esa-sceva/satcom-chunk-collection huggingface dataset:
# Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach
## 6 File Replication  
Back to Equation (1), the third component of the system is \(\mathbb{F}\), the set of files described in Equation (4) which are accessed in the System \(\Xi\). The cost of accessing file \(f\in\mathbb{F}\) depends on many factors including how far the file from the destination node \(\alpha\in\Omega\) and whether that \(\alpha\) is a cloud or an edge server. In this paper, we assume that the total cost \(\eta\) of file replication is to be the data transfer delay that will be saved in \(\tau\), in addition to, the data processing cost \(\pi\) and the load on the hosting node \(\rho\). These factors are weighed and weights differ from a server to another.  
\[\eta=\beta_{1}\pi+\beta_{2}\rho+\beta_{3}\tau \tag{51}\]  
The unit costs for every independent variable in Equation (51) is determined by the host and the weights are learned through historical data. When replicating a file \(f\in\mathbb{F}\), the cost of data transfer and the hosting depend on how big the file is, this means that replicating segments of a file could minimise both hosting and data transfer costs. File is being segmented to a number of segments according to accessibility. For a region \(s\) of a large file \(f\), if this region is frequently accessed, this \(S\) will be a separate segment which means file will be divided into three segments, segment \(s_{b}\) which is the portion before the target segment, segment \(s\) which is the required one and segment \(s_{a}\) which is the part of the file after the required segment. This means that file \(f\) after partition is:  
\[f=\{s_{b},s,s_{a}\}.\]  
A segment can be partitioned into several sub segments the same way. The details of how this is carried out is being handled in a future study and in this paper we assume that files are already partitioned.
# Microwave and Ultrasound Based Methods in Sludge Treatment: A Review
## 2 Efficiency Indicators
As its definition suggests, a given process can be evaluated in terms of its effectiveness by calculating DD; the more increment the process can cause in DD, the more it disintegrates the structure of the sludge, hence increasing the SCOD, and ultimately, the anaerobic digestibility. The estimation of WAS disintegration degree is generally based upon the values of SCOD, STOC and STN, as suggested by [PERSON] et al. [14]. From an experimental point of view, there are some novel methods for the assessment of sludge DD, for example, differential centrifugal sedimentation [15]. Total solid (TS) and volatile solid (VS) percentages are also rather important in terms of sludge characterization, and for that commonly used as indicators as well. During the treatment of wastewater in a wastewater treatment plant, an excessive amount of wastewater sludge is produced. The resulting sludge can be and is used for either fertilization purposes [16; 17] or anaerobic digestion (AD), i.e., biogas production. The product of the AD, as discussed in the Introduction, is mainly methane and carbon dioxide. It can be stated that the more methane is being produced during AD, the more effective the process is (but of course there is a limitation in CH\({}_{4}\) extent). For these considerations, scientific literature often uses methane yield as an indicator, and when comparing different treatment or pre-treatment processes to each other in terms of effectivity, it is usually calculated how they can increase the overall methane yield in the following AD process. A slightly simpler method is to determine the exact biogas yield; however, it carries less information about the usable content of the biogas itself. Since several of the applicable pre-treatment methods means external energy investment, it is very common to determine specified indicators based upon the amount of transmitted energy to be able to objectively compare different types of treatment methods. There are numerous variations of these specified indicators, non-exhaustively kJ/kg TS, kJ/kg VS, \(\Delta\)SCOD/kj, \(\Delta\)BOD/kj, \(\Delta\)CH\({}_{4}\)/kg, etc. On the other hand, it can be observed that many scientific research papers use quite a unique unit of measurements to describe certain processes due to various reasons (e.g., the experimental design or setup, uniqueness of the applied equipment or method, etc. ), which makes it difficult or sometimes impossible to compare different experimental results in research articles.
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 4 Proposed Approach
### Cluster Maintenance
#### 4.7.2 Node Joining Operation  
When a node exits the old cluster, it sends its HELLO message to the nearest CH to join a new cluster network. The CH will reply with an ACK message to the new node. Then, the new node will update its CH information, and the CH will add the new node to its members list.  
