esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Wireless Channel Analysis  
Hyperloop communication applications have strict requirements for QoS metrics, such as data rate, transmission delay, and BER. Due to these factors as well as a desire to use mature and low-cost technology, we use off-the-shelf technologies and add applications to meet specific services and demands.  
Exact and detailed broadband wireless channel characterization is a prerequisite for the deployment and performance analysis of the Hyperloop wireless communication system. Moreover, it provides an effective evaluation of further advanced transmission technologies and resource management.
# Research of Modal Analysis for Impeller of Reactor Coolant Pump
## 3 Resonance Analysis of the RCP Impeller
### Resonance Analysis of the Impeller  
[PERSON] et al. [22] held that a positive work by the exciting force is necessary for the resonance of a rotor system. Thus, when an impeller resonates, nodal diameters, blade numbers of the impeller and the vane diffuser should satisfy the following equation:  
\[v_{2}Z_{v}=v_{1}Z_{b}\pm m, \tag{1}\]  
with the number of impeller blades \(Z_{b}\), the number of vane diffuser blades \(Z_{v}\), any integer \(v_{1}\) and \(v_{2}\), and the number of nodal diameters \(m\).  
When the excitation frequency is close to the natural frequency of the system, the amplitude of the forced vibration will soar. Due to the rotor-stator interaction [23], the impeller is excited by periodic fluctuating pressure. Because the number of excitations per revolution is equal to the number of impeller blades, the frequency of the pressure on the impeller is the product of static blade number and the shaft frequency. All in all, if the natural frequency is the same as the frequency (or its multiples) of fluctuating pressure and the number of modal nodal diameters satisfies Equation (1), hydraulic resonance will occur.  
Figure 5: Pressure distribution on both sides of the blade at \(\text{span}=0.5\).  
Figure 6: Comparison of natural vibration modes before and after impeller improvement.  
In our pump design, the number of impeller blades is 7, and the number of vane diffuser blades is 18. Because the shaft frequency is 30 Hz, the frequency of hydraulic load on the impeller is 540 Hz. As shown in Figure 6, the value of natural frequency with three nodal diameters mode shape is 542.44 Hz. Since this frequency is close to the frequency of fluctuating pressure on the impeller and the number of nodal diameters accords with Equation (1), hydraulic resonance may occur. Thus, it is necessary to improve the design of the impeller.
## 4 Deployment and Model Training of the PLA Scheme
### Machine Learning and Deep Learning Models  
The success of the multi-attribute PLA scheme relies heavily on ML and DL models to classify sensors as legitimate or spoofed. LR serves as a lightweight binary classification model that evaluates the likelihood of a sensor's legitimacy based on its attributes. Its simplicity and computational efficiency make it well suited for real-time scenarios [21]. RF constructs multiple decision trees, providing robust classification performance, even in noisy data environments. It is particularly effective for handling non-linear relationships [22]. KNN is a proximity-based classification algorithm that relies on the closeness of data points. While simple, it may struggle with scalability in high-dimensional spaces [22].  
Figure 3: Phase 0, Initial Join Procedure for LoRaWAN; Phase 1, Multi-Attribute PLA.  
DL models include the MLP, which is a fully connected network designed to capture non-linear dependencies. Its architecture incorporates dense layers and dropout mechanisms to prevent overfitting, making it effective for structured input features [23]. The CNN uses convolutional layers to extract spatial and temporal patterns from time-series data, enhancing the representation of attributes like RSSI, BL, and altitude for improved classification accuracy [24].
###### Abstract  
Current developments and the need for high-performance devices that provide safe and reliable communications present a future perspective by using visible light as an alternative solution that can substantially improve road and pedestrian safety. The daily use of smartphones is imperative; thus one can build on this premise a system dedicated to the aforementioned problem. However, the problem of the visible light communication channel (VLC) is highly dynamic and becomes extremely unpredictable in terms of exposure to noise sources. Developing applications dedicated to direct communications with infrastructure and vehicles using portable devices is becoming a challenge and at the same time a necessary solution. The article proposes the shaping of an emission-reception architecture dedicated to adaptive fuse light communications using CCC (optical camera communication) but also standard VLC communications using ambient light sensors via an Android application. This approach aims to provide a first step in shaping information-sharing applications using VLC communications. As far as we know, this approach has not been implemented in external VLC systems. The performance of the architecture and the application was demonstrated by practical tests that confirmed the capacity of the technology even if we are in the first stage.  
