esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Discussion
### Recommendations
#### 8.2.4 An Increase in Energy Efficiency  
It goes without saying that any sustainable growth must pay strict attention to energy use. As a result, 6G will need to design communication tactics that are both effective and energy-efficient. In order to provide battery-free communication whenever possible, the goal is to achieve communication efficiency on the order of 1 pJ/b. Some key performance indicators (KPIs), such as delay jitter and energy per bit, are not stated in 5G since they do not represent a primary focus of 5G, although they are critical KPIs for 6G.  
Figure 7: Categories of Motivations.
###### Abstract  
Currently, analysts in a variety of nations have developed various WSN clustering protocols. The major characteristic is the Low Energy Adaptive Clustering Hierarchy (LEACH), which attained the objective of energy balance by sporadically varying the Cluster Heads (CHs) in the region. Nevertheless, because it implements an arbitrary number system, the appropriateness of CH is complete with suspicions. In this paper, an optimal cluster head selection (CHs) model is developed regarding secure and energy-aware routing in the Wireless Sensor Network (WSN). Here, optimal CH is preferred based on distance, energy, security (risk probability), delay, trust evaluation (direct and indirect trust), and Received Signal Strength Indicator (RSSI). Here, the energy level is predicted using an improved Deep Convolutional Neural Network (DCNN). To choose the finest CH in WSN, Bald Eagle Assisted SSA (BEA-SSA) is employed in this work. Finally, the results authenticate the effectiveness of BEA-SSA linked to trust, RSSI, security, etc. The Packet Delivery Ratio (PDR) for 100 nodes is 0.98 at 500 rounds, which is high when compared to Grey Wolf Optimization (GWO), Multi-Objective Fractional Particle Lion Algorithm (MOFPL), Sparrow Search Algorithm (SSA), Bald Eagle Search optimization (BES), Rider Optimization (ROA), Hunger Games Search (HGS), Shark Smell Optimization (SSO), Rider-Cat Swarm Optimization (RCSO), and Firefly Cyclic Randomization (FCR) methods.  
WSN; improved DCNN; trust evaluation; RSSI; security
# Coupled Vibration Analysis of Submerged Floating Tunnel System in Wave and Current
## 4 Parametric Influence Study
### Effect of Wave Parameters  
Figure 8 gives the SFT maximum amplitude response when the wave period \(T\) varies from 6 to 16 s and the wave height \(H\) varies from 4 to 16 m [36], under the same working conditions given in Figure 3a. As illustrated in Figure 8, in general, the structural response increases with the increase in wave height, but the effect of the wave period is more complicated.  
In Figure 8a, the maximum value of \(A_{ci}/d_{i}\) (the normalized maximum amplitude of the cable) is obtained at \(T\approx 10\) s. However, since the performance of the anchor cable is mainly dominated by the vortex-induced forces, the change in the vibration amplitude is negligible.  
It can be seen from Figure 8b that, with the increase of the wave period, the amplitude of the tube \(A_{t}\) first shows a rising trend, and then a decreasing trend, which is consistent with the \"wave loads vs. wave numbers\" curves in references [7; 10]. Also, \(A_{t}\) reaches a maximum at \(T\approx 9\) s. This is because,  
Figure 7: SFT structural response under different current velocity (frozen parameters: \(H=8.2\) m, \(T=10.8\) s, gravity-buoyance ratio (GBR) = 0.65 and inclined mooring angle (IMA) = 90\({}^{\circ}\)): (**a**) normalized maximum amplitude of the cable \(A_{ci}/d_{i}\); (**b**) maximum vibration amplitude of the tube \(A_{t}\), and maximum stress variation in the cables \(A_{\Delta\sigma}\).  
in Equations (6) and (7), the amplitude and frequency of the wave force on the SFT tube are functions of the wave period, so that the structure response may exist at extreme points as the wave period changes.
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 2 Related Works  
In this section, existing prediction methods and adaptive transmission methods designed for the dynamic communication environment are briefly reviewed.