The pseudocode of cluster maintenance is listed in Algorithm 4.  
```
1:if ACK from CM\({}_{j}\) is not received by CH\({}_{i}\)
2:CH will deplete CM\({}_{j}\) from its members list
3:The node which just exited the cluster will set its CH to null and broadcast its HELLO message
4:The nearest CH\({}^{\prime}\) will reply with an ACK message to the node immediately
5:This node will update its CH information to CH\({}^{\prime}\)
6:CH\({}^{\prime}\) will add this node to its members list
7:endif
```  
**Algorithm 4** Cluster Maintenance
# Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model
The attention mechanism serves as a resource allocation strategy that selects the most relevant information for the current task from a large pool of data, thereby enhancing the model's ability to learn and represent critical features [32]. [PERSON] et al. [31] proposed the SECNN-E attention network for estimating soil organic carbon content, which effectively manages complex soil spectral data and mitigates the impact of redundant features on predictive accuracy. This approach facilitates the selection and learning of more meaningful features. Although some studies have proposed hybrid deep learning methods that capture various aspects of soil spectral data, such as local features and temporal dependencies, these elements are often processed independently, constraining the models' ability to holistically integrate spatial and temporal information. To address this limitation, we propose an LSTM-CNN model enhanced with an attention mechanism for soil property prediction. The model prioritizes sensitive spectral bands--specific regions of the spectrum that exhibit strong and consistent correlations with soil properties--by assigning weights. This approach effectively captures the nonlinear spatial and temporal relationships between spectral data and soil properties. In this study, a comprehensive topsoil dataset was employed, and deep learning models were leveraged as robust and precise tools for data mining. Furthermore, we will objectively evaluate this approach by benchmarking its performance against traditional machine learning models and established methods reported in the literature. Finally, we will explore the model's applicability and advantages in predicting various soil properties. The key contributions of this study are the following:  
* A novel LSTM-CNN-Attention model is developed for predicting soil properties from hyperspectral data;
* The model integrates temporal and spatial feature extraction with attention mechanisms to improve predictive accuracy;
* The proposed model outperforms not only traditional machine learning models but also previous deep learning approaches.
# A Distributed Sensor System Based on Cloud-Edge-End Network for Industrial Internet of Things +
## 3 System Model and System Architecture
### Data Preprocessing and Visualization  
Data preprocessing is an essential step in data analysis, and it aims to clean, transform, and normalize data to prepare it for further analysis. In industrial applications, preprocessing techniques are used to reduce the impact of noise, missing values, outliers, and other anomalies in the data. Coordinated filtering is a common technique for removing noise from sensor data by taking the average of multiple sensor readings. K-means clustering can be used to group similar data points together, making it easier to identify patterns and anomalies. Singular spectral analysis is a method for decomposing time-series data into their underlying components, which can be used to extract trends and patterns. An LIN  
Figure 8: A/D and D/A conversion principle of data acquisition and storage process.  
interpolation algorithm can be used to fill in missing values in the data, which is important for time-series data that have regular time intervals.  
Visualization is another important aspect of data analysis, as it allows users to explore and understand the data. The system shown in Figure 9 has a data visualization module that allows users to interactively explore the data and generate visualizations, such as charts, graphs, and heatmaps. Data visualization tools can be used to identify patterns and relationships in the data and to communicate the results of the analysis to stakeholders. By using these tools, users can gain a deeper understanding of the data and make more informed decisions.  
It is great to see that the system is equipped with a comprehensive data preprocessing and visualization system. Data preprocessing is crucial to ensure that the collected data are accurate, complete, and consistent. By filtering, sorting, and cleaning the collected data, the system can generate high-quality datasets that can be used for further analysis. Additionally, the system can synchronize multisensor information using the finite state machine algorithm, which is a useful technique for integrating data from different sources.  
The visualization system is also an important component of the system as it allows users to monitor data in real-time and detect anomalies or issues with devices. With real-time monitoring, enterprises can maintain their devices in a timely manner and optimize their performance. Moreover, by making packaged datasets publicly available, users can contribute to the community and promote collaboration in the industry.  
