esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations  
\begin{tabular}{l l}
**Symbols** & **Meanings** \\ \(M_{TX}\) & Transmitter \\ \(M_{RX}\) & Receiver \\ \(k\) & Number of bits \\ \(s\) & Distance \\ \(M_{dec}\) & Amount of energy \\ \(s_{0}\) & Threshold distance \\ \(\epsilon_{ad}\) & Energies for the free space \\ \(\epsilon_{np}\) & Multipath model \\ \(g(x)\) & Fitness function \\ \(P_{best}\) & Best position \\ \(P_{best1}\) & First mayfly in the swarm \\ N & total number of male mayflies \\ \(x_{j}^{i}\) & Male mayfly's present location \\ \(j\) & Search space \\ \(t\) & Time \\ \(C_{k}^{t+1}\) & Velocity \\ \(x_{j}^{i+1}\) & Updated position of male mayfly \\ \(y_{1}\) and \(y_{2}\) & Positive attraction constraints \\ \(Pbest_{jk}\) & Optimal position of mayfly had ever visited \\ \(r^{2}\) & Distance between male and female mayflies \\ \(h\) & Gravity coefficient \\ \(\alpha\) & Fixed visibility coefficient \\ \(y_{j}^{t}\) & Female mayfly's present location \\ \(y_{j}^{t+1}\) & Updated position of female mayfly \\ \(fitness\big{(}y_{j}\big{)}\) & Fitness value of female mayfly location \\ \(fitness\big{(}x_{j}\big{)}\) & Fitness value of male mayfly location \\ \(Y_{jk}\) & The location of the female mayfly in dimension k \\ \(X_{jk}\) & \(j\)th element of the location of the male mayfly in dimension k \\ \(rmf\) & Cartesian separation between the male and female mayflies \\ \(r\) & Random number between \([-1,1]\) \\ \end{tabular}
## 4 Results and Experimental Evaluation
### Uncertainty Quantification and Trajectory Distribution  
The primary advantage of our StochasticTrajectory model lies in its ability to quantify uncertainty in predictions, which is critical for safety-critical applications in general aviation. To thoroughly investigate this capability, we analyzed the statistical properties of the input and target trajectory data at different sampling rates.  
Table 2 presents a comparative analysis of statistical measures in different sampling rates for both input trajectory data (historical points used for prediction) and target trajectory data (future points to be predicted). \"Uncertainty Ratio\" is calculated as the ratio of target trajectory standard deviation to input trajectory standard deviation.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **SR = 1** & **SR = 2** & **SR = 4** \\ \hline Input Std. Dev. & 1.6048 & 1.5870 & 1.5583 \\ Target Std. Dev. & 1.1700 & 1.1651 & 1.1658 \\ Uncertainty Ratio & 0.7291 & 0.7341 & 0.7481 \\ Input Range (Max-Min) & 9.2766 & 9.2621 & 9.2245 \\ Target Range (Max-Min) & 7.2578 & 7.2568 & 7.2568 \\ Range Expansion Ratio & 0.7824 & 0.7835 & 0.7867 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical properties of trajectory data across sampling rates.  
As the sampling rate decreases (from SR = 1 to SR = 4), we observe a consistent increase in the uncertainty ratio (from 0.7291 to 0.7481). This indicates that with sparser temporal resolution, our model appropriately captures greater uncertainty in predictions.  
Although the input trajectory standard deviation slightly decreases with lower sampling rates (1.6048 at SR = 1 to 1.5583 at SR = 4), the target trajectory standard deviation remains relatively stable (approximately 1.17 across all sampling rates). This suggests that our model maintains consistent uncertainty quantification regardless of the input data density.  
The target trajectory range (distance between maximum and minimum values) remains almost identical across all sampling rates (approximately 7.26), despite slight variations in input range. This indicates robust boundary prediction regardless of temporal resolution.
