esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Fire Evacuation Drills through a Web-Based Serious Game
## 3 Technical Methods
### Optimization Solution
#### 3.3.3 Rendering Test  
The rendering efficiencies before and after optimization were tested to demonstrate the effectiveness of the above optimization solution. A six-story dormitory was selected as a test scene, which was also used as a case study, as shown in Figure 3. The total height and built-up area of this building are 21.60 m and 9600 m\({}^{2}\), respectively. The fifth floor of the building was selected to perform a fire evacuation drill with dozens of NPCs. A low-voltage laptop was used for testing, and its specific parameters are listed in Table 1.  
The test browser is Firefox because its web development tools provide a test program for rendering the frame rate. The fire evacuation scene in Figure 4 is separately rendered 10 times with and without the proposed optimization solution, and the corresponding frame rates are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline
**Hardware** & **Specifics** \\ \hline CPU & i5-6200U @ 2.3 GHz \\ RAM & 8 G \\ GPU & NVIDIA GeForce 940 MX \\ \hline \end{tabular}
\end{table}
Table 1: Hardware specifics for rendering test.  
Figure 3: Test scene of fire evacuation.  
According to the test results in Figure 4, the average frames per second (FPS) of the web application without and with the optimization solution are 25.94 and 30.93, respectively. Generally, these programs run smoothly when the frame rate is greater than 30 FPS. Evidently, the web-based fire evacuation drill system has a good rendering performance when using the proposed optimization solution. Note that the performance could be further improved if better hardware was used.
# Optimal Model-Free Mean-Square Consensus for Multi-Agents with Markov Switching Topology
###### Abstract  
Due to the real applications, optimal consensus reinforcement learning with switching topology is still challenging due to the complexity of topological changes. This paper investigates the optimal consensus control problem for discrete multi-agent systems under Markov switching topologies. The goal is to design an appropriate algorithm to find the optimal control policies that minimize the performance index while achieving consensus among the agents. The concept of mean-square consensus is introduced, and the relationship between consensus error and tracking error to achieve mean-square consensus is studied. A performance function for each agent under switching topologies is established and a policy iteration algorithm using system data is proposed based on the Bellman optimality principle. The theoretical analysis shows that the consensus error realizes mean-square consensus and the performance function is optimized. The efficacy of the suggested approach is confirmed by numerical simulation using an actor-critic neural network. As a result, the value function is the optimum and the mean-square consensus can be reached using this technique.  
optimal consensus control; multi-agent systems; Markov switching; reinforcement learning
# Non-Contact Dermatoscope with Ultra-Bright Light Source and Liquid Lens-Based Autofocus Function
## 1 Introduction
Thus, there is a need for simple, easy-to-operate and yet accurate dermatoscope systems capable of providing the functionality required for proper skin examination without distortion of the geometry, i.e., in non-contact mode. In this work, we present a novel non-contact dermatoscope featuring properties advantageous for more reliable and comparable skin examinations in the future. The light source of the developed prototype is an ultra-bright white light-emitting diode (LED), which allows for full control of the lighting situation to be independent from the surrounding light. Also, by realizing a calibrated display, it is possible to display the natural colors of the skin area. Furthermore, the non-contact approach enables a much larger field of view. While pigmented nevi that are examined in melanoma screening are often 0.5-2-cm wide, inflammatory lesions are generally several centimeters wide. Thus, their examination requires a much larger field of view of the imaging system compared to a device designed for skin cancer screening only. The prototype described here has a field of view of about \(17\times 13\) mm\({}^{2}\) and can, in the future, be combined with a second camera to record images of even larger skin areas.
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 4 Routing-Protocol Proposals Based on Metrics
### Multimetric-Routing Proposals
#### 4.2.10 Multimedia Multimetric Map-Aware Routing Protocol (3 MRP)  
One of the most important applications in VANETs is the efficient management of accidents. When an accident occurs, a vehicle could transmit a short video about the situation through the VANET and alert the emergencies services or other vehicles in the area. In this context in [10], the proposal was an effective routing protocol to operate video-reporting messages in vehicular ad hoc networks; the proposal provides video-reporting messages over VANETs in smart cities, and is called the multimedia multimetric map-aware routing protocol (3 MRP). The proposal included five metrics to improve the selection of the best routing path, distance, density, trajectory, available bandwidth estimation, and MAC losses.  
