esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robust Image Watermarking Scheme Based on SVD in the Spatial Domain
## 3 SVD-Based Watermarking and Its Impact on the Spatial Domain
### SVD-Based Watermarking  
In SVD-based image watermarking [14], the watermark \(\mathbf{W}\) is embedded into the host image by modifying its singular values. Firstly, the watermark image is decomposed into three parts by SVD:  
\[\mathbf{W}\Rightarrow\mathbf{U}_{\text{w}}\mathbf{S}_{\text{w}}\mathbf{V}_{\text{w}}^{ \text{T}}. \tag{3}\]  
Then, the host image is decomposed by DWT. The low frequency sub-band (LL) is divided into several non-overlapping blocks. For each block, the SVD is applied. The largest singular value in each block is selected and modulated by the principal component of the watermark image, which can be expressed as:  
\[\lambda^{\prime}_{\text{max}}=\lambda_{\text{max}}+\alpha\mathbf{W}_{\mathbf{U}_{\text {w}}\mathbf{S}_{\text{w}}}, \tag{4}\]  
where \(\lambda_{\text{max}}\) and \(\lambda^{\prime}_{\text{max}}\) represent the original and watermarked largest singular values in the image block, respectively. \(\mathbf{W}_{\text{Iw}}\mathbf{S}_{\text{w}}=\mathbf{U}_{\text{w}}\mathbf{S}_{\text{w}}\) denotes the principal component of the watermark image, and \(\alpha\) is the scaling factor used to control the embedding intensity. In watermark extraction, the principal component of the embedded watermark can be computed by:  
\[\mathbf{W}_{\mathbf{U}_{\text{w}}\mathbf{S}_{\text{w}}}=\frac{1}{\alpha}(\lambda^{ \prime}_{\text{max}}-\lambda_{\text{max}}). \tag{5}\]  
The right singular vector \(\mathbf{V}_{\text{w}}\) obtained in the watermark embedding process is used as the supplementary information to extract the watermark, which can be expressed as:  
\[\mathbf{W}_{\text{Iw}}\mathbf{S}_{\text{w}}\mathbf{V}_{\text{w}}^{\text{T}}\Rightarrow \mathbf{W}. \tag{6}\]  
Due to the good stability of the SVD transform, the SVD-based watermarking scheme and its improvements have achieved great success in copyright protection. However, the false positive problem and the selection of the scaling factor are two major challenges in SVD domain watermarking schemes. In addition, the side information \(\mathbf{U}_{\text{w}}\) or \(\mathbf{V}_{\text{w}}\) is needed in watermark extraction, which causes great inconvenience for practical application.
# Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste
## 3 Results
The results of the comparison of the analyzed models are presented in Table 8. The quality of each model was compared using indicators such as R, MSE, RMSE, RIE and MAE. The ElasticNet model shows a lower R-squared value of 0.90716, suggesting it explains less variance in the data compared to the other two models, which both exhibit R-squared values closer to 1, indicating a very high degree of variance explanation. The MSE and RMSE for the ElasticNet model are substantially higher than those for the Decision Trees and Neural Networks, implying larger average and root-mean-squared prediction errors. In terms of the RIE, ElasticNet has the highest value, and Decision Trees the lowest, indicating that Decision Trees have the least relative error. Finally, the MAE is significantly higher for ElasticNet, somewhat lower for Decision Trees, and the lowest for Neural Networks, suggesting that the Neural Network model has the best average prediction accuracy among the three. In conclusion, while all models show competency in predictions, the Neural Network model outperforms the other two in terms of these quality indicators, with Decision Trees following and Elastic
# Continuous Adaptation with Online Meta-Learning for Non-Stationary Target Regression Tasks
## 4 Related Work
### Kalman Filter  
Several of the ideas present in this paper can also be interpreted from a traditional filtering perspective. Particularly relevant is the Kalman filter [24, 25], as an algorithm that estimates non-stationary unknown variables from a series of observations. Originally developed within system control, it is widely used in time series analysis for various applications, such as signal processing, social science and economics. It is possible to apply the Kalman filter to our task by considering the regression parameter \(K_{t}\) as the non-stationary unknown variable and then derive similar recursive update equations as in CORAL. However, there are some key differences. First, how the Kalman filter maintains the priors for the estimating parameter differs from our method. CORAL uses online meta-learning to update the priors, whereas the Kalman filter implicitly computes the prior from the previous time step estimation and its evolution model. Secondly, the Kalman filter's forgetting mechanism is different from CORAL's and the other methods described in the previous subsection, as it adds the process noise covariance matrix to the target variable covariance matrix, whereas CORAL subtracts the old data contributions from the target variable precision matrix (Equation (7)). Although, under its assumptions, the Kalman filter approach is optimal, in practice, it requires knowing all the evolution model parameters, including the process noise covariance matrix. In many cases, this unrealistic, especially when the target variable is high dimensional. On the other hand, the CORAL approach does not require full knowledge of the evolution model. It has fewer parameters to tune--the sliding window length and online meta-learning learning rate--and it is easy to optimise even for the high-dimensional target variable cases. Extensions of the Kalman filter to adaptively estimate the model parameters, including the process noise covariance matrix, include [26, 27, 28, 29, 30, 31]. As these should be discussed with a comparison to the sliding window adaptation algorithms, it is left as future work.