Finally, the system model's hardware independence is a significant advantage as it reduces the cost of manufacturing for enterprises. Overall, the system's data preprocessing and visualization capabilities, combined with its hardware independence, provide a powerful tool for enterprises to improve their operations and achieve better outcomes.
## 2 Preliminaries
### Attack Algorithms
#### 2.2.2 Jacobian Saliency Map Attack  
The Jacobian Saliency Map Attack (JSMA) [9] perturbs the input image using \(L_{0}\) distance metric. Among pixels in the input image, JSMA iteratively selects the pixel that can will increase the chance of misclassification by the highest amount. To do that, the attacker first calculates gradient of the output layer neurons with respect to each pixel of the input image. The result is the Jacobian matrix that is shown in Equation (3). In the equation, \(l(x)\) is the values of output layer neurons before applying softmax, also called _logits_. \(i\) is the index of the input pixels and \(j\) is the index of the logits.  
\[\bigtriangledown l\left(x\right)=\frac{\partial l\left(x\right)}{\partial x}= \left[\frac{\partial l_{j}\left(x\right)}{\partial x_{i}}\right]_{i\times j} \tag{3}\]  
If the attack is a targeted attack, JSMA selects \(i\) which has the maximum absolute value among the elements in the column \(j\) of the Jacobian matrix, where \(j\) is the target class. Then, the pixel is changed in the direction that will decrease \(l_{j}(x)\). Once the pixel is changed, a new Jacobian matrix is calculated with respect to the modified input. This process is repeated until the adversarial example \(x^{\prime}\) is misclassified as the target class by the target network, or we reach the maximum number of pixels allowed to be modified. For an untargeted attack, a target class can be selected randomly or the target class which requires the minimum amount of modified pixels can be searched. The third row of Figure 1 shows the images created using JSMA. In contrast with FGSM, images created using JSMA has dots that were not in the original input while most of the background stays black.
# Rethinking Modbus-UDP for Real-Time IIoT Systems
## 2 Related Work
Its performance and CPU/memory requirements in comparison to TCP, together with its resilience against cyber attacks, have been thoroughly evaluated in [32]. Its suitability to transport Modbus traffic has been investigated in the past, and an experimental implementation of Modbus-QUIC written in Python is available [33]. The QUIC specification includes a sophisticated and, as shown in [32], very effective datagram retransmission mechanism, which would undoubtedly eliminate the message delivery reliability concern mentioned previously. However, the official specification [30] states that QUIC is a connection-oriented protocol, like TCP, and hence does not support broadcast. This fact is also reflected in the main implementations currently available and makes it not directly comparable with the Modbus-UDP proposal presented in this paper. The crucial role that cyber-security plays in today's IoT and IIoT systems has led to the introduction of the Modbus-TCP Security extension [34]. The extension makes use of a Transport Layer Security (TLS) [35] handshake to establish a security context between the two communication endpoints, the same method also used by QUIC [36]. Being designed with point-to-point connections in mind, this method is not directly applicable to Modbus-UDP, which is connectionless and supports one-to-many data exchanges. However, significant research has been performed to enhance CAN security, for instance, within the Automotive Open System Architecture (AUTOSAR) standard [37]. Given the significant analogies between Modbus-CAN and the Modbus-UDP proposal presented here, those techniques are likely applicable to Modbus-UDP too and represent an important area of future work.
# Integrated DVB-X2 Receiver Architecture with Common Acceleration Engine
###### Abstract  
This paper proposes an integrated DVB-X2 receiver architecture to support multi-mode broadcasting standards such as DVB-T2, DVB-C2, and DVB-S2 in a single platform. The entire system consists of a tuner block, a H/W-based receiver engine, a frame processor, and an A/V decoder. Specifically, an integrated architecture to solve key design and technical issues such as reducing the complexity of the receiver, efficiently accessing the H/W-based receiver engine, and simplifying an OFDM demodulator is proposed. The H/W-based receiver engine for DVB-X2 demodulation and channel decoding functions is implemented in two FPGA devices. The frame processor is implemented with 256 MB memory and a DSP operating at a clock speed of 1.0 GHz. To verify functionalities of the proposed DVB-X2 receiver, various test scenarios were considered in the laboratory setting. In particular, the proposed system was tested under various operating modes, as specified in standards such as DVB-T2, DVB-C2, and DVB-S2, and demonstrated successful operations in all test scenarios.  