When a sender vehicle receives hello messages (HM) from its neighbors in transmission range, the node updates its neighbors' list with all those vehicles in line of sight (LOS), only considering the neighbors that sent their HM with enough power. Then, the source evaluates and assigns a total multimetric score to each neighbor as a possible candidate for the next forwarding node. As a first step, the authors considered the same weights (\(w_{1}\), \(w_{2}\), \(w_{3}\), \(w_{4}\), \(w_{5}\)) to each metric (\(u_{dst,N_{gh}}\), \(u_{trj,N_{gh}}\), \(u_{dns,N_{gh}}\), \(u_{abe,N_{gh}}\), \(u_{los,N_{gh}}\)), respectively, in the multimetric score \(\overline{u}_{N_{gh}}\) of each neighbor \(N_{gh}\). Finally, a multimetric score was obtained for each candidate node using Equation (12). The final quantification varied between 0 and 5. The neighbor with the highest multimetric value is selected as the best next forwarding node.  
As mentioned before, the authors first considered the same rank of importance to all the used metrics, i.e., \(w_{i}=1/5\), \(1\leq i\leq\)5; as the next improvement, the authors proposed to dynamically actualize the scores of the candidate neighbor vehicles using an algorithm to calculate the self-configured weights of the metrics.  
\[\begin{array}{ll}\overline{u}_{N_{gh}}&=\sum_{i=1}^{5}u_{i,N_{gh}}\cdot w_{i }=u_{dst,N_{gh}}\cdot w_{1}+\\ &u_{trj,N_{gh}}\cdot w_{2}+u_{dns,N_{gh}}\cdot w_{3}+\\ &u_{abe,N_{gh}}\cdot w_{4}+u_{los,N_{gh}}\cdot w_{5}\end{array}. \tag{12}\]
## 3 Results
### Loss of Control Predictive Model for Time Series data  
Equations (3) and (6), respectively for Model-1 (simplest model) and Model-3 (best performance) were applied to each instant to predict the time series loss of control probability (see Equations (6) and (9) with the functions \(\mathrm{F_{Max}(Xt)}\), \(\mathrm{F_{RMS}(Xt)}\) and \(\mathrm{F_{peak-to-RMS ratio}}\) (Xt) defined in Section 2.5.  
\[Yt\ (model\text{-}1)=\mu_{0}+\mu_{1^{*}}F_{RMS}(Gyro\ Norm_{t})+\mu_{2^{*}}F_{ Max}(Gyro\ Z_{t})+\mu_{3^{*}}F_{Max}(Lat\ Accel_{t}) \tag{10}\]  
\[Yt\ (model\text{-}3)=\beta_{0}+\beta_{1^{*}}F_{Max}(Yaw\_Acc_{t})+\beta_{2^{*}}F_ {peak\text{-to-RMS ratio}}(Roll\_Acc_{t}) \tag{11}\]  
where \(\mu_{k}\)_and_\(\beta_{k}\) are the coefficients estimated previously for the Model-1 and Model-3, respectively.  
Time series predictive model based on 'advanced' model Model-3 predicted all 11 _loss of control_ cases (100% accuracy) and the model based on 'basic' model Model-1 predicted 10 cases (90.9%), corresponding to the same performance achieved off-line. Model-3 detected loss of control cases 19 ms earlier than Model-1 in average (SD = 93 ms), but no significant differences were found (Wilks' Lambda = 0.992, \(F(1,9)\) = 0.072, \(p\) = 0.561). No differences were found in the vehicle speed at which loss of control was detected for each model (mean of 21.3 and 21.1 km/h for Model-1 and Model-3, respectively; Wilks' Lambda = 0.995, \(F(1,9)\) = 0.047, \(p\) = 0.833). Figure 11 shows examples of four riders performing braking trials with the real-time loss of control predictors adapted from Model-1  
Figure 10: Scatter plot of _loss of control_ probability by subject for the 165 braking trials for peak-to-RMS ratio of roll acceleration and “steering rate” (best decision threshold in dotted line).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Predictor** & **Threshold** & **Specificity** & **Sensitivity** & **AUC (CI)** \\ \hline Roll Acc peak-to-RMS & 4.49 & 0.974 & 1 & 0.996 (0.989–1.00) \\ Steering rate Max (\({}^{\circ}\)/s) & 39.5 & 0.945 & 1 & 0.980 (0.960–1.00) \\ Gyro-Z Max (rad/s) & 0.269 & 0.922 & 0.909 & 0.970 (0.930–1.00) \\ Yaw Acc Max (rad/s\({}^{2}\)) & 5.6 & 0.994 & 0.818 & 0.943 (0.859–1.00) \\ Lat Accel Max (m/s\({}^{2}\)) & 1.85 & 0.955 & 0.909 & 0.911 (0.766–1.00) \\ Gyro Norm Max (rad/s) & 0.773 & 0.987 & 0.818 & 0.887 (0.728–1.00) \\ Lock front-wheel (s) & 0.09 & 0.903 & 0.455 & 0.673 (0.513–0.833) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Prediction Performance and best thresholds for the feature input.  