# DGSS-YOLOv8s: A Real-Time Model for Small and Complex Object Detection in Autonomous Vehicles
## 4 Experiments and Results
### Implementation  
To systematically evaluate DGSS-YOLOv8s' road detection efficacy in vehicular environments, we established a reproducible experimental protocol with the following specifications:  
* Hardware platform: Intel(r) Xeon(r) Silver 4214R CPU @2.40 GHz, 90 GB DDR4 RAM, NVIDIA GeForce RTX 4090 (24 GB GDDR6X Graphics Processing Unit (GPU)).  
* Software stack:
* PyTorch 1.11.0 + Torchvision 0.12.0 (CUDA 11.3 backend).
* Ultralytics YOLOv8 reference implementation (v8.1.0) [18].  
The learning regimen utilized a batch dimension of 8 over 200 training epochs. Input tensor dimensions were standardized to \(640\times 640\) through bicubic interpolation, achieving an optimal trade-off between small-object preservation (\(>\)95% sub-50 px targets retained) and edge deployment feasibility. Data augmentation strategies, detailed in Table 1, were applied during training to enhance model robustness and generalization against potential dataset shifts and variations encountered in real-world driving. All comparative analyses employed randomly initialized parameters under identical initialization seeds to eliminate pretraining bias. Network scaling strictly followed YOLOv8' original width/depth coefficients.
# Strategies for Scaleable Communication and Coordination in Multi-Agent (UAV) Systems
## 5 A Proposed Control/Communication Architecture for Multi-Agent Systems
### Developing a Proof-Of-Concept: A Structured Approach
The objective is to verify that neural networks can model tracking policies for various interest-point trajectories. To achieve this milestone, it is logical to start with one agent and one stationary interest-point, where the reward function has a simple discernible structure. The next step is to let the interest-point move predictably (i.e., around a circle at constant velocity). Next, add two interest-points. Then incrementally increase the complexity of the system, verifying at each stage that the extra complexity is captured in training. 4. _The Scaleable Multi-Agent Scenario._ The system maintains approximately optimal performance as the number of agents grows large. Training is performed using deep RL, but each agent relies on its local state and a compressed/aggregated version of the global state space. Macro-actions now include unmanned traffic management (UTM) to support large-scale high-traffic drone transportation. The \"dynamic sky-way network\" redistributes agents over the grid to keep the distribution of agents and interest-points in equilibrium, and consists of a few high-throughput airways for long distances and many low-throughput airways for finer redistribution over short distances. The shape of the network adapts to the distribution of interest-points in real-time. Agents collectively determine the trajectories of airways that traverse their respective sub-regions. 5. _The Multi-Agent Multi-Level Clustering Scenario._ Agents rely on a multi-level clustering scheme to disseminate/aggregate their local state information and infer a common view of the global state. Cluster-heads compress state information to ensure the information throughput has sub-linear scaling. Each cluster organizes local activity and cooperates with higher-level clusters to ensure the system behaves in a coordinated manner. The system supports essential distributed tasks like cluster-formation, cluster-maintenance, and synchronization. The clustering algorithm is fully integrated into the RL training process developed in the previous milestones.
