esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Particle Swarm Optimization for High-Dimensional Latin Hypercube Design Problem
## 2 Technical Background
### Particle Swarm Optimization and Comprehensive PSO  
PSO is a population-based, heuristic method to solve high-dimensional optimization with optimal solutions. Since the proposal of PSO [26], most of the variants in PSO have been developed to focus on the optimization problems in different scenarios [30; 33; 34; 38; 44; 45; 46; 47; 48]. After PSO initializes the position of each particle, it will determine the speed of each particle based on the self-cognition and social learning factors of each particle. The position is then updated, and the evaluation value at this new position is assessed. In the whole process, all particles seek the optimal value through social interaction and cooperation [35].  
Figure 1: Two LHD based examples where \(n=10\), \(m=2\).  
The detailed updated processes of speed and position of particles are shown as:  
\[v_{i}^{t+1}=\omega v_{i}^{t}+c_{1}r_{1}^{t}(pBest_{i}^{t}-x_{i}^{t})+c_{2}r_{2}^{t }(gBest^{t}-x_{i}^{t}) \tag{2}\]  
\[x_{i}^{t+1}=x_{i}^{t}+v_{i}^{t+1} \tag{3}\]  
where \(\omega\) is the weight of the inertia, constants \(c_{1}\) and \(c_{2}\) are acceleration coefficients, and \(r_{1}^{t}\) and \(r_{2}^{t}\) are two uniformly distributed random numbers generated independently in the range of [0, 1]. In Equation (2), \(pBest_{i}^{t}\) is the best adaptive position of the \(i\)-th particle found thus far, and \(gBest_{i}^{t}\) is currently the best overall position. \(c_{1}\) represents self-cognition factors, and \(c_{2}\) represents social learning factors. \(c_{1}\) and \(c_{2}\) decide the learning degree in which they balance the individual and global best position, searching for the optimal particle.  
The search environment of LHD is complex, and a large number of the local optimal solutions are assembled in it. Searching the LHD by the PSO can easily cause particles to be attracted to the \(gBest\) zone and fall into the local optimal solution. The current particles may find the corresponding regions of the optimal solution in some dimensions, but they may have poor fitness due to poor performance in other dimensions. This paper proposed an improved hybrid method to use the different dimensions of learning different particles to improve the searching ability of PSO.  
For each dimension of each particle, a random number is generated. If it is greater than the set learning probability \(Pi\), the \(pBest\) of the corresponding dimension will be learned. Otherwise, the \(pBest\) of other particles will be further learned. The method of selecting other particles is to randomly select two particles from the population and choose the current dimension of the particle with better applicability for learning. The speed update equation of the comprehensive learning strategy is shown in Equation (4):  
\[v_{i}^{d}=\omega\times v_{i}^{d}+c\times rand_{i}^{d}\times(pBest_{fi(d)}^{d}- x_{i}^{d}) \tag{4}\]  
and the flow of the comprehensive learning strategy is shown in Figure 2.
# A 7.5-9 GHz GaAs Two-Channel Multi-Function Chip
## 2 Circuit Design
### Serial-to-Parallel Converter Design  
Based on the 0.5 \(\upmu\)m GaAs E/D pHEMT process, which has both enhancement/depletion mode pHEMTs on one substrate, a 27-bit serial-to-parallel converter is designed. The depletion mode (D-mode) pHEMT works as a load to constrain the current, while the enhancement mode (E-mode) pHEMT works as a switch to achieve logic function. In contrast to other logic families (e.g., two-phase dynamic FET logic (TDFL), source-coupled FET logic (SCFL), pseudo-complementary FET logic (PCFL), feedback FET logic (FFL), etc.), the direct-coupled FET logic (DCFL) is the most commonly used logic style and is chosen in this design, which has compact topology and low power dissipation [11].  
The circuit structures of NO and NAND are shown respectively in Figure 5a,b, which consists of 1 \(\times\) 5 \(\upmu\)m size E/D-mode pHEMT. Shift and hold registers are composed of a D-flipflop, which contain 4 NAND blocks and 4 NO blocks, as shown in Figure 5c.  
