esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intelligent Approach to the Unit Nesting Problem of Coil Material
## 2 Problem Model
### Mathematical Model  
The variable definitions, used to establish a mathematical model for the irregular part nesting problem of coil material, are as shown in Table 1.  
In Figure 4, '\(L\)' represents the distance between the rightmost and leftmost points of a unit, and '\(L\)'' represents the distance between the leftmost and rightmost points of a combined unit.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Variable Definitions** \\ \hline \(W\) & The width of the coil \\ \(D\) & Within the area of coil \\ \(r\) & The quantity of part types \\ \(N\) & The maximum quantity of parts in a unit \\ \(M_{i}\) & The number of parts \(i\) in a unit \\ \(r_{i}\) & Within the area of part \(i\) \\ \(S_{i}\) & The area of parts \(i\) \\ \(a_{i}\) & The angle of parts \(i\) \\ \(I\) & The placement order of parts \\ \(L\) & The maximum length of a unit \\ \(L^{\prime}\) & The maximum length of two units after combination \\ \(sl\) & The minimum distance of the unit movement without colliding (\(sl=L^{\prime}-L\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variable definitions.  
Figure 3: Flowchart of the unit nesting approach.  
In the nesting process of coil material, the position and orientation of parts are changed to achieve maximum utilization of the unit. The formula is shown as follows:  
\[\max f(I,\ [\alpha_{1},\ \alpha_{2},\ \cdots,\ \alpha_{N}])=\frac{\sum\limits_{i=1}^ {r}S_{i}\times M_{i}}{sl\times W}, \tag{1}\]  
\[\text{s.t.}\ r_{i}\in D, \tag{2}\]  
\[r_{i}\cap r_{j}=\varnothing,i\
eq j, \tag{3}\]  
\[M_{i}\geq 1, \tag{4}\]  
\[\sum\limits_{i=1}^{r}M_{i}\leq N, \tag{5}\]  
\[sl\leq 15,\!000. \tag{6}\]  
In the above mathematical model, Equation (1) represents the optimization objective of the model. \(f^{\prime}\) is the utilization rate of the unit in the nesting layout, and it is influenced by the position and the orientation of parts. Equations (2) and (3), respectively, indicate that the parts should be placed within the boundaries of the coil material and do not overlap with others. Equations (4) and (5) represent the limitation of the number of parts in the unit to be met. Equation (6) represent that the maximum value of step length is 15,000.
## 4 Empirical Study
### Technical and Development Trends of 5G Optical Networks  
To present the technical development trajectory of 5G optical networks, the researchers examined the changes in the focus of research and development over the years by using the technology networks of different stages. Using 2016 as a cutoff point, they segmented the time period into an early and a later stage. The development trends of each technology were illustrated using the increase or decrease in centrality of its CPC codes. Table 5 displays the results:  
Figure 1: Level-4 CPC network of 5G optical networks. Note: The size of a node indicates the number of nodes it is connected to. The thickness of an arc indicates the strength of the connection, namely the number of patents shared. To keep the figure succinct, only nodes that were connected to more than 25 other nodes were retained.  
Table 5 indicates that among the patents approved prior to 2008, H04W12 (security arrangements), H04W84 (network topologies), as well as H04W40 and H04W72 (communication routing and allocation of wireless resources) surpassed the other technology domains in terms of their closeness, betweenness, and fragmentation centrality; this differs from the technological focuses of the later stage, which consisted of H04B10 (transmission systems employing electromagnetic waves other than radio waves), as well as H04L5 and H04Q11 (multiplex communication systems). Both H04L5 and H04Q11 technology nodes have been recognized as having high betweenness centrality and fragmentation centrality in terms of key player problem-negative. This indicates that they both play a bridging and critical role in connecting technology clusters within 5G optical networks.