pedestrian safety; safety driving; infrastructure-to-pedestrian; mobile communication; vehicle-to-everything communication; visible light communication +
Footnote †: journal: Sensors
###### Abstract  
A strengthening method using steel fiber-reinforced mortar (SFRM), proposed for the seismic retrofit of masonry buildings, is verified experimentally in this study. The SFRM is overlaid on masonry walls directly, which is possible to implement while the building is occupied for residence. First, tests of workability and material strengths were conducted for SFRM itself in order to find SFRM mixing ratios appropriate for overlay construction and strengthening. Then, masonry prisms were produced using two types of bricks and strengthened with SFRM for the chosen mixing ratios and test variables such as the number of overlaid sides and the fiber volume fraction. Compressive strength tests and diagonal tension tests for those specimens were conducted. Both compressive and shear strengths were improved the most highly by overlaying the SFRM with a fiber volume fraction of 1.3%, which is the highest among the test variables, on both sides. Overlaid SFRM tends to be detached without cracking almost at the maximum strength in both compression and diagonal tension tests. For red clay brick prisms, the compressive and shear strengths increased by up to 61% and 138%, respectively. For concrete brick prisms, the compressive and shear strengths increased by up to 26% and 67%. Finally, a design formula for the shear strength of strengthened masonry prisms is compared with the experimental results, and it is observed that the design formula gives slightly higher results.  
seismic retrofit; masonry prism; steel fiber-reinforced mortar; overlay construction +
Footnote †: journal: Applied Sciences
## 6 Influence of Beam Length on the Ductility Demand of the Connection
### Global Behavior of the Deck  
The non-dimensional load-deflection curves are shown in Figure 6. Points on the curves corresponding to significant slip values in the end studs (the more stressed) are evidenced. The significance slip values considered are 2 mm, which is the maximum elastic slip; 6 mm, which is the minimum ultimate limit of a ductile connection according to Eurocode 4 [10]; and 10 mm, which is possibly a new definition for ductile connections. Furthermore, the slip demand is reported at a maximum deflection of 1/50 L because higher deflections cannot be considered reliable results due to second-order effects and loss of equilibrium on the supports for excessive deformations. The required maximum slip at 1/50 L is the same, approximately 14 mm, for the three spans. The longer the span, the lower the non-dimensional load that occurs at the same slip; at 6 mm, spans of 25 m, 37 m, and 50 m attain 91%, 88%, and 76% of q\({}_{\text{p}}\), respectively. In other words, the ductility capacity of the connection depends on the beam length and not only on the stud ductility, although the reduction in ductility is much lower than proportional to the increment in length. The results also emphasize that the ductile stud defined by Eurocode 4 with a slip capacity of 6 mm could not be safe for all lengths in the plastic analysis. If the ultimate slip is enhanced to 10 mm, the non-dimensional load increases to 95%, 94%, and 81% for the three lengths, respectively.  
The model allows for the evaluation of the distribution of shear and slips in the studs along the deck. In Figure 7b,d, the shear (non-dimensional with respect to the reference stud strength P\({}_{\text{R}}\)) in the studs along the central beam is reported for the various beam lengths at slip = 6 mm or slip = 10 mm at the end. In Figure 7a,c, the slip in the studs along the spans is depicted when the maximum slip at the end is 6 mm or 10 mm.  
The shear in the studs is less than the shear strength near the middle span for a length that depends on the ductility of the studs. The change in trend when the linear behavior of the slips is overcome is also clear in the distribution of the slip along the beam.  
Figure 6: Load–deflection behavior of bridges.
# Fuzzy Graph Learning Regularized Sparse Filtering for Visual Domain Adaptation
## 5 Discussion
### Does Iterative Learning Help Improve the Model?  
To answer the question \"Does iterative learning help improve the model\", we report the performance of 18 tasks during training, whose results are shown on Figure 4. Specifically, we divide the full training process to 10 parts, and record current accuracy. Obviously, the model becomes more precise as training goes. On the other hand, we can also see some fluctuations, which suggests that our approach cannot completely eliminate the effects of mislabeling.  
Figure 3: Performance on three subtasks with different \(\lambda\). X-axis: tasks. Y-axis: accuracy.
