esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The data presented in this contribution were obtained during the pilot testing of fire blankets on vehicles by the Fire and Rescue Corps within the Slovak Republic. Conducting large-scale tests is demanding. However, through experimental research, we obtained the following results:  
* The application of the car fire blanket has a significant impact on the temperature decrease in the interior (temperature dropped by 584 \({}^{\circ}\)C) but also on the body (temperature dropped by 97 \({}^{\circ}\)C) of the ICEV. Thermocouples T0 and T3 at a lateral distance of 0.7 m did not measure significant temperatures during the whole experiment.
* In the large-scale ICEV fire test, the maximum temperature reached was 724 \({}^{\circ}\)C in thermocouple T1 located in the interior.
* There was no significant impact on temperature decrease after the car fire blanket application in the large-scale HTB fire test.
* The large-scale HTB fire test results show that after car fire blanket application, the temperature inside the HTB increased (temperature rose by 39 \({}^{\circ}\)C).
* Mechanical damage to the integrity of the HTB enclosure did not initiate a long-term lithium fire in the HTB fire test.
* In the large-scale HTB fire test, the maximum temperature reached was 335 \({}^{\circ}\)C in thermocouple T0 located on the left side at a lateral distance of 0.7 m.  
We consider the findings based on the results of this research to be of interest in not only the area of testing car fire blankets but also in ICEV and HTB fire testing. We also believe that the methodology of experiments applied in this research is repeatable and can bring more results in the area of car fire blanket testing. However, we acknowledge the limitations of the conducted experiments. Our prospective aim to enhance future experiments is to incorporate heat release rate (HRR) measurements. Since the car fire blanket was not applied during thermal runaway, we cannot assess its effect on this phenomenon in detail.  
Conceptualization, J.S. and M.K.; methodology, K.S. and J.S.; formal analysis, K.S. and J.S.; investigation, J.S., M.K., M.B. and K.S.; resources, M.B.; data curation, J.S. and K.S.;  
Figure 24: Comparation of temperatures of [7] and large-scale ICEV fire test.  
writing--original draft preparation, K.S.; writing--review and editing, K.S.; visualization, K.S.; project administration, K.S. All authors have read and agreed to the published version of the manuscript.  
This article was supported by University of Zilina, Faculty of Security Engineering. Department of Fire Engineering.  
Not applicable.  
The data given in this study is available on the request of the corresponding author because of the anonymousness of the manufacturers who cooperated on the research.  
This article was supported by the Fire and Rescue Corps of Slovak Republic with Research task PPZ-OAI-2022/002671-037--Testing the effectiveness of extinguishing electric, hybrid vehicles and other dangerous substances in vehicle fires in road transport using different types of special fire blankets.  
The authors declare no conflicts of interest.  
References
# Deep Learning Forecasting Model for Market Demand of Electric Vehicles
## 4 Experimental Works
### Dataset  
In the study, BEV, PHEV, and HEV were used as dependent variables; gasoline prices (GP), diesel prices (DP), electricity prices (EP), monthly real gross domestic product (MRGDP) index, total renewable energy production (TREP), total number of charging stations (TNCSs), price producer index (PPI), consumer price index (CPI), and new auto loan rate (NALR) were used as independent variables. Monthly data of all variables for the period June 2015-December 2022 were included in the study. Data on BEV, PHEV, and HEV are from Argonne National Laboratory; data on gasoline, diesel and electricity prices, total renewable energy production, and total number of charging stations are from the Energy Information Administration in the U.S. SP Global provided the data for the MRGDP index, the