esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology
### Ten-Fold Cross-Validation and Judgment Index  
#### 3.3.1 Ten-Fold Cross-Validation  
Ten-fold cross-validation is a commonly used model validation technique primarily aimed at assessing the specific performance and generalization capability of machine learning models [44; 45; 46]. The main approach of this technique involves dividing the dataset into multiple parts, repeatedly training and validating the model on different subsets, and using the results from multiple validations to cross-check the model's predictive ability, thus providing a more stable and reliable performance estimate. The specific process is as follows:  
1. The entire data set is randomly divided into 10 subsets of similar size.
2. One subset is selected as the verification set and the other nine subsets are selected as the training set. The training set is used as the data for model construction and training. The rest of the verification set is used to check the accuracy of the model built at this time.
3. The above steps are repeated 10 times, each time selecting a different subset as the validation set to ensure that the validation set used in each experiment is unique.
4. The performance indicators after each iteration were recorded and the values of ten different experiments were compared in order to evaluate the accuracy and generalizability of the model.  
Of course, in different cases, the original data set can be divided into different amounts to improve the number of model tests and to enhance the precision of the model's predictive ability. Depending on the size of the original quantity set and the choice of the specific model, the number of tests in this paper will be taken as ten.
# Bar-Wave Calibration of Acoustic Emission Sensors
## Appendix A Asymmetric Excitation  
In most of this study, bar waves were produced utilizing a symmetric excitation method. This is shown in the top part of Figure 11. A transmitter (FC500) is glued (using epoxy) at the central position and most of the end surface of the bar was subjected to the displacement pulse from the transmitter. Because of the symmetry, bar-wave modes similar to symmetric plate waves are produced on the test bar. Another approach is to apply the displacement pulse in asymmetric manner. This study used an arrangement shown in the lower part of Figure 11. Here, a square-rod of 3.2 mm was inserted (by epoxy bonding) between the transmitter and the test bar and only one side of the bar is excited, favoring the generation of asymmetric bar-wave modes.  
When asymmetric excitation is used, the output waveforms on opposite surfaces showed different trend from that found in Figure 7 with the symmetric excitation, where the wave phases matched well from the initial arrival up to 150 \(\upmu\)s. Here, a shorter bar of 1.05 m was used to compare the excitation methods. As Figure A2a shows, the phases started to shift from about 70 \(\upmu\)s and asymmetric wave modes became evident at 90 \(\upmu\)s or the velocity of 3 mm/\(\upmu\)s. Figure A2b covers the period between 90 and 150 \(\upmu\)s and in this segment, the two waveforms showed the features of opposite phase or the wave modes to be fully asymmetric. Beyond 150 \(\upmu\)s, Figure A2c clearly exhibits asymmetric behavior.  
Figure 14 is CWT spectrograms, comparable to Figure 6, except this plot was obtained using the asymmetric excitation method. This figure shows weaker peak intensities at peak positions corresponding to cw1 through cw5. The low frequency (below 100 kHz) peaks are seen, but shifted to later arrival (>150 us). These are possibly related to x- and y-displacement dominated modes, seen in Figure 5a. A new peak appears at 140 us and 700 kHz, which was absent in Figure 6. For this position, there are numerous modes indicated in Figure 5a and its origin is indeterminate.
# Functional Connectome Fingerprinting Through Tucker Tensor Decomposition
## 3 Results
### Evaluating the Impact of Brain Parcellation Rank and Participant Rank on Fingerprinting  
To obtain a holistic view of how brain parcellation rank and participant rank affect fingerprinting, we computed matching rates under different combinations of both ranks for all fMRI conditions. Results shown in Figure 3 indicate that higher brain parcellation ranks led to higher matching rates compared to lower brain parcellation ranks. However, the impact of the participant rank on matching rates depends on the fMRI condition. Specifically, with Emotion and Motor tasks we can achieve near-optimal matching rates with a participant rank of 300 or higher, while for all the other fMRI tasks, we achieve near-optimal matching rates earlier, starting at a participant rank of 150. Resting-state matching rates were the highest among all fMRI conditions, reaching optimal scores starting at a participant rank 100, with 100% matching accuracy. In contrast, Emotion had the lowest matching rate.  
