esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Implementation and Verification
### Mobile App Function  
The primary function of the mobile app is to develop data linkages between mobile devices and sensors in the home monitoring system. When users open this app for the first time, they are required to register their user information. Only after registration can they proceed to log in. The registration form includes four fields: username, email, password, and password confirmation. The system will verify whether the email format is correct and whether the password matches the password confirmation. After registration, users will be redirected to the login screen. After visiting the homepage of the app, the user needs to confirm whether the app is connected to the database or not by clicking on the \"CONNECT\" icon found at the bottom of the homepage. If the \"home monitor\" icon found in the upper right corner of the homepage is selected, the app will display the status of the devices and sensor data from all the sensors connected to the gateway of the home monitoring system. Moreover, the app can remotely switch on/off the fan or the warning light. When the indoor temperature is \(>\)27 \({}^{\circ}\)C, the user will automatically be notified with a warning stating that the current ambient temperature is too high. The notification will ask whether the user wishes to switch on the fan. Other functions available on the homepage are \"IP CAMERA\", \"COMMUNICATION\", \"GPS\", \"SOS\", and \"FALL DETECTION\". Figure 15 illustrates the designed mobile app functions available on the homepage of the home monitoring system.
## 4 Evaluation
### Ranging Accuracy
#### 4.3.3 Outdoor Long Range Test  
Longer range measurements were performed on an empty parking lot. Test samples were recorded at two fixed distances (50 and 75 m) at 2 m height, and the cumulative distribution of these experiments is given in Figure 9a. The statistics of these measurements are given in Figure 9b. The mean reported distances are 49.99 and 74.95 m, deviating only 1 and 5 cm from the real distance. The mean receive power at 75 m is \(-101.08\) dB m, resulting in a packet receive ratio of 91.55 %. The precision for the measurements at 1, 2, 5, and 50 m is very similar while the precision for 75 m is much lower due to the small link margin at 75 m. Ranging in indoor environments are typically more challenging than in open space. Reflections from ground, walls, objects, and ceiling will interfere at the receiver's antenna influencing the signal strength level of the received packet.  
A second outdoor measurement was carried out, where the distance between sender and receiver was gradually increased when registering receive power, first path power, and reported distance until a distance of 100 m was reached. The anchor and tag nodes were placed at a height of 2 m. The receive powers and reported ranges are displayed in Figure 10, and simulation results based on the theoretical model (Section 4.3.1) were added. Ranges were received until the previously measured power level (Section 4.2) is reached \(-106\) dB, and packets with lower receive powers (\(-106\) to \(-109\) dB m) still were received, but no ranging could be determined. The simulation predicted that ranging was possible between 1 to 81.55 m and from 116.25 up to 220.35 m. If the transmit power gain of the DW1000 is increased to the maximal value, the simulation predicts UWB range until 1008.95 m and communication until 1227.65 m when the anchors are placed 2 m above ground. Adjusting the height will change the length of the ground reflected path and influences the range. The sub-GHz backbone permits to report the ranges from anchor to tag node over large distances.  
Figure 9: Indoor short distance and outdoor long distance measurements. (**a**) Cumulative distribution function of the reported ranges. (**b**) Statistics measurements.
## 4 Tuning Parameters and Results Assessment
### Parameter Optimization and Accuracy Assessment
#### 4.2.2 The SVM Algorithm  
SVM is a supervised ML algorithm that can be used to resolve the classification and regression problems, based on a separating hyperplane [34]. In this study, the radial basis function (RBF) kernel was used. Two parameters that need to be optimized for the RBF kernel SVM are Cost and Gamma. When the Cost is small, low bias but high variance may occur, and vice versa. Gamma affects the smoothing of the hyperplane shape [35]. Therefore, optimizing these parameters is a critical procedure. This optimization can be done by the CV method [36]. In this study, a very wide range of Cost values was proposed: from \(2^{-5}\) to \(2^{4}\). Similarly, the Gamma range was from \(2^{-2}\) to \(2^{7}\).  
