esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Three Layer Hybrid Clustering Mechanism for Energy Efficient Routing in IoT
## 5 Performance Evaluation
As explained in Section 4, higher value of \(TL\) results in more balanced energy consumption of sensor nodes in the network, but on the cost of higher energy consumption due to frequent control packet exchange. As shown in the Figure 6, the reason that we cannot set very low value to \(TL\) is due to its tradeoff with balance energy consumption between all deployed sensor nodes. Based upon the network parameters which are defined in Table 1, the proposed scheme outperforms with value of \(TL\) =10. Figure 7 shows the network life of WSN based on two metrics called First Node Dies (FND) and Half of the Nodes Alive (HNA), which are proposed in [29]. FND metric shows the number of rounds in which the energy of first sensor node is exhausted and is considered dead. HNA metric on other hand shows number of rounds in which at least 50 percent of the deployed sensor nodes are functional. As WSN becomes almost useless after half of the deployed sensor nodes die out, we therefore focus more on HNA metric rather than Last Node Dies (LND) metric. HNA is also more useful metric in  
Figure 4: Network lifetime of proposed mechanism is increased by limiting wasteful operations. Figure 5: Total number of received data packets at base station. denser WSN as compared to FND metric, as network performance is not affected by dying out of single sensor node. As shown in Figure 7, the proposed scheme outperforms rest of techniques based on FND and HNA metrics.
## 3 Test Results and Analysis
### Microscopic Analysis  
To further analyze the hydration products of AOD slag mixtures, X-ray diffraction (XRD) tests were conducted on the specimens aged 90 days (A-0, A-3, and A-12), with the outcomes presented in Figure 7. Comparing the silica (SiO\({}_{2}\)) dosage among the three groups revealed that the sample with \(R\) = 3% contained the least amount of SiO\({}_{2}\), while exhibiting the highest strength. This confirms AOD slag provides an alkaline environment, facilitating a pozzolanic reaction between SiO\({}_{2}\) and Ca(OH)\({}_{2}\), leading to a reduction in SiO\({}_{2}\) dosage and a corresponding increase in the quantities of hydration products, such as calcium  
Figure 5: Relationship between peak stress and dosage.  
Figure 6: Relationship between elastic modulus and dosage.  
hydroxide (CH), calcium silicate hydrate (C-S-H) gel, and ettringite (AFt). The highest SiO\({}_{2}\) dosage observed at \(R\) = 0% primarily stems from the inherent chemical composition of fly ash. However, due to the lack of an alkaline environment provided by the AOD slag, there are relatively few hydration products. Conversely, at \(R\) = 12%, a similar scenario arises due to the deficiency of SiO\({}_{2}\) typically supplied by fly ash. These experimental results illuminate precisely why AOD slag combined with fly ash is stronger than AOD slag or fly ash alone.  
Building upon XRD analysis, Scanning Electron Microscopy (SEM) was employed for the microstructural analysis of the 90-day-old mixture specimens (A-0, A-3, and A-12), with the results illustrated in Figure 7. The comparative analysis of Figure 8a,b clearly shows that at \(R\) = 0%, fewer hydration products are present alongside incompletely hydrated spherical particles of C\({}_{2}\)S and C\({}_{3}\)S. Conversely, when \(R\) = 3%, flocculent C-S-H gel and needle-like AFt structures become more abundant. Deeper analysis integrating the XRD patterns reveals that as the hydration products from the pozzolanic reaction increase, the pores in the mixture are gradually filled by these products, densifying the structure. However, at \(R\) = 12%, with less fly ash, and consequently insufficient SiO\({}_{2}\) to facilitate a pozzolanic reaction, there is a notable reduction in hydration products, leading to the reappearance of pores within the mixture. This negatively impacts the strength performance of the mixture, as depicted in Figure 8e,f.  
Figure 8: \(Cont.\)  
Figure 7: XRD patterns of hydration products in mixtures at 90 days.