and Model-3. Each example of Figure 11 from (a) to (d) includes three plots: the upper plot shows the loss of control (LC) probability for Model-1 and Model-3 and the vehicle and wheel speed; the middle plot shows the LC probability for Model-1 and its two most significant predictor variables; the lower plot shows the LC probability for Model-3 and its two predictor variables. The left \(y\)-axis represents the LC probability defined by the models and right \(y\)-axis represents the magnitude of speed for the upper plot and of the predictor variables for the rest.
# The Availability of Space Service for Inter-Satellite Links in Navigation Constellations
## 4 Numerical Simulation  
The number of navigation satellites that can be scheduled to serve for spacecraft is restricted by ISL's own work and the time division spot beam limits the number of users that can be served simultaneously, although the availability may be enhanced if the navigation signal is combined. Therefore, this paper analyzes the availability in the following cases: (1) all of the satellites are scheduled to serve a single target (ASST); (2) only some of the satellites are scheduled to serve a single target (SST); (3) all of the satellites are scheduled to serve multiple targets (ASMT); (4) only some of the satellites are scheduled to serve multiple targets (SSMT); (5) none of the satellites are scheduled (NS); and (6) the navigation signal is combined with the ISL (NSCI).  
Figure 3: Geometry of ISLs at a specific time: (**a**) ISL signals among the navigation satellites; and (**b**) ISL signal to space users.  
In this section, we present the results of the numerical simulation that we used to characterize the availability of ISLs. All of the simulations were implemented using M-files in MATLAB R2010a (MathWorks, Natick, MA, USA). The tested navigation system was the MEO satellites in BeiDou. The maximum scanning angle was \(\pm 55^{\circ}\), the sphere was evenly divided into \(6^{\circ}\times 6^{\circ}\) grids at each altitude, the step size was 60 s, the simulation time was 48 h. Table 1 lists the parameters and values used.
## 3 Method
### Algorithm Design  
The processing pipeline initiates with spatial-temporal feature extraction. Radar micro-Doppler signals are decomposed into 16 sub-bands via Meyer wavelet transforms, with limb velocity components isolated using [PERSON] et al. (2023) Doppler-kinematic mapping. TOF point clouds are structured as dynamic graph tensors, where skeletal nodes update connections through anatomical adjacency matrices derived from anthropometric reference data [22].  
A multi-modal attention transformer processes fused features through three computational stages:  
Cross-modal feature correlation: Radar spectrograms and skeletal graphs are projected into orthogonal feature spaces using rotation matrices inspired by quantum computing principles. These matrices help identify correlations between different sensor modalities.  
Classical Attention: A 12-head transformer layer computes token affinities in the residual subspace.  
Multi-Head Attention Mechanism:  
\[\text{Attention}(\text{Q},\text{K},\text{V})=\text{softmax}\Bigg{(}\frac{\text {QK}^{\text{T}}}{\sqrt{\text{d}_{\text{k}}}}\Bigg{)}\text{V}\]  
Feature Space Rotation:  
\[\mathcal{P}(\text{x})=R_{radar}\times R_{TOF}\ x\]where \(R_{r\it{radar}}\) and \(R_{\it{TOF}}\) are rotation matrices for radar and TOF features, respectively, and \(\times\) denotes standard matrix multiplication. This transformation aligns features from different sensors for improved correlation detection.  
Safety-critical gesture verification employs a state-machine model based on [PERSON] et al.'s (2011) research, where emergency commands must satisfy two clear conditions:  
The radar Doppler velocity exceeds 2.3 m/s (95 th percentile of play motions);  
TOF skeletal angles matchbraking postures within a 50 ms temporal window.  