Figure 3: Brain parcellation rank and participant rank effects on matching rate for all eight fMRI conditions, ordered by scan length (represented by the number of time points or “TPs” in their time series) for granularity of Schaefer parcellation 414. For each condition, the fingerprinting framework is applied in both test\(\rightarrow\)retest andretest\(\rightarrow\)test directions. Each curve illustrates the average matching rate computed across both settings at each combination of brain parcellation ranks and participant ranks.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{8}{c}{**Runtime (Seconds) per Parcellation Granularity**} \\ \cline{2-10}  & **114** & **214** & **314** & **414** & **514** & **614** & **714** & **814** & **914** \\ \hline PCA & **0.38** & 0.85 & 1.93 & 3.74 & 6.26 & **8.75** & **12.45** & 21.23 & **24.71** \\ CP Decomposition & 2.15 & 12.88 & 45.82 & 108.23 & 158.75 & 222.00 & 288.06 & 378.45 & 465.32 \\ ST-HOSVD & **0.38** & **0.73** & **1.58** & **3.20** & **5.15** & 8.78 & 13.24 & **19.55** & 27.62 \\ HOOI & 0.87 & 1.76 & 3.81 & 6.41 & 11.70 & 18.03 & 26.51 & 36.20 & 72.07 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between the worst runtimes of different dimensionality reduction methods. For each parcellation granularity \(PG\), a tensor of dimension \(PG\times PG\times 426\) was decomposed by CP Decomposition, ST-HOSVD, and HOOI. Equivalently, PCA was applied to a matrix of dimension \((P_{2}^{G})\times 852\), where the rows consist of vectorized upper triangular FCs, and the columns correspond to the participants’ test and retest sessions. Bold values indicate the shortest runtime across methods.
## 7 Summary of MIR Gas Sensing
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Species** & **Bands** & **Wavelength/nm** & **Laser Type** & **Techniques** & **LOD 1/ppb** & **Refs** & **Applications** \\ \hline \multirow{3}{*}{H\({}_{2}\)O} & \(v_{2}\) & 6700 & QCL & OA-ICOS [14] & 280 & [21] & Chemical  analysis \\ \cline{2-7}  & \(v_{3}\) & 2666.7 & OPO & FCS & 5.3@30 s & [210] & Technique  research \\ \hline \multirow{3}{*}{H\({}_{2}\)O\({}_{2}\)} & \(v_{6}\) & 7730 & QCL & QEPAS & 12@100 s & [236] & Breath diagnosis \\ \cline{2-7}  & \(v_{3}\) & 3760 & OPO & FCS & 8 & [171] & Exhaled breath  analysis \\ \hline \multirow{3}{*}{H\({}_{2}\)S} & \(v_{2}\) & 7900 & QCL & QEPAS & 450@3 s & [237] & Environmental  pollution \\ \cline{2-7}  & \(v_{3}\) & 10,540 & QCL & QEPAS & 0.05@1 s & [238, 239] & Technique  research \\ \hline \multirow{3}{*}{CS\({}_{2}\)} & \(v_{1}+v_{3}\) & 4590 & QCL & QEPAS & 28@1 s & [240] & Industrial  process \\ \cline{2-7}  & \(v_{1}+v_{3}\) & 4590 & QCL & DOAS; & 10.5; 60@240 s & [27, 241] & Atmospheric \\ \hline \multirow{3}{*}{OCS} & \(v_{3}\) & 4860 & QCL & DAS & [EMAIL_ADDRESS] s & [242] & Exhaled breath  analysis \\ \cline{2-7}  & \multirow{3}{*}{\(v_{2}\)} & 3393 & & DAS & [EMAIL_ADDRESS] s & [68] & Industrial  emission \\ \cline{2-7}  & \multirow{3}{*}{\(v_{2}\)} & 3392 & ICL & & & & & \\ \cline{2-7}  & & 3392 & WMS & 7.1@295 s & [165, 243] & Atmospheric \\ \hline \multirow{3}{*}{CH\({}_{3}\)SCH\({}_{3}\)} & \multirow{3}{*}{\(v_{18}\)} & 3370 & ICL & WMS-2f/1f & 2.8@125 s & [69] & Environmental \\ \cline{2-7}  & & 3337 & ICL & WMS & 9.6@164 s & [164] & Atmospheric \\ \hline \hline \end{tabular}  
* \({}^{1}\) LOD: Limit of Detection; \({}^{2}\) HPSDS: Heterodyne phase sensitive dispersion spectroscopy; \({}^{3}\) OF-CEAS: Optical feedback cavity-enhanced absorption spectroscopy; \({}^{4}\) QW-DFB-DL: Quantum wells distributed feedback diode laser; \({}^{5}\) DROPO: Doubly resonant optical parametric oscillator; \({}^{6}\) NICE-OHMS: Noise-immune cavity-enhanced optical heterodyne molecular spectrometry; \({}^{7}\) CLADS: Chirped laser dispersion spectroscopy; \({}^{8}\) DF-RFM: Dual-feedback RF modulation; \({}^{9}\) VECSEL: Vertical-external cavity surface-emitting laser; \({}^{10}\) DIAL: Differential absorption lidar; \({}^{11}\) I-QEFAS: Intracavity Quartz-Enhanced Photocaustic Spectroscopy; \({}^{12}\) QEFAS: Quartz-enhanced photoacoustic spectroscopy; \({}^{13}\) DIAS: Differential absorption spectroscopy; \({}^{14}\) OA-ICOS: Off-axis integrated cavity output spectroscopy;  
\end{table} 
Table 2: _Cont._
# Design of a Configurable Spike-Encoding Circuit Based on Focal Plane Array
## 2 System Requirement and Coding Theory
### Technical Difficulties  
In the early stages of research, the photoelectric sensor in the FPA system had a developed solution. In addition, the SNN used for target recognition and classification, including its model architecture, deployment method, and training method, had a reference design scheme [14; 15; 16]. Therefore, the technical difficulty of the system resides in the FPA FPA-integrated encoding circuit. Depending on the requirements of the system, the encoding circuit must support two distinct spike-encoding modes: a coarse single-spike encoding and a precise spike-sequence encoding. This requires judicious selection from existing coding theories. While designing, consideration must also be given to the circuit implementation's dependability, reconfigurability, and reusability.