As shown in Figure 6, the serial-to-parallel converter contains 4 TTL-direct-coupled FET logic (DCFL) level shifters, 27 shift registers, 27 hold registers, and 27 output buffers. The level shifter converts the external TTL signals into internal DCFL signals. The shift registers transform the DATA into binary numbers where the 1-state stands for 0 V while the 0-state is \(-\)3 V. The shift registers shift the 0/1-state to next shifter register when CLK falling edge comes. When the LD signal arrives, hold registers receive the parallel data from the shifter register and transmit to the output buffers, which provide differential voltages to RF FETs. The power consumption of the 27-bit serial-to-parallel converter is less than 70 mW, while \(-\)5 V and 5 V voltages are supplied. The power consumption ratio of level shifts; output buffers, shift registers, and hold registers are around 1:15:35:35. The serial DATA out bit stream (SDout) is available and can be cascaded to a second chip or be used to check the working state of the serial-to-parallel converter. The integration of the serial-to-parallel converter decreases the number of control pads from 54 to 9.
# Visual Detection of Surface Defects Based on Self-Feature Comparison in Robot 3-D Printing
## 1 Introduction
The experiment showed potential application in processing satellite and medical imagery, geophysical data, and in other areas of digital image processing. In the existing works, most researchers focus on detection defects of the upper surface in FDM. Outer surface detection has some advantages in FDM because of its invariant layers. When the gaps of the layers are irregular, defects are likely to appear. In our previous work, a multiview and all-round vision detection system has been presented for the outer surface according to the invariance of the gaps between layers on the outer surface, which can detect the defects on the outer surface online and monitor the 3-D printing process of creating an object [19]. However, it is only applicable to the parts whose outer surface change is gentle. For the steep changed outer surface in the field of view, it can hardly distinguish the defects from its self-feature, such as the eyes on the face, which may be identified as defects. This paper presents a self-feature extraction method of shape defect detection of robot 3-D printing products, which distinguish the defects and their own characteristics by comparing the theoretical projection contours and the experimental contours of products. The outline of this paper is organized as follows. Section 2.1 introduces the whole process of this method. In Section 2.2, the implementation details of feature extraction of the model itself are analyzed in detail. Section 3 proposes an evaluation function to judge the similarity between the self-feature contours and the defects contours.
# A Novel Index for Detecting Bare Coal in Open-Pit Mining Areas Based on Landsat Imagery
## 4 Results and Discussion
### BC Mapping in the Study Area  
The index was constructed based on the bare coal spectra of typical coalfields in the Inner Mongolia Autonomous Region. To assess the applicability of the index on a large scale, we acquired images from July to September using the GEE platform. We then used the BCI to detect bare coal mining areas with bare coal fractions greater than 90% across the entire Inner Mongolia Autonomous Region. The accuracy of the identification for each bare coal patch was then verified through visual interpretation, in conjunction with Google Earth imagery from the same time period. The identification results are shown in Figure 15.  
The total number of patches identified in Inner Mongolia is 466, with 422 of these being bare coal areas, resulting in a correct classification accuracy of 90.56%. Among these, 164 patches are open-pit mining areas, 258 are identified as being coal storage areas or areas used for industry, and 44 are misidentified patches. The misidentified patches are characterized by smaller sizes, more dispersed distribution, and BCI values fluctuating around the minimum threshold. Most of the misclassified patches are gray rooftops, and setting a minimum recognition patch area can help eliminate some of these errors.  
Additionally, we analyzed the coal mining activities across different prefecture-level cities in Inner Mongolia using the detected bare coal information. The statistical results indicate that Ordos has the highest intensity of coal mining, with mining patches accounting for 66.5% of the total. This is followed by Wuhai and the area adjacent to Alxa League,  
Figure 14: Detection results and accuracy of bare coal areas in different months.  
which together account for 13.4% of the mining patches. This information can better serve relevant departments by providing data support for land reclamation, ecological restoration, and regulatory inspections of mining areas.
