esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Microcontroller Section and Data Acquisition  
This subsection studies the correct connection of the distance sensors to microcontrollers for data sampling and data acquisition, the number of microcontrollers needed, and microcontroller communication. Data acquisition posed a few challenges, which are detailed here along with their corresponding solutions. (1) Electricity shortage: By using a single Arduino Uno with 75 sensors, the input voltage for the ultrasonic was lower than 3.5 V, which resulted in no measurement estimation from HC-SR04. An Arduino Mega connected to a spread power source was used to fix this issue. The sensors were distributed among these two Arduinos. (2) Single output: The serial communication of Arduino Uno was connected to the Arduino Mega to save data from only one Arduino and have the sensors measuring simultaneously. During the distance measurement, these Arduinos were in touch with each other. First, both Arduinos checked their connected sensors. Second, the Arduino Uno printed a character that ordered the Arduino Mega to activate its connected distance sensors. Third, the Arduino Mega received the character and confirmed with the Arduino Uno by sending a different character. At this moment, both microcontrollers had already forced their connected sensors to measure the distance of the targeted object. The Arduino Uno then printed the values of its sensors in its serial communication and waited for another character from the Arduino mega. When the Arduino Mega was finished with its measurements, it triggered a short delay (making sure that Arduino Uno was finished with its printing and was waiting for Arduino Mega), and it sent the character. Shortly after printing this character, the Arduino Mega sent all of its measurements to the Arduino Uno. Finally, by receiving the Arduino Mega character, Arduino Uno started printing the outputs of the Arduino Mega with a space from its printing. This way, the information was only received from the Arduino Uno. (3) Data acquisition: Raspberry Pi was connected to the master Arduino (Arduino Uno) for saving all the measurements. The Raspberry Pi provided power for the Arduino Uno and acquired its output through a written Python code. (4) Supply: The Raspberry Pi requires input power with 5 V and 2.5 A. Standard adaptors for mobile charging or power-banks only provide 5 V and 2.1 A. Consequently, the Raspberry Pi showed a low-power error that affected the connected Arduino Uno. However, the Arduino Mega can connect to any habitual power bank or micro-USB mobile charger.
# A Study on the Roof-Cutting and Pressure Releasing Technology of Roof Blasting
## 5 Conclusions  
Numerical simulations were conducted to analyze the damaged area of different diameter charges to study the influence of in situ stress on the blasting damage area. Field tests were conducted on the 6302 working face in the Xinhe Coal Mine. The main conclusions are as follows:  
1. The explosion load mainly affects the damage zone near the borehole, while the in situ stress mainly affects far-field crack propagation. The crack propagates in the direction of high surrounding rock pressure. The shock wave adopts the oblique incidence method at the joint surface, effectively reducing the reflection frequency and achieving the optimal rock-breaking effect.
2. The shock and stress waves generated by the detonation of different diameter charges determine the crushing area size. The explosion gas determines the fracture zone's secondary expansion and extension length. When the non-coupling coefficient is 1.5, the impact pressure of the 60 mm diameter borehole is eight times that of the 20 mm, and the impact speed can reach two times that of the 20 mm.  
After optimization of the blasting parameters in the Xinhe coal mine, the radius of the single-hole blasting crack zone is about 5 m. The high energy of the roof is transferred to the front of the working face, the distribution is no longer concentrated, and a better blasting pressure-relief effect is achieved. Overall, the research results have a definite guiding significance for underground engineering in deep projects.  
Methodology, X.X.; Software, J.G.; Writing--original draft, X.H.; Writing--review & editing, Y.L. and F.H.; Visualization, H.W.; Supervision, Y.M. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Natural Science Foundation of Shandong Province of China (No. ZR2019 BA023), the Natural Science Foundation of China (Nos. 51879135, 52064004), and the Foundation of Hubei Key Laboratory of Blasting Engineering (No. BL2021-06).  
All authors have read and agree to the published version of the manuscript.  
Date cannot be shared openly but are avaiable on request from authors. Except for the data provided in the article, other data is our specialized research and therefore cannot be shared.  
The authors wish to express their thanks to all of their sponsors.  
The authors declare no conflict of interest.  
Figure 13: Variation of energy events.
# A Novel Multi-Objective Harmony Search Algorithm with Pitch Adjustment by Genotype
## 3 Proposed Pitch Adjustment by Genotype
### Pitch Adjustment by Genotype  
According to the previous subsection, the Pareto-optimal front can be reflected in the search space both by dispersed solutions and by close solutions that form promising areas. This can be determined by means of crowding distance by genotype, opening two possibilities:  
1. Intensity exploitation as the algorithm advances, but this time depending on the conformation of promising areas.
2. Maintain higher exploration in dispersed solutions and higher exploitation in promising areas, with the objective of achieving a better balance of the algorithm, based on the behavior of \(HM\) during the formation of the Pareto-optimal front.  
