esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Evaluation on an Embedded System
#### 5.5.2 Evaluation of Classifiers per Dataset Running on an Embedded System  
Table 14 summarizes the measured accuracy and timing of the five classifiers for the BDLib, ESC-10, ESC-50 and the UrbanSound datasets running on the Raspberry Pi. The performance of the classifiers is not affected by the platform, achieving similar accuracy to the one depicted in Table 4. As expected, the execution time needed for the sound classification is significantly affected, becoming around 10 times higher in many cases.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Time [ms]** \\ \hline Mfcc 0 - Mfcc 12 & 622.22 (163.71) \\ Sp. Contrast 0 - Sp. Contrast 5 & 336.39 (64.87) \\ Sp. Centroid & 300.24 (52.81) \\ Sp. Roll off & 315.79 (61.37) \\ Sp. Bandwidth & 349.83 (63.33) \\ Rms & 1089.10 (260.62) \\ Zero Crossing & 64.22 (15.19) \\ \hline
**Total time ( \(t_{extraction}\) )** & 3077.79 (125.21) \\ \hline \hline \end{tabular}
\end{table}
Table 13: Average time required for the feature extraction on the Raspberry Pi. The values in brackets are the Standard Deviation (SD).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{2}{c}{**BDLib**} & \multicolumn{2}{c}{**ESC-10**} & \multicolumn{2}{c}{**ESC-50**} & \multicolumn{2}{c}{**UrbanSound**} \\ \cline{2-9}  & **Accuracy [\%]** & **Timing [ms]** & **Accuracy [\%]** & **Timing [ms]** & **Accuracy [\%]** & **Timing [ms]** & **Accuracy [\%]** & **Timing [ms]** \\ \hline k-NN & 59.17 (4.73) & 12.83 (8.05) & 73.50 (10.01) & 9.54 (3.39) & 48.70 (4.19) & 114.53 (5.94) & 50.48 (2.91) & 80.70 (33.21) \\ Naive Bayes & 68.33 (10.24) & 2.42 (5.29) & 69.00 (12.87) & 10.11 (3.27) & 42.10 (3.48) & 82.18 (4.42) & 49.29 (2.62) & 11.85 (4.87) \\ ANN & 50.83 (9.38) & 2.20 (0.84) & 64.50 (10.12) & 1.85 (0.57) & 20.90 (4.39) & 5.95 (0.29) & 50.6 (6.73) & 4.34 (1.72) \\ SVM & 30.00 (8.96) & 5.01 (0.22) & 36.00 (12.65) & 4.94 (0.31) & 9.40 (3.89) & 16.79 (1.07) & 25.29 (7.73) & 6.44 (2.52) \\ Decision Trees & 29.58 (8.43) & 4.36 (0.21) & 38.00 (11.34) & 4.42 (0.24) & 6.80 (2.48) & 4.85 (0.35) & 34.7 (2.09) & 6.14 (2.41) \\ \hline \hline \end{tabular}
\end{table}
Table 14: Experimental values obtained in the Raspberry Pi. The classifiers’ accuracy per dataset and the average execution time of the classifiers with default configuration are measured. Timing values are expressed in milliseconds. The values in brackets are the Standard Deviation (SD).
## 3 System Model
### Sensing Model  
Assume that RSU \(n\) and vehicle \(un\) are equipped with sensors featuring circular sensing ranges with radii \(R_{n}\) and \(R_{un}\), respectively. To define the redundant sensing data between RSU \(n\) and vehicle \(un\), quad-tree compression technology is employed to partition the sensing areas into equal-area blocks, ensuring that each block contains the same amount of data [29]. Therefore, the redundant sensing data between RSU \(n\) and vehicle \(un\) are defined as the total area of the overlapping blocks. Additionally, considering the uncertainty  
Figure 1: The considered ISCC system within the IoV scenario.  
in sensor performance and environmental influences [30], the sensing overlap rate \(O_{un}\) between RSU \(n\) and vehicle \(un\) is expressed as  
\[O_{un}=\begin{cases}1,&D_{un}+r_{e}\leq R_{n}-R_{un},\\ \alpha\cdot e^{-\varphi(D_{un}+r_{e})\phi},&\text{otherwise}.\end{cases} \tag{1}\]  
where \(D_{un}=\sqrt{\left(x_{un}-x_{n}\right)^{2}+\left(y_{un}-y_{n}\right)^{2}}\) is the Euclidean distance between vehicle \(un\) and RSU \(n\). \(\alpha=\frac{S_{un}^{un}}{S_{un}^{un}}\) indicates the proportion of the intersecting area between the sensing ranges of RSU and vehicle \(un\) relative to the sensing area of vehicle \(un\). The variables \(r_{e}\), \(\varphi\) and \(\phi\) are determined by the characteristics of the sensors and environmental factors.
