esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Model
### Verification of Academic Information
#### 3.2.1 Case 1: The Holder Sends a Third Party the Certificate and Allows Its Verification  
In this scenario, the holder \(H\) sends to the third-party \(T\) using a secure channel all the information contained in the certificate (\(c\)), the blockchain transaction \(id\), and grants access to the data by adding \(T\)'s account in the _SCAccess_ (\(aa\)), indicating use case number one. When \(T\) asks for \(id\) in _SCData_ to obtain the encrypted root of the Merkle tree \(h\), it checks \(aa\) to validate that is authorized and, regardless of the result, logs in _SCLog_ that holder's data tried to be accessed by \(T\). If access is granted (\(T\)'s address was included in \(aa\)), and the requested information is the same indicated by \(H\) in \(aa\), \(T\) receives the validity of the data and \(h\), decrypts \(h\) with the institution's public key, and compares the result with the root of the Merkle tree (\(mt\)) of the certificate (\(c\)) to verify and check if it has not been revoked.  
The data exchanged between the holder and a third party, together with the interactions with the different smart contracts in this scenario, are depicted in Figure 3.
## 3 Results  
### Dataset Introduction  
We evaluated the model on two datasets: the German-Street dataset and the Massachusetts roads dataset.  
* The German-Street dataset, a subset of the CITY-OSM dataset, was published by [PERSON] et al. [35] and includes images from various cities and regions, with annotations generated via OpenStreetMap. It consists of 4000 street images measuring 512 \(\times\) 512 pixels, divided into 3600 for training, 40 for validation, and 360 for testing.
* The Massachusetts Roads dataset, created by [PERSON] et al. [36], consists of 1171 aerial images of Massachusetts, each measuring 1500 \(\times\) 1500 pixels and covering an area of 2.25 square kilometers. The dataset includes a wide range of landscapes such as urban, suburban, and rural areas and is randomly split into 1108 for training, 14 for validation, and 49 for testing.  
The two datasets encompass diverse urban and rural road scenarios, presenting challenges like road occlusion, inter-class similarity with the background, and scattered yet continuous road distributions. These characteristics facilitate evaluation of a model's local precision and global coherence in road extraction. The datasets' differences in geographic distribution, road features, and remote sensing styles further enable assessment of a model's generalization across complex urban and varied rural environments.  
Figure 3: The LCM uses four-directional convolutions to capture contextual information, with skip-connections to retain key pixel data.
## 7 Analysis of Semi-Active Vibration Absorption Effect  
### Analysis of MR Damper Energy Dissipation  
In the event of an earthquake, the energy transferred to the structure from earthquake motion has a significant relationship with damage to the structure. The semi-active control system reduces the seismic response of the structure by increasing the energy dissipation due to the setting of the dampers. This section analyzes the magnitude of the energy consumed by various semi-active control algorithms under the action of earthquake motion from the view of energy and compares it to the energy consumption of the active control. The time-history curves of the energy dissipated by the dampers for each control algorithm are shown in Figure 5, and the total energy dissipated by the dampers of the PC continuous girder bridge with CSWs under various algorithms are given in Table 3.  
Figure 5 and Table 3 show that the energy dissipation of the composite bridge structure under various semi-active control algorithms is mainly concentrated in the first 15 s under the same earthquake motion. The energy consumed by each algorithm is different with different control algorithms; Semictrl2 consumes the most significant power, Semictrl1 is the second, and Semictrl3 is the most minor. Further, due to the energy consumption being more significant, the acceleration of the main beam under the action of Semictrl2  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ground Motion** & \multicolumn{4}{c}{**EI Centro (PGA = 0.2 g)**} \\ \hline
**Coefficient of Weight Matrix** & \(\alpha\) = 40 & \multicolumn{4}{c}{\(\beta\) = 0.00001} \\ \hline Number of MR damper & \#1 & \#2 & \#3 & \#4 \\ Active optional control force(N) & 253,280 & 894.6936 & 253,480 & 253,540 \\ Point in time (s) & 2.34 & 2.23 & 2.34 & 2.34 \\ Relative velocity (m/s) & 0.3889 & \(-\)0.0008959 & 0.3897 & 0.3900 \\ Adjustable multiple of damping force & 8 & 8 & 8 & 8 \\ Viscous damping coefficient of MR & 81,409.10 & 124,838.64 & 81,306.13 & 81,262.82 \\ damper (N-s/m) & 221,620.00 & 782.86 & 221,795.00 & 221,847.50 \\ Maximum Coulomb force of MR & 0 & 0 & 0 & 0 \\ variable damper (N) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter design table of MR damper.  