# Efficient Removal of Heavy Metal Ions in Wastewater by Using a Novel Alginate-EDTA Hybrid Aerogel
## 3 Results and Discussion
### Effect of pH  
Approximately 100 mg of Alg-EDTA was weighed and added to 50 mL of 1.5 mM Cd\({}^{2+}\) (Pb\({}^{2+}\), Cu\({}^{2+}\), Cr\({}^{3+}\) or Co\({}^{2+}\)) solution of different pH values, stirred for 6 h, and filtered, and the concentration of residual metal ions in the filtrate was determined by ICP-OES. As shown in Figure 5, when the solution pH is less than 1.0, Alg-EDTA can only adsorb 21.3% of Cd\({}^{2+}\) (19.2% of Pb\({}^{2+}\), 17.1% of Cu\({}^{2+}\), 14.4% of Cr\({}^{3+}\) or 13.7% of Co\({}^{2+}\)) probably due to the protonation of carboxyl and amino groups under strongly acidic conditions, thereby reducing the binding sites of Alg- EDTA with heavy metal ions. With increased solution pH, the adsorption capacity of Alg-EDTA for metal ions increases significantly and then stabilizes. The solubility product rule suggests that Pb\({}^{2+}\), Cd\({}^{2+}\), Cu\({}^{2+}\), Cr\({}^{3+}\) and Co\({}^{2+}\) in the solution will separately form the corresponding hydroxide precipitation when the solution pH is higher than 6.5. Therefore, the optimum pH lies in the range of 4.0-6.5 for adsorption of heavy metal ions by Alg-EDTA.
###### Abstract  
Temperature data, as a key meteorological parameter, holds an indispensable position in meteorological research and social management. High-resolution data can significantly enhance these tasks, whether it is accurate climate prediction or the prevention of meteorological disasters. Unfortunately, due to economic or geographical factors, among others, some regions are unable to obtain detailed temperature data, which is a concern for researchers. This study proposes a ResNet-based model aimed at high-resolution reconstruction of 2 m temperature data. In this study, we utilized the ERA5 dataset and applied the method to the South China region (SC). The paper constructs a neural network architecture that integrates a sub-pixel convolution module with a residual structure, which can effectively capture regional temperature characteristics and achieve high-precision data reconstruction. Compared with traditional interpolation methods, this method is more accurate, reduces the initial parameter settings, and lowers the risk of excessive human intervention. Moreover, it is not restricted by the super-resolution ratio. In this paper, experiments with 2\(\times\) and 4\(\times\) super-resolution were conducted, respectively. These outcomes indicate that the neural network model presented in this article is a promising approach for generating high-resolution climate data, which holds significant importance for climate research and related applications.  
Keywords:two-meter temperature data; super-resolution; ResNet; sub-pixel convolution +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Comparison of Mycotoxins of Rice Grains Stored under Different Conditions  
Ochratoxin and aflatoxin are commonly detected mycotoxins that are produced by fungi during the storage of cereals [41]. Moreover, ochratoxin A and aflatoxin B1 are highly toxic and are commonly detected in rice grains [42]. To compare the content of mycotoxins in rice grains under different storage conditions, the content of mycotoxins was determined. Among the three storage conditions, the content of ochratoxin A and aflatoxin B1 under quasi-low temperature, horizontal ventilation, and nitrogen gas atmosphere conditions was 3.67 \(\upmu\)g/kg and 3.19 \(\upmu\)g/kg, which was the highest (Figure 1H,J). According to international regulations, ochratoxin A and aflatoxin B1 could not be detected higher than 5 \(\upmu\)g/kg and 10 \(\upmu\)g/kg in samples [43]. The ochratoxin A and aflatoxin B1 of rice grains stored in the three storage conditions did not exceed the national limited safety standards of China and reached the requirement. Previous studies have demonstrated that lower temperatures can effectively inhibit microbial growth and toxin production [44;45]. Additionally, higher temperatures and moisture levels are associated with increased accumulation of aflatoxin B1 and ochratoxin A, with aflatoxin B1 reaching its highest concentration [46]. However, the rice grains stored with the condition of quasi-low temperature had a higher content of ochratoxin A and aflatoxin B1. This may be due to 18 \({}^{\circ}\)C also being the optimal condition for Aspergillus to produce toxins [47].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**N0**} & \multirow{2}{*}{**Compound Name**} & \multicolumn{3}{c}{**Storage Conditions (Mean \(\pm\) SD)**} \\ \cline{3-5}  & & **RA** & **RB** & **RC** \\ \hline
42 & 2,5-Dimethyltridecane & ND & ND & \(3.5\pm 1.53\) a \\
