esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Summary
### FPGA Coprocessors  
Table 8 depicts the results of the FPGA coprocessors described in Section 4, which were proposed as part of the sensor nodes. As noted before, the power consumption on the IGLOO predominates. Besides that, even low-cost FPGAs (Spartan-3, XC3S1000) had a decent power consumption ([75]) at high frequency (137.44 MHz) performing the Running Gaussian Average in order to do background subtraction of a captured image. However, the consumption of the implementation is not coherent with the static power consumption of the platform, _i.e._, 98 mW, according to XPE.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Work** & **Platform** & **Model** & **Frequency (MHz)** & **Power Consumption (mW)** & **Application** \\ \hline
[58] & Spartan-3 & XC3S5O & 28 & 38 & General / Routing Acceleration \\
[66] & Virtex-5 & XC5 VLX33 OT & 100 & 364.85 & Multimedia / Loeffler DCT \\
[75] & Spartan-3 & XC3S1000 & 137.44 & 8.57 & Multimedia / Running Gaussian Average \\
[65] & Spartan-3 & XC3S400 & - & - & Multimedia / Image Compression \\
[68] & Virtex-5 & - & 139.49 & 267 & Multimedia / Image Compression \\
[80] & Virtex-4 & FX100 & 209 & 1,794 & Environmental Monitoring / Kalman Filters \\
[86] & IGLOO & - & 10 & 5 & Security / Dijkstra Algorithm \\
[28] & Cyclone I & EP1C4F & 50 & 98.92 & Security / AES \\
[29] & Stratix I & EP1S10F & 3 & 29 & Security / AES \\
[88] & Spartan-3E & XC3S500E & 20 & 98.9 & Security / Skijpakc \\
[89] & IGLOO & AGLN250V2 & 27 & - & Security / ECC \\ \hline \hline \end{tabular}
\end{table}
Table 8: FPGA coprocessors for research sensor nodes.  
Finally, two high-end (Virtex) FPGAs were used. In [66] a Virtex-5 is utilized in order to implement the Loeffler DCT running at 100 MHz. Despite the high operating frequency, according to the authors, the implementation behaves better than the same implementation in the MICA2 nodes (0.42 mJ in comparison to 37.826 mJ in the MICA2 platform). Moreover, the use of a high-end FPGA in a sensor node can be justified since partial reconfiguration is usually available in such FPGAs. In this respect, document [80] performs Partial Reconfiguration over different configurations of Kalman filters which, according to the authors, produced between 5% and 25% power reduction.  
Given the results of the two 802.15.4 cryptographic accelerators, it is possible to make a trade-off between throughput and energy consumption. In [28], the core is clocked at 50 MHz and processes 57 Mbps. However, its power consumption exceeds the total active power of all commercial nodes depicted in Table 4. On the other hand, document [29] minimized the power consumption reducing the clock at 3 MHz and providing a throughput of 600 kbps (2.4 times the ZigBee bitrate of 250 kbps). The core consumes 29 mW, compared to 38.52 mW for the previous design.  
To conclude, since most of the authors (Tables 6-8) did not provide enough power consumption details, it is still difficult to make a thorough comparison between such experimental nodes and commercial ones.
## 4 Results and Analysis
### Stepwise Regression Model
#### 4.2.2 Model Fitting and Prediction  
Based on the significant impact of annual average rainfall and average wind speed on the frequency of fires, as well as the particularly notable impact of annual average rainfall on the area affected by fires, a stepwise regression equation model was developed to precisely fit the relationship between these meteorological variables and fire characteristics. As illustrated in Figure 7 and Table 2, Figure 7a displays the fitted curve for the number of fires: \(\hat{y}=1601.75-0.481x_{1}-190.484x_{2}\) (where \(x_{1}\) represents annual relative humidity, where \(x_{2}\) represents annual average rainfall) with an \(R^{2}\) of 0.244 and \(p=0.008<0.01\). Figure 7b presents the fitted curve for the area affected by fires: \(\hat{y}=9964.326-7.493x_{1}\) (with \(R^{2}=0.572\) and \(p=0.00<0.01\)). The closer the data points cluster near the diagonal line, the higher the concordance between the model predictions and actual observations.  