## 5 Results and Discussion
### Real-Time Experiment Results  
The FBBA was controlled in real time by using Arduino as a communication medium between Simulink and the real plant. The program consisted of a two block diagram. Figure 11 shows the block diagram that was built into the Arduino hardware and Figure 12 shows the block diagram that was run by simulation in the Simulink software. The block diagram used in the program creates a connection between Simulink software and Arduino hardware.  
Figure 11: The block diagram that is embedded into Arduino.  
Figure 12: The block diagram used in simulation to manage FBBA in real time.  
Figure 10: Step response of PID-auto-tuned and PID-GA.  
In the experiment of real-time control, the same PID parameters as in the simulation were used. Figure 13 depicts the step response. The experimental result follows the simulation pattern, with PID-GA outperforming PID-auto-tuned in terms of Tr and Ts. A multistep reference angle, as shown in Figure 14, was used as the input signal to evaluate the controller's effectiveness. This clearly demonstrates that PID-GA improved significantly. The study found that the system had a low to no OS %.
# A Novel Anomaly Behavior Detection Scheme for Mobile Ad Hoc Networks
## 2 Background
### Ad Hoc On-Demand Distance Vector (AODV)  
AODV follows a hop-to-hop routing methodology and belongs to the category of reactive protocols. In AODV, the node issues route requests (RREQs) if there is a requirement to identify the route for a specific target node. The transitional/intermediary nodes forward the RREQs; a reverse route is also generated for the target node [6]. When the target node gets the route to the destination request, it creates a route reply (RREP) that comprises multiple hops needed to reach the target, as shown in Figure 1.  
In AODV, the connection setup delay is less, as there is on-demand routing; to find the latest routes, sequence numbers are used [5]. However, AODV can have multiple route reply packets generated in reply to a particular route request packet, which may result in heavy control overhead.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 2 Research Status of the Precision Formation of Distributed Spacecraft Project
### Irassi  
InfraRed Astronomy Satellite Swarm Interferometry (IRASSI) [26] was proposed by the German Aerospace Center's Space Administration (DLR) in 2016. It consists of five spacecraft, forming a 3.5 m infrared interferometer with orbits distributed at point L2 and  
Figure 10: Schematic diagram of Taiji.  
Figure 9: Schematic diagram of TianQin.  
an effective detection range of 1-6 Hz, as shown in Figure 11. This project uses far-infrared heterodyne interferometry to study the physical formation process of terrestrial planets, mainly in the cold regions of the universe, stellar disks, etc. The formation range of the telescope satellites is 7-850 m, the baseline measurement accuracy must be less than 5 \(\upmu\)m, and the pointing accuracy of the telescope needs to be less than 0.4 arcsec to complete high-precision detection. Each spacecraft is 5.5 m high, equipped with a 6-meter-diameter shading plate and a ranging system composed of four laser modules, forming a three-dimensional asymmetric double-cone telescope array. A detection mission is performed through free drift expansion and the controlled contraction of the formation shape, with a free drift period of 26 h and a controlled contraction period of 22 h [27].
## 4 Evaluation and Benchmarking  
We now present a set of evaluation scenarios and quantitative evidence on the performance of the system that we collected from the operation of our real-world installation present in Section 3. We split our evaluation into multiple domains, based on the technologies used and the devices involved.
