esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Communication-Motion Planning in Networked Robotic Systems
## 2 Conventional Relay-Assisted Robot System
### Performance Assessment: Orient
#### 2.2.2 Motion Performance Assessment  
In order to accurately evaluate the motion performance, we take the robot energy consumption model provided in [17] as a reference. Ref. [17] is committed to providing a more accurate energy consumption model of robot. They believed that the energy consumption of robots mainly came from motion, sensing of the external environment, microcontrollers and embedded computers. In terms of the relationship between motion and energy consumption, they pointed out that the motor carried by the robot converted electrical energy into mechanical energy. Therefore, the motion energy consumption was equal to the motor power consumption. The motor power consumption included the mechanical output power and the energy conversion loss. Let the mass of the robot be \(m\) and the friction coefficient be \(\mu\). If the velocity of the robot at a certain time is \(v\), the acceleration is \(a\), the gravity constant is \(g\), and the energy conversion loss is \(p_{l}\), then according to the laws related to physics, it can be concluded that the traction force required by the robot at this time is \(m(a+g\mu)\), and the corresponding output power is \(m(a+g\mu)v\). Based on the above conclusion, the motion power \(p_{m}\) can be expressed as \(p_{m}(m,v,a)=p_{l}+m(a+g\mu)v\). This conclusion was verified in the form of measured experiments in [17], so this model is accurate and reliable. If we let \(\kappa_{1}=m(a+g\mu)\), \(\kappa_{2}=p_{l}\), and \(P_{mo}=p_{m}(m,v,a)\), we can adopt the linear motion power consumption model as in  
\[P_{\text{mo}}=\begin{cases}\kappa_{1}v+\kappa_{2}&\text{if}0<v\leq v_{\text{ max}}\\ 0&\text{if}\qquad v=0\end{cases} \tag{4}\]  
and obviously, we only need to integrate (4) in time to obtain the corresponding energy consumption  
\[E_{\text{mo}}=\kappa_{1}I_{\text{R}}+\kappa_{2}t_{\text{mo}} \tag{5}\]  
where \(\kappa_{1}\), \(\kappa_{2}\) are constants, \(v_{\text{max}}\) is the maximum velocity of the mobile robot, and \(I_{\text{R}}\) is the trajectory length during \(t_{\text{mo}}\). It is assumed that the trajectory is a straight line connecting the current position and the next position of the mobile robot, while no physical obstacle is in between. It may be verified that when adopting the maximum velocity \(v_{\text{max}}\), the motion time \(t_{\text{mo}}\) (\(t_{\text{mo}}=l_{\text{R}}/v_{\text{max}}\)) is minimized. With a specific trajectory length, the motion energy consumption \(E_{\text{mo}}\) is also minimized when adopting \(v_{\text{max}}\). Therefore, we use \(v_{\text{max}}\) as the velocity of the mobile robot.
## 5 Discussion and Future Work
Future research should focus on addressing these limitations by collecting more diverse and representative datasets and developing techniques to handle imbalanced or sparse data. Additionally, exploring dimensionality reduction directly on covariance matrices in Riemannian space and investigating different manifold-based classifiers would be worthwhile. Furthermore, integrating EEG signals with other physiological signals, such as electromyogram (EMG) and electrocardiogram (ECG), as well as non-physiological signals like facial expressions or body gestures, could further enhance the accuracy of emotion recognition. Therefore, our logical next step involves exploring these multimodal approaches and applying the method presented in this work.
# Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer's Disease Detection
## 4 Results and Discussion
### Computational Efficiency Analysis
#### 4.4.2 Ablation Study Insights  
Figure 13 presents an ablation study evaluating the model's performance when one or more components are removed. **Critical Components**: removing **ViT** from the ensemble causes a 1.4% accuracy drop, followed by **CNN** (0.3% drop), confirming these are the most valuable contributors. **Redundant Components**: removing **DenseNet** or **VGG19** actually improves ensemble performance slightly, suggesting they introduce noise or redundancy. **Optimization Opportunity**: the ablation study suggests a more efficient ensemble could be constructed by eliminating **DenseNet** and potentially **VGG19**, reducing computational overhead while maintaining or improving accuracy.