U.S. Bureau of Labor Statistics provided the consumer and producer price index data, and the Federal Reserve Economic Data provided the new auto loan rate data. The dataset contains a total of 90 monthly observations. There is no missing data in the dataset, so no missing data filling method was applied. However, in order for the model to produce more accurate results, all variables were scaled between 0 and 1 with the min-max normalization method. The trends of the data were examined for outliers, and no extreme points were detected. These preprocessing steps were performed to optimize the learning processes of the model. Table 3 displays the monthly values of the input and output variables for 2022.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Month** & **BEV** & **PHEV** & **HEV** & **GP** & **DP** & **EP** & **MRGDP** & **TREP** & **TNCS** & **PPI** & **CPI** & **NALR** \\ \hline
1 & 42.780 & 11.983 & 63.093 & 3.413 & 3.724 & 0.147 & 21,759.622697.568 & 48,717 & 248.201 & 167.582 & 4.58 \\ \hline
2 & 46.859 & 12.563 & 58.175 & 3.611 & 4.032 & 0.148 & 21,747.366651.899 & 48,283 & 248.848 & 168.027 & 4.87 \\ \hline
3 & 64.160 & 16.200 & 76.683 & 4.322 & 5.105 & 0.15 & 21,713.761733.189 & 48,661 & 249.613 & 168.056 & 4.87 \\ \hline
4 & 52.537 & 17.875 & 71.849 & 4.213 & 5.12 & 0.151 & 21,676.476712.467 & 49,464 & 251.747 & 169.699 & 4.87 \\ \hline
5 & 52.502 & 15.263 & 68.737 & 4.545 & 5.571 & 0.154 & 21,721.769742.753 & 50,438 & 257.198 & 171.350 & 5.15 \\ \hline
6 & 74.262 & 14.838 & 61.039 & 5.032 & 5.754 & 0.16 & 21,730.470725.578 & 51,196 & 260.776 & 172.546 & 5.15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Monthly values of input and output variables in 2022.
# An Adaptive Back-Off Mechanism for Wireless Sensor Networks
## 4 Simulation and Analysis
### Network Topology  
Since the network topology mainly consists of mesh, and the linear topology of real WSNs, we adopted these two different topologies to evaluate our proposed back-off mechanism. Mesh topology as shown in Figure 4a, this contains nine nodes. One sender is Node 5 whose destination is Node 6, and the other one is Node 7 whose destination is Node 8. In Figure 4b, the linear topology consists of five nodes, and the sender and receiver are Node 0 and Node 4, respectively. The distance between adjacent nodes for both topologies is 200 m.
## 2 Internet of Vehicles (IoV)
### IoV Applications  
IoV opens up new opportunities and applications and offers many benefits to drivers, pedestrians, society and business. According to the World Health Organization (WHO), every year, approximately 1.3 million people die and 7 million are injured in traffic accidents. Moreover, people waste billions of hours because of traffic problems (e.g., through traffic jams and accidents) [31].  
According to the 5G Automotive Association (5 GAA), IoV applications are clustered into four categories.  
* Safety-based: The primary goal is to reduce the severity and frequency of vehicle crashes. Connected vehicles exchange warning alerts to prevent possible collisions, and provide real-time crash information with location information to emergency teams.
* Traffic efficiency: Traffic efficiency covers a range of applications intended to optimize the flow of road traffic. For instance, scheduling traffic signals at intersections according to the volume of traffic to reduce waiting time.
* Cooperative driving: Cooperative adaptive cruise control (CACC) requires throughput of the order of 5 Mbps and latency of the order of 2-10 ms.
* Infotainment: Efficient provision of non-driving-related information and entertainment to drivers and passengers with stringent QoS is crucial for IoV. Many vehicular users are now interested in accessing HD video streaming and use various latency-sensitive mobile applications, such as real-time online gaming, virtual sports, video streaming, instant messaging among passengers, and geo-specific advertisements. Such services are characterized by relatively short latency.  
Typical IoV applications and their requirement are listed and described in Table 1.
