esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Battery Pack Cooling Mathematical Model
### Validation of the Mathematical Model  
In order to verify the accuracy of the proposed flow resistance and heat dissipation models, this section establishes a battery pack model with \(5\times 8\) battery cells.  
Due to the high calculation accuracy of the CFD method, this paper used FLUENT software for CFD simulation to verify the proposed mathematical model. The properties used in the CFD method and the mathematical model are shown in Table 2. The Realizable k-epsilon model was used to simulate the calculation of the three-dimensional battery pack. The air inlet uses a velocity inlet boundary condition, which describes the velocity of the cooling air at the inlet. The outlet uses outflow boundary condition. The boundary between the battery and the air uses a temperature continuity boundary condition. The computational region is discretized in space by finite volume method, and the discrete  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Re** & **Y** & **y** \\ \hline
1–4 & 0.989 & 0.330 \\
4–40 & 0.911 & 0.385 \\
40–4000 & 0.683 & 0.466 \\
4000–40,000 & 0.193 & 0.618 \\
40,000–250,000 & 0.0266 & 0.805 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of Y and y under various Reynold numbers in Equation (22).  
Figure 4: Heat transfer between the battery unit and the air.  
The battery unit temperatures obtained by the mathematical model and the CFD method are shown in Figure 7. It can be seen that the battery unit temperatures calculated by the CFD method and the mathematical model were in good agreement. Under three inlet airflow rates, the maximum errors between the two methods were 0.3 K (Q0 = 0.040), 0.3 K (Q0 = 0.045), 0.5 K (Q0 = 0.050), respectively. Since the CFD method is calculated using nonlinear partial differential equations, the calculation was very complicated and CPU-intensive, requiring a large amount of time for each calculation. In contrast, the flow resistance model and the heat dissipation model calculated the battery unit temperatures much faster, saving a lot of time in the optimization process that required repeated modification of the model parameters. Therefore, in the next structural optimization section, the flow resistance and heat dissipation models were used instead of the CFD method to calculate the temperature distribution in the battery pack.
## 4 WSN Clustering
### K-Means Algorithm  
K-means algorithm is an unsupervised algorithm as it groups points that are close to one another [73]. The position of a new point within a given group is determined by its Euclidean distance from all other groups. The data is then regrouped, with the number of clusters that produces the least variance being optimal. This iterative procedure continues until the stated number of repetitions is reached, at which point the repeat terminates. [PERSON] and [PERSON] [74] devised the K-means algorithm-based KCED protocol for determining the cluster header in order to reduce the power consumption of wireless sensor networks. The operating idea is to only seek an update from the first and subsequent rounds when a node has died. To build the cluster, the most potent and nearby nodes are chosen. The study's findings demonstrated that the proposed strategy was successful at selecting an appropriate cluster head and extending the network's lifespan. In another work by [PERSON] and [PERSON] [73] the K-means clustering algorithm was utilized. Where the SAW and WPM methods were used to segment the performance of the decision-making process. In terms of selection of CHs, process distribution, package delivery time, and energy usage, the results demonstrate that the proposed method outperforms the alternatives. Presented cluster head selection strategies to reduce the power consumption of nodes and extend the whole network lifespan by improving energy consumption of nodes as cluster-based techniques used. The authors in [75] suggested MR-LEACH model in the whole procedure was split into three stages (build clustering, re-clustering of nodes, and CH selection). The LEACH procedure is used to decide the selection of the first CH. The network is divided into clusters, depending on the Euclidean distance nodes connecting their closest CH. Once the nodes are attached to the CH, the center of each cluster is given an ID depending on the distance from the center. The node nearer to the center has a lower number. CH is rotated, and a new CH is chosen for the next comparable closer node to the center. Compared to other clusters, it increases overall network life, but frequent cluster reforms provide extra overhead networks and high energy consumption. In addition, since clusters are randomly generated, it may originally lead to sub-optimal clusters and unfair load distribution. Some of these modifications include LEACH-B [76], and ER-LEACH version [77]. Due to its probabilistic approach in selecting the cluster heads, LEACH has various downsides, especially increased energy demand in the sub-optimal CH node. A bad network's performance is a side effect of the dynamic overhead clustering and non-uniform CH distribution, both of which require more energy. Furthermore, compares various clustering routing models proposed for WSNs. The summary of classes and clustering routing protocol differences is presented in Table 4.