Therefore, a pitch adjustment based on the crowding distance by genotype is proposed as established in expression (11), where \(bw\) is obtained from Equation (12):  
\[x_{i}=\left\{\begin{array}{ll}HM(r,i)+rand(-1,1)\times bw(r,i),&probability \ (PAR)\\ HM(r,i),&probability\ (1-PAR)\end{array}\right. \tag{11}\]  
\[bw(r,i)=\frac{x_{i}^{j+1}-x_{i}^{j-1}}{2} \tag{12}\]  
For the upper and lower extremes of each front, \(bw\) is given by Equations (13) and (14), respectively:  
\[bw(r,i)=x_{i}-x_{i}^{j-1} \tag{13}\]  
\[bw(r,i)=x_{i}^{j+1}-x_{i} \tag{14}\]  
Since the bandwidth \(bw(r,i)\) is the crowding distance component of the variable \(x_{i}\) for the solution vector in the position \(r\), Equation (12) includes no summation operator. That is, \(x_{i}\) may vary by adjusting the pitch up to a range equal to the distance from the neighboring points. An example considering an \(HM\) with four harmonies is shown in expression (15), where a non-dominated ranking was performed and harmonies 1,2,4 turned out to be non-dominated (_Front 1_) while harmony 3 was dominated only once (_Front 2_):  
\[HM=\left[\begin{array}{ccc}9&7&1\\ 3&1&9\\ 6&2&4\\ 2&5&8\end{array}\right]\rightarrow\begin{array}{ll}Harmony\ 1&(Front\ 1)\\ \rightarrow&Harmony\ 2&(Front\ 1)\\ \rightarrow&Harmony\ 3&(Front\ 2)\\ \rightarrow&Harmony\ 4&(Front\ 1)\end{array} \tag{15}\]  
Suppose that a variable \(x_{2}\) in the new harmony is created by pitch adjustment, and the parameter \(r\) is randomly generated with a value 4. The substitution of these values in expression (11) produces Equation (16), for calculating \(x_{2}\):  
\[x_{2}=HM(4,2)+rand(-1,1)\times bw(4,2) \tag{16}\]  
Vector 4 is a part of _Front 1_. Since the analyzed variable is \(x_{2}^{j}=HM(4,2)=5\), the neighboring points are 7 and 1, from harmonies 1 and 2, respectively. Equations (17) and (18) are generated by substituting these values in Equations (12) and (16). Note that solution vector 3 is not considered because it belongs to _Front 2_.  
\[bw(4,2)=\frac{(x_{2}^{j+1}-x_{2}^{j-1})}{2}=\frac{(7-1)}{2} \tag{17}\]  
\[x_{2}=5+rand(-1,1)\times 3 \tag{18}\]
# Improving Polar-Coded SCMA System by Information Coupling and Parity Check
## 6 Performance Evaluation  
In this section, the simulation results are provided to evaluate the block (TB and CB) error rate performance and complexity of the uplink SCMA system over the AWGN channel and Rayleigh fading channel. Assuming there are six users multiplexed over four orthogonal resources, the overloading is 150%. The mapping matrix has already been given in (1), and the corresponding SCMA codebook is designed according to [30]. Base systematic polar code is constructed by Bhattacharyya parameter-bound method. For each user, the base code length is set to \(N=192\), 200, and the code rate is \(R=1/3\), 1/2. To ensure that systematic PCCA-polar code and CA-polar code have the same number of assistant bits, the CRC-8 with the generator polynomial \(g(x)=x^{8}+x^{2}+x+1\) and CRC-11 with the generator polynomial \(g(x)=x^{11}+x^{10}+x^{9}+x^{5}+1\) are used in PCCA-polar code and CA-polar code, respectively. The number of parity check bits \(n_{pc}\) is set to 3. The weight factor \(\alpha\) and coupling ratio \(\delta\) are selected according to the values optimized in Section 5. The number of blocks in a TB and the window size of coupled polar decoder are set to \(L_{c}=10\) and \(L_{w}=10\). For the base SCL decoder, the minimum and maximum list sizes are configured as \(L_{\min}=4\) and \(L_{\max}=32\). Additionally, the rate-matching pattern is obtained according to the introduced method of [14]. As the comparative scheme, the LDPC encoder and rate-matching algorithm used in 3 GPP TS 38.212 are employed, where the CRC bits are generated by CRC-11. In addition, the log-Belief Propagation (log-BP) algorithm with 30 inner-loop iterations is used for LDPC decoding. In the receiver, the MPA detection within one inner-loop iteration  
Figure 11: Transfer characteristics of PIC PCCA-polar code with different \(\delta\). (**a**) \(K=100\), \(R=1/2\); (**b**) \(K=64\), \(R=1/3\).  
