esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Fuzzy Logic Controller Used for a Differential Drive Wheeled Mobile Robot
## 2 Problem Description
### Optimization of the Fuzzy Logic Controller Based on the Genetic Algorithm
#### 3.2.1 Parameterization of the Membership Functions  
Many types of the MFs have been used in fuzzy logic control systems, such as a triangular function, a trapezoidal function, a Gaussian function, etc. In the WMR field, the triangular and trapezoidal functions are applied more often than others [36]. Thus, in this study, the triangular function is selected to represent the MFs of the FLC because the triangular function is determined by just three parameters. Also, its evaluation is quite cheap, and the main advantage of the triangular function is the possibility to define it fully by just three parameters. Such a low number of parameters helps to lower the dimensionality of the optimization problem.  
The triangular functions can be represented in different ways. A set of parameters defining these MFs is depicted in Figure 9.  
In Figure 9, the (min, base1) is the set of parameters defining the 1 st Membership Function (MF), the (base2, A1, base3) is the set of parameters defining the 2 nd MF, the (base4, A2, base5) is the set of parameters defining the 3 rd MF, the (base6, A3, base7) is the set of parameters defining the 5 th MF. Thus, there is a total 11 parameters (base1, base2, A1, base3,..., A3, base7, base8). Here, min and max are the minimum and the maximum of the universe of discourse, respectively. A1-A3 and base1-base8 can take any value in the range [min; max] with the constraint (min < A1 < A2 < A3 < max).  
The FLC has two inputs and two outputs as mentioned in the Section 3.1. Each input and output has five MFs described by 11 parameters, thus there is a total of 44 parameters for all inputs and outputs of the FLC. Here, the parameters of input_1 are (base1, base2,  
Figure 8: The genetic algorithm cycle block diagram.  
Figure 9: The triangular membership functions with their parameters.  
A1, base3, base4, A2, base5, base6, A3, base7, base8), the parameters of input_2 are (base9, base10, A4, base11, base12, A5, base13, base14, A6, base15, base16), the parameters of output_1 are (base17, base18, A7, base19, base20, A8, base21, base22, A9, base23, base24), the parameters of output_2 are (base25, base26, A10, base27, base28, A11, base29, base30, A12, base31, base32). These 44 parameters are encoded to 44 gens of each chromosome used in the GA, i.e., each chromosome represents all parameters of the input and output MFs. The structure of each chromosome can be represented as follows:  
\[\begin{array}{ccccc}&44&\text{gens}\\ \{&\\ \text{Chromosome}=\text{base1},&\ldots,\text{base8 base9},&\ldots,\text{base16 base17 },&\ldots,\text{base24 base25},&\ldots,\text{base32}\\ \{&\}&\}&\{\\ \text{Input\_1}&\text{Input\_2}&\text{Outout\_1}&\text{Output\_2}\end{array}\]
## 3 Problem Definition
If user \(u_{i}\) interacts with item \(v_{j}\), then \(F(u_{i},v_{j})\)'s value is set to 1, indicating a positive reaction from the user to the item; if there is no interaction between user \(u_{i}\) and item \(v_{j}\), then \(F(u_{i},v_{j})\)'s value is set to 0, indicating no direct interest in the item, but not necessarily a dislike. Increasing the number of interactions with the target item (i.e., setting more 1 values) can enhance the exposure of the target item, reflecting its importance and visibility in the recommendation system, thereby increasing the likelihood of it being recommended. After generating synthetic user accounts and their interaction data, the subsequent procedure involves integrating these data into the original training set to enhance the visibility of target items in user recommendation lists through training. A white-box setting is ideal, where attackers, having a basic understanding of the recommendation algorithm, can replicate it to obtain user recommendation lists. However, this ideal scenario is hard to achieve in reality; in a black-box setting, user recommendation lists are private and only visible to the user themselves, making it difficult for attackers to assess the promotion of target items. To overcome this challenge, we introduce the concept of \"spy users\" as an innovative solution. Spy users refer to a group of synthetic user accounts created and controlled by attackers, designed to mimic the normal interaction behaviors of real users within the recommendation system. By reading the user-item interaction records from the training dataset, these users are categorized into two types: active users and inactive users, to cover different user behavior patterns:  
**Active users:** For explicit datasets, users who frequently rate items highly (e.g., scores between 1 and 5) are considered active users. A user is deemed active if their average rating for items is three or higher. For implicit datasets, which lack ratings, users whose number of item interactions exceeds the average are regarded as active users. **Inactive users:** Conversely, any user not classified as an active user within the dataset is considered inactive. These users are often a focus of the recommendation system, because once they engage with an item from the recommendation list, the system can quickly analyze their preferences and subsequently recommend more related items to them. To effectively target the recommendation system, constructing spy users to simulate the behaviors of inactive users requires meticulous planning. By introducing spy users, we provide a feasible source of feedback for attack strategies in black-box settings. By analyzing the presence of target items in the recommendation lists of spy users, attackers can indirectly assess the impact of their actions and adjust their strategies accordingly.