# A Multimode Fusion-Based Aviation Communication System
## 3 Hardware Design  
Based on the design concepts of aviation systems engineering, including comprehensiveness, generalization, and modularity, the hardware of the aviation multimode fusion communication system consists of an ad hoc network communication module, a public network communication module, a BeiDou satellite communication module, an RTK high-precision positioning module, an ADS-B broadcast communication module, a fusion control module, a power module, and an antenna. The composition diagram is shown in Figure 2.
# A New Methodology for Type Synthesis of Planar Linkages for Exoskeletons up to Five Angular Outputs
## 4 Type Synthesis
### Parallel Connection Method  
In this method, each additional RRR dyad starts from the latest angular output. In other words, the new RRR dyad is connected to the latest angular output and any other link of the original linkage system. That can be achieved by applying the following steps:  
1. All angular output links should be represented usually at the right of the graph.
2. Adding the RRR dyad starts from the last presented angular output link (including two links and three revolute joints).
3. The first additional link is located directly above the current output link to represent the 'newer angular output'. This link is represented as a hollow circle. Therefore, all angular outputs are located in parallel arrangement as shown in Figure 2.
4. The second additional link is located at the left of the new angular output link. This link is denoted by 'distributive link'.
5. Joints between the previous output, newer output, and distributive links are represented by solid edges (i, j). In other words, angular outputs are presented vertically above each other.
6. The last revolute joint (k) has some alternatives to connect the distributive link with other links, excluding the connections that produce rigid sub-chains. These alternatives generate the available topological graphs that can achieve the required number of angular output motions.
# Dynamic Mixed Reality Assembly Guidance Using Optical Recognition Methods
## 3 Materials and Methods
### Software and Equipment  
MR system software and equipment utilized in this work are chosen according to that which is currently state of the art and to respect the following criteria concerning application requirement: ease-of-access, ease-of-use, and practicality for long-term use.  
As for software, the MR system is designed by using Unity Engine and Vuforia Engine Software Development Kit (SDK). They are widely used in the development of MR system. Vuforia, in particular, was chosen given its broad range of recognition and tracking capabilities. Among the different target recognition techniques supported by Vuforia, Model Targets (MT), and Image Targets (IT) are the most suitable for the MR system developed in this work.  
The MT is an object-tracking method that allows for real-world recognition and tracking of objects based on their shapes and features. It can be applied using a 3D model of the object, which is then converted into a Vuforia Engine model using the MT Generator (MTG) tool and stored in a database collecting all the design features that allow it to detect the objects in the real-world scene. An Advanced Model Target Database (AMTD) can be produced by training a database with one or more Model Targets, each containing advanced guide views ranging up to 360\({}^{\circ}\) in all rotations. Advanced guide views allow for recognizing and tracking objects from different angles in a defined range. As the objects modeled in the system move in the environment, the positions of the objects need to be anticipated or tracked. For this reason, each object needs its own coordinate system (CS) to be anticipated or tracked in the MR system.  
The IT is an image recognition technique that allows the detection and tracking of images by comparing the features extracted from camera images with known targets stored in a database. Once detected, Vuforia Engine augments data of the target's features in the real-world scene that can be used to interact with the users.  
As for equipment, the display device chosen was the HoloLens 2, i.e., perspective holographic glasses that allow the users to interact with the augmented- and real-world environment. The choice fell on the HoloLens 2 device because of its ability to map the real-world environment with built-in cameras and sensors and its compatibility with both software Unity and Vuforia engine selected for MR applications.  
The physical tools that the users needed to use while performing the experiment comprised three battery-powered screwdrivers with heads fitting the screw sizes (two  
Figure 2: Completed assembly.  
for assembling the block components, one for assembling the label screws). No other equipment was needed to perform the simple assembly operations, and nothing more was given to the users to avoid confusion.
