esa-sceva/satcom-chunk-collection huggingface dataset:
# SSKM_DP: Differential Privacy Data Publishing Method via SFLA-Kohonen Network
## 4 The Proposed Data Publishing Method
### Data Noise  
Satisfying differential privacy noise added to each cluster after conversion can be greatly reduced compared to adding to each record. The privacy budget satisfying differential privacy is allocated to the clustering center of the subset cluster formed by the SFLA-Kohonen network clustering, and then the corresponding noise is added to the clustering center of the cluster where the sensitive attribute is located and the cluster with the non-sensitive attribute is associated with the sensitive attributes. For the cluster center of each cluster, the calculation method is described as follows:  
Given data composed of \(n\) records \(U=\left\{x_{1},x_{2},\ldots,x_{n}\right\}\), each record has \(q\) attributes, \(U\) forms, and \(m\) clusters through SFLA-Kohonen network clustering. Assuming \(A_{i}^{q}\) that the attribute completes the clustering, there are \(m_{j}\) records in the clustering \(v_{j}(j=1,2,\ldots,m)\). The calculation of the clustering center is shown in Equation (18):  
\[Center(v_{j}(A_{i}^{q}))=\frac{\sum\limits_{p=1}^{m_{j}}v_{jp}(A_{i}^{q})}{m_{j}} \tag{18}\]  
where \(v_{jp}(A_{i}^{q})\) is the \(A_{i}^{q}\) value of \(p\) records in \(v_{j}\), and \(m_{j}\) represents the number of records in \(v_{j}\).  
The Laplace mechanism is used to add noise to each cluster center to make it meet differential privacy protection, and the _Ue_ of differential privacy data is generated. The method of adding noise is shown in Equation (19):  
\[Noise(v_{j}(A_{i}^{q}))=Center(v_{j}(A_{i}^{q}))+Y \tag{19}\]  
where \(Y\sim Lap(\Delta f/\varepsilon)\) is the random noise, obeying the Laplace distribution of the scale parameter \(\Delta f/\varepsilon\).
## 3 Materials and Methods
### Deep Learning-Based Visual Detection and Tracking of Target Ship
#### 3.1.1 Target Ship Detection
This is achieved by reshaping \(X\) into \(X_{r}\in\mathbb{R}^{S^{2}\times\frac{HW}{S^{2}}\times C}\). We then derive the query \(Q\), key \(K\), and value tensor \(V\in\mathbb{R}^{S^{2}\times\frac{HW}{S^{2}}\times C}\), using linear projections. \[X_{r}\in reshape(X,S^{2},\frac{HW}{S^{2}},C)\] (1) \[Q=X_{r}W_{q},K=X_{r}W_{k},V=X_{r}W_{v}\] (2) where \(W_{q}\), \(W_{k}\), and \(W_{v}\) represent the learnable weight matrices. 2. Region-to-region routing with a directed graph: We then find the attending relationship by constructing a directed graph. Specifically, we first compute region-level queries and keys by applying an average to \(Q\) and \(K\) within each region, resulting in \(Q_{r}\) and \(K_{r}\in\mathbb{R}^{S^{2}\times C}\). Subsequently, we derive the adjacency matrix \(A_{r}\in\mathbb{R}^{S^{2}\times S^{2}}\) for the region-to-region affinity map by performing matrix multiplication between \(Q_{r}\) and the transposed \(K_{r}\): \[Q_{r}=mean(Q,1),K_{r}=mean(K,1)\] (3) \[A_{r}=Q_{r}K_{r}^{T}\] (4)  
The values in the adjacent matrix, \(A_{r}\), quantify the semantic relationships between pairs of regions. The core step that we perform next is to prune the affinity graph by keeping only top-\(k\) connections for each region. To achieve this, we construct a routing index matrix, \(I_{r}\in\mathbb{N}^{S^{2}\times k}\), using a row-wise top-\(k\) selection operation:  
\[I_{r}=topIndex(A_{r}) \tag{5}\]
3. Token-to-token attention: Finally, token-to-token attention is performed within the specified regions. Each query token selectively attends to the query and context pairs corresponding to the defined routing areas. The attention function is then applied to these refined feature pairs to generate the final output. This process is expressed as follows: \[K_{g}=\text{gather}(K,I_{r}),V_{g}=\text{gather}(V,I_{r})\] (6)  
Figure 4: (a) Details of a BiFormer block; (b) Structure of the BRA. Figure 2:0  
In this, \(K_{g}\), \(V_{g}\) are the gathered key and value tensor, \(Attention(\cdot)\) represents the attention function, and \(LCE(\cdot)\) denotes the term that enhances local context, parameterized through deep convolution. The BiFormer block significantly reduces computational complexity by adopting a BRA strategy. Under a proper region partition factor \(S\), its complexity is \(O(S^{2}\cdot k\cdot d)\). In contrast, the traditional global attention mechanism has a complexity of \(O((HW)^{2})\), while the axial attention mechanism has a complexity of \(O((HW)^{\frac{3}{2}})\). This sparse computation strategy not only improves computational efficiency but also effectively reduces interference from irrelevant regions, thereby enhancing the robustness and accuracy of the model in handling small target detection tasks on the water surface.
