esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation Study on the Installation of Helical Anchors in Sandy Soil Using SPH-FEM
## 4 Conclusions  
This paper is the first to employ the SPH-FEM to simulate the installation and extraction process of helical anchors in sandy soil foundations, analyzing the impact of the advancement ratio on the installation force, installation torque, uplift capacity, and torque correlation coefficient. The following principal conclusions have been reached:  
1. The \(AR\) significantly influences the installation force and torque of the helical anchor. Compared to the \"pitch-matched\" (\(AR=1.0\)) installation method, employing a high \(AR\) (\(AR=1.2\), 1.5) results in a significant increase in both installation force and torque, with the force increase being more pronounced than that of the torque. Conversely,  
Figure 14: Comparison of calculated torque capacity correlation factor \(K_{t}\) at various ARs with [PERSON] (2010) [18] and [PERSON]’s (2009) [6] results.  
using a low \(AR\) (\(AR\) = 0.8) for installation can significantly reduce the installation force, although the change in installation torque is relatively minor.
2. The ultimate uplift capacity of the helical anchor foundation is linearly and negatively correlated with the \(AR\). At a low \(AR\) (\(AR\) = 0.8), the foundation achieves its maximum ultimate uplift capacity, which is approximately 4% higher than that obtained with the \"pitch-matched\" installation (\(AR\) = 1.0).
3. The \(AR\) affects the torque correlation coefficient \(K_{\text{t}}\). The \(K_{\text{t}}\) value peaks when using the \"pitch-matched\" installation (\(AR\) = 1.0). Utilizing the \(K_{\text{t}}\) value determined at this \(AR\) to predict the ultimate uplift capacity of the helical anchor under other \(AR\) will likely result in an overestimation.  
Conceptualization, H.Z. and H.H.; methodology and software, H.H.; validation, H.H. and C.Y.; investigation and data curation, H.H.; writing--original draft preparation, H.H.; writing--review and editing, C.Y. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 52130905 and 52079002.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Survey of Smart Parking Systems
## 3 Types of SPS Reported in the Literature
### Other Categories  
* DS: This category of studies focuses only on information concerning VDT and parking slots that can used in an SPS, for example, measuring the impact that web services have on WSN when they are used for estimating the free size of parking places in smart cities [57], proposing smart parking slot occupation monitoring solutions based on the new tendency of sensors [58], or using image and video technologies to identify vehicles in a parking slot [59].
* APS: This category focuses on the research on algorithms for SPS in terms of routing vehicles, information on the current state of parking, or solving potential problems. For example, a study for parking sensor networks focused particularly on delay constraints and energy efficiency issues from a network traffic viewpoint [60], where two types of traffic models performed with different rate parameters. Another task, for example, was an analysis and proposal of an algorithm to select the best route for a vehicle based on uncertain influencing factors. Routes were selected by taking into account the uncertainty of the road conditions information, the factors influencing it, and their uncertainty. In addition, the algorithm proposed using decision rules based on the user's attitude or preference [61].
## 2 Materials and Methods
### Acoustic Equipment System and Data Analysis Method  
The acoustic system used for the survey was a split-beam scientific echosounder (EK60, Kongsberg Simrad, Norway), and acoustic data were collected for frequencies 38 and 120 kHz. The acoustic data were stored by continuously receiving position information from DGPS (SPR-1400, Samyoung, Seongnam, Korea). The acoustic data collected in situ were subsequently processed in the laboratory using acoustic data analysis software (Echoview V 8.0, Echoview Software Pty Ltd., Hobart, Australia). The flow diagram of data processing for the fish species identification is shown in Figure 2. As shown in the figure, after filtering the noise around the sea surface, sea bottom, and other places, the integration section is set to compose a matrix for each frequency, thus creating a new echogram. In this study, a cell size of 5 ping \(\times\) 5 m (width \(\times\) height) was used to examine the dB-difference. When the dB-difference between fish species becomes clear, the range of values is set, the data range bitmap is created, and the mask is created with an echo that matches the size of the cell at 38 kHz. We then extract echoes consistent with the 38 kHz echo, which is removed noise, by dividing them again into ping intervals. Through the above methods, fish species can be identified by separating the echoes of fish suitable for each characteristic.  