Figure 5: Time-history of energy loss of dampers under various algorithms.  
is amplified compared to the uncontrolled state. By comparing the energy consumption time-history of the damper under three different semi-active algorithms to the active control algorithm, it is found that the energy dissipation time-history curve of the Horvat algorithm is closest to that of the active control algorithm, which shows that the Horvat algorithm has a better vibration absorption effect than the other two semi-active control algorithms in this composite bridge structure.
## 3 Results and Discussion
### Data Processing using Machine Learning Methods
#### 3.4.3 Neural Characterization Strategy  
With account of the unsupervised nature of the problem, the CNN-based autoencoder was chosen for extracting hidden features from image arrays. Random-crop, random-rotation, and random vertical flip were applied simultaneously to generate a dataset of images from an available experimental data. Ten thousand samples of \(64\times 64\) pixels were isolated from the whole dataset of 919 grayscale AFM-scans of \(256\times 256\) pixels. The diagram of that process is shown below (Figure 8). So significant augmentation is reasoned by the transformations applied (rotation, flip, random selection).  
From 10 to 11 objects (sites) were generated from each experimental sample. Each sample was then normalized by subtracting its mean value and dividing by standard deviation. Such a method was used to obtain enough data for convolutional autoencoder to fit the inner structure of substrates. By implementation of this procedure 10,000 sites were available. Approximately 6000 of them were used for training of the neural network, 2000 were used for validation of the model, and 2000 for the test. Learning and testing were performed on different experimental samples.  
Figure 7: Clusters representation with highlighted spectral activity. Numbers correspond to substrate types defined above.
## 3 Results and Discussion
### NO Formation Characteristics of NH\({}_{3}\)/CO Combustion
#### 3.3.4 Effect of Residence Time \(t\) on NO Production  
To investigate the effect on residence time on NO production, the combustion temperature was 1500 \({}^{\circ}\)C; the excess air coefficients were 0.9, 1.0, and 1.1; and the residence times varied from 565 ms to 2258 ms. The experimental result is shown in Figure 10. With the gradual increase in residence time, the amount of NO produced by combustion gradually decreased, and with the decrease in excess air coefficient or a gradual increase in residence time, the influence of NO amount on residence time also gradually reduced.
## 4 Results
### Spatial Analysis
#### 4.1.3 Spain  
The Spanish coastline was affected by 74 extreme weather events during the review period, which represents almost 78% of the total number of events recorded in the study area. The Mediterranean coastline stands out, with 44 events inventoried and accounting for almost 61% (102 victims) of the total number of fatalities recorded in the area of analysis (168). Economic losses amount to 6827.9 M, although no reliable information is available for 30% of the events, so the figure is certainly underestimated.  
Most of the events recorded on the Spanish Mediterranean coast took place during the early autumn after the intense heat of the summer, giving rise to HLIDs (High Level Isolated Depressions). The heat and humidity accumulated in the lower layers and in the sea meets the cold that arrives in the upper layers of the atmosphere, producing strong storms and downpours, with very intense rainfall concentrated in short periods of time (stationary or quasi-stationary convective nuclei). Depending on the orography, flash floods often occur. As a significant example of an HLID during the period analysed, the tragedy in the coastal town of Sant Llorenc des Cardassar in Majorca on 9 October 2018, which caused 13 fatalities, stands out. An extraordinary convective rainfall event (400 mm in 6 h), unforeseen by most numerical prediction models, generated a devastating flash flood in the urban centre of Sant Llorenc, crossed by a torrent (Ses Planes: 23.4 km\({}^{2}\) in drainage area) that reached a peak flow of 305 m\({}^{3}\)/s. This event far exceeds the extension for a 500-year return period [32; 33].  
