esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of Deformation Predictions
In this paper, the input sequence lengths are three, five, seven, nine, and the output sequence lengths are three, five, seven, nine, respectively. Therefore, there are 16 groups of input and output combinations, and the results are shown in Figure 6.  
Figure 4: Monitoring data from Songxi Station. (**a**) Cumulative value. (**b**) Rate of change. Figure 5: Parameter selection. (**a**) SVR. (**b**) DTR. (**c**) GPR. 3.5 and the RMSE ranges from 0 to 4.5. From Figure 5(e,f), the MAE of pile horizontal displacement ranges from 0 to 4.5 and the RMSE ranges from 0 to 5.5. For the small sample of data in this paper, the GPR model and stacked ML gives the best prediction, followed by the SVR model and finally the DTR model. As can be seen from Figure 6, the \"red circle position\" is the lowest point of error. At the same time, considering the complexity of the model, the GPR model is simpler, easier to implement, and more difficult to overfit. Therefore, this paper chooses the GPR model as the prediction model. 3. The smaller the number of days of prediction, the better the model prediction. As can be seen in Figure 5(a)-f, the number of prediction days is set as three, five, seven and nine. When the number of prediction days is three, the model error is lower, the MAE is usually kept within 1.5, and the RMSE is kept within 2.5. But when the number of days of prediction is five, seven, and nine, the error value becomes higher in that order. Therefore, the forecasting model in this paper selects the number of forecasting days as three. 4. The GPR model performs best with a training sample length \(p\) of five when the number of prediction days is q of three. From the \"red dots\" in Figure 5(a), it can be seen that when the number of prediction days is three, the training sample of five days has the smallest error value. Therefore, the prediction model in this paper chooses the GPR model with a training sample length of 5 days and 3 prediction days, denoted as GPR (5,3). In summary, the GPR (5,3) model is the model with the best predictive performance in the small sample of data in this paper. Based on the rolling time series forecasting method, the number of forecasting days has a better forecasting effect when the number of forecasting days is 3 days. In addition to obtaining the predicted values of deformation, it is also possible to predict the trend of deformation. The model training sample length \(p\), on the other hand, is related to the data themselves and needs to be adjusted according to the specific time series and test results. The optimal training sample length for this paper is five. Based on the above analyses, the GPR (5,3) is used in this paper. The indicators of the six monitoring points at Luochongwei station are selected for prediction, and the cumulative values for the next 3 days are predicted on a rolling basis using a training sample length of 5 days, and the results of the model predictions are used for fusion early warning of pit risk.
# Simultaneous Lightwave and Power Transfer for Internet of Things Devices
## 5 Conclusions  
In summary, we provided an overview of the SLIPT background and the co-existence of this technology with OWC systems. We also demonstrated a self-powered system that can harvest energy for various light sources and detect signals modulated using an OFDM modulation. A net data rate of 1.3 Mbps was reported using 16-QAM OFDM scheme with an adaptive data loading (bit and power loading) algorithm at a distance of 2 m with an average BER of \(2.4\times 10^{-3}\). The designed system was equally able to detect the movements of a UAV using an IR motion sensor. Our demonstrations can be of interest to next-generation autonomous IoT devices. Finally, a discussion on the progress of SC technology and the practicality of the wide-scale deployment of SLIPT-based IoTs was included.  
Conceptualization, J.I.D.O.F. and A.T.; methodology, J.I.D.O.F., O.A. and A.T.; software, J.I.D.O.F. and O.A.; validation, J.I.D.O.F., O.A. and A.T.; formal analysis, J.I.D.O.F., O.A. and A.T.; investigation, J.I.D.O.F., O.A. and A.T.; resources, B.S.O., M.-S.A. and K.N.S.; data curation, J.I.D.O.F., O.A. and A.T.; writing--original draft preparation, J.I.D.O.F., O.A. and A.T.; writing--review and editing, B.S.O., M.-S.A. and K.N.S.; visualization, J.I.D.O.F., O.A., A.T., B.S.O., M.-S.A. and K.N.S.; supervision, B.S.O., M.-S.A. and K.N.S.; project administration, B.S.O., M.-S.A. and K.N.S.; funding acquisition, B.S.O., M.-S.A. and K.N.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by King Abdullah University of Science and Technology.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **SC Technology** & **Active Area (mm2)** & **Max Data Rate (Mbps)** \\ \hline
[5] & Multi-crystalline Si & 43,200 \({}^{a}\) & 11.84 (OFDM) \\ \hline
[6] & Organic (PTB7:PC\({}_{71}\)BM) & 8 & 34.2 (OFDM) \\ \hline
[9] & GaAs & 0.8 & 1000 (OFDM) \\ \hline
[10] & Triple-cation perovskite & 6.5 & 56 (OFDM) \\ \hline This work & Mono-crystalline Si & 3850 & 1.4 (OFDM) \\ \hline \hline \end{tabular} \({}^{a}\) Size of the full solar panel. Considered active area in the experimental demonstration is significantly smaller.  