43 & 4,8-Dimethyltridecane & ND & ND & \(5.32\pm 0.1\) a \\
44 & 3-Methyleneundecane & ND & ND & \(3.96\pm 1.47\) a \\ Others & & & & \\
1 & 1,3-Dichlorobenzene & 19.07 \(\pm\) 17.9 b & \(50.8\pm 3.13\) a & ND \\
2 & 2,4-Di-tert-butylphenol & 13.53 \(\pm\) 1.20 a & 14.22 \(\pm\) 0.96 a & ND \\
3 & 2-Pentylfuran & ND & 10.57 \(\pm\) 0.44 a & ND \\ \hline \hline \end{tabular} Note. RA: room temperature, horizontal ventilation, nitrogen gas atmosphere; RB: room temperature, vertical ventilation, nitrogen gas atmosphere; RC: quasi-low temperature, horizontal ventilation, nitrogen gas atmosphere. Different letters indicate significant difference (\(p<0.05\)) among contents of rice grain in three storage environments. \"ND\" means the substance was not detected.  
\end{table}
Table 2: _Cont._
###### Abstract  
Arabic is one of the official languages recognized by the United Nations (UN) and is widely used in the middle east, and parts of Asia, Africa, and other countries. Social media activity currently dominates the textual communication on the Internet and potentially represents people's views about specific issues. Opinion mining is an important task for understanding public opinion polarity towards an issue. Understanding public opinion leads to better decisions in many fields, such as public services and business. Language background plays a vital role in understanding opinion polarity. Variation is not only due to the vocabulary but also cultural background. The sentence is a time series signal; therefore, sequence gives a significant correlation to the meaning of the text. A recurrent neural network (RNN) is a variant of deep learning where the sequence is considered. Long short-term memory (LSTM) is an implementation of RNN with a particular gate to keep or ignore specific word signals during a sequence of inputs. Text is unstructured data, and it cannot be processed further by a machine unless an algorithm transforms the representation into a readable machine learning format as a vector of numerical values. Transformation algorithms range from the Term Frequency-Inverse Document Frequency (TF-IDF) transform to advanced word embedding. Word embedding methods include GloVe, word2 vec, BERT, and fastText. This research experimented with those algorithms to perform vector transformation of the Arabic text dataset. This study implements and compares the GloVe and fastText word embedding algorithms and long short-term memory (LSTM) implemented in single-, double-, and triple-layer architectures. Finally, this research compares their accuracy for opinion mining on an Arabic dataset. It evaluates the proposed algorithm with the ASAD dataset of 55,000 annotated tweets in three classes. The dataset was augmented to achieve equal proportions of positive, negative, and neutral classes. According to the evaluation results, the triple-layer LSTM with fastText word embedding achieved the best testing accuracy, at 90.9%, surpassing all other experimental scenarios.  
sentiment analysis; opinion mining; Neural Network; LSTM; Arabic
## 4 TRSA: Trust-Aware Evidence Reasoning and Spatiotemporal Feature Aggregation Model
### Semantic Aggregation of Evidence Based on a Capsule Network
#### 4.3.1 Semantic Interactions between Evidence and Source Information Based on Multi-Head Attention  
Although the fusion gate can efficiently aggregate the spatiotemporal neighborhood information of evidence to obtain a reliable evidence representation, it cannot model the fine-grained semantic interactions between evidence and source information. To capture the focus of evidence on source information, we adopted a multi-head attention mechanism [46] to model the semantic interactions between evidence and source information. Specifically, we considered the evidence representation set \(\mathbf{X}=\{\mathbf{x}_{0},\mathbf{x}_{1},\dots,\mathbf{x}_{m}\}\in R^{\ (m+1)\times h}\) obtained in Section 4.2 as query vectors \(Q\) and considered the semantic representation of source information \(\mathbf{S}=\{\mathbf{w}_{1},\mathbf{w}_{2},\dots,\mathbf{w}_{l}\}\in R^{l\times d}\) as keys (\(K\)) and values (\(V\)). We used each piece of evidence in \(\mathbf{X}\) to assign attention to each word in \(\mathbf{S}\) through scaled dot-product attention, and then applied the resulting attention weights to the source information as follows:  
\[Attention(Q,K,V)=\mathbf{softmax}\bigg{(}\bigg{[}\frac{\mathbf{X}\mathbf{K}^{T}}{\sqrt{d }}\bigg{]}V\bigg{)} \tag{13}\]  
To prevent the model from focusing too heavily on a particular location, we first mapped queries, keys, and values to different spaces through different types of linear transformations. We then performed attention calculations in different spaces in parallel to obtain representations of each comment (evidence) in different subspaces.  