Storm \"Gloria\", that occurred on 19-20 January 2020, activated the maximum alert level for wind gusts, rain, snow and coastal phenomena (Figure 5) in a large part of the eastern peninsular and in the Balearic Islands [34]. The storm, with heavy rainfall of up to 400 mm/24 h and winds of up to 140 km/h [19], caused 13 fatalities, 3 missing persons and economic damages valued at \(\epsilon\)71 M (Consorcio de Compensacion de Seguros). Gloria set a milestone in the Mediterranean, breaking records of all kinds: snow thickness (86 cm in Vilafranca, Castellon), wave height in the western Mediterranean (8.44 m significant height at the Valencia buoy), strong winds (up to 140 km/h), the maximum daily rainfall collected during the month of January (400 mm/24 h), etc. [35].  
On the Spanish Atlantic coast, 30 significant events were recorded during the period analysed, with 9 fatalities. The most significant event occurred on the coast of Galicia,between 4-7 January 2014, with a sea storm of strong intense waves that triggered the overflowing of rivers and streams and significant flooding on the coast. Three people lost their lives and their bodies were rescued by the Maritime Safety and Rescue Society of Spain.
# A Lyapunov-Optimized Dynamic Task Offloading Strategy for Satellite Edge Computing
## 7 Future Work  
This paper provides some insights into the future deployment of satellite-based Internet services for terrestrial users. However, the offloading model proposed in this study remains relatively simplistic. Future research should aim to address the complexities introduced by the heterogeneity of satellite computing resources and communication capabilities, as well as the more uneven frequency of task generation. At the same time, this paper focuses solely on simulating the architecture of the Iridium NEXT constellation, and thus the impact of traditional satellite constellations on the proposed offloading strategy is explored. However, the potential impact of emerging giant constellations, such as Starlink, has yet to be studied. These factors are likely to further complicate the task offloading problem, and warrant further study.  
Conceptualization, Y.H.; Formal analysis, Y.H.; Investigation, Y.H.; Methodology, Y.H.; Project administration, W.G.; Software, Y.H.; Supervision, W.G.; Validation, Y.H.; Visualization, F.Z.; Writing--original draft, Y.H.; Writing--review and editing, F.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
## 3 The Proposed Method
### CNN-Transformer Module  
The distance between different regions has changed in the images' polar representation. As the input areas have a significant impact on the model output, without a larger receptive field, the network may obtain incorrect segmentation results by misleading local features. In the OD and OC segmentation task, these U-Net variants have limited effective receptive fields and only focus on the local information of images.  
To solve this problem, we first exploit CNN to extract the local visual features to obtain more discriminative features. Next, we take advantage of the feature pyramids network to improve their spatial resolution, which handles multi-scale feature information. Then, we propose a transformer-based module, which is used to obtain better feature representation by aggregating long-range context information. Our module computes pairwise interactions (self-attention) between the optic cup and optic disc features, combining their features and generating contextualized features. The results of the experiment proved that transformer could obtain global contextual information.
# Enhanced Precision in Chinese Medical Text Mining Using the ALBERT+Bi-LSTM+CRF Model
## 3 Model Training  
When a knowledge graph is constructed, a systematic approach is adopted to process unstructured medical texts and extract entities and related data. Firstly, a deep analysis of texts using NLP is conducted, and then key entities such as diseases, body parts, and examination items are identified and extracted, which are the fundamental elements for constructing a medical knowledge graph. To recognize the correspondence between entities, label categories are assigned to each entity in the training process. Thus, the accuracy and reliability of knowledge graphs are ensured. Finally, these accurately labeled entities and relationships are integrated into a database, thus constructing structured and information-rich knowledge graphs. Therefore, the proposed algorithm not only advances the reliability of the database but also lays a solid foundation for subsequent data analysis and applications.  
