esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Security Threats
### Methodology for Attack Classification  
Our approach to classifying blockchain security threats involved a targeted literature review across major academic databases, including IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar. The review process followed three key steps:  
* Article Selection: We searched for papers using terms such as \"blockchain attacks\", \"security taxonomy\", and \"vulnerabilities in distributed ledgers\". Studies were included if they (i) focused on blockchain-based systems, and (ii) presented or discussed a classification of security threats or attack types.
* Relevance Screening: Titles and abstracts were reviewed to filter out papers that were either not technical, not related to blockchain, or purely focused on cryptographic primitives. This helped ensure focus on architectural and systemic security threats.
* Classification Analysis: For each selected study, we examined how attacks were categorized (e.g., by type, impact, architecture layer). This comparative analysisinformed the design of our own five-layer taxonomy, which maps attacks to the data, network, consensus, contract, and application layers of blockchain systems.  
This structured yet flexible approach ensured that our taxonomy is informed by prior literature, while offering a more architecture-aligned and extensible classification framework.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 6 Clinical Applications of Commercially Available TD-NIRS Systems by Japanese Researchers
### Other Groups in Japan  
In 2006, [PERSON] et al. made measurements for six healthy human subjects with pharmacologically perturbed cerebral hemodynamics using the TRS-10 and PET simultaneously, and reported a correlation between the hemodynamic changes obtained by the TRS-10 and the PET-parameter changes in gray matter regions [242]. [PERSON] et al. used the TRS-10 with eight adult male heads during carotid endarterectomy and reported that TD-NIRS measurements were useful for monitoring the cerebral blood circulation and oxygenation during neurosurgical operations [243]. [PERSON] et al. used the TRS-20 on the foreheads of pregnant women during caesarean section [244]. In the case of massive bleeding due to placenta previa, \(S_{02}\) measured by the TRS-20 showed clear decreases from 67% to 54%, while \(S_{02}\) measured by pulse oximetry did not change. The TD-NIRS systems were found useful for monitoring bleeding during caesarean section. [PERSON] et al. evaluated the usefulness of TD-DOS imaging of primary breast cancers using the TRS-20 [245]. Lesions with \(C_{total-Hb}\) 20% higher than normal tissue exhibited more advanced cancer stage, higher mitotic counts, and higher \({}^{18}\)Fluoro-deoxy-glucose uptake in PET, and TD-DOS imaging of \(C_{total-Hb}\) was found useful for prediction of patient prognosis and potential response to treatment.
# Analysis of the Efficiency of Slope Evacuation for Dense Crowds in Urban Street-Type Public Spaces
## 7 Conclusions  
This study provides a theoretical framework for understanding pedestrian evacuation dynamics in sloped street-type spaces. The findings highlight the importance of slope gradients, street width, obstacle layout, disability type, and group movement in influencing evacuation efficiency. While the study is primarily theoretical, it offers guidance for urban planners and public safety managers to improve the design and management of street-type spaces. Based on these experimental results, the main conclusions can be summarized as follows:  
1. From the perspective of spatial design, the slope of the street-type space should not be more than 4\({}^{\circ}\), which can maintain the continuity of the flow of people while avoiding the congestion and stagnation caused by excessive slope.  
Figure 16: Preventive mechanisms for safe evacuation of slopes in urban street-type public spaces.  
From the perspective of site management, when the width of the evacuation channel is less than 2 m, it is more likely to cause congestion of dense crowds, which falls into the high-risk zone. Relevant management units should increase the deployment of security forces and enhance police dispatch to deal with emergencies.
3. In order to optimize pedestrian evacuation, it is necessary to reasonably plan the setup and layout of obstacles when designing public space, to improve the pedestrian's access efficiency and comfort, and to ensure the smoothness of the evacuation channel.
4. From the perspective of crowd control, the age of evacuees and the variability of their evacuation ability should be taken into account to formulate an emergency evacuation plan to maximize the overall evacuation efficiency through reasonable control and guidance.
