esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The Yangtze Estuary and its adjacent areas are an important channel for material transportation and exchange between the Yangtze River, the East China Sea and the Yellow Sea [1]. The runoffs from the Yangtze River continuously transport nutrients to the area, making the estuary a habitat and a baiting, fattening and breeding site for many commercial fishes and invertebrates. Therefore, the estuary has become a pivotal fishing ground in China [1]. _Portunus trittuberculatus_ ([PERSON], 1876), an edible crab inhabiting the sandy or pebble seafdoors throughout the majority of seawaters in China, is the perennial dominant species of invertebrates in the Yangtze Estuary [2]. Over the past few decades, sustained human activity has had serious effects on its biomass and abundance. The catch of _P. trittuberculatus_ in China had increased significantly from 130,000 tons in 1989 to 490,000 tons in 2018, among which the catch from the Yangtze Estuary and its adjacent areas accounted for about 50% [3]. As an important commercial species in China, the wild biomass of _P. Trittuberculatus_ has been continuously decreased due to overfishing, pollution and habitats' destruction in the past decades [4, 5]. Meanwhile, the swimming crab, with high economic value, was also an important breed of aquaculture. Artificial propagation, restocking and stock enhancement are crucial ways to supply fishery biological resources and restore aquatic ecology [6, 7]. The aquaculture yield of the swimming crab also revealed a trend of rapid growth since recorded from 2003, the proportion of which was about 25% of the total catch of swimming crab [3].  
Traditional fishery resource assessments require not only value for life history invariant [8] but also, in some cases, catch and effort data [9], length-frequency data or age-structure data [10]. Due to the high cost of data collection and analysis, resource assessments existed for less than 1% of fishery species in the world [11]. Therefore, the lack of survey data for most sea-life made it difficult to assess their maximum sustainable yield (MSY).  
The length-based Bayesian biomass estimator (LBB) is a powerful tool only using length-frequency (LF) or width-frequency (WF) data to evaluate the stock status of marine species growing throughout their lives, such as a large number of fishes and invertebrates [12]. Through the analysis of LF or WF data, the LBB theory estimates several parameters for the target species including, but not limited to, asymptotic length/width (\(L_{inf}/W_{inf}\)), length/width at first capture (\(L_{c}/W_{c}\)), relative natural mortality (\(M/K\)) and relative fishing mortality (\(F/K\)) [13,14].  
This study presents the LBB method for the analysis of the carapace width of _P. trittuberculous_ captured from the Yangtze Estuary. The purpose was to evaluate the size structure of stocks for the swimming crabs in this sea area via derived fishery reference points such as \(B/B_{0}\) and \(B/B_{MSY}\), which would be used as the theoretical foundations for managers to take corresponding measures for rationally utilizing the biological resources.
# UAV IoT Framework Views and Challenges: Towards Protecting Drones as \"Things\"
## 3 Proposed Framework and Methodology  
### Overall Concept and Methodology  
The fundamental concept of the proposed framework to protect drones lies in the fact that mobile semiautonomous devices are expected to enter the IoT architecture as another type of smart devices and, due to their significant impact on several everyday activities (sensitive/critical or not), they require well-established and high-quality security support. On this ground, the proposed framework envisions to provide the necessary security components that would facilitate the process of interconnecting drones and UAVs under the umbrella of the IoT, while exhibiting advanced intelligence and self-management characteristics.  
In more detail, considering the use of drones for different types of scenarios as representative application, the current state is actually characterized by separated, isolated, and custom approaches. As illustrated at the top of Figure 3, each operator currently uses their control center to communicate and control drone flight based on their own perceived processes. The adoption of security tools is, in fact, optional, their quality is questionable and not certified, as well as not compatible (in the general case) with security measures applied by other operators. The flight license (wherever required, based on local regulations) is also separate and difficult to verify compliance. Moreover, there are no fleet-management processes and no interdrone communications.  
The introduced framework actively inserts drones in the IoT architecture by properly securing them both at device and network level.  
* At device level, the proposed framework enhances drones with embedded lightweight security, privacy, and safety based on cutting-edge vision-based techniques, which also enable advanced scene/path identification.