D vB-T2; DVB-C2; DVB-S2; receiver Article  
Integrated DVB-X2 Receiver Architecture with Common Acceleration Engine
## 4 Results
### Analysis of the Contextual Relevance of Distractors
#### 4.1.2 Two-Way Analysis of Variance (ANOVA)  
To further analyze the contextual relevance of distractors, we conducted a two-way Analysis of Variance (ANOVA). Before performing the analysis, we checked the assumptions of the ANOVA, including normality (using the Shapiro-Wilk test) and homogeneity of variances (using [PERSON]'s test). The results indicated that the data generally met these assumptions (\(p>0.05\)).  
Table 4 presents the results of the ANOVA, where the dependent variable is the distractor relevance score, and the independent variables are the generation method and knowledge type. The analysis revealed: (1) The significant main effect of the generation method: F(2, 348) = 19.85, \(p<0.001\), partial \(\eta^{2}\) = 0.08. According to [PERSON] (1988), this is considered a medium effect size, indicating substantial differences in distractor relevance across different generation methods. (2) The significant main effect of knowledge type: F(3, 348) = 4.34, \(p\) = 0.005, partial \(\eta^{2}\) = 0.03. This is a small effect size, suggesting that the type of knowledge significantly affects the relevance scores of the distractors. (3) Significant interaction between the generation method and knowledge type: F(6, 348) = 5.79, \(p<0.001\), partial \(\eta^{2}\) = 0.07. This medium effect size indicates that the combination of the generation method and knowledge type significantly influences distractor relevance scores, showing distinct performance patterns.  
\begin{table}
\begin{tabular}{c l c c c} \hline
**Generation Method** & **Knowledge Type** & **Mean** & **Standard Deviation** & **N** \\ \hline \multirow{3}{*}{Multi-Examiner} & Factual & 4.03 & 1.00 & 30 \\  & Conceptual & 3.00 & 0.91 & 30 \\  & Procedural & 3.57 & 0.94 & 30 \\  & Metacognitive & 3.73 & 0.94 & 30 \\ \hline \multirow{3}{*}{GPT-4} & Factual & 3.33 & 0.99 & 30 \\  & Conceptual & 2.53 & 0.97 & 30 \\  & Procedural & 2.40 & 1.10 & 30 \\  & Metacognitive & 3.20 & 0.99 & 30 \\ \hline \multirow{3}{*}{Human} & Factual & 3.07 & 1.14 & 30 \\  & Conceptual & 3.63 & 0.81 & 30 \\ \cline{1-1}  & Procedural & 3.77 & 0.82 & 30 \\ \cline{1-1}  & Metacognitive & 3.60 & 1.00 & 30 \\ \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of distractor relevance scores by generation method and knowledge type.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Generation Method** & **Conceptual Relevance** & **Logical Rationality** & **Clarity** \\ \hline Multi-Examiner & 3.83 & 3.65 & 3.38 \\ GPT-4 & 3.12 & 2.73 & 2.68 \\ Human & 3.82 & 3.45 & 3.33 \\ \hline \end{tabular}
\end{table}
Table 3: Breakdown of distractor relevance sub-criteria by generation method (mean scores).
# Ginger: Panacea or Consumer's Hype?
## 2 Pharmacology of Ginger
### Safety and Interactions
#### 2.2.1 Adverse Effects  
Ginger consumption rarely induces side effects. Gastrointestinal complications are reported in a clinical trial with patients taking 2 g of ginger powder per day, but the incidence is in similar numbers in the test group and the placebo group [27].  
Anticoagulant activity with platelet inactivation may occur with very high doses of ginger. Aggregation of platelets was reduced by roughly one third in patients with a previous history of myocardial infarction taking a daily dose of 10 g ginger powder [28]. There is no action on platelet aggregation at daily doses of 4 g or less [28; 29; 30].