## 6 Field Tests  
### Overview of the Test  
The ZKdj37 drilling hole is located in the southeastern part of the Tibet Autonomous Region. The designed depth is 152 m, with a hole angle of 90\({}^{\circ}\). The drilling is conducted using a three-stage wellbore structure. The first stage has a borehole diameter of \(\phi\)172, and a \(\phi\)168 casing is inserted. The second stage has a borehole diameter of \(\phi\)150, and a \(\phi\)146 casing is inserted for guidance. The third stage has a borehole diameter of \(\phi\)122. The core recovery rate for the entire hole is required to be greater than 80%. In extremely loose formations, the core recovery rate should not be less than 70%, and the interval without core must not exceed 2 m. The wellbore structure is illustrated in Figure 19.  
Figure 18: Coring Performance of Core Claw.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Closure Pressure** & **Simulated Coring** & **Protective Rate (\%)** \\  & **(MPa)** & **Closure Pressure (MPa)** & **Protective Rate (\%)** \\ \hline standard core claw & 2.21 & 2.22 & 95 \\ core claw A & 3.15 & 3.27 & 88 \\ core claw B & 2.32 & 2.38 & 85 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of Core Claw Compression Test.  
The drilling hole is located in an area where the main lithology consists of loosely fragmented Quaternary overburden. The rock mass is loosely developed, with low cohesion, and its mechanical properties further deteriorate when exposed to water, making it extremely difficult to recover cores. The test borehole encompasses six strata: fine sand, sand gravel, cobbles, medium sand, coarse sand, and silt, which cover almost all lithological types of loose formations. The experimental results have strong representativeness for rope coring in loose strata. During the drilling process using traditional S/55 wireline coring tools with a borehole diameter of \(\phi\)122, difficulties in core recovery and low core recovery rates were encountered, especially when drilling down to 34.83 m. To address these issues, a combination of a hydraulic closed-type coring mechanism and traditional S/5 wireline coring tools was employed for testing. The test section ranged from 9.99 m to 60.65 m, with drilling parameters set as follows: drilling pressure of 3 to 10 KN, rotation speed of 100 to 207 r/min, and pump volume of 50 to 150 L/min. Figure 20 illustrates the core recovery results before and after the change in drilling tools. Rounds 1 to 18 utilized the traditional S/5 drilling tools, while rounds 19 to 36 adopted the newly developed drilling tools, achieving a total footage of 50.66 m. The basic information regarding each drilling round is summarized in Table 6.  
Figure 19: ZKdj37 Wellbore Structure.  
Figure 20: Comparison of Core Recovery Effectiveness.
## 5 Conclusions  
This study demonstrated how, with a dimensionality reduction algorithm, it is possible to describe the underlying phenomena by highlighting spectral bands and relevant time periods within a time-series of imagery.  
This was demonstrated by analysing the footprint of an extreme climatic event and predicting yield losses from a heat wave in 2019 in vineyards in the Languedoc-Roussillon region. In this case, spectral bands around the Red Edge (700 nm) and the SWIR region (1600 nm) were found relevant, as their spectral profile showed a deepening of the time profile during the period of the heat stress, which permitted the band information to be related to reported yield losses.  
The proposed method cannot handle temporal data gaps due to clouds and inconsistent availability of satellite images during the acquisition period. Interpolation of the data to achieve a three-way data structure was mandatory for the development of the methodology. By doing this, the method was able to deal with a time-series data set with different sets of missing values and to identify a noise reduction approach with a 15-day time step, which led to a consideration of only seven dates over the study period.  
Further investigation is needed to (i) complete the results of this study, expanding the analysis to the spatial distribution of the phenomenon in order to determine its regional dynamics and thus the reasons for its main effects, and to (ii) confirm the applicability of the N-PLS method to different time-evolving phenomena.  
