esa-sceva/satcom-chunk-collection huggingface dataset:
# Indoor MIMO-VLC Using Angle Diversity Transmitters
## 3 MIMO-VLC Using ADTs
### Average Achievable Rate  
In this work, spatial multiplexing (SMP)-based MIMO-VLC is considered and, hence, different ADTs transmit different data streams simultaneously [10]. According to (2), (3) and (13), the \(m\)-th data stream at the \(k\)-th receiver location in the \(N_{r}\times N_{t}\) MIMO-VLC system can be expressed by  
\[\hat{x}_{m}^{k}=x_{m}^{k}+\sum_{i=1}^{N_{r}}\tilde{h}_{i,m}^{k}w_{i}, \tag{14}\]  
where \(\tilde{h}_{i,m}^{k}\) denotes the element in the \(i\)-th row and \(m\)-column of \(\mathbf{\tilde{H}}^{k}\) with \(\mathbf{H}^{k}\) being the corresponding \(N_{r}\times N_{t}\) MIMO channel matrix at the \(k\)-th receiver location.  
Based on (14), the signal-to-noise ratio (SNR) of the estimate of the \(m\)-th data stream at the \(k\)-th receiver location can be obtained by  
\[\Gamma_{m}^{k}=\frac{P_{s}}{\sum_{i=1}^{N_{r}}(\tilde{h}_{i,m}^{k})^{2}P_{w}} =\frac{\Gamma_{0}}{\sum_{i=1}^{N_{r}}(\tilde{h}_{i,m}^{k})^{2}}, \tag{15}\]  
where \(P_{s}\) is the electrical power of the transmitted signal, \(\Gamma_{0}\) is the transmitted SNR, which is defined as \(\Gamma_{0}=\frac{P_{s}}{P_{w}}\)[9].  
As a result, the achievable rate (bits/s/Hz) of the \(m\)-th data stream at the \(k\)-th receiver location is calculated by [14]  
\[R_{m}^{k}=\frac{1}{2}\log_{2}(1+\Gamma_{k,m})=\frac{1}{2}\log_{2}\left(1+\frac {\Gamma_{0}}{\sum_{i=1}^{N_{r}}(\tilde{h}_{i,m}^{k})^{2}}\right). \tag{16}\]  
Hence, the overall achievable rate of the ADT-based \(N_{r}\times N_{t}\) MIMO-VLC system at the \(k\)-th receiver location can be achieved as follows':  
\[R^{k}=\sum_{m=1}^{N_{t}}R_{m}^{k}=\frac{1}{2}\sum_{m=1}^{N_{t}}\log_{2}\left(1 +\frac{\Gamma_{0}}{\sum_{i=1}^{N_{r}}(\tilde{h}_{i,m}^{k})^{2}}\right), \tag{17}\]  
and the average achievable rate at \(K\) different receiver locations over the receiving plane, with \(k=1,2,\cdots,K\), can be given by \[\mathbb{R}=\frac{\sum_{k=1}^{K}R_{k}}{K}=\frac{1}{2K}\sum_{k=1}^{K}\sum_{m=1}^{N_{ t}}\log_{2}\left(1+\frac{\Gamma_{0}}{\sum_{i=1}^{N_{t}}(\tilde{h}_{i,m}^{2})^{2}} \right). \tag{18}\]  
By observing (18), we find that the average achievable rate of the ADT-based \(N_{r}\times N_{t}\) MIMO-VLC system is related by the term \(\tilde{h}_{i,m}^{k}\), which is largely determined by the structure and the spacing of the ADTs. Therefore, it is feasible to optimize the structure (e.g., the inclination angle of the side LEDs) and the spacing of the ADTs to maximize the average achievable rate of the system.
# Jamming and Spoofing Techniques for Drone Neutralization: An Experimental Study
## 3 System Design and Methodology  
### Hardware and Software  
#### 3.1.1 Hardware  
The system implementation consists of four key components: a computer for signal generation and processing, the ADALM-PLUTO for signal modulation and transmission, a power amplifier to boost transmission power, and frequency-specific antennas to ensure optimal signal transmission.  
The ADALM-PLUTO is a versatile RF learning module from Analog Devices Inc., designed for SDR, RF, and wireless communications education. It offers a tuning range of 325 MHz to 3.8 GHz with tunable bandwidths of 200 kHz to 20 MHz [28]. The device can be modified to extend its frequency range up to 6 GHz [29].  