# Geometry-V-Sub: An Efficient Graph Attention Network Struct Based Model for Node Classification
## 2 Materials and Methods
### Reduce Parameters by Modifying GAT  
In this part, we try to improve the performance of the model by slightly adjusting the structure, which is adjusted by 2 aspects:  
1. After the feature transformation, the activation function is added to ensure that the parameters of the feature transformation are relatively independent compared with the later processing steps, and more node features can be learned.
2. The attention vector is widened by two or more times to enhance the expression of opposite edge information.
3. By scaling the edge vectors spliced by nodes, the attention vectors on the edges can learn more hidden features of edges.  
When dealing with graph structure data, it is often necessary to map the sparse representation node information from the sparse representation space to the dense representation space, so as to facilitate the subsequent steps and reduce the scale of the model. Most models in graph learning usually have this step. This process can be completed by matrix transformation. We can get the transformed feature \(h^{1}\) by transforming the input value \(h^{0}\) through the matrix \(W\), as shown in Equation (1):  
\[h^{1}=Wh^{0} \tag{1}\]  
In order to adapt the attention matrix of the attention matrix \(2\times n\), it is necessary to expand the copy of the input feature \(h^{1}\) with the feature matrix size of \(2\times n\) to \(h^{2}\) shown as flow (2):  
\[h^{2}=expand(h^{1}) \tag{2}\]  
In order to fully characterize the transformed nodes, \(LeakyReLU\) is used as an activation function to process \(h^{2}\). The purpose is to use the activation function to isolate the feature transformation and edge attention to enhance the generalization ability, as shown in equation:  
\[h^{3}=LeakyReLU(h^{2}) \tag{3}\]Like GAT, the feature representation of nodes and between nodes is the processed feature of node \(i\) and node \(j\) connected to edge \(ij\) by splicing, and the attention mechanism is used to achieve the purpose of edge learning. The activation function \(\sigma\) is applied to process the results to increase the generalization ability of the model network. Here, \(LeakyReLU\) with negative slope \(\alpha=0.2\)is selected as the activation function, as shown in Equation (4):  
\[e_{ij}=\sigma(a(h_{i}^{2}||h_{j}^{2})) \tag{4}\]  
To get the message passing weight in probability form \(w_{ij}\), where the softmax function is used to calculate the attention of each edge, as shown in Equation (5):  
\[w_{ij}=softmax_{j}(e_{ij}) \tag{5}\]  
To fit \(h^{1}\) with the size \(1\times n\), here \(w_{ij}\) sum column directions to get \(w^{\prime}_{ij}\), now \(w_{ij}\) is converted from a matrix of \(2\times n\) to a vector of \(1\times n\). By multiplying the message passing weight \(w_{ij}\) with the \(h_{i}^{1}\), the message \(m_{i}\) transmission is achieved, as shown in Equation (6):  
\[m_{i}=h_{i}^{1}w_{ij} \tag{6}\]  
After message passing step, messages aggregate through Equation (7) as current layer output features \(h^{out}\) shown as follow:  
\[h^{out}=\sum_{i}m_{i} \tag{7}\]
## 3 Materials and Methods  
### Sample Preparation  
Four sweet potato cultivars--Anna, Jieshu95-16, Ayamurasaki, and Shuangzai--were used as experimental plant materials in this study. All cultivars were planted on the 11 July 2020 using the standard production practices at the Baiyun Experimental Station (23 deg23' N, 113 deg26' E; 20 m above sea level) of the Guangdong Academy of Agricultural Sciences, Guangzhou, China. On 23 November 2020, triplicate samples were harvested. For each cultivar, 10 medium-sized tuberous roots were selected, washed with abundant tap water, rinsed with distilled water, dried with paper towel, cut into strips with a high-grade stainless knife, mixed well, weighed to 200 g, and then frozen immediately in liquid nitrogen. All of the samples were separately ground in a liquid nitrogen grinder (A10 basic, IKA, Staufen, Germany) and stored at \(-80\)\({}^{\circ}\)C prior to GC-MS analysis. One gram of sweet potato sample was stored in a 15 mL headspace vial with 1 mL saturated NaCl solution added. Then, 5 \(\upmu\)L of 1-pentanol solution (0.4055 mg mL\({}^{-1}\)) was added and the mixture was gently homogenized. The vial was capped with a crimp type cap and a PTFE/silicone septum (CNW, Shanghai, China) to prevent the leakage of volatile compounds.  