Figure 12: Transfer characteristics of polar code. (**a**) \(K=100\), \(R=1/2\); (**b**) \(K=64\), \(R=1/3\).  
is employed for MUD, and the number of outer-loop iteration between the MPA detector and channel decoder is set to 5.
# Day-Ahead Dispatch Model of Electro-Thermal Integrated Energy System with Power to Gas Function
## 2 The Concept of P2G  
### Water Electrolysis  
The chemical principle of water electrolysis technology is that the water is reduced to form hydrogen at the cathode and oxidized to form oxygen at the anode when the current through the water. The chemical formula is as follows:  
\[2\mathrm{H_{2}O}\overset{\mathrm{energetic}}{\rightarrow}2\mathrm{H_{2}}+ \mathrm{O_{2}} \tag{1}\]  
The essence of an electrolytic cell is composed of a series of electrochemical cells containing metal poles and electrolyte. At present, there are two main technologies of water electrolysis: alkaline electrolyzer and proton exchange membrane electrolyzer. The third technology, solid oxide electrolysis has not been widely used. The structure of these electrolyzers are shown in Figure 1.
## 4 Use Case Evaluation
### Performance Evaluation
Table 4 summarizes the performance evaluation results. Despite being a preliminary performance evaluation, this study highlights the effectiveness and efficiency of S2 NetM in facilitating applications such as the Green Route recommendation system, enabling timely responses while considering important factors including setup time, processing delay and memory usage.
# An Efficient Trust-Based Scheme for Secure and Quality of Service Routing in MANETs
## 3 Techniques and Methods
### The QoS Parameters
#### 3.2.1 Channel Quality  
In our scheme, channel quality means the availability of the channel during transmission. Measurement of interference in a channel can help utilize resources more effectively to ensure reliable communication. The use of channel quality metric also helps to reduce end-to-end delay through the allocation of on-the-fly radio resources. The capacity of a channel (\(H\)) is defined as the rate at which data can be reliably transmitted over the medium which can be calculated as follows:  
\[H=G_{\mathrm{t}}\times G_{\mathrm{r}}\left(\lambda/4\pi d\right)^{2} \tag{1}\]  
where \(G_{\mathrm{t}}\) is the transmitter gain, \(G_{\mathrm{r}}\) is the receiver gain, \(\lambda\) is the wavelength of the signal and \(d\) is the distance between the source and the destination.
# A New Algorithm for Simultaneous Retrieval of Aerosols and Marine Parameters
###### Abstract  
We present an algorithm for simultaneous retrieval of aerosol and marine parameters in coastal waters. The algorithm is based on a radiative transfer forward model for a coupled atmosphere-ocean system, which is used to train a radial basis function neural network (RBF-NN) to obtain a fast and accurate method to compute radiances at the top of the atmosphere (TOA) for given aerosol and marine input parameters. The inverse modelling algorithm employs multidimensional unconstrained non-linear optimization to retrieve three marine parameters (concentrations of chlorophyll and mineral particles, as well as absorption by coloured dissolved organic matter (CDOM)), and two aerosol parameters (aerosol fine-mode fraction and aerosol volume fraction). We validated the retrieval algorithm using synthetic data and found it, for both low and high sun, to predict each of the five parameters accurately, both with and without white noise added to the top of the atmosphere (TOA) radiances. When varying the solar zenith angle (SZA) and retraining the RBF-NN without noise added to the TOA radiance, we found the algorithm to predict the CDOM absorption, chlorophyll concentration, mineral concentration, aerosol fine-mode fraction, and aerosol volume fraction with correlation coefficients greater than 0.72, 0.73, 0.93, 0.67, and 0.87, respectively, for \(45^{\circ}\leq\) SZA \(\leq 75^{\circ}\). By adding white Gaussian noise to the TOA radiances with varying values of the signal-to-noise-ratio (SNR), we found the retrieval algorithm to predict CDOM absorption, chlorophyll concentration, mineral concentration, aerosol fine-mode fraction, and aerosol volume fraction well with correlation coefficients greater than 0.77, 0.75, 0.91, 0.81, and 0.86, respectively, for high sun and SNR \(\geq 95\).  
