esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Delay Optimization of Compressing Shipborne Vision Sensor Video Based on Deep Learning
## 2 SVSV Image Characteristics
### Features of Shipborne Visible Light Camera Video Images
The average intra-frame pixel difference probability density of the video is shown in Figure 2, and the average inter-frame pixel difference statistics are shown in Figure 3. It can be seen that the intra-frame pixel difference probability density and inter-frame pixel difference statistics are mainly concentrated in the range around 0, and the frequency of inter-frame pixel difference being 0 is more than 90%. Therefore, it can be inferred that the spatial correlation and temporal correlation of the SVLC video images are high, and therefore, the application of HEVC advanced PC technique can well improve the compression efficiency of SVLC video. \[p(d)=\frac{1}{\sqrt{2}\sigma_{d}}EXP\left(-\frac{\sqrt{2}|d|}{\sigma_{d}}\right) \tag{1}\]  
\[d_{k}(i,j)=f_{k}(i,j)-f_{k-1}(i,j) \tag{2}\]  
where \(d\) denotes the difference of neighboring pixels in the frame, \(\sigma_{d}\) is the mean squared difference, \(f_{k}(i,j)\) denotes the pixel value with coordinates \(i,j\) in the current frame, and \(f_{k-1}(i,j)\) denotes the pixel value with coordinates \(i,j\) in the previous frame. Figure 2: Intra pixel difference statistics of shipborne visible light camera video: (**a**) Level pixel difference probability density; (**b**) Vertical pixel difference probability density.
# Comparing Speaker Adaptation Methods for Visual Speech Recognition for Continuous Spanish +
## 4 Model Architecture  
### Visual Speech Recogniser  
The VSR system employed in our research is based on the state-of-the-art CTC/Attention architecture proposed by [PERSON] et al. [22], which comprises about 52 million parameters. As Figure 2 reflects, this end-to-end system is composed of different modules:  
* **Visual Front-end:** Consists of a 2D ResNet-18 [55] in which, in order to deal with temporal relationships in the data, the first layer has been replaced by a 3D convolutional layer with a kernel size of 7 \(\times\) 7 pixels and a receptive field of five frames.
* **Conformer Encoder:** Defined as a 12-layer encoder based on Conformers [56], an approach explicitly designed to capture both global and local speech interactions from the previous latent representation provided by the visual front-end.
* **Hybrid CTC/Attention Decoder:** Composed of a six-layer Transformer decoder [24] based on the Cross-Entropy (CE) loss and a linear layer as the CTC-based decoding branch [23]. Both decoders are followed by a softmax activation layer. Details on how these loss functions are combined can be found in Section 4.3.  
It should be noted that in all our experiments our VSR system was initialised using the weights publicly released by [PERSON] et al. [22] for the Spanish language. As mentionedin Section 2, this model was able to reach around 45% WER on the Spanish partition of the CMU-MOSEAS database [52]. This performance was possible thanks to a two-step training process. First, more than 1500 h of data from different English corpora were used in a pretraining stage. Then, the model was fine-tuned using the Spanish partition of the Multilingual-TEDx [57] and CMU-MOSEAS databases [52].