## 5 Review of the Original GN-AK Protocol
### Protocol Steps  
The original GN-AK protocol consists of the following steps:  
Step 1: Initiator \(T\) computes and sends:  
1. Generate a random pseudorandom scalar \(d_{T}\).
2. Compute \(T_{1}=d_{T}(P^{-1}+Q)\).
3. Compute hash \(T_{2}=h(PK_{T}\|T_{1})\).
4. Compute digital signature or encryption \(T_{3}=\mathrm{Enc}_{SK_{T}}(T_{2})\).
5. Send \(\{T_{1},T_{3}\}\) to \(R\). Step 2: Responder \(R\) receives \(\{T_{1},T_{3}\}\) and responds:  
1. Compute \(T^{\prime}_{2}=h(PK_{T}\|T_{1})\).
2. Verify authenticity by checking \(\mathrm{Dec}_{PK_{T}}(T_{3})=T^{\prime}_{2}\).
3. If valid, generate pseudorandom scalar \(d_{R}\).
4. Compute \(R_{1}=d_{R}(P^{-1}+Q)\).  
\begin{tabular}{l l}
5. & Compute \(R_{2}=h(PK_{R}\|R_{1})\). \\
6. & Compute \(R_{3}=\text{Enc}_{SK_{R}}(R_{2})\). \\
7. & Compute session key \(K_{R}=d_{R}T_{1}\). \\
8. & Send \(\{R_{1},R_{3}\}\) to \(T\). \\  & Step 3: Initiator \(T\) verifies and derives key: \\
1. & Compute \(R_{2}^{\prime}=h(PK_{R}\|R_{1})\). \\
2. & Verify \(\text{Dec}_{PK_{R}}(R_{3})=R_{2}^{\prime}\). \\
3. & If valid, compute session key \(K_{T}=d_{T}R_{1}\). \\ \end{tabular}  
Assuming all computations are valid and participants behave honestly, we have:  
\[K_{T}=d_{T}R_{1}=d_{T}d_{R}(P^{-1}+Q)=d_{R}T_{1}=K_{R}. \tag{8}\]
## 5 Experiment
### Experimental Setup
#### 5.1.2 Evaluation Metrics  
The qualitative evaluation metrics refers to evaluate the detection performance based on human subjective feeling. For example, the boundary of surface defect is clear, and the contrast between defect object and background is obvious.  
There are five quantitative evaluation metrics, including precision-recall (P-R) curve, receiver operating characteristic (ROC) curve, average F-Measure (\(F_{\xi}\)), area under ROC (AUC) and mean square error (MAE). They are defined as follows:  
\[FPR=\frac{FP}{FP+TN} \tag{27}\]  
\[TPR=\frac{TP}{TP+FN} \tag{28}\]  
\[F_{\xi}=\frac{1}{N}\sum_{i=1}^{N}\frac{\left(\zeta^{2}+1\right)\times\text{ precision}\times\text{recall}}{\zeta^{2}\times\text{precision}+\text{recall}} \tag{29}\]\[\text{MAE}=\frac{\sum_{i=1}^{H}\sum_{j=1}^{W}\lvert S(i,j)-G(i,j)\rvert}{H\times W} \tag{30}\]  
where a pixel that belonging to defect is defined as a positive example, and a pixel that belonging to background is defined as a negative example; true positive (\(TP\)) indicates that the positive pixel is judged correctly, true negative (\(TN\)) indicates that the negative pixel is judged correctly, false positive (\(FP\)) indicates that the positive pixel is judged as the negative pixel mistakenly, false negative (\(FN\)) indicates that the negative pixel is judged as the positive pixel mistakenly; \(\text{precision}=TP/(TP+FP)\), \(\text{recall}=TP/(TP+FN)\); \(N\) represents the number of surface defect image samples of the same class, \(H\) and \(W\) denotes the height and width of surface defect image, respectively; precision is defined as the percentage of defect pixels correctly assigned, while recall is the ratio of correctly detected defect pixels to all true defect pixels. \(F_{\xi}\) represents the weighted harmonic mean of precision and recall. Besides, P-R curve is obtained by binarizing the saliency map using a number of thresholds ranging from 0 to 255; \(TPR\) represents true positive rate, \(FPR\) represents false positive rate; \(MAE\) measures the dissimilarity between the saliency map \(S\) and the ground truth \(G\).
## 2 Materials and Methods
### Experimental Procedure  
Before starting each testing session, noise and speech level intensities were measured and calibrated using a digital sound level meter (Extech 407730, Waltham, MA, USA) to ensure that the ambient noise in the testing room (college research laboratory) was within an acceptable environment noise range (below 50 dB SPL) during testing, which is an important factor for such a mobile and outside of clinic hearing screening tool.  
The testing procedure was administered with a headphone set (Sennheiser HD-280 pro, Wedemark, Germany), which was verified for audiometry testing [33, 36]. Additionally, the output sound volume of the personal computer was measured and adjusted accordingly to ensure that the noise files used in this experiment were fixed at 60 dB using the digital sound meter before each session.  