\(\bullet\) Classification Function  
To evaluate the classification ability of SVM, we used the CV technique to assess how accurately this method can perform in practice. The final mean scores corresponding to the values of Cost and Gamma after 10-fold CV are illustrated in Figure 13. The highest mean score appeared when Cost = \(2^{3}\) and Gamma = \(2^{4}\). These values were used throughout this study when the area division process with SVM was employed.  
\(\bullet\) Regression Function  
In a process similar to the classification process above, we first searched for the best positioning accuracy according to Gamma and Cost parameters, and then we verified the robustness of the SVM algorithm corresponding to the values found by 10-fold CV. On the whole floor and edge area, the best positioning accuracy occurred when Cost = \(2^{4}\) and Gamma = \(2^{7}\) (Figures 14 and 15). There was a minor problem occurring in the remaining case (Figure 16), in that the best tuning parameters were not the choice when reassessed by CV. For instant, the best choice in Figure 16b appeared when Gamma = \(2^{5}\) and Cost = \(2^{4}\) (orange text), while the best value of Gamma and Cost, in Figure 16a, were \(2^{7}\), and \(2^{4}\), respectively (red text). However, in Figure 16b, the mean CV score in both cases: Gamma = \(2^{5}\), Cost = \(2^{4}\) (orange text) and Gamma = \(2^{7}\), Cost = \(2^{4}\) (red text) are nearly the same. We, therefore choose Gamma = \(2^{7}\) and Cost = \(2^{4}\) for our work.  
Figure 13: Mean score after CV.  
Figure 14: The mean score in the whole area of: (**a**) positioning accuracy and (**b**) CV.  
Figure 15: The mean score in the edge area of: (**a**) positioning accuracy and (**b**) CV.
# Energy Efficient Policies for Data Transmission in Disruption Tolerant Heterogeneous IoT Networks
## 5 Results
In comparison, policies that rely only on local network availability information, although easier to implement, lead to a significant increase in energy cost and packet drops.
# Systems of Variational Inequalities with Nonlinear Operators
## 1 Introduction
In particular, if \(B_{1}=B_{2}=B\), then problem (3) reduces to the following system of variational inequalities (SVI) in Banach spaces:  
Find \((x^{*},y^{*})\in C\times C\) such that  
\[ \begin{cases}&\langle\mu_{1}By^{*}-y^{*}+x^{*},j(x-x^{*})\rangle\geq 0,\quad \forall x\in C,\\ &\langle\mu_{2}Bx^{*}-x^{*}+y^{*},j(x-y^{*})\rangle\geq 0,\quad\forall x\in C. \end{cases}  \tag{4}\]  
Furthermore, if \(x^{*}=y^{*}\), then we obtain the following variational inequality (VI) in Banach spaces: Find \(x^{*}\in C\) such that  
\[\langle\mu Bx^{*},j(x-x^{*})\rangle\geq 0,\quad\forall x\in C. \tag{5}\]  
Let \(\text{VI}(C,B)\) denote the set of solutions to problem (5). Whenever \(E=H\) a real Hilbert space, it is easy to see that the GSVI (3) reduces to the following problem of finding \((x^{*},y^{*})\in C\times C\) such that  
\[ \begin{cases}&\langle\mu_{1}B_{1}y^{*}-y^{*}+x^{*},x-x^{*}\rangle\geq 0,\quad \forall x\in C,\\ &\langle\mu_{2}B_{2}x^{*}-x^{*}+y^{*},x-y^{*}\rangle\geq 0,\quad\forall x\in C, \end{cases}  \tag{6}\]which is called a GSVI in Hilbert spaces. In [11], the GSVI (6) was transformed into a fixed-point problem by [PERSON], [PERSON] and [PERSON] in the following way. **Lemma 1**. : _[_14_]_ _For chosen \(x^{*},y^{*}\in C\), \(x^{*}\)-\(y^{*}\) is a solution of GSVI (1.6) if and only if \(x^{*}\in\mathrm{GSVI}(C,B_{1},B_{2})\), where \(\mathrm{GSVI}(C,B_{1},B_{2})\) is the fixed-point set of the mapping \(G:=P_{\mathrm{C}}(P_{\mathrm{C}}(I-\eta B_{2})-\rho B_{1}P_{\mathrm{C}}(I- \eta B_{2}))\), and \(y^{*}=P_{\mathrm{C}}(I-\eta B_{2})x^{*}\)._  
Recently, many authors studied problems (3)-(6) via projection-based methods in Hilbert or Banach spaces; see [15; 16; 17; 18; 19; 20; 21; 22] and the references therein. In this paper, we introduce a hybrid viscosity implicit iteration method that is based on [PERSON]'s extragradient method, the viscosity approximation method and the [PERSON] iteration method for finding a common solution of the GSVI (3) for two inverse-strongly accretive mappings, a common fixed-point problem (CFPP) of a countable family of uniformly Lipschitzian pseudocontractive mappings and an asymptotically nonexpansive mapping in the intermediate sense. We prove the strong convergence of the proposed method to a common solution of the GSVI (3) and the CFPP, which solves a certain variational inequality on their common solution set in 2-uniformly smooth and uniformly convex Banach spaces. Additionally, we give an application to solve CFPPs of asymptotically nonexpansive and pseudocontractive mappings, and variational inequality problems for strict pseudocontractive mappings in Banach spaces.