# A Swarming Approach to Optimize the One-Hop Delay in Smart Driving Inter-Platoon Communications
###### Abstract
Hence, a long and unbalanced one-hop delay of the inter-platoon communications cannot enable the member vehicles to obtain some emergency changes of vehicles' kinestate within a limited time and take prompt action accordingly to keep a platoon formation. Therefore, the one-hop delay of inter-platoon communications is an important performance metric. To the best of our knowledge, there are no research works on optimizing the one-hop delay for inter-platoon communications. That motivates us to conduct this work. In this paper, we propose a swarming approach to optimize the one-hop delay of inter-platoon communications by adjusting the minimum contention window size of each backbone vehicle in two steps. Then, we can get the adjusted minimum contention window sizes under different numbers of backbone vehicles in a multi-platoon. The performances such as one-hop delay, end-to-end delay, one-hop throughput, end-to-end throughput and transmission probability have been discussed by using the adjusted minimum contention window sizes. The rest of this paper is organized as follows. Section 2 reviews the relevant recent works. The system model is introduced in Section 3. The swarming approach that optimizes the one-hop delay is described in Section 4. In Section 5, we discuss the network performance, and, finally, we conclude this paper in Section 6.
## Appendix D The ST-FIS Method
### Acquiring Input Variables
\begin{array}{ccc}0&if&z_{3}<\left(a_{A_{2}}\times \left(\frac{TA}{10}\right)\right)\\ \\ \frac{z_{2}-a_{A_{2}}\times\left(\frac{TA}{10}\right)}{\left(b_{A_{2}}\times \left(\frac{TA}{10}\right)\right)-\left(a_{A_{2}}\times\left(\frac{TA}{10} \right)\right)}&if&\left(a_{A_{2}}\times\left(\frac{TA}{10}\right)\right) \leq z_{3}\leq\left(b_{A_{2}}\times\left(\frac{TA}{10}\right)\right)\\ \\ 1&if&\left(b_{A_{2}}\times\left(\frac{TA}{10}\right)\right)<z_{3}<\left(c_{A_{ 2}}\times\left(\frac{TA}{10}\right)\right)\;for\;0\leq z_{3}\leq TA,\\ \\ \frac{z_{3}-d_{A_{2}}\times\left(\frac{TA}{10}\right)}{\left[\left(d_{A_{2}} \times\left(\frac{TA}{10}\right)\right)-\left(c_{A_{2}}\times\left(\frac{TA}{10 }\right)\right)\right]}&if&\left(c_{A_{2}}\times\left(\frac{TA}{10}\right) \right)\leq z_{3}\leq\left(d_{A_{2}}\times\left(\frac{TA}{10}\right)\right)\\ \\ 0&if&z_{3}>d_{A_{2}}\times\left(\frac{TA}{10}\right)\end{array} \right. \tag{100}\]  
where \(a_{*}\), \(b_{*}\), \(c_{*}\) and \(d_{*}\) are real numbers (see Table 10), and the parameter \(TA\) is a positive integer (cf. Equation (55)).