* At network level, drones become part of the IoT architecture and they are accessed/controlled through it. Furthermore, agile communications among drones are enabled, providing self-organizing capabilities that set the basis for innovative features, namely, device registration, flight dynamic monitoring, trust establishment through a distributive reputation point system, enforcement and verification of flight-plan regulations, and extensive fleet management via advanced interoperability.  
For achieving the aforementioned security and privacy goals, the proposed framework introduces: (a) a lightweight security toolbox, (b) vision-based solutions, and (c) privacy prevention and anonymity techniques for mobile things.
# Fine-Tuned Visual Transformer Masked Autoencoder Applied for Anomaly Detection in Satellite Images
## 2 Materials and Methods
### Proposed Approach  
The visual masked autoencoder used for the purpose of this paper was the model used in [42]. Here, the autoencoder is organized in an asymmetric design that allows the model to operate only on partial information during handling input images while the remaining tokens are masked. The patches that were chosen to be masked were selected according to a uniform distribution to ensure equal and non-deterministic masking. Other issues that were taken into account were the requirement to disable the algorithm from creating extrapolations of neighboring patches and to exclude center bias, which is primarily visible when patches around the image center are masked. It should be mentioned that the original encoder-decoder architecture was published in [43] and was reused in [42]. In this model, each single image is divided into equal patches that pass through parallel linear projection modules. Next, a learnable embedding is created in the sequence of embedded patches to model image representation. Position embeddings are added to the patch embeddings to retain positional information. The Transformer encoder [1] consists of alternating layers of multi-headed self-attention and multi-layer perceptron blocks. Layer normalization is applied before each block. The model used in our research, the ViTMAE Large variant, uses 24 layers combined with 16 self-attention heads. The Masked Autoencoder reconstructs the input by predicting the input pixel values for each patch that was previously masked. The last layer of the decoder is a linear projection with the number of output channels equal to the number of pixel values in a patch. The decoder output is then reshaped to form a reconstructed image. This process is the basis for loss function calculations. The loss for the model is calculated as Mean Squared Error between the reconstructed and original images in the pixel space. The non-masked pixels are not considered. After the reconstruction error is calculated for each image in the validation dataset, the results are stored for a later training phase. With the acquired data, a second classification model is added to the pipeline to classify samples based on the reconstruction error rate. Using Microsoft Azure's Automated Machine Learning (SDK v2) software, a threshold-based classification task is performed. The task selects a threshold that results in the best performance indicators. Then, the threshold is applied in the final model.  
Figure 2: Comparison of normal vs. anomalous samples. (**a**) Sample of normal image. (**b**) Sample of anomalous image.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 6 Models of Communication in the Internet of Vehicles
### Vehicle-to-Pedestrian (V2P)  
The IoV relies on vehicle-to-pedestrian communication as a vital aspect of improving pedestrian safety on roads. This technology provides direct V2P communication for real-time information sharing that can prevent accidents. To support direct V2P communication in urban environments, wireless local area networks (WLANs) are used for V2P [91]. This technology is used to help vehicles transmit essential data to pedestrians like warnings, alerts, and notifications, to help improve safety. Additionally, vehicles with light-emitting diode (LED) projection modules are able to project warning signals and traffic information onto the road or sidewalks for pedestrians to prevent accidents [92]. This cooperative V2P system promotes better communication and understanding of the vulnerabilities of road users by vehicles [93]. The exchange of information enables both drivers and pedestrians to make more decisions, which thereby reduces the possibility of accidents. It is a key enabling feature of smart transportation systems, driving safer and more efficient mobility in smart cities and beyond.
## 3 Experimental Results and Analysis
### Secret Message Embedding Results  
Figure 4 lists 20 stego HDR RGBE images produced by our scheme, each conveying a different number of secret bits. Note that these images are tone-mapped low dynamic range images, and we adopted the tone-mapping algorithms introduced in [2; 37].  