## 2 Materials and Methods
### Evaluation Metrics for Model Accuracy  
The coefficient of determination (R\({}^{2}\)) and root mean square error (RMSE) were employed to assess the model's accuracy. R\({}^{2}\) reflects the goodness of fit, while RMSE quantifies the deviation between predicted and measured values. A higher R\({}^{2}\) with a value closer to 1, and a lower RMSE with a value closer to 0 indicate a better prediction model. The formulas are as follows:  
\[\mathrm{R}^{2}=\sum_{i=1}^{n}\left(\hat{y}_{i}-\overline{y}\right)^{2}/\sum_{ i=1}^{n}\left(y_{i}-\overline{y}\right)^{2} \tag{1}\]  
\[\mathrm{RMSE}=\sqrt{\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}/n} \tag{2}\]  
where, n denotes the number of samples. \(y_{i}\) represents the measured value of the sample i, \(\overline{y}\) represents the average of the measured values of the sample, \(\hat{y}_{i}\) represents the estimated value of the sample i, and RMSE represents the root mean square error.
# Novel Methods of AI-Based Gait Analysis in Post-Stroke Patients
## 1 Introduction
### Former Approaches to Post-Stroke Gait Analysis  
Gait dysfunction is considered one of the most common and disabling effects of stroke [29]. To date, instrumental gait analysis has been used to analyze the causes of gait deviations in post-stroke patients, but it generates a large amount of complex data, which poses a problem when there is a need to generate a short, simple report [30]. To date, three main methodological shortcomings have been identified: small sample sizes, lack of testing and selection on the basis of the method/methods best suited for analysis, and lack of detailed validation procedures [30]. A fundamental problem that occurs in clinical gait analysis is that large amounts of data are generated by laboratory analyses of human gaits, and this results in alternative attempts at reliable gait analysis based on relatively limited data sets (including heuristic methods), e.g., for the needs of the Children's Health Centre, based on the use of artificial neural networks. A separate issue is the problem of the physical interpretation of the obtained results. An attempt to solve this problem using decision trees, neural networks, and fuzzy logic was presented by [PERSON], [PERSON] et al. [31; 32; 33].
# Development of Height Indicators using Omnidirectional Images and Global Appearance Descriptors
## 1 Introduction
The altitude estimators we propose go beyond the classical topological notion of connectivity and introduce the concepts of closeness and farness, thus some altitude estimators are proposed that estimate the relative height of a robot except for a scale factor. The results of this paper jointly with the developments presented in [26; 34] prove the usefulness of the global appearance descriptors to estimate the position and orientation of the robot in the ground plane and its altitude with respect to this plane, in a straightforward way. This supposes a step ahead towards the definition of global appearance descriptors that permit building models of the environment and localization when the robot moves with 6 DOF. The remainder of the paper is structured as follows. Section 2 makes a review of some techniques to describe globally the appearance of omnidirectional visual information. Then, in Section 3 the methods implemented to estimate topological height from visual information are detailed. After that, Section 4 describes the geometry of the vision system and the sets of images used to carry out the experiments, whose results are presented in Section 5. At last, the conclusions and future works are outlined in Section 6.
# Reducing the False Negative Rate in Deep Learning Based Network Intrusion Detection Systems
## 4 Materials and Methods
### Model Architecture
This function was designed to overcome the vanishing gradient problem and it works in the way that it returns 0 for any negative input, but for a positive input, it returns the value of the input back. It can be defined as:  
\[f(x)=max(0,x) \tag{2}\]  
The second, third and fourth hidden layers have 248, 124 and 62 nodes, respectively. The output layer has a different activation function and a different number of neurons based on the type of classification which is being done. For binary classification, the output layer uses the sigmoid activation function and has only one neuron. The sigmoid function takes a value as the input, and outputs another value between 0 and 1. It can be defined as:  
\[f(x)=\frac{1}{1+e^{-x}} \tag{3}\]  
On the other hand, for the multiclass classification, the output layer has the number of neurons which is equal to the number of the attack categories in the dataset, and the activation function which is used is the softmax function. This function converts a vector of K real values into a vector of K real values that sum to 1 [41]. It can be defined as:  
\[f_{i}(\vec{x})=\frac{e^{x_{i}}}{\sum_{j=1}^{J}e^{x_{j}}}\text{ for }i=1,...,J \tag{4}\]  
Additionally, to prevent overfitting during the training phase, we implemented the dropout on all the hidden layers. Dropout is a regularization method that causes some of the neurons of a layer to be randomly dropped out (ignored) during the training of the network. Dropping out the neurons means that they will not be considered during the specific forward or backward passing through the neural network. The dropout rate chosen for this network, for each hidden layer, was equal to 0.1. This means that 10% of the units will be dropped (set to 0) at each step. The units that are not dropped are scaled up by \(\frac{1}{(1-rate)}\) so that the sum of all the units remains unchanged. A graphical representation of the architecture of the neural network can be seen in Figure 3. \begin{table}
\begin{tabular}{c l} \hline \hline
**Hyperparameter** & **Value** \\ \hline Batch Size & 64 \\ \hline Epochs & 100 \\ \hline Optimizer & Stochastic Gradient Descent (SGD) with Nesterov momentum \\ \hline Momentum & 0.9 \\ \hline Learning rate & 0.01 \\ \hline Regularization & \(1\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Final values chosen for the training phase.