## 5 Conclusions  
The development of advanced photovoltaic panels is a critical path for designing zero-energy buildings in the near future. The objective of the present analysis was to examine three promising PV cooling cell designs in different operating conditions and determine the most effective cooling techniques depending on the climate data of every location. The studied cooling techniques were the use of radiative PV cells, the addition of external fins in the down part of the PV and the application of PCM in the down part of the PV. These ideas were systematically compared with the conventional PV design for the climate conditions of Athens, Barcelona, Munich and Stockholm. In every location, two different building typologies were investigated and the goal was to design zero-energy buildings. In the cases that the roof PVs could not cover the total yearly electrical load, BIPVs were also added in the south part of every building. The most remarkable conclusions of the present analysis are summarized as follows:The application of radiative and finned PVs always leads to higher performance compared to simple PV, while the use of PCM has to be selected only in warm climates. The finned PV leads to higher electrical efficiency enhancements which range between 0.58% and 0.89%, while the radiative PVs lead to 0.52% up to 0.71% electrical efficiency enhancements. - The PCM PVs lead to significantly lower cell temperature levels compared to the other technologies during the summer, but to higher cell temperature levels during the winter. The radiative and the finned designs lead to a bit lower cell temperatures compared to the simple PV configuration. - For the climatic conditions of Athens and Barcelona, electrical positivity is achievable only with the use of rooftop PVs, in contrast to Munich and Stockholm, where the use of BIPVs is necessary. - The use of the cooling PV designs can significantly enhance positive electricity production in cases with marginal solar potential. Specifically, for building B1 in Stockholm, the fined PVs in combination with BIPVs led to a 212 kWh electricity surplus, or a 136% increase, in contrast to the use of simple PVs in combination with BIPVs. Conceptualization, G.M. ; Methodology, G.M., A.T. and V.K. ; Software, G.M. ; Validation, G.M. ; Investigation, G.M., A.T., V.K. and D.K. ; Writing--original draft, G.M. and V.K. ; Supervision, A.T. and D.K. All authors have read and agreed to the published version of the manuscript. This research project is implemented in the framework of H.F.R.I call \"Basic research Financing (Horizontal support of all Sciences)\" under the National Recovery and Resilience Plan \"Greece 2.0\" funded by the European Union--NextGenerationEU (H.F.R.I. project number: 14812, BIPV-City). Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request.
## 2 Theoretical Discussion
### Dynamic Factor Calculation Methods Outlined
#### 2.3.2 AREA Calculation Method (DAF\({}_{\text{Area}}\))  
This calculation method is based on the geometrical characteristics of the wheel, which was proposed to increase by 1/100 of the value obtained by dividing 33 inches (83.8 cm) by the driving wheel diameter of the locomotive as the speed increases by 1 mile/hour. In the case of a diesel locomotive in operation, the wheel diameter is 40 inches (101.6 cm), so 33/40 = 0.825 per mile, and when converted to km, it is 0.513%, as shown in the following equation [24].  
\[DAF_{\text{Area}}=1+0.513V/100 \tag{9}\]  
where,  
\(DAF_{\text{Area}}\): dynamic factor using the AREA method  
\(V\): train speed
# Blockchain Solutions for Enhancing Security and Privacy in Industrial IoT
## 2 Literature Review
### Smart Contracts and Automation  
Smart contracts have emerged as a powerful tool for automating processes in Industrial Internet of Things (IIoT) systems. [PERSON] et al. [23] investigated the role of smart contracts in automating workflows within Industrial Internet of Things (IIoT) ecosystems, highlighting their potential to reduce human intervention and operational costs. However, they also identify challenges related to contract complexity and execution speed, which require further research. [PERSON] et al. [24] extended this concept by applying smart contracts to predictive maintenance, demonstrating how blockchain can securely store and share maintenance data to improve operational efficiency.  
Despite the promising advancements in integrating blockchain with the Industrial Internet of Things (IIoT), significant challenges prevent its widespread adoption. Studies [21; 22; 23; 24; 25] highlight critical barriers to scalability, interoperability, and regulatory compliance. Additionally, the energy consumption of blockchain protocols, particularly those based on Proof-of-Work (PoW), poses a concern for energy-sensitive industrial environments. Addressing these challenges requires innovative solutions, including the development of more efficient consensus mechanisms, improved interoperability with existing Industrial Internet of Things (IIoT) infrastructures, and the careful consideration of regulatory and ethical implications.  
In response to these challenges, this paper proposes a novel blockchain-based framework designed to address critical vulnerabilities in Industrial Internet of Things (IIoT) systems, including unauthorized access and data manipulation. The framework leverages decentralized security mechanisms to ensure data integrity and trust, utilizes smart contracts for automated process enforcement, and employs edge computing to enhance real-time data processing while reducing latency. By integrating these technologies, the proposed solution mitigates security risks while maintaining operational efficiency in resource-constrained Industrial Internet of Things (IIoT) environments. The framework is rigorously evaluated to assess its practicality, scalability, and compatibility with the dynamic and demanding nature of Industrial Internet of Things (IIoT) systems. The evaluation results demonstrate its potential to effectively address the existing security challenges while supporting the growing demands of industrial applications. This study underscores the importance of advancing blockchain technology to meet the specific needs of IIoT ecosystems, paving the way for secure, scalable, and sustainable industrial solutions.
