esa-sceva/satcom-chunk-collection huggingface dataset:
# Loosely Coupled GNSS and UWB with INS Integration for Indoor/Outdoor Pedestrian Navigation +
## 4 Test Setup
### Ultra-Wideband (UWB) System Setup  
In order to employ the UWB technology for positioning, different algorithms and estimation procedure have been developed which relies on measurements based on the radio signal traveling between the fixed nodes and target nodes in addition to the position information of the fixed nodes.  
The Pozyx system used in this work provides high-precision distance measurement between a network of fixed sensors (anchors) placed on the edge of the system's operating area and a mobile sensor (tag) using a two-way ranging approach. In particular, 8 anchors were placed around the testing area and their position was calculated with high accuracy through detailed measurements (angles and distances) made with a total station. As previously described, the total station was placed on a known point belonging to the topographic network in order to trace the local system back to a global georeferenced system. Consequently, any estimation algorithm used for positioning (least-square, multilateration, Kalman filtering, etc.) provides the tag position in the absolute reference frame that in this work correspond to the WGS84 reference system with the UTM32N projection.
# Assessing Cyber Challenges of Maritime Navigation
## 2 Methodology  
A systematic literature review was conducted, based on and structured according to documented guidelines [41; 42; 43] through a comprehensive, explicit, reproducible, and implicit idiosyncratic method of data collection is followed. This method consists of ten steps that can be grouped into three main phases:  
1. Planning the review: The planning phase focused on defining a review question to guide the search: \"What are the effects of cyberattacks and cybersecurity in the maritime domain?\"
2. Conducting a review: In the search phase, the relevant research databases, the keywords to be used during these searches, and the proper timeframe for the resulting documents to be included were identified. Data for the study were available in the databases such as Scopus, Web of Science, Google Scholar, and open sources. The search keywords were determined from a knowledge domain analysis around the concept of \"maritime cyber\". The two main knowledge domains to be scanned were identified as \"maritime cyber\" and \"cybersecurity\". After the broad initial literature search, explicit inclusion and exclusion criteria--i.e., refined selection (e.g., document type, themes, research area) to identify relevant documents for this analysis--were applied. The documents were analyzed and synthesized according to contexts, methodological approaches, and outcomes. Our final list consisted of 171 documents (76 articles, 52 peer-reviewed journal papers, and 43 reports by specialized agencies) that covered the area of \"maritime cyber\", ranging from 2016 to 2020. While the results of this article are novel, a few earlier studies on this topic were also taken into account as references.
3. Reporting and dissemination: In the next section, we report on our findings from the literature review.  
The specific aspect of AIS/GPS spoofing is reinforced by an analysis in Chapter 3 by the Faculty of Maritime Studies and Transport, the University of Ljubljana, research and analysis [44] regarding a particular AIS spoofing event at Elba Island at the end of 2019.
## 4 Discussion
### System Capacity  
When the initial position of marine mammals is different, the system capacity of the underwater acoustic network is greatly different. However, the sensor of the underwater acoustic cognitive network adopted in this paper can sense the position information of marine mammals and calculate its own transmission power when sending data, so as to improve the system capacity.  
Figure 5 shows the change in network system capacity when the network load increases.  
As can be seen from Figure 5, with the increase of network load, the system capacity of the three different algorithms increases continuously. When the network load is not large, the system capacity of the three algorithms has little difference. However, starting from \(k=6\), the system capacity obtained by applying this algorithm increases significantly compared with the other two algorithms. The system capacity of the proposed algorithm is 36.6% higher than that of conventional cognitive radio technology and 62.4% higher than that of the conventional spectrum allocation method.
## 6 Conclusions  
The tests of the PCS model showed that the developed solution, including the dedicated electronic controller, can effectively reduce the local skin temperature and stabilize its value even outside the TEMs at the appropriate level, thus positively affecting the subjective sensation of overall thermal comfort. The obtained results are promising and encourage further research aimed at (1) a comprehensive evaluation of the PCS, including an analysis of its influence on human thermal states expressed by differences in mean skin temperature and body temperature, and an ergonomics evaluation under simulated utility conditions; (2) increasing energy effectiveness; (3) improving wearability and maintenance; as well as (4) adjusting to the requirements of other specific working environments (e.g., glass factory or mine).  