\end{table}
Table 3: Comparison between data rates in SLIPT-based demonstrations in the literature.
# Detection of Intelligent Intruders in Wireless Sensor Networks
## 3 Intrusion Algorithms
### Flood-Fill Intrusion Algorithm
Some critical functions and data structures are described as follows:  
* _directions_ is an array containing the four basic directions: up (\(<0,1>\)), down (\(<0,-1>\)), left (\(<1,0>\)) and right (\(<-1,0>\)). Figure 3: An illustration of the flood fill algorithm. * _steps_ is an array generated every loop containing the position of the intruder if it were to take each of the directions in _directions_
* \(Count(array)\) counts the number of elements in an array
* _map_ is a data structure mapping keys (cell positions) to values (cell values). Accessing a cell's value is expressed as \(map[position]\). ```
1:while position! = intruderDestinationPosition do
2:for\(i=0;i<\text{Count(directions)};i++\)do
3:steps[i] \(\leftarrow\) position + directions[i] \(*\) cellSize
4:for\(i=0;i<\text{Count(directions)};i++\)do
5:if!map.containsKey(directions[i]) then
6:map[steps[i]] \(\leftarrow\) IsCovered(steps[i])
7:for\(i=0;i<\text{Count(directions)};i++\)do
8: hits[i] \(\leftarrow\) map[steps[i]]
9: targetVector \(\leftarrow\) targetPosition - position
10: maxDot \(\leftarrow\) \(-\infty\)
11: generalDirection \(\leftarrow\) \(-1\)
12:for\(i=0;i<\text{Count(directions)};i++\)do
13: dot \(\leftarrow\) targetVector \(\cdot\) directions[i]
14:if dot \(>\) maxDot then
15: maxDot \(\leftarrow\) dot
16: generalDirection \(\leftarrow\) i
17: min \(\leftarrow\) \(-1\)
18: for\(i=0;i<\text{Count(directions)};i++\)do
19: j \(\leftarrow\) (generalDirection + i)%4
20: if hits[j]! \(=\) \(-1\) then
21: if hits[j] \(<\) min then
22: min \(\leftarrow\) hits[j]
23: if minHit! \(\overset{\text{minHit}}{=-1}\) then
24: position \(\leftarrow\) position + directions[minHit] \(*\) cellSize
25: map[steps[minHit]] \(\leftarrow\) map[steps[minHit]] \(+1\)
```  
**Algorithm 2** Flood-fill.
# A Line Segment Detector for Space Target Images Robust to Complex Illumination
## 3 Results
### Quantitative Evaluation Results  
Table 1 provides the line segment detection results of the five methods on the MNS-LS dataset. As shown in the results, ST_LSD is better than other methods in precision,  
Figure 10: Some images in the MNS-LS dataset. (**a**) High-brightness image. (**b**) Low-brightness image. (**c**) An image with noise.  
Figure 9: Semiphysical simulation platform.  
recall, and F-score. This is mainly attributed to the adaptive bilateral filtering and anchor map extraction method combined with Otsu introduced in this paper. In addition, the aggregation method introduced also ensures the integrity of line segments as much as possible. AG3 line ranks second in precision, recall, and F-score because AG3 line uses the combination of active grouping and geometric gradients to facilitate the extraction of line segments, thus ensuring the recall of line segments. In precision, AG3 line adopts two methods to control false positives from coarse to fine, which is also more effective. EDlines and Linelet perform very similarly in three aspects, but Linelet does better overall in precision and F-score. The performance of Linelet in precision benefits from its effective control of false positives in the validation phase. After the original author's optimization, the performance of EDlines is significantly improved, and it performs better than Linelet in the recall, but its precision and F-score are worse than Linelet, which indicates that EDlines detect more ground truths as well as more false positives than Linelet. The classical LSD performs poorly and has a certain gap with the previous four detectors.  
