esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### HM Contamination in Soil  
The average concentrations of the investigated HMs (except Pb) in the soil located within 50-700 m of the Alaverdi copper smelting plant (sites S-50-S-700) were significantly higher (\(p<0.05\)) than those at the reference site (S-R). The contents of As, Cu, Zn, Pb, and Mo at sites S-50-S-700 exceeded the reference levels by 3.6-23.7, 6.2-10.0, 2.0-3.6, 0-1.6, and 3.6-6.8 times, respectively (Figure 2). The concentrations of As, Cu, Zn, and Mo mostly decreased with an increase in distance from the smelter, while the levels of Pb between sites S-50 and S-200 increased slightly, then decreased at the further sites (Figure 2). Pearson's correlation analysis showed a strong positive relationship between the concentrations of the investigated HMs (except Pb; Table 2), which indicates that they were likely induced by the same source.  
Figure 2: Concentrations of some HMs in the soil located at different distances from the Alaverdi copper smelter.  
According to the Igeo values (Table 3), the soil located nearest the copper smelter (sites S-50 and S-100) was heavily and moderately contaminated with As and Zn, respectively, while the contamination decreased with an increase in distance from the smelter, and the soil at sites S-200-S-700 showed moderate contamination and non-contamination to moderate contamination with As and Zn, respectively. Cu showed moderate to heavy contamination at all investigated sites (S-50-S-700). Mo was also an ecologically risky element in soil, particularly at sites S-50-S-200 where the soil was moderately to heavily (S-50-S-100) and moderately (S-200) contaminated with Mo. The ex-operation of the Alaverdi copper smelter did not cause ecologically risky contamination with Pb in the investigation area where the soil was either uncontaminated or uncontaminated to moderately contaminated with this metal.
# Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images
## 5 Discussion  
### Overall Performance Analysis of RS-YOLOX-Nano  
Utilizing the YOLOX-Nano model as the foundational network, this study integrates data augmentation strategies that combine Mosaic and Mix-up, employing the \(EIoU\) loss function to replace the original \(IoU\) loss function. Additionally, it incorporates redesigned Focus, SPP, and PAFPN modules, enhanced with various attention mechanisms, to substitute the original modules in the RS-YOLOX-Nano improvement network.
# Feature-Based Image Watermarking Algorithm Using SVD and APBT for Copyright Protection
## 2 Related Work
### Apbt  
APBT [26] is an improved transform firstly proposed by [PERSON] et al. which can be applied in the field of image processing, such as image compression, image denoising, image demosaicking, and so on [27]. In practice, owing to the fact that APBT has better energy concentration in low frequencies and attenuation characteristics in high frequencies, it has better applications in image processing. Suppose that \(\mathbf{X}\) is the data of a \(B\times B\) image block, and \(\mathbf{V}\) represents the APBT matrix with the size of \(B\times B\), respectively, the transform coefficients block \(\mathbf{Y}\) after two-dimensional APBT transform can be denoted by:  
\[\mathbf{Y}=\mathbf{V}\mathbf{X}\mathbf{V}^{\mathrm{T}}, \tag{1}\]  
where \(\mathbf{V}^{\mathrm{T}}\) is the transpose matrix of \(\mathbf{V}\). In the contrary, we use:  
\[\mathbf{X}=\mathbf{V}^{-1}\mathbf{Y}(\mathbf{V}^{-1})^{\mathrm{T}}, \tag{2}\]  
to reconstruct the image, and \(\mathbf{V}^{-1}\) is the inverse matrix of \(\mathbf{V}\).  
When transform matrix \(\mathbf{V}\) is set as the different forms as Equations (3) and (4) separately, the corresponding transforms can be defined as all phase discrete cosine biorthogonal transform (APDCBT) and all phase inverse discrete cosine biorthogonal transform (APIDCBT), respectively.  
\[\mathbf{V}_{1}(p,q)=\left\{\begin{array}{ll}\frac{B-p}{B^{2}},&p=0,1,\cdots,B-1,q=0,\\ \frac{B}{B^{2}}[(B-p)\cos\frac{pq\pi}{B}-\csc\frac{q\pi}{B}\sin\frac{pq\pi}{B} ],&p=0,1,\cdots,B-1,q=1,2,\cdots,B-1,\end{array}\right. \tag{3}\]  
\[\mathbf{V}_{2}(p,q)=\left\{\begin{array}{ll}\frac{1}{B},&p=0,\ q=0,1,\cdots,B-1,\\ \frac{B-p+\sqrt{2}-1}{B^{2}}\cos\frac{p(2q+1)\pi}{B},&p=0,1,\cdots,B-1,\ q=1,2, \cdots,B-1.\end{array}\right. \tag{4}\]  
Figure 1: General framework of digital watermarking.