Keywords:algorithms; radiative transfer; remote sensing; atmospheric optics; neural networks; CDOM absorption; chlorophyll concentration; mineral concentration; aerosol +
Footnote †: journal: _algorithms_
## 2 Design of Components and Fabrication
### Test Specimens  
In this study, the dynamic response of steel plates was assessed under low-velocity impact conditions using a three-point loading system, highlighting the versatility of the  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Component** & **Parameter** & **Value** & **Range** \\ \hline \multirow{3}{*}{Velocity sensor} & Sensitivity, (mV/m/s\({}^{2}\)) & 100 & 10 to 100 \\ \cline{2-4}  & Accuracy, (\%) & \(\pm\)0.5 & \(\pm\)1 to \(\pm\)5 \\ \hline \multirow{3}{*}{Linear potentiometer–Displacement sensor} & Measurement range, (mm) & 10–100 & - \\ \cline{2-4}  & Repeatability, (mm) & \(\leq\)0.002 & - \\ \hline \multirow{3}{*}{Load cell} & Weight, (kg) & 3.5 & Variable \\ \cline{2-4}  & Measuring range, (N) & 1000 & 0–2000 \\ \hline \multirow{3}{*}{Impactor} & Material & Steel & - \\ \cline{2-4}  & Weight, (kg) & 35.0 & 1–250 \\ \cline{2-4}  & Impact velocity, v (m/s) & 0.40–0.65 & 0.1–32 \\ \cline{2-4}  & The shape of the impactor tip & Cylindrical & - \\ \hline \multirow{7}{*}{Spring} & Material & Steel–ASTM A228 & - \\ \cline{2-4}  & Diameter, D (mm) & 60 & 0.5–50 \\ \cline{2-4}  & Diameter of the wire, d (mm) & 8 & 0.1–20 \\ \cline{2-4}  & Length, L\({}_{\rm F}\) (mm) & 528 & 0.1–1000 \\ \cline{2-4}  & Coils count, n & 66 & - \\ \cline{2-4}  & Extension range, x (mm) & 0–800 & 0–800 \\ \cline{2-4}  & Shear modulus, G (GPa) & 84 & 79–90 \\ \cline{2-4}  & Shear strength, \(\tau\) (MPa) & 550 & 420–650 \\ \cline{2-4}  & Spring constant, k (N/m) & 3016.8 & 100–100,000 \\ \hline \multirow{3}{*}{Specimen} & Material & Steel S235 JR & - \\ \cline{2-4}  & Cross section shape & Rectangular & - \\ \cline{1-1} \cline{2-4}  & Impact Angle (\({}^{\circ}\)) & Perpendicular to the weak axis & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters and properties of the various setup components.  
experimental device in precisely tuning impact scenarios. A total of ten steel plates, each 5 mm thick and 20 mm wide, were tested. The plates measured 850 mm in length, with an 800 mm support span. The specimens conformed to EN 10025-2 S235 JR (ST37) grade steel, which has a minimum yield stress of 235 MPa and an elastic modulus of 165 GPa, determined under quasi-static conditions. The chemical composition of this steel grade is listed in Table 4[25]. This characterization provides a robust basis for analyzing the dynamic behavior of steel under varying impact conditions.
###### Abstract  
The complexity of power system networks is increasing continuously due to the addition of high capacity transmission lines. Faults on these lines may deteriorate the power flow pattern in the network. This can be avoided by the use of effective protection schemes. This paper presents an algorithm for detecting and classifying faults on the transmission network. Fault detection is achieved by utilizing the fault index, which depends on a combination of characteristics extracted from the current signal by the application of the Stockwell transform and Wigner distribution function (WDF). Various faults are categorized using the quantity of phases with a faulty nature. The fault events like phase-to-ground (L-G), two phases (LL), two phases to-ground (LL-G), and three phases to-ground (LLL-G) are investigated in this study. The performance of the algorithm designed for the protection scheme is tested for the variations in the impedance during the fault event, variations in the angle of the fault incidence, different fault locations, the condition of the power flow in the reverse direction, the availability of noise, and the fault on the hybrid line consisting of two sections of underground cable and the overhead line. The algorithm is also analyzed for discriminating switching incidents from fault cases. A comparative study is used to establish the superiority of the proposed technique as compared to the Wavelet transform (WT) based protection scheme. The performance of the protection technique is established in MATLAB/Simulink software using a test network of the transmission line with two terminals.  