Using the above method and procedure, each species can be identified by separating the echoes of the respective fish species suitable for the characteristics [10].
# Fuzzy Risk Evaluation and Collision Avoidance Control of Unmanned Surface Vessels
## 2 Actions for Collision Avoidance
### Methods of Collision Avoidance  
A sailing vessel can monitor the relative information of other surrounding vessels, such as the location of target ships or obstacles, using the automatic identification system (AIS) and automatic radar plotting aids (ARPA), respectively.  
After acquiring all the data, the sailors of the vessels will make their decision to avoid collision. There are two main methods to avoid collisions:  
1. Alteration of course;
2. Reduction of speed.  
If there is sufficient sea-room, alteration of course alone is the most effective action to take [11].
# A Simulation Study on Pressure Control in Oil Well Drilling Using Gain-Scheduled PID Controllers
## 4 Results  
The simulation results for the IMC methodology with two degrees of freedom (IMC+2 DOF) at different depths (500, 1000, 2000, 3000, 4000, and 5000 m) are presented for analysis purposes. These results detail the response of the IMC+2 DOF-controlled system for various robustness levels (\(M\)s) and depths, using a unit step input at the start, followed by a disturbance step after 100 s, as illustrated in Table 3 and Figure 5. These results present  
Figure 4: Optimal parameters of the two-degrees-of-freedom IMC controller for four levels of robustness [29].  
parameters related to the performance of the response, including overshoot, rise time, settling time, and the integral absolute error in the presence of disturbance (IAE-d), at each operating point [23].  
The main feature of the IMC+2 DOF methodology was that the transient response overshoot was zero, due to the action of the reference filter \(F(s)\), as observed in these results. Unlike other traditional IMC controllers, the system's robustness was inversely proportional to the disturbance rejection performance index, meaning it showed better transient response performance with higher disturbance rejection. This behavior can be explained by the rise time parameter in this procedure being directly proportional to the system's robustness, a trend also present in other controllers that lack a reference filter, resulting in damped oscillations in the transient response, extending the settling time [34]. Therefore, this behavior did not manifest with the IMC+2 DOF controller because the filter caused the response to become over-damped with a significantly faster settling time.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Depth [m]**} & \multirow{2}{*}{**Robustness**} & \multirow{2}{*}{**Disturbance**} & \multicolumn{2}{c}{**Transient Response**} & \multicolumn{2}{c}{**Controller Parameters**} \\ \cline{2-7}  & & \(M\)**s** & **IAE-d** & **Rise Time [s]** & **Settling Time [s]** & \(k_{p}\) & \(t_{i}\) & \(t_{d}\) \\ \hline \multirow{4}{*}{500} & 2 & 83.788 & 6.2954 & 13.6552 & 0.0
## Appendix A Surrogate Models
### Gbdtr  
Boosting methods are powerful techniques in which the final \"strong\" regressor model is based on an iteratively formed ensemble of \"weak\" base regressor models [41]. The main idea behind boosting is to sequentially add new models to the ensemble, iteratively refining the output. In GBDTR models, boosting is applied to arbitrary differentiable loss functions. In general, GBDTR models are additive models, where the samples are modified so that the labels are set to the negative gradient, while the distribution is held constant [42].  