Figure 1(a) illustrates the product design concept showing the three operational modes (standing, seated, and balance) of the adaptive mobility platform, demonstrating how the  
Figure 1: (**a**) Product design and usage scenarios; (**b**) hardware architecture and design.  
reconfigurable chassis adapts to different user needs and ages. The mechanical reconfiguration controller (Figure 1b) implements interval arithmetic constraints, limiting standing-to-seated mode transitions to chassis angles below 45\({}^{\circ}\) with joint torque thresholds \(<\) 8 N-m.
# Green Spare Parts Evaluation for Hybrid Warehousing and On-Demand Manufacturing
## 2 Materials and Methods  
### Spare Parts Evaluation  
Spare parts evaluation refers to the process of evaluating and determining the quantity, location, and lead time of spare parts. Several standards such as NORSOK Z-008 recommend that spare parts evaluation studies be performed in the project phase in what is commonly known as the spare parts interchangeability record (SPIR) [1]. Complex assets such as oil and gas platforms must have a spare parts inventory plan and SPIR documents [2]. The NORSOK Z-008 standard [1] provides a workflow to determine the optimal location to store spare parts, based on a risk matrix (shown in Figure 2) that considers both the consequences of not having spare parts in place and the demand rate. The ideal location should minimize both the risk and the cost associated with the inaccessibility of spare parts. Potential locations may vary from stocking (1) via stock at the site, (2) in a central warehouse, or (3) no stock at all. In other words, the standards help SPIR engineers determine what and how many spare parts should be on-site to ensure the desired production uptime, and what and how many spare parts should be stocked at the central warehouse. It is clear that all these recommendations and stocking options are related to physical warehousing, whereas the digital warehousing option has not been considered yet. Digital warehousing supported by on-demand manufacturing could impact stocks on-site or at the central warehouse if it is used.  
The second important issue to determine is the number of spare parts. To keep the spare parts stock at a certain level, the quantity of orders and the reorder point must be determined. If the demand rate and supply lead time are constant, the Economic Order Quantity (EOQ) model can be used. It is a model used in inventory management to determine the optimal order quantity that minimizes total inventory costs (ordering and holding costs). However, asset failure rates are not constant and the supply lead times  
Figure 2: Spare parts risk matrix by NORSOK Z-008 [1].  
also vary from time to time. Therefore, stochastic models can be used to determine the preferable order quantity and reorder point considering the variability of demand rates and lead times.
# Energy-Aware Wireless Sensor Networks for Smart Buildings: A Review
## 2 Literature Review  
In Wireless Sensor Networks (WSN), sensor nodes are deployed either uniformly (controlled), randomly (uncontrolled), or in a hybrid manner [2]. In uniform deployment, sensors are placed based on pre-calculated and pre-engineered network architecture, while nodes are deployed at random positions in the random deployment. For uniform deployment, network connectivity and coverage are controlled. The random deployment is used in large WSNs where sensor nodes' connectivity and coverage are subject to random network modeling. The deployment choice depends mainly on the accessibility to the deployment environment.  
On the one hand, deploying sensor nodes in a harsh environment like a battlefield, disaster region, or a forest can be possible only by throwing sensors from an airplane. Hence, the deployment, in such a case, will be random. On the other hand, uniform deployment is the placement of sensor nodes in a controlled manner. This type of deployment is used in a user-friendly environment such as a Smart Building. Since the Smart Building is our deployment environment, in this paper, we discuss the design criteria and challenges of indoor WSN.  
Several challenges arise while designing a WSN [3], such as network connectivity, network coverage, data fidelity, and network lifetime [4]. In the context of a Smart Building, network coverage, data fidelity, and network connectivity issues can be handled by the uniform deployment (Figure 1). Good coverage and connectivity can be achieved by well positioning nodes and choosing devices with the optimal signal range. Additionally, data fidelity can be ensured by placing multiple sensors to sample a single area. For example, to measure temperature value in a room, there might be a need to place numerous temperature sensors where the average of the sampled values represents the real temperature value of the room. However, in all WSN types (indoor, outdoor, static, or even mobile) [5], extending the network's lifetime to the maximum is a goal that is needed whenever there is room for doing so. This challenge can be achieved by optimizing the energy consumption at the level of WSN nodes.  