# Spatial Data Analysis for Deformation Monitoring of Bridge Structures
## 4 The Liberty Bridge--A Case Study  
The Liberty Bridge is part of the cycling route between the Bratislava district of Devinska Nov\(\acute{\text{a}}\) Ves (the Slovak Republic) and Schlosshof (Austria). It spans the river Morava and a stagnant pool of associated flows with a total length of 525.0 m [29]. The bridge is located in an inundation area on both sides in a protected floodplain forest (Figure 5). The substructure consists of reinforced-concrete pillars in which the supports are anchored. The superstructure of the bridge over the river consists of three bridge sections with a total span of 180.0 m. The main part of the superstructure is a steel triangular truss beam with an orthotropic deck. The suspended bridge section has a radius of 376.35 m and is suspended on four pylons, which create dual-hinged rectangular frames. The pylons' diameter is 0.914 m, while the height of each is 17.7 m.
## 4 Experiment Results and Visualization
### Abstract Clustering Results and Visualization
Beneath are the five clusters, sorted by Silhouette score, along with the number of abstracts and the frequent terms of each cluster. The frequent terms allow further examination of the content of a cluster. For example, the cluster #1 is about _urban air quality_ and relevant technologies such as management system, information system, and control system; the cluster #2 _urban heat_ and _air temperature_; the cluster #3 _urban environment_ and _human perception_; the cluster #4 _urban land use_ and _urban growth_; and the last cluster #5 contains a diversity of terms that do not appear in other clusters such as _urban governance_, _urban system_, and _urban form_. Clicking the cluster #2 in Figure 5 displays its total number of abstracts and its frequent terms, as shown in Figure 6. The top panel lists the top 10 frequent terms in a cluster, and the bottom panel displays the details of the abstracts. The number (12) in brackets following  
Figure 5: Abstract cluster visualization for all clusters from the iterative clustering phase. On the left, the scatter chart of abstract vectors reveals three color regions (blue, orange, and green) along with three gray regions for outliers. Clicking on the blue region retrieves and displays the abstract clusters in the right panel. a term, e.g., _surface temperature_ (12), is the number of abstracts containing the term. Clicking a term, say _temperature data_, displays the abstracts containing the term in the bottom panel. Each abstract highlights the selected term along with the auto-generated keywords in the keyword extraction phase. More information about an abstract, including article titles, authors, author keywords, citations, publication year, and DOI, are given at the end. This result shows a very limited number of overlapping terms in the cluster. Only four terms (_surface temperature_, _urban heat_, _heat island_, and _land surface_) occur in more than 10 abstracts (half of the cluster) whilst most of the terms occur only once. The diverse terms in this cluster make it difficult to find common terms representing the topics.