# Design and Evaluation of IoT-Enabled Instrumentation for a Soil-Bentonite Slurry Trench Cutoff Wall
## 4 Data Transport
### XTP Evaluation
It is designed to operate efficiently over wireless links where bi-directional communication is generally possible using standard per-packet acknowledgements. In higher loss situations, we suggest improving link quality through other means to achieve reliable communication and then applying XTP for data transfer. This could include using higher powered radios, directional antennas, reducing the data rate to improve receiver sensitivity, or using more advanced signal encoding with forward error correction.
## 5 Case Study
### Experiment Results
#### 5.3.2 Depth Information Transmission  
Positioning is inherently a 3-dimensional problem, but is often simplified to a 2-dimensional solution for underwater positioning in practice. In the experiment, we investigate the benefit brought about by depth information, supported by a communication service.  
With depth information, the positioning problem can be reduced to 2D calculation, and detailed work can be referred in [53]. In addition, it's useful to increase the positioning continuity. For traditional TDOA positioning, 4 or more beacons are needed. When the number of beacon information received is less than 3, the beacon location can not be calculated. With depth information, the position can still be resolved. During this experiment, 729 positioning points are obtained, and among which 52 points are calculated by 3 points, improving the positioning continuity by 7.68%. The detailed information is shown in Table 5.  
Figure 15: Localization results with timing error \(\sigma_{t}=1\times 10^{-4}\). (**a**) shows the comparison of actual and estimated trajectory of beacon with a zoomed-in details. (**b**) shows the localization error distribution with timing error \(\sigma_{t}=1\times 10^{-4}\).
# A Physical Approach to Simulate the Corrosion of Ceramic-Coated Magnesium Implants
## 1 Introduction
Besides the diffusion coefficient in Equation (1), the controlling parameters in the model include the saturation limit of the magnesium ions in the environment and the initial ions concentration in the solid magnesium and the environment. The initial concentration of the magnesium ions in the environment is well established and is used as the concentration level in the blood plasma. The concentration in the solid is assumed equivalent to the partial density of the magnesium in an alloy or the total density for pure magnesium. The saturation limit is used following the assumption that the transport of the ions follows the saturation of the environment on the corrosion boundary. This value was approximated in the previous models by the saturation limit of the anhydrous magnesium chloride in water at room temperature [14; 18]. The diffusion coefficient was not available prior to the work by [PERSON] et al. [14] where they calibrated the model against mass loss data collected using hydrogen evolution testing in the study by [PERSON] et al. [19]. Although the hydrogen evolution test can provide more data points along with the test duration, it was found to highly underestimate the corrosion rate as compared to weight loss data [19]. In this study, a physical and diffusion-controlled corrosion model was adopted to describe the uniform galvanic corrosion observed in pure magnesium. The saturation limit of the magnesium ions was investigated experimentally for the first time in a simulated  
Figure 1: Mechanical properties of magnesium alloys vs. natural bone and other used metals for orthopedic implants. body environment to be used in the subsequent calibration of the diffusion coefficient. The calibration of the diffusion coefficient was based on the weight loss data which was found to best represent the actual corrosion in magnesium [20]. Furthermore, the effective diffusivity concept was utilized to study the effect of adding a porous coating layer of a given thickness.