We present a larger size of the stego image \"dani_cathedral\" for the purpose of visualization, as shown in Figure 5. This stego image has conveyed around 5.92 million of secret bits prior to the encryption process. As shown in Figure 5a, direct display of a stego HDR image is not satisfying because over- and under-exposed areas hide image features. In contrast, the tone-mapped (TM) stego images reveal their full details and give them a dynamic twist as well as a realistic look, as shown in Figure 5b,c, respectively, Nevertheless, even though our scheme offers a significant high embedding rate, reaching 7.550 bpp, the distortions in these stego images are difficult to be perceived by human eyes.  
Table 4 shows the features of test images and the embedding capacity as well as the results of the image quality assessments. Depending on the image resolutions, the embedding capacity ranges from 51.25 million bits (No. 15, 3025 \(\times\) 2129) to 2.59 million bits (No. 13, 720 \(\times\) 480). Depending on the distribution of E-channel, the embedding rate ranges from 7.386 bpp (No. 11) to 8.258 bpp (No. 16). Nevertheless, all the test images comply with the demand of 7.3 bpp requested by the end user. As observed in Table 4, the mean squared error of three channels, MSE(RGB), in the stego HDR RGBE image varies from 3.183 to 45.546, depending on the image resolution, the distribution of the E-channel, and that of the secret message. In this table, we show the PSNR values of the tone-mapped images as they are now in the low dynamic range, thereby using 8 bits per sample to represent a pixel. Apart from the \"Still Life\" image, the PSNR values are all larger than 40 dB, while some of them are even higher than 50 dB. In addition, the structural similarity index measure (SSIM) and the universal image quality index (Q-index) are all very close to 1.0. Image assessment results indicate that the stego tone-mapped image has good image quality.
## 3 Results
### Critical Synthesis and Future Directions  
The six analyzed themes reveal interconnected challenges in DES. On the one hand, stochastic-robust optimization models (Section 3.1) have been shown to reduce operational costs by up to 21.49% [16], but their reliance on centralized computations clashes with the decentralized nature of multi-agent systems (Section 3.2). This disconnect suggests the need for hybrid approaches, such as federated ADMM algorithms [25; 42], which could bridge the gap between global optimization and distributed control. On the other hand, while blockchain (Section 3.4) offers transparency in P2P trading, its high energy consumption in protocols like PoW [66] limits its adoption in microgrids with practical constraints (Section 3.3). Emerging solutions, such as lightweight PoA variants [19] or sharding techniques [69], could balance this tension between security and efficiency.  
A key finding is the disparity between theoretical studies and real-world implementations. For example, hierarchical frameworks for autonomous microgrids (Section 3.5) show annual savings of 8.96% in simulations [81], but often overlook human behavior variability or communication failures in real environments [17]. Similarly, traditional electric machines (Section 3.6), while robust, face unprecedented challenges in dynamic scenarios like V2G integration, where their efficiency declines under fluctuating loads [95]. This underscores the urgency for adaptive designs, such as CNN-LSTM-based predictive control systems [24; 85], capable of anticipating real-time fluctuations.
## 3 Error Correction Codes in Wireless Sensor Network
### Richardson Scheme as the Encoding Technique  
The encoding method proposed by [PERSON] _et al._[13] assumes that \(H\) can be converted to an approximate lower triangular matrix. The authors worked on an \(m\times n\) parity check matrix \(H\) over \(F\) where \(n\) is the number of variable nodes and \(m\) is the number of check nodes. The parity check matrix \(H\) is transformed in the form of  
\[H=\left[\begin{array}{cc}A&B&T\\ C&D&E\end{array}\right], \tag{1}\]  
where \(A\) is \((m-g)\times(n-m)\), \(B\) is \((m-g)\times g\), \(T\) is \((m-g)\times(m-g)\), \(C\) is \(g\times(n-m)\), D is \(g\times g\), and E is \(g\times(m-g)\) where \(g\) is denoted as the gap. Further, all these matrices are sparse and \(T\) is lower triangular with ones along the diagonal. This matrix is multiplied from the left by  
Figure 4: Bipartite graph corresponding to an irregular parity check matrix.  