The implementation of three separate output channels of the electronic controller provided a greater coherence between different TEMs in terms of cooling power. The parallel arrangement of TEMs in pairs decreased their required maximum supply voltage to about 3 V, which enhanced the electrical safety of the user even in the presence of sweat. Moreover, this allowed a 5 V powerbank to be used for energy storage without requiring a step-up power converter.  
The integration of the developed PCS model within ASSIST-IoT will increase the level of automation of the former, reducing the required amount of user interaction. The use of biometric data and environmental data will allow the cooling intensity to be adjusted to the current worker's activity and site conditions. Moreover, taking into account possible changes to system controls made by the user will make it possible for the system to learn about their individual preferences, and the use of AI to adapt cooling to these changes will allow it to find appropriate cooling settings. The proposed architecture takes special care to preserve the user's privacy while using data from several sources to adjust the cooling power in real time.
# FlowHaptics: Mid-Air Haptic Representation of Liquid Flow
## 3 Methodology
### Results and Discussions  
Figure 10 shows the result of fitting the psychometric curves to the probabilities using a normalized cumulative distribution function. To evaluate the discrimination performance quantitatively, we calculated the Just-Noticeable Difference (JND) as half of the difference threshold levels between the inverse of the curves at 0.25 and 0.75.  
Figure 9: (**a**) Experimental setup; (**b**) front view of the device; (**c**) one finger; (**d**) three fingers.  
From the fitted curves, we could divide the subjects into two groups, which were those who successfully discriminated the output pressure (solid lines) and those who guessed the majority of the stimuli (dashed lines). More than 80% and 70% of the participants were able to successfully discriminate the output pressure using our novel device with one tube and three tubes, respectively. This result is in agreement with other types of haptic devices [24,25], for which a small number of participants were not able to discriminate the sensation.  
From those who successfully discriminated the output pressure, we found that the JNDs were 8.88%, 10.80%, 8.42%, and 13.05% for 1F/1T, 1F/3T, 3F/1T, and 3F/3T, respectively. The box plot of the JND for each case is shown on the right side of Figure 10a-d. Based on the JND results for 1F/3T and 3F/3T compared to the JND results for 1F/1T and 3F/1T, it seemed that it was more difficult for the subjects to discriminate the output of the three-tube condition (3T) compared to the one-tube condition (1T). However, the JND results for 3F/1T were about the same as for 1F/1T, and the JND results for 3F/3T were higher than for 1F/3T. Compared with the one-finger condition (1F), the greater sensing area in the three-finger condition (3F) did not strongly affect the discrimination, as the  
Figure 10: The probability of the subjects responding that the “comparison is stronger” with respect to the standard stimuli from the psycho-physical evaluation results (CS = comparison stimuli, SS = standard stimuli). (**a**) 1F/1T (**b**) 1F/3T (**c**) 3F/1T (**d**) 3F/3T.  
greater sensing area in 3T significantly resulted in participants feeling confused about the difference in flow velocity when compared to 1T.  
From this analysis, we can confirm that our developed controlled air jet device is able to sufficiently reproduce the feeling of fluid flow such that a human can classify the different flow velocities.
## 4. SoSCO-Based Architectural Steps and Application Example
### Step 3: Define Capabilities Based on Factors  
This step addresses question 1 and involves identifying capabilities by combining the three capability factors using a three-level approach to defining capabilities.  
At the top level, basic capabilities are derived from a combination of _TemporalFactors_ and _StructuralFactors_. The Semantic Web Rule Language (SWRL) is a rule language that semantic web reasoners can process to enable automated semantic inference [54]. SWRL rules can be specified in an implicit form and are well suited for describing filtering rules [55]. Propositions involved in the rules are represented as atoms of \(P(x,y)\), where \(P\) is an object property. In \(P(x,y)\), the source element \(x\) represents the client, and the target element \(y\) represents the supplier. Table 3 shows an example of SWRL rules for creating the generalization of capability, where the atom correspondsToCapability (?sos,? c) represents a transformation of OWL class type from _CapabilityFactor_ to _Capability_. Figure 10 illustrates the excerpt facts of SoSCO in SAR after reasoning with SWRL rules in Protege with a Pellet reasoner [56]. SAR includes SAR phase 1 and SAR phase 2 as _TemporalFactor_, and SAR can be further subdivided into maritime and land-based SAR based on _StructuralFactor_. These attributes correspond to various sub-capabilities, and the SoS _Search and Rescue_ corresponds to the top-level capability _SAR_. The object property _isParentOf_ represents the generalization of capabilities. The capabilities can be modeled in the Strategic Taxonomy (St-Tx) view of the UAF implemented by the SysML BDD, as shown in Figure 11.  