# Development of Highly Sensitive Temperature Microsensors for Localized Measurements
## 2 Materials and Methods
### Microsensors Readout Electronics  
Figure 8 shows the control and readout electronics for the microsensors temperature measurements. The microsensors readout electronics require a precision power source for excitation, especially when a high resolution is required. Any fluctuation of the excitation current will originate a change in the electrical potential of the sensors, and consequently, will provide inaccurate temperature values. So, an adjustable current source (LM134 from Texas Instruments, Dallas, TX, USA) was used to provide a stable current of 100 \(\upmu\)A. This device was chosen because it allows a current adjustment from 1 \(\upmu\)A to 10 mA, with high accuracy and low noise. The supply of the circuit was performed by batteries for assuring independence of the power supply and portability.  
The high-sensitivity and noise-free RTDs readout circuit usually relies in a Wheatstone bridge or a four-wire configuration analog circuit for sensor output [25]. In this particular case, a four-wire configuration was used, which features high accuracy and an output voltage directly proportional to the RTD resistance only (Figure 8). The analog signals of the RTDs circuit were amplified using the AD620 from Analog Devices and converted into a usable voltage level for the microcontroller's Analog Digital Converter (ADC), and for further stored in a SD card or presented in a display.  
Figure 8: Schematic of the readout and control electronics for temperature microsensors.  
Figure 7: Experimental setup used for the temperature microsensors characterization.
# Mechanical and Acoustic Emission (AE) Characteristics of Rocks under Biaxial Confinements
## 2 Experimental Methodology
### Stress Paths  
Two kinds of stress loading paths in biaxial compression tests were applied in this study. The detailed loading steps of Path I were as follows: Firstly, the maximum principal stress \(\sigma_{1}\) and the intermediate principal stress \(\sigma_{2}\) are loaded synchronously to the preset value of \(\sigma_{2}\) at the same loading rate (0.2 MPa/s). Then, the \(\sigma_{2}\) remained at the predefined levels, and the \(\sigma_{1}\) continued to increase until the final failure of the rock specimen (Figure 3a). Path II was applied to check the bearing capacity of rocks under \(\sigma_{1}=\sigma_{2}\). The \(\sigma_{1}\) and \(\sigma_{2}\) are loaded synchronously at the same loading speeds (0.2 MPa/s) until rock failure (Figure 3b). The peak values of the \(\sigma_{1}\) were defined as the biaxial compressive strength \(\sigma_{\text{bcs}}\) of rocks. The \(\sigma_{\text{bcs}}\) of rocks under \(\sigma_{2}=0\) was also called the uniaxial compressive strength \(\sigma_{\text{ucs}}\).  
Figure 2: Rock specimens used in this study: (**a**) specimen size, and (**b–d**) mineral components of granite, marble, and sandstone, respectively.  
The preset values of \(\sigma_{2}\) varied for different rocks in Path I, which were 0, 5, 10, 20, 40, and 60 MPa for granite, 0, 5, 10, 20, and 40 MPa for marble, and 0, 5, 10, and 20 MPa for sandstone, as shown in Table 1.
# Advancement of the DRPE Encryption Algorithm for Phase CGHs by Random Pixel Shuffling
## 4 Results Discussion and Performance Analysis
### Visual Assessment  
Our method enables the storage, encryption, and decryption of multichannel scenes and images. Computer graphics allows for the generation of any multichannel image, which can be stored in a color CGH [44]. This study presents results for a single wavelength. To achieve this, we converted Figure 2a to luminance values to facilitate the comparison between simulation and laboratory results. Nevertheless, the obtained results are extrapolable to color CGHs generated from the multichannel scene.  
The quality of the final image is usually affected by several factors: Working with only the phase and encoding it with integer numbers within the allowed range impacts the final scene quality. The dynamic range or the pixel size of the SLM (real or simulated) also directly affects the final quality. Additionally, speckle phenomena always appear, which are  
Figure 6: Basic scheme for capturing the scene in the laboratory.  
not attributable to the encryption technique. To improve the final image, the process can be iterated by generating multiple CGHs and applying statistical filtering techniques [45,46].
## 4 Proposed Architecture and Implementation
### Security and Privacy Measures Implemented  
In our implementation, we prioritized security and privacy by integrating several essential mechanisms into the blockchain-based vehicular communication system. This part of the research followed three key aspects:  
* End-to-end encryption-we implemented end-to-end encryption to ensure that all communication between vehicles (V2V) and vehicles and infrastructure (V2I) is encrypted. Public-private key pairs were used, with each vehicle and RSU having their own cryptographic keys.