## 4 Discussion
### Applicability of the Proposed Model to Match Internal and External Features  
The proposed model is primarily designed for high-density internal corrosion, where features are often in close proximity. In contrast, external corrosion typically occurs in isolated locations, resulting in reduced complexity and less manual effort for feature matching. Nevertheless, the proposed model is well-suited for both internal and external feature matching. Like the annealing and Voronoi models, the proposed model also addresses internal and external feature matching independently as two separate problems. Mixing internal and external features during data clustering is not methodologically appropriate, as it could result in mixed clusters and/or matching problems, ultimately compromising the model's feasibility.
## 6 Optimization Results  
According to the regression model analysis, only the C factor is required for fitting the data to the AIC response. It is proposed to evaluate the means of observing whether there is any statistical difference between levels. The results are shown in Table 13. In this case, we propose minimizing the AIC response with a minimum number of neurons.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Coefficient** & **Typical Error** & **t-Statistic** & \(p\)**-Value** \\ \hline Number of neurons & 16.46 & 1.44 & 11.38 & 1.9 \(\times\) 10\({}^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Coefficients test.  
Figure 3: Residuals for the C factor.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Degrees of** & **Sum of** & **Mean** & **F-Ratio** & \(p\)**-Value** \\  & **Freedom** & **Squares** & **Square** & **F-Ratio** & \(p\)**-Value** \\ \hline Regression & 1 & 142,381.8 & 142,381.8 & 129.677347 & 4.7736 \(\times\) 10\({}^{-7}\) \\ Residues & 11 & 12,077.6 & 1097.97 & & \\ Total & 12 & 154,459.5 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 12: ANOVA for model analysis.  
Time series data of Figure 4 are used as a reference to evaluate the results from Section 7. Figures 5 and 6 show that an increase in the C factor only increases the model complexity, without guaranteeing better performance in terms mainly of MAPE.  
Additionally, we show how it is possible to obtain better performance by varying the number of neurons, allowing a lower computational cost due to a training process with a lower time requirement. Table 15 shows the relationship between the number of neurons and the training time for data from the 24 periods of energy demand according to the considerations addressed in [19].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Neurons** & **Training Size (s)** \\ \hline
1 & 0.285186 \\
10 & 0.284743 \\
20 & 0.466448 \\
50 & 7.847768 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Relationship between number of neurons and training time.  
Figure 5: Multi-comparison test: AIC vs. number of neurons.  
Figure 6: Multi-comparison test: MAPE vs. number of neurons.  
Table 15 shows that a higher number of neurons in the training process implies an exponentially higher computational cost. Therefore, the proposed approach is key when defining an experimentation zone that reduces the time required for the training process, searching for an optimal performance value of the forecasting model.  
The results in the implementation of an energy demand prediction model are shown in Figure 7, using the same methodology in the training process of the models as is used in [16].  
Figure 8 shows the MAPE performance of the proposed approach for the forecasting process of the Atlantico electricity market, considering the testing data from 1 April 2019 to 30 September 2019.  
The forecasting models obtained through the proposed methodology evidence a better performance due to this method's radar chart area being less than that of the Atlantico electricity market operator.
# An Improved Forwarding of Diverse Events with Mobile Sinks in Underwater Wireless Sensor Networks
## 5 ESDR: Event Segregation Based Delay Sensitive Routing
### Network Initialization and Node Deployment  
ESDR considers deep underwater environment which comprises of \(l\) m\(\times\)\(b\) m\(\times\)\(D_{max}\) m. Initially \(\mathcal{N}\) nodes having same initial energy \(E_{o}\) are randomly deployed. Each node is equipped with required sensing capabilities and a communication module. Each node is aware of its depth which is calculated using pressure sensors. We have considered the following assumption about the deployed nodes and network.  
It is assumed that the horizontal mobility of nodes is negligible while the vertical motion of nodes may change their depth. However, since it is a localization free scheme depending on the instantaneous depth, the change in depth is already accounted in the protocol operation. Hence the impact of mobility of water on nodes position is not taken into consideration. It is also assumed that each sensing node on the basis of value of sensed attribute can determine its type and delay criticality associated with it. One way to realize this assumption is by predefining some threshold levels of the being sensed parameter and then conditioning their type by the threshold values. After sensing the event a node determines its types by comparing the sensed value with the thresholds.  