Figure 9: Strategic Processes (St-Pr) of the SoS phases.  
At the middle level, the mission should be decomposed into sub-missions, and the corresponding capabilities characterized by the _OperationalFactor_ of the mission view can be traced. Figure 11 shows that the capabilities of search, assistance, and recovery are modeled by verbs based on operational factors of the St-Pr view.  
At the bottom level, the captured capabilities are supplemented by additional _OperationalFactors_ based on domain knowledge to provide a more comprehensive context. On the one hand, capabilities captured based on sub-mission may lack the information necessary for explaining their relationship with other operational factors. For example, Figure 11 shows that the C2 center should control the search capability and track information from the distress signal monitor. On the other hand, capabilities captured based on _TemporalFactor_ and _StructuralFactor_ also require additional capabilities based on operational factors. For example, maritime SAR has more capabilities than land-based SAR.
## 3 Results
### Comprehension Analysis  
Comprehension analysis consists of single word comprehension and simple command comprehension tasks. Figure 6 illustrates participants' comprehension levels in both tasks. However, Figure 6 does not represent non-aphasic participants, since they demonstrated 100% accuracy in both tasks. Figure 6 illustrates the number of accurate responses by [PERSON] and [PERSON]'s participants per each target word. It is clear from the results that the Anomic participants have a higher level of single word comprehension than the [PERSON]'s participants. The simple command comprehension task separately analyzes participants' comprehension levels of objects, sequences, and actions, and the performance results are presented in Figure 6, respectively. Aligning with single word comprehension, Anomic participants secured a higher level of comprehension in the simple command task as well. A summary of the comprehension task is given in Table 4. Although the comprehension analysis was not automated, all evaluation points were enclosed with the assessment and prompt the SLP to input only quantitative observations, e.g., number of objects, sequences, and actions identified. Hence, SLP input is not subjective and maintains consistency throughout. A performance summary of the comprehension task is given below in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Criteria**} & \multirow{2}{*}{
\begin{tabular}{c} **Single** \\ **Word Task** \\ \end{tabular} } & \multicolumn{8}{c}{**Simple Commands Task**} \\ \cline{3-11}  & & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Objects & 10 & 1 & 1 & 1 & 2 & 1 & 2 & 3 & 3 & 3 \\ Sequences & - & 1 & 1 & 1 & 2 & 1 & 1 & 2 & 2 & 2 & 2 \\ Actions & - & 1 & 1 & 1 & 2 & 1 & 1 & 2 & 2 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comprehension task summary (number of objects, sequences, and actions to comprehend).  
Figure 6: Comprehension performance of the [PERSON] and [PERSON]’s subjects. (**a**) Average accuracy of single word comprehension; (**b**) object identification accuracy in commands; (**c**) sequence accuracy in commands; (**d**) action identification accuracy in commands.
# Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review
## 2 E-Nose System
### The Electronic Nose's Brain: Algorithms and Visualization
#### 2.4.5 Artificial Neural Networks  
ANNs replicate brain-like problem-solving and data processing. Composed of interconnected processing elements, ANNs excel at handling complex, nonlinear data. They consist of input, hidden, and output layers, with nodes transmitting signals for decision-making. ANNs originated from [PERSON] concept in 1943 [93] and have been applied to electronic noses.  
In ANNs, data are processed in two phases: forward-pass (signal propagation) and backward-pass (error correction). Synaptic weights connecting nodes are adjusted to minimize output errors. ANNs are adept at supervised learning when trained with labeled data using algorithms like the vanishing gradient method [94; 95; 96].  
In electronic noses, ANNs learn from labeled data, recognizing patterns and making accurate predictions. Their strength lies in efficiently capturing and analyzing complex, multi-dimensional data patterns, which is crucial in the diverse and variable-rich environments wherein electronic noses are deployed. This capability makes ANNs exceptionally well suited for enhancing the accuracy and reliability of odor detection and classification in these systems. Table 2 provides a succinct overview of the fundamental features and functionalities of the discussed computational treatments.