# Golgi Metal Ion Homeostasis in Human Health and Diseases
## 3 Zn\({}^{2+}\) Transporters
### Zn\({}^{2+}\) Function in the Earlier Secretory Pathway  
In the Golgi, Zn\({}^{2+}\) is a cofactor of glycosylation enzymes including \(\alpha\)-manosidase 2 (MAN2A1 and MAN2A2), involved in both the substrate specificity and catalytic activity of the enzyme [124]. In mammary epithelial cells, ZnT4 is identified on the _trans_-Golgi, providing Zn\({}^{2+}\) for the Zn\({}^{2+}\)-dependent enzyme carbonic anhydrase VI, and regulating its cellular abundance [114]. On the other hand, the activity of \(\beta\)-4-galactosidaseyltransferase, a _trans_-Golgi resident glycosylation enzyme that catalyzes the transfer of galactosyl to N-acetylglucosamine on glycoproteins, is also regulated by the Zn\({}^{2+}\) level. Excessive Zn\({}^{2+}\) competes with Mn\({}^{2+}\), the cofactor of galactosyltransferase, and inhibits the enzyme [125]. Indeed, overexpression of ZnT4 significantly decreased galactosyltransferase activity without affecting the protein abundance. Different from ZnT5 and ZnT6, ZnT4 is localized on _trans_-Golgi instead of _cis_-Golgi, which may explain why it shows no effect on ALP activity [114]. On the other hand, ZIP7, ZIP9, and ZIP13 are reported to export Zn\({}^{2+}\) from the ER and Golgi lumen to the cytosol, which is also considered to contribute to the function of the early secretory pathway. Endogenous ZIP7 is localized to the Golgi and exports Zn\({}^{2+}\) from the Golgi lumen [117]. But this conclusion was challenged by another study concluding that only ZIP13 is the Zn\({}^{2+}\) transporter in the Golgi, while ZIP7 is located in the ER and involved in ER stress regulation [126]. ZIP9 is localized to the _trans_-Golgi independent of Zn\({}^{2+}\) status; stable expression of ZIP9 reduces ALP activity, indicating its function in Zn\({}^{2+}\) homeostasis in the secretory pathway [118].
## 5 Conclusions  
In this study, a web application for the quantification of CS was implemented. Three measurements were proposed: (1) without using any additional processing for variations in the image, (2) using a Gaussian blur filter to attenuate the noise generated in the CTCS, and (3) using an algorithm that ensures a minimal number of pixels with the same density score. The data were compared against commercial software OsiriX DICOM Viewer where the third applied algorithm had a higher correlation (r = 0.9997). The average error that was obtained for the CS comparison in the proposal without filter was 3.8%,4.7% for the proposal with Gaussian filter, and 1.9% for the minimal mode algorithm.  
Of the studies, 96% for the original proposal and 100% using any of the proposed filtering algorithms were correctly classified in the corresponding risk group. The implementation of some additional processing to reduce noise in the image and thus have more stable HU values was considered to be important. This proposal offers a web application available from any device with Internet access and a web browser to perform the interpretation and calculation of the calcium score in the coronary arteries without the need for installing additional software. The medical specialist who supported the development of this application found it to be a notable and very useful tool that facilitates the calculation of the calcium score from any device and without the need for a specific DICOM viewer. He found that the application was not complicated in its operation, it was very intuitive, and that further it simplified the selection of calcified areas by having the high intensity areas marked before marking them. The clinical cardiologist would be able to elaborate, consult, and compare studies of their patients at any time. Lastly, it facilitates cardiologists or other medical specialists in interacting with one of the tools that is gaining the most acceptance worldwide in the prevention of cardiovascular risk.  
Conceptualization, J.A.-A. and S.O.-T.; methodology, J.A.-A. and J.M.-N.; software, J.A.-A. and J.P.-M.; validation, S.O.-T. and J.M.-N.; formal analysis, S.O.-T.; investigation, V.S.-O., M.B.-S. and J.P.-M.; resources, S.O.-T.; writing--review and editing, V.S.-O. and M.B.-S.; supervision, J.M.-N.; project administration, J.M.-N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.  
The authors declare no conflict of interest.
# PV Sizing for EV Workplace Charging Stations--An Empirical Study in France
###### Abstract  
Photovoltaic (PV) powered Electric Vehicle Charging Stations (PVCS) have received extensive attention recently due to the complementary relationship of PV energy and electric vehicles. This paper proposes a methodology aimed at assisting a Charging Point Operator (CPO) in determining the size of the main components of such PVCS. The modular structure of the method gives flexibility for possible use on a new sizing problem by modifying key parameters such as the EV charging demand (i.e., arrival/departure times and energy needed to fill the battery), the EV charging strategy or the business model, independently from each other. It is of particular interest for a CPO that sizes many PVCS operated in the same environment (for example, a car park at a workplace). In that case, the CPO first has to apply the method on a representative charging station. Next, he can re-use parts of the obtained results to drastically speed up (from weeks to hours) the sizing of the other charging stations. The proposed method has been applied to the EVCS of an industrial research complex in southern France. The input dataset used to apply the method consists of more than 32,000 charging transactions spanning over 6 years with 350 EV users and 80 charging points. Three charging strategies with different levels of complexity are investigated, including Mean Power, Plug and Charge, and Solar Smart Charging. The considered business model is based on the maximization of the self-production rate. The numerical findings reveal that employing a straightforward charging strategy, such as Mean Power, leads to a substantial reduction of nearly half in the required size of the PV plant compared to the basic Plug and Charge mode. In addition, our analysis demonstrates that Solar Smart Charging has the potential to decrease the PV plant size by nearly three times.  