## 4 EFTs Classification Scheme
### Estimation of Functional Attributes  
In order to summarize the information provided by the NDVI, LST and Albedo time series, several metrics of the annual average curve for the period 2000-2009 have been derived for every pixel of the image composite. The metrics used in this paper include: maximum value (Max), minimum value (Min), mean value (Mean), annual integral (I) as the area under the seasonal curve, date of maximum (D) as the date when the maximum value occurs, relative range (R) as the difference between maximum value and minimum value divided by the annual integral. A total of eighteen functional attributes have been derived, six for each of the NDVI, LST and Albedo series. These cumulative remote sensing indices or metrics [70] have been reported to capture significant features of ecosystem functioning. In particular,  
Figure 2: Flowchart of the methodology used to derive the ecosystem functional types (EFTs).  
the annual integral of NDVI (INDVI) shows the ecosystem's long-term capacity to sustain biomass whereas the date of maximum NDVI (DNDVI) shows its resilience for recovering from disturbances, mainly rainfall fluctuations [45]. To keep the continuous nature of the annual period and the relative distance between months (_i.e._, December is as close to February as May is to July), we transformed months into polar coordinates to compute the date of maximum. Months were therefore characterized by their sine and cosine values (30\({}^{\circ}\) for January and 360\({}^{\circ}\) for December).  
Additionally, the Thornthwaite moisture index was computed as the difference between monthly precipitation (P) and potential evapotranspiration (PET) [71]. This climate index is a useful indicator of the supply of water in an area relative to the demand for water (P) under prevailing climatic conditions (PET). Therefore, P-PET is used as an estimate of the atmospheric balance between water supply and water demand, which reflects the portion of total precipitation used to nourish vegetation over a certain area. To derive PET we apply the Thornthwaite method [72] to monthly precipitation maps (2000-2009 period) from the AEMet dataset. The budget between PET and rainfall yields the yearly humidity and aridity indices. The linear combination of them produces the moisture index.
# Prediction of Oleic Acid Content of Rapeseed Using Hyperspectral Technique
## 2 Materials and Methods  
### Materials  
The Selected Materials  
Two rapeseed varieties (Xiangyou 708 and Xiangyou 710) are used as the research objects. Our field experiment is located in the Yunyuan Base of Hunan Agricultural University (113\({}^{\circ}\)4\({}^{\prime}\) E, 28\({}^{\circ}\)10\({}^{\prime}\) N) in 2018 and 2019. The field soil is black loam soil with rice-rapeseed rotation.
## Appendix A The LoRaWAN Communication Solutions
### LoRaWAN Implementations  
Currently there are several different LoRaWAN solutions available; some of them are based on private infrastructure connecting proprietary nodes for specific applications [58; 59; 60], others, are based on public infrastructure offering free access for non-profit usage (despite some constraints in terms of available bandwidth exist) [61; 62]. Additionally, some open-source implementations of the backendare available [63]. Notably, the LoRa Server [44] is a complete, ready-to-use and particularly easy to setup solution that includes both a web-based and a Representational State Transfer (REST) APIs for integration with third party applications.
# A Systematic Literature Review on Military Software Defined Networks
## 7 Discussion and Recommendations for Future Work
### Tools  
Validation and evaluation of performance, resilience and security solutions for complex systems like military networks remain difficult problems. Simulators, emulation platforms and test-beds are useful for proving the efficiency and feasibility of new network architectures/designs and algorithms, and evaluate their capability to address specific challenges of military networks. Future research directions include:  
* Designing experiments to study SDN-enabled multi-domain heterogeneous networks, non-IP protocol innovations, and building SDN network exchange (SDX) to act as policy based SDN network peering point for connecting various SDN network prefixes.