* When data (e.g., vehicle location, speed, and hazard warnings) are transmitted, they are first encrypted with the sender's private key to ensure that the datacannot be read by unauthorized parties. Only the recipient who has the corresponding public key can decrypt the message. This guarantees that the data remain confidential and tamper-proof throughout the entire communication.
* We used elliptic curve cryptography (ECC) for public-private key pair generation, as it offers strong security properties and efficient key generation, which are crucial for resource-constrained environments such as vehicular networks.
* Tamper-proof ledger-the blockchain acts as a tamper-proof ledger that records all vehicle interactions and data exchanges. Each transaction between vehicles and RSUs is validated via a consensus mechanism and stored in blocks that are cryptographically linked to previous blocks.
* Once data have been written to the blockchain, they are immutable. This means that no vehicle, RSU, or external company can change or delete previous data entries, ensuring an accurate and transparent record of all events.
* We configured the blockchain to be permissioned so that authorized nodes (such as vehicles and RSUs) can access the ledger while maintaining transparency across the network. This ensures accountability, as all interactions are logged, and the integrity of the data can be verified by all participants.
* Decentralization-to ensure decentralization, we established an approved blockchain with multiple participating nodes. Vehicles, RSUs, and infrastructure nodes (e.g., cloud servers) each act as decentralized nodes in the network. No single node controls the entire system, making the network resilient to attacks.
* We used the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm to reach an agreement across the network, even in the presence of faulty or malicious nodes. This protocol ensures that the network can still reach consensus and validate transactions as long as fewer than 1/3 of the nodes are compromised.
* Decentralization makes the system resistant to denial of service (DoS) and Sybil attacks. For example, in a Sybil attack, where an attacker creates multiple fake identities to manipulate the network, the blockchain's consensus protocol ensures that only authenticated nodes can participate in the decision-making process. In addition, the distributed nature of the system means that there is no single point of failure, which minimizes the likelihood of successful DoS attacks.
## 2 Related Works  
### CSI-Based HAR  
Within the existing literature, numerous WiFi-based HAR systems have been researched and analyzed, capitalizing on the widespread availability of WiFi signals. Notably, [PERSON] et al. [21] presented a system called WiGest, which consists of three integral components: initial feature extraction, gesture recognition, and motion mapping. This system employs RSS measurements for accurate gesture identification. Additionally, [PERSON] et al. [22] proposed an alternative approach that leverages WiFi RSS to recognize human activities. Through manual extraction of significant features from raw RSS measurements, they introduced a fusion algorithm capable of identifying essential movements such as standing and walking.  
The efficacy of activity recognition mechanisms based on RSS measurements is limited by the presence of instability and disorder caused by multi-path and fading effects, even when considering basic activities. While RSS provides a broad understanding of communication links, CSI offers more intricate details about the condition of the communication channel [23]. Notably, the enhanced reliability and informativeness of WiFi CSI have attracted considerable attention. [PERSON] et al. [24] devised a Fresnel zone model for HAR that employs WiFi CSI signals, enabling the assessment of WiFi signals' sensing capabilities. Through their proposed model, they achieved remarkable accuracy in detecting human behaviors at centimeter and decimeter scales, such as respiration rate and the orientation of walking. Furthermore, [PERSON] et al. [25] introduced a location-based movement identification system that utilizes WiFi CSI readings.  
Previous studies relied on hand-crafted features, which obligate expert knowledge and may not capture the implicit features necessary for accurate HAR using WiFi CSI. To address this issue, some researchers have proposed using DL techniques to automatically learn essential features for this task.
# Prediction of the Moisture Content in Corn Straw Compost Based on Their Dielectric Properties
## 2 Materials and Methods
### Methods of the Prediction Model
#### 2.5.2 Support Vector Machine  
Support vector machine (SVM) is a machine learning method that was developed in the mid-2000s as a tool to solve problems in machine learning using optimization methods [29]. SVM improves the generalization ability of the learning machine by seeking the minimum structured risk to minimize the empirical risk and confidence range to obtain superior statistics when the statistical sample size is small [30].  