## 5 Results
### Performance of Hybrid Features and WTSST-DCNN  
The DWT features were combined with statistical and nonlinear features, and the performance of this proposed approach was experimented with and evaluated with the deep neural network and SVM classifier. Different kernel functions of SVM and three types of SVM were experimented with to find the optimum and most robust classifier. Figure 11 shows the proposed approach's performance for different types of SVM, while Table 5 shows different SVM kernel function performances. Finally, a comparison of 1D-CNN, best SVM classifier performance, and the performance of the proposed new transform WTSST-DCNN were evaluated, as presented in Table 6. From the table and figure, it is clearly shown that the polynomial kernel function with the LS_SVM method outperforms other kernel functions and SVMs with accuracy, sensitivity, and specificity of 99.3%, 99.5%, and 99.0, respectively. However, the proposed deep learning network of WTSST-2D-DCNN shows an improvement in performance when compared to the SVM best-optimized classifier with 99.7%, 99.5%, and 99.  
Finally, we compared our proposed algorithms with other recent works in the literature that used the same Bern-Barcelona database, as shown in Table 7. From the table, the proposed algorithms in this work showed improvements in performance as compared with some recent work that experimented with the same dataset. [PERSON] et al. [12] used reconstructed phase space (RPS), EWT, and LS_SVM to characterize focal and non-focal EEG signals and obtained an accuracy of 90%. [PERSON] et al. [51] employed nonlinear features and reported an accuracy of 87.93%. Details of comparison can be found in Figure 7 and Section 5.3.
## 3 Results  
### Overall Findings  
There is an increasing trend in the number of publications related to driver emotion modulation or driver emotion regulation from 2018 to 2023, as shown in Figure 2. We analyzed 802 articles obtained from the Web of Science and Scopus databases using CiteSpace software. Based on the findings, most of the relevant studies are from China, the United States, Germany, and India, as shown in Figure 3. The United States (\(n\) = 147) has the highest number of publications, followed by China (\(n\) = 127), Germany (\(n\) = 107), and India (\(n\) = 63). This indicates an increasing trend in research programs in China and the United States. In contrast, the number of relevant studies in Germany was highest in 2018, followed by a decline.  
The results of the keyword co-occurrence analysis are shown in Figure 4. There is a gradual increasing trend for the combination of the \"driver emotion\" and \"artificial intelligence\" keywords. From a temporal perspective, the dark red color represents a time closer to the present, indicating that research on drivers' emotions gradually shifted from driver behavior in 2018 to emotion regulation in 2020. Research became more prevalent from 2019 to 2020, gradually focusing on in-vehicle emotion regulation. In 2022, there was an increase in the number of studies pertaining to the integration of artificial intelligence for in-vehicle monitoring. After keyword clustering, there are six clusters: (1) user experience, (2) driver distraction, (3) emotional intelligence, (4) driver emotion regulation, (5) in-vehicle frustration, and (6) autonomous vehicles.  
The six key clusters can be organized into three distinct groups, each emphasizing different aspects of the driving experience and their impact on emotion modulation. The first group, drivers' emotional and behavioral factors, focuses on emotional intelligence and driver emotion regulation, emphasizing the importance of managing emotions for behavioral decision-making and composure under stress, which directly affects driving safety. The second group, driver emotion modulation based on human sensory perception, includes user experience and driver distraction. This group analyzes how interaction with the vehicle through sensory inputs impacts drivers' emotional states and attentiveness. A well-designed user experience reduces stress and the potential for accidents. The third group, driver emotion modulation related to cross-cultural backgrounds and driver personality, addresses in-vehicle frustration and the impact of autonomous vehicles. It considers how personal and cultural differences affect drivers' emotional responses and interactions  
Figure 3: Map of national cooperation networks.  
Figure 2: Number of publications from 2018 to 2023.  
with autonomous technologies. This is essential for creating adaptable systems for diverse global populations, thereby enhancing both user experience and safety.