## 2 Bullet Low-Overlap Point Cloud Registration Based on Line Feature Detection
### Algorithm Complexity Analysis  
This algorithm mainly includes the following four steps: voxel downsampling, fitting the central axis, using the discrete Hough transform to extract line features, and improving the Cuckoo algorithm to find the optimal rotation angle to complete the matching. The total computational complexity is O(m + k\({}^{3}\) + n. + T-m + m), where m is the number of points in the point cloud, and assuming that there are k voxels on each axis, and the number of populations n is set to 20 and the maximal number of iterations is T, then k\({}^{3}\ll\) m and n \(\ll\) m and h \(\ll\) m, where the overall complexity is then mainly controlled by O(T-m). The ICP algorithm consists of initializing the alignment, iterating the nearest-point matching, calculating the transformation matrix and applying it, and iterating until convergence, and the computational complexity is O(T - (mlogm + m)) = O(mlogm). Similarly, the computational complexity of TRICP and GOICP is O(T - (mlogm + m)) and O(T - (mlogm + m) + bd), where the GOICP algorithm branch delimitation is obtained by the branching factor b = 3 and depth of tree d = 10. The algorithms in this paper have some advantages in terms of complexity.
## 2 Background Fundamentals
### Randomized Low-Rank Approximations
As the collected data are independent of each other, finding the inter-relations or inter-associations between the attributes can be recognized using the multi-collinearity test with the help of variance inflation factors (VIF). Therefore, the following section deals with identifying significant attributes using the multi-collinearity test. ### Feature Selection Using Multi Collinearity Test

This section aims to identify the selection of significant attributes using a multi-collinearity test. For each attribute, variance inflation factors (VIF) identify the multi-collinearity number using Equation (1), where the unadjusted determination coefficient for the \(i\)th independent attribute over the other attributes is determined by \(R_{i}\)2. The VIF threshold value (\(\tau\)) was assumed on a better understanding of the data pattern. For the collected data with a dimension of 12,000 \(\times\) 32, the VIF is calculated on each attribute and discards the attributes that fail to have an association in the prediction process. The process of VIF calculation is given in Algorithm 1. The calculated VIF values for all 32 attributes are depicted in Table 6. Figure 7 clearly illustrates the attribute reduction process, and better features are selected. The reduced sample dataset with 18 attribute values is presented in Figure 8.

Figure 4: Generalized architecture of ensemble learning process. Figure 4.0 Four divisions of India. Figure 4.1 Properties of input variables. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Attribute** & **VIF** & **Attribute** & **VIF** & **Attribute** & **VIF** & **Attribute** & **VIF** \\ \hline \(a_{1}\) & 1.2 & \(a_{9}\) & 2.99 & \(a_{17}\) & 3 & \(a_{25}\) & 1.01 \\ \(a_{2}\) & 3.8 & \(a_{10}\) & 2.48 & \(a_{18}\) & 10.2 & \(a_{26}\) & 1.63 \\ \(a_{3}\) & 1.6 & \(a_{11}\) & 1.34 & \(a_{19}\) & 9.25 & \(a_{27}\) & 7.89 \\ \(a_{4}\) & 1.04 & \(a_{12}\) & 9.7 & \(a_{20}\) & 2.25 & \(a_{28}\) & 4.75 \\ \(a_{5}\) & 8.27 & \(a_{13}\) & 1 & \(a_{21}\) & 2.46 & \(a_{29}\) & 4.35 \\ \(a_{6}\) & 7.78 & \(a_{14}\) & 2.22 & \(a_{22}\) & 3.59 & \(a_{30}\) & 8.66 \\ \(a_{7}\) & 5.07 & \(a_{15}\) & 1.79 & \(a_{23}\) & 3.55 & \(a_{31}\) & 3.32 \\ \(a_{8}\) & 1.35 & \(a_{16}\) & 9.93 & \(a_{24}\) & 5.12 & \(a_{32}\) & 1.2 \\ \hline \end{tabular}
\end{table} 
Table 6: VIF calculation for 32-attributes.
# Beyond Kanban: POLCA-Constrained Scheduling for Job Shops
## 3 POLCA and the Enhanced Contraint Programming: A Basic Case
### Modeling Assumptions  
The environment analyzed in the Basic Case is modeled as a classical job shop with finite capacity and stochastic processing times. Each operation requires exclusive access to a single workcenter, with every workcenter modeled as a single-capacity, continuously available resource. Once the processing of a job on a given machine has started, preemption is not permitted: operations are executed non-preemptively and must run to completion without interruption. All workcenters are assumed to have identical processing capabilities; in other words, the processing speed is homogeneous, and the distinction between resources arises solely from their assignment in the job routings. Nevertheless, each workcenter can execute only one operation at a time, and alternative routing or parallel servers are not considered.  
