esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
Apply a kernel trick to classify the training data in transformed feature space in case of soft margin SVM. \[K(x_{1},x_{2},...x_{m})= \begin{bmatrix}x_{1}.x_{1}&x_{1}.x_{2}&\dots&x_{1}.x_{m }\\ x_{2}.x_{1}&x_{2}.x_{2}&\dots&x_{2}.x_{m}\\ \vdots&\vdots&\ddots&\vdots\\ x_{m}.x_{1}&x_{m}.x_{2}&\dots&x_{m}.x_{m}\end{bmatrix} \]
4. Find the value of Lagrange multiplier (\(a\))
5. Find the value of \(w\) using \[w=\sum_{i=1}^{m}\alpha_{i}y_{i}x_{i}\]
6. Find the value of basis (\(b\)) using \[b=y_{i}-w.x_{i}\]
7. Repeat Step 1 to 6 for each class of training set. It is not always possible to get a hyperplane with zero training error so, we go with soft margin SVM by accepting some mistakes. In some cases, we get the hyperplane in acceptable error, but when we have mixed samples of all classes, as shown in Figure 10, then the soft margin will not work in an acceptable range. To solve the mentioned problem, the RBF kernel is applied (\(7\)) to transform the data into kernelized feature space. After classifying data in feature space, we need to bound a class within its area. The weighted mean is computed to get the upper bound of radius, and then the circumference is calculated to get the whole area of the class. Our proposed method targets those samples located in an intersected area of both classes as shown in Figure 12. In Figure 12 the mean of a class is computed to get the upper bound of radius. Figure 14 shows both mean and weighted mean of class in two-dimensional space. In experiments, all datasets are multi-dimensional. WR-SVM shows improvement in terms of accuracy compared to existing methods such as SVM and F-SVM using linear and RBF kernels. Figure 13: Original and kernelized feature space [49]. Therefore, it is noticed that many samples are in the intersected area and far from the mean of its actual class. When weighted mean is used to get the upper bound of class, then it is clear that the number of samples in an intersected area in Figure 14 is less than Figure 15.
## 4 Experiment Results
Combined with Equation (22), the estimated result of the side length of the cube target is 5.1814 mm with a relative error of 3.63%. Figure 10: Time-frequency feature extraction experimental results of cube targets. (**a**) Spin rate = 1.4 Hz. (**b**) Spin rate = 2.5 Hz. (**c**) Spin rate = 3.8 Hz. (**d**) Spin rate = 5.0 Hz. (**e**) Spin rate = 6.4 Hz.
# Hybrid Chirp Signal Design for Improved Long-Range (LoRa) Communications
## 4 Proposed Modulation Scheme
### Hybrid Demodulation  
In this section, we propose a hybrid demodulation algorithm to further improve the performance of the proposed FFT-based demodulator. In the presence of noise/interference, sometimes the FFT demodulator incorrectly detects the transmitted up-chirp (down-chirp)-based signal as a down-chirp (up-chirp)-based signal. To tackle this ambiguity, we perform two cross-correlation operations after performing the FFT-based demodulation, as cross-correlation-based demodulation provides optimum detection. After performing the multiplication of the received signal with raw down- and up-chirp signals, we find one up-chirp-based candidate transmitted LoRa symbol and one down-chirp-based candidate transmitted LoRa symbol. Then, we perform cross-correlation between the received signal and the candidate symbols, from where the best matched symbol is considered the transmitted symbol. The proposed hybrid demodulation scheme is summarized in Algorithm 1.  
```
1:Find \(z_{k_{u}}(t)\) and \(z_{k_{d}}(t)\) from (9) and (10), respectively.
2:Following (11) and (12), find the estimates of the up- and down-chirp-based transmitted symbol's indexes, i.e., \(\hat{k}_{u}\) and \(\hat{k}_{d}\), respectively.