Support vector regression machine (SVR) is a generalized form of SVM applied to regression fitting and is widely used in regression analysis. In SVR, a low-dimensional nondifferentiable problem is transformed into a high-dimensional problem by nonlinear mapping to render the model linearly differentiable. Compared with conventional fitting methods, SVR has the kernel function, which not only can adapt to the nonlinearity of the training sample set but also reduces the risk of overfitting through adjustable parameters. Replacing the linear term in the linear equation with a kernel function can render the original linear algorithm nonlinear [31; 32]. In this study, SVR was used to predict and analyze the moisture content of straw compost, and the radial basis function (RBF) with superior stability and higher accuracy was used as the kernel function of SVR.
## 4 Design Implementation
### FPGA Bus Components
#### 4.3.2 Scatter-Gather Direct Memory Access  
Moreover, the SG-DMA controller (as shown in the blue box in Figure 8) comprises an internal CSR, which the micro-processor can access to control SG-DMA operations as drawn in Figure 9. Upon receiving a start command from the micro-processor, the dispatcher fetches a descriptor, based on priority. These descriptors contain essential information for the read and write controllers to execute memory access for a specified length of a data block. An SG-DMA task involves the continuous execution of multiple descriptors. This functionality allows SG-DMA to effectively gather several small scattered data blocks into a larger data stream within a contiguous memory space, forming a substantial datagram, which can then be transmitted to the network's physical layer (PHY).  
The execution flow of an SG-DMA task is illustrated in Figure 10. For a fair comparison, the system time is recorded by a hardware timer (as a controller attached to the system bus shown in Figure 8 and described in Section 4.3. FPGA Bus Components). The performance calculation is from \"Task Start\" to \"Task End\" in Figure 10, including the total execution time, which consists of time required for programming the descriptors, starting the dispatcher, transferring data blocks, and executing the interruption of services. Moreover, test datagrams are first initialized as a sequential number (0, 1, 2, 3,...), as the assigned length of memory transfer by the micro-processor from a source address of the memory. After completion of the SG-DMA execution, the data at the destination address were verified to validate the correctness of execution. For more information regarding the  
Figure 8: FPGA system bus components with external memory and network interface.  
performance metrics, the following sub-sections in Section 5 \"Experimental Results\" will provide the details.
## 5 Gates Formula Improvement  
### Data Fitting  
The comparative analysis demonstrates that the Hiley formula offers the most reliable prediction for pile capacity. However, the Hiley formula requires the rebound value, which occurs momentarily and is challenging to capture, limiting its general application in the field. In contrast, the Gates formula requires only penetration and hammer energy measurements. Due to the ease of obtaining variables and its satisfactory accuracy, the Gates formula gains preference.  
No formula can entirely overcome inherent assumptions and errors. Experienced engineers may suggest modifications for greater reliability or adopt a rule-of-thumb approach. Correction coefficients or re-fitting can be established by comparing results from the dynamic load tests [16]. Thus, this study aims to improve the Gates formula to enhance the usability and reliability of the pile driving formula. The existing Gates formula shows high accuracy in low-capacity ranges (approximately below 3000 kN) but tends to underestimate bearing capacity compared to dynamic load tests as capacity increases [19]. Moreover, the original Gates formula, initially recommended for use only up to 2650 kN, reflects the lower pile driving energy available at empirical formula creation, making it less applicable to current PHC pile conditions and hammer.  
To enhance the accuracy of the Gates formula, two empirical approaches were considered. The first approach involved modifying the constants of the Gates formula using 258 PDA data points. This was achieved through data curve fitting, a process that ensures the reliability of the data used for the fitting. Outliers, defined as cases where values calculated by the three pile driving formulas (Hiley, Gates, and Danish) differed by more than 20% from the bearing capacity value obtained through PDA, were first removed. After excluding 16 outlier cases, 242 data points underwent data curve fitting as shown in Figure 3. Figure 3a shows the data fitting for the new formula, while Figure 3b illustrates the errors of the new formula compared to the 242 data points.  