## 3 Results
This region was previously identified as significant in familial linkage analyses of a large AIS familial cohort. A neighboring region, distal chromosome 16p11.2 duplication, has been recently identified as a significant risk factor for severe AIS. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{\(N\) Families} \\
**Containing Variant** & **Gene Names** \\ \hline \multirow{8}{*}{2 families} & _TMEM52, GCFC2, RTP5, SLC10A6, SAMD9L, PTPRD, ASCL1, GALC, CD34, ALPPL2, FAM189B, NTRK1, CHIT1, DCBLD2, COL6A5, ATG9B, NUTM2F, NME3, GFAP, KRTAP10-5, ZNF644, FLG, OR6P1, CSMD1, UCGR, LANTD2, THAP11, MLIT1, OR14A2, VWA3B, CFC1B, MSH3, AKAP3, GNB1L, RAB36, FGD1, THOCs, TNXB, AHNAK2, XYLT1, APOL3, CACFD1, RPL3L, PIGT, CTNNA3, LRIT1, TYMP, ASXL2, GPAT2, AARD, MGA, LGALS9C, C18 orf65, AHCY, CSMD2, VP54B1, BHLE22, CCDC68, SYNJ1, HMGCS2, SYN2, DNAES1L3, CASP12, FSIP2, TET2, FAM153B, FAM153A, PFAS, PDZD2, KILH32, ANKRD18A, MCM8, NPHP4, USP32, COL21A1, SSTR5, CEACAM21, FAM26F, OR4A5, KRT81, BDNF, SLC1A7, SPTA1, TNK2, PTPRE, CYBS7R, MTUS2, FANCA, KATNAL2, OTC, GPH1 BP1, CLE18, SLC2A31, SETDB1 \\ \hline \multirow{8}{*}{3 families} & _GPRIN1, SKIV2L, PRR23D1, TPSED126, KIR3 DL1, TTN [61], KRTAP4-3, DLL3 [58,59], OBSCN, KCNQ5, CCDC168, PRR25, ANKRD11 [62], HRC, GPR179, USP26, FCCBP, KCNN3, CACNA1H, ANKRD30B, KIAA1785, MNCI, ACCT4, KIAA0556, ERCCGL, DYSF, CEP170, AHNAK [60]_ \\ \hline \multirow{2}{*}{4 families} & _GPRIN1, SKIV2L, PRR23D1, TPSED126, KIR3 DL1, TTN [61], KRTAP4-3, DLL3 [58,59]_ \\  & _OBSCN, KCNQ5, CCDC168, PRR25, ANKRD11 [62], HRC, GPR179, USP26, FCCBP, KCNN3, CACNA1H, ANKRD30B, KIAA1785, MNCI, ACCT4, KIAA0556, ERCCGL, DYSF, CEP170, AHNAK [60]_ \\ \hline \multirow{2}{*}{4 families} & _GPRIN2, PKD112, TMPR5S13, KRT2, ZNF17, PDE4 DIP_ \\ \hline \multirow{2}{*}{\(\geq\)5 families} & _AL589743.1, TPRX1, FAM47A, WIPF3_ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Genes containing variants in multiple idiopathic scoliosis (IS) families. Genes with the same variant identified across families are indicated with an *. Citations are given for genes previously associated with scoliosis (including idiopathic, degenerative, or infantile). Full variant results by family are provided in Supplementary Files S3. Figure 2: Summary information for total gene list (\(n=1160\)) of familial AIS-associated variants across families with a minor allele frequency (MAF) \(<\) 0.05 passing bioinformatic filtering, as described in the Methods. (**A**) Variant type with \(n\) variants and \(\%\) of total provided. (**B**) Minor allele frequency of all variants using ExAC. (**C**) Variant counts by chromosome. (**D**) Top GO Cellular Component, 2018 terms using EnrichR. See Table 2 for top GO terms using DAVID. (**E**) Volcano plot of top enriched KEGG terms over expected.
## 3 Current and Future Legal Constraints  
In the EU, regulation will require the mandatory installation of telematic devices on all new vehicles by 2024 [15]. Vehicles that are fitted with telematic devices can collect a vast amount of personal and private information from the user. Current EU regulations that cover data processing and collection within a vehicle are the ePrivacy Directive [47] and General Data Protection Regulation (GDPR) [48]. The ePrivacy directive requires consent from the data controller in the event of requiring access to or storing of data from a device capable of communication.  
The International Working Group on Data Protection in Telecommunications (IWGDPT) and the European Commission have both stated the importance of data privacy and user consent in connection to personal driver data [2; 13]. The process of requiring consent should also be afforded to passengers and users of rental vehicles [1; 2; 49]. The user of the vehicle should also have the ability to delete profile information and other personal information relating to them from the vehicle [1; 2]. The European Data Protection Board (EDPB) recommends that manufactures should implement a profile management system in the vehicle. This will allow a user to easily agree, reject, or delete specific data types from different vehicle systems.  