Figure 11 shows the detailed performance of the different methods in precision, recall, and F-score on the MNS-LS dataset. As shown in the picture, ST_LSD performs better on images 1-6 with high-brightness illumination. For images with a large gradient difference between the background and the target, the anchor map extraction method combined with Otsu can effectively separate the background and target so as to extract more meaningful line segments. For images 7-13 with too-dark illumination, the gradient difference between the background and the target is very small. The anchor map extraction method combined with Otsu fails to effectively separate the target and the background, which leads to the performance degradation of ST_LSD in images 7-13. However, compared with other traditional methods, ST_LSD still maintains certain advantages. In addition, for images 14-21 with noise interference, ST_LSD is slightly better in all aspects, mainly due to the bilateral filtering method introduced in this paper, which can remove noise and effectively maintain edge information. In general, compared with other methods, ST_LSD performs better with complex illumination and noise interference.
## 2 Augmentation of Li-Fi Wireless DMT Transmission Capacity Through Sparse Coded Mask Optimization
### Impact of the Masking Ratio on the Channel Capacity
#### 2.2.3 Mathematical Derivation  
Let us restate key equations to show how the \"masking ratio to channel capacity\" link can be expanded in the paper:  
* Masked DMT Signal Length, \(L_{masked}\) can be described as Equation (12). \[L_{masked}=(1-r)\times L_{orig}\] (12) where \(L_{orig}\) is the original sample length and \(0\leq r\leq 1\).
* Per-Symbol Transmission Time
* If the system transmits each masked symbol in \((1-r)\times T_{orig}\), then the instantaneous symbol rate can be increased proportionally if we keep the same overall bandwidth or sampling rate.
* Maximum Achievable Data Rate* Denote \(\beta\) bits per DMT symbol (depending on QAM order and number of subcarriers). In an ideal scenario with no overhead, \(C_{ideal}(r)\) can be expressed as Equation (13). \[C_{ideal}(r)=\frac{\beta}{(1-r)T_{orig}}\] (13)
* Realistic Rate Improvement
* Let \(\alpha\) be an experimental efficiency factor (\(0\leq\alpha\leq 1\)), representing overhead or imperfection. Our measured rate gain is organized as Equation (14). \[C_{real}(r)\approx\alpha\times\frac{\beta}{(1-r)T_{orig}}\] (14)
* In practice, we empirically observed \(\alpha\) to be significantly below 1, resulting in about 1.85\(\times\) final measured improvement for \(r=0.85\).  
These derivations clarify that while the theoretical capacity can rise dramatically with large \(r\), real-world overhead and constraints limit how much we can exploit the reduced signal length. Nevertheless, the improvement remains significant (nearly doubling the net throughput at 85% masking).
# HMM Adaptation for Improving a Human Activity Recognition System
## 5 Experiments Carried out in This Work
### User Adaptation Experiments  
For the user adaptation experiments, all user sessions used for testing the system are randomly divided into two sub-sessions including 50% of the activities each. The average duration of an activity is 440 s. The first sub-session is used for adapting the HMMs to the user and the second sub-session is used for testing the system. There is not any overlap between these two sub-sessions. It is important to remark that the sub-session used for testing is the same along all the experiments to allow a fair comparison.  
Figure 6 represents the evolution of the ARER depending on the adaptation coefficient. This representation also includes the confidence intervals at 95% along the curve. When MU = 0 no adaptation is done and the system obtains the same results as when using general HMMs. When MU increases, the ARER decreases until reaching a minimum for MU = 15. After this value, the ARER increases because the available user-dependent data is limited and it is not productive to increase its weight for HMMs training.  
The next figure (Figure 7) shows the ARER depending on the amount of data to adapt. Instead of using the whole sub-session (50% of the user session) for adapting the HMMs, different amounts have been considered: 10%, 20%, 30%, 40% and 50% of the user session.  
Figure 6: Activity recognition error depending on the adaptation coefficient.  
Figure 7: Activity recognition error rate depending on the amount of data for HMMs adaptation (% of the user session).  