* Simulating/experimenting developed mathematical models for realizing a unified programmable control plane, with support of a realistic number of nodes for wireless heterogeneous network.
* Testing the content-based security concept in network exchange points such as Software Defined Exchange.
* Examining the performance of operational aspects of Software Defined Coalitions (SDC) in an emulated test-bed.
* Implementing an operational military Network with SDN where management links are suffering from variances.
* Implementing an operational military Network by including third-party platforms such as Pyretic to the applications code, in order to explore the advantages of the policy enforcement approach.
* Conducting experimentation on an at-scale real SDN testbed for large-scale military networks.
* Extending the testbed setups to be able to demonstrate enforcement of security policies in respect to all three dimensions of security goals, i.e., confidentiality, integrity and availability, and performing experiments involving a combination of cross-layer and network-specific (e.g., network intrusion detection mechanisms)
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 3 Overview of Telemedicine
### Components of Telemedicine
#### 3.1.1 Patient's WBAN Unit  
The patient's WBAN unit comprises the remote patient along with any biosensors, physiological sensors, or external sensors around the patient. These sensors are physical devices used for the collection of vital signs from the patient, and are processed for transmission through the communication unit. These devices have the ability to store and retrieve stored data and to interface with application programs to share acquired data. Sensors for telemedicine are usually of two categories, namely, physiological sensors and environmental sensors. Physiological sensors are wearable or embedded/implanted in the human body. These sensors are designed considering the need for ease of wearing, energy efficiency, data security, standardization, and skin protection. Environmental sensors are those sensors used to measure or collect environmental signals around the patient. Several sensors for collecting physiological signs and measurements of interest for medical prognosis have been identified in previous research, prominent among which are [47; 48; 49; 50; 51; 52; 53; 54; 55; 56]:  
1. Electrocardiogram (ECG), which records the heart's electrical signals.
2. Pulse oximeter, which records the hemoglobin oxygen concentration in the blood.
3. Pulse/heart rate.
4. Blood pressure.
5. Body temperature.
6. Electroencephalogram (EEG), for measuring electrical activity in the human brain.
7. Electromyography [57], for the assessment of nerve and muscle cells.  
Other sensors used in telemedicine include, as in [19]:  
1. Photoplethysmography (PPG).
2. Electroocologram (EOG), for eye diagnosis.
3. Diet sensors, for monitoring eating patterns and assessing Type II diabetes and coronary heart disease.
4. Galvanic Skin Response (GSR).  
Environmental sensors include thermometers, accelerometers, motion detectors, GPRS, humidity sensors, weight sensors, gyroscopes, and piezoelectric sensors. Very recently there have been a variety of nano-engineered electrochemical sensors used specifically for COVID-19 diagnosis and management, as discussed in [39], including:  
1. Field Effect Transistor-based (FET-based) sensors; these are ultrasensitive, and are used to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) antigens.  
Figure 3: Components of a Telemedicine System.  
2 Laser-engraved graphene-based immunosensors, which are used o obtain a diagnosis from saliva.
* Nanomaterial-based hybrid sensors for exhaled breath analysis, which enable machine learning-assisted COVID-19 diagnosis.  
With respect to WBAN, the provider should have a secure database to securely save patients' data regardless of whether the telemedicine mode of operation is synchronous or asynchronous, and each user's data should be uniquely assessed based on authorization and authentication. Such a database usually contains the EHR for proper follow-up, tracking of patients' progress [58], and management of health issues.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 2 Background and Preliminaries
### Beyond IoT Technologies
#### 2.6.1 Edge Computing  
Edge computing enhances smart grid security by enabling local, real-time data analysis and response at substations and energy devices, thereby minimizing reliance on centralized systems that are susceptible to latency or attack [56]. The decentralization of detection and response capabilities enhances system resilience [33]. Edge computing enables IoT sensors and meters to optimize fault isolation, energy routing, and intrusion detection close to the source by allowing these devices to not only gather data but also respond instantly [57].