The training model consists of three parts, ALBERT, Bi-LSTM, and CRF. Figure 3 depicts the model structure. The text is first embedded into ALBERT to extract features, providing a hidden-state representation of a sequence after multiple embeddings are run. Then, the Bi-LSTM layer captures the long-distance dependencies in the sequence according to the output of ALBERT. Finally, the CRF layer performs sequence labeling and outputs the labeled results in the BIO format. To categorize named entities, each entity is assigned to a label, namely, B-X, I-X, or O. B-X signifies that a given word is categorized under class X and is positioned at the start of the entity. I-X denotes that the word belongs partially to the category X, but it appears in the middle of the entity's span. O signifies that a word is not associated with any of the categories.
## 4 Methodology
### Model Operation
#### 4.2.2 Spatiotemporal Relation Attention Model
Then, \(Y_{t}(X)\) is obtained by the nonlinear activation function to represent the region feature weight tensor \(g^{t}\) that is sensitive to time change as follows:  
\[g^{t}=\text{Relu}(Y_{t}(X)) \tag{7}\]  
Finally, the attention weighted output of the spatiotemporal relationship attention module \(Y\) is as follows:  
\[Y_{c}(\text{i},\text{j})=X_{c}(\text{i},\text{j})\times g_{c}^{h}(i)\times g_{ c}^{w}(i)\times g_{t}^{h}(i,j) \tag{8}\]  
Hence, the network can more precisely determine the spatiotemporal links among objects because the three transformations capture the long-range temporal dependencies and spatial relationships in the feature space. Accordingly, the introduced attention mechanism captured the region of interest by using spatial connections and temporal interdependence among objects. Moreover, the STR attention module could be plugged into the model without altering the input-output structure. The mean squared error (MSE) is applied as a loss function by computing the average squared difference between the input and output frames to estimate how far the reconstructed frame is from the original. Because the model has only been trained for normalcy, the MSE values will be higher for anomalous frames and lower for regular ones. The goal is to reconstruct the normal video segments in a meaningful way. Thus, stable structures must be used to minimize the reconstructive loss between the ground truth Y and decoder output Y. Furthermore, the obtained reconstruction error is used as an anomaly score to distinguish between the normal and anomaly frames. The reconstruction error could be calculated as follows:  
\[L_{rec}=\left\|\hat{\text{Y}}-Y\right\|_{2} \tag{9}\]  
In addition, an experimental threshold is established, meaning that if a frame's anomaly score exceeds a threshold value, it is regarded as abnormal, and vice versa.
## 3 Problem Statement  
Consider a Wireless Sensor Network (WSN) where an adversary makes passive attacks to identify (or locate) the critical nodes. The adversary is free to choose any attack mechanisms, including, for example:  
* Eavesdropping.
* Tracing Back.
* Traffic Analysis.
* Node Compromised.
* ID Analysis.
* Collusion Stacks.  
Let \(A\) be the set of attacking mechanisms available with the adversary and \(a\in A\) be an attack mechanism. It is assumed that the adversary is capable of launching local or hotspot-level attacks on the WSN. The goal of the WSN manager is to maximize the safety period, while ensuring the Quality of Service (QoS) with reasonable energy consumption. In this work, we assume that the WSN is designed such that multiple routing mechanisms can adaptively be employed, so the WSN manager (defender) could command the nodes in a WSN to choose among these different routing mechanisms. For example, the WSNmanager (defender) could choose any of the following routing mechanisms for a certain period, and then choose another one for the next period, and so on according to a specific defense strategy:  
* Random Walk.
* Ring Routing.
* Phantom Base Station.
* Fake Packet Injection.
* Multi-path Routing.
* Data Mule.
* Hiding Mechanism.
* Directional Communication.
* Isolation Mechanism.  
Let \(D\) be the set of options available with the defender and \(d\in D\) be a defense mechanism. Let \(h_{d,a}\) be the total survival time (the time from the start of attacks to the detection of critical station(s)) when the adversary picks an attack mechanism \(a\) and the defender picks a defense mechanism \(d\). The primary goal of the defender is to have high survival time, which is contrary to the goal of the adversary. Therefore, the payoff for the adversary conflicts with the defender and can be taken as \(-h_{d,a}\). Thus, the conflict between adversary and defender can be modeled as a two-player zero sum-game. However, unlike the conventional game theory models, the manager has to satisfy the QoS and energy requirements. In addition to that, the data of survival time may contain uncertainty. All the notations that are used in this paper are defined in Abbreviations. In the following section, a game-theory-based approach to LPP is proposed.