## 2 Materials and Methods
### Statistical Analysis  
A sample size of 47 participants was deemed adequate based on a 0.95 statistical power (\(\beta\)), a 0.50 large effect size, and a 5% level of significance (G*Power 3.1.9.7, Heinrich-Heine-Universitat Dusseldorf, Dusseldorf, Germany). The post hoc (correlation: bivariate normal model) power analysis showed that correlations between the variables selected and WA points presented a statistical power from 0.93 to 1.00. Normality of distribution and variance homogeneity were confirmed using Shapiro-Wilk and Levene tests, respectively, and descriptive analysis (mean and standard deviation) were obtained for all variables (IBM(r) Corp, Armonk, NY, USA, SPSS(r) Statistics 27.0.1.0). Student's \(t\)-test for independent samples with effect size estimation [32] was used for comparing swimmers (separated by sex) from elite and non-elite groups. Pearson correlation coefficients (controlled for sex) and 95% confidence intervals were calculated between age, training background and duration,  
Figure 2: Distribution of swimming events used to assess competitive swimmers’ performance based on World Aquatics points (model output).  
anthropometry, countermovement jump, and general biomechanical-related variables and WA Points.  
After inferential procedures, we developed a predictive deep artificial neural network model to assess the relationship between the dependent variables and WA points. The model structure was developed in Python (version 3.9.2) using the TensorFlow library (v2.10.0, Google). A total of 16 variables were included in the input layer, 1000 neurons in each of the three hidden layers (hyperbolic tangent, sigmoid, and linear rectified activation functions), and one variable in the output layer (WA points; Figure 3).  
The sample was then divided randomly into data sets for the model training (70%) and testing (30%), and the seed identifier (SID) was saved to ensure model reproducibility and consistency. The current model training used the root mean square propagation adaptive learning algorithm with a loss function based on the mean square error and a learning rate of 0.001 [20]. A limit of 200,000 epochs was established for training (since computational power was not an issue), and to avoid overtraining, the information of the best weight configuration (by monitoring the root mean square error) was stored by a checkpoint function [20]. The final model reached a root mean square error of 0.0001 after 25,640 epochs. The model training, test, and total sample sets were measured through linear regressions and correspondent coefficients of determination between targets (predicted) and output (real) values (see Figure 4; predictive validity), and interpretation of the model performed by computing Shapley additive explanations values through the SHAP model (v0.041.0) for Python [19].  
Figure 3: Artificial neural network model structure: 16 variables in the input layer, hyperbolic tangent (Tanh), sigmoid and linear rectified activation (ReLu) functions corresponding to the three hidden layers with 1000 neurons each and World Aquatics points in the output layer.
# Wireless Sensor Network Energy Model and Its Use in the Optimization of Routing Protocols
## 2 Description of the Energy Model
After this basic single packet cycle, the node continues to receive, listen, transmit,and changes states until the simulation time has ended and the node turns off using shutdown energy (\(E_{OFF_{i}}\)). During all the processes performed by a node as part of a network, the node is in active mode and the microcontroller remains active throughout the sampling period, consuming microcontroller energy (\(E_{MC_{i}}\)). Algorithm 1 shows the model operation pseudo-code for each of the energies previously exposed in the main processes executed by a node within the network. ```
E_{ON-Total} = 0 E_{MC-Total} = 0 E_{OFF-Total} = 0 E_{Switching-Total} = 0 E_{CSMA/CA-Total} = 0 E_{TX-Total} = 0 E_{RX-Total} = 0 FOR{Every node i} Node i turns on with a coverage radius; E_{ON-Total} = E_{ON-Total} + E_{ON_i} E_{MC-Total} = E_{MC-Total} + E_{MC_i} Node i listens to the channel; E_{CSMA/CA-Total} = E_{CSMA/CA-Total} + E_{CSMA/CA_i} E_{MC-Total} = E_{MC-Total} + E_{MC_i} IF{channelisfree} Send neighboracknowledgment packet; E_{TX-Total} = E_{TX-Total} + E_{TX_i} E_{RX-Total} = E_{RX-Total} + E_{RX_i} E_{MC-Total} = E_{MC-Total} + E_{MC_i} ELSE Increasedelay and re-check channel; E_{MC-Total} = E_{MC-Total} + E_{MC_i} ENDIF Node i populates its neighbors table; IF{Node i requires sending information} Find a route in neighbors table; IF{channelisfree} Send neighboracknowledgment packet; E_{TX-Total} = E_{TX-Total} + E_{TX_i} E_{RX-Total} = E_{RX-Total} + E_{ERX_i} E_{MC-Total} = E_{MC-Total} + E_{MC_i} ELSE Increasedelay and re-check channel; E_{MC-Total} = E_{MC-Total} + E_{MC_i} ENDIF IF{SimulationTime == end} E_{OFF-Total} = E_{OFF-Total} + E_{OFF_i} E_{MC-Total} = E_{MC-Total} + E_{MC_i} ENDIF ENDFOR