3:Perform cross-correlation (the closed-form expression for the cross-correlation can be found in [25]) between the received signal and the LoRa symbols with indexes \(\hat{k}_{u}\) and \(\hat{k}_{d}\) and find the absolute cross-correlation values \(X_{\hat{k}_{u}}(\ell)\) and \(X_{\hat{k}_{u}}(\ell)\), respectively. \[X_{\hat{k}_{u}}(\ell) =\left|\int_{-\infty}^{\infty}y_{k}(t)\cdot x_{\hat{k}_{u}}^{*}( t-\ell)\right|\] \[X_{\hat{k}_{d}}(\ell) =\left|\int_{-\infty}^{\infty}y_{k}(t)\cdot x_{\hat{k}_{d}}^{*}( t-\ell)\right|\]
4: Find the estimated index of the transmitted symbol by the following equation: \[\hat{k}=\operatorname*{argmax}_{i\in\{\hat{k}_{u},\hat{k}_{d}\}}(X_{i}[0])\]
```  
**Algorithm 1** Hybrid Demodulation Technique  
The closed-form expression for the cross-correlation can be found in [25].
# Soil Hg Contamination Impact on Earthworms'
## 4 Discussion
### Effect of Hg on Earthworm Gut Parts  
By looking closer into the digestive system of the earthworm, we were able to characterize differences in the bacterial community structure along the gut tract; the bacterial composition shifted gradually from soil in the pot to earthworm stomach, upper gut, and lower gut parts. This is in line with previous findings that the majority of bacterial taxa in the earthworm gut are highly similar to those in the soil [53], and that only few taxa are truly endemic to the gut microbiome (e.g., [54]). The ingested soil microbial taxa might therefore still be very abundant in the stomach and upper gut compartments, while taxa more specific to the earthworm microbiome dominate in the lower gut, such as the Gammaproteobacteria, whose relative abundance increased in the lower gut samples in both the uncontaminated and Hg-contaminated treatments.  
In contrast to our third hypothesis, we did not find a clear difference in the Hg tolerance of the microbial community among earthworm gut parts. The shift in both bacterial and fungal community structure between upper and lower gut parts was less clear in the Hg-contaminated treatment. Furthermore, bacterial Mollicutes and Verminephrobacter sp., as well as fungal Yarrowia sp. and Penicillum sp., showed a consistently higher abundance in Hg-contaminated soil in all gut parts, suggesting that highly Hg-tolerant taxa are ubiquitous throughout the earthworm gut tract, similar to the persistence of gut-associated bacterial taxa observed in arsenic-contaminated soils [55].  
The bacterial merA gene, encoding mercury reductase, is phylogenetically widely dispersed and is thus a good biomarker for Hg tolerance [22]. The merA gene was reported to be present in many bacterial phyla, including the dominant phyla found in the Hg-treated samples (e.g., Actinobacteria and Proteobacteria). This is a potential explanation for the phylogenetically highly widespread Hg-tolerant taxa found in the Hg-contaminated treatment. However, it is possible that external environmental conditions favor Hg reduction via the activation of the Hg reductase enzyme encoded by the merA gene.  
The elevated concentration of Hg in the earthworm gut, highest in the gastodermis [49], might thus favor microbial Hg reduction. Moreover, the microhabitat conditions of the earthworm gut, such as high moisture, sufficient availability of organic nutrients and adequate carbon substrate [55; 56], might augment the efficiency of the Hg reduction, similar to what has been described for denitrification in the earthworm gut [57]. However, no clear trend emerged from the present study to help understand the link between the earthworm gut microhabitat and bacterial Hg reduction. Future investigations should be conducted to foster a mechanistic understanding of bacterial Hg reduction within the earthworm gut.
## 5 Experiment
### Ablation Analysis (RQ2)  
To verify the effectiveness of each MKCLN component, we conducted ablation experiments on the model, exploring the contribution of a component to the final performance by corrupting a variant of the MKCLN model for the component. We define two variants of the model as MKCLN that corrupts the local-level comparison learning component MKCLN (w/L) and MKCLN that corrupts the global-level comparison learning component MKCLN (w/G), respectively. The experimental results are shown in Table 3, and by observing them we can conclude the following.  