# Graph Convolutional Spectral Clustering for Electricity Market Data Clustering
## 4 Discussion  
### Advantages of the Proposed Method  
In our research on power data analysis, we innovatively combine the isolation forest algorithm with the radial basis function (RBF) neural network for data fusion, integrating spatial and temporal data attributes. This allows for a more comprehensive analysis of different users' electricity usage patterns. Additionally, we introduce a graph convolutional network (GCN) and spectral clustering framework for dimensionality reduction and feature extraction of power data. This method effectively captures and analyzes the non-Euclidean structured data commonly present in power distribution systems. By leveraging the unique advantages of GCN in learning data topology, combined with spectral clustering, our approach can identify subtle relationships and patterns in electricity usage that traditional analysis methods cannot detect. This enables us to more effectively handle high-dimensional, multi-source datasets.  
In this study, the Isolation Forest algorithm has proven to be efficient in detecting anomalies in end-user electricity consumption data, with linear time complexity and low memory usage, and the detection results of the algorithm are quite accurate. The algorithm achieves this by constructing a tree structure to isolate abnormal data from normal data, effectively identifying potential anomalies in power consumption. Furthermore, integrating the RBF neural network during the interpolation process after anomaly detection has proven feasible, enhancing the accuracy and reliability of data reconstruction. It adeptly handles missing or abnormal data, contributing to smoother and more accurate data processing, thereby establishing a more reliable foundation for subsequent analysis.  
The method involving segmented aggregation and approximate feature extraction based on information entropy achieves significant dimensionality reduction in high-dimensional data. This approach not only minimizes redundant information but also preserves key electricity consumption characteristics, offering more informative data for subsequent analysis. The GCN utilized in the method proves effective in learning the relationships between nodes in the graph structure. This capability enables a better understanding of the similarity and dissimilarity between different users in the clustering of user load curves. The spectral clustering algorithm performs well in extracting typical load curves of users. It proficiently clusters similar load curves, providing robust support for power system optimization and the quantification of user demand.  
Overall, the combination of isolation forest, RBF neural network, segmented aggregation, approximate feature extraction, GCN, and spectral clustering contributes to a comprehensive and effective approach for analyzing end-user electricity consumption data, enhancing anomaly detection, and supporting power system optimization efforts.  
Figure 9: The computational time of different algorithms.
# A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments
## 4 Waveform Selection  
In this section we will introduce some candidate waveforms for massive MIMO systems and explain some of the studies done in this field.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 3 Survey of State-Of-The-Art MAC Protocols
### Random Access Protocols
#### 3.1.2 Cross-Layer MAC for Congestion-Contention and Power Control  
The optimal Cross-Layer Cognitive MAC Protocol (OCC-MAC) [67] proposed for multi-hop CRAHNs is a slotted random access MAC protocol that employs the OSA concept. It also uses slotted \(p\)-persistent CSMA to control replaced messages through the CCC. In OCC-MAC, persistence probability and the transmission power are balanced on the premise of the entrance rate controlled by sources to expand the aggregate net income of the auxiliary framework while the impact probability to the PU is kept lower than the allowable threshold. OCC-MAC divides time into fixed length intervals. CR nodes synchronize and they use a predefined slot at the beginning of each time.  
In Heuristic Cross-Layer Cognitive MAC (HCC-MAC) [67], SUs do not interchange control data through any of the cognitive radios; therefore, one of their cognitive radios is used for eavesdropping on current communications (idle, success or collision) inside each band. To utilize the spectrum opportunity, both HCC-MAC and OCC-MAC try to balance the contention and interference level among the cognitive links. However, due to overhead blockage in the network and the long queue of unsuccessful control messages in CCC, the OCC-MAC is not a suitable option to implement in practice.