Figure 5: \(H\) matrix used in the simulation.  
\[\left[\begin{array}{cc}I&0\\ -ET^{-1}&I\end{array}\right] \tag{2}\]  
And the \(H\) matrix is found as  
\[\left[\begin{array}{cc}A&B&T\\ -ET^{-1}A+C&-ET^{-1}B+D&0\end{array}\right]. \tag{3}\]  
They then break the codeword as \(x=(s,p_{1},p_{2})\) where \(s\) denotes the systematic part, \(p_{1}\) and \(p_{2}\) denote the parity part, \(p_{1}\) has length \(g\), and \(p_{2}\) has length \((m-g)\). After that, the equation \(Hx^{T}=0^{T}\) is used to state the following two equations  
\[As^{T}+Bp_{1}^{T}+Tp_{2}^{T}=0, \tag{4}\]  
\[(-ET^{-1}A+C)s^{T}+(-ET^{-1}B+D)p_{1}^{T}=0. \tag{5}\]  
Taking \((-ET^{-1}B+D)\) as nonsingular, it is concluded that  
\[p_{1}^{T}=-(-ET^{-1}B+D)^{-1}(-ET^{-1}A+C)s^{T}. \tag{6}\]  
\[p_{2}^{T}=-T^{-1}(As^{T}+Bp_{1}^{T}). \tag{7}\]  
By using the step by step procedure, it is shown that the complexity of calculating \(p_{1}\) and \(p_{2}\) are \(O(n+g^{2})\) and \(O(n)\) respectively. The matrix used in our simulation can also be written in ALT form and is shown in Figure 5.
###### Abstract  
Pipe-jacking construction technology is favored in urban construction due to its advantages of high safety and being a non-ex excavation technique. However, instability of the tunnel face often occurs due to unfavorable conditions, such as pipe jacking across the river channel, shallow soil cover, and improper control of the support pressure. In this study, we made a use of the limit balance method and mud-water balance theory. At this moment of passive damage and active destruction occurring at the pipe-jacking tunnel face, the general mathematical expressions of the tunnel-face support pressure (with lower limit value P\({}_{\text{min}}\) and upper limit value P\({}_{\text{max}}\)) are derived. In the non-river impact area and river impact area, the optimal value P\({}_{\text{0}}\) of support pressure at the tunnel face is thus derived. Then, based on the Y25-Y26 pipe-jacking project across the Chu River channel in Hefei North District, a numerical simulation method is used to support further discussion. The results indicate that, when the river overburden is 3 m, the ultimate support pressure calculated by means of numerical simulation is 881.786 kN, and the optimal support ratio \(\lambda\) is taken in the interval of 1.0-1.5. Secondly, the upper limit value P\({}_{\text{max}}\), lower limit value P\({}_{\text{min}}\), and optimum value P\({}_{\text{0}}\) calculated using the theoretical equations are 2669.977 kN, 309.910 kN, and 1044.870 kN, respectively. These results leads us to recommend setting the support pressure of the tunnel face in a reasonable range between the upper limit value P\({}_{\text{max}}\) and the lower limit value P\({}_{\text{min}}\), to ensure that the tunnel-face support pressure and resistance during pipe jacking always remain in a balanced state. The relevant research results from this study provide an important technical guarantee for the successful implementation of the examined project and, at the same time, can serve as a reference example for similar projects.  
pipe jacking; mud-water balance; tunnel face; support pressure Article
## 4 Analysis of Security and Performance
### Performance Analysis
#### 4.2.2 Functionality Comparison  
The functions of message authentication schemes are anonymous, conditional un-traceability, message authentication in sparse RSU, or needing certification. Table 6 compares schemes in terms of functionality, and the proposed scheme fits all functional requirements.