## 2 I-Pro MONALISA: A New Semiautomatic GIS Approach
### Preprocessing  
To evaluate the feasibility of this approach over the area under investigation, a preprocessing step is required: Therefore, it is important to verify whether the acquisition of SAR images to assess intensity of instability phenomena, is affected by geometric and radiometric distortions. To estimate the visibility of an investigated area and the effect of topography on the presence of radar targets, the R-Index map [38; 39] is generated starting from Digital Terrain Model (DTM) and geometric parameters of satellite orbit (incidence angle and orbit angle). The maximum value of the R-Index, equal to 1, corresponding to the best geometry detectable by the satellite, occurs when the slope is parallel to the 'Line of Sight' (LoS). On the other hand, the smaller the R-index, the more difficult it will be to detect PS. The extreme case is when R value is less than 0.4 and it tends to be 0. This is the case of the foreshortening effect, when no PS can be detected. The layover effect and the shadow effect occur when R-index is negative and no PS can be detected.  
Figure 1: Flow chart of the proposed methodology.  
Furthermore, since SAR data are measured just along the LoS direction, a projection of these measurements along the direction of the maximum slope can be performed following the procedures proposed by [40] and subsequently by [41; 42; 43]. For each available point, the real displacement along slope direction is obtained by means of the \"C\" correction factor depending on satellite acquisition parameters (incidence angle and orbit angle), as well as topographic parameters (terrain slope and facing). The \"C\" factor represents the percentage of the target real movements which can be detected by satellite acquisition with a specific geometry of acquisition.
## 5 Related Blockchain-Based National Population Census Work
#### Countries Exploring the Integration of Blockchain in the Census System  
There are currently no countries that have fully integrated blockchain into their national census systems. However, there are some examples of pilot projects and early-stage experimentation with blockchain for census-related applications:  
* Dubai-launched the Dubai Blockchain Strategy in 2016, with census data collection mentioned as a future application, but a concrete implementation is still awaited [43].
* India-proposed using a permissioned blockchain for future census data collection by the government. Academic researchers have published design frameworks, but no system has been implemented yet [44].  
Several startups like Census Chain and Procivis [47] have worked on blockchain-based identity and census data solutions, but wide-scale adoption by governments remains elusive currently.  
While promising proofs-of-concept exist, most governments are still assessing feasibility or running small trials. Large-scale adoption faces challenges like population coverage, technology readiness across census machinery, and legal/regulatory hurdles. However, opportunities remain for selective blockchain integration to enhance data integrity and efficiency in the future.
# Stain Defect Classification by Gabor Filter and Dual-Stream Convolutional Neural Network
## 4 Feature Vector Extraction Based CNN
### Dual-Stream Extraction  
A stream is an independent layer structure before the feature maps merge in a CNN. In a Single-stream extraction, there is only one stream because there is no merging process. The feature map of the Gabor filter image and the gray-scale inspection image pass through a Single-stream, so the features of the Gabor filter image and the gray-scale inspection image are mixed in the training process. This process may cause a decrease in classification accuracy.  
Alternatively, a Dual-stream extraction is composed of two streams. In this study, separate streams for the gray-scale inspection image and the Gabor filter image were designed to solve this decrease in classification accuracy. A CNN independently extracted features for each gray-scale inspection image and the Gabor filter image was constructed through the separate streams.  
Figure 5 shows the dual-stream extraction of the network proposed in this paper. The Dual-stream extraction has two streams that extract features from the input gray-scale inspection image \(I_{Gray}\) and Gabor filter image \(S=\{s_{1},s_{2},\cdots,s_{N}\}\), and each stream for extracting features can be configured. The upper stream extracts a \(2048\times 1\) size feature vector \(V_{Gray}\) for the gray-scale inspection image through the convolution layer, pooling layer, and Res_Block. At the same time, the stream at the bottom, which has the same structure as the upper stream but has a different weight, extracts the \(2048\times 1\) size feature vector \(V_{Gabor}\) for the Gabor filter image through the convolution layer, pooling layer, and Res_Block. The feature vector \(V_{Concat}\) of \(4096\times 1\) size, connecting the two feature vectors \(V_{Gray}\) and \(V_{Gabor}\), is converted to \(O\) as a classification result of the fully connected layer. The two are separate streams that do not share weights, and their features are preserved independently without mixing.  