Figure 5: Partial least squares-discriminate analysis (PLS-DA) score plot of the GC-MS data. A variables important in projection (VIP) score close to or greater than 1 of the compound was considered to be a differential volatile in the cultivars.
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 3 Proposed Fingerprinting System
### Proposed Fingerprinting Strategies
#### 3.2.4 Weighted Coverage  
The _Weighted Coverage_ strategy explicitly merges network availability and RF information by combining the metrics defined in (2) and (6):  
\[\begin{split} d_{W\_Cov}(i,j)&=d_{Net}(i,j)\cdot d _{Cov}(i,j)\\ &=\frac{1}{L_{i,j}}\sqrt{\sum_{l=1}^{N}\Bigl{(}p_{l}^{i}-p_{l}^{ j}\Bigr{)}^{2}}.\end{split} \tag{7}\]  
RPs are sorted based on increasing \(d_{W\_Cov}(i,j)\), and the first \(k\) is selected for estimating the position of TP \(j\).
# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 2 Related Work
### Multi-Server Federated Learning in Vehicular Networks  
[PERSON] et al. [17] propose a clustered architecture for vehicular federated learning that integrates both learning and scheduling mechanisms to address the unique challenges of vehicular networks. Their framework leverages vehicular-to-vehicular (V2V) communication to mitigate communication constraints, enabling clusters of vehicles to train models concurrently. In each cluster, vehicles collaborate locally by aggregating their model updates, and only the aggregated output is transmitted to a multi-access edge computing (MEC) server for further global aggregation. This clustered approach not only supports both single-task and multi-task learning, but also accounts for critical factors such as data heterogeneity and mobility constraints. Although the design emphasizes client-side operations and efficient intra-cluster cooperation, its reliance on a centralized MEC server for final aggregation distinguishes it from fully decentralized models, potentially impacting scalability in more dynamic scenarios.  
[PERSON] et al. [10] introduced a two-layer federated learning architecture for the 6G-enabled Internet of Vehicles (IoV), where a central cloud server aggregates model parameters from RSUs based on their contextual information, such as vehicle locations and computational power. At the RSU level, local models from vehicles are aggregated using a weighted mechanism that prioritizes data from nearby vehicles with higher-quality contributions. The cloud server then performs global aggregation, improving model accuracy and convergence speed. While this framework enhances learning efficiency, it relies heavily on frequent communication with the central cloud server, which may introduce latency and centralization bottlenecks.  
The authors in [21] proposed a trust-based knowledge-sharing mechanism within a multi-server FL architecture. In this approach, each server calculates trust scores for other servers based on the feedback received from its clients. This method demonstrated improved accuracy and reduced loss, particularly during the initial training rounds. However, the framework selects only one global model for the subsequent training round based on the highest trust score, potentially overlooking valuable contributions from other servers. This limitation can be critical in vehicular networks with non-IID data, where diverse contributions from multiple servers may be necessary to improve the overall model performance.
# Joint Content Recommendation and Delivery in Mobile Wireless Networks with Outage Management
## 4 Heuristic Algorithms for Joint Content Recommendation and Delivery
### Content Recommendation  
Based on the pre-allocated user capacity, we can formulate two content recommendation problems as follows.