Figure 4: The results of recall@k on two datasets.  
Figure 5: The results of ndcg@k on two datasets.  
* Removing both local and global levels of contrast learning models significantly degrades the performance, which indicates that collaborative information, semantic information, and high-order structural information have a significant contribution to the recipe recommendation task.
* MKCLN (\(w/L\)) performs worse than MKCLN (\(w/G\)). This indicates that for the recipe recommendation task, the collaborative and semantic information contributes more to the fine-grained node representations, and also illustrates the importance of collecting information through two different views.
## 2 Mathematical Modeling  
### Geometric Model  
#### 2.1.1 Basic Geometric Parameters  
The main geometric parameters are shown in Figure 6. As the gap width of leading side equals to that of the trailing side (the gate-rotor is drawn by solid line), \(\delta\) is set zero. If the gate-rotor deflects anticlockwise in the screw groove (indicated by dashed line), \(\delta\) is set positive. On the contrary, \(\delta\) is set negative.
# C-SHAP: A Hybrid Method for Fast and Efficient Interpretability
## 3 How SHAP Interprets Model Decisions  
SHAP [21, 52] interprets model decisions by assigning importance values to each feature based on its contribution to a prediction, which is crucial for understanding the role of different features in machine learning models. Interpretability, the ability to explain or understand the reasoning behind a model's predictions, is key to building trust in machine learning systems. It refers to a model's capacity to provide human-understandable insights into its decision-making process, making it easier to understand how and why certain predictions are made.  
Grounded in cooperative game theory, SHAP uses Shapley values to fairly and consistently distribute credit among features, ensuring a balanced representation of their importance in the decision-making process. The Shapley value for a feature \(i\) is given by  
\[\phi_{i}=\sum_{S\subseteq N\setminus\{i\}}\frac{|S|!(|N|-|S|-1)!}{|N|!}(v(S \cup\{i\})-v(S))\]  
Here,  
* \(\phi_{i}\) is the Shapley value for feature \(i\).
* \(N\) is the set of all features.
* \(S\) is a subset of features that does not include feature \(i\).
* \(v(S)\) is the prediction based only on the subset \(S\) of features.
* \(v(S\cup\{i\})\) is the prediction based on the subset \(S\) plus the feature \(i\).
* The fraction is the weighting factor, representing how to fairly distribute the marginal contributions across all possible subsets.  
However, while SHAP provides highly accurate and consistent feature attributions, it is computationally expensive, especially when applied to larger datasets and complex algorithms like Random Forest and SVC. This results in slow execution times in real-world applications, limiting its practicality in scenarios where both speed and interpretability are crucial, such as medical diagnostics or financial modeling [30]. Even with optimizations like TreeSHAP, SHAP's computational burden can hinder its use in time-sensitive environments [25, 26].  
In summary, SHAP is a powerful tool for explaining model decisions, offering both fairness and accuracy in feature attribution. However, its computational cost remains a significant challenge, particularly in large-scale applications [22, 28, 53, 54].
# Detecting Change at Archaeological Sites in North Africa Using Open-Source Satellite Imagery
###### Abstract  
Our paper presents a remote sensing workflow for identifying modern activities that threaten archaeological sites, developed as part of the work of the Endangered Archaeology of the Middle East and North Africa (EAMENA) project. We use open-source Sentinel-2 satellite imagery and the free tool Google Earth Engine to run a per-pixel change detection to make the methods and data as accessible as possible for heritage professionals. We apply this and perform validation at two case studies, the Aswan and Kom-Ombo area in Egypt, and the Jufra oases in Libya, with an overall accuracy of the results ranging from 85-91%. Human activities, such as construction, agriculture, rubbish dumping and natural processes were successfully detected at archaeological sites by the algorithm, allowing these sites to be prioritised for recording. A few instances of change too small to be detected by Sentinel-2 were missed, and false positives were caused by registration errors, shadow and movements of sand. This paper shows that the expansion of agricultural and urban areas particularly threatens the survival of archaeological sites, but our extensive online database of archaeological sites and programme of training courses places us in a unique position to make our methods widely available.  