## The Contribution of Various Partial Waves in Each Lamb Wave
### Using Symmetry to Summarize the Contribution of Longitudinal and Shear Partial Waves  
One of the advantages of the partial-wave method approach is that the contribution of each partial wave can be determined for each Lamb wave mode. That is, if the amplitudes are normalized by dividing by:  
\[B_{mag}=\left(\left|B^{(+L)}\right|^{2}+\left|B^{(-L)}\right|^{2}+\left|B^{(+ SV)}\right|^{2}+\left|B^{(-SV)}\right|^{2}\right)^{1/2} \tag{46}\]  
and since \(\left|B^{(+L)}\right|\approx\left|B^{(-L)}\right|\) due to the symmetry of the problem, a representation of the amount of longitudinal partial wave contribution can be calculated using the average of \(B^{(+L)}\) and \(B^{(-L)}\):  
\[B^{(L)}_{avg}=\frac{\left|B^{(+L)}\right|+\left|B^{(-L)}\right|}{2B_{mag}} \tag{47}\]This average is used to account for the approximate nature of the solutions. \(B_{avg}^{(L)}\) is representative of the amount of the longitudinal partial waves that make up a given Lamb wave mode, and is overlaid on the dispersion curves as an intensity plot in Figure 8a.  
Similarly, since \(|B^{(+SV)}|\approx|B^{(-SV)}|\) due to the symmetry of the problem, the contribution of the shear vertical partial waves can also be shown by calculating:  
\[B_{avg}^{(SV)}=\frac{|B^{(+SV)}|+|B^{(-SV)}|}{2B_{mag}}, \tag{48}\]  
and plotting it in Figure 8b.  
In Figure 8, it can be seen that in Region 1 (Figure 1a), the apportionment of longitudinal (i.e., \(B_{avg}^{(L)}\)) and shear vertical (i.e., \(B_{avg}^{(SV)}\)) partial waves are similar. However, in regions 2 and 3 (Figure 1b,c) however, this is no longer the case. In Region 2 (Figure 1b), the Lamb waves around 4000 m/s are almost entirely made up of bulk shear vertical partial waves, and Lamb waves just below 6000 m/s are made up of mostly surface longitudinal partial waves. Interestingly, Lamb modes have phase and group velocities of \(\sqrt{2}c_{s}\) and are comprised of shear bulk waves propagating at \(\pm 45^{\circ}\)[14]. Region 2 (Figure 1b) also has a clear phase velocity dependence on the amount of each partial wave, but no frequency dependence. In Region 3 (Figure 1c), a certain degree of mode dependence appears, but it does not appear to differentiate between symmetric and anti-symmetric modes.
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
## Appendix A  
### Proof of Theorem 1  
**Lemma A1**.: _If there is a disparity in delay between two satellites, we can reduce the large delay and increase the small delay by reallocating resources, which ultimately leads to the same delay for both satellites._  
Proof.: According to the expression of delay (9), (10), it can be observed that delay increases monotonically with the allocation ratio of satellites and decreases monotonically with the transmission power. Assuming there exist two satellites named Satellite 1 and Satellite 2, and the delay of each is named as \(t_{1}\) and \(t_{2}\), where \(t_{1}\geq t_{2}\), we can achieve \(t_{1}^{*}=t_{2}^{*}\) by implementing one or both of the following actions:  
1. Transfer a number of data from **Satellite 1** to **Satellite 2**: by shifting some data processing tasks from **Satellite 1** to **Satellite 2**, we can reduce the load and processing time on **Satellite 1**, potentially decreasing \(t_{1}\) and increasing \(t_{2}\).
2. Transfer a portion of power from **Satellite 2** to **Satellite 1**: by reallocating power resources from **Satellite 2** to **Satellite 1**, we can enhance the performance and transmission capability of **Satellite 1**, potentially reducing \(t_{1}\) and multiplying \(t_{2}\).  