## 3 Results and Discussion  
### Impact of Ultrasound on Extraction Performances  
The effect of acoustic cavitation on extraction performances of phenolic compounds was assessed by submitting the leaves to increasing sonication durations (5 min, 15 min, and 60 min). Control experiments were performed using stirring to evaluate the impact of the immersion of solvent on the leaves. Results expressed in terms of extraction yield and extract composition in phenolic compounds are presented in Table 1. Results show an increase in polyphenol concentration in extract with increasing sonication duration (from 6.96 \(\upmu\)g eq. O/mL to 48.75 \(\upmu\)g eq. O/mL for sonication duration from 5 min to 60 min, respectively). Given the yields obtained by stirring extractions (0.36 and 1.46 \(\upmu\)g eq. O/mL for stirring durations from 5 to 60 min), it can be concluded that ultrasound led to a sharp increase in polyphenol extraction. This effect has been reported by other authors for extraction of polyphenol from olive leaves [28; 29].  
Polyphenol composition indicates that oleuropein was the major polyphenol extracted (up to a concentration of 28.16 \(\upmu\)g/mL for 60 min of sonication, Table 1). Hydroxyrosol extraction seemed to be mainly impacted by the type of process used: hydroxytyrosol concentration in extracts is close to zero for stirring and for all durations of ultrasound-assisted extraction, its concentration is comprised between 0.20 and 0.26 \(\upmu\)g/mL. Tyrosol concentrations in extracts are very low, which could be due to the fact that the batch of leaves contained low amounts of tyrosol. In the literature, oleuropein is reported as the major polyphenol extracted from olive leaves and hydroxytyrosol, in a lesser proportion, is also a characteristic phenolic in olive leaves [30; 31]. Oleuropein and hydroxytyrosol account for less than 50% of total phenolic compounds extracted (Table 1). Other phenolic  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & \multicolumn{3}{c}{**Extract Composition \({}^{*}\)**} \\ \hline
**Extraction** & **Extraction** & **Total Polyphenol** & & & \\
**Process** & **Duration** & **Content \({}^{*}\)** & & & \\ \hline
**(-)** & **(min)** & **(\(\upmu\) eq. O/mL) \({}^{**}\)** & **(\(\upmu\)g/mL)** & **(\(\upmu\)g/mL)** & **(\(\upmu\)g/mL)** \\ \hline stirring & 5 min & 0.36 & 0.10 & 0.00 & 0.00 \\  & 60 min & 1.46 & 0.36 & 0.00 & 0.04 \\ \hline ultrasound & 5 min & 6.96 & 2.75 & 0.02 & 0.24 \\  & 15 min & 22.44 & 13.09 & 0.00 & 0.20 \\  & 60 min & 48.75 & 28.16 & 0.02 & 0.26 \\ \hline \hline \multicolumn{6}{l}{\({}^{*}\)\(n=3\), \({}^{**}\) ug. O/mL: micro equivalent Oleuropein/mL of extract.} \\ \end{tabular}
\end{table}
Table 1: Comparison of extraction performances (yield and polyphenol composition of extracts) for stirring and ultrasound.  
compounds have been identified in olive leaf extracts in previous studies (e.g., verbascoside, apigenin-7-glucose, and luteolin-7-glucoside [30; 31]).
# Multi-Hop Routing Protocols for Oil Pipeline Leak Detection Systems
## 2 Related Work  
In this section, we present the related systems that are used in pipeline leakage detection and specifically the routing protocols that are used in these systems.
## 4 Open Challenges and Future Research Directions
### AI-Driven Security: Federated Learning and Edge AI Challenges  
AI is increasingly employed in IoE systems for tasks such as threat detection, anomaly recognition, and proactive defense mechanisms. However, AI-based security solutions themselves are becoming vulnerable to attacks.  
