esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability Conditions in Lignite Open Pits from Romania, Case Study: Olteu Open Pit
## 3 Results and Discussion
### Determination of the Stable Geometry of the Working Front and Lateral Slopes
#### 3.2.2 Stable Geometry of Lateral Slopes  
For this case, section P1 was taken into account, where the thickness of the rock package between the lignite layer no. I bed (elevations +345 to +334 m above sea level) and the land surface (elevations +350 to 380 m above sea level) records maximum values, respectively, 41-46 m at a distance of 70-90 m from the outcrop area of the lignite layers.  
In these conditions, it is necessary to adopt a technology of excavation in steps and sub-steps to ensure stability conditions.  
The first step (from the base of the open pit) up to +370 m surface level should be of maximum 26 m (crossing the lignite layers, the sandy marl between and above the lignite layers and the yellow-brown clay at the top on maximum thickness of 7.6 m).  
The second step, up to +380 m surface level, will be excavated exclusively in the yellow-brown clay layer, and will have a maximum height of 10 m.  
When the yellow-brown clay layer exceeds 10 m thickness (above the elevation of +380 m surface level), it is necessary to divide the second (upper) step into sub-steps so as not to exceed the maximum allowed height of 10 m. The upper sub-step will have a variable thickness, between 3 and 10 m, as the excavation advances toward the planned configuration (in the final stage, the slope will be divided in two steps, one of 26 m (the lower one) in height and one of 20 m (the upper one) divided in two sub steps of 10 m in height each).  
a. Lower step with lignite layers (Figure 12):  
* H = 26 m;
* \(\alpha=55^{\circ}\);
* Yellow-brown clay layer thickness: 7.6 m;
* F\({}_{\rm 81}\) = 1.25.
* b. Upper step and sub-steps in yellow-brown clay (Figure 13):
* Height of sub-steps H = 10 m;
* The height of the sub-step system (upper step) H\({}_{\rm gen}\) = 20 m;
* The width of the intermediate sperm B = 60 m;
* The width of the safety sperm b = 30 m;
* Inclination of sub-steps slopes \(\alpha_{\rm ind}\) = 45\({}^{\circ}\);
* General inclination of slope \(\alpha_{\rm gen}\) = 25\({}^{\circ}\);
* The stability factor for sub-steps F\({}_{\rm 81 ind}\) = 1.15;
* The stability factor for general slope (sub-step system) F\({}_{\rm 81 gen}\) = 1.26.  
Figure 11: Section L1–Stability analysis for the proposed design.
# WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction
## 3 Theoretical Frameworks
### XGBoost  
XGBoost, on the other hand, is an optimized gradient-boosting algorithm. It uses a gradient-boosting framework to train multiple decision trees sequentially, each of which corrects the errors of its predecessors. XGBoost is renowned for its efficiency, scalability, and robustness, making it an excellent choice for many machine-learning tasks. What sets XGBoost apart from other gradient-boosting algorithms is its adoption of a more regularized model formulation, which effectively mitigates the risk of overfitting and, in turn, contributes to superior performance [21]. This entails the learning of individual functions, each structured as a tree with associated leaf scores. Given a dataset comprising m samples and n-features (\(D=(X_{j},y_{j})\) where \(|D|=m,X_{j}\in R_{n},y_{j}\in R\)), a tree ensemble model leverages a collection of L additive functions to make predictions, as elucidated in Equation (4).  
\[\hat{y}_{j}=\varphi(Xj)=\Sigma h_{l}(X_{j}),hl\in H \tag{4}\]  
where \(h(a)\) represents base learners.  
In this context, \(H\) represents the space of regression trees, denoted as \(H=h(X)=w_{q}(X)\) (with \(q:R^{n}\to U\) and \(w\in R^{U}\)). Here, \(q\) characterizes the unique structure of each tree, mapping a sample to its respective leaf index, with \(U\) representing the total number of leaves within the tree. It is worth noting that each \(hl\) corresponds to an independent structure defined by tree \(q\), and the leaf weights \(w\).