archaeology; change detection; North Africa; Libya; Egypt; Google Earth Engine Article
###### Abstract  
In this study, the ability of a bench-scale simulated constructed wetland (CW) to remove organic matter (OM) and tetracycline (TC) from water was examined. The performance of CW was evaluated by varying the initial concentrations of the target compounds and the hydraulic retention times (HRTs). Findings showed that OM removal efficiencies were 55.2-80.8%, 28.1-71.9% and 72.1-79.7% for ultraviolet absorbance at 254 nm (UV-254), dissolved organic carbon (DOC) and soluble chemical oxygen demand (sCOD) respectively, under 1 day-HRT, whereas higher initial DOC concentration achieved better removal efficiencies. Changing from 1 day-HRT to 2 day-HRT, the removal efficiency of OMs remained practically unchanged, while that of NH\({}_{3}\)-N increased considerably, from 61.7% to 73.0%, implying that the removal of ammonia in CW needs a longer time for complete treatment. CW also showed an excellent performance in removing TC, especially in the first two hours of operation through the absorption process. In addition, the findings from this research revealed an improvement in effluent water quality when photocatalysis (TiO\({}_{2}\)/a-Al\({}_{2}\)O\({}_{3}\), with ultraviolet A (UVA) irradiation) was used as the post-treatment following CW, presented by the increase in removal efficiency of OMs of the combined system compared to that of CW alone. This study points to the possible and promising application of the low-cost water treatment system for dealing with OMs and TC in water.  
constructed wetlands; organic matter; tetracycline; removal efficiency; photocatalysis; water quality +
Footnote †: journal: Applied Sci. 2019, 9, 2680; doi:10.3390/appr9132680
## 2 Preliminaries and Problem Description
### False Data Injection Attacks  
It is noteworthy that deception attacks in communication links can disrupt the system's state estimation by injecting falsified state information (such as fabricated position or velocity data). Collaborative communication channels between MHSTs, relying heavily on wide-area wireless transmissions, are particularly vulnerable to these attacks compared to local closed-loop control signals. Furthermore, considering the attacker's limited capabilities and the necessity of concealing their malicious activities, attack attempts do not always succeed. Hence, this paper employs a success probability to characterize the attacker's strategy. The measurements received by the controller from the leading train and neighboring vehicles are defined as follows:  
\[\begin{split}\bar{v}_{j}(\ell)&=v_{j}(\ell)+\beta_{j}( \ell)\chi_{j}(\ell)\\ \bar{v}_{d}(\ell)&=v_{d}(\ell)+\beta_{d}(\ell)\chi_{d}(\ell) \end{split} \tag{9}\]  
the variable \(\bar{v}_{j}(\ell)\) denotes the neighboring train speed signal under deception attacks, while \(v_{j}(\ell)\) indicates the original speed signal. The variable \(\bar{v}_{d}(\ell)\) denotes the leading train speed signal under deception attacks, while \(v_{d}(\ell)\) indicates the original speed signal. \(\beta_{j}(\ell)\chi_{j}(\ell)\) and \(\beta_{d}(\ell)\chi_{d}(\ell)\) characterize the attacker's impact on the train system, where \(\beta_{j}(\ell)\) and \(\beta_{d}(\ell)\) specify whether the attack succeeds or fails, and \(\chi_{j}(\ell)\) and \(\chi_{d}(\ell)\) are the actual injected deceptive data. Consequently, the expression can quantify both the success rate and the influence of the attacker on the control system. The statistical properties of \(\beta_{j}(\ell)\) and \(\beta_{d}(\ell)\) are predefined with \(\mathbb{E}\{\beta_{j}(\ell)=1\}=\bar{\beta}_{1}\), \(\mathbb{E}\{\beta_{d}(\ell)=1\}=\bar{\beta}_{2}\), and \(\mathbb{E}\{\beta_{j}(\ell)=0\}=1-\bar{\beta}_{1}\), \(\mathbb{E}\{\beta_{d}(\ell)=0\}=1-\bar{\beta}_{2}\), where \(\bar{\beta}_{1},\bar{\beta}_{2}\in[0,1)\) are constants. Additionally, the unknown deceptive attack data are constrained by cyber-attack energy limitations, satisfying \(|\chi_{j}(\ell)|\leq d_{1},|\chi_{d}(\ell)|\leq d_{2}\).