The processing requirements of data processing and collection from a vehicle will change. The IWGDPT stipulates that the processing of personal data should be prioritized within the vehicle, as to avoid transmitting sensitive information to remote servers [2] (p. 10). The EDBP endorses this requirement, host-data processing should be prioritized over data transmission. In the event host-data processing is not possible, a qualified third party should be selected to process the raw telematic data [1] (p. 16). The aforementioned regulation ensures that a minimal amount of personal data leaves the vehicle.
## 4 Analytical Methods  
### Zircon U-Pb Dating  
Zircons were extracted from four samples (FY-80418, FY-80618, FY-80703, FY80704) by the Langfang Regional Geological Survey in Hebei Province, China. Standard density and magnetic separation methods were employed, after which zircon grains were manually selected under a binocular microscope. These grains were then analyzed using both transmitted and reflected light microscopy. To examine their internal structures and identify suitable targets for U-Pb dating, cathodoluminescence (CL) images were captured using a JEOL JSM6510 scanning electron microscope equipped with a GATAN Chroma CL detector at Gaonianlinghang Company in Beijing, China. U-Pb geochronological analysis was conducted using an LA-ICP-MS system located at the Inner Mongolia Geological Survey Institute in Hohhot, China. Zircon 91500 [50,51] was used as an external standard for age calibration Zircon standards GJ-1 [52] were analyzed as unknown samples that were inserted between 91500 and the samples, and NIST SRM 610 silicate glass was applied for instrument optimization. Detailed information on the instrument configurations and analytical protocols can be found in prior publications [53]. Corrections for common Pb were applied based on established methods [54], and [PERSON] [54] elaborated on a novel common lead correction method applicable to U-Pb analyses lacking \({}^{204}\)Pb data. This approach determines the radiogenic lead content, total lead content, common lead proportion, initial crystallization age, lead loss age, and lead loss magnitude in zircon or other U/Th-rich minerals by numerically solving a set of equations. In this study, we implemented common lead corrections using the Excel spreadsheet tool provided by the authors in their original publication. Data reduction was performed using ICP-MS-DataCal (Ver. 6.7 [55]), and concordia diagrams were generated using Isoplot (Ver. 3.0; [56]).
# A Hybrid Mapping Method with Position and Stiffness for Manipulator Teleoperation
## 4 Stiffness Mapping Method for Compliant Teleoperation
### Stiffness Mapping in Contact Space  
According to the stiffness classification from the sEMG signals, the classified stiffness at the human arm endpoint can be obtained in real time, which can be mapped to naturally control the stiffness of the slave robot in contact space.  
The overall block diagram of stiffness mapping for teleoperation is shown in Figure 11. In contact space, the CyberForce without force feedback and TrignoTM Wireless system are utilized simultaneously to teleoperate the slave manipulator. The feedback force is not applied to the human arm because it influents the stiffness of human arm. The Cartesian impedance controller in which the stiffness parameters can be adjusted is designed in the slave robot to execute the compliant tasks. The reference position \(\mathbf{P}_{end}\) of the impedance controller is provided by the human arm motion measured by CyberForce. Besides, the classified stiffness at the human arm endpoint \(\mathbf{K}_{end}\) can be used as the reference stiffness of the Cartesian impedance controller to regulate the compliant behaviors of the slave robot.
# A Novel Deep Reinforcement Learning Approach to Traffic Signal Control with Connected Vehicles +
## 3 Methodology  
The deep reinforcement learning (DRL) approach has gained much attention due to its capability in learning high-level decision-making processes, as demonstrated in [39]. As a data-efficient method, it enables learning of decision-making by agents through interactions with the environment. A typical DRL-based control framework consists of three basic components: environment sensing, the action space of the agent, and a learning goal for the agent [4]. As demonstrated in previous studies, data-driven methods applied to traffic signal control problems outperform conventional methods in simulations involving large amounts of data.  
The objective of this research is to improve the overall performance of data-driven signal control frameworks by enhancing training outcomes and yielding more adaptable SPaT results. As highlighted in the previous section, the existing DRL-based methods face various challenges in terms of learning efficiency and optimality. In particular, the learning structure needs to be revised to accommodate dynamic traffic conditions and improve learning efficiency. With this in mind, this paper presents a novel data-driven optimization framework for traffic signal control that leverages innovative DRL techniques. The structure of the proposed method is depicted in Figure 1.