###### Abstract  
The expected benefit of composite adsorbents generally consists in their growing applicability, thanks to the combination of the adsorption properties of individual components. Composite adsorbents were prepared as mixtures of kaolin from a Czech deposit (kaolin Sedlec, SK) and two fly ashes (FAs) from a fluidised bed boiler in Czech operations differing in fuel type. The mixtures of SK with FA in a ratio of 50:50% wt. were prepared at 20 \({}^{\circ}\)C, 65 \({}^{\circ}\)C, and 110 \({}^{\circ}\)C in an autoclave. The source materials and composite adsorbents were tested for the adsorption of lead as Pb\({}^{2+}\), and arsenic as AsO\({}_{4}\)\({}^{3-}\) from model solutions in laboratory conditions. The adsorption of Pb\({}^{2+}\) proceeded quantitatively on the source materials except SK, and on both the composites, with an adsorption yield of \(>\)97% and a low adsorbent consumption (\(\sim\)2 g.L\({}^{-1}\)). The AsO\({}_{4}\)\({}^{3-}\) adsorption proceeded selectively only on both FAs, with an adsorption yield of \(>\)97% again. The adsorption of AsO\({}_{4}\)\({}^{3-}\) on the composite adsorbents achieved a worse yield (\(<\)80%), with about ten times more adsorbent consumption (\(\sim\)20 g.L\({}^{-1}\)). An increased preparation temperature did not affect the Pb\({}^{2+}\) adsorption at all, but it reduced the efficiency of AsO\({}_{4}\)\({}^{3-}\) adsorption by up to 30%. The SK-FA composites proved to have promising properties, mostly as cation-active adsorbents.  
kaolin; fly ash; composite adsorbent; lead; arsenic +
Footnote †: journal: Applied Sciences
# Traceability and Spectral Analysis of Transmission Error in Rotary Vector Planetary Gear Reducers
## 4 Transmission Error Spectrum Experiment
### Test Prototypes  
The experiment selected two models of RV reducers, 190 BX-121-RVE and 65 BX-121-RVE, with all the prototypes assembled for the first time using newly processed components. The actual prototypes are depicted in Figure 5, and their main parameters are listed in Table 5. Based on Table 5 and Equation (14), the fundamental frequencies are determined as follows: \(f_{\mathrm{sun}}\) = 0.5 Hz, \(f_{\mathrm{pla}}\) = 0.1612 Hz, and \(f_{\mathrm{cyc}}\) = 0.004132 Hz. From this, the theoretical kinematic error frequencies for the test prototypes are determined as shown in Table 6.  
Figure 4: ZFUN ZRT-II robot reducer performance testing system.  
Figure 5: Tested RV reducer: (**a**) 190 BX-RVE; (**b**) 65 BX-RVE.  
The sole distinction between the 190 BX-121-RVE and 65 BX-121-RVE models lies in the characteristic frequency of the sun gear, which is attributed to the differing number of planet gears; all the other characteristic frequencies remain identical. The laboratory is maintained at a constant temperature of 25 \({}^{\circ}\)C. Before commencing any tests, the prototype is operated continuously on the test bench at an input speed of 2000 rpm for 30 min without any load to ensure thermal equilibrium between the prototype and the environment. Subsequently, TE testing is conducted immediately without load.