Four courier nodes are also placed at different depths as shown in Figure 1. Courier nodes are deployed for data collection and have the capability of vertical motion.  
Packet format for ESDR is shown in Figure 2. S.ID is sender ID, PSN is packet sequence number, depth of previous sender is kept in \(D_{p}\), \(E_{r}\) is the remaining energy of previous node. One important field is packet type which can be very critical, critical or ordinary.  
Figure 1: Node deployment.  
Figure 2: Packet format.
## 1 Introduction
According to this channel model we can find the channel responses regarding intra-flight deck, cabins, cabin to lower lobe, cabin to exterior, cabin to landing gear, and exteriors;
* We investigate eight schemes, which are beamforming schemes (including the analog beamforming (ABF) and digital beamforming (DBF)), non-cooperative schemes (including single-hop, multi-hop), cooperative schemes (including the amplify and forward (AF), the DF, the incremental amplify and forward (IAF), and the IDF), and represent the energy consumption and throughput analysis with the aircraft channel model;
* We show the optimal transmitted power regarding eight schemes, which satisfy the transmitted power requirement based on ITU-R M.2283. Thus, this paper can be cited when one of the transmission schemes is considered for the aircraft wireless sensor networks;
* The simulation results clearly show that first, the incremental cooperative scheme had the lowest total energy consumption in overall spectral efficiency. In terms of the throughput performance, the beamforming scheme had the best performance in the overall SNR (signal-to-noise ratio) range from \(-20\) to \(30\). Whereas, in terms of normalized throughput performance the ABF had the best performance in low SNR range from \(-20\) to \(1\) dB, but in a high SNR range from \(2\) to \(30\) dB the IDF had the best performance. This paper is organized as follows. In Section 2, the system model of the aircraft is described. In Section 3, wireless communication schemes for an aircraft are represented. In Section 4, simulation results of the two metrics are shown. Finally, conclusions and future works are drawn in Section 5.
## 3 Experiments and Results
### Verification of the UWB/IMU/Encoder Fusion Position Estimation  
#### 3.2.1 Verification of the Proposed UWB Data Pre-Processing  
Since the distance information data have strong outliers and noise in the UWB sensor, data pre-processing is required, and an experiment was conducted to compare pre-processing and post-processing of the distance information data in the UWB sensor by applying the pre-processing algorithm proposed in this study. The experiment was conducted by collecting data at 15 Hz intervals for 30 s in the environment presented in Figure 7. As a result of the experiment, it was confirmed that the outlier removal weight \(\alpha=8\) cm of the pre-processing algorithm and the average number of sample data n = 10 were ideal. Figure 11 shows the experimental graph, and it was verified that the noise and outliers of the data graph were reduced according to the application of pre-processing. Therefore, it was verified that the distance data were stably acquired regardless of the strong outliers and noise of the UWB sensor data.  
Table 1 shows the comparison of data before and after pre-processing, and through this, the performance of the pre-processing algorithm can be confirmed. In the case of the raw data, the deviation PP (peak to peak) between the maximum and minimum values is large and the SD (standard deviation) is high, whereas in the case of the filtered data, which are the data after pre-processing, it was verified that both the deviation and standard deviation of the maximum and minimum values were smaller than that of the raw data. Therefore, it was verified that the influences of the noise and outliers were reduced through the pre-processing algorithm proposed in this study.  
Figure 10: Wheelchair platform and structural diagram used in this experiment. (**a**) Intelligent wheelchair configuration chart; (**b**) intelligent wheelchair.
## 3 Results and Discussion
### Optimization and Validation of Model  
The optimum extraction conditions estimated by RSM were 147 \({}^{\circ}\)C, 3 min, and 10 mL/g for temperature, time, and liquid/solid ratio, respectively. The desirability value for these conditions was 0.99. Pectin yield was estimated to be 27.74% under the predicted optimum conditions. Experimental validation was performed in triplicate and a pectin yield of 27.69 \(\pm\) 0.07% was obtained. A one-sample _t_-test was applied to find significance between experimental and predicted yield. There was no significant difference (\(p>\) 0.05) between the predicted and experimental yield values, which means that the model was confirmed.