```  
**Algorithm 1**Algorithm for the proposed energy model.
# A Hierarchical Control System for Autonomous Driving towards Urban Challenges
## 2 Hierarchical Control System
### Local Path Planning
#### 2.2.2 Obstacle Avoidance Based on Hybrid A* Algorithm  
One of the important features of the path-planning module in an autonomous vehicle framework is the obstacle avoidance mechanism [16; 30]. This mechanism is desired when an autonomous vehicle must detour the predefined global path in order to avoid any hindrance. Typical examples of such environments are of car crash-like obstacles, or construction sites on the highways. The path planner, upon receiving the information from the perception module about an obstruction in the way, activates the obstacle avoidance mechanism. Based on the information from the perception module and global path, sub-goals of starting and goal poses of the vehicle are defined, and an ideal path is generated that takes non-holonomic constraints into account. The realtime hybrid A*-based LPP is performed that mainly inherits the implementation of [31; 32]. The algorithm is performed when DMM requests by providing start and goal location and occupancy grid map as the inputs. The goal is selected from the global path planning, whereas the perception within a sliding window continuously updates the local grid map. The algorithm starts with the initialization by generating a collision lookup table and obstacle distance lookup table. The Reed Shepp curves are calculated for the constrained heuristics, whereas an A* search was conducted for the unconstrained heuristics. The analytical collision-free path is fed to the smoother that optimizes the obstacle distances and treats it for smoothness. Later, the refined path is fed forward to the control module. The main improvement compared with the previous research [31] is a large static occupancy grid map is stored as initialization and updated during the operating of this algorithm. The local region about the obstacle is employed for path planning. This methodology enables the safe path generation when dealing with the uncertainty of dynamic objects from the perception.
# Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
## 3 Proposed Scheme Based on Homomorphic Encryption and Classic McEliece
### Proposed Non-Device-Centric Authentication Scheme
#### 3.3.1 Setup and Enrollment Phases  
1. The authentication server \(A\) generates its pair of public and private keys \((T^{A},\ \Gamma^{A})\) by means of the Classic McEliece _KeyGen_ algorithm. They are stored in a secure way.
2. The authentication server \(A\) sends the public key to the client device \(U\) and it is stored.
3. A threshold value \(th\) is set to determine the recognition decision.
4. The client device \(U\) acquires the user identifier \(ID^{U}\) and the biometric samples \(S^{UR}\).
5. Binary biometric features \(b^{UR}\in\mathbb{F}_{2}^{t}\) that preserve the distances are extracted from \(S^{UR}\).
6. The transformation of the enrollment binary features is applied to \(b^{UR}\), thus, obtaining the substrings \(eb_{i}^{UR}\in\mathbb{F}_{2}^{n}\) with Hamming weight greater than \(t\).
7. The \(ENCODE\) algorithm is employed to encode the substrings \(eb_{i}^{UR}\) by using the authentication server public key \(T^{A}\), resulting the encoded substrings \(cb_{i}^{UR}\in\mathbb{F}_{2}^{n-k}\).
8. The \(ID^{U}\) is mapped to an index \(x^{U}\) in a way that is only known by the client device.
9. The substrings \(cb_{i}^{UR}\) and the index \(x^{U}\) are sent to the database server to be stored. The setup and enrollment phases are illustrated in Figure 4.
# Optimizing Electric Vehicle Routing Efficiency Using K-Means Clustering and Genetic Algorithms
###### Abstract  
Route planning for electric vehicles (EVs) is a critical challenge in sustainable transportation, as it directly addresses concerns about greenhouse gas emissions and energy efficiency. This study presents a novel approach that combines K-means clustering and GA optimization to create dynamic, real-world applicable routing solutions. This framework incorporates practical challenges, such as charging station queue lengths, which significantly influence travel time and energy consumption. Using K-means clustering, the methodology groups charging stations based on geographical proximity, allowing for optimal stop selection and minimizing unnecessary detours. GA optimization is used to refine these routes by evaluating key factors, including travel distance, queue dynamics, and time, to determine paths with the fewest charging stops while maintaining efficiency. By integrating these two techniques, the proposed framework achieves a balance between computational simplicity and adaptability to changing conditions. A series of experiments have demonstrated the framework's ability to identify the shortest and least congested routes with strategically placed charging stops. The dynamic nature of the model ensures adaptability to evolving real-world scenarios, such as fluctuating queue lengths and travel demands. This research demonstrates the effectiveness of this approach for identifying the shortest, least congested routes with the most optimal charging stations, resulting in significant advancements in sustainable transportation and EV route optimization.  