Suppose that when the system reaches minimum delay in a fixed time slot, the delays of individual satellites are not identical. There exists a set \(\mathcal{A}\) satisfying  
\[t_{j}=S=\max\{t_{0,i},t_{1,i},\ldots,t_{M,i}\}\quad\forall j\in\mathcal{A}\]  
Correspondingly, there exists a set \(\mathcal{B}\) where  
\[t_{k}<S\quad\forall k\in\mathcal{B}\]  
According to Lemma A1, we can allocate resources by pairing any \(t_{j,i}\) from set \(\mathcal{A}\) with any \(t_{k,i}\) from set \(\mathcal{B}\). This yields the reallocated relationship:  
\[t_{j,i}\geq t_{j,i}^{*}=t_{k,i}^{*}\geq t_{k,i}\]  
where \(t_{j,i}^{*}\) and \(t_{k,i}^{*}\) denote the updated delays after resource reallocation. Extending this process to all satellites in \(\mathcal{A}\), we obtain:  
\[t_{k}\geq t_{k}^{*}\quad\forall k\in\mathcal{A}\]  
Define the new maximum delay:  
\[S^{*}=\max\{t_{0,i}^{*},t_{1,i}^{*},\ldots,t_{M,i}^{*}\}\]  
It necessarily follows that:  
\[S^{*}\leq S\]  
This contradiction implies \(S\) cannot be the minimum delay. Therefore, when the system achieves minimum delay in a fixed slot, all satellite delays must satisfy:  
\[t_{0,i}=t_{1,i}=\cdots=t_{M,i}\]
# MEXN: Multi-Stage Extraction Network for Patent Document Classification
## 3 Multi-Stage Extraction Network: MEXN
### Paragraph Encoder (P-Block)  
P-Block function \(\mathcal{F}\) encodes the feature matrix \(P_{i}\in\mathbb{R}^{D\times l}\) to a paragraph feature vector \(h_{P_{i}}\in\mathbb{R}^{D\times 1}\), which takes fixed-size length input which is pre-splitted paragraph by Preprocessing step. This operation summarizes the feature matrix \(P_{i}\) based on the composition of words and sentences only using its own contexts in the paragraph, which means \(\mathcal{F}(P_{i})\) does not consider the information of neighbor paragraphs for the \(P_{i}\). The information of all other neighbor paragraphs will be aggregated in D-Block operation. P-Block can be easily implemented by CNN or RNN layers. Again, all words in paragraphs are embedded into the pre-defined feature vector with length \(l\). Next, we describe three different instances of P-Block operation, \(\mathcal{F}\).  
**CNN.** A simple instance for P-Block is CNN, which is the straight-forward approach to apply to P-Block encoder because \(P_{i}\) is a fixed-size feature matrix:  
\[\mathcal{F}(P_{i})=W_{\theta}P_{i}+b, \tag{5}\]  
where \(i\) is the index of paragraph position. \(W_{\theta}\) and \(b\) indicate learnable convolutional weight parameters and bias, respectively.  
Figure 1: A concept figure of MEXN.  
RNN.The traditional way to compute features from sequential inputs is RNN, which is not restricted in the input length size. It also can be applied for fixed size feature input.  
\[\mathcal{F}(P_{i})=tanh(W_{\theta}h_{P_{i}}+W_{\phi}P_{i}+b), \tag{6}\]  
where \(W_{\theta}\), \(W_{\phi}\) and \(b\) indicate learnable RNN weight parameters and bias, respectively. Before feeding P-Block with CNN or RNN, we use Glove model [29] when embedding words in the paragraphs.  
Attention.We can also design a stacked feature encoder such as Transformer mechanism [18], which extracts the paragraph feature hierarchically as:  
\[\mathcal{F}(P_{i})=\sum_{l=1-s}^{L}\mathcal{A}^{l}_{P}, \tag{7}\] \[[CLS]^{l}_{i}=\mathcal{A}^{l}_{P}(Q^{l-1}_{i},K^{l-1}_{i},V^{l-1} _{i}), \tag{8}\]  
where \(Q\), \(K\), and \(V\) indicate query, key, and value for the Transformer encoder, respectively. These feature vectors are also embedded by concatenating \([CLS]\) token and \(P_{i}\) as \(Q_{i}=K_{i}=V_{i}=\{[CLS],P_{i}\}\). Here, \(\{\cdot,\cdot\}\) denotes concatenation. \(L\) is the total number of stacked encoders, and \(l\) is the index of the stacked encoder. \(L\) and \(s\) determines how many encoder layers are used to summarizing paragraph \(P_{i}\). In attention based P-Block, unlike the previous two method (e.g., CNN, and RNN), we use contextualized word embedding method [30] as used by [PERSON] [19].
## 8 Conclusions
### Work Limitations  
The first limitation identified in this work can be derived from Figures 2 and 5. That is, collected data cover selected countries from Europe. This is because we have found that it is unfeasible to reach the personnel associated with AI, IoT, and EC teaching areas from all the universities reliably and efficiently. We have seen that several universities do not publicly share the instructors' (or Program Coordinators) names/email addresses on their course programs' websites, or the available data placed there are wrong/misleading. Maybe, this could be motivated by the constant role changes in terms of training activities to which academic staff is exposed. Also, the number of interviewed academics from the same university is relatively small, which might give partial or biased views of the reality.  
This is because the knowledge areas (i.e., AI, IoT, EC) are very specific and, thus, there is typically a small amount of personnel associated with them. However, we believe that the amount of data that we have collected for this work can capture the current status of Higher Education regarding these scientific fields. At the same time, one can see that the geographical distribution of academics is not uniform. Knowing that the quality of education differs significantly from country to country, it can quickly be concluded that the research results are not trustworthy. However, analyzing them, we have seen similar trends between data from different countries, which endorses the reliability of this study.  