# Usage of V2X Applications in Road Tunnels
## 5 Discussion
The construction of tunnels includes several junctions, large elevation gain, but also the fragmentation in the vertical arrangement (road bends), and thus thevehicle's own signal propagation is also limited, and it also creates an increased potential of accidents, emergencies, and extraordinary events in the tunnel. This could negatively affect the operation of the tunnel itself, and the impact on the safety and fluidity of traffic should be taken into account as well. The C-ITS can be seen as an appropriate technology to help with the safety management and has real benefits for the future autonomous operation of smart cities, and also for tunnels. Therefore, it is appropriate to examine the possibilities of using the C-ITS in more difficult and demanding conditions in more complex structures and environments, not only on the surface, but also below the surface, i.e., in tunnels.
## 5 Empirical Case Study: Courier Company in Bangkok
### From a Fixed Time Instance to Time-Dependent Traffic Information Urban Logistics Problems
#### 5.5.1 From Time Instances to Time-Dependent Traffic Information with 480-Min Working Time Limit  
Now we examine the transferability of the route solution obtained earlier with a fixed time instance to the problem with time-dependent traffic information or actual traffic time. In other words, using the route solution obtained in Section 5.3.1, we query point-to-point the actual traffic time from map API to get the actual vehicle time. Since there are altogether 11 time instances, we will retrieve and compare actual times of the routes constructed from each time instance. Results of time violation amount (from 480 min) are given in Figure 10.  
Recall from Section 5.3.1 that for problems in Scenario A when a fixed time instance was used, no vehicle violated the working time of 480 min at any time instance. Unfortunately, using these route solutions, the retrieved actual vehicle time of almost all time instances violated the 480-min working time limit except for 11 a.m. and 12 p.m.  
As for Scenario B, we saw earlier from Section 5.3.1 that some vehicles at time instances 11 a.m. and 12 p.m. violated the 480-min working time limit. Now, using these route solutions, Figure 9(b) reveals that the retrieved actual vehicle time of all time instances violated the 480-min working time limit. In addition, the amount of violations could be as much as 100 min as in the case of Problems B--11 a.m, 6 p.m. and 7 p.m.  
This problem arose as the effect of the imbalance of vehicle working times. Referring back to the results obtained in Section 5.3.1, Figure 7(b) revealed that applying the route adjustment algorithm with 480-min working time limit resulted in solutions with highly unbalanced route time. Let us take Problem B--7 p.m. as an example. Although no vehicle violated the 480 min, Figure 7(b) indicated that 10 out of 15 vehicles worked up to nearly 480 min, while the other 5 vehicles still had about 60-300 min remaining. Once we calculated the route time using the actual traffic time, those vehicles with nearly zero minutes of working time remaining will likely violate the working time limit of 480 min in the case of actual time. Finally, notice also the highly imbalanced working times with the time-dependent traffic information displayed in Figure 10.
## 2 Theoretical Background
### Environmental Regulation  
The international standards ISO 1996-1:2016 and ISO 1996-2:2017 [6; 7], used as the basis for the Brazilian standard NBR 10.151:2019 [8], define the nomenclature and procedures for the measurement of environmental noise in community environments and noise assessment in inhabited areas, aiming at the comfort of the community. The sound pressure levels are limited by each city zone, typically based on the applicable standards, considering the A-weighting, and divided by day time and night time periods.  
Furthermore, the aforementioned Brazilian standard defines that, in the case that specific frequencies are identified in the measured noise--characterizing the so-called tonal noise--the measured sound pressure levels shall be penalized in \(K_{T}\) = +5 dB. Noise is considered to be tonal if there is a difference larger or equal to 15 dB between the specific frequencies and its side frequencies, for frequencies between 25 Hz and 125 Hz; 8 dB, for frequencies between 160 Hz and 400 Hz; or 5 dB, for frequencies between 500 Hz and 10 kHz, as depicted in Table 1.  
This is the criterion that characterizes and quantifies the limits for framing the noise component as containing tonal components. As an example, it should be noted that, in the frequency range from 25 Hz to 125 Hz, the penalty imposed by legislation (i.e., adding  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frequency Band (Hz) 1/3 Octave of** & **AP (dB), Difference between Sound Pressure** \\
**the Frequency of Interest** & **Measured at Both, Central and Adjacent Frequencies** \\ \hline
25 Hz to 125 Hz & \(\geq\)15 dB \\
160 Hz to 400 Hz & \(\geq\)8 dB \\
500 Hz to 10,000 Hz & \(\geq\)5 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characterization of a tonal sound according to the ABNT NBR 10151:2019 standard.  
+5 dB to the measured sound power value) only occurs if the difference between the sound pressure measured at the central frequency and at both adjacent frequencies is higher than or equal to 15 dB.