# An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks
## 3 Satellite Resource Scheduling Problem Formulation  
### Preliminaries  
To provide a clear description of the data model, we present a list of the main variables used in the paper. As shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & **Description** \\ \hline \(T\) & Discrete time \\ \(t\) & Slot time \\ \(S\) & Satellites \\ \(Q\) & Task queue \\ \(\tau\) & Time Slice \\ \(c(u,v)\) & Time consumption from node \(u\) to \(v\) \\ \(f_{s}^{r}(t)\) & The amount of resource \\ _sours_ & Source Node Satellite Collection \\ _nei_ & Neighborhood Nodes \\ \(K\) & Request tasks \\ \(s_{source}\) & The source satellite \\ \(s_{sink}\) & The sink satellite \\ \((u,v)\) & The edge from \(u\) to \(v\) \\ \(f_{u,w}^{r}(t)\) & The data amount on the edge \((u,w)\) \\ \(g_{q}(t)\) & The time cost which consumed to complete a request \\ \(\Phi\) & The waiting delay and the corner time of antenna equipment \\ \(f_{s}(x)\) & The time consumption for all tasks performed on satellite \(s\) \\ \(U_{s}^{r}(t)\) & The time cost consumption for the resource request \\ \(\mathop{\max}\limits_{s,s^{\prime}}^{\max}\) & Link bandwidth \\ \(V\) & Nodes in a network representing satellites \\ \(E\) & Edge in a network representing an inter-satellite link \\ \(G\) & The spatial information network \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of variables.
## 2 The Need for Traceability in Model-Based Testing  
The Institute of Electrical and Electronics Engineers (IEEE) defines traceability as the degree to which relationships can be established between two or more products of the development process, especially products having a predecessor-successor or master-subordinate relationship to one another [5]. Typically, traceability relations denote satisfiability, dependency, evolution, and rationalization relations between software artifacts [6]. [PERSON] and [PERSON] in [7] define traceability as the ability to describe and follow the life of a requirement in both a forwards and backwards direction. [PERSON] _et al._ refer to traceability as the ability to identify requirements at different levels of abstraction and to show that they have been implemented and tested [8].  
The above definitions do not cover all aspects of traceability, and most of them focus on requirements traceability. Since design models and test models also play a major role in model-based testing, there is a need for defining the design model and test model relationship as well. The automation of bidirectional traceability between requirements and test cases is a key aspect of MBT [9]. Bidirectional traceability is the ability to trace links between two parts of the software developmentprocess with respect to each other [9]. [PERSON] _et al._[4] suggest that defects of a model can be minimized by ensuring the traceability from requirements to the part of models.  
Traceability is widely used in the software development life cycle and is an active research area in software engineering [10]. Since tests are derived from models, the correctness of model and the consistency of requirements are important. Therefore, there is a need to understand the following:  
* What are the requirements?
* How to derive tests from system design and the requirements model?
* How are they related?
* What is the impact on other models, when any of the models is changed?  
Traceability between these models will help to understand the above questions. Traceability in model-based testing helps to:  
* Trace back to the respective requirements and design models when a test fails.