Table 1 shows the Dual-stream extraction structure. \(I_{Gray}\) stream is the first flow in Figure 5, and gray-scale images are input. For the structure, the resnet50 structure was used. The \(S=\{s_{1},s_{2},\cdots,s_{N}\}\) stream is the second flow in Figure 5 and inputs the selected Gabor images. The structure uses resnet50 in the same way. \(V_{Gray}\) and \(V_{Gabor}\) in Figure 5 output to each flow are merged with \(V_{Concat}\), and the classification result is output as a fully connected layer. In this paper, ResNet is used as an example, but o  
Figure 5: Dual-stream network structure based on ResNet50.  
extraction may be used. We propose improving classification performance depending on the input and Gabor images, where the network is unimportant.
# An Improved Lightweight YOLOv8 Network for Early Small Flame Target Detection
## 4 Yolov8 Improvements
### Improvement of Loss Function
\begin{bmatrix}c_{x}\\ c_{y}\end{bmatrix} ,\sum=\left[ \begin{array}{cc}\frac{w^{2}}{4}&0\\ 0&\frac{b^{2}}{4}\end{array} \right] \tag{8}\]  
In addition, the resemblance between bounding boxes A and B can be converted into the divergence between two Gaussian distributions. The NWD uses the Wasserstein distance of optimal transport theory to calculate the distribution distance. For two 2D Gaussian distributions \(\mu_{1}=\mathcal{N}(m_{1},\Sigma_{1})\) and \(\mu_{2}=\mathcal{N}(m_{2},\Sigma_{2})\), the second-order Wasserstein distance between \(\mu_{1}\) and \(\mu_{2}\) is defined as  
\[W_{2}^{2}(\mu_{1},\mu_{2})=\left\|m_{1}-\,m_{2}\right\|_{2}^{2}+\mathrm{Tr}( \Sigma_{1}+\Sigma_{2}-2(\Sigma_{2}^{1/2}\Sigma_{1}\Sigma_{2}^{1/2})^{1/2}) \tag{9}\]  
The above formula can be simplified to  
\[W_{2}^{2}(\mu_{1},\mu_{2})=\left\|m_{1}-\,m_{2}\right\|_{2}^{2}+\left\|\Sigma_ {1}^{1/2}-\Sigma_{2}^{1/2}\right\|_{F}^{2} \tag{10}\]  
Among them, \(\left\|.\right\|_{F}\) represents the Frobenius norm. Furthermore, for Gaussian distributions \(A=(cx_{a},cy_{a},w_{a},h_{a})\) and \(B=(cx_{b},cy_{b},w_{b},h_{b})\), modeled from bounding boxes \(\mathcal{N}_{a}\) and \(\mathcal{N}_{b}\), Equation (10) can be further simplified to  
\[W_{2}^{2}(\mathcal{N}_{a},\mathcal{N}_{b})=\left\|([cx_{a},cy_{a},\frac{w_{a}} {2},\frac{h_{a}}{2}]^{\mathrm{T}},[cx_{b},cy_{b},\frac{w_{b}}{2},\frac{h_{b}}{ 2}])\right\|_{2}^{2} \tag{11}\]  
However, \(W_{2}^{2}(\mathcal{N}_{a},\mathcal{N}_{b})\) is a distance measure and cannot be used directly as a similarity measure. Accordingly, the exponential form is normalized to yield a novel measure, designated as the normalized Wasserstein distance (NWD). \[NWD(\mathcal{N}_{a},\mathcal{N}_{b})=\exp(-\frac{\sqrt{W_{2}^{2}(\mathcal{N}_ {a},\mathcal{N}_{b})}}{C}) \tag{12}\]  
where \(C\) is a constant that is closely related to the data set. The NWD (normalized Wasserstein distance) loss function is specifically designed to measure the similarity between small objects, making it more effective in tasks such as early fire detection. It uses 2D Gaussian distribution modeling by modeling the bounding box as a 2D Gaussian distribution; the NWD loss function captures the spatial information and distribution characteristics of the object, providing a more accurate similarity measure. It can measure similarity even in cases where there is little or no overlap, making it more robust for scenes with small objects and limited overlap. Given the advantages of the NWD in detecting small objects, it is used to replace the original bounding loss function in YOLOv8 and optimize it, thereby further improving detection accuracy.