Conceptualization, E.L.-F., G.B. and N.D.; formal analysis, E.L.-F. and J.-M.R.; methodology, E.L.-F., B.G. and M.M.; validation, E.L.-F. and F.R.; writing--original draft preparation, E.L.-F.; writing--review and editing, E.L.-F., B.T. and J.T.; visualization, E.L.-F.; supervision, J.-M.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank for the opportunity to have participated in the \"Projet Canicule 2019\". This work was supported by the French National Research Agency under the Investments for the Future Program, referred as ANR-16-CONV-0004.  
The authors declare no conflict of interest.
## 4 Implementation Strategy
### Phase III--Autonomous Execution  
The third phase closes the loop between planning and physical action. The robotic platform autonomously executes validated interventions while the digital twin monitors outcomes and refines its models through feedback. This enables the continuous improvement of decision-making accuracy and operational reliability. Major functionalities include the following:  
* Autonomous task execution, governed by a human-in-the-loop scheduling system, as detailed in Algorithm 1. The user initiates the process via a graphical interface, selecting the target robot, defining the action type (e.g., harvesting), and specifying timing parameters such as date, time, and repetition frequency. The system then assigns a navigation route within the greenhouse and activates execution controls for pausing, stopping, or resuming operations in real time. Throughout task execution, the robot's status is continuously monitored via a feedback loop that checks for failure states or completion events. In the case of anomalies, alerts are triggered to inform the operator, ensuring safe and robust operation. Upon successful task completion, a notification is issued. This logic closes the control loop between virtual planning and physical action, and supports the high-level orchestration of horticultural interventions. While predictive planning, strategy ranking, and decision-making are handled in the preceding layers of the DigiHortiRobot architecture, the task assignment algorithm is dedicated to runtime scheduling and actuator coordination. Its logic is composed of fixed-branch operations and condition checks, resulting in constant-time execution per monitoring cycle. Therefore, the overall computational complexity is constant per loop iteration, and the total runtime is primarily dominated by the physical duration of robotic task execution. As a result, the algorithm imposes minimal computational load on the central node and ensures responsive operation under standard greenhouse operating conditions. * Response monitoring, measuring crop reactions (e.g., growth rate, fruit development) to specific control actions. * Predictive maintenance, achieved through an analysis of sensor logs and robotic performance data to detect early signs of failure or degradation. Figure 6: IsaacSim simulation—hydrogenic greenhouse environment. By integrating perception, simulation, planning, and actuation into a unified feedback system, DigiHortiRobot evolves from a modeling framework into a fully adaptive, autonomous horticultural assistant. The digital twin not only maintains a synchronized representation of the greenhouse and robotic system, but also actively contributes to optimizing the coordinated behavior through data-driven reasoning and continuous interaction with the physical environment.
## 4 Experimentation and Results
### Experiment 2: How Well SAFEPA Performs in the Facial Expression Recognition Task
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Emotions** & **Samples** & \begin{tabular}{c} **Correct** \\ **Prediction** \\ \end{tabular} & \
\begin{tabular}{c} **Incorrect** \\ **Prediction** \\ \end{tabular} &
\begin{tabular}{c} **Percentage** \\ **\%** \\ \end{tabular} \\ \hline Afraid & 40 & 29 & 11 & 72.50 \\ Angry & 36 & 31 & 5 & 86.11 \\ Disgusted & 52 & 50 & 2 & 96.15 \\ Happy & 41 & 41 & 0 & 100.0 \\ Neutral & 46 & 42 & 4 & 91.30 \\ Sad & 39 & 21 & 18 & 53.85 \\ Surprised & 37 & 31 & 6 & 83.78 \\ Total & 291 & 245 & 46 & 84.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Prediction detail of SAFEPA model for 7 emotions on frontal face samples of KDEF dataset. select the most informative features from the extracted geometric and color-based features, and then modeled inter-subject variability using a generalized linear mixed effects probit model. To evaluate their approach, the authors extracted a set of features from the facial regions of each video frame and trained a regression model to predict pain intensity [38]. They reported an accuracy of 27.13% based on 10-fold cross-validation [38]. However, the SAFEPA's accuracy is slightly lower than the accuracy of 37.42% reported in [39]. In [39], [PERSON] and his colleagues proposed a face analysis CNN combined with an LSTM network for pain assessment. They trained and tested their model on selected data belonging to 30 participants. They do not report which subset of the dataset was used for their result. Table 6 presents the performance of the SAFEPA model on the entire BioVid dataset, presenting accuracy and processing time metrics alongside accuracy scores obtained from three other studies (including each method's main characteristics) conducted on the entire dataset or a subset of it. Notably, previous studies did not take processing time into account and in contrast with results in the SAFEPA used various validation methods instead of testing using the entire dataset for testing. The average processing time for the SAFEPA system is 17.82 s per video. It is important to note that each video in the BioVid dataset lasts for 5 s. Thus, the SAFEPA can efficiently process each video in the dataset within a reasonable time, indicating its potential for real-world application.