electric vehicle; smart charging; empirical data; electric vehicle charging infrastructures (EVCI); PV-powered electric vehicles charging stations (PVCS); collective self-consumption rate (SCR); self-production rate (SPR) +
Footnote †: journal: Applied sciences
# Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN
## 6 Numerical Results and Discussions
### Full Communicating Sensor-Energy Consumption Results  
In this part, we will evaluate the performance of our energy consumption model using LoRaWAN mode 3 (Table 5) because the provided range is enough for our application and this allows saving the use of the battery. For that, the following scenario is proposed:  
The sensor node realizes measurements of the acceleration and transmits acceleration value every 30 s. It is noted that the microcontroller operating frequency is equal to 4 MHz in this study. Table 6 shows the power and time parameters of the model. These parameters are given in the _BMA220_, _STM32L073_ and _SX1272_ datasheets [29; 32; 33].  
Figure 14 depicts possible scenarios of the sensor node based on LoRaWAN Class A. The first scenario is to transmit data to the gateway without receiving both RX1 and RX2. The second one is to transmit data and receive RX1 without receiving RX2. The third scenario is to transmit data and demodulate RX1 which contains transmission error, so that the node must demodulate RX2.
## 2 Materials and Methods
### Evaluation of Virtual Reality as a Technological Tool  
#### 2.7.1 Technology Acceptance Model (TAM) Development of Virtual World Technology  
Learning Media  
The main goal of the technology acceptance model (TAM) is to provide insight into the process of technology acceptance and forecasting. To explain technology's success to potential practitioners on pre-system implementation measures, in this instance, VR training [37]. Based on social psychology and behavioral science, TAM maintains that perceived utility and simplicity of use impact technology adoption. Among TAM's essential elements are:  
* Perceived usefulness refers to users' subjective evaluation of the extent to which technology enhances performance or facilitates job processes. Individuals are more likely to embrace and utilize technologies that offer significant value.
* Perceived ease of use: the ease or difficulty of utilizing a certain technology; higher acceptance levels are characterized by ease of use.
* Behavioral intention refers to a user's expressed willingness or intention to employ a certain technology. According to TAM, the perceived usefulness and simplicity impact this objective.  
The present study employed TAM to examine the acceptance or rejection of VR training for simulating radiopharmaceutical production among users. Hence, to assist in designing a VR training module, we explored the factors that could influence users' acceptance of the module by investigating the aspects that might affect the user acceptability of the VR training module to understand the numerous factors affecting students' expectations and build a training package that supports their engagement. In order to achieve this objective, a cohort of 60 students studying RT was selected to participate in an online survey employing a Likert scale. The scale ranged from 1, indicating significant disagreement, to 5, indicating substantial agreement.
## 3 Method
### Two-Stage Pre-Training  
As shown in the frame diagram, before the training of fitting and reconstruction of CMG data, the Sinc-LSTM network is pre-trained in two stages.  
Firstly, MRO dataset \(S_{1}\) was input into the constructed Sinc-LSTM network for time series reconstruction training, and the reconstructed data \(S_{1}^{R}\) was obtained. After training, the mapping model about MRO dataset reconstruction \(f_{1}:S_{1}\xrightarrow{W_{1}}S_{1}^{R}\) is obtained. The first stage of pre-training is to enhance the ability of the network to extract extensive features from one-dimensional data and to reconstruct them.  