Federated Learning for Privacy-Preserving AI SecurityFL enables AI models to be trained without transmitting user data to a central server. The use of FL in IoE devices provides significant advantages, especially in data-sensitive domains such as healthcare, finance, and smart cities [15; 25; 27; 59; 106]. Nevertheless, several critical challenges remain:  
* Data synchronization and model update frequencies must be optimized for IoE environments.
* Blockchain-based verification mechanisms should be incorporated to ensure the reliability of model sharing.  
Real-Time Threat Detection with Edge AIEdge AI allows IoE devices to analyze data locally without the need for cloud processing. This enables instant responses to security events such as DDoS attacks, data forgery, and malware detection. However, edge devices face limitations in computational power, requiring optimized AI models. Additionally, secure model updates are necessary, where blockchain and FL can play supportive roles [16; 28].  
AI-Based Attacks and Model SecurityAI models in IoE systems are susceptible to adversarial attacks, model poisoning, and data manipulation, which can lead to incorrect decisions or system vulnerabilities. To enhance the security of AI models used in IoE, advanced FL and Edge AI solutions are under development [108; 112; 113].  
Open Research Areas
* Efficient implementation of FL in IoE devices and Edge AI
* Optimization of Edge AI for real-time IoE security
* Protection of AI models against Adversarial Attacks and Model Poisoning
* Secure sharing of AI model updates using blockchain-supported mechanisms  
Model Complexity and Edge ConstraintsEdge AI applications enable fast and local analysis in IoE devices. However, these devices often have limited computational power and memory, making it challenging to deploy complex models such as deep learning. Furthermore, continuously updating these models imposes communication and resource burdens on the network and hardware.  
Research DirectionTechnologies such as TinyML, model quantization, knowledge distillation, and adaptive update strategies are at the forefront of developing AI systems optimized for resource-constrained edge devices [165; 166]. Additionally, FL allows data to be processed locally, enhancing privacy protection while reducing dependence on centralized servers [167; 168; 169; 170]. Future research should focus on combining TinyML and FL to develop low-resource yet secure Edge AI solutions. Moreover, blockchain-supported FL frameworks and privacy-preserving update mechanisms should be further investigated to strengthen security in IoE environments.
# ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network
However, we aim to design a routing algorithm adapted to both static and dynamic networks with minimal traffic overhead for better availability. In this paper, we propose ACE: a Routing Algorithm based on Autonomous Channel Scheduling for Bluetooth Mesh Network. Four major advantages of ACE are shown as follows:  
* Compared with Bluetooth Mesh, ACE can create and maintain routes without any additional route management packets nor connection state transitions. * With the well-designed routing algorithm, the transmission latency of ACE is much lower than standard Bluetooth Mesh. * The ACE can fully utilize all the 40 channels of BLE, but standard Bluetooth Mesh only use three advertising channels for data transmission. * A beacon-aware routes recovery mechanism is proposed. It can recover routes mainly by adaptively listening beacon packets without any traffic overhead. First, through reusing BLE Mesh messages (e.g., heartbeat message), Second, the routing algorithm can choose the route with minimum latency even in high dynamic scenarios. The end-to-end latency is greatly reduced compared with standard Bluetooth Mesh. Third, ACE integrated with autonomous channel scheduling scheme, which can utilize all the 37 data channels. Last, we create a beacon-aware routes recovery mechanism, which can enable movable nodes recovering routes mainly by listening beacon packets on a previously known channel adaptively without any traffic overhead. We implement ACE on the nRF52840 SoC [13] and evaluate its performance both on our testbed and simulation, and also evaluate Bluetooth Mesh for comparison. The experimental results show that our routing algorithm on the nRF52 reduces the end-to-end latency by 16%, increases packet delivery ratio (PDR) by 30% under heavy traffic, improves scalability, BLE channel resource utilization, and can be less affected by external BLE interference. The contributions of this work are threefold. * We propose a proactive routing algorithm for Bluetooth Mesh network. And an autonomous channel scheduling mechanism is applied to fully utilize all the 40 BLE channels. * We customize the routing algorithm for Bluetooth Mesh containing the following advantages: (1) Route creation reuses existing mesh messages without inner-node negotiation nor path reservation, and (2) routes recovery using an adaptively beacon-aware mechanism without additional traffic overhead. * We implement ACE on the nRF52840 SoC, and the experimental results show that ACE outperforms Bluetooth Mesh in the following aspects: end-to-end latency, reliability, spectrum utilization, and scalability. Moreover, unmodified smartphones can control the mesh network without knowing the existence of ACE.