The additive method of GBDTR is the following:  
\[\hat{y_{i}}=F_{\text{G}}(x_{i})=\sum_{g=1}^{\text{G}}h_{g}(x_{i}) \tag{11}\]  
where \(\hat{y_{i}}\) is the prediction for a given input \(x_{i}\), and \(h_{g}\) are the fitted base tree regressors. The constant \(G\) is the number of base tree regressors. The GBDTR algorithm is greedy, where a newly added tree regressor \(h_{g}\) is fitted to minimize the loss \(L_{g}\) of the resulting ensemble \(F_{g}=F_{g-1}+h_{g}\), i.e.,  
\[h_{g}=arg\min_{h}L_{g}=arg\min_{h}\sum_{i=1}^{n}l(y_{i},F_{g-1}(x_{i})+h(x_{i})) \tag{12}\]  
Here, \(l(y_{i},F(x_{i}))\) is defined by the loss parameters, and \(h(x_{i})\) is the candidate base regressor. With the utilization of a first-order Taylor approximation:  
\[l(z)\approx l(a)+(z-a)\frac{\partial l(a)}{\partial a} \tag{13}\]  
where \(z\) corresponds to \(F_{g-1}(x_{i})+h_{g}(x_{i})\) and \(a\) corresponds to \(F_{g-1}(x_{i})\), we can approximate the value of \(l\) with the following:  
If the function \(f(x)\) under consideration is modeled by a Gaussian process, i.e., if \(f(x)\sim\mathcal{GP}(\mu(x),K(x,x^{\prime}))\), then we have the following  
\[\mathbb{E}[f(x)] =\mu(x) \tag{11}\] \[\mathbb{E}[(f(x)-\mu(x))(f(x^{\prime})-\mu(x^{\prime}))] =K(x,x^{\prime}) \tag{12}\]  
for all \(x\) and \(x^{\prime}\). Thus, we can define the Gaussian process as the following:  
\[f(x)\sim\mathcal{N}(\mu(x),K(x,x)) \tag{13}\]  
We use the notation that matrix \(D=(X_{D},Y_{D})\) contains the training data with input data matrix \(X_{D}=(x_{1},\ldots,x_{n})\) and output data matrix \(Y_{D}=(y_{1},\ldots,y_{n})\), and test data matrix \(T=(X_{T},Y_{T})\) contains the test data with \(X_{T}=(x_{n+1},\ldots,x_{n+m})\) as input and \(Y_{T}=(y_{n+1},\ldots,y_{n+m})\) as output. We can define that they are jointly Gaussian and zero mean with consideration of the prior distribution:  
\[\begin{bmatrix}Y_{D}\\ Y_{T}\end{bmatrix}\sim\mathcal{N}(0,\begin{bmatrix}K(X_{D},X_{D}))&K(X_{D},X_ {T}))\\ K(X_{T},X_{D})&K(X_{T},X_{T}))\end{bmatrix} \tag{14}\]  
The Gaussian process makes a prediction \(Y_{T}\) for \(X_{T}\) in a probabilistic way, where, as stated before, the posterior distribution can be fully described by the mean and the covariance.  
\[\begin{split} Y_{T}|X_{T},X_{D},Y_{D}&\sim\mathcal{N}(K(X_{T},X_{D})K(X_{D},X_{D})^{-1}Y_{D},\\ K(X_{T},X_{T})-K(X_{T},X_{D})K(X_{D},X_{D})^{-1}K(X_{D},X_{T})) \end{split} \tag{15}\]
# Acoustic TDOA Measurement and Accurate Indoor Positioning for Smartphone
## 2 Basic Acoustic Positioning System  
The acoustic positioning system is composed of a wireless scheduler, acoustic nodes as the signal transmitters and a smartphone as the signal receiver with the localization to be estimated. The wireless scheduler is mainly used to synchronize all acoustic nodes on a unified time axis. The acoustic nodes mainly transmit signals, and the microphone in the smartphone receives the signals transmitted by the acoustic nodes. The hardware architecture of the acoustic node and scheduler is shown in Figures 1 and 2. The acoustic node is composed of a wireless receiver, FPGA, analog-to-digital converter and speaker. The scheduler is mainly composed of FPGA, wireless transmitter, and key module. When the wireless receiver module receives the signaling sent by the scheduler, the FPGA in the acoustic node starts the interrupt arbitration mechanism to drive the speaker to send the signal. The benefits of our system are the following: (1) the acoustic nodes can be easily synchronized within 0.5 milli-second level with the low-cost wireless scheduler. Such synchronization is supportive to achieve the positioning of sub-meter accuracy in the acoustic positioning system. (2) As the acoustic signals are in essence transmitted by broadcasting, there is no limit on the capacity of the receivers, which is beneficial for massive amount of users in large-scale positioning scenarios.  
To describe the hardware in more detail, the DAC module is used to convert the digital signal to the output of the analog acoustic signals and drive the speaker into vibrations of the membrane, which are transmitted through the medium of air. The key module is reserved to start or stop the system. The FPGA in the acoustic node has two functions: (1) storing the signal without losing data. (2) controlling and managing different modules.