The SDR output power was insufficient for the intended application, fluctuating between \(-4\) dBm and 0 dBm at target frequencies of 1.5 GHz and 2.4 GHz [30]. To address this, a WYDZ-PA-1G-3 GHz-1W power amplifier was added. This broadband amplifier operates from 1 GHz to 3 GHz, providing a gain of 30 dB. The amplifier's PIOP is 30 dBm at 2 GHz, equivalent to 1 \(W\)[31].  
The WIFI YAGI ABAKS is a high-performance directional antenna operating in the 2.4 GHz to 2.5 GHz range, with an 18 dBi gain. The measured S11 magnitude, shown in Figure 1, indicates effective impedance matching, especially at 2.405 GHz, where the return loss is is above 5 dB. When measured in dB, the return loss is \(-\,\)\(\,\)\(\,\)\(\,\  
In addition to these components, drones also play an important role. Most drones commonly operate within the 2.4 GHz and 5.8 GHz frequency bands. The DJI Mini 3 and DJI Mini 3 Pro support communication in the 2.4 GHz and 5.8 GHz frequency ranges [32; 33]. The Autel Evo Nano+ also operates in these bands, with an additional frequency range of 5.150-5.250 GHz, enhancing its resilience against interference [34; 35]. The Husban Zino Mini Pro primarily operates in the 2.405-2.475 GHz band and includes 4G connectivity as a backup in case of interference in the 2.4 GHz range [36]. Finally, the ZLL SG 108 drone operates only in the 2.4 GHz band for both RC and video link communication [37]. The drones were selected based on their weight (\(\leq\)250 g) to comply with legal restrictions. Within this class, models were chosen in ascending price order, under the assumption that higher-priced drones have more advanced communication algorithms, enabling a better characterization of jamming effectiveness.
## 2 Materials and Methods  
### Primary DPSC Culture  
All animal experiments were approved by the local ethics committee at Osaka Dental University (Approval No.: 23-01018 and 24-02018). Primary DPSCs were isolated from Wistar/ST rats (5-week-old male, Shimizu Laboratory Supplies, Kyoto, Japan). First, the mandible of each rat was extracted, the two incisors were isolated, and the dental pulp was digested in 3 mg/mL collagenase type I (Wako Pure Chemical Industries, Osaka, Japan) in a shaking bath (Yamato, Tokyo, Japan) at 37 \({}^{\circ}\)C for 40 min. After centrifugation, the cells were suspended in \(\alpha\)-Modified Eagle Minimum Essential Medium (\(\alpha\)-MEM, Nacalai Tesque, Kyoto, Japan) supplemented with 20% fetal bovine serum (FBS; Nichirei, Tokyo, Japan) and 1% penicillin-streptomycin (P.S., Nacalai Tesque). The primary DPSC suspension was seeded into culture flasks (Violamo, Osaka, Japan). Cells were maintained in an incubator at 37 \({}^{\circ}\)C under 5% CO\({}_{2}\). The culture medium was changed every 3 days. When the cells reached 80-90% confluence, the cells were detached with trypsin (Nacalai Tesque) and passaged at a 1:3 ratio. The DPSCs passaged 3, 5, 15, and 26 times (hereafter, passage times are designated as 3, 5, 15, 26 P) were used in subsequent experiments (Figure 1). Cells were captured using a microscope (IX70, Olympus, Tokyo, Japan) at random with a phase-contrast mode. The area of the cells in each group was measured using ImageJ software (1.53k, NIH Image, Bethesda, MD, USA).
## 4 System Performance Discussion
### Performance Comparison Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Radar Type**} & **Target Distance** & **Range Error Rate** & **Target Speed** & **Speed Error Rate** \\ \hline Single Link MIMO Radar & 9082.5 m & 0.9\% & 262.5 m/s & 2.8\% \\ Traditional MIMO Radar & 9052.5 m & 0.6\% & 271.875 m/s & 0.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation results of two radar systems under ideal conditions. \[P_{dp\_mc}=\frac{1}{N}\sum_{j=1}^{N}D(t) \tag{23}\]  
The receiver operating characteristic (ROC) curve, or the sensitivity curve, can be used to characterize the detection performance of MIMO radar [38]. The working characteristic curve of the subjects is composed of false positive rate as the horizontal axis and true positive rate as the longitudinal axis. The true positive rate can also be denoted as the sensitivity, which means the accurate prediction probability for each radar signal of all enemy aircrafts. The true positive rate can also be called the specificity, which indicates the false prediction probability of each radar signal of all non-enemy aircrafts. Because each radar's prediction standard varies, the sensitivity and specificity combinations are also different. The more that the ROC curve is distributed toward the top left corner, the more accurate the detection system. The ROC curve closest to the upper left corner has the best threshold with the least error and the lowest total number of false positives and false negatives. The ROC curves of the two radar systems are drawn under the same coordinates to compare their performance, as is shown in Figure 12. As can be concluded from the results, the single-link MIMO radar is nearer to the upper left corner than the traditional MIMO radar. The larger area under curve (AUC) indicates a better performance and higher accuracy of the single-link RF MIMO radar.