After the training, the SMAP&MSL dataset \(S_{2}\) from NASA was input into the Sinc-LSTM network after the first stage of pre-training for training, and the model parameters were fine-tuned to complete the second stage of pre-training and obtain the reconstructed data \(S_{2}^{R}\). In the second stage of pre-training, anomaly detection training is carried out on labeled spacecraft data to enhance the capability of feature extraction, sequence reconstruction, and anomaly detection of the model on spacecraft telemetry data. After training, the mapping of the second stage \(f_{2}:S_{2}\xrightarrow{W_{2}}S_{2}^{R}\) is obtained. The weight parameters are composed as follows:  
\[W_{k}=\left[W_{k}^{S},W_{k}^{L1},W_{k}^{L2},W_{k}^{L3}\right] \tag{1}\]  
where, \(W_{k}^{S}\) denotes the Sinc layer parameter of the \(k^{th}\) stage training, and \(W_{k}^{Li}\) denotes the \(i^{th}\) LSTM layer parameter of the \(k^{th}\) stage training.  
The Sinc function filter parameters \(W_{2}^{5}\) and LSTM unit internal parameters after two-stage pre-training \(\left[W_{2}^{L1},W_{2}^{L2},W_{2}^{L3}\right]\) are used as the initial weight parameters for CMG data training, and the parameters of Sinc function filter layer \(W_{2}^{5}\) are frozen to maintain the feature extraction ability of one-dimensional data. Parameters of LSTM layers \(\left[W_{2}^{L1},W_{2}^{L2},W_{2}^{L3}\right]\) participate in training for fine tuning.
# Analysis of Acoustic Emissions for Determination of the Mechanical Effects of Scratch Tests
## 5 Conclusions  
This research set out to investigate the AE response of different materials when there is insufficient load to initiate galling and compare the scratch tests to single gift tests. This research found the following for scratch tests:  
* Significant frequencies for the tests were found at far higher values than expected, at 400 kHz, 500 kHz, 700 kHz, 800 kHz, and 950 kHz. The expected frequency range, 60 kHz to 125 kHz, was found to be dominated by noise.
* There was no difference found between the AE signature of the aluminium or the steel for indenter scratch tests in the frequency domain.
* The mean frequency, centroid frequency, and Shannon entropy parameters showed a difference not reflected in Fourier techniques. Steel had a higher mean and centroid frequency (600 kHz) than aluminium (500 kHz), attributed to a difference in hardness, as demonstrated by a difference in depth of cut.
* Mean frequency performs as well as centroid frequency (for scratch tests).
* Visual inspection showed that galling did not occur, so differences are mostly attributed to differing frictional forces between the counter face and samples.  
* In addition, this research found the following for single grit scratch tests:
* From the SG tests, the NI-based alloys appear to exhibit a distinguishable AE signature from one another in terms of frequency response.
* The frequency responses for the SG tests were found in the expected region, unlike the indenter scratch tests.
* Looking further into the SG tests, for each individual material there are differences in terms of AE response, This needs to be taken into consideration when carrying out scratch tests and using AE to correlate damage mechanisms.
* Centroid frequency and Shannon entropy can be used to discriminate between some materials.  
Formal analysis, T.D.; Investigation, B.R. and M.P.; Methodology, J.M.G.; Software, T.D.; Supervision, B.R., M.P. and J.M.G.; Validation, T.D.; Writing--original draft, J.M.G.; Writing--review & editing, T.D., B.R. and M.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data will be available upon request to the corresponding author.  
The authors declare no conflict of interest.
# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 4 Performance Evaluation
### Result Analysis
#### 4.3.1 Performance Comparison in Terms of Varying UE Velocity
The inclusion of ST in IHD-V2X and CAP contributes to reducing the number of ping-pong HOs as a result of prohibiting triggering HOs if the current cell is sufficient to meet UE requirements, with the lowest in IHD-V2X at an average of 15% among all. Primarily, the ACHQ algorithm exhibited the highest number of ping-pong HOs owing to the absence of stay time and resource allocation parameters. The average performance of different algorithms is shown in Figure 7. ACHQ fails to prevent unnecessary HOs with approximately 20% of the total number of handover attempts, leading to degraded network performance and unnecessary signaling overhead. The CAP significantly improves this part, compared to the total number of HOs attempts, by implementing a minimum stay period before starting an HO. The most notable improvement is observed with IHD-V2X, which dynamically adjusts to mobility settings and halts the highest percentage of unnecessary HOs, optimizing the overall network performance to approximately 75% of the total HOs attempts. These results indicate the effectiveness of IHD-V2X in supporting seamless connections in a dynamic network environment. Figure 6: Handover performance results of IHD-V2X algorithm vs. UE velocities.