As it is shown in Figure 7, the ARER decreases when we increase the amount of data (this representation also includes the confidence intervals at 95% along the curve). Regarding the differences between users, the absolute ARER reduction varies from 0.5% (the lowest reduction) to 1.5% (the highest reduction). The curve presented in Figure 7 does not show any saturation tendency, so it means that if we increase the adaptation data, the ARER can continue decreasing. For further works, a bigger dataset will be considered in order to analyze this effect.  
In order to complete the analysis, a new experiment has been carried by training the HAR system with the data of a single user. In this case, the first user sub-session has been used for training the system (instead of adapting the system) and the second sub-session for testing. In this case, the ARER increases to 9.4%. This significant degradation is due to the important reduction in the amount of data for training the system. This result supports the utility of the adaptation algorithm proposed in this paper as the best solution for developing a user-dependent HAR system when there is a small amount of data per user.
## 2 Related Work  
### Image Classification  
Early image classification techniques were primarily based on traditional machine learning techniques. These techniques mainly involved manually extracting certain features from images and then training a classifier based on these features. Typical feature extraction methods include scale-invariant feature transform (SIFT) [9] and histogram of oriented gradients (HOG) [10], and classifiers based on these features include the support vector machine and K-nearest neighbors (K-NN) [11]. While these techniques have been successful for simple image classification tasks, they have limitations when dealing with increasing image complexity and large datasets. In particular, manually defined features lack robustness to image rotation, size changes, lighting changes, noise, etc. [12].  
Deep learning techniques, notably, the CNN, emerged as a solution to these issues. The CNN provides the spatial structure of an image to extract features automatically and learn high-level patterns through various layers. This approach overcomes the limitations of manual feature extraction and has shown excellent performance on large image datasets. CNNs use filters to learn local patterns and compress them through a pooling process to achieve spatial invariance. The features learned in this process are robust to changes in image size and location [13]. Since the introduction of the CNN, various models that further improve the performance of image classification have emerged. For instance, the VGG uses a deep network structure to learn high-level patterns, and ResNet introduces residual connections to solve the problem of gradient dissipation in deep networks, allowing further expansion of network depth [14; 15]. The Inception model introduces a structure that simultaneously uses filters of different sizes to extract features at different scales [16]. While all of these models are CNN-based, they leverage different structural properties to maximize performance.  
Recently, transformer-based models such as ViT have emerged in an attempt to overcome the limitations of CNN [17]. The shift from traditional to deep learning methods has revolutionized the performance of image classification, and various deep learning models, including CNNs, have been successfully applied to large-scale image classification tasks.
# Elastoplastic Constitutive Model for Energy Dissipation and Crack Evolution in Rocks
## 5 Validation of the Theoretical Model
### Argillaceous Silsttone Model  
As shown in Table 2, the constructed elastoplastic constitutive model for argillaceous silstone can simultaneously characterize the energy dissipation and crack evolution. Moreover, when the plastic shear strain \(\gamma_{p}\) is greater than or equal to 2%, \(\gamma_{p}\) remains constant at 2%.
## 3 Results and Discussion
### High-Pressure Liquid Chromatography Size Exclusion for Molecular Mass Distribution (HPLC-SEC)  
Both Common buckwheat control and enzymatically hydrolyzed extracts were lyophilized, and identification of molecular mass distribution was evaluated and performed using  
Figure 2: SEM images of common buckwheat spent grain before enzyme-assisted extraction (**a**) and after enzyme-assisted extraction (**b–d**). The images are at a scale bar of 1.00 mm and 500 \(\upmu\)m.  