# Using Optimization Techniques in Grammatical Evolution
## 3 Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **NNC-S (\(R=2\))** & **NNC-S (\(R=5\))** & **NNC-S (\(R=10\))** & **NNC-S (\(R=20\))** \\ \hline MAMMOGRAPHIC & 17.18\% & 16.78\% & 16.58\% & 16.62\% \\ \hline PARKINSONS & 12.68\% & 12.16\% & 12.26\% & 11.95\% \\ \hline PIMA & 25.72\% & 25.11\% & 25.26\% & 26.33\% \\ \hline POPFAILURES & 5.87\% & 5.72\% & 5.52\% & 5.58\% \\ \hline REGIONS2 & 23.55\% & 24.04\% & 24.47\% & 24.08\% \\ \hline SAHEART & 29.48\% & 28.96\% & 29.52\% & 29.24\% \\ \hline SEGMENT & 40.32\% & 40.23\% & 39.38\% & 40.82\% \\ \hline STUDENT & 4.18\% & 4.50\% & 4.52\% & 4.78\% \\ \hline TRANSFUSION & 24.60\% & 24.12\% & 24.33\% & 24.36\% \\ \hline WDBC & 6.09\% & 5.68\% & 5.59\% & 5.46\% \\ \hline WINE & 11.00\% & 10.30\% & 11.47\% & 9.41\% \\ \hline Z\_F\_S & 8.33\% & 8.30\% & 7.93\% & 8.50\% \\ \hline Z\_O\_N\_F\_S & 41.70\% & 43.42\% & 40.42\% & 41.44\% \\ \hline ZON\_NF\_S & 7.58\% & 7.72\% & 6.60\% & 7.10\% \\ \hline ZONF\_S & 2.50\% & 2.54\% & 2.36\% & 3.00\% \\ \hline ZOO & 6.80\% & 6.30\% & 7.20\% & 6.10\% \\ \hline
**AVERAGE** & **18.61\%** & **18.52\%** & **18.43\%** & **18.28\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._  
Figure 7: Time comparison between the initial method and the proposed modifications. Once again, the present method outperforms the original artificial neural network construction technique and the performance appears to be stable as the critical parameter \(g\) grows.
# Acoustic Backscatter Communication and Power Transfer for Batterless Wireless Sensors
## 1 Introduction
However, additional challenges appear when transferring the concepts to true wireless sensor nodes with a strictly limited energy budget, such as Sampling Frequency Error (SFE) between the reader and tag. Especially in ultra-low-power devices, we must expect inaccurate oscillators and implement countermeasures. Lastly, the existing investigation of wireless power transfer and active communication only covers short distances between the reader and tag--often less than a meter--and only investigates simple, isolated plates or beams. In real applications, we expect larger and more complex structures. This work bridges the gap between the lab and the real world. We demonstrate how backscatter communication and power transfer can be combined to create a fully batteryless miniaturized tag. Therefore, we synthesize approaches from acoustic power transfer and backscatter communication from other domains and focus primarily on practicability. We design low-power hardware from commercially available off-the-shelf (COTS) components instead of using high-cost and high-power signal generators and oscilloscopes. We imple  
Figure 1: Concept of a potential application, e.g., an acoustic pipeline monitoring network. A reader powers multiple embedded sensor nodes using acoustic-guided waves. ment a harvester and modulator module working on the same transducer to enable cheap and small tag devices. Furthermore, we employ Digital Phase Locked Loop (DPLL)s to cope with inaccurate and unstable low-power oscillators. More specifically, we detect the time-varying phase offsets and track the sample timing using a derivative timing detector. In our evaluation, we achieve combined power transfer and communication over up to three meters and uplink data rates of 2 kbit s\({}^{-1}\). The remainder of this article is organized as follows: Section 2 gives an overview of existing approaches to acoustic wireless power transfer and communication through metals, after which we explain our contribution to the field in more detail. Backscatter communication and the design of our prototype are explained in Section 3. The evaluation in Section 4.1 first evaluates the potential of acoustic power transfer through lamb waves and then moves on to the efficiency of the presented harvester. Section 4.2 compares the ultra-low-power oscillator with a more accurate external one. We then investigate the backscatter communication's power consumption in Section 4.3 and look at the decoding performance of the software-defined receiver in Section 4.4. Finally, we discuss the results and conclude our work.