Similarly, we have examined training programs from selected universities in Europe. Again, up-to-date syllabus details regarding these programs are often unavailable on public websites. We understand that some universities see their syllabus as their Intellectual Property and, thus, are averse to sharing this information in an out-of-control way, fearing other universities to _copy_ their training programs. However, we believe that a training program is far more than a course syllabus and involves teaching methodologies, instructor skill set, available facilities (e.g., laboratories), university added-value services, etc. Therefore, we think that making the information related to training programs publicly available would improve similar training programs from different universities (i.e., competency) and would ease the exchange of students among universities.  
This leads to the following limitation of this work. As envisaged in Figure 9, Artificial Intelligence, Internet of Things, and Edge Computing are fields under constant evolution. Day to day, new algorithms, technologies, and use-cases emerge as promising alternatives to the current developments achieved so far. Typically, these advancements shape the evolution of training programs. Therefore, the results presented in this work are limited to showing the current picture of training in AI, IoT, and EC nowadays. Nonetheless, these results should be taken as a starting point to build training programs for shaping the future professionals of Industry 4.0.
# Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications
## 2 Proposed RHRSE-20T SRAM
### Double Node Upset Recovery Analysis
#### 2.6.1 Single Event Transient Occurs Simultaneously at Nodes Q and QB  
Q changes from '1' to '0', and QB changes from '0' to '1'. In this situation, transistor P3 is activated, and P4 is deactivated. However, the changes in Q and QB do not affect the values stored in the other nodes. Therefore, node S3 activates transistor N4, causing QB to recover to '0'. Consequently, node Q also recovers to '1'. Through this process, all nodes are restored to their original states.
# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 3 Results and Discussion
### Parallel Pipeline Performance and Scalability
#### 3.5.2 Throughput and Task Latency  
Throughput, measured as the number of tasks completed per second, is an essential metric for evaluating the processing capability of parallel execution. The Outlier Detection stage achieves the highest throughput at 40.8 tasks per second, benefiting from its simple computations being easily parallelizable across cores. However, more complex stages such as Bi-LSTM Optimization and Imputation have lower throughput values of 1.86 and 0.078 tasks per second, respectively, due to their higher computational demands.  
Interestingly, task latency remains nearly unchanged across different configurations, particularly in computationally expensive stages such as Bi-LSTM Optimization and Forecasting LSTM. While total execution time is significantly reduced through parallelization, the per-task execution duration remains largely consistent, indicating that the primary benefit of parallel computing in this context is increased task concurrency rather than per-task acceleration.  
Figure 9 visualizes throughput performance across different computational stages.
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 3 Proposed Framework
### Performance Enhancement Strategies  
The framework's practical feasibility is heavily dependent on two complementary optimization methodologies that overcome the conventional performance limitations of homomorphic encryption systems.  
Our key performance enhancement is using SIMD vectorization. The CKKS scheme's inherent slot-packing capacity allows for the encryption of thousands of pixel values into a single ciphertext. Homomorphic processes then run concurrently across all slots, resulting in a computational model in which the processing time is virtually independent of image resolution up to the scheme's capacity. This feature is especially useful for high-resolution applications, where typical pixel-based techniques become computationally intractable.  
The FHE public key materials, despite their large size, have good amortization features across numerous operations. By having the client transmit these materials to the server once per session for caching, we effectively spread the initial transmission cost over all subsequent activities. This amortization can increase effective throughput by orders of magnitude in batch processing scenarios, such as those used in medical imaging or surveillance.  
These improvements work together to deliver practical performance while providing extensive security assurances against both classical and quantum adversaries.  
The profound impact of this SIMD architecture is best understood when contrasted with a traditional, non-batched FHE approach. A pixel-wise method would require encrypting each of an image's thousands of pixels into a separate ciphertext. Consequently, any operation, such as a brightness adjustment, would need to be performed thousands of times in a loop on the server. Our SIMD framework avoids this prohibitive overhead by performing only a single, highly parallelized operation on one ciphertext that contains the entire image.
# An Image-Encryption Algorithm Based on Stage-Merging Bit Scrambling
## 2 Materials and Methods
### Encryption Process
#### 2.4.1 Key Handling  
In order to prevent differential attacks, this algorithm uses a hash algorithm to calculate the plain image, generates a hash digest that is highly sensitive to the plain image, and associates it with the key-processing process. In this way, different initial parameters can be generated for each different plain image, thereby generating completely different cipher images.  