## 3 Results
### Time-Dependent Performance Analysis
#### 3.3.2 Work Consistency over Time  
Figure 9 illustrates how the coefficient of variation (CV) for work cycle duration changes over time for each task under different conditions. The data presented in these graphs provides evidence of how movement consistency is affected by exoskeleton use throughout the 2 h work period. Open circles (\(\Circle\)) represent data points classified as statistical outliers in the box plots.  
For the sack carrying task (CT) under low-intensity conditions, workers without exoskeletons showed a CV increase from 12.8% in the first interval to 17.3% in the final interval, while with the exoskeleton, the CV increased more modestly from 8.5% to only 10.0%. Under high-intensity conditions, the effect was more pronounced, with the CV without exoskeleton rising from 15.3% to 20.8%, while with the exoskeleton it increased from 9.2% to only 11.4%.  
The masonry bricklaying task (MT) showed similar patterns. Under low-intensity conditions, the CV without exoskeleton increased from 10.5% to 14.4%, compared to a smaller rise from 7.6% to 9.1% with the exoskeleton. The high-intensity condition demonstrated an even greater effect, with the CV increasing from 14.2% to 19.5% without the exoskeleton, while increasing more modestly from 9.3% to 11.6% with the exoskeleton.  
For the scaffolding installation task (ST), the low-intensity condition showed the CV without exoskeleton increasing from 11.3% to 15.3%, while with exoskeleton it increased from 8.1% to only 9.6%. In the high-intensity condition, the CV without exoskeleton rose from 11.4% to 16.8%, compared to a smaller increase from 7.6% to 9.3% with exoskeleton.  
Even in the first interval (0-30 min), the exoskeleton provided improvements in movement consistency, with lower CV values than without exoskeleton. This advantage consistently increased over time, with the difference between conditions growing from the first interval to the final interval. The average increase in CV when using the exoskeleton across all tasks was significantly smaller from the first to last interval, compared to the increase without exoskeleton.  
The data demonstrates that the exoskeleton not only improves initial movement consistency but becomes increasingly valuable as work duration extends, effectively mitigating the natural deterioration in movement precision that occurs with fatigue. The stability of CV values when using the exoskeleton, even after two hours of continuous work, suggests that this technology could significantly contribute to maintaining both work quality and worker wellbeing during extended construction activities.
# Underwater Wireless Sensor Networks Performance Comparison Utilizing Telnet and Superframe
## 3 Background on Communication Protocols
### Location-Aided Routing 1 (Lark1)  
In wireless ad hoc networks, the LAR protocol is deployed as a kind of location-based and reactive routing. It accomplishes this by utilizing three separate packets, namely, the route request, the route reply, and the route error, in order to send and receive information between nodes and maintain stable connections. The LAR on-demand routing protocol functions in a manner that is comparable to that of the DSR (dynamic source routing) protocol. The LAR protocol, as opposed to the DSR protocol, makes use of location data in order to establish the borders of the \"request zone\", which is the area in which it is feasible to discover new routes. This zone is defined in contrast to the DSR protocol, which does not make use of location data. As a direct result of this, the route requests will not be broadcast to the full of the network, but rather will only be transmitted by the nodes that are a part of the request zone. The originator provides a forecast about a spherical region that is termed the predicted zone. This region is the location in which the target object is most likely to be discovered at this same current time. The prediction is made by using data from the past [18].
# Research on the Decoupling of the Parallel Vehicle Tilting and Steering Mechanism
## 3 Simulation and Experiment
### Simulation  
Through the simulation of the established model, it can be seen that when the vehicle does not tilt, the steering angle of the wheels on both sides is such that is shown in Figure 4.  
Taking the process of left steering as an example, compared with when the vehicle is not tilting, the influence of the tilting angle on the steering angles is that which is shown in Figure 5. \(\Delta\delta\) and \(\Delta\delta^{\prime}\) represent the errors due to the tilting angle.  
When \(\left|\theta_{v}\right|\leq 20\) deg, the maximum error of the outer and inner wheel steering angles is 0.166 deg and 0.282 deg, respectively.  
When the vehicle is driving at the speed of \(v=25\) km/h, the target tilting angle is given by [10]. The tilting angle and the steering angles of the wheels on both sides are shown in Figure 6.  
Figure 4: Simulation without tilting.  