HPLC-SEC (Figure 3). Buckwheat is a pseudocreal with a high starch content of starches. However, in origin, buckwheat typically has low levels of amylases, which must be implemented and added when used in beer production [6; 7]. In this study, we compared two different enzymatic hydrolysis methods for common buckwheat flour. By incorporating amylase and a non-starch polysaccharide enzyme cocktail into the extraction, long-chain carbohydrates were cleaved from 7,000,000 kDa to \(\leq\)7000 kDa (Figure 2a,c). This process mainly created DP3 length oligosaccharides, which were found in lower concentrations in the control extract. However, when additional glucoamylase enzymes were incorporated into the buckwheat flour extraction process, the product contained mostly monomeric sugars (Figure 3e). Interesting molecular mass distribution was observed in all the extracts after IVD. The control extract demonstrated cleavage of soluble polysaccharides resulting in higher DP3-DP4 oligosaccharides content. Also, the formation of DP2 and DP3 after IVD was observed in samples to which amylase, glucoamylase, and non-starch polysaccharide enzyme cocktails were added. This means that additional isomerization and metabolic pathways were formed with the digestive fluids. The water-soluble fibres of buckwheat seeds mostly consisted of pectins, arabinadactans, and xyloglucans, which in turn consist of monomeric sugars such as glucose, xylose, arabinose, and galactose [36; 37]. This composition indicates possible variation in sugar composition ranging between DP1 and DP4 following enzymatic extraction.  
Figure 3: Common buckwheat molecular mass (MM, kDa) distribution identified using HPLC-SEC. Graph (**a**)—buckwheat as control (BE Control); Graph (**b**)—buckwheat as control after IND (BE Control IVD); Graph (**c**)—batch I buckwheat flour enzyme-assisted extract (BE I); Graph (**d**)—batch I buckwheat flour enzyme-assisted extract after IVD (BE I IVD); Graph (**e**)—batch II buckwheat flour enzyme-assisted extract (BE II); Graph (**f**)—batch II buckwheat flour enzyme-assisted extract after IVD (BE II IVD). DP1–DP4 represent the degree of polymerization, showing the lengths of the sugars and oligosaccharides. Different colors of the curves represent the repeatability of each sample.
# A Portable Optical Sensor for Microplastic Detection: Development and Calibration
## 3 Results and Discussion
### Operation and Data Acquisition  
The operation of the prototype sensor involves a coordinated sequence of steps designed to effectively detect and analyze microplastics. The workflow is as follows:  
1. The process commences with the selection of wavelengths to scan the sample using the multiplexer, ensuring precise and targeted analysis of the material;
2. Then, the four-bar structure performs a systematic scan across the filter containing microplastics, ensuring comprehensive coverage of the sample area;
3. During the scan, the photodiode captures the transmitted light from the microplastics. The absorbance intensity varies depending on the material's optical properties;
4. The photodiode generates an analog signal, which we route through an analog-to-digital converter (ADC) circuit that digitizes it for further processing;
5. The Raspberry Pi processes the digitized data, identifying peaks in the signal that correspond to specific wavelengths. These peaks provide critical information about the sample's optical response;
6. Finally, the intensity of the detected signals determines the identification of the microplastics. The system utilizes these intensity patterns to differentiate microplastics from other materials in the sample.  
The prototype's operational workflow allows it to efficiently acquire and process data, enabling the rapid identification of microplastics in diverse sample environments. The integrated mechanism, photodiode, and ADC circuit ensure high-resolution scanning, while Raspberry Pi's processing capabilities support accurate signal analysis. Together, these components form a robust, portable solution for precisely detecting microplastics.
## 4 Implementation and Results
### Creation of Daily Polygons and Alerts  
We create daily polygons, which consist of transforming the deforestation map from raster into vector (polygons). Figure 6 shows the obtained polygons that represent defor  
Figure 4: Deforestation of North Sumatra, Indonesia, 1988–2021.  
Figure 5: Map of oil palm and water grabbed from the classification map.  
estation. This transformation allows us to define each deforestation patch as a polygon instead of pixels. The reason for the vectorization (transforming raster to vector) is to use the area that each deforestation patch occupies as an independent feature that can be easily described by a set of attributes. Based on polygon features, we can clearly define the boundary of the deforestation patch. To create daily polygons, we developed a Python using rasterio, shapely, and geopandas libraries.  
We then create alerts by clustering daily polygons based on time and distance. Alerts are basically multipolygons; polygons are grouped together if they are within 45 days and the distances between patches are not more than 250 m. We set the threshold as 45 days for the time and 250 m for the distance based on heuristic analysis. Alerts and polygons are shown in Figure 7. The highlighted polygon on the left image depicts a daily polygon detected on 7 July 2010, while the highlighted multipolygon on the right side depicts an alert (multipolygon) from 7 to 25 July 2010.  
Figure 6: Creating daily polygons.  
Figure 7: Daily Polygons (**left**) and Alerts (**right**).