Job routings are predetermined at release and must respect strict technological precedence constraints, prohibiting overtaking or re-sequencing of operations within the same job. Each job follows a fixed sequence of operations defined by its specific routing vector, which establishes the ordered list of required workcenters. Sequence-independent set-up times, if present, are included in the reported processing times, ensuring that no additional idle periods are introduced between consecutive jobs at the same workcenter.  
Regarding job prioritization, jobs awaiting service at the same workcenter are dispatched according to an Earliest Due Date (EDD) policy; ties between jobs with the same due date are broken according to a First-In-First-Out (FIFO) rule. The model assumes that once jobs enter the system, no order cancellation or reneging is allowed: every released order is processed through to completion.  
The POLCA mechanism is introduced as the sole production control constraint: each POLCA loop is associated with a finite set of authorization cards, and no starvation cards are employed in the basic configuration. The number of cards per loop is constant and identical for all loops. This restriction ensures that the number of jobs authorized to move between workcenters at any given time is strictly bounded, directly linking the capacity control policy to the structure of the job routings.  
Although the algorithm in Figure 1 supports a rolling-schedule paradigm--where the optimizer can be re-invoked whenever shop-floor deviations exceed a threshold \(\Delta\)--for the _Basic Case_ analyzed in Section 3, a single static schedule is generated at time zero and retained for the whole simulation horizon. Consequently, the rescheduling branch of the diagram is inactive in the numerical experiments, ensuring consistency with the assumptions of the computational study.
## 4 TRSA: Trust-Aware Evidence Reasoning and Spatiotemporal Feature Aggregation Model
### Evidence Representation Based on Spatiotemporal Structure
#### 4.2.1 Evidence Temporal Sequence Representation Unit  
The evolution of source information is triggered by a sequence of forwards or comments over time, as shown in Figure 2. We aimed to exploit the initial semantic representation of source information and related evidence posts (comments or forwarded content) in combination with the temporal structure \(P(S)=\left\{(\mathbf{c}_{0},t_{0}),\dots,\left(\mathbf{c}_{j},t_{j}\right),\dots\right\}\) to learn evidence representation regarding temporal sequence. To obtain a more reliable sequence representation, we integrated the reliability of temporal neighborhood information into the sequence modeling process and used bidirectional long short-term memory (Bi-LSTM) [43] to model the temporal dependency of the information.  
\[\overset{\rightarrow}{\mathbf{h}_{i}}=\overrightarrow{LSTM}(\exp(r_{i})\mathbf{c_{i}}) \tag{5}\]  
\[\overset{\leftarrow}{\mathbf{h}_{i}}=\overbrace{LSTM}(\exp(r_{i})\mathbf{c_{i}}) \tag{6}\]  
\[\mathbf{h}_{i}=\left[\overset{\rightarrow}{\mathbf{h}_{i}};\overset{\leftarrow}{ \mathbf{h}_{i}}\right] \tag{7}\]  
The above equations demonstrate how to model the temporal sequence representation of evidence \(i\). Specifically, we applied the corresponding credibility weights (calculated in Section 4.1.2 upon each piece of posts in \(P\)) and captured reliable context from temporal neighbor posts around evidence \(i\). In the equations, \(\overrightarrow{h_{i}}\in R^{l}\) and \(\overleftarrow{h_{i}}\in R^{l}\) denote the hidden state of the forward LSTM \(\overrightarrow{LSTM}\) and backward LSTM \(\overleftarrow{LSTM}\). \(l\) is the number of hidden units in the LSTM and [:] denotes a concatenation operation. \(r_{i}\in[0,1]\) represents the credibility of information. To avoid the vanishing gradient problem when \(r_{i}\) is too small, we used \(\exp(r_{i})\) instead of \(r_{i}\). \({\mathbf{h}}_{i}\in R^{2l}\) is a semantic representation of the temporal structure of evidence \(i\) (when \(i\) = 0, it represents source information).
# Evaluation of BLE Star Network for Wireless Wearable Prosthesis/Orthosis Controller +
## 2 Background
### BLE Parameter Definitions  
The BLE configuration parameters of the connection interval and event length were considered in our measurements. The maximum transmit unit was scaled with respect to the connection interval to maximize the amount of data sent in each BLE transmission. These configuration parameters are defined as [24]:  
1. **Connection Interval:** The time between consecutive connection events of the connected devices.
2. **Maximum Transmission Unit (MTU):** The maximum size of the data packet transferred between connected devices.
3. **Event Length:** The time allocated within a connection interval for the data transfer to occur between the connected devices (including required interframe and subevent spaces).