Figure 5: Simulation with tilting. (**a**) The influence of \(\theta_{v}\) on \(\delta\); (**b**) The influence of \(\theta_{v}\) on \(\delta^{\prime}\).  
Since the motion of the vehicle is symmetrical when turning to both sides, the vehicle steering to the left can be taken as an example. The SGCR (\(K\)) of the vehicle in this context is shown in Figure 7.  
When 0 deg < \(\theta_{\rm v}\)\(\leq\) 20 deg and 0 deg < \(\delta\)\(\leq\) 40 deg, the SGCR of the vehicle is 0.87 \(\leq K\)\(\leq\) 1.25. This is within an acceptable range (in low-speed vehicles, \(K\) should be in the range of 0.4 \(\leq K\leq\) 1.6) [22].  
Figure 6: Simulation when the vehicle is driving. (**a**) \(\theta_{\rm v}\) when the vehicle is steering; (**b**) \(\delta^{\prime}\) and \(\delta\) when the vehicle is tilting; (**c**) the error in \(\delta^{\prime}\) and \(\delta\) because of the tilting angle.  
Figure 7: Simulation of the SGCR.
## 3 Results and Discussion
### Data Processing Method  
The 1 Hz GPS data provided by Japan's GEONET (GNSS Earth Observation Network System) and the Crustal Movement Observation Network Of China (CMONOC) are used for the analysis of the earthquakes in Tohoku-Oki and Lushan, respectively. During the data processing, the precise orbit and clock products from IGS are adopted. Those GPS data are processed under the modes of SF-PPP and GAMIT/TRACK.
## 6 Experimental Results
### Results for Individual Vowels  
The results of individual classifiers were also compared by evaluating each vowel separately. As in the previous case, we distinguished between male, female, and mixed types of samples, and we employed the same cross-validation approach as before. Each of these sample types was subdivided into four subsets of samples. Each subset contained records of only one kind of vowel. The number of features for each subject was 1560. The best value among the vowels is highlighted with bold font in each column in Table 3.  
Table 3 shows that the vowel /u/, pronounced in all intonations, achieved significantly better results than the other vowels. In the case of women, the accuracy of each classifier is higher for the vowel /u/ than for the other vowels. Another finding is that the fusion of all intonations of the particular vowel has a positive influence on the overall accuracy of the classification. The best results for mixed samples were achieved by the SVM classifier, although results are very similar to RFC. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**SVC Accuracy (\%)**} & \multicolumn{3}{c}{**KNN Accuracy (\%)**} & \multicolumn{3}{c}{**RFC Accuracy (\%)**} \\ \hline \multirow{4}{*}{**Type of Samples**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Cross**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Cross**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Cross**} \\  & & & & & & & & & & & \\ \cline{1-1}  & Women \& Men & 76.5 & 75.51 & 78.0 (\(\pm\)5) & 73.0 & 65.31 & 67.2 (\(\pm\)5) & 80.5 & 73.47 & 77.6 (\(\pm\)5) \\  & Women & 75.0 & 65.38 & 70.3 (\(\pm\)7) & 72.6 & 53.85 & 66.3 (\(\pm\)9) & 74.5 & 65.39 & 69 (\(\pm\)7) \\  & Men & 80.3 & **86.96** & **80.7 (\(\pm\)7)** & 68.0 & 60.87 & 67.6 (\(\pm\)7) & 81.2 & **91.30** & 79.3 (\(\pm\)8) \\ \hline \multirow{4}{*}{**Vowels**} & Women \& Men & 81.1 & 87.75 & 80.3 (\(\pm\)4) & 69.7 & 69.39 & 70.6 (\(\pm\)6) & 82.4 & 75.51 & 81.8 (\(\pm\)5) \\  & Women & 71.5 & 80.77 & 80.6 (\(\pm\)5) & **77.5** & 65.39 & **74.4 (\(\pm\)7)** & 81.0 & 65.38 & 0.78 (\(\pm\)8) \\ \cline{1-1}  & Men & **81.2** & **86.96** & 86.2 (\(\pm\)6) & 73.5 & **73.91** & 67.4 (\(\pm\)8) & **84.7** & 82.61 & **83.7 (\(\pm\)0)** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparing the results of classification according to gender and feature selection. All features and the first 300 features selected by feature selection were compared. Both groups are divided by gender.