Based on the literature review of WSN, this paper examines the network lifetime with the focus on the energy consumption of the sensor node at the hardware layer, operating system layer, networking layer, and application layer (see Figure 2). This study took into consideration three factors: first, the network topologies such as Mesh, Cluster, Tree, and Star topologies; second, the node's role in the WSN such as End node, Router node, Cluster-Head, or Gateway; third, the investigations included the data processing approaches where WSN nodes are involved, for instance, In-node processing and In-network processing. As a result of this study, this paper guides the WSN designer on extending the WSN's lifetime by optimizing the node's resources.
## 2 Materials and Methods
### RNA-Seq  
Libraries were created using the NEBSnext Ultra II RNA Library Prep Kit for Illumina. Library quality was checked using an Agilent 2100 Bioanalyzer (Agilent, 2100, Santa Clara, CA, USA) and Agilent High Sensitivity DNA Kit (Agilent, 5067-4626). Total library concentration was detected using Pico green (Quantifluor-ST fluorometer, Promega, Madisson, WI, USA, E6090; Quant-iT PicoGreen dsDNA Assay Kit, Invitrogen, P7589, Waltham, MA, USA) and validated library concentration was quantified by QPCR \(<\)Thermo Scientific, Waltham, MA, USA, StepOnePlus Real-Time PCR Systems\(>\). The mixed libraries were diluted step-wise and quantified before sequencing in PE150 mode on an Illumina (San Diego, CA, USA) sequencer.  
The filtered high-quality sequences (Clean Data) were aligned to the reference genome of the species. Moreover, the Read Count value of each gene was counted using HT-Seq (0.9.1) as the raw expression of the gene. Expression was normalized (Normalization) using FPKM to assess gene expression levels. Differential expression analysis between two comparison combinations was performed using DESeq (1.20.0). Differentially expressed genes (DEGs) were screened under the conditions of expression difference ploidy \(\mid\) log2(FoldChange) \(\mid\) \(>\) 1, and significance \(p\)-value \(<\) 0.05.
# Measurement and Analysis on Magnetic Field Influence of Substation for Magnetic Shielding Device
## 4 Test Results and Discussion
### Magnetic Field near substation 3
According to the spectrum data, when measured at 5 m from the substation 3, the magnetic field fluctuation approaches the fluctuation level of the Earth's magnetic field. Based on the analysis of DC magnetic field and fluctuating magnetic field, when substation 3 is used, the construction site of magnetic shielding device should be at least 5 m away from the radius of substation 3.
## 2 ANC Method Based on VMD and GBDT  
### Preliminaries  
The schematic diagram of the proposed ANC method based on VMD and integrated learning algorithm is shown in Figure 1. The proposed ANC method includes an active noise control module, feature extraction module and gradient boosting decision tree module.  
The ANC is a modular feedback control system, where \(x(n)\) represents the reference signal, \(y(n)\) represents the secondary path output (control output), \(p(n)\) represents the primary noise, \(e(n)=p(n)-y(n)\) represents the error signal, \(s=[s_{0},\ s_{1},\ \cdots,\ s_{M-1}]\) represents the secondary path. In this model \(\overset{\mathsf{A}}{\mathbf{s}}\) represents the estimated secondary path, and generally \(\overset{\mathsf{A}}{\mathbf{s}}=\mathbf{s}\). \(\mathbf{w}(n)=[w_{0}(n),\ w_{1}(n),\ \cdots,\ w_{N-1}(n)]^{\mathsf{T}}\) represents the adaptive controller.  
The feature extraction module includes two parts: a noise feature extraction based on VMD and noise clustering analysis based on PCA and k-means++. The module extracts the IMFs of noise data through VMD and gets the AE for each IMF of noise. The VMD processing result is taken as the input for the PCA and k-means to classify the noises. On the basis of the feature extraction and cluster analysis of noise data, the GBDT module generates different GBDT regressors for different noises, thereby creating an ANC for noise of different types.
# Prophet-CEEMDAN-ARBiLSTM-Based Model for Short-Term Load Forecasting
## 3 Experiment Results and Analysis  
### Data  
#### 3.1.1 Data Collection  
The paper employs real data from the New England region to evaluate the generalization ability of the methods. During the data collection process, the sensor records the hourly data points. The dataset contains the hourly data for 4324 days from 2003 to 2014, and the total number size is 103,776. The paper adopts the hourly data from March 2003 to December 2005 as the training set, while the test set consists of data from the next year.