###### Abstract  
Clouds are of high importance for the climate system but they still remain one of its principal uncertainties. Remote sensing techniques applied to satellite observations have assisted tremendously in the creation of long-term and homogeneous data records; however, satellite data sets need to be validated and compared with other data records, especially ground measurements. In the present study, the spatiotemporal distribution and variability of Total Cloud Cover (TCC) from the Satellite Application Facility on Climate Monitoring (CM SAF) Cloud, Albedo And Surface Radiation dataset from AVHRR data--edition 2 (CLARA-A2) and the International Satellite Cloud Climatology Project H-series (ISCCP-H) is analyzed over Europe. The CLARA-A2 data record has been created using measurements of the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the polar orbiting NOAA and the EUMETSAT MetOp satellites, whereas the ISCCP-H data were produced by a combination of measurements from geostationary meteorological satellites and the AVHRR instrument on the polar orbiting satellites. An intercomparison of the two data records is performed over their common period, 1984 to 2012. In addition, a comparison of the two satellite data records is made against TCC observations at 22 meteorological stations in Europe, from the European Climate Assessment & Dataset (ECA&D). The results indicate generally larger ISCCP-H TCC with respect to the corresponding CLARA-A2 data, in particular in the Mediterranean. Compared to ECA&D data, both satellite datasets reveal a reasonable performance, with overall mean TCC biases of 2.1 and 5.2% for CLARA-A2 and ISCCP-H, respectively. This, along with the higher correlation coefficients between CLARA-A2 and ECA&D TCC, indicates the better performance of CLARA-A2 TCC data.  
Keywords:total cloud cover; CLARA-A2; ISCCP-H; ECA&D; satellites; ground measurements; Europe; comparison; validation; climatology +
Footnote †: journal: Remote Sensing
## 2 Complexity Classification for General Digraphs
**Proposition 1**. : Shortest Supported Path _can be solved in polynomial time if \(G\) is a bi-star._  
Proof. : Let \(G\) be a bi-star with non-leaf vertices \(u\) and \(v\). Every \((D,G)\)-supported path \(P\) thus contains either \(u\) or \(v\). In the case where \(D[N_{G}[u]]\) contains an arc incident with \(u\), then there is a \((D,G)\)-supported path of length one containing \(u\), and we are done. Similarly, in the case where \(D[N_{G}[v]]\) contains an arc incident with \(v\), then there is a \((D,G)\)-supported path of length one containing \(v\). Let us now consider the case where \(D[N_{G}[u]]\) does not contain an arc incident with \(u\), and \(D[N_{G}[v]]\) contain no arc incident with \(v\). Let \(P\) be a \((D,G)\)-supported path (if any). Thus, \(P^{*}\) (the underlying undirected path of \(P\)) contains no edge, which is also present in \(G\). Consequently, \(P\) links a neighbor of \(v\) to \(u\), or a neighbor of \(u\) to \(v\), or a neighbor of \(v\) to a neighbor of \(u\), or a neighbor of \(u\) to a neighbor of \(v\). If \(u\) or \(v\) does not belong to \(P\), then \(G[V(P)]\) cannot be connected. Thus, \(P\) contains \(u\) and \(v\). Further, since \(G\) is a bi-star with non-leaf vertices \(u\) and \(v\), every path in \(D\) containing \(u\) and \(v\) is supported. Hence, the problem can be solved by computing the shortest path between \(u\) and \(v\) in this case (considering both possible directions of the path). If such a path does not exist, then there is no \((D,G)\)-supported path. The Longest Supported Path problem is more difficult, since it remains NP-hard even if \(G\) is a star: a simple reduction from the NP-hard Hamiltonian Path problem, which asks for a simple path on all vertices of a directed graph \(D\), can be achieved by making \(G\) an arbitrary star on the same vertex set as the input graph \(D\) of the Hamiltonian Path instance. Then, by finding the longest \((D,G)\)-supported path, one can clearly solve the Hamiltonian Path. Figure 1: Illustration of the reduction in the proof in the proof of Theorem 1. The arcs/edges of \(H,D\) and \(G\) are drawn with plain lines. The dotted lines represent, respectively: in \(H\), the paths joining \(s_{1}\) to \(t_{1}\) and \(s_{2}\) to \(t_{2}\); in \(D\), the corresponding \((D,G)\)-supported path; in \(G\), the connected subgraph given by the vertices of the \((D,G)\)-supported path. **Theorem 2**. : _The decision version of Longest Supported Path is NP-hard, even if \(G\) is a star._  