# Environment-Aware Knowledge Distillation for Improved Resource-Constrained Edge Speech Recognition
## 2 Background Material
### Hubert and Variants  
The HuBERT model, as introduced in [3], represents a speech pre-training technique designed to learn effective speech representations by means of masked feature reconstruction. This architecture comprises two major components: the encoder and the context network. The encoder network, denoted as \(f\), operates as a mapping function from the raw waveform sample \(x_{i}\in\mathcal{X}\) to an intermediate feature representation \(z_{i}\in\mathcal{Z}\). It is structured as a 1D convolutional network, encompassing seven layers with 512 feature maps. The kernel and stride sizes for each layer vary accordingly. Following this convolutional network, a GELU non-linear activation function [21] is applied. The context network, represented by the mapping function \(g:\mathcal{Z}\mapsto\mathcal{C}\), serves a crucial role in the HuBERT architecture. Its fundamental principle revolves around mapping diverse features into a unified context vector via multiple Transformer encoders, thereby capturing long-range information. The interested reader can refer to the first figure of [3; 22] for more details on the HuBERT model and the Transformer architecture, respectively.  
Different versions of the HuBERT model have been developed, including the HuBERT _Base, Large_, and _X-Large_ versions. The main difference between them is the size of their respective Transformer networks, comprising 12, 24, and 48 layers, respectively. Furthermore, it is worth noting that the HuBERT Base model was trained on a dataset encompassing 960 h of the LibriSpeech dataset [23], while the Large and X-Large versions were trained on a substantially larger dataset consisting of 60,000 h from the LibriLight dataset [24].
# Decentralized Identity Authentication with Auditability and Privacy
## 5 Privacy-Preserving Protocol
### Final Protocol  
The insecurity of the previous approach is caused by the fact that the verification is implemented only at the service, since verifying the token authenticity with the blockchain nodes would violate privacy. In our final protocol, we employ a computational integrity proofing system, such as zk-SNARK, to \"move\" the verification from the service to the blockchain nodes, but while preserving privacy. Therefore, in addition to ensuring consecutive counters, a client needs to--non-interactively (as the proof is to be verified by the blockchain nodes) and with zero knowledge--prove that tokens were created under the same private key, and the previous token is in the blockchain. This requires a composition of non-interactive zero-knowledge VRF and signature verifications. Although a system such as zk-SNARK can be seen as a \"heavyweight\" tool, it seems unavoidable to realize this. We see the optimization of this construction as interesting future work.  
The registration process in the final protocol is the same as that in the intermediate protocol (see Section 5.1.1). In addition, the blockchain platform in this protocol maintains the same data structures (i.e., the identity map and the token set). Prior to the protocol's deployment, the proofing system requires a setup phase in which the proving and verification keys are generated for a circuit, i.e., \(\langle z_{pk},z_{ik}\rangle\gets zkGen(Token,\lambda)\) in our case, and the blockchain platform is pre-loaded with the verification key. In some systems, the setup phase is assumed to be run by a trusted party. zk-SNARK shares this limitation, and, in practice, this assumption can be relaxed by a multi-party computation ceremony [41].
# Predicting Multi-Gene Mutation Based on Lung Cancer CT Images and Mut-SeResNet
## 1 Introduction  
According to 2022 National Cancer Center data, China ranked first with 4.064 million new cancer cases and 828,000 [1] lung cancer cases. The mortality rate of lung cancer in China has increased by 465% [2] in the past 30 years, making it one of the most significant in numbers of lung cancer countries. Non-small cell lung cancer (NSCLC) accounts for 80% to 85% of lung cancers [3, 4]; lung adenocarcinoma and lung squamous cell carcinoma are common non-small cell lung cancers [5, 6].  
Medical images contain a large amount of potential pathophysiological information. Computer-aided [7, 8, 9, 10] diagnosis using CT images can improve the accuracy and stability of clinical diagnosis, reduce the time needed for clinical diagnosis, and decrease clinical misdiagnosis risks and judgment omissions caused by various subjective factors [11].  
In recent years, studies have been proposed to predict gene mutations based on CT images. Most of these are based on imaging-omics approaches, and a few are based on machine learning or statistical approaches to predict gene mutations. Although imaging-omics, statistics, and machine learning methods have achieved some results in the research using CT images to predict gene mutations, these methods often require complex processes and the guidance of an experienced imaging physician throughout the process. In this paper, we proposed a multi-task deep learning model (Mut-SeResNet) for extracting deep features from CT images to predict both EGFR and KRAS mutations.