# A Survey on Recent Advanced Research of CPS Security
## 4 Discussion
### Architecture Layers
#### 4.1.2 Network Layer  
Network layers are used to receive the gathered data and transmit them to the control center or applications. As the basis of networks, protocols among diverse devices vary and face serious challenges. Encryption technology in a network needs to be improved for embedded devices. Moreover, traffic analysis is also significant in security defense.  
(1) IoT protocol: Various devices (router, gateway, switching, cloud server, etc.) link objects via various protocols, such as ZigBee, ZWave, WiFi, Bluetooth, and HDMI (high definition multimedia interface). They can be divided into two basic types: network and data protocols, which should guarantee the integrity and credibility of packets when transferring data. Under the guidance of this principle, researchers have provided more secure protocols in patient monitoring [16], vehicle authentication [21], and house controls [35]. They have also investigated the vulnerabilities in existing widely used protocols and proposed corresponding solutions, such as HDMI [36], ZigBee [37], and EC-JPAKE [81].  
(2) Lightweight Crypto: Cryptography is the cornerstone of information security. As the computation and memory resources of embedded devices are limited, these devices require more lightweight encryption algorithms. Some studies proposed lightweight encryption schemes for secure communication among resource-limited devices [101; 103; 104] or image encryption [20]. They also investigated the problem of crypto misuses [100] and performed ciphertext-only fault analysis on lightweight ciphers [102]. In addition, to efficiently manage a secret key among devices deployed distributively, a key manager and distribution scheme [19] is also needed to establish trusted communication.  
(3) Traffic Analysis: Through traffic analysis, intrusion behaviors or abnormal statuses can be detected to help block an attack before it occurs. Typically, rule-based matching is the easiest and fastest way to detect attacks, but it cannot adapt to new attack models. Recent studies have attempted to solve this problem with new approaches, such as behavior-rule specification-based techniques [17], artificial neural networks (ANNs) [89], pattern matching accelerated by GPUs [90], and model-checking [91]. Moreover, they also leveraged fingerprinting techniques to enhance the intrusion detection scheme [11], as minor differences in devices can be used to identify spoofed command responses. These methods are well adapted to specific application scenarios with affordable overhead.
## 5 Experiments
### Acted Emotional Speech Dynamic Database (AESDD)
#### 5.2.3 Auditory Emotion Detection  
In this subsection, we evaluate the performance of the auditory emotion detection model, which employs a 1D convolutional neural network (1D CNN). The main objective is to assess the model's ability to accurately predict emotions from audio data, using metrics such as accuracy and loss plots. We also analyze which emotions are easier or more difficult for the model to classify. Additionally, we compare the model's performance across various datasets, both individually and in combination, with and without the application of data augmentation techniques. This evaluation will help identify the best data configuration for training and guide the fine-tuning process.  
In the final experiment, we combined all datasets with data augmentation and conducted fine-tuning using a personalized dataset.  
The results of these experiments show a significant improvement after fine-tuning, with the model achieving an accuracy of 100%. However, it is important to highlight that the effectiveness of the fine-tuning process is closely tied to the user's ability to distinguish and express different emotions clearly in the training examples. The aim is to enable users to create their own personalized dataset in approximately 5 min. However, due to the limited amount of data in this short recording period, the system may be sensitive to factors like microphone distance or changes in the user's vocal tone from day to day.  
Ideally, the system would benefit from a more extended data collection period. For instance, if a user could record four messages each day over the course of a week--one for each emotion, while varying their tone and the distance from the microphone slightly--the model would become more robust to such variations. Nonetheless, the system performs well under stable conditions, provided that there are no dramatic shifts in tone or microphone positioning.  
A video demonstrating an interaction between the user and the robot can be found at the following link: [[https://www.youtube.com/watch?v=dkulXqDm8v8](https://www.youtube.com/watch?v=dkulXqDm8v8)]([https://www.youtube.com/watch?v=dkulXqDm8v8](https://www.youtube.com/watch?v=dkulXqDm8v8)) (accessed on 16 December 2024).