fault; power system; Stockwell transform; transmission line protection; Wigner distribution function +
Footnote †: journal: Applied Physics Letters  
16 October 2020; 10, 7985; doi:10.3390/app10227985
# Method for Recognizing Disordered Sugarcane Stacking Based on Improved YOLOv8n
## 2 Materials and Methods
### Mpdiou  
MPDIOU is an optimized loss function for bounding box regression in object detection tasks, designed to enhance accuracy and efficiency in computer vision tasks such as object detection, scene text detection, and instance segmentation [29]. It improves the localization accuracy of bounding boxes by minimizing the Euclidean distance between key points (typically the top-left and bottom-right corners) of the predicted and true bounding boxes. The specific formula is as follows:  
\[d_{1}^{2}=\left(x_{1}^{B}-x_{1}^{A}\right)^{2}+\left(y_{1}^{B}-y_{1}^{A} \right)^{2} \tag{4}\]  
\[d_{2}^{2}=\left(x_{2}^{B}-x_{2}^{A}\right)^{2}+\left(y_{2}^{B}-y_{2}^{A} \right)^{2} \tag{5}\]  
\[MPDIOU=\frac{A\cap B}{A\cup B}-\frac{d_{1}^{2}}{w^{2}+h^{2}}-\frac{d_{2}^{2}}{ w^{2}+h^{2}} \tag{6}\]  
In the formula, A and B represent two random convex shapes, respectively. w and h denote the width and height of the image dimensions. \(\left(x_{1}^{A},y_{1}^{A}\right),\left(x_{2}^{A},y_{2}^{A}\right)\) respectively represent the coordinates of the top-left and bottom-right corners of A. \(\left(x_{1}^{B},y_{1}^{B}\right)\), \(\left(x_{2}^{B},y_{2}^{B}\right)\)  
Figure 4: Schematic diagram of the ECA module.  
respectively represent the coordinates of the top-left and bottom-right corners of B. \(\mathrm{d}_{1}^{2}\), \(\mathrm{d}_{2}^{2}\) respectively denote the squares of the Euclidean distances between the top-left corners and the bottom-right corners of A and B.  
Using the MPDIOU loss function simplifies the comparison of similarity between two bounding boxes in object detection tasks, aiding the algorithm in more precisely selecting the bounding box that best matches the real target, thereby improving localization accuracy. MPDIOU is particularly effective in target detection scenarios involving object overlap, such as stacked sugarcane, because it minimizes the distance between key points, reducing the distortion of detection boxes caused by overlap and effectively decreasing the rate of target misses.
## 6 Prospects
### Future Development  
To enhance the \"Co-Relay\" system and explore additional research avenues, forthcoming studies should focus on several key areas. Firstly, expanding the user sample size to encompass individuals from diverse backgrounds and age brackets will bolster the generalizability of findings. Secondly, optimizing the comfort and interaction performance of VR devices, reducing technical barriers, enhancing user acceptance, and investigating cost-effective device solutions are crucial. Moreover, incorporating user feedback to refine interaction mechanisms, introduce more interactive content and challenging tasks, and enhance emotional depth and narrative complexity is essential. Strengthening user interaction and collaboration, exploring cooperative modes and gameplay, and supporting a broader range of VR platforms and devices will enhance cross-platform narrative system compatibility and accessibility. Future research should delve into cross-platform interaction and collaboration to enrich user experience diversity and flexibility. Long-term user research is essential to monitor and analyze user experience and feedback of the virtual reality narrative collaboration system over time, assess its sustainability and user engagement, and refine human-computer interaction design and user experience through ongoing data collection and analysis. Additionally, we aim to investigate the integration of emerging technologies such as augmented reality (AR), mixed reality (MR), and artificial intelligence (AI) in education, game, and therapy to enhance the interactivity and intelligence of the multiplayer narrative collaboration platform, offering a more diverse user experience. We believe the present study expands the practical application of personalized virtual reality educational interventions. Building upon the work of [PERSON] et al. (2024) [35], it contributes additional case studies on the personalized design of virtual reality-based educational tools. Building on the work of [PERSON] et al. (2022) regarding virtual reality design in higher education [36], our study, \"Optimizing VR Devices to Enhance Educational Applicability,\" offers a systematic review of VR design in education and serves as a guide for future research directions. Furthermore, the findings extend the research of [PERSON] et al. (2016) on the use of virtual reality in pediatric emergency medicine education [37], thereby enriching the discourse on the cross-disciplinary value of this technology.  
This study underscores the significant potential of VR technology in interactive narrative collaboration systems through the development and evaluation of the Co-Relay virtual reality narrative collaboration system. The focus is on advancing VR technology in narrative co-creation environments and emotional computing story systems. While acknowledging existing limitations, continuous optimization, future research, and exploration are expected to drive further innovations and enhancements in design and user experience.