### Designing of Control System from Continuous Domain to Networked Control Domain
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Networked System** & **Conventional System** \\ \hline Vital Function & Control of physical equipment & Data processing and transfer \\ \hline \multirow{2}{*}{Applications} & Manufacturing, processing and utility distribution & Corporate and home environments \\ \hline \multirow{2}{*}{Hierarchical Process} & Deep, functionally separated hierarchies & Shallow, integrated hierarchies \\ \hline Failure Mode and its effect & High & Low \\ \hline Reliability Requirement & High & Moderate \\ \hline Deterministic & High & Low \\ \hline \multirow{2}{*}{Data Composition} & Small packets of periodic and aperiodic traffic & Large, aperiodic packets \\ \hline Reliability & Required & Not required \\ \hline \multirow{3}{*}{Operating Environment} & Hostile conditions, often featuring high levels of dust, heat, and vibration & Clean environments, often specifically intended for sensitive equipment \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \end{tabular}
\end{table} 
Table 1: Differences between networked and conventional control systems. Figure 2: Trade-off between control performance and the sampling rate.
## 4 Experiments and Results  
### Implementation Details  
The experiments were conducted on the PyTorch platform (version 2.3), utilizing a Tesla T4 GPU with 12 GB of VRAM. The specific environment included CUDA version 12.1 and CuDNN version 8.9.6. The deep learning models were trained over 50 epochs with a fixed learning rate of 1 \(\times\) 10\({}^{-4}\), which were selected based on preliminary experiments and validation performance. The error for each epoch was computed using the cross-entropy loss function and optimized using the Adam optimizer [59]. The input patch size of 16 \(\times\) 16 was determined through experimental analysis (Section 4.8).  
A stratified K-fold sampling technique was employed to validate the performance of the classification methods. In this approach, K = 3 was considered to provide a balance between training and testing datasets. Each of the three folds was designed to contain an approximately equal number of samples, ensuring a nearly uniform distribution of samples across each class within every fold.  
To achieve this, we spatially distributed the samples such that the first fold primarily included samples from the northern part of the study area, the second fold included samples from the middle, and the third fold included samples from the southern part. This spatial distribution reduces the spatial dependency of samples between folds, thus reducing the spatial dependency of the train and test samples, leading to a more rigorous validation of model performance.  
For thorough evaluation, we repeated the K-fold procedure five times for each model, resulting in a total of 15 training iterations. In each training iteration, one fold (approximately 33% of the data) was left out for testing, while the remaining two folds (approximately 67% of the data) were used for training. This process ensured that each sample was used for both training and testing, enhancing the robustness of the validation. Furthermore, within the training samples, 10,000 samples (approximately 5%) were randomly selected for validation, employing a stratified random sampling technique. This ensured that the validation set was also representative of the overall class distribution, providing a reliable measure of model performance during training.  
This stratified K-fold validation approach ensures a well-balanced distribution of training and testing samples, enhancing the reliability of our assessment. By spatially and statistically distributing the samples across the folds, land covers, and iterations, we can more rigorously assess the generalizability and robustness of our classification methods.