A similar hardness result for Longest Supported Path can be easily obtained if \(G\) is a path and apparently even Supported Path is NP-hard in this case [21].
# Exploring the ViDiDetect Tool for Automated Defect Detection in Manufacturing with Machine Vision
## 1 Introduction
A station with a conveyor belt was proposed and implemented, on which a camera, illuminator, and control cabinet equipped with a PC computer with the possibility of data acquisition were mounted. An algorithm was developed to analyze the quality of the machined surface, on the basis of which the decision to replace the cutting tool is made. The project used available machine learning tools included in the Cognex In-Sight Vision Suite system. Cutting tool wear is chosen as the focal point in this study for several reasons:  
* Direct Impact on Product Quality: Cutting tool wear directly influences the quality of machined parts. As tools wear down, the surface finish of components can deteriorate, leading to higher rejection rates and increased scrap production;
* Efficiency and Cost Savings: Excessive tool wear can result in longer machining times, increased energy consumption, and more frequent tool changes. By addressing tool wear proactively, manufacturers can significantly reduce operational costs and improve overall efficiency;
* Tool Life Optimization: Understanding the wear patterns of cutting tools allows manufacturers to optimize tool life. This means tools are replaced at the right time, preventing premature wear-related failures and maximizing their useful life;
* Process Consistency: Consistent tool wear assessment ensures that machining processes remain stable over time. This consistency is essential for meeting quality standards and reducing the need for rework. By focusing on cutting tool wear and leveraging machine vision technology, manufacturers can move towards a proactive and data-driven approach. This advancement promises to revolutionize the way machining operations are managed, empowering manufacturers to achieve higher efficiency, reduced costs, and enhanced product quality in today's competitive industrial landscape. The paper is organized as follows. Section 2 describes the state of the art in machine vision systems for cutting tool wear assessment. The proposed solution for the vision system is presented in Section 3. Section 4 includes the experimental results and a comparison of the introduced method with state-of-the-art approaches. The conclusions and future research directions are given in Section 5.
# Model Establishment of Cross-Disease Course Prediction Using Transfer Learning
## 4 Experimental Evaluation
### Experimental Settings  
This study uses transfer learning as the framework of the research method, so the training data setup for the source disease course prediction model and the target disease course prediction model will be explained separately. The course development prediction model of the source disease was trained using the disease course representation tensors created from the medical records of 724,155 PD patients extracted from National Health Insurance Research Database in Taiwan [24]. The disease course representation tensors of 30% of the patients were used as test data, and among the remaining 70% of patients (i.e., training dataset), 5-fold cross-validation were performed to train the prediction model. In other words, 20% of the patients in the training dataset were randomly extracted during every epoch training to serve as validation data to evaluate the performance of the model of each epoch and determine early stop. The batch size of the model training was set to 1000, the epoch was set to 50, the
# Modeling of Historical Marine Casualty on S-100 Electronic Navigational Charts
## 4 Case Study
### Data Construction and Encoding  
Once the attribute values were manually mapped for each selected marine casualty case, the structured data was prepared for encoding in compliance with the S-100 framework. The transformation process involved aligning each record with the feature types and attribute definitions specified in the application schema. To ensure interoperability with S-100-based systems, the data was encoded using Geography Markup Language (GML), a widely supported XML-based encoding format.  