# Multi-Dimensional Fusion Attention Mechanism with Vim-like Structure for Mobile Network Design
## 4 Experiment
### Object Detection  
#### 4.4.1 Implementation Details  
To demonstrate the superiority of the proposed method in object detection, we evaluated MDVA on the COCO dataset. The COCO dataset's training set consists of 118,000 images, and the standard preprocessing requires resizing images to a uniform size of \(640\times 640\). The implementation is based on PyTorch and YOLOv5s.  
During the training, we set the size of the input image to \(640\times 640\), and we trained the model on an RTX 2080 ti GPU, setting the batch size to 5 and using synchronized batch normalization. Cosine annealing learning rate scheduling was employed, with an initial learning rate set to 0.01. The model was trained for a total of 300 epochs.  
These experimental settings allowed us to thoroughly evaluate the performance of MDVA in object detection and compare it with other methods. The experimental results  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Backbone** & **\#Param. (M)** & **FLOPs (G)** & **Top-1 (\%)** \\ \hline Baseline [46] & & 2.35 & 0.33 & 76.0 \\ +SE [6] & & 4.62 & 0.33 & 77.3\({}_{+1.3}\) \\ +CBAM [8] & MobileNetV2 & 2.92 & 0.33 & 75.6\({}_{-0.4}\) \\ +CA [21] & & 2.80 & 0.34 & 76.7\({}_{+0.7}\) \\ +MDVA & & 2.37 & 0.35 & 76.9\({}_{+0.9}\) \\ \hline Baseline [47] & & 1.36 & 0.15 & 75.1 \\ +CBAM & ShuffleNetV2 & 1.36 & 0.15 & 75.2\({}_{+0.1}\) \\ +MDVA & & 1.36 & 0.15 & 75.3\({}_{+0.2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of various attention methods on CIFAR-100 in terms of network parameters (in millions), FLOPs, Top-1 Validation Accuracies%.  
Figure 6: Trade-offs between accuracy and training throughput. Measured with batch size of 32 on a GTX 2080 ti GPU (graphics processing unit).  
show that the new multi-dimensional attention mechanism significantly outperforms traditional attention mechanisms in terms of performance. Notably, in object detection tasks, MDVA demonstrates superior localization and recognition capabilities.
## 5 Conclusions  
This study demonstrates the potential of FPGA-based DL acceleration in addressing the challenges of resource-constrained and real-time applications. By implementing VGG16 and VGG19 using the Xilinx Vittis-AI framework, we achieved an effective balance between accuracy, inference latency, and resource efficiency. VGG16 and VGG19 delivered Top-1 accuracies of 89.54% and 87.46%, respectively, while reducing inference latency by 86.3% and 84.9% compared to CPU-based implementations. These results position our design as an ideal candidate for edge AI applications, such as autonomous systems and IoT devices, where both speed and precision are paramount. Our study not only identifies pathways for performance improvement, but also highlights the practicality of using framework-based FPGA deployment over custom implementations, which are time-intensive and may not always yield proportionate benefits. Exploring the PyTorch framework for broader compatibility with pretrained models, employing real-time datasets, and integrating advanced architectures like Transformers represent promising future directions. In conclusion, this work underscores the competency of our approach as a scalable and efficient method for deploying advanced DL models in real-world applications. By demonstrating the balance of performance and efficiency, this study paves the way for innovations in FPGA-based DL acceleration, setting a foundation for more adaptable and practical solutions in edge computing and beyond.  