## 3 Results and Discussion
### Periodic Length and Grid Resolution
Figure 7 shows the comparison of equilibrium position as a function of confinement with three different definitions for \(AR\) = 1-4. For each confinement, changes in the equilibrium position with increasing \(AR\) are described. For comparison among \(C_{1}\), which corresponds to the comparison with the same minor axis, the equilibrium position was shifted to the center with increasing \(AR\). On the contrary, for comparison among \(C_{2}\), which corresponds to the comparison with the same major axis, the equilibrium position was slightly shifted to the wall side (\(AR\) = 2), then shifted to the center (\(AR\) = 4) with increasing \(AR\). For comparison among \(C_{3}\), which corresponds to the comparison with the same area fraction, the equilibrium position was shifted to the center with increasing \(AR\). These results showed that the effects of aspect ratio on the equilibrium position strongly depend on the confinement definition. Figure 6: Lift coefficient profiles of a single circular and elliptical particle with (a) \(C_{3}\) = 0.05, (b) \(C_{3}\) = 0.1, and (c) \(C_{3}\) = 0.2 for different aspect ratios \(AR\) = 1, 2, and 4. Note that the vertical axis scale is different for each figure for clarity. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{\(AR\) [-]} & \multicolumn{3}{c}{\(y_{\text{eq}}\)/\(D\) [-]} \\ \cline{2-4}  & \(C_{3}\) = 0.05 & \(C_{3}\) = 0.1 & \(C_{3}\) = 0.2 \\ \hline
1 & 0.258 & 0.263 & 0.277 \\
2 & 0.260 & 0.267  
Our results are qualitatively consistent with other results for case 1 and 2. [PERSON] et al. [4], who discussed the effects of aspect ratio in a comparison similar to case 1, showed that the equilibrium position with higher \(AR\) is closer to the center. [PERSON] et al. [16], who discussed the effects of aspect ratio with \(C_{2}\) for non-Newtonian fluids, reported that the equilibrium position shifted toward the wall with increasing \(AR\). [PERSON] et al. [5] also discussed the effects of aspect ratio with \(C_{2}\) on the equilibrium position. They reported that the equilibrium position changes from being closer to the wall to being closer to the center when a certain aspect ratio was exceeded. Our results are consistent with their results. The novel contribution of the present study is to determine the equilibrium positions in case 3 and to compare them among their definitions.
# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 4 Materials and Methods
### Datasets
#### 4.1.1 FLIR Dataset  
The FLIR ADAS dataset presents a significant challenge for multispectral target detection, encompassing complex scenes captured during both day and night. However, the original dataset contains a substantial number of misaligned visible-thermal image pairs, which hinders the effective training of deep learning models. To mitigate this issue, an \"aligned\" version of the dataset has been introduced, wherein misaligned image pairs have been manually filtered to enhance data quality and usability. The aligned dataset comprises 5142 precisely aligned multispectral image pairs, with 4129 designated for training and 1013 reserved for testing. It includes three target categories: pedestrians, cars, and bicycles. In this study, all experiments were conducted using the \"aligned\" version of the FLIR dataset, and all subsequent references to the FLIR dataset pertain exclusively to this version.
## 2 Materials and Methods
### Experimental Procedure and Setting Up Toxin Ion Drift Model  
The principle of the method and the assumptions made for setting up the device are described below. Water contaminated with the bacterial toxins flows inside the duct of the device. It is considered for reasons of simplicity that contaminated water contains only one type of bacterial toxin. Due to the applied electric field, toxin ions are drifted towards positive or negative electrodes of the conductor, resulting in an almost toxin-free water in the other parts of the duct, thus leading to a considerable reduction of toxin concentration in aqueous solutions in the largest part of the duct. The configuration of the toxin drift model is presented in Figure 1.  
The device consists of the following components:  
1. Two electrodes which are charged by V voltage, and which produce among themselves almost homogeneous electric field intensity with direction from positive to negative electrode.