## 3 Theoretical System Construction
### Application of Grounded Theory in Data Classification and Structure Construction
#### 3.2.1 Primary Coding: Extraction of Original Concepts  
The primary coding stage, also known as the open coding stage in grounded theory, is the initial data classification stage and serves as the foundation for revealing the visual expression and cultural significance of the phoenix pattern. This study systematically constructs the corpus and combines the text description with the image entity for multidimensional analysis, one by one, to extract the original concepts with analytical potential. According to Figure 4, taking the sample number \"SQ001\" as an example, the phrases in its description text, such as \"two birds facing the sun composition\", \"feathers spread\", \"symmetric central axis\", \"upper sun wheel\", and so on -- that is, the original concept units in the open coding stage-- are extracted one by one and numbered and recorded. Ultimately, this study extracted more than 2600 valid concept items from 426 phoenix pattern samples, forming the \"original concept pool\" of phoenix pattern image data. At the same time, in the first-level coding table structure, each concept item is accompanied by preliminary cultural semantic attribution information and image category labels, forming an original information unit of \"keyword + primary coding field\". For example, in the description of sample JXD012, \"Copper-plated gold dragon and phoenix double-wing pavilion number  
Figure 3: Flow chart of phoenix pattern research based on grounded theory.  
Figure 2: Schematic diagram of raw data collection of phoenix patterns.  
bronze plaque\", the extracted keywords \"copper\", \"gold plating\", \"double-wing pavilion number\", \"Republic of China period\", \"practical tools\", and so on were initially marked as PP category (metal material), PC category (pattern structure), AS category (usage context), and RF category (era cultural attribution).  
In short, in the first-level coding stage, its core value lies not only in making implicit semantic elements in massive text-image data explicit and labeled, but also in establishing a cognitive pathway from raw linguistic inputs to conceptual recognition and semantic categorization of phoenix pattern visual information. This move aims to activate the quantitative potential of traditional pattern images and lay a semantic foundation for subsequent core category clustering and theoretical model construction.
## Materials and Methods
### LST, NDVI Climate Data Records, and SPEI Dataset
In order for the datasets to be consistent, the temporal coverage is truncated to span the period between January 1991 and December 2009 for LST and NDVI datasets, and the period between December 1990 and November 2009 for the SPEI dataset (see Section 2.2 for details about this 1-month lag). Furthermore, PU LST is aggregated into monthly diurnal cycle composites to match the CM-SAF LST datasets. Monthly means of the three LST datasets are then estimated by averaging the month diurnal cycle. NDVI bi-monthly values are averaged to get monthly values. NDVI and CM-SAF LSTs are interpolated to a 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) spatial grid. Finally, SPEI time-scales of 1-, 3-, 6-, 9-, and 12-months are chosen. Linear trends on NDVI associated to the region's greening or browning [60; 61], and those associated to global warming on LST, are removed by applying a linear regression algorithm. Furthermore, the biome classification is also re-gridded to a 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) spatial resolution by choosing the dominant biome. In summary, this study considers four monthly Climate Data Records (three LST and one NDVI) and one SPEI dataset collocated over a grid of 0.5\({}^{\circ}\) and spanning a period of 19 years.
# Visual Image Dehazing Using Polarimetric Atmospheric Light Estimation
## 5 Results and Discussion
### Experimental Results
Image entropy describes the quantity of information in the image, local contrast describes the algorithm's capacity to improve the distinguishability of object edges, and SSIM quantifies the retention of structural information in images before and after dehazing. Table 2 displays the evaluation results for each subfigure in Figure 16.  
For each measure, we use bold type to indicate the highest value and underlining to represent the second-highest value in Table 2. It can be observed that our approach obtains the maximum entropy value across all scenes. RTVP has very high contrast in Scene 1; however, this is due to overexposure in the sky area. [PERSON]'s approach provides the strongest contrast in Scenes 2 and 3, which is related to its extreme color distortion. The approach we propose preserves acceptable brightness and color accuracy while maintaining a high level of contrast. In terms of structural similarity, GLPF performs best because its dehazing impact is less significant, resulting in structural similarity that is closest to the original picture. However, our approach preserves the structure of the original image while producing a good dehazing effect.