## 2 Related Works
In recent years, many broadcast protocols for VANETs have been proposed by the research community. In [18], one can find a possible classification of broadcast protocols. In [19], the authors focus, from a theoretical perspective, on efficient broadcasting for mobile _ad hoc_ networks. Position-based broadcast protocols (see [20] and references therein) exploit the knowledge of some geographical characteristics of the network, to improve the retransmission efficiency. For example, the Emergency Message Dissemination for Vehicular environments (EMDV) protocol achieves remarkable performance exploiting pure geographical information and information on the local network topology [21]. The major drawback shared by all position-based protocols, however, is the need for information on the network topology and the geographical characteristics of the environment where the nodes are located [21]. Since collecting this information may be very difficult, several alternative broadcasting protocols have been recently proposed with the goal of achieving the same performance level of position-based protocols without the need for major information exchange. The Urban Multihop Broadcast (UMB) [22], the Smart Broadcast (SB) [23] and the Binary Partition Assisted Protocol (BPAB) [24] are good examples of threshold-based protocols, based on the idea of partitioning the transmission range of the source in distinct regions.
# A Novel Non-Coherent \(Q\)-Ary TH-PPM Transceiver
## 2 System Model  
In this section, the conventional TH-PPM transceiver is introduced first. Then the new TH-PPM transceiver is proposed. For simplicity, single-user communication is considered in this work.
## 4 Traffic Filtering-Based Techniques
### Filtering HSR Traffic (FHT)
#### 4.5.3 Advantages and Disadvantages  
The FHT approach prunes unicast traffic for not only unused DANH rings but also unused QuadBox rings. Additionally, it also removes duplicated and circulated traffic from the active rings. These features make the FHT approach become one of the most efficient HSR traffic reduction techniques. Since FHT's MAC tables contain only MAC addresses of DANH nodes that belong to DANH rings, the FHT approach requires low memory requirements compared with the PL and EPL approaches. The main drawback of the FHT approach is that it uses control messages to learn MAC addresses and build MAC tables. This results in additional control overhead in HSR networks.
# Design of Remote Upgrade System for Data Processing Unit in Marine Engine Room Simulator
## 4 Firmware Remote Upgrade Design
### Remote Upgrade Program Design  
Within the Local Area Network, the DPU will first accomplish the activities associated with the marine engine room simulator [29]. If the simulator is linked to the Internet and communicates with the Web server, the DPU can start a remote upgrade. First, DPU will send the \"GET\" seek to the Web server, which returns the file number, file length, and the beginning and ending addresses. When the DPU needs to upgrade based on a comparison of the file number, it will set the upgrade flag \"UpgradeState\" to 1 and simultaneously switch the work area. Then, it enters the bootloader program section and queries the command to request the server to deliver the most recent version of the program file. The server gets the command and sends the \"Devicenumber1\" program file to the simulator device. If DPU fails to obtain the upgrade file within the allotted time, the \"Overtime\" counter will increase by 1. If the \"Overtime\" counter value is more than 5, the remote upgrade fails and returns the corresponding fault code. If a server-sent upgrade file is received, the \"Overtime\" counter will be reset to 0 and the received program upgrade file will be verified. If the verification result is incorrect, the upgrade process will be stopped. If accurate, the subsequent data will be sent and written to flash. When DPU receives and verifies the \"Devicenumber1\" program file, it returns the command \"upgrade successful\" and restarts and runs the upgraded application. The system's upgrading flow chart is depicted in Figure 6.  
The program upgrade has a good fault tolerance mechanism. If the program fails to receive, the backup program will be rewritten into the APP running area. Jumping between programs ensures that the marine engine room simulator will continue to work despite a failed upgrade [30].
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Sponge-Like Graphene ECG Electrodes
#### 2.6.2 PVA/PED/GO Sponge ECG Electrode  
Gel ECG electrodes lose water over time, resulting in increased impedance and poor signal quality [76], which is not suitable for long-term ECG monitoring. However, hydrogel is still an efficient basis for electrodes, as it gives electrodes flexibility and helps the electrode materials mix evenly. To make full use of the advantages of hydrogel, give it a structure more suitable for electrode application, and solve the problem of easy water loss, [PERSON] et al. proposed a three-dimensional network structured polyvinyl alcohol/polyethylene glycol/graphene oxide (PVA/PED/GO) gel through cyclic freezing-thawing [77]. During the freezing process, a complete polymeric net was formed by dense hydrogen bonds, giving the composite good electrical conductivity and high hydrophilicity and elasticity (Figure 5a). Unlike the commercial gel electrode, the PVA/PED/GO electrode did not show signs of allergic reaction during in vitro testing.