Keywords: electric vehicle routing; dynamic recharging queue; Genetic Algorithm; K-means +
Footnote †: journal:  
_Article_
# XOR Multiplexing Technique for Nanocomputers
###### Abstract  
In emerging nanotechnologies, due to the manufacturing process, a significant percentage of components may be faulty. In order to make systems based on unreliable nano-scale components reliable, it is necessary to design fault-tolerant architectures. This paper presents a novel fault-tolerant technique for nanocomputers, namely the XOR multiplexing technique. This hardware redundancy technique is based on a numerous duplication of faulty components. We analyze the error distributions of the XOR multiplexing unit and the error distributions of multiple stages of the XOR multiplexing system, then compare them to the NAND multiplexing unit and the NAND multiplexing multiple stages system, respectively. The simulation results show that XOR multiplexing is more reliable than NAND multiplexing. Bifurcation theory is used to analyze the fault-tolerant ability of the system and the results show that XOR multiplexing technique has a high fault-tolerant ability. Similarly to the NAND multiplexing technique, this fault-tolerant technique is a potentially effective fault tolerant technique for future nanoelectronics.  
Keywords:nanoelectronic circuit; fault tolerant; XOR multiplexing technique; bifurcation analysis +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussion
### Effect of Operating Conditions
#### 3.2.6 Effect of Methanol Synthesis Operating Pressure  
Figure 11 shows the effect of methanol synthesis operating pressure on yield. It is observed that the methanol yield improves with increasing operating pressures, which is due to the reversible nature of the reaction (reducing the gas volume). Therefore, increasing reaction pressure favors the methanol production. For example, when the pressure increases from 1 bar to 50 bar, the methanol yield increases significantly from 0 kmol/1000 kg to 7.99 kmol/1000 kg. However, when the pressure increases from 50 bar to 150 bar, the yield increases barely from 7.99 kmol/1000 kg to 11.35 kmol/1000 kg. The methanol yield increase rate from 0 bar to 50 bar is more significant than the yields at a pressure between 50 bar and 150 bar. Besides, using strong operating pressures involve higher equipment requirements, price, and operational risks. Therefore, although the methanol production is favored at higher pressures, a pressure of 50 bar appears as a good compromise.  
Figure 11: Effect of pressure on methanol yield (250 \({}^{\circ}\)C).  
Figure 10: Effect of temperature on methanol yield (50 bar).
## 3 Methods
### Spatial Autocorrelation
#### 3.1.2 Multivariate Spatial Cross Correlation  
Spatial cross correlation indicates the extent to which the multiple attributes of a feature are related to nearby features. The exploration of multivariate spatial cross correlation is a core functionality of current exploratory data analysis (EDA), knowledge discovery, and data mining tools [61]. [PERSON] et al. (2002) proposed bivariate global (Equation (3)) and local (Equation (4)) Moran's I to quantify bivariate spatial cross correlation [62]. They are calculated from:  
\[I_{ab}=\frac{n\sum_{i}^{n}\sum_{j}^{n}w_{ij}(a_{i}-\overline{a})(b_{j}- \overline{b})}{\left(\sum_{i}^{n}\sum_{j}^{n}w_{ij}\right)\sum_{i}^{n}(a_{i}- \overline{a})^{2}} \tag{3}\]  
\[I_{ab}(i)=\frac{n(a_{i}-\overline{a})}{\sum_{i}^{n}(a_{i}-\overline{a})^{2}} \sum_{j}^{n}w_{ij}(b_{j}-\overline{b}) \tag{4}\]  
where \(a_{i}\) and \(b_{j}\) are the observed values of variables \(a\) and \(b\) at positions \(i\) and \(j\), respectively; \(n\) is the total number of observations; \(\overline{a}\) and \(\overline{b}\) are the mean values of the observations of variables \(a\) and \(b\), respectively; and \(w_{ij}\) is the spatial weighted matrix.  