\[\begin{gathered} Head_{i}=Attention\Big{(}\mathbf{X}\mathbf{W}_{i}^{Q}, \mathbf{S}\mathbf{W}_{i}^{K},\mathbf{S}\mathbf{W}_{i}^{V}\Big{)}\\ \mathbf{E}=MultiHeadAttention(\mathbf{X},\mathbf{S},\mathbf{S})\\ =ReLU([Head_{1}||Head_{2}||Head_{3}\dots Head_{n}])\end{gathered} \tag{14}\]  
Here, \(\mathbf{W}_{i}^{Q}\in R^{\ h\times p}\), \(\mathbf{W}_{i}^{k},\mathbf{W}_{i}^{p}\in R^{\ d\times p}\) and \(\mathbf{E}=\{\mathbf{e}_{0},\mathbf{e}_{1},\dots,\mathbf{e}_{m}\}\in R^{\ (m+1)\times np}\) represent collections of underlying evidence capsules.
# Joint Design of Transmit Waveforms for Object Tracking in Coexisting Multimodal Sensing Systems
## 5 Simulations Results
### Approach I-B Simulation  
Following the optimization procedure of Section 4.2.2, we initially computed the optimal selection of communications users FM rates from Equation (27) that satisfy Equation (6) given \(N_{\mathrm{c}}=4\). The optimization parameters for the radar in Equation (31) are constrained such that \(1\leq T_{\mathrm{r}}\leq 4\)\(\upmu\)s and the minimum and maximum bandwidth of the radar waveform are \(3\leq 2b_{\mathrm{r}}T_{\mathrm{r}}\leq 10\) MHz. A plot of the feasible radar parameters \(\Psi_{\mathrm{r}}\) and their associated cost value from Equation (30) using the optimized selection of communications users are demonstrated in Figure 5b.  
The BER performance for the users is demonstrated in Figure 9a as a function of energy per bit for the case where the radar waveform is chosen to minimize system interference. The corresponding time-delay estimation MSE obtained by the radar waveform that minimizes the correlation in Equation (32) is shown in Figure 9b in red. This is compared to the MSE obtained by a radar waveform that directly minimizes the MSE (green). As can be seen, the radar waveform that minimizes system interference does not, in general, provide the best MSE performance for the radar. As a result, we utilized the multiobjective waveform approach to derive radar parameters that jointly optimize both the radar system and communications system performance.  
Figure 8: MSE for range (**top**) and range–rate (**bottom**) estimation when the radar OLFM waveform FM rate is \(-b_{20}\) (**red**) and \(-b_{1}\) (**blue**) for three users at \(-18\) dB SINR at the radar receiver.
# Spectroscopic Approach for the On-Line Monitoring of Welding of Tanker Trucks
###### Abstract  
The appearance of defects during the manufacture of tanker trucks via arc-welding is a significant problem in the industry. A reliable low-cost and non-destructive on-line method could aid the discovery of solutions to overcome productivity problems. Plasma optical spectroscopy was employed in this study to correlate the presence of several elemental emission lines with certain quality-related events, such as the appearance of porosities. Results obtained through field trials and also during in-line production show that the convenient processing of acquired process signals facilitates not only the detection of defects, but also the identification of their causes. Output monitoring signals have been compared with X-ray inspections of the seam welds performed. It has been demonstrated that the spectroscopic monitoring variables obtained are good indicators for evaluating contamination in the process and therefore the occurrence of welding defects.  
arc-welding; quality monitoring; on-line monitoring; plasma optical spectroscopy; defect detection; X-ray inspection; non-destructive evaluation Article