## 4 Experimental Analysis
### Presentation of the Result of Training Models
The high AUC signifies the model's strength in handling complex patterns, making it one of the most reliable models for terrorism prediction in this dataset (see Figure 11). Extra Trees also achieves a near-perfect AUC of 0.98, indicating excellent model performance. This high score highlights its ability to accurately distinguish classes, confirming the effectiveness of ensemble methods with randomized trees after fine-tuning (see Figure 12). The observed differences in model performance can be more deeply understood by considering the structural characteristics of the algorithms in relation to the properties of  
Figure 11: The XGBoost model ROC curve on GTD dataset. Figure 12: The Extra Trees model ROC curve on GTD dataset. the datasets. For instance, XGBoost consistently demonstrated superior F1-scores across both datasets, which aligns with its gradient boosting mechanism that iteratively reduces residual errors and adapts to difficult cases--an advantage when dealing with ambiguous or overlapping lexical patterns common in terrorism discourse. Its ability to model complex interactions between features allows it to capture nuanced co-occurrence patterns that simpler models may overlook. In contrast, the Extra Trees algorithm, while achieving the highest overall accuracy, tended to produce conservative predictions, as evidenced by its relatively lower recall. This is attributable to its use of extreme randomization in feature splits, which, although effective in reducing variance and avoiding overfitting, may fail to capture subtle patterns associated with positive (i.e., terrorism-relevant) terms, especially in the presence of class imbalance. Random forest and Bagging models displayed more balanced profiles, benefiting from ensemble averaging to mitigate overfitting while still capturing moderately complex relationships. However, these models may lack the iterative error-correction refinement seen in boosting algorithms. The single decision tree model, while interpretable and efficient, suffered from relatively low recall, indicating susceptibility to both overfitting and underfitting depending on tree depth. The data characteristics further amplify these behaviors. The open-source dataset is lexically rich but semantically noisy, with high-dimensional TF-IDF vectors and considerable synonymy. This setting favors models that can handle sparse and noisy input (e.g., XGBoost), whereas more rigid algorithms may underperform. The GTD dataset, although more structured, still contains categorical and lexical ambiguity that benefits from models capable of fine-grained feature interaction modeling. The results suggest that the algorithmic structure should be matched to the data complexity. Boosting-based models like XGBoost appear better suited to handling heterogeneous, imbalanced, and lexically complex terrorism data, while ensemble bagging methods provide robustness but may require careful tuning to avoid recall deficiencies.
# A Model for Simulating the Upward Flow of a Viscous Fluid in a Fracture Network
## 3 The Upward Flow of Liquid in a Bifurcated Fracture
### Model
#### 3.1.1 Governing Equations  
In channels 0, 1, and 2, we describe the evolution of the fracture aperture using the following nonlinear equations, respectively,  
Figure 11: Model of a branch fracture consisting of channels 0, 1, and 2. For an open channel, the triangles surrounded by the dashed line are removed from the outlets of channels 1 and 2, while, for the closed channel, the triangles remain.  