Table 9 presents the structured attribute values extracted from the raw GISIS reports. Each column represents a different incident category, and the rows indicate specific attributes defined in the proposed data model, such as the incident date, IMO number, number of casualties, location, description, and incident subtype. The values were organized according to the standardized attribute structure defined in the feature catalogue. In particular, the 'Subtype' row indicates the specific subcategory selected from the corresponding code list defined for each feature type. Using these structured values, a GML-based dataset was constructed for use in S-100-compliant visualization systems.
# Analysis of a Collision-Energy-Based Method for the Prediction of Ice Loading on Ships
## 2 Background
### Ship-Ice Contact and Ice Crushing Pressure  
To manage the complexity of the ship-ice interaction process, ship-ice contact forces can be estimated based on the average pressure and contact area in terms of so-called pressure-area models [26]. These are typically presented in the form of pressure-area relationships \(P(A)\) describing the development of the average pressure throughout the ice indentation process [27; 28]. Because the ship-ice interaction process is complex and not fully known, \(P(A)\) relationships can only be reliably determined based on full-scale measurements.  
The maximum force generated by ice acting upon a structure depends on the strength of the ice in the relevant mode of failure [29]. Because in ship-ice interactions, the ice often fails by crushing, ice crushing strength is an important factor for estimating ice loading on ships [30]. As pointed out by [PERSON] [8], the crushing strength of ice depends on multiple parameters, including ice salinity, ice temperature and loading rate, but all of this is not fully understood. However, as demonstrated empirically by [PERSON] and [PERSON] [31], ice crushing strength is proportional to ice compressive strength \(\sigma_{co}\), \([MPa]\) as per  
\[\sigma_{cr}=C\cdot\sigma_{co},[MN] \tag{3}\]  
where \(C\) is an indentation coefficient which for the brittle range for sea ice has an empirically determined value of 1.57 [32; 33].
# Coseismic Stress Change and Viscoelastic Relaxation after the 2008 Great Sichuan Earthquake
## 1 Introduction
Thus, viscoelastic relaxation transfers the coseismic stress from the lower crust and upper mantle to the overlying seismogenic upper crust, resulting in a more complex regional stress distribution [36; 57]. In this study, the evolution of stress is focused on the broad Sichuan area, which is the most seismically active region in China. The stress variation in this area were investigated for a period of more than 10 years since 2008, and the effects of coseismic stress changes and postseismic viscoelastic relaxations associated with large earthquakes in the past decade were examined. The main aim of the present study is to investigate the state of stress evolution in this region by considering the viscoelastic relaxation of the lower crust and upper mantle. The modeling technique and the parameters used in the calculation are carefully outlined. Then, the results of long-term stress evolution are obtained through the calculation of coseismic stress change and postseismic relaxation based on Coulomb failure stress change theory. Furthermore, the potential triggering mechanism and seismic risk of the fault system are discussed.
## 2 Materials and Methods
### Preparation of Extract
#### 2.4.2 Phytochemical Analysis  
Determination of Total Phenolic Content (TPC)  
The total phenolic content (TPC) of the different studied extracts was evaluated according to the method reported by [PERSON] et al. [69] in three replicates. The method was based on the interaction, in an alkaline medium, between the components of the Folin-Ciocalteu reagent and the phenolic compounds present in the analyzed extracts. A 20 \(\mu\)L volume of the extract or standard <<Gallic acid>> was added to 100 \(\mu\)L volume of Folin-Ciocalteu reagent (10-fold diluted). After 4 min, the reaction medium was alkalinized by adding 75 \(\mu\)L of sodium carbonate (Na\({}_{2}\)CO\({}_{3}\), 7.5%). The absorbance was measured after 2 h of incubation in the dark at ambient temperature using a Multiskan SkyHigh 96-well microplate reader (Thermo Fisher Scientific, A51119600C, Illkirch-Graffenstaden, France) at a \(\lambda_{\text{OD}}=765\) nm against a blank (ethanol/water 80/20 _v/v_).  