## 5 MTMD Applications on Taizhou Bridge
### Parametric Analysis on the MTMD Application  
Before the parametric analysis on the MTMD application, the dominant frequency and corresponding equivalent mass should be firstly determined. Since the vertical buffeting response of Taizhou Bridge is dominated by the AS-V-1 mode, the modal frequency and equivalent modal mass for the vertical MTMD design is definite, as shown in Table 2. However, the lateral buffeting response is mainly dominated by two modes (AS-L-1 and S-L-1). Hence, a weighting calculation is performed on the two modes. The weight coefficients are given according to the average energy represented by the two modes over the time. For example, if the averaged energies on two frequencies are \(E_{1}\) and \(E_{2}\), respectively, the corresponding weight coefficients will be \(E_{1}/(E_{1}+E_{2})\) and \(E_{2}/(E_{1}+E_{2})\). Then, the dominant modal mass and frequency are expressed as  
\[f_{L}=w_{1}f_{\text{AS-L-1}}+w_{2}f_{\text{S-L-1}} \tag{20}\]  
\[M_{L}=w_{1}M_{\text{AS-L-1}}+w_{2}M_{\text{S-L-1}} \tag{21}\]  
where \(f_{L}\) and \(M_{L}\) are the dominant modal mass and frequency, respectively. \(f_{\text{AS-L-1}}\) and \(f_{\text{S-L-1}}\) are the modal frequencies of Mode AS-L-1 and Mode S-L-1, respectively. \(M_{\text{AS-L-1}}\) and \(M_{\text{S-L-1}}\) are the equivalent modal masses of Mode AS-L-1 and Mode S-L-1, respectively. \(w_{1}\) and \(w_{2}\) are weight coefficients, and in this study correspond to 0.7 and 0.3, respectively.  
In such a case, the dominant modal mass for the lateral MTMD is set as 25,318.6 t and the dominant modal frequency is 0.07868 Hz. The modal mass for vertical MTMD is 24,200.4 t and the modal frequency is 0.08023 Hz. Then, a parametric analysis is conducted in the following section.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dominant Mode**} & \multicolumn{2}{c}{**Lateral**} & \multicolumn{1}{c}{**Vertical**} \\ \cline{2-4}  & **AS-L-1** & **S-L-1** & **AS-V-1** \\ \hline Equivalent modal mass/t & 25,988.8 & 23,754.8 & 24,200.4 \\ Modal frequency/Hz & 0.07163 & 0.09512 & 0.08023 \\ Weight coefficient & 0.7 & 0.3 & 1 \\ Dominant modal mass/t & 25,318.6 & 24,200.4 \\ Dominant modal frequency/Hz & 0.07868 & 0.08023 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dominant frequency and corresponding equivalent mass for MTMD design.
# Chlorobenzene Removal Using DBD Coupled with CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) Catalyst
## 3 Results and Discussion
### Effect of SED on the Performance of DBD Reactor  
#### 3.2.1 Chlorobenzene Removal Efficiency  
Figure 5 shows the removal efficiency of chlorobenzene under different SED in a single DBD reactor and two combined plasma-catalytic reactors. Compared with the other two types of plasma-catalytic reactors, the CuO packing reactor performed better chlorobenzene degradation (58.9%) when the SED is 750 J\(\cdot\)L\({}^{-1}\), about 10% higher than that in the DBD reactor loaded with \(\gamma\)-Al\({}_{2}\)O\({}_{3}\) powder (53.8%) and 66% higher than that in the single DBD reactor (32.9%). In the meanwhile, the increase in SED is beneficial for the chlorobenzenes to degrade. However, when the SED of plasma exceeds 1000 J\(\cdot\)L\({}^{-1}\), this enhancing effect of CuO loading is weakened. As SED reached 1350 J\(\cdot\)L\({}^{-1}\), over 90% of chlorobenzene removal can be achieved by these reactors, and the differences among these reactors are ignorable.  
Figure 4: XPS spectra of the CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) catalyst before and after discharge: (**a**) before; (**b**) after.
# Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities
## 2 Materials and Methods
### Performance Evaluation  
To check the appropriate classification model before comparing ANN and ANFIS techniques, 10-fold cross-validation was used. The recorded data were randomly divided into 10 parts: nine parts taken as the training data, in turn, and the last part as the testing data. Then, cross-validation averaged the results system accuracy of 10 loop experiments to the final classification performance. The classification methods used signals with a ratio of 2:1 of training and performance testing data. Performance testing measures used to evaluate the classifiers were system accuracy (_ACC_), sensitivity (_SE_), specificity (_SP_), and positive predictivity (_PPR_), as shown in (6)-(9).  