**Author Contributions:** Conceptualization, A.M.Z.K. and A.C.; methodology, A.M.Z.K.; software, A.M.Z.K.; validation, A.M.Z.K. and A.C.; formal analysis, A.M.Z.K.; investigation, A.C.; resources, A.C.; data curation, A.M.Z.K.; writing--original draft preparation, A.M.Z.K.; writing--review and editing, A.C.; visualization, A.M.Z.K.; supervision, A.C.; project administration, A.C.; funding acquisition, A.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the \"National Institute of Health\" (NIH) research project in South Korea (project No. 2024 ER080300) and Gachon University research fund of 2024 (GCU-202404610001).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Mathematical Model of the BE-LRP
### Model Assumptions and Variable Definitions  
The relevant symbols and variables used in the mathematical model are shown in Table 1. Based on the consideration of the actual ship transportation mode, the established model is assumed to be subject to the following three constraints [32]:  
**Assumption 1**.: _During the entire delivery process, each cabin can only be served once by delivery._  
**Assumption 2**.: _Each distribution center and cabin location are known, and not all distribution centers are selected._  
**Assumption 3**.: _Each vehicle departs from the distribution center and returns to the distribution center after completing the distribution task._  
\begin{table}
\begin{tabular}{c c} \hline Set & Definition \\ \hline \(I\) & Set of distribution centers, \(I=\{1,2,3,\ldots,i\}\) \\ \(J\) & Set of cabin positions, \(J=\{1,2,3,\ldots,j\}\) \\ \(K\) & Set of delivery vehicles, \(K=\{1,2,3,\ldots,k\}\) \\ \(R_{k}\) & Ordered set of delivery service routes, \(R_{k}=\{1,2,3,\ldots,n\}\), \(k\in K\) \\ \(U_{k}\) & Set of vehicles for cabin service \\ \(N_{kn}\) & Set of service cabins in the \(n\)th route for the vehicle \(k\), \(k\in K,n\in\mathrm{R}_{k}\) \\ \hline Parameter & Definition \\ \hline \(M_{k}\) & Loading capacity of vehicle \(k\) \\ \(V_{k}\) & Speed of vehicle \(k\) \\ \(M_{i}\) & Storage capacity of distribution centers, \(i\in I\) \\ \(Q_{j}\) & Material demand for cabins, \(j\in J\) \\ \(|R_{k}|\) & Number of service routes for car \(k\), \(k\in K\) \\ \(|I|\) & The total number of cabins in the delivery area \\ \(|N_{kn}|\) & Cabin serviced by car \(k\) on route \(n\), \(k\in K,n\in\mathrm{R}_{k}\) \\ \(|U_{k}|\) & Number of vehicles serving all cabins, \(k\in K\) \\ \hline \end{tabular}
\end{table}
Table 1: Model variable definition.
# Solar Irradiance Forecasting Using Dynamic Ensemble Selection
## 3 Background
### Autoregressive and Moving Average Model  
The autoregressive and moving average models (ARMA) belongs to the [PERSON] family of linear models [11]. While the AR part considers the lags of the series, the MA creates the output response addressing random shocks \(a_{t-P-j}\)[44], which are weighted by \(\theta_{j}\) coefficients, as in Equation (1):  
\[\hat{x}_{t}=\phi_{1}x_{t-P}+\ldots+\phi_{p}x_{t-P-p+1}-\theta_{1}a_{t-P}-\ldots- \theta_{q}a_{t-P-q+1}+a_{t}, \tag{1}\]  
where \(\phi_{i}\) and \(\theta_{j}\) are the autrorregressive and moving average coefficients, respectively; \(P\) is the number of steps ahead considering the direct approach; and \(i=1,\ 2,\ldots,p\) and \(j=1,\ 2,\ \ldots,\ q\), are free coefficients [44].  
The most usual way to apply the model considers the random shocks \(a_{t}\) as equivalent to the residual set of the previous samples [45].  
Finally, to adjust the free coefficients of the ARMA model, we used the maximum likelihood estimator (MLE), which is the usual way [11]. Note that this model can also be named the autoregressive and moving average model (ARMA) when no differentiations are performed in the time series [11].