* Identify the problems in the design model and the inconsistencies in the requirements elicitation and tests.
* Fills the gap between business analyst, system designer, and test developer.
* Specify the evolution of design model from requirements.
* Show the evolution of test models from requirements and design model.
## 5 Computational Experiments
### Computational Experiments for Static Problems
#### 5.3.3 Routing Example  
An example of a route obtained by the proposed method is shown in Figure 12. In this example, the number of AGVs is set to 7, and the initial and destination positions of each AGV are shown in Table 2. As shown in Figure 12, each AGV was verified to move toward the destination position while avoiding collisions. AGV1 reached the destination position in only five steps and did not move thereafter, forcing AGV2 to take a long way around. Here, the overall optimization is for AGV1 to stop a few steps just before reaching  
Figure 11: Comparison of computation times on static problems. 10 different initial conditions were prepared at each number of AGVs, and the average of the 10 times under each initial condition was plotted.  
Figure 10: Comparison of total transfer period on static problems. The computation time was measured when the AGV was increased by one unit. For each number of AGVs, 10 initial conditions were prepared, and the average values are shown for each of the 10 experiments conducted under each condition.  
the destination position and give way to AGV2. This problem arises because the proposed method cannot predict states far into the future from the present.
# A Parameter-Driven Methodology of Wheel Flat Modeling for Wheel-Rail Impact Dynamics
## 5 Simulation and Discussion
### Effect of Flat Geometry on Wheel-Rail Impact Force  
According to the simulation methodology proposed in Section 5.1, the time-domain solution for the wheel-rail vertical force when the wheel has a flat is presented in Figure 10. The wheel-rail vertical force exhibits periodic impacts in the time domain, characterized by high-frequency vibrations with very short impact cycles. As the wheel enters the flat region, a brief wheel-rail separation occurs, followed by the force reaching its maximum peak value, referred to as the P1 force, and then gradually decaying to near the steady state value.  
Figure 9: Wheel profile input for model. (**a**) Wheel circumferential input. (**b**) Wheel tread profile input.  
The time-domain solution in Figure 10 shows good consistency with the wheel-rail impact force patterns computed in [1,2,17], validating the accuracy of the dynamic model. The discrepancy in the amplitude of the time-domain results compared with the literature is due to the larger axle weight of the vehicle used in this paper, which results in a higher impact force.  
Based on the time-domain characteristics of the wheel-rail force, Figure 11 compares the P1 force variation with velocity for different wheel flat geometries at various depths. The velocity dependence of the maximum wheel-rail impact force is consistent across different wheel flat geometries and depths. The impact force at the new flat stage is larger than at the worm flat stage, with the maximum peak value corresponding to a lower velocity in the new flat stage. This is because the length of the new flat is smaller than that of the worm flat, and the horizontal velocity corresponding to the same drop in the wheel center of mass height is lower in the new flat stage. The maximum impact force is negatively correlated with the minimum circumferential curvature of the flat, and the curvature discontinuity in the new flat leads to a singularity in contact force, as detailed in [32].  
The geometric description method for the wheel flat proposed in this paper shows good agreement with the surface reconstruction method based on measured roughness, indicating that the basic assumption of the worn stage in the analytical description method is reasonable and the computational accuracy meets the requirements.  
In addition, since the analytical form is parametrically controllable, when \(\beta\) is close to \(\pi/2\) ( \(\beta=1.8\)) and \(\gamma=\beta/2\), the maximum impact force in stage III differs from the haversine model by only \(0.51\%,0.97\%\), and \(1.3\%\) for the three depth conditions, respectively. This indicates that the analytical model proposed in this paper exhibits a high degree of flexibility.