## 5 Benchmarks for Federated Learning
Benchmark results are reported, along with validation of the conversion procedure by comparison of classification results on converted NIST and MNIST digits. * FedEval: [PERSON], et al, [86] suggested a thorough approach to FL system evaluation in their works. Particularly, they first offer the ACTPR model, which identifies five metrics--accuracy, communication, time efficiency, privacy, and robustness--that cannot be disregarded in the FL assessment. Then, using FedEval, a benchmarking system they developed and put into use, it is possible to compare and evaluate previous works in a systematic manner while maintaining a constant experimental environment. The authors next present a thorough benchmarking analysis between FedSGD and FedAvg, the two most used FL methods. According to the benchmarking results, FedSGD and FedAvg both have benefits and drawbacks when using the ACTPR model. For instance, the non-IID data issue has little to no effect on FedSGD, while in their studies, FedAvg has an accuracy loss of up to 9%. However, when it comes to communication and time consumption, FedAvg outperforms FedSGD. Finally, they uncover a series of key findings that will be highly beneficial for researchers working in the FL region. * OARF: An Open Application Repository for FL (OARF) is presented by [PERSON], et al, [87]. It is a benchmark set for federated ML systems. OARF simulates more realistic application situations. The benchmark suite is heterogeneous in terms of data quantity, dispersion, feature distribution, and learning task difficulty, as observed by their categorization. The thorough analyses with reference implementations highlight potential areas for future FL system development. The key components of FL, such as model correctness, communication cost, throughput, and convergence time, have been constructed as reference implementations and reviewed. They made several intriguing discoveries as a result of these tests, such as the fact that FL may significantly boost end-to-end throughput. From the papers we gathered, there have been some separated AQI datasets, as shown at the end of Table 1. To the best of our knowledge, there is presently no standard dataset designed expressly for AQI analysis, even though there are several benchmarks for FL in general. Future studies in this area must consider this problem.
## 4 Conclusions  
In this paper, a novel MRA-based MPPT control approach is developed using a low-cost microcontroller to considerably reuse the waste heat that is produced during the industrial process. A detailed modeling of the TEG system, MRA algorithm. and operating conditions was performed. The results are compared to the well-established stochastic algorithms GWO, WOA, and PSO. The MRA is compared to other meta-heuristic methods in terms of the tracking time, energy harvest, % age power tracking efficiency, MPPT rating, and hardware experimental emulator setup. The MRA demonstrates superior performance. The MRA settle at the GM with improved efficiency of up to 99.95% and less than 38-70% faster tracking times. To verify the MPPT control's capacity to converge and its resilience, the MRA is put through a variety of tests. The MRA is evaluated on a low-cost TEG emulator and implemented on a low-cost microcontroller, demonstrating the higher performance of the suggested MPPT control approach. This provides enough evidence for a practical application of the MRA as a cost effective MPPT control of a centralized TEG system in an industrial environment.  
* The MRA-based MPPT technique tracks the GMPP under NUTD with 99.96% efficiency within 220 ms.
* The strong capability to differentiate between the LMPP and GMPP with an improvement of up to 70% lesser tracking time.
* The experimental verification is achieved on a low-cost microcontroller, thus facilitating large-scale adoption in existing and new MPPT applications.  
In future, large-scale TEG shall be studied with an AC grid using inverter topologies to gauge the impact of the TEG system as a heat recovery unit in the rotary kiln of a 10,000 TPD cement plant.  
Figure 15: PSO, GWO, and MRA hardware results.  
**Author Contributions:** Data curation, Resources, Writing--original draft, Visualization, M.H.Z.; Formal analysis, Methodology, Writing--review & editing, M.A.H.; Investigation, M.M.; Methodology, Validation, N.M.K.; Investigation, Writing--original draft, S.K.R.M.; Software, Resources, Visualization, M.K.K.; Project administration, Funding acquisition, Supervision, N.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not Applicable.  
**Informed Consent Statement:** Not Applicable.  
**Data Availability Statement:** No data is used in this study.  
**Conflicts of Interest:** The Authors declare no conflict of interest.
# Research on a Novel Nanosecond Marx Generator and Its Efficiency Analysis
## 2 The Proposed Nanosecond Pulse Generator  
The new topology of the nanosecond Marx generator is proposed, as shown in Figure 2. It consists of the series-resonant charging power supply and a 3 \(\times\) 5 stage Marx generator based on avalanche BJTs.  
Figure 1: The traditional N \(\times\) M stage Marx generator based on avalanche BJTs.  
Figure 2: The schematic of the proposed nanosecond Marx generator (circuit I).