Figure 10: The profiles of (**a**) the liquid pressure, (**b**) the liquid speed, and (**c**) the liquid temperature in the closed fractures with different propagating speeds.  
\[\frac{\partial h_{0}}{\partial t} =\frac{(h_{0})^{3}}{12\mu_{0}}\frac{\partial^{2}p_{0}}{\partial z^{2 }}-\frac{(h_{0})^{3}}{12(\mu_{0})^{2}}\frac{\partial\mu_{0}}{\partial z}\frac{ \partial p_{0}}{\partial z}+\frac{(h_{0})^{2}}{4\mu_{0}}\frac{\partial p_{0}}{ \partial z}\frac{\partial h_{0}}{\partial z}-2u_{L,0}\,,\] \[\frac{\partial h_{1}}{\partial t} =\frac{(h_{1})^{3}}{12\mu_{1}}\frac{\partial^{2}p_{1}}{\partial z ^{2}}-\frac{(h_{1})^{3}}{12(\mu_{1})^{2}}\frac{\partial\mu_{1}}{\partial z} \frac{\partial p_{1}}{\partial z}+\frac{(h_{1})^{2}}{4\mu_{1}}\frac{\partial p _{1}}{\partial z}\frac{\partial h_{1}}{\partial z}-2u_{L,1}\,,\] \[\frac{\partial h_{2}}{\partial t} =\frac{(h_{2})^{3}}{12\mu_{2}}\frac{\partial^{2}p_{2}}{\partial z ^{2}}-\frac{(h_{2})^{3}}{12(\mu_{2})^{2}}\frac{\partial\mu_{2}}{\partial z} \frac{\partial p_{2}}{\partial z}+\frac{(h_{2})^{2}}{4\mu_{2}}\frac{\partial p _{2}}{\partial z}\frac{\partial h_{2}}{\partial z}-2u_{L,2}\,, \tag{22}\]  
where the subscripts 0, 1, and 2 represent the variables in channels 0, 1, and 2, and the leakage speeds in the channels can be computed as follows,  
\[u_{L,0}(z,t) =\frac{\hat{k}_{p,0}}{\mu_{0}}(p_{0}(z,t)-p_{b,0})\,,\] \[u_{L,1}(z,t) =\frac{\hat{k}_{p,1}}{\mu_{1}}(p_{1}(z,t)-p_{b,1})\,,\] \[u_{L,2}(z,t) =\frac{\hat{k}_{p,2}}{\mu_{2}}(p_{2}(z,t)-p_{b,2})\,. \tag{23}\]  
Based on the balance between the elastic force and the liquid pressure, we describe the relationship between the fracture aperture and the pressure in the three channels as follows,  
\[h_{0}(z,t) =h_{o,0}+\frac{p_{0}(z,t)-p_{o,0}}{k_{n,0}}\,,\] \[h_{1}(z,t) =h_{o,1}+\frac{p_{1}(z,t)-p_{o,1}}{k_{n,1}}\,,\] \[h_{2}(z,t) =h_{o,2}+\frac{p_{2}(z,t)-p_{o,2}}{k_{n,2}}\,. \tag{24}\]  
We solve the governing equations (Equations (20) and (22)) for a total of six variables: three pressure distributions within three channels, \(p_{0}\), \(p_{1}\), and \(p_{2}\), and the apertures of the three channels, \(h_{0}\), \(h_{1}\), and \(h_{2}\).
# Bayesian Optimization Based Efficient Layer Sharing for Incremental Learning
## 3 Proposed Algorithm
### Combined Classification Accuracy  
The combined classification accuracy measures the quality of incremental learning with \(n\) initial sharing layers by activating the combined softmax of both the base network and the new branch network simultaneously. The equation of the combined classification accuracy is as follows:\[L_{Acc}(n)=\frac{1}{N}\sum_{x_{i}\in D_{N}}n(x_{i},d_{i}) \tag{2}\] \[n(x_{i},d_{i})=\begin{cases}1&\text{if }F_{base,new}(x_{i})=d_{i},\\ 0&\text{otherwise}\.\end{cases}\]  
where \(N\) denotes the total number of data for testing combined classification, \(x_{i}\) is the \(i\)th data for testing, \(d_{i}\) is the label of \(x_{i}\), and \(n(x_{i},d_{i})\) denotes accuracy on \(x_{i}\), respectively. Therefore, if the output of the combined network has the same value with the ground truth on \(x_{i}\). i.e., \(F_{base,new}(x_{i})=d_{i}\), it provides 1 or otherwise 0.