# Fault Imaging of Seismic Data Based on a Modified U-Net with Dilated Convolution
## 3 The Architecture of the Modified U-Net
In these operations, the purpose of down-sampling is to obtain more global information, the purpose of up-sampling is to restore the size of feature maps, and the purpose of softmax is to enable the module to automatically select multi-scale information. Three feature maps with spatial weights are multiplied by three feature maps of different scales obtained by dilated convolution to get three new feature maps. Finally, after splicing the three groups of new feature maps, a residual operation is performed with the input feature map to acquire the final prediction results. In summary, under the guidance of spatial information, the GSM module can select multi-scale information among a group of feature maps. Figure 4: The architecture of GCM. The proposed neural network is based on the U-Net network and has two functional modules, the GCM and GSM, which can finely describe faults of different scales. Its architecture is shown in Figure 6. Due to the powerful multi-scale information selection ability of GCM and GSM modules, this paper only uses a 4-layer U-Net based on a coding-decoding structure as the basic network. In the coding path, only two 3 \(\times\) 3 convolution and maximum pooling are used to quickly obtain feature maps with different resolutions. In the decoding path, multiple simple decoding blocks are used to quickly and effectively recover feature maps with high resolution. In this neural network, the data distribution after convolution is corrected by BN and ReLU, and the GCM module is placed at the connection layer of the network to automatically select multi-scale information, which makes up for the lack of transmitting single information to the decoder by the encoder of conventional U-Net. At the same time, the GSM module between groups is placed in the path of decoder to realize the function of multi-scale information selection, which makes up for the disadvantage of losing global information in up-sampling process.
## 4 Results
### Complementarity of MEVO Municipal Electric Bike Systems against Other Forms of Urban Transportation  
As indicated above in the literature review, municipal bike schemes aim, for example, to make it easier for passengers to travel to and from public transport terminals or stations (the so-called first and last mile). Therefore, municipal bike schemes should stimulate the demand for public transport services through increasing their time-based availability, since the total urban travel time in regard to door-to-door systems, thanks to the municipal bikes, should be reduced (referring to people who walked the first and the last mile before introducing the municipal bike scheme). The reduced total travel time in regard to the door-to-door system resulted from the higher average speed of electric bike travel compared with walking trips or trips by traditional bicycles. The average speed of walking trips totaled roughly 4.5 km/h [63], whereas the trips by traditional bicycles amounted to 18.8 km/h. Meanwhile, bikes with electric power-assisted steering (Pedelec) reached 21.9 km/h, and electric bikes (S-pedelec) reached 27.9 km/h [64]. The data show that traveling by a bike equipped with electric power-assisted steering was nearly, on average, five times faster than walking. In order to increase the time-based availability of public transport through offering intermodal travels combined with municipal bikes, it is necessary to ensure proper quality of the bike-sharing systems. In this context, the particularly important features of bike-sharing systems include the number of municipal bikes per city resident or system user (which affects their availability) and the mode of bike relocation in systems with no docking stations (which affects the distance between the user and the closest vacant bike).  
The data presented in Table 8 indicate that only every ninth surveyed respondent used a MEVO municipal bike most frequently for travel combined with public transport. The majority of the surveyed respondents used the municipal bike for direct trips to their destinations. Therefore, a MEVO municipal bike was used only to a small extent as a means of transportation complementary to public transport.
# Fabrication of Stretchable Transparent Electrode by Utilizing Self-Induced Vacuum Force
###### Abstract  
The key challenge in fabricating a stretchable transparent electrode is the effective transfer of an electric conductor to a stretchable substrate. To this end, we used vacuum force to fully permeate the elastomer substrate into the electric conductor. The vacuum force was self-induced from the evaporation of the solvent in the electric conductor. Hence, a solvent, having a high evaporation rate, is postulated to exhibit superior fabrication quality. To demonstrate this, three different solvents were tested for preparation of the conductor slurry. In the test, the high-vapor-pressure solvents resulted in the superior quality of the fabricated stretchable electrode. Furthermore, the heating direction was changed during thermal curing to maximize the self-induced vacuum force. The plate-heating curing exhibited better transferring efficiency of the electric conductor because the evaporation of the solvent in the conductor slurry was accelerated faster than that of the thermal curing of the elastomer substrate. Besides the achieved high quality of the electrode, the fabrication cost can be drastically reduced because the extra process required to dry the electric conductor is omitted by simultaneous curing of the electric conductor and the stretchable elastomer substrate.  