\[System\ Accuracy\ (ACC)=\frac{TP_{All\ Bents}}{n} \tag{6}\]  
\[Sensitivity\ (SE)=\frac{TP_{BT}}{TP_{BT}+FN_{BT}} \tag{7}\]  
\[Specificity\ (SP)=\frac{TN_{BT}}{TN_{BT}+FP_{BT}} \tag{8}\]  
\[Positive\ Predictivity\ (PPR)=\ \frac{TP_{BT}}{TP_{BT}+FP_{BT}} \tag{9}\]  
TP(AF) is the number of true positive beats when an AF beat is classified as an AF diagnosis. FN(AF) is the number of beats while an AF beat is classified as another (NSR or PAF) diagnosis. TN(AF) is the number of true negative beats when other beats are classified as another diagnosis. FP(AF) is the number of beats when other beats are classified as an AF diagnosis.
## 4 Rate-Distortion Analysis of Using CS Encoding in WSN
### Energy Consumption Evaluation of a Multi-hop CS-based WSN: A Case Study
Regarding to the energy consumption, the smaller the value of \(\delta\), the higher the number of transmissions, leading to a high energy expenditure when DECA is used. On the other hand, when sensor node uses the CS framework, the \(N\) acquired samples have to be stored before the transmissions, consuming energy. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(B\) **(bit-depth)** & \(\mathrm{R}\) **(bits/sample)** & **NMSE (dB)** & **Energy Cons. (J)** \\ \hline
4 & 0.034 & \(-13.9\) & 0.56 \\
6 & 0.086 & \(-16.3\) & 0.74 \\
8 & 0.140 & \(-18.6\) & 0.90 \\
10 & 0.199 & \(-19.2\) & 1.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Energy consumption of compressive sensing (CS) scheme, applied to the reconstruction of \(N=512\) samples of a temperature signal, using 14 CS measurements.
## 2 Design of DSCT Specimens
### Section Design  
The cross-section of the single-body DSCT wind turbine tower was designed before the modularized DSCT tower. Considering the capacity of loading equipment, the outer diameter of the specimen was selected as 600 mm. The thickness of the outer tube was determined as 4 mm, which is the minimum value to avoid the local buckling failure of the outer tube. Therefore, the diameter of the outer tube was 596 mm. The hollow ratio of the specimen was set as 0.8 and the outer diameter of the inner tube was determined as 474 mm. Table 1 lists the properties of the used materials for pre-analysis. The height from the top of the base to the loading point was set as 2050 mm.  
The required thickness of the inner tube was calculated using Equations (1) and (2) [13]. Equation (1) was used for calculating the minimum thickness (t\({}_{y}\)) required to resist yield failure, and Equation (2) was used for determining the minimum thickness (t\({}_{\text{bk}}\)) to prevent buckling failure.  
\[t_{y}=\frac{D_{i}f_{oty}t_{ot}}{D^{\prime}f_{y}} \tag{1}\]  
\[t_{bk}=\sqrt{\frac{6}{2.27}\frac{D_{i}^{2}f_{oty}t_{ot}}{D^{\prime}E_{it}}} \tag{2}\]  
where  
\(D_{i}\): inner diameter of confined concrete (=outer diameter of inner tube),  
\(D^{\prime}\): outer diameter of confined concrete (=inner diameter of outer tube)  
\(f_{oty}\): yield strength of outer tube  
\(t_{ot}\): thickness of outer tube  
\(f_{y}\): yield strength of inner tube  
\(E_{it}\): modulus of elasticity of inner tube.  
The design thickness of the inner tube should be greater than the value calculated from Equations (1) and (2). The required minimum thickness from Equation (1) was 3.2 mm, while 2.2 mm was the result using Equation (2). The thickness of the inner tube was finally designed as 3.2 mm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Property** & **Value (MPa)** \\ \hline \multirow{3}{*}{Steel} & Modulus of elasticity & 205,000 \\ \cline{2-3}  & Yield strength & 245 \\ \cline{2-3}  & Ultimate strength & 400 \\ \hline Concrete & Compressive strength & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties for section design.  
Figure 7: Bolting, welding, and shear key connector (Type-W).
# DEM Simulation of the Load Transfer Mechanism of a GRPS Embankment with a Fixed Geogrid Technique
## 3 Results and Discussions
### Pile Efficacy
Under the surcharge load of 500 kPa, the vertical load that was transferred by the reinforcement increased to 28%, and the proportion of the soil between the piles increased further. This means that the soil arching reached the bearing limit, and the load transfer capacity of the reinforcement was gradually fully utilized.