# Prediction of Structural Type for City-Scale Seismic Damage Simulation Based on Machine Learning
## 3 Supervised ML Solution  
### Determination of Models and Platforms  
Many ML models and implementation platforms exist [22; 23; 24; 25; 26; 27], and each has its own advantages and disadvantages. Therefore, appropriate models and platforms must be determined.  
(1) ML models  
The purpose of this study is to predict the structural type with other building attribute data. Structural types are generally limited, e.g., masonry, frame, and shear-wall structures; therefore, the prediction of structural type is a multi-class classification problem in ML. The existing studies indicate that artificial neural network [22], decision forest [23], support vector machine (SVM) [24], and logistic regression [25] are suitable for the classification problem.  
The artificial neural network model that simulates the synaptic connection of the brain comprises a large number of neurons and their interconnections; it can be used for multi-class classification problems [22]. The decision forest model is equivalent to an upgraded decision tree model. The decision forest [23] is composed of many decision trees, and each decision tree is independent. For classification problems, the prediction result with the highest accuracy in all the decision trees will be selected as the result of the decision forest. Logistic regression is the appropriate regression analysis to conduct when the dependent variable is binary or multi-class, because it can describe data and explain the relationship between one dependent variable and one or more independent variables. The SVM method is mainly used to segregate the two classes. However, the prediction of structural types in this study is a multi-class problem. Therefore, except SVM model, the artificial neural network, decision forest and logistic regression models were adopted in this study. The prediction results of these three models can be compared with each other, and more accurate results will be applied in the city-scale seismic damage simulation.  
(2) Implementation platforms  
Currently, many implementation platforms exist for ML, e.g., BigML [26], Microsoft Azure (hereinafter referred to as Azure) [27], Google's TensorFlow [28], and Amazon Machine Learning [29]. Azure has integrated lots of the existing ML models, e.g., the artificial neural network, decision forest models, and logistic regression models, and it can be freely employed for a long time. Therefore, Azure was adopted in this study.
# Ensemble Modelling for Predicting Fish Mortality +
## 5 Results and Discussion
We close this section with the presentation of the results concerning the total number of fish deaths (due to all causes), during the examined time period. Accordingly, Figure 6 depicts the estimation of the developed final stage ensemble model in the validation dataset, for the total number of fish deaths on the next day. For that case, the MAE of the final stage ensemble model is 1850, which is thus lower than the MAE of the standalone ANN and SR models developed in [11], which was 1900 and 2070, respectively. This represents an improvement of approximately 2.6% over the ANN, and a more substantial 10.6% reduction compared to the SR model. The intermediate stage ensemble model achieved an MAE of 1880, indicating a 1% improvement over the ANN and a 9.2% reduction compared to the SR model of [11]. While both ensemble stages demonstrate significant enhancements over the standalone models of [11], the final stage ensemble model achieves slightly better results than the intermediate-stage model, reflecting a consistent trend of incremental improvement in the final stage. Figure 5: A concise presentation of the Decision Trees of the DT models of the final stage ensemble models. The variables shown in the nodes of the trees are as follows: _Deaths_:_ number of fish deaths due to a disease recorded \(i\) days ago, _Temp_:_ water temperature recorded \(i\) days ago, _Food_: amount of food administered \(i\) days ago, _Medicated_Food_j_:_ amount of the medicated food \(``\)\(\prime\)\(\prime\)\(\prime\) administered \(i\) days ago. Nodes that do not correspond to a variable are leaf/terminal nodes. Lastly, the mathematical expression encoded into the respective SR model is stated subsequently. \[D_{+1}=0.746\cdot D_{-3}+\sqrt{\frac{D_{-1}\cdot D_{-2}-\sqrt[1/4]{D_{-2}}-0.85 \cdot D_{-3}-0.85\cdot\sqrt[3/2]{D_{-3}}+\frac{0.104}{D_{-3}}}{D_{-3}}}\]  
The derived mathematical expression asserts that the total number of fish deaths on the next day (\(D_{+1}\)) is a (rather complex) function of the total number of fish deaths recorded on each one of the last four days (\(D_{0}\), \(D_{-1}\), \(D_{-2}\), and \(D_{-3}\)).