# Flutter Reliability Analysis of Xiangshan Harbor Highway Cable-Stayed Bridges in Service
###### Abstract
[PERSON] [28] analyzed the sensitivity of flutter parameters for the open section and typical closed-mouth segment model, respectively, by the two-dimensional frequency-domain flutter analysis method, and discussed the sensitivity of flutter critical wind speed to structural parameter difference. [PERSON] [29] and [PERSON] [30; 31] quantified the uncertainty of flutter derivatives of bridges by wind tunnel tests, and carried on the probabilistic flutter analysis of the bridge. [PERSON] [32] pointed out that reliability analysis methods have been developed rapidly in bridge engineering design, and reliability studies and applications considering the uncertainty of structural parameters and dynamic loads are more and more widespread. Flutter reliability of long-span bridges is still a hot topic in the bridge field. Although some scholars conducted flutter reliability analyses of long-span bridges from different perspectives and focuses based on the reliability theory, most of the studies on parameter sensitivity were deterministic analyses and considered flutter influencing factors only in the design stage, while few studies have analyzed the flutter parameters of cable-stayed bridges in a service state. In this paper, the time-history analysis method of the bridge flutter and reliability theory are introduced in Section 2. Then, the functional function and parameters distribution for flutter reliability analysis are also determined in Section 2. Furthermore, the building of the finite element model of Xiangshan Harbor Bridge, the calculating of its flutter derivatives and critical flutter wind speed, and the exploring of the change law of the critical flutter wind speed under different parameters take place in Section 3. In Section 4, the flutter reliability index of Xiangshan Harbor Bridge is calculated based on the checking point method and Latin hypercube sampling, considering the operation stage of the bridge and parameter uncertainty. Finally, some conclusions are summarized in Section 5.
## 2 Materials and Methods
### Oxidant and Bioaugmentation Foam Generation and Characterization  
The surfactant, sodium C14-16 alpha olefin sulfonate (AOS; AK Precision Chemical Co., Ltd., Daejeon, Korea), was used to generate the foam. In this study, two types of remedial foams were prepared, oxidant foam for the pretreatment of TPH by oxidation and bioaugmentation foam for the biodegradation of TPH. The oxidant foam was generated by injecting air at a flow rate of 800 cc/min through a capillary tube into the mixture of oxidant (50 mN Na2S2O8) and 0.1% AOS solution. In this study, persulfate oxidation (\"PS\") foam refers to sodium persulfate-surfactant foam. The bioaugmentation foam was generated by injecting air at the same flow rate into the mixture of bacterial solution inoculated in R2A, 0.1% AOS, and inorganic nutrients (1:1 ratio of bacterial solution with the AOS solution). It was found that the surfactant (AOS) did not show a negative impact on the population of bacteria (_Acinetobacter_ sp K-6). An analysis using bacterial solution (prepared in R2A medium) found that the colony forming units (CFU) were 6.1 \(\times\) 10\({}^{9}\) CFU/L and 5.2 \(\times\) 10\({}^{9}\) CFU/L before and after adding AOS, respectively.  
We characterized the two foams using several techniques. Foam quality was identified as suggested in the literature [19; 27]. The foam stability was determined in terms of half-life, defined as the time taken for the foam to decline to half the original aerated volume [28]. Foamability [29] was identified as the time taken to generate 1000 mL of surfactant foam inside a mass cylinder. From the half-life and foamability, we determined the performance of the foam in terms of the Foam Composite Index (FCI) [30]. The foam density was measured by pouring surfactant foam in an apparatus of known volume and measuring its weight [31]. Foam expansion ratio was identified as the ratio of liquid used to produce a certain volume of foam [32]. In this experiment, we produced 1000 mL of foam by which an expansion ratio was calculated. Finally, in situ generation of surfactant bubbles was captured using 0.001 g of brilliant blue dye that was added to 65 mL of the surfactant solution. The properties of the foam were not affected by the addition of the dye.
###### Abstract  
The control performance of a dynamic system can be checked by the degree of controllability. In this work, we present a new method for determining the degree of observability of state variables for the linear quadratic optimal estimation (LQE) problem. We carried out the calculation of the degree of controllability for the linear quadratic optimal control (LQR) problem using a duality theorem. Compared with the traditional measures of controllability such as determinant, trace, and maximal eigenvalue of the inverse controllability Gramian, the proposed degree of controllability was developed for each state variable and takes into account both the controllability Gramian and the cost function. The new method is convenient to apply to LQR problem. In the numerical simulation, we determined the influence of the model parameters on the degree of controllability. Besides that, we analyzed the degree of controllability, which gives an insight into the relationship between the system model design and the control performance.  
Keywords:degree of controllability; degree of observability; linear quadratic control; duality; controllability Gramian +
Footnote †: journal: Applied Sciences
# Multimodal AutoML via Representation Evolution
## 4 Experiments
### Baselines and Evaluation
#### 4.2.3 Multimodal Baselines  
We also consider several common multimodal approaches that combine information from images and texts using both early fusion and late fusion.  