The flowchart of the proposed Arabic word-in-noise hearing assessment tool is shown in Figure 2. During each testing session, all participants were instructed to identify the presented Arabic words in both white noise lists (Word lists 1, 2, and 3) and multi-talker babble noise (Word lists 4, 5, and 6). Participants were instructed to listen carefully to the presented words. Moreover, the words in each list were delivered with a gradual decrease in the SNR for both the white and multi-talker babble noise (+15 to 0 dB), while the noise intensity was kept constant at 60 dB across all SNR conditions. Subjects were encouraged to guess the correct word from a multiple-choice list after each word. Participants were also able to select the last choice, \"I do not know-- \(\big{\{}\big{\}}\)\" \(\forall\)\" only if they did not perceive the word. In addition, the subjects would be excluded from the study if they failed to identify the first word in each word list, as they were presented without noise, and the session would be terminated. The order of the presented noise type in each session and the word lists was randomly assigned and counterbalanced across subjects to avoid an order effect.  
The acquired responses were given a score of zero for wrong word identification and a score of one for correct word identification. In addition, an overall average score of three word lists was calculated for each noise type. The percent-correct word identification score was then computed and used for statistical analysis.  
Figure 1: Illustration of the SNR conditions presented for both white noise and multitasker babble.
## 3 Methodology
### Object Re-Identification
#### 3.4.1 Explanation  
Multimedia broadcast content, such as SOAP, news, or talk shows, often reuse locations that conserve the objects that they contain. Then, based on the above hypothesis, the objects are the same if they are present in the same location. For example, in Figure 8, Image 1 is frame 26070 of the video and image 2 is frame 27604. Although they are approximately 1500 frames apart, they both pertain to the same location, and thus the objects in them are the same. An important point to note is that the hypothesis holds only for stationary/static  
Figure 6: Overview of the LSU-detection module. Given the input video, the framework extracts audio–visual features to predict logical story unit boundaries based on semantic similarity between temporally coherent _shots_. The final decision boundary is based on thresholding the distance between consecutive _shots_.  
Figure 7: Estimated shot similarity for RAI video 23353. The figure also shows key frames of a selected LSU (red box).  
objects; if there are dynamic objects present in the shots (e.g., persons) the hypothesis will fail. Our approach focuses only on static object re-identification--thus, the current paper will only address problems of this kind.  
Based on the number of occurrences of a same class object in the same frame, the re-ID algorithm is composed of two sub-parts:  
* Single instance
* Multiple instance
# Two-Stage Beamforming Technique for GNSS Applications +
## 3 Experimental Results and Discussion  
To validate the proposed technique, an experiment was conducted in an open-sky static environment. The experimental setup and results are presented hereafter.
## 3 Experiments and Functioning Tests
### Quadruped Robot Dog Air Quality Monitoring Test  
One of the main technologies in the proposed framework is the air quality monitoring QRD. To confirm its effectiveness, a sample data acquisition was conducted by maneuvering the QRD and attached sensor board into a confined space filled with CO\({}_{2}\) gas, as demonstrated in Figure 8. The gas was discharged at a constant rate from a RedRock 16 G CO\({}_{2}\) cartridge and funneled through a syringe into the restricted enclosure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hardware** & **Model** & **Specifications** & **Function** \\ \hline \multirow{3}{*}{QRD} & \multirow{3}{*}{Puppy Pi Pro} & L \(\times\) W \(\times\) H & \multirow{3}{*}{SHM robot} \\  & & (226 \(\times\) 149 \(\times\) 190 mm) & \\  & & weighs (720 g) [47] & \\ \hline \multirow{3}{*}{AR headset} & \multirow{3}{*}{HoloLens 2} & See-through, 2k & \multirow{3}{*}{AR monitoring} \\  & & resolution, 6 Dor & \\  & & tracking [48] & \\ \hline \multirow{3}{*}{Microcontroller board} & \multirow{3}{*}{Arduino Uno} & Operating voltage: 5 V, & \multirow{3}{*}{Data acquisition} \\  & & clock speed: & \\  & & 16 MHz [49] & \\ \hline \multirow{3}{*}{Microcontroller board} & \multirow{3}{*}{Arduino Nano 33 