## 2 Materials and Methods
### Battery Life and Thrust-to-Power Analysis  
Battery life is pivotal for UAVs, especially when considering energy management for long-duration missions or remote operations. The thrust-to-power ratio, which determines the efficiency of the motor-propeller combination, plays a key role in this context. By analyzing this ratio, we can determine the most energy-efficient configurations that align with the UAV's power availability and battery capacity.  
In our analysis, simulated battery cycles were used to evaluate battery health over time, taking into account the impact of energy management strategies, such as optimal charging, propulsion needs, and the potential for integrating hybrid storage systems. The findings from this analysis inform decisions on mission length, payload capabilities, and energy-intensive operations, ensuring that the UAV operates within its energy constraints while maximizing performance [9; 12; 45; 52].
# Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer's Disease Detection
## 4 Results and Discussion
### Performance Analysis Using the Confusion Matrix  
The color intensity in the confusion matrix visualizes the frequency of classifications, with darker colors representing higher counts (primarily along the diagonal for correct predictions) and lighter colors representing fewer occurrences (for misclassifications).  
The confusion matrix in Figure 5(a) illustrates the performance of the hard voting ensemble technique in classifying the stages of Alzheimer's disease across three categories: CN, MCI, and AD. For the CN class, 858 instances were correctly classified, with 16 misclassified as MCI and 2 as AD. The MCI class showed 880 correctly classified instances, with 7 misclassified as CN and 5 as AD. For the AD class, 404 instances were correctly classified, while 11 were misclassified as CN and 15 as MCI. This evaluation highlights the effectiveness of the hard voting ensemble technique, demonstrating strong classification accuracy with minimal errors across the three categories.  
The confusion matrix in Figure 5(b) illustrates the classification performance of the stacking ensemble technique for Alzheimer's disease across three categories: CN, MCI, and AD. For the CN class, 863 instances were correctly classified, with 14 misclassified as MCI and 2 as AD. The MCI class showed 880 correctly classified instances, with 7 misclassified as CN and 5 as AD. For the AD class, 404 instances were correctly classified, while 11 were misclassified as CN and 15 as MCI. This evaluation highlights the effectiveness of the hard voting ensemble technique, demonstrating strong classification accuracy with minimal errors across the three categories.  
Figure 5: The 95% confidence intervals for performance metricsohlfor each of the ensemble architecture.  
Figure 6: Confusion matrix for (**a**) hard voting and (**b**) stacking ensemble classifiers for [PERSON]’s disease classification.  
MCI and 2 as AD. The MCI class showed 897 correctly classified instances, with 8 misclassified as CN and 5 as AD. For the AD class, 387 instances were correctly classified, while 3 were misclassified as CN and 19 as MCI. This analysis demonstrates the model's strong classification accuracy, with relatively few misclassifications observed across all categories.
# Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration
## 3 System Model Design and Optimization Framework
### Neural Network Framework
Clearly, there is a trade-off between the lifetime extension and coverage percentage as soon as constraints are fulfilled, and the objective function gets lower for each topology. Therefore, node relocation is a function of that trade-off. Figure 2: Flowchart diagram of neural network solver. On the contrary, the situation is different for an initial random topology deployment. In that case, nodes' coordinates are derived, for example, from a Gaussian distribution, and are initially scattered around near the sink node. To this end, the coverage rate is low. The NN solver needs to stretch the initial topology to the extent where the final sensor coordinates fulfil both coverage and connectivity constraints and extend the network lifetime. In Figure 3, we depict a snapshot topology example of (a) a deterministic topology and (b) a random topology, in both of which the sink node is placed at (0, 0) in the middle of the FoI. In case (a), we depict the initial node positions after the relocation proposed by the solver, in respect of the constraints, as red dots and the final ones as green dots. We clearly observed that, in this case, the solver needs to evaluate the trade-off between energy consumption and coverage rate, and it turns out that the relocation is close to the initial points, which are the ones proposed by the CVT topology, which is the best one in terms of geometry; in contrast, in case (b), the solver starts from a random topology and has more degrees of freedom to relocate nodes according to the constraints and to extend the network lifetime.