In this regression analysis, the results of the model, as shown in Table 3, reveal significant correlations between varying levels of rainfall, wind speed, and the number and area of forest fires. Positive coefficients indicate a direct correlation between the independent and dependent variables, whereas negative coefficients reflect an inverse relationship. The standard error measures the precision of the estimates. The \(p\)-values of all coefficients (all at 0.000) are statistically significant, providing a robust foundation for further predictive analysis and theoretical inferences.  
After conducting a paired T-test between the actual and predicted values, as presented in Table 4, we obtained a T-value of 0.000 and a \(p\)-value of 1.000, which is greater than 0.01. This result indicates no significant difference between the actual and predicted values, confirming the high accuracy of the model. Root mean square error (RMSE) tests, based on the fitting equation, revealed that the RMSE for the number of fires was 200 occurrences, while the RMSE for the area affected by the fires was 1130 hectares.
## 2 Materials and Methods
### Data Collection and Measurements  
#### 2.3.1 Soil Moisture Content  
Soil moisture content (SMC, cm\({}^{3}\) cm\({}^{-3}\)) was measured at 10 cm increments to a depth of 100 cm using Insentek soil moisture sensors (Zhejiang Oriental Insentek Technology Co., Ltd., Hangzhou, China). The sensor is a portable wireless sensor based on Frequency Domain Reflectance (FDR), which can realize automatic collection, wireless transmission, portable charging and other functions. The previous study indicated that the Insentek sensor was a reliable tool to represent real SMC values in the field with a root mean square error of 0.927 cm\({}^{3}\) cm\({}^{-3}\) between the Insentek sensor and oven-dry method [19]. The  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Treatments** & **Border Width (m)** & **Irrigation Quota (mm)** & **Water Pressure (MPa)** & **Single Invigation Water (mm)** & **Irrigation Time (h)** \\ \hline G1Q2 & 2.3 & 75 & 0.05-0.06 & 83 & 0.67 \\ G1Q3 & 3.3 & 75 & 0.06-0.08 & 79 & 0.85 \\ G1Q5 & 5.3 & 75 & 0.1 & 85 & 1.9 \\ G2Q2 & 2.3 & 45 & 0.05-0.06 & 47 & 0.35 \\ G2Q3 & 3.3 & 45 & 0.06-0.08 & 54 & 0.60 \\ G2Q5 & 5.3 & 45 & 0.1 & 48 & 0.68 \\ \hline \hline \end{tabular}
\end{table}
Table 2: First-year boundary test design scheme.  
Figure 1: The layout of soil moisture distribution experiment in the second experimental year of 2021–2022.  
oven-drying method was adopted to measure SMC before sowing at 10 cm increments to a depth of 100 cm.  
To measure soil moisture distribution, Insentek sensors were installed between rows at the distance of 15 m, 30 m and 45 m from the reference border along the direction of micro-spray tapes. At the same time, soil sampling was also made to measure soil moisture content at similar positions. Moreover, perpendicular to the direction of micro-spray tapes, soil sampling was also made to measure soil moisture content at the distance of 15 m, 30 m and 45 m from the tapes. Soil samples were taken 30 cm away from the Insentek sensors.