Through iteration, constants were identified to minimize the errors in Figure 3b. The resulting Gates (PHC) formula is as follows.  
\[Q_{u}=7.99\sqrt{(W\cdot H)}log(10N)-608.5 \tag{7}\]  
where \(W\) = weight of hammer (kN), \(H\) = height of hammer (m), \(N\) = number of blows to penetrate 25.4 mm.  
The improved Gates formula, the Gates (PHC) formula was used for a comparison with the PDA results (Figure 4). Figure 4a shows the comparison between the traditional Gates formula and the PDA results, while Figure 4b presents the comparison between the Gates (PHC) formula and PDA results. The Gates (PHC) formula shows a significant reduction in the underestimation of values beyond 3000 kN. However, Gates (PHC) displays a slight underestimation trend beyond 5000 kN, with an increase in variance compared to the Gates.  
Compared to the Gates formula, although the dispersion in Gates (PHC) increased, the accuracy definitely improved.
## 4 Proposed Solution
### Pre-Processing of the RSS Data  
As the measured RSS values are noisy [42], we propose spatial and temporal smoothing RSS filters to mitigate measurement noise and multipath fading effects.
## 3 Proposed Method
### Denoising  
The denoising part of CMD-Net is an encoder-decoder architecture. The encoder of the denoiser represents points involved in a cloud through a PointNet++-based 3 rd set  
Figure 2: CMD-Net: Canonical Mapping (**light green**) and denoising (**dark blue**) parts are encoder-decoder architectures based on PointNet++. Canonical Mapping (auxiliary task) serves as a proxy for denoising (downstream task). See Section 3 for details.  
abstraction (SA) module (see Figure 2). Given the noise-corrupted canonical input cloud \(\mathbf{N}^{can}=\{\mathbf{n}_{1},\mathbf{n}_{2},...,\mathbf{n}_{M}\}\), the module samples a new set of points \(\mathbf{W}^{can}=\{\mathbf{w}_{1},\mathbf{w}_{2},...,\mathbf{w}_{q}\}\) (\(\mathbf{n}_{i},\mathbf{w}_{i}\in\mathbb{R}^{3}\)). Each point \(\mathbf{w}_{i}\) is then represented with a \(z\) dimensional feature vector \(\mathbf{f}_{i}\in\mathbb{R}^{2}\), and the feature set \(\mathbf{F}^{can}\) of the sampled points \(\mathbf{W}^{can}\) is as follows: \(\mathbf{F}^{can}=\{\mathbf{f}_{1},\mathbf{f}_{2},...,\mathbf{f}_{q}\}\). Hence, the input of the 3 rd SA module is \(M\times 3\) dimensional point cloud, and its output is \(q\times c\) dimensional tensor. Note that \(q\) is less than or equal to \(M\) (\(q\leq M\)).  
The decoder of the denoiser processes \(\mathbf{W}^{can}\) and its feature representation \(\mathbf{F}^{can}\) with a shared multi-layer perceptron (MLP) block (shown in Figure 3) to obtain the noise-free canonical point cloud that lies within the shape's convex hull \(\mathbf{H}^{can}_{u}=\{\mathbf{u}_{1},\mathbf{u}_{2},..,\mathbf{u}_{M}\}\) where \(\mathbf{u}_{i}\in\mathbb{R}^{3}\). Inspired by [11], we apply softmax activation at the shared MLP exit to generate probability map \(P_{i}=\{p_{i}^{1},p_{i}^{2},..,p_{i}^{q}\}\) for each point \(\mathbf{u}_{i}\) in \(\mathbf{H}^{can}_{u}\). Each element \(p_{i}^{j}\) in the probability map depicts the probability of the point \(\mathbf{w}_{j}\) being the denoised point \(\mathbf{u}_{i}\)[11]. The set \(P=\{P_{1},P_{2},...,P_{M}\}\) is the probability map for the complete cloud. The denoised cloud \(\mathbf{H}^{can}_{u}\) is then calculated as in the following:  
\[\mathbf{u}_{i}=\sum_{j=1}^{q}\mathbf{w}_{j}p_{i}^{j}\quad\text{and}\quad\sum_{ j=1}^{q}p_{i}^{j}\quad\text{and}\quad i=1,2,...,M. \tag{3}\]