###### Abstract  
This study aims to accurately determine the distribution of landslides in the complex terrain of Jiangdingya, Nanyu Township, Zhouqu County, Gansu Province. The digital orthophoto map (DOM) and digital elevation model (DEM) are used to accurately identify landslide areas and analyze associated data. Based on image-based supervised classification, the influence factor constraint analysis is used to further identify and delineate the landslide area. Three mathematical morphology operations--erosion, dilation, and opening--are then applied to automatically identify and extract landslides. Experimental results demonstrate that achieving an accuracy, precision, and recall of 98.02%, 85.24%, and 84.78% shows that it is possible to better avoid interference caused by complex terrain with rich features. High-resolution DEM and DOM data contain rich spectral and texture information. These data can accurately depict geomorphic features of complex terrain and aid in identifying landslide-prone areas when combined with mathematical morphology processing. This contribution is important for identifying landslides in complex terrain and emergency disaster management.  
Keywords:Jiangdingya landslide; landslide extraction; image classification; constraint analysis; morphology +
Footnote †: journal: Medical Imaging
## 2 Materials and Methods
### Automatic Target Extraction and Pips Recognition  
As all facets of a die are planar, the first step of automatic extraction of dice from the entire point cloud (Figure 5a) is basically planar feature segmentation. An improved random sample consensus (RANSAC) algorithm proposed by [PERSON] et al. can be employed to extract all planar features due to its high validity and robustness [26]. The method first decomposes the original point cloud to voxel grids, then it fits an individual small plane to each voxel so that normal vectors of the grids are computed. Then, it randomly samples the normal vectors iteratively to group points belonging to the same planes that are large enough. After obtaining all planar features (Figure 5b), the facet of the dice is identified by setting a threshold of maximum size of the plane. Then, two criteria are chosen to confirm which planar features belong to the die: (1) the dot product of the normal of three neighboring planar features approximately equal zero; (2) the centers of the three planes above are close to each other. Since each facet is orthogonal to another, the dot products of the normals are close to zero. As a result, we detected planar features with dot products close to zero as the facet of dice (Figure 5c). In addition, since the facets are close to each other, their centers (mean) are approximately the same. If the distances between the centers are within a certain threshold (e.g., the length of the die), the planar features likely belong to the die.  
Figure 4: Chirality of the dice used for the proposed method.  
Figure 3: Workflow of the proposed point cloud registration method.  
For recognition of the pips printed on each facet, the facet's point cloud is first transformed with the normal of the plane and then projected as a two-dimensional (2D) image (rasterization) [27]. After that, a low-pass filter is applied to smooth the image, and binarization is performed for the subsequent edge detection [28]. Finally, we applied the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method to segment individual pips and simultaneously recognize them [29]. The key steps are summarized in Figure 6.  
Figure 5: Key steps of the automatic target (die) extraction. The NDT-RANSAC is adapted for the planar feature segmentation. (**a**) Original point cloud. (**b**) NDT-RANSAC plane segmentation. (**c**) Extracted die.  
Figure 6: Key steps of the pips recognition for individual facets. (**a**) Dice plane division. (**b**) Rasterization and raster image convolution. (**c**) Raster image binarization. (**d**) Canny edge detection. (**e**) DBSCAN circle clustering.
# Triple-Attention Mixed-Link Network for Single-Image Super-Resolution
## 3 Proposed Method
### Triple Attention
#### 3.3.3 Spatial Attention  
To further enhance the details in reconstructed images, a spatial attention module for fusing global and local information is added in the reconstruction network, as shown in Figure 4. The reconstruction network also adopts the strategy of global residual learning. The global residual features are increased to twice the original. Half of the channels are weighted by global information, and the other half retains the local information. Then, the two are summed and averaged as the fusion of global and local information. The network generates the final high-resolution image from all the blocks with spatial attention according to the following equations. Figure 5 gives a visualization on how spatial attention affect the global residual, which directly related to the high frequency details in the images.  
\[R_{1},R_{2}=S(M(x)) \tag{10}\]  
\[I_{HR}=\sum_{b}^{B}w^{b}\frac{1}{2}(R_{1}+R_{2}G(M(x)))+U(I_{LR}) \tag{11}\]  
where B denotes the AE-MLBs, \(w^{b}\) denotes the weight for the images. \(R_{1}\) and \(R_{2}\) mean the features, G means the channel squeeze convolution, M means the channel multiplier convolution. \(U\) denotes the subpixel shuffler [16] for upscaling which increases the width and height of \(I_{LR}\) to the desired size.  