## 4 Applications
### Application to the Bilevel Variational Inequality Problem
\begin{cases}\min\Bigl{\{}\frac{(s_{k}+s)\|w_{k}-v_{k}\|}{\| \triangledown hv_{k}-\triangledown hv_{k}\|},\lambda_{k}+p_{k}\Bigr{\}},& \text{if}\;\;\triangledown hw_{k}\
eq\triangledown hv_{k},\\ \lambda_{k}+p_{k},&\text{otherwise}.\end{cases} \]  Put \(k:=k+1\) and return to Step 1. ```

**Algorithm 5** A Modified Accelerated Projection and Contraction Method

_Then, sequence \(\{p_{k}\}\) converges strongly to an element in \((\triangledown h+\partial g)^{-1}(0)\)._

**Corollary 4**. : _Let \(\mathcal{H}\) be a real Hilbert space, \(\triangledown h:\mathcal{H}\rightarrow\mathcal{H}\) be a \(L\)-Lipschitz continuous operator, and \(\partial g:\mathcal{H}\to 2^{\mathcal{H}}\) be a maximal monotone operator. Assume that \((\triangledown h+\partial g)^{-1}(0)\
eq\emptyset\) and that assumptions \((A_{4})\)-\((A_{5})\) hold. If \(\{p_{k}\}\), a sequence generated by Algorithm 6:_```
Initialization: Choose \(\phi>0\), \(\lambda_{1}>0\), \(\varrho\in\left(0,\frac{2\eta}{L_{1}^{2}}\right)\) and \(\{\theta_{k}\}\subset[a,b]\subset(0,1]\). Let \(p_{0}\), \(p_{1}\in\mathcal{H}\) and set \(k=1\). Iterative steps: Calculate the next iteration point \(p_{k+1}\) as follows: Step 1: Choose \(\phi_{k}\), such that \(\phi_{k}\in[0,\bar{\phi}_{k}]\), where \[\bar{\phi}_{k}= \begin{cases}\min\Bigl{\{}\frac{k-1}{k+\phi-1},\frac{e_{k}}{\|p_ {k}-p_{k-1}\|}\Bigr{\}},&\text{if }\ p_{k}\
eq p_{k-1},\\ \frac{k-1}{k+\phi-1},&\text{otherwise}.\end{cases} \] Step 2: Set \[w_{k}=p_{k}+\phi_{k}(p_{k}-p_{k-1}),\] and compute \[v_{k}=(I+\lambda_{k}\partial g)^{-1}(I-\lambda_{k}\triangledown h)w_{k},\] Step 3: Compute \[z_{k}=(1-\theta_{k})w_{k}+\vartheta_{k}(v_{k}+\lambda_{k}(\triangledown hw_{k} -\triangledown hv_{k})).\] Step 4: Compute \[p_{k+1}=(I-\alpha_{k})z_{k}+\alpha_{k}f(z_{k}),\ \forall k\geq 1.\]  Update \[\lambda_{k+1}= \begin{cases}\min\Bigl{\{}\frac{(\alpha_{k}+z)\|p_{k}-p_{k}\|}{ \|\triangledown hv_{k}-\triangledown hv_{k}\|},\lambda_{k}+q_{k}\Bigr{\}},& \text{if }\ \triangledown hw_{k}\
eq\triangledown hv_{k},\\ \lambda_{k}+q_{k},&\text{otherwise}.\end{cases} \]  Put \(k:=k+1\) and return to Step 1. ```  
**Algorithm 6** A modified accelerated Tseng splitting method. _Then, sequence \(\{p_{k}\}\) converges strongly to an element in \((\triangledown h+\partial g)^{-1}(0)\)._
# Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8
## 3 Method  
Firstly, high-resolution old landslide image data in the study area are collected based on Google Earth images. These images are subsequently segmented into sizes of 640 \(\times\) 640, labels are marked, and data enhancement is performed to complete the dataset production. Next, the network structure of the YOLOv8 model is improved by adding a CBAM attention mechanism to the backbone network and modifying the IoU loss function at the detection head to enhance model accuracy. Finally, the finished dataset was trained on the improved model for accuracy assessment. Figure 3 illustrates the old landslide detection process using the improved YOLOv8 algorithm.