# A New Method to Lightweight Magnesium Using Syntactic Composite Core
## 2 Materials and Methods
### Characterizations  
Experimental and theoretical density measurements were performed on polished as-cast monolithic Mg and Mg/Mg-20 GMB hybrid composite samples, using Archimedes' principle and the rule-of-mixtures principle, respectively. X-ray diffraction (XRD) analysis was carried out by using an automated Shimadzu LAB-XRD-6000 diffractometer (Kyoto, Japan). The specimens were subjected to Cu-K\({}_{\alpha}\) radiation (0.1542 nm) with a scanning speed set at 2\({}^{\circ}\)/min. Grain size studies were performed on polished as-cast Mg/Mg-20 GMB hybrid composite using a Leica DM 2500 M Optical microscope. Morphologies of the as-cast Mg/Mg-20 GMB hybrid composite specimens were observed by the JEOL, JSM-6010 scanning electron microscope (SEM) equipped with a spectrometer to perform energy dispersive X-ray spectroscopy (EDX). The Vickers micro-hardness measurement of the cross-section of the as-cast hybrid composite was conducted on a Matsuzawa MXT50 automatic digital microhardness tester. Compressive tests were performed using a Shimadzu AG-25 TB at room temperature on cylindrical as-cast pure Mg and Mg/Mg-20 GMB hybrid composite. This test was carried out in accordance with the ASTM E9-89a standard method. The fracture surface of post-compression samples was analyzed using a JEOL, JSM-6010 scanning electron microscope (SEM).
## 4 Enabling Technologies for FSO Communication
### Adaptive Detection Thresholds
#### 4.10.2 Mechanism of ADTs  
ADTs work by continuously monitoring the received signal's parameters, such as BER, SNR, and scintillation index. Algorithms, often based on ML or statistical analysis, process these real-time data to determine the optimal threshold. The primary mechanisms include the following:  
1. Feedback Loops: Real-time feedback from the Rx to the Tx helps adjust the power levels and detection thresholds. This feedback loop ensures that the system adapts to varying conditions promptly [194; 424].
2. Threshold Adjustment Algorithms: Algorithms such as the Kalman filter, maximum likelihood estimation, and adaptive filtering techniques dynamically adjust the detection thresholds. These algorithms analyze the statistical properties of the received signal and make predictive adjustments [413].
3. Diversity Techniques: Implementing spatial or temporal diversity techniques can also aid in adaptive threshold adjustment. By combining multiple received signals, the system can better estimate the optimal threshold [423].
## 9 Application of Derivatives of Styrene in Membrane of Fuel Cells
### Poly(Styrene Acrylonitrile) [San]  
Poly(styrene acrylonitrile) (SAN) is a copolymer of styrene used for making proton exchange membranes (PEMs) due to its high mechanical and chemical stability, as well as its sheet-forming ability. According to research, the conductivity of sulfonated SAN polymer reaches a value of 10\({}^{-2}\) S cm\({}^{-1}\)[130]. When SAN is sulfonated, its conductivity further improves, positively impacting the overall efficiency of the fuel cell. As an example, [PERSON] and his colleague scientists [130] sulfonated the poly(Styrene-Co-Acrylonitrile) (PSAN) by treating the pure PSAN with H\({}_{3}\)PO\({}_{4}\) followed by dipping it in a mixture of CH\({}_{2}\)CL\({}_{2}\) and sulfuric acid. The modified membrane was dried in a vacuum oven for further use. The results show that the conductivity of the PSAN/H\({}_{3}\)PO\({}_{4}\) membrane is in between 10\({}^{-9}\) mS cm\({}^{-1}\) and 10\({}^{-5}\) mS cm\({}^{-1}\). However, when the doping of acid increases in the membrane, conductivity increases but it is still not applicable in fuel cells. Post-sulfonation with sulfuric acid attaches sulfonic groups to the polystyrene chains, forming covalent bonds. The water uptake of the modified membrane shows a value of 45.7%. Ultimately, the ion conductivity boosts and reaches a value of 1 mS cm\({}^{-1}\) at room temperature and 10 mS cm\({}^{-1}\) at a temperature of 80 \({}^{\circ}\)C. A summary of these articles is given in Table 3.