stretchable transparent electrode; vacuum force; highly porous carbon Article
## 4 Optimization Strategy and Classification
### Optimization Strategy of LPLC-HDL
#### 4.1.2 Class-Specific Dictionary Learning
By using (12) and denoting \(\bar{L}_{i}\) as \(L_{i}-l_{i}\) with the identity matrix \(l_{i}\in\mathbf{R}^{N_{i}\times N_{i}}\), we can further obtain a matrix version for \(Z_{p}^{i}\) update as:  
\[\hat{Z}_{p}^{i}=\Big{(}D_{p}^{\mathrm{T}}D_{p}+\lambda P_{i}^{\mathrm{T}}P_{i }+2\gamma l_{i}\Big{)}^{-1}\Big{(}D_{p}^{\mathrm{T}}X_{i}^{\prime\prime}- \gamma Z_{p}^{i}\bar{L}_{i}\Big{)}. \tag{22}\]  
By repeating the steps (a)-(d), we can iteratively obtain all the optimal variables \((D_{0},L_{0},Z_{0},D_{p},Z_{p})\). The overall optimization of our LPLC-HDL is summarized in Algorithm 1. Moreover, the objective function value of Equation (4) decreases as the number of iterations increases on a given dataset. For example, the convergence curve of LPLC-HDL on the Yale face dataset [29] is illustrated in Figure 1. From it we can see that the proposed algorithm converges quickly, no more than 20 iterations. Figure 1: The convergence curve of the proposed LPLC-HDL with the number of iterations on the Yale face dataset. In Algorithm 1, the time complexity of our LPLC-HDL mainly comes from these parts: \(O(k_{0}^{3}mN)\) for updating the shared dictionary \(D_{0}\), \(O(k_{0}^{2})\) for updating the graph Laplacian matrix \(L_{0}\), \(O(k_{0}^{3})\) for updating the commonality representation \(Z_{0}\), \(O(K_{p}mN)\) for updating the class-specific dictionary \(D_{p}\) and \(O(K_{p}^{3})\) for updating the particularity representation \(Z_{p}\). Thus the final time complexity is \(O((k_{0}^{3}+K_{p})mN+K_{p}^{3})\) in each iteration. ```
1:Input: Train samples \(X\), class label \(Y\), the parameters \(\lambda\), \(\gamma\), \(\eta\)
2:Initialization: Using the k-means algorithm, initialize \(D_{0}\) by all the training samples, compute the matrix \(L_{0}\) with Equation (6); Initialize \(D_{i}\) (\(i=1,2,\cdots,C\)) by the training samples \(X_{i}\), concatenate these sub-dictionaries as the initialized \(D_{p}\); Initialize the coefficients \(Z_{0}\) and \(Z_{p}\) with the MVRR algorithm; Construct the serial of matrices \(P_{i}\) and \(L_{i}\) with Equations (9) and (12), respectively. 3:while converge condition no satisfied do
4: (a) Update \(D_{0}\) by solving Equation (14), update \(L_{0}\) by using Equations (5) and (6);
5: (b) Update \(Z_{0}=\big{(}D_{0}^{\mathrm{T}}D_{0}+2\eta(L_{0}+l_{0})\big{)}D_{0}^{\mathrm{ T}}X^{\prime}\);
6: (c) Update \(D_{p}\) as follows:
7: for\(i=1\) to \(C\)do
8: Compute \(E=X^{\prime\prime}-\sum_{t\
otin S_{i}}d_{p}^{t}z_{p}^{t}\);
9: for\(k\in S_{i}\)do
10: Update the atom \(d_{p}^{k}\) by solving Equation (19);
11: endfor
12: endfor
13: (d) Update \(Z_{p}\) as follows:
14: for\(i=1\) to \(C\)do
15: Update the coding coefficients \(Z_{p}^{i}\) by solving Equation (22);
16: endfor
17:endwhile
18:Output: The variables \(D_{0},L_{0},Z_{0},D_{p},Z_{p}\). ```  
**Algorithm 1** Optimization procedure of the proposed LPLC-HDL.