## 3 Proposed Extended Chaotic-Map-Based User-Authentication and Key-Agreement Scheme
### System-Parameter Initialization Phase  
Medical-center server _MCS_ selects a secure hash function \(h(\cdot)\), a random number \(r_{\text{MCS}}\) and a random variable \(x\) in \((-\infty,+\infty)\). Then, _MCS_ computes \(T_{r_{\text{MCS}}}(x)mod\ p\) and publishes \(\{x,T_{r_{\text{MCS}}}(x)\text{mod}\ p,h(.)\}\). Patient _Pat_ selects a random number \(r_{P}\), computes \(T_{rp}(x)\) mod \(p\) and publishes \(\{T_{rp}(x)mod\ p\}\). Medical service staff (or Doctor) _Doc_ selects a random number \(r_{D}\), computes \(T_{r_{D}}(x)mod\ p\) and publishes \(\{T_{r_{D}}(x)mod\ p\}\).
## 5 Types of Phage-Based Biosensors
### Phase-Based Electrochemical Biosensors
#### 5.2.2 Electrochemical Impedance Spectroscopy (EIS) Biosensors  
Electrochemical impedance spectroscopy (EIS) is a novel biosensor that uses functional sinusoids. The analysis is carried out based on changes in the electrical impedance (conductance, impedance, and capacitance) of the medium (Figure 5). The microbial metabolism in the medium reduces the capacity of the impedance [111]. Bacteriophages immobilized on an electrode are used as probes in this platform to detect bacterial strains at the electrode's surface [112]. This technique is applicable for the detection of _E. coli_ in inoculated samples or pure culture media ranging from \(10^{4}\) to \(10^{7}\) CFU/mL [113]. [PERSON] et al. designed a phage-based impedimetric microelectrode array biosensor. The results indicated that the sensitivity of the impedimetric biosensor was enhanced by reducing the gap and width of the electrode and by using a lower relative dielectric permittivity [114]. An impedimetric biosensor (a label-free system) was proposed by [PERSON] et al. for the analysis of _E. coli_ B with T4 phage-based EIS by covalently immobilizing them on a gold surface (cysteineamine-modified) with a detection limit of 8 \(\times\) 10\({}^{2}\) CFU/mL in less than 15 min [115]. A screen-printed graphene sensor surface (electrode) was immobilized by highly specific lytic phages for the quick detection of _Staphylococcus arlettae_[116]. Table 2 summarizes some of the foodborne pathogens that have been detected using this technique.  
Figure 5: Schematic illustration of phage-based impedimetric biosensor, showing steps involved in phage immobilization and target bacteria detection: (**A**) surface modification of gold electrode using chemical linker (e.g., cysteamine); (**B**) cross-linker/enhancement using 1,4 dithiocyanate (PDICT); (**C**) immobilization of phages and treatment with ethanolamine to block nonspecific binding; (**D**) capture of target bacteria; and (**E**) impedance measurement (detection of target pathogen).
## 3 Analytical Procedures
### Zircon LA-ICP-MS U-Pb Dating  
Each fine-grained granite sample was crushed to 80-100 mesh, and more than 500 zircon crystals were obtained by the gravimetric-magnetic separation technique. Subsequently, no fewer than 200 grains with a diameter greater than 40 um, where no visible inclusions and fractures developed, were handpicked with a binocular microscope and then were mounted in epoxy resin to be polished so as to reveal the center of zircon crystals. Based on the cathodoluminescence (CL) images and the reflected and transmitted light micrographs, the detailed observation of the internal structure of zircon was performed in order to obtain the zircon grains, which contain neither microcracks nor fluid/mineral inclusions, used for zircon U-Pb dating.  
Zircon U-Pb dating was carried out at Hefei University of Technology, China, using an Agilent 7500a ICP-Mass equipped with a GeolasPro laser (Agilent technology Co., Ltd., La Jolla, CA, USA). The laser spot diameter was 32 um with a frequency being 6 Hz. Harvard zircon 91500 was adopted as the external standard for U-Pb dating, and NIST610 was used as the external standard for the calculations of U, Th and Pb contents. The ICPMSDataCal 11 program was used to obtain the ratios of \({}^{207}\)Pb/\({}^{206}\)Pb and \({}^{206}\)Pb/\({}^{238}\)U [57], and the common Pb was corrected based on the method of Ref. [58]. The Concordia diagram, histogram and weighted average age were obtained using the Isoplot 3.75 software [56]. Detailed analytical methods and procedures refer to those described by Refs. [59; 60].