[PERSON] (2019) proposed using the univariate local Geary's C to measure the squared distance in attribute space (i.e., along a line for the univariate case) between the values at a geographic location and its neighboring locations, which is summarized in the form of a weighted sum [13]. This indicator can be readily extended to a multivariate context. For example, consider two variables, \(p\) and \(q\). The squared distance, \(d_{ij}^{2}\), in two-dimensional attribute space between the values at observation \(i\) and its geographic neighbor, \(j\), is:  
\[d_{ij}^{2}=\big{(}p_{i}-p_{j}\big{)}^{2}+\big{(}q_{i}-q_{j}\big{)}^{2} \tag{5}\]  
The bivariate local Geary's C can be defined as:  
\[\begin{array}{l}\mathrm{c}_{ab}(i)=\frac{1}{2}\sum\limits_{j}w_{ij}d_{ij}^{ 2}=\frac{1}{2}\sum\limits_{j}w_{ij}\Big{[}\big{(}p_{i}-p_{j}\big{)}^{2}+\big{(} q_{i}-q_{j}\big{)}^{2}\Big{]}\\ =\frac{1}{2}[\sum\limits_{j}w_{ij}\big{(}p_{i}-p_{j}\big{)}^{2}+\sum\limits_{ j}w_{ij}\big{(}q_{i}-q_{j}\big{)}^{2}]=\frac{1}{2}(c_{a}(i)+c_{b}(i))\end{array} \tag{6}\]  
where \(p_{i}\) and \(p_{j}\) are the observed values of variable \(a\) at positions \(i\) and \(j\), respectively; \(q_{i}\) and \(q_{j}\) are the observed values of variable \(b\) at positions \(i\) and \(j\), respectively; and \(w_{ij}\) is the spatial weighted matrix.  
Following standard practice in multivariate clustering analysis, these variables have been standardized such that the mean of the transformed variable is zero and its variance is one. Moreover, the concept of a local Geary' C is additive in the attribute dimension. Therefore, a multivariate local Geary's C can be defined as:  
\[c_{total}(\mathrm{i})=\sum\limits_{v=1}^{k}\ c_{v}(i)/\mathrm{k} \tag{7}\]  
where \(k\) represents \(k\)-dimensional attribute space, and \(c_{v}(i)\) represents the univariate local Geary's C of variable \(v\).
## 3 Case Study
### Indirect or Contributing Causes  
1. The factors that did not directly cause the fire but may have contributed indirectly to the incident and the consequences were also identified. These may have contributed to creating an environment to make the three elements of fire present in sequences leading to the incident, or they may have contributed to the failure of response systems to mitigate the consequences of the incident after the fire occurred. For example, after use of the portable fire extinguisher to extinguish the fire, the operator proceeded to look for the suprvisor, leaving the scene unattended, and the fire was reignited. With proper communication systems, communication should have occurred immediately at the scene to trigger emergency response teams. The operator's failure to fully extinguish the fire with a portable fire extinguisher can also be attributed to the organisation's failure to provide the operator with fire fighting training. The contributing causes are summarized below:
2. **Poor risk management:** No release or commissioning process was performed to certify the installations of the electricals for the additional systems.
3. **Lack of OEM consultation:** The management of the change process did not involve the OEM to approve the installation of the electrical components of the additional systems.
4. **Inadequate standards and procedures:** There was no standard procedure for the installation of the fire suppression system. The fire procedures did not outline the need for a fire watch in the event of a fire.
5. **No fire fighting training for the operator:** The operator had only received training on how to activate the fire suppression system and had not received any fire fighting training.
6. **Poor communication systems:** The underground phone used to report the issue was further away from the scene, and the operator was unable to immediately report or call for assistance while still at the machine.
7. **Poor Emergency mobilization:** The mobilization of resources and transportation of the emergency response teams took a long time. There was also no clarity of roles between the fire fighting crews and emergency response teams.
## 2 Fundamental Concepts and Background
### Types of Explanations
#### 2.4.1 Ante-Hoc Methods  
Additionally, techniques can be split into ante-hoc and post hoc explainability methods. Ante-hoc methods are explainable by design or inherently explainable methods, and are also referred to as transparent or white box/glass box approaches. These methods, which are model specific by definition, include linear and logistic regression, decision trees, k-nearest neighbors, fuzzy inference systems, rule-based learners, general additive models, and Bayesian models [3, 18]. However, even for these methods, they can only be considered explainable, transparent, or interpretable up to a point, for example, in high-dimensional scenarios with complex interaction terms or deep decision trees, these methods can become difficult to interpret [67].  
ML algorithms such as random forests, support vector machines, neural networks (including Deep Neural Networks) are, within practical limits, inherently non-explainable and are typically referred to as \"black-box\" models [3, 19, 21]. Post-hoc methods, which are typically model agnostic, might not explain how black-box models work but they may provide local explanations for a specific decision [3, 18, 46]. One way to do this is to build simpler transparent models that provide interpretable approximations of the black-box [63].