# Temporal Variations Dataset for Indoor Environmental Parameters in Northern Saudi Arabia
## 3 Methodology
#### Data Acquisition  
In this section, we describe the data acquisition process, which involves collecting and storing sensor data using the Arduino kit shown in Figure 2. The data acquisition system consists of multiple sensors connected to an Arduino board, which is responsible for reading sensor values and transmitting the data to a storage device or a computer for further analysis. The steps involved in the data acquisition process are as follows:  
1. Sensor Selection and Integration: We selected the appropriate sensors based on the project's objectives to measure the required parameters. These sensors were built on the Arduino board. The dataset was collected from six sensors using the Arduino board. Arduino boards can control and manage home activities, such as reading input from temperature, humidity, and light sensors. This research used six sensors, measuring temperature, humidity, pressure, altitude, light, and timestamps. Arduino boards read the sensor data in real time with a timestamp column.
2. Arduino Programming: We developed an Arduino program to read data from the connected sensors. The program used built-in functions and libraries specific to each sensor for accurate and  
Figure 2: Arduino sensor kit used for the data collection.  
efficient data collection. The Arduino code was written in C++, leveraging the Arduino IDE to compile and upload the code to the board.  
3. Data Collection:  
The Arduino board was programmed to collect sensor data at regular intervals. We chose an appropriate sampling rate to ensure sufficient data points were captured while avoiding excessive data storage requirements or potential sensor overloading. The data was temporarily stored in the Arduino's memory before being transmitted to a computer or storage device.  
4. Data Transmission:  
To transmit the data from the Arduino board to a computer or storage device, we used one of the following methods:  
* Serial Communication: We utilized Arduino's built-in serial communication capabilities to send data directly to a computer via USB. This method allowed real-time monitoring and data collection using a serial monitor or a custom software interface. We imported the library and used a serial monitor, which begins at 9600, which is the speed. The sensor reads the temperature in Celsius, and we set a delay of 5 min before accessing the sensor to integrate these sensors.
* Wireless Communication: In cases where a wired connection was not feasible, we employed wireless communication modules such as Bluetooth, Wi-Fi, or LoRa to transmit data to a nearby computer or a cloud-based storage solution.  
5. Data Storage and Organization:  
Upon receiving the sensor data, we stored the information in a structured format, such as a CSV file. This allowed for easy access and analysis of the collected data. We also timestamped each data entry to provide a chronological context for the measurements.
# An Optimal Sizing Design Approach of Hybrid Energy Sources for Various Electric Vehicles
## 3 Case Study for Five Types of Electric Vehicles  
### Electric Sedan  
With the rapid popularization of commercialized electric sedans, all international automakers have produced several types of EVs. However, the trade-off between high power (good acceleration and high maximum speed) and high energy (traveling mileage) makes a vehicle with a single energy source difficult to produce. To optimize the HEES, the cost function is listed as follows:  
\[J=E_{HEES}\times SR\times SP_{HP}+E_{HEES}\times(1-SR)\times SP_{HE}+C_{DCDC}+\gamma \tag{20}\]  
where the optimized cost is calculated by the summation of the cost of a high power source (LTO), the cost of a high energy source (Li3), and the cost of a DC/DC converter. The constraints are as follows:  
\[\begin{split} P_{max}&\left\{\begin{array}{l}0.95 \times(P_{HE}+P_{HP})>P_{max}\\ P_{HE,max}=\frac{E_{HEES}}{SW_{HE}\times SP_{HP}}\\ P_{HP,max}=\frac{E_{HEES}-E_{HE}}{SW_{HE}\times SP_{HE}}\\ C_{HE}=E_{HEES}\times SR\times SP_{HE}\\ C_{HP}=E_{HEES}\times(1-SR)\times SP_{HP}\\ C_{DCDC}=P_{HP}\times SPDC\times x(i)+P_{HE}\times SPDC\times[1-x(i)]\\ \end{array}\right.\\ W_{max}&\left\{\begin{array}{l}W_{HE}+W_{HP}<1.22\times W_{HE}\\ W_{HE}=\frac{E_{HP}}{SW_{HE}}\\ W_{HP}=\frac{E_{HEES}-E_{HE}}{SW_{HP}}\\ \end{array}\right.,\ D_{max}\Big{\{}E_{HEES}>\frac{P_{00}\times D_{max}\times 3 600}{100\times 50} \tag{21}\]  
where the constants 0.95, 1.22, and \(P_{00}\) represent battery efficiency, safety factor, and power demand at the constant velocity of 50 km/h, respectively. Because the battery efficiency was influenced by SOC, which varies for energy management but not for the sizing optimization, the efficiency was set as a constant average value of 95%. For the safety factor, 1.22, for the weight, cost, and volume, the references [25; 26] were considered. The subscripts \(HE\)  
Figure 4: Flowchart of the GSA for HEESs.  