## 2 Materials and Methods
### Reagents  
Acetonitrile (CH\({}_{3}\)CN, 99.9% LC-MS) and methanol (MeOH, 99.8% LC-MS) were purchased from Fisher Scientific (Thermo Fisher Scientific, Waltham, MA, USA). The formic acid (HCOOH, 98% p.a.) was purchased from Merck (Darmstadt, Germany). The ultra-pure water (18.2 MO.cm) was obtained from a Millipore-Direct Q3 UV system (Millipore, Burlington, MA, USA). Fluorescein sodium salt, Folin-Ciocalteu's reagent Trolox (6-hydroxy-2,5,7,8-tetramethyl chromane-2-carboxylic acid) and AAPH (2,2-azobis(2-methylpropionamidine)dihydro), were all obtained from Sigma-Aldrich (Darmstadt, Germany). Standard phenolic compounds, namely gallic acid (PubChem CID: 370) and quercetin-3-glucoside (PubChem CID: 25203368), were obtained from Sigma-Aldrich (Darmstadt, Germany), and chlorogenic acid (PubChem CID: 1794427) and quercetin-3-O-(6-acetylglucoside) were obtained from Extrasynthese (Genay, France). MTS reagent (CellTiter 96(r) Aqueous One Solution Reagent) was obtained from Promega (Madison, WI, USA).
# UnifiedCut: A Simple and Efficient Neural Model for Thai, Burmese and Khmer Word Segmentation
## 1 Introduction
Given the word formation characteristics of southeast Asian Abugida languages, using excessively long context does not enhance segmentation performance and instead adds unnecessary model complexity. Our approach follows this modeling strategy, learning segmentation features for the target character based on fixed-length windows of multiple n-gram sequences. The n-gram inputs do not require language-specific preprocessing, such as syllable segmentation or character type recognition, making it more versatile. Our method introduces an attention mechanism that learns segmentation features from windowed contexts alongside a multi-head attention design that models these features from multiple perspectives. Unlike the convolutional networks used in DeepCut and AttaCut, the multi-head attention mechanism enables the model to learn more nuanced and effective segmentation features within the same computational framework. The multi-head attention features help enhance the model's generalization ability and improve the recall rate for out-of-vocabulary (OOV) words. Moreover, compared to the CNN-based model, the proposed approach also supports greater parallelism, leading to improved segmentation efficiency. Experiments demonstrate that UnifiedCut surpasses state-of-the-art systems in in-domain word segmentation accuracy and provides comparable or superior results in cross-domain scenarios. Additionally, UnifiedCut proves versatile and effective for syllable segmentation tasks, offering a robust and efficient solution across applications. Our contributions can be summarized as follows:  
1. We propose a simple and efficient unified neural model for Thai, Burmese, and Khmer word segmentation, which learns word segmentation features from windowed multiple characters n-gram sequences by an attention mechanism. 2. The proposed multi-head attention encoder has much fewer learnable parameters than the CNN-based models and supports greater parallelism. The proposed Uni-fiedCut runs about 4x times faster than the state-of-the-art Thai word segmentation model, DeepCut. * Experiments on public Thai, Burmese, and Khmer word segmentation datasets show that our model consistently outperforms the state-of-the-art word segmentation models, resulting in higher recall values of out-of-vocabulary words.
# AI-Enabled Electrocardiogram Analysis for Disease Diagnosis
## 3 ECG Signal Analysis
* Wrapper-based methods assess the relevance of a subset of features by training and evaluating the machine learning algorithm on various feature subsets [62]. Although these methods are computationally expensive, they can yield better accuracy than filter-based methods. Recursive feature elimination (RFE) is a well-known wrapper-based method that recursively eliminates the least essential features until the desired number of features is selected. * Embedded-based: Embedded-based methods are a combination of filter-based and wrapper-based methods. These methods perform feature selection during the machine learning algorithm's training process [62]. Lasso (Least Absolute Shrinkage and Selection Operator) and Ridge regression are typical examples of embedded-based methods that use regularization techniques. Recently, there have been evolutionary algorithms that have been used for the selection of ECG features, some of the algorithms are genetic algorithm [63], particle swarm optimizer [64], firefly algorithm [64].