Figure 4: Reconstruction net with spatial attention.
###### Abstract  
Constraint satisfaction problems (CSPs) are widely used in domains such as product configuration, scheduling, and resource allocation. However, formulating constraint models remains a significant challenge that often requires specialized expertise in constraint programming (CP). This study introduces the Automatic Constraint Model Generator (ACMG), a novel framework that leverages fine-tuned large language models (LLMs) to automate the translation of natural language problem descriptions into formal CSP models. The ACMG employs a multi-step process involving semantic entity extraction, constraint model generation, and iterative validation using the MiniZinc solver. Our approach achieves state-of-the-art (SOTA) or near-SOTA results, demonstrating the viability of LLMs in simplifying the adoption of CP. Its key contributions include a high-quality dataset for fine-tuning, a modular architecture with specialized LLM components, and empirical validation which shows its promising results for complex configuration tasks. By bridging the gap between natural language and formal constraint models, the ACMG significantly lowers the barrier to CP, making it more accessible to non-experts while maintaining a high level of robustness for industrial applications.  
Keywords:large language models; constraint programming; constraint satisfaction problem; MiniZinc; code generation +
Footnote †: journal: Applied sciences
## 4 Materials and Methods
### Analysis of Volatile Metabolites
#### 4.3.3 GC-MS Analysis  
Following HS-SPME extraction, the fibre was injected into an Agilent 7890B gas chromatograph coupled with a 5977A inert mass spectrometer (GC-MS) under splitless mode at 250 \({}^{\circ}\)C and held in the injection port for 1 min to allow complete desorption of analytes. The fibre was held at 250 \({}^{\circ}\)C for a further 1 min to ensure the fibre was clean for subsequent analyses. The GC oven started at an initial temperature of 35 \({}^{\circ}\)C. Following injection, the temperature was raised at 4.5 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) to 180 \({}^{\circ}\)C. The temperature was then raised at 40 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) to 160 \({}^{\circ}\)C. Finally, the temperature was raised to 260 \({}^{\circ}\)C at 10 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\). The carrier gas used was helium (He) at 7.7 psi with a flow rate of 1.09 mL\(\cdot\)min\({}^{-1}\) through a Rtx-5 MS column with a coating thickness of 0.25 \(\upmu\)m, diameter of 0.25 mm, and total length of 30 m. Interface temperature was held at 250 \({}^{\circ}\)C and quadrupole temperature was at 200 \({}^{\circ}\)C. The ion source was operated in electron impact ionisation mode at 70 eV. Compounds were detected using mass spectra acquired in scan mode in the range of 33 to 400 m/z.
## 3 Results and Discussion
### Viability and Cell Migration Assays
The overall antimigration activity against cancer MCF-7 cells was highest for the 0.1% H2, 0.1% H3, and 0.1% H1 samples, as the wound closure values were 64.68%, 65.66%, and 65.98%, respectively (Figure 2). The lowest wound closure value was observed for H4 (71.99%). The rapeseed honey's antimigration properties were only 1.17% weaker than the untreated control, which indicates its low inhibition of MCF-7 cells migration in comparison with the tested samples. The inhibition of cancer cell migration by honeybee may have resulted from its antioxidant activity [61; 62]. An increase in the antioxidant status during apoptosis in hepatocellular carcinoma cells was observed previously [62]. The migration of normal fibroblasts was less affected by the tested samples; the degree of wound closure ranged from 79.47% (0.1% H1) to 92.89% (1% H4), while for rapeseed honey this value was only 3.39% lower than for the nontreated control. The inhibitory effect on MCF-7 cell migration was previously tested for propolis's extracts, showing large differences depending on the origin of the sample [38]. However, it is not possible to directly compare the effects obtained for propolis and honey due to the much more complex composition of the propolis's extract compared to honey. In the case of propolis, an isolated fraction of bioactive ingredients was used for research, while in the current study whole diluted honey was tested. It is interesting that against both normal and cancer cell lines, the 0.1% concentrations of honey'hew'e inhibited cell migration to a greater extent. Hence, the wound closure value was inversely proportional to the concentration of tested samples, including rapeseed honey. However, it was already described that a 0.1% \(v/v\) honey' concentration supports wound healing in vitro under normoxia and hypoxia (3%), indicating that a honey dilution can be effective in hypoxic wound healing [63].