2. An insulated duct in which the contaminated water flows through. This is placed along the electrodes and at the minimum distance from them to ensure that the external electric field is almost homogeneous. Therefore, the contaminated water with velocity \(\overrightarrow{v}\) flows perpendicular to the external electric field intensity, which is considered along the \(y\)-axis (Figures 1 and 2), and a change of the toxin ion concentration along the \(y\)-axis of the duct is observed (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**Type of Toxin**} & \multirow{3}{*}{\
\begin{tabular}{c} **Toxin 1** \\ _Staphylococcus aureus_ \\ **Alpha-Hemolysin** \\ **(TSST-1)** \\ \end{tabular} } & \multicolumn{3}{c}{**Toxin 4:**} \\  & & & & **E. coli** **S**higa Toxin 2** \\  & & & **Toxic Shock** & _Staphylococcus aureus_ & **(One Subunit A and** \\  & & **Syndrome Toxin-1** & **Enterotoxin Type A** & **Five** \\  & & **(TSST-1)** & & **Subunits B)** \\ \hline \multirow{6}{*}{Reference} & \multirow{6}{*}{GenBank: QTN48712.1} & \multirow{6}{*}{GenBank: KIT87450.1} & \multirow{6}{*}{GenBank: QTN49470.1} & \multirow{6}{*}{
\begin{tabular}{c} Shiga toxin Stp2 \\ subunit A \\ phage Lyz12581 Vzw] \\ NCBI Reference \\ Sequence: \\ Yp\_009907811.1 \\ Sibia toxin Stp2 \\ subunit B [_Escherichia_ \\ \textbackslash{}}\textbackslash{}\textbackslash{}\textbackslash{}\text  
Figure 1: Configuration of the toxin drift model (V: voltage of the electrodes, L: width of the duct, E: electric field intensity, \(\overrightarrow{v}\): velocity of contaminated water). The electrodes of the capacitor that create the electric field between them are represented as well as the duct with the contaminated water flowing inside it perpendicular to the electric field intensity.  
Figure 2: Configuration of ion toxin movement through the duct. (V: voltage of the electrodes; L: width of the duct, E: electric field intensity, \(\overrightarrow{v}\): velocity of ions). Due to the electric field created by the electrodes, the ions according to their charge accumulate on both sides of the duct.
# Research Progress in the Early Warning of Chicken Diseases by Monitoring Clinical Symptoms
## 2 Early Diseases Detection through Physiological Characteristics
### Abnormal Localization
Therefore, deep learning models were applied to address this problem, which can extract features from input data automatically and avoid complex feature engineering [46]. In the study carried out by [PERSON] et al., two deep learning models RNN (Recurrent Neural Network) and CNN (Convolutional Neural Network) models were used to classify the chicken sounds of healthy ones and those infected with (AI) [47]. The results of this study showed that the highest recognition accuracies of CNN were 93.01%, 95.05% and 97.43% on the second, fourth and sixth day after injection with the H9N2 virus, respectively, which were much higher than the recognition accuracies of RNN (49.97%, 55.86% and 57.10%, respectively). This means that the method could deliver warnings to farmers to take necessary measures before an outbreak of AI, which would greatly reduce economic losses. In a recent study, [PERSON] et al. [48] successfully achieved early warning of ND by analyzing the sound of chickens, and the results showed that the accuracies within the first, second, third and fourth days after infection were 82.15%, 90.00%, 93.60% and 98.50%, respectively, which is important for the improvement of animal welfare and automated monitoring of poultry production. The cost of identifying animal diseases by sound signals is relatively low, and the application prospect is broad but not yet mature. There are two main difficulties. The first is denoising. In the animal living environment, fan rotation, conveyor belt operation and other activities will produce large environmental noises, which may affect the sound preprocessing and final results. The second is the high sparsity of abnormal sounds. Chicken sound data are continuous, while indicative abnormal sounds usually occupy just a small fraction of a long recording duration in a random way. So, how to design and implement algorithms to improve recognition accuracy and efficiency in the complex environment are problems that must be solved in the future. Furthermore, if the abnormal sound in the chicken house can be spatially located [49], it would further improve the management level of the modern poultry industry and reduce the burden on farmers.
## 9 Routing in CRSNs
### CRSN Routing Modules
#### 9.1.2.3.1 Complete Avoidance of PU Zones
##### 9.1.2.3.3 Prediction Schemes  
Prediction schemes are usually used to predict the arrival of PUs so that effective actions will be taken in a timely manner to avoid data loss or time-outs. In this respect, [78] introduces a channel-aware transmission mechanism based on CSMA to optimize energy efficiency. The pushback mechanism simply predicts the channel status to determine the optimal times to transmit and to refrain from transmission to conserve energy. Without additional packet overhead and with its minimal computational requirements, the mechanism significantly improves the packet success rate and the energy without degrading throughput. This result was achieved by modeling the channels as an HMM. Another related work along this line is [79], which presents a MAC layer spectrum sensing scheme using random sampling and sensing based on a maximum likelihood (ML) estimation strategy. The authors focused on both scheduling the sensing activity and the estimation of statistical properties of random variations in the channel.