The total polyphenol content was estimated in micrograms of gallic acid equivalent per milligram of dry plant extract (\(\mu\)g GAE/mg DE). It was calculated from the calibration curve performed with the standard <<Gallic acid>> at different concentrations (12.5-100 \(\mu\)g/mL). This curve had a linear regression (y = 0.0064x + 0.0061) with R\({}^{2}\) of 0.9952.  
Determination of the Total Flavonoid Content (TFC)  
The total flavonoid content (TFC) of the different samples was determined in three replicates using the aluminum chloride method described by [PERSON] et al. [70]. A volume of 100 \(\mu\)L of each extract or standard <<Quercetin>> was mixed with 100 \(\mu\)L of aluminum chloride solution (AlCl\({}_{3}\)-6H\({}_{2}\)O, 2%). After 15 min in the dark at 20 \({}^{\circ}\)C, absorbance was measured against the blank (ethanol/water 80/20 _v/v_) with a Multiskan SkyHigh 96-well microplate reader (Thermo Fisher Scientific, A51119600C, Illkirch-Graffenstaden, France) at a \(\lambda_{\text{OD}}=430\) nm.  
The total flavonoid content was expressed in micrograms of Quercetin Equivalent per milligram of dry plant extract (\(\mu\)g QE/mg DE). It was determined from the calibration curve realized with the standard <<Quercetin>> at different concentrations (3.125-75 \(\mu\)g/mL). The curve had a linear regression (y = 0.0233x + 0.0035) with a R\({}^{2}\) of 0.9981.
# Development of a Datalogger for Submarine Glider: Integration of Fault-Tolerant Software Layers
## 2 Datalogger Design
### LFSR Development for the BCH Code (255, 223, 4)  
An ECC code is composed of message bits and parity bits. For a BCH code, systematic coding is done using the Equation (6).  
\[c(x)=x^{n-k}d(x)+b(x) \tag{6}\]  
where \(d(x)\) represents the message, \(b(x)\) the added parity bits, \(n\) the length of the code, \(k\) the length of the message, and \(c(x)\) the code. The parity bits \(b(x)\) are obtained by modulo operation as presented in the Equation (7):  
\[b(x)=x^{n-k}d(x)\ mod\ g(x) \tag{7}\]  
where \(g(x)\) is the polynomial generator of the BCH code, which can be obtained with the MATLABTM function bchgenpoly. This function requires the length of the code and the length of the message. The generator polynomial given by the function is the following:  
\[g(x)=1+x^{2}+x^{3}+x^{4}+x^{5}+x^{6}+x^{7}+x^{9}+x^{14}+x^{16}+x ^{17}+x^{19}+x^{20}+x^{22}\] \[+x^{25}+x^{26}+x^{27}+x^{29}+x^{31}+x^{32} \tag{8}\]  
To obtain the parity bits, a systematic LFSR cyclic code encoder circuit is used as shown in Figure 5, where \(g_{n-k}\) is the coefficient of the second term with the highest exponent of the generator polynomial and always takes the value of 1 as well as \(g_{0}=1\).  
The way the LFSR circuit in Figure 7 encodes is described below.  
1. The shift registers \(R\) must be initialized with the 0 value.
2. During the initial \(k\) cycles, the term \(A\) is defined with the 1 value. The value taken by the multiplexer is defined with the 1 value, and the most significant bit of the datum \(d(x)\) is shifted into the LFSR first. The input \(d(x)\) in this state are the 223 message bits and the output \(c(x)\) takes the message bits.  
Figure 6: Comparative graph of different BCH codes with code sizes \(n=255\) and \(n=511\). As more parity bits are added, the probability of on-chip error decreases. The dotted box contain ECCs that do not allow the encoding of the information in real-time for the used controller.  
For the last \(n-k\) cycles, \(A\) has the 0 value. That means, the values in the multiplexer are used in the most-right register which are the parity bits. In this research work, the registers \(R\) are deleted at the end. Each bit that goes through the multiplexer is concatenated with the previous one. In other words, at the end of each cycle, the code is constructed from the message and 32 parity bits [49].  
Writing on a page of the NAND flash chip was performed in portrait orientation as shown in Figure 8. The reason it is available this way is because it takes advantage of the logical operations of the LFSR. In the next section, writing on the NAND flash memory chip is described.