* **Late fusion (sum)** An image-only classifier (MobileNet + SVM) and a text-only classifier (N-grams + SVM), both as described above, are trained independently. Class probability distributions are extracted from both models and combined by a sum. Combined probability for class \(i\) is defined as: \[P(i)=softmax(P_{img}(i)+P_{txt}(i))\]
* **Late fusion (max)** Class probabilities are obtained from both models and combined by a maximum. Combined probability for class \(i\) is defined as: \[P(i)=softmax(max(P_{img}(i),P_{txt}(i)))\]
* **Early fusion + SVM** Image features are extracted with a MobileNetV3 model, much like in Section 4.2.1. Text features are extracted using a pre-trained Sentence-BERT sentence embedding model [46], trained using a multilingual MPNet objective [47]. Features from both modalities are then concatenated and fed into a linear SVM for prediction. The SVM classifier is trained for a maximum of 1000 iterations with \(C=2\).
* **Early fusion + NN** Again, image features are extracted with a MobileNetV3 model and text features are extracted using a Sentence-BERT model. Features from both modalities are then concatenated and fed into a two-layer fully-connected network. The neural classifier uses a 100-dimensional hidden layer and ReLU activation and is trained for 30 epochs, at a base learning rate of \(10^{-3}\) using the Adam optimizer with an L2 penalty of \(10^{-4}\).  
We consider a MuRE baseline, given at most one hour for training. The variant adopts minority class upsampling (addition of artificial instances up to the point of uniform distribution of labels). We hypothesized that by considering upsampling, the MuRE's classifier layer (linear) could perform better.
## 3 Proposed Architecture
### Weighted Word Representation  
Term frequency-inverse document frequency (TF-IDF): Studies show that the weighted averages of word embedding can improve unsupervised NLP tasks, especially sentiment classification performance [49]. TF-IDF is an unsupervised term weighting scheme for information retrieval and text mining. TF-IDF represents the relative frequency of a word t in a text document, and inverse document frequency scales with the number of documents. It can process as follows:  
\[W_{d,t}=tf\times log(\frac{N}{df}) \tag{1}\]  
\(W\) indicates the weight value for term \(t\), and document \(d\), the total number of the document in the corpus defines as \(N\), \(tf\), and \(df\) denote the term frequency indicating the number of times and number of the document in a particular term, respectively.  
The global vectors (GloVe): GloVe is a word2 vec-based word representation to learn word embeddings from text documents effectively [50]. A pre-trained word embedding model GloVe with 2 billion tweets, 27 billion tokens, and 1.2 million vocabularies was used to generate a word vector matrix as a 200-dimensional vector. TF-IDF with GloVe models utilized to observe the proposed model performance. If each input k-word is represented as \(T(t_{1},t_{2},\ldots,t_{k})\), then each word is converted into word vector of d dimension. Hence, \(R^{d}\) will be the dimensions space of each word; then, each input text is represented as \(R^{k\times d}\) dimension space and input text matrix generation as \(T=\{t_{1},t_{2},\ldots,t_{k}\}\in R^{k\times d}\). Finally, the feature vector \(f_{v}\) for the \(T\) document concatenated \(\oplus\) with word embedding is shown as follows:  
\[f_{v}=w_{1}\oplus w_{2}\oplus w_{3}...\oplus w_{n-1}\oplus w_{n} \tag{2}\]  
To improve the text representation, we integrate pre-trained Glove word embedding with TF-IDF weighing.  
\[V_{i}=W_{d,t}\times f_{v} \tag{3}\]  
where \(f_{v}\), as mentioned above, is a word vector matrix obtained by Glove, and \(W_{d,t}\) is the TF-IDF weighing of document \(d\) and term \(t\). This method can solve the dimensional problem of the high dimensional sparse matrix. We also applied Gaussian Noise and Gaussian Dropout after receiving text representation from the word representation layer. The Gaussian Noise and Gaussian Dropout process can be used as a regularization method, making the model more vital and less prone to overfitting. Similarly, since Gaussian noise is applied directly to the word embedding, this process serves as a random data augmentation during training time.