IoT} & Operating voltage: 3.3 V, & \multirow{3}{*}{Wireless data acquisition} \\  & & clock speed: & \\  & & 48 MHz [50] & \\ \hline \multirow{3}{*}{Microcontroller board} & \multirow{3}{*}{ESP32-CAM} & 802.11\(\mathrm{b/g/n\ Wi\text{-}F_{i}}\), & \multirow{3}{*}{Wireless monitoring} \\  & & OY2640 cameras [51] & \\ \hline \multirow{3}{*}{Single-board processor} & \multirow{3}{*}{Raspberry Pi 4B} & Quad-core 64-bit SoC @ & \multirow{3}{*}{Edge processing} \\  & & 1.8 GHz, 8 GB & \\ \cline{1-1}  & & SDRAM [52] & \\ \hline \multirow{3}{*}{Wi-Fi hotspot} & \multirow{3}{*}{HTC 5G Hub} & 802.11 \(\mathrm{a/b/g/n/ac/ad}\) & \multirow{3}{*}{Mobile hotspot} \\  & & 20 connections [53] & \\ \hline \multirow{3}{*}{HEXBUG device} & \multirow{3}{*}{Nano} & L \(\times\) W \(\times\) H & \multirow{3}{*}{Confined space SHM} \\  & & (44.45 \(\times\) 12.7 \(\times\) 19.05 mm) & \\ \cline{1-1}  & & weighs (7.09 g) [54] & \\ \hline \multirow{3}{*}{HEXBUG device} & \multirow{3}{*}{Spider} & L \(\times\) W \(\times\) H & \multirow{3}{*}{Confined space SHM} \\  & & (114.3 \(\times\) 88.9 \(\times\) 76.2 mm) & \\ \cline{1-1}  & & weighs (76.2 g) [55] & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hardware specifications utilized in the experiments.  
The purpose of this test is to verify the capability of the QRD with external networked sensor circuit by maneuvering it into a confined space built for CO2 testing and successfully detecting elevated levels of CO2 at the 1000 PPM threshold.
## 3 Materials and Methods
### Signal Loss Measurement in IBC Channel  
In [35], the authors presented a dataset on signal loss in galvanic-coupled IBC channels [21], containing over 17,000 measurements. The data were measured on 30 experimental participants (subjects) in a frequency range from 50 kHz to 20 MHz. The signal was transmitted from a signal generator through the human body using galvanic coupling to a receiver. Stable skin-electrode contact was achieved by applying ECG gel to the skin under the electrode contact plates. One oscilloscope measured the signal on the transmitter side, and the second on the receiver side. The IBC channel gain was calculated based on the measurement data.  
The dataset contains the following data: participant identifier; experiment number; participant's height and weight, body mass index (BMI), percentage of body fat, age, and gender (male or female); transmitter point number; receiver point number; distance between transmitter and receiver; average fat content at the transmission point; average fat content at the reception point; sum of fat levels at the transmission and reception points; measurement frequency; receiver gain using a 50 \(\Omega\) load resistance; and receiver gain using a 1 m\(\Omega\) load resistance. Currently, study [35] is the most extensive experiment on measuring IBC characteristics; therefore, in our study, we used that dataset.
# A Quality Control Method for High Frequency Radar Data Based on Machine Learning Neural Networks
## 2 Materials and Methods
### Prediction-Based Anomaly Detector
#### 2.2.1 Data Preprocessing  
Before the radar data are fed into a predictive model, they undergo essential data preprocessing steps. In the real-world operating environment of radar systems, various interference factors often lead to the omission of data. In this study, to align radar data with the requirements of the model, two critical processes were applied: the imputation of missing data and standardization of data.  
To maintain the continuity of the time series data, a thorough timestamp check was conducted on radar time series. This ensures that the time intervals between data points within each segment of the time series remain consistent. If, within a segment, the time interval between adjacent data points exceeds a predefined standard time interval (typically 1 h in this study), it indicates the presence of missing data. In such cases, a linear interpolation technique is employed to reconstruct the missing data. Moreover, recognizing that ocean-current data often exhibit periodicity, in this study, we refrained from imputing missing data for segments with time gaps exceeding 6 h.  
To enhance the training effectiveness of neural network models, it is customary to standardize input data. In this research, the z-score standardization method was utilized. This method involves transforming the data to have a mean of zero and a standard deviation of one, making them suitable for input into the neural network model. These preprocessing steps ensure that radar data are effectively prepared for model training and utilization.