and \(HP\) represent a high-energy and high-power battery, respectively. To satisfy the first constraint, \(P_{max}\), the maximal power of high energy or the high power were derived from the electric capacity divided by the specific weight and the specific price. The maximum cost of the HEES was a factor (1.22) multiplied by the summation of the total cost of the LTO batteries, Li3 batteries, and the DC/DC converter. The maximum weight of the HEES was an allowable factor (i.e., 1.22) multiplied by the summation of the total weight of the LTO batteries and the Li3 batteries. The minimum electric capacity was calculated by giving the required traveling distance at a constant speed of 50 km/h. With the boundary conditions of these four limitations, the optimal cost function concerning the vehicle performance requirement can be globally searched for.
## 4 Discussion
### Summary  
To summarize the main findings of this study, Table 2 has been included to indicate for each specific combination of plasma characteristics which reactive plasma species were generated. More specifically, it was indicated if these conditions resulted in the generation of UV photons, H\({}_{2}\)O\({}_{2}\), nitrite, and nitrate, and/or a drop in pH. In Table 3, on the other hand, it was included for both biofilm forming species if the lethal effect of CAP was (partially) due to (i) the generation of intracellular ROS, (ii) the presence of UV light, (iii) leakage of the membrane, and/or (iv) DNA damage. For the presence of UV light, this was indicated as 'yes' if the log-reduction values with implementation of the quartz disc were higher than 1 log\({}_{10}\)(CFU/cm\({}^{2}\)), which is the detection limit of the applied enumeration method. For all other attributes, 'yes' was indicated if the values were significantly different from the untreated control. Finally, it was also included if CAP treatment resulted in an increased porosity of the biofilms.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **DBD-0\%-** & **DBD-1\%-** & **DBD-0\%-** & **DBD-1\%-** & **SD-0\%-** & **SD-1\%-** & **SBD-0\%-** & **SBD-1\%-** \\  & **21.88 V** & **21.88 V** & **13.88 V** & **13.88 V** & **21.88 V** & **21.88 V** & **13.88 V** & **13.88 V** \\ \hline UV photons & & & & & & & & & \\ detected using UV-TEC strips & & & & & & & & & \\ \hline H\({}_{2}\)O\({}_{2}\) & Yes & Yes & No & No & No & No & No & No \\ \hline Nitrite & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline Nitrate & Yes & Yes & Yes & Yes & No & No & Yes & No \\ \hline pH drop & Yes & Yes & Yes & Yes & Yes & Yes & No & No \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary table indicating whether the different combinations of plasma characteristics resulted in the generation of UV photons, H\({}_{2}\)O\({}_{2}\), nitrite, and nitrate, and/or a drop in pH.
## 3 Generation of a Cachexia-Induced Phenotype  
To establish a cachexia-induced phenotype several methods and agents have been employed to induce skeletal muscle wasting, adipocyte tissue loss and myocardial atrophy (Figure 1). From direct agents such as cell medium from tumour cells [82], inflammatory factors [23], hormones [83], chemotherapeutic drugs [84] and gene regulation ( upregulation and downregulation) using miRNAs [85]. The methods used to establish CAC cell models and cell mechanisms are summarised in (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cell Line** & **Induction Method** & **Cell Effect** &
\begin{tabular}{c} **Mechanism of** \\ **Action** \\ \end{tabular} & **References** \\ \hline \multirow{8}{*}{**C2C12**} & Conditioned medium from patient-derived pancreatic cancer cells & Upregulation of UPS proteins, and increase in autophagy & Activation of p38 MAPK and expression of transcription factor C/EBP\(\beta\) & [86; 87] \\ \cline{2-5}  & Conditioned medium from PC3 and DU145 cell lines & Inhibition of the formation of microtubules; decreasing of differentiation & Expression of transcription factor C/EBP\(\beta\) & [87] \\ \cline{1-1} \cline{2-5}  & & Reduction in myotubes number and width; increased expression of Atropin-1 and Murf1 & Protein O-GlcNAcAcylation & [88] \\ \cline{1-1} \cline{2-5}  & & Increased expression of ubiquitin ligases; decrease in myogenic differentiation & Autophagy pathway, myostatin pathway and UPS system & [89] \\ \cline{