This paper uses the MD5 hash algorithm to calculate the plain image, and obtains a hexadecimal string with a length of 32 bits, denoted as, _hash_ and evenly splits it into four strings with a length of 8 bits, denoted as _hash\({}_{1}\)_, _hash\({}_{2}\)_, _hash\({}_{3}\)_, and _hash\({}_{4}\)_. The key of this algorithm is a hexadecimal string with a length of 48 bits, denoted as _key_, which is evenly divided into four strings with a length of 12 bits, denoted as _key\({}_{1}\)_, _key\({}_{2}\)_, _key\({}_{3}\)_, and _key\({}_{4}\)_.  
The initial parameters of Kent chaos and 2D logistic chaos are calculated by Equation (3) and Equation (4) respectively:  
\[\left\{\begin{array}{l}x_{1}=\text{mod}(\frac{\text{hex2 dec}(key_{1})}{2^{ 28}}+\frac{\text{hex2 dec}(hash_{1})}{2^{22}},1)\\ a=\text{mod}(\frac{\text{hex2 dec}(key_{2})}{2^{28}}+\frac{\text{hex2 dec}(hash_{ 2})}{2^{22}},1)\end{array}\right. \tag{3}\]  
Figure 4: Flow chart of the image-encryption algorithm.  
\[\left\{\begin{array}{l}{x_{1}}^{\prime}=\text{mod}(\frac{\text{hex2 dec}(kvg_{1})}{ 2^{8}}+\frac{\text{hex2 dec}(hash_{2})}{2^{22}},1)\\ {y_{1}}^{\prime}=\text{mod}(\frac{\text{hex2 dec}(kvg_{4})}{2^{8}}+\frac{\text{ hex2 dec}(hash_{4})}{2^{22}},1)\end{array}\right. \tag{4}\]  
where hex2 dec(_key_) represents the conversion of the hexadecimal string _key_ to a decimal value, mod represents the modulo operation, and mod(_x_,_1) returns the remainder of dividing \(x\) by 1.  
Mark the plain image to be encrypted as \(I\) and its size as \(M\times N\). Iteratively calculate the Kent chaos. In order to eliminate the influence of the transient effect of the chaotic system, the first 1000 iterations are discarded and two chaotic sequences with length \(MN-2\) are obtained, which are denoted as \(K_{1}\) and \(K_{2}\). The 2D logistic chaotic system is iteratively calculated \(1000+MN\) times, and the first 1000 calculation results are discarded, and two sequences \(L_{1}\) and \(L_{2}\) of length \(MN\) are obtained.
# Improving Threat Detection in Wazuh Using Machine Learning Techniques
## 3 Materials and Methods  
### Design of ML-SOC Architecture and Integration  
Designing an ML-SOC architecture involves addressing various challenges to ensure effective threat detection, efficient response times, and reduced false positives [7]. This section outlines a systematic approach to the design and development of the ML-SOC architecture, focusing on data collection, preprocessing, model selection, and performance evaluation. To develop an effective ML-SOC architecture, it is essential to understand the core objectives and requirements that guide its design. Table 1 below outlines the primary objectives and the corresponding requirements. This section elaborates on the process of integrating ML to improve real-time threat-detection capabilities, as shown in Figure 2. We discuss how data is prepared, preprocessed, and used for training various ML models to enhance the system's ability to identify and respond to threats effectively. The implementation of ML techniques enables Wazuh to transition from a traditional rule-based approach to a more intelligent, adaptive security system capable of detecting sophisticated cyber threats, such as advanced persistent threats (APTs), zero-day exploits, and polymorphic malware that evade predefined rules, while minimizing human intervention [13; 14]. This paper details the technical implementation of ML models tailored for Wazuh, emphasizing data preparation, feature extraction, model training, and evaluation. Our integration leverages Wazuh's three-tier architecture, consisting of Wazuh Manager, Wazuh Indexer (Elasticsearch-based), and Wazuh Dashboard (OpenSearch Dashboards). Machine learning enhancement occurs at multiple integration points to ensure comprehensive threat analysis while maintaining system reliability and performance. **Wazuh Manager Enhancement:** The Wazuh Manager receives custom output modules that extract structured data from processed alerts for ML analysis. Custom decoders parse security events to extract relevant features, including temporal patterns, event characteristics, source information, and contextual metadata. Enhanced correlation rules provide initial threat scoring and categorization that informs ML model selection and processing priorities. Configuration modifications include custom output plugins that format alert