## 5 Fabrication Techniques for Chitosan-Based Nanoparticles
One of the major risk factors associated with diabetes mellitus is the development of cataract and diabetic retinopathy. The presence of insulin receptors on the ocular surface has indicated a promising strategy for treating these complications. Peptides like insulin undergo rapid degradation when administered orally, thus the encapsulation of insulin in chitosan nanocarriers can achieve sustainable release with small initial burst profiles in the eye [40,41];
5. EMUSSIFICATION SOLVENT EVAPORATION: The technique of ESE is a well-defined method that comprises two steps: the process of emulsification of the solution consisting of the polymer and the substance to be encapsulated. This is followed by solvent evaporation or precipitation of polymer that results in particle hardening. The application of high sheer due to high-intensity sonification or homogenization during the emulsification process results in the breakdown of polymer into micro- or nanodroplets in the presence of surface-active agents. The solvent evaporation process majorly influences the morphology, encapsulation, and release properties of the encapsulated moiety. The single emulsion consists of the oil-water phase, while a double emulsion consists of the water-oil-water phase [42]. After the FDA approval of a cyclosporine A as an anti-inflammatory agent in the treatment of dry eye syndrome, attention has shifted to explore its uses in ophthalmology [43]. The encapsulation of cyclosporine A in poly(lactide-co-glycolide) nanoparticles coated with chitosan was prepared with single-emulsification solvent evaporation and demonstrated efficient ocular binding and prolonged anti-inflammatory properties [44,45];
6. SELF ASSEMBLY: Amphiphilic derivatives of chitosan are formed by attaching hydrophobic groups like cholesterol, cholic acid, deoxycholic acid, alkyl, acyl, or 5\(\beta\)-cholanic acid on chitosan backbone. At critical aggregation concentration, the amphiphilic derivatives of chitosan undergo spontaneous nanoparticle formation. The nanoparticles are characterized by the core-shell structure, as they consist of a hydrophobic core in a hydrophilic shell. Hydrophobic drugs with considerable water solubility have been loaded into the nanoparticles by direct addition to aqueous polymer dispersion [46,47].
## 3 Results and Discussion  
### Spatial Pushover Analysis (SPA)  
A pushover analysis is applied to the tested eccentric structures in this section. The lateral loads are assigned to the mass center of each floor. The roof displacements and first floor displacements of the eccentric structures of different eccentricities under lateral load are shown in Figure 6a,b, respectively. The resistance force decreases as the eccentricity increases, which means the seismic capacity of the structure is weaker. The structure with 5% eccentricity is an exception. The structure with 0% eccentricity (i.e., a symmetrical structure) has the best seismic capacity, and the structure with 30% eccentricities has the worst seismic capacity.
## 3 Test Results and Discussion
### Durability  
Figure 6 presents the Cantabro test results of PAC mixtures under immersion and freeze-thaw conditions. Both conditions had similar effect on the durability of PAC mixtures: i.e., as the number of conditioning cycles increases, the Cantabro loss becomes larger. Among various mixtures, the Cantabro loss of PAC10-I4 is obviously larger, and those of the others are relatively close. For the three novel PAC10s, the Cantabro loss decreased with the increasing content of the mineral fillers, but the Cantabro loss of PAC10-I8 is slightly smaller than that of PAC10-I6, indicating that increasing the mineral filler content can improve the durability of novel PAC10 mixture, but when the mineral filler content reaches a certain level, further increase of mineral filler content might not be effective in improving the durability of PAC. In other words, for the three mineral filler contents considered in this study, 6% is the optimum value in terms of the durability of the novel PAC10.
# Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM
## 4 Proposed Method  
### LSTM Block  
There are three stages in the process of the sequence entering the LSTM block: the segmentation stage, the block processing stage, and the overlap and addition stage. In the segmentation stage, the sequential feature input is divided into overlapping blocks, and they are connected to form a 3-D tensor. As is shown in Figure 3, in the block processing stage, the 3-D tensor enters the Bi-LSTM sequentially in the time sequence of the segmented feature sequence, and then the fully connected layer and the SE module are connected (98). The output of the SE module is multiplied element-by-element with the output of the fully connected layer, and Group Normalization (GN) is performed on the result obtained. Then we can get the final result through residual connection. The SE module is an attention mechanism that can learn to use global information to selectively emphasize useful channel information and suppress useless channel information. It learns the correlation between channels and can perform dynamic channel feature recalibration to adjust the network and improve the network expression ability. The SE module can achieve the purpose of capturing global information with a simpler structure and a smaller network scale. Global Average Pooling (GAP) (99) is used in the SE module to regularize the entire network to avoid the risk of overfitting caused by the full connection, and it can replace the conversion function of the full connection layer. In the overlap and addition stage, the 3-D output of the last LSTM block is converted back to sequential output by performing overlap addition on the block.
## 2 Data and Methods  
### Study Sites  
Observed WUE was calculated by the ratio of GPP and ET, which was collected at eight ChinaFLUX eddy covariance flux sites from 2003-2008. Detailed information for each flux site is listed in Table 1. These eight flux sites cover the main dominant vegetation types in China, including Changbaishan temperate mixed forest (CBS), Qianyanzhou evergreen needle leaf forest (QYZ), Dinghushan evergreen mixed forest (DHS), Xishuangbanna evergreen broadleaf forest (XSBN), Inner Mongolia temperate steppe (NM), Haibei alpine shrub-meadow (HBGC), Damxung alpine steppe-meadow (DX) and Yucheng cropland (YC).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Sites**} & \multirow{2}{*}{\begin{tabular}{c} **Location and** \\ **Altitude** \\ \end{tabular} } & \begin{tabular}{c} **Mean Annual** \\ **Temperature** \\ **(°C)** \\ \end{tabular} & \begin{tabular}{c} **Mean Annual** \\ **Precipitation** \\ **(mm)** \\ \end{tabular} & \multirow{2}{*}{**Soil Type**} & \multirow{2}{*}{**Predominant Species**} \\  & & & & & \\ \multirow{4}{*}{CBS} & 42.40\({}^{\text{o}}\)N, & & & & \\  & 128.10\({}^{\text{o}}\)E; & 3.6 & 713 & \begin{tabular}{c} Upland dark brown \\ forest soil \\ \end{tabular} & \
\begin{tabular}{c} _Pinus koriaensis, Tilia_ \\ _amurensis, Quercus_ \\ _mongolica and_ \\ _Faxinus mandhurica_ \\ _Pinus eliottii [PERSON], Pinus_ \\ _massoniana Lamb and_ \\ _[PERSON]_ \\ _lancelate Hook_ \\ \end{tabular} \\  & 23.15\({}^{\text{o}}\)N, & & & & \\ \multirow{4}{*}{DHS} & 112.50\({}^{\text{o}}\)E; & 20.9 & 1956 &
\begin{tabular}{c} _Pinus elliottii Engelm, Pinus_ \\ _massoniana Lamb and_ \\ _[PERSON]_ \\ _lancelate Hook_ \\ \end{tabular} \\  & 300 m & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Site descriptions (Changbaishan temperate mixed forest (CBS), Qianyanzhou evergreen needle leaf forest (QYZ), Dinghushan evergreen mixed forest (DHS), Xishuangbanna evergreen broadleaf forest (XSBN), Inner Mongolia temperate steppe (NM), Haibei alpine shrub-meadow (HBGC), Damxung alpine steppe-meadow (DX) and Yucheng cropland (YC).