esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work and Our Contribution in Context
### Suffix Trees and CSTs  
A _suffix trie_ is a trie (or digital tree) storing all the suffixes of \(T\). This is a labeled tree where each text suffix is read in a root-to-leaf path, and the edges toward the children of a node are labeled by different characters. The concatenation of the string labels from the root up to a node \(v\) is called the _path-label_ of \(v\), \(\pi(v)\). A _suffix tree_ is obtained by \"compacting\" unary paths in a suffix trie, which means they are converted into a single edge labeled by the string that concatenates the involved character labels. The suffix array alone is insufficient to efficiently emulate all the relevant suffix tree operations. To recover the full suffix tree functionality, we need two extra pieces of information: (1) The tree topology; (2) The _longest common prefix_ (_LCP_) information, that is, _LCP\([i]\)_ is the length of the longest common prefix between \(T[A[i-1],n]\) and \(T[A[i],n]\) for \(i>1\) and _LCP\([1]=0\)_ (or, seen another way, _LCP\([i]\)_ is the length of the string labeling the path from the root to the lowest common ancestor node of the \(i\)th and \((i-1)\)th suffix tree leaves). Indeed, the suffix tree topology can be implicit if we identify each suffix tree node with the suffix array interval containing the leaves that descend from it. This range uniquely identifies the node because there are no unary nodes in a suffix tree. Consequently, a _compressed suffix tree_ (_CST_) is obtained by enriching the CSA with some extra data. [PERSON] [11] added the topology of the tree (using \(4n\) extra bits) and the LCP data. The LCP data was compressed to \(2n\) bits by noticing that, if sorted by text order rather than suffix array order, the LCP numbers decrease by at most 1: Let _PLCP_ be the permuted _LCP_ array, then _PLCP\([j+1]\geq\)_PLCP\([j]-1\)_. Thus the numbers can be differentially encoded, \(h[j+1]=\)_PLCP\([j+1]-\)PLCP\([j]+1\geq 0\)_, and then represented in unary over a bitmap \(H[1,2n]=0^{h[1]}10^{h[2]}\ldots 10^{h[n]}1\). Then, to obtain _LCP\([i]\)_, we look for _PLCP\([A[i]]\)_, and this is extracted from \(H\) via _rank/select_ operations. Here \(rank_{b}(H,i)\) counts the number of bits \(b\) in \(H[1,i]\) and \(select_{b}(H,i)\) is the position of the \(i\)-th \(b\) in \(H\). Both can be answered in constant time using \(o(n)\) extra bits of space [33]. Then _PLCP\([j]=select_{1}(H,j)-2j\)_, assuming _PLCP\([0]=0\)_. [PERSON] _et al_. [14] get rid of the parentheses, by instead identifying suffix tree nodes with their corresponding suffix array interval. By sampling some suffix tree nodes, most operations can be carried out by moving, using suffix links, towards a sampled node, finding the information stored in there, and
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 4 Results  
### Hierarchical Decision Tree  
For each mapping unit, a series of classification trees generated by the decision tree classifier (See Section 5) were conducted to produce the CLC-SW2010 product in this study. Owing to spatial limitations, this paper only shows three typical decision trees, which were conducted for distinguishing between vegetation and non-vegetation, vegetation divisions and forest divisions, respectively, in the junction zone (path/row: 138/38, WRS2) of the Sichuan province and Tibet autonomous region.
# Numerical Simulation of Liquid Film Characteristics during Atomization of Aluminum Alloy Powder
## 2 Atomization Model and Basic Theory  
### Geometric Model and Structural Parameters  
The high-fluidity aluminum alloy powder atomization experimental device used for additive manufacturing is mainly composed of a melting furnace, induction heating tundish, centrifugal atomization rotating disk, strong cooling high-speed motor, atomization chamber, powder collection tank, etc. The principle of the atomization device is shown in Figure 1. The outdoor atomizing wall is a water-cooled wall that serves to accelerate atomization drop cooling, and the upper part of the atomizing chamber is installed with a tundish. Aluminum alloy raw materials are poured into the induction heating tundish after melting in the melting furnace, and they then flow into the atomizing disk through the nozzle at the bottom of the tundish. The molten liquid aluminum alloy forms a liquid film under the action of friction and centrifugation on the surface of the high-speed rotating atomizing disk, and the certain characteristics of the liquid film develop and it breaks into droplets, which are cooled and solidified in the atomizing chamber. The simulation geometric model of the above atomizing device is shown in Figure 2, and the specific structural parameters are shown in Table 1.  
Figure 1: Schematic diagram of atomizing device.  
Figure 2: Geometric model and curvature of spherical disk.  
According to the geometric model of the atomization chamber and the actual working conditions, the irrelevant structural parts are simplified and omitted, and the simulation model and boundary conditions after transformation are shown in Figure 3. The specific treatment methods are as follows:  
1. The tundish is simplified, the outer wall of the tundish is retained, the actual liquid aluminum alloy is not filled in the simulation, and the velocity inlet boundary condition (constant atomization flow rate) is simplified at the nozzle outlet;
2. The drive shaft and motor of the rotating atomization disk are omitted, and only the atomization disk is retained;
3. The external cooling of the cooling wall of the atomization chamber is omitted and the temperature wall boundary conditions are equivalent.
# Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis
## 6 Conclusions  
In this study, we propose the CIED method to address the challenges faced by existing gradient-based methods in processing low-contrast medical images with blurred edges. The method selects the three MSB planes to extract edge information from medical images. This is due to the three MSB planes containing important contour features. Using bit planes for edge detection reduces the effect of noise, naturally enhances contrast, and improves computational efficiency. The CIED is thoroughly compared with existing edge detection methods. Qualitative analysis shows that the edges extracted by the CIED are more complete, continuous, and less noisy. Quantitative analysis shows that the CIED demonstrates superior performance with a mean precision of 0.408, a mean recall of 0.917, and a mean F1-score of 0.550. It has good robustness under different contrasts, and the CIED can extract complete and valid edges even at very low contrasts. The CIED provides a new approach in the domain of medical image-assisted diagnosis, which contributes to enhancing the performance of disease diagnosis and the effectiveness of treatment. In future research, we will try to explore and extend the CIED method to non-medical image applications such as industrial defect detection, satellite image analysis, natural scene image processing, and remote sensing image processing. These extensions can further validate the universality and robustness of the CIED method and enhance its practical value in different fields.  
D.L.: Conceptualization, Methodology, Data Curation, Software, Visualization, Writing--Original Draft. P.C.-I.P.: Conceptualization, Supervision, Validation, Writing--Review and Editing. C.-K.L.: Methodology, Writing--Review and Editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Macao Science and Technology Development Fund (funding ID: 0088/2023/ITP) and Macao Polytechnic University research grant (funding ID: RP/FCA-13/2022; submission code: fca.e22a.7 ee.c).  
Not applicable.  
The dataset is publicly available at www.kaggle.com (accessed on 12 January 2025).  
The authors declare no conflicts of interest.
## 2 Taxonomy of DDoS Attacks
### DDoS Attack Architecture
#### 2.2.3 Application-Based DDoS Attacks
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Attack Type** & **Features** & **Attack Magnitude** & **Effect on Target Server** & **Attack Complexity** & **Affected Layer** & **Frequency of Occurrence** \\ \hline \multirow{4}{*}{Volumetric-based} & The use of a huge amount of traffic to saturate the bandwidth of the target server & \multirow{4}{*}{Bits per second (bps), Gbps, flood target server} & Access to the target resources may be totally blocked by the attack’s sheer volume of traffic. & Easy to generate using simple amplification techniques & Network layer & Most common \\ \hline \multirow{4}{*}{Protocol-based} & The use of a huge amount of traffic to saturate the bandwidth of the target server & \multirow{4}{*}{Bits per second (bps), Gbps, flood of traffic.} & It disrupts service by consuming all the target server’s processing power or resources, including the firewall. & \multirow{4}{*}{Less complex} & Network and transport & \multirow{4}{*}{More common} \\ \cline{1-1} \cline{3-3} \cline{5-6}  & It exploits the weakness in layers 3 and 4 of the protocol stack to make the target server not accessible. & \multirow{4}{*}{Requests per second (rps)} & It creates a session with the target and then uses up its resources by completely dominating processes. & \multirow{4}{*}{Complex and difficult to detect} & \multirow{4}{*}{Application Less common} \\ \cline{1-1} \cline{3-3} \cline{5-6}  & It harnesses the flaws in layer 7 of the protocol stack to make the target server not accessible. & \multirow{4}{*}{Requests per second (rps)} & It creates a session with the target and then uses up its resources by completely dominating processes. & \multirow{4}{*}{Complex and difficult to detect} & \multirow{4}{*}{Application Less common} \\ \cline{1-1} \cline{3-3} \cline{5-6}  & It harnesses the flaws in layer 7 of the protocol stack to make the target server not accessible. & \multirow{4}{*}{Requests per second (rps)} & It creates a session with the target and then uses up its resources by completely dominating processes. & \multirow{4}{*}{Complex and difficult to detect} & \multirow{4}{*}{Application Less common} \\ \cline{1-1} \cline{3-3} \cline{5-6}  & It makes the target server not accessible. & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of the three major DDoS attack classification.
## 2 Materials and Methods
### Collection of ATR-FTIR Spectra  
Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectra was collected from the range of 4000 cm\({}^{-1}\) to 600 cm\({}^{-1}\) with the resolution of 4 cm\({}^{-1}\) using the VERTEX 70 spectrometer (Bruker Optics Inc., Ettlingen, Germany), coupled with an attenuated total reflection (ATR) accessory (Pike Technologies, Germany). Then, the spectral signals were digitalized with 2 cm\({}^{-1}\) intervals in the Fourier transform. The repeat scan number was set to 16 times, and the displayed curve was the averaged spectrum, which was obtained using OMINIC software (Version 6.5, Bruker, Inc.). Notably, each sample was updated through the cuvette (10 mm) and six times were measured repeatedly. Therefore, a total of six spectra were collected for further modeling analysis.
## 5 Discussion  
It can be seen from Table 3 that the I-NSGA-III-VLC could obtain solutions equal to or even better than the enumeration method within a certain error and in a shorter time than other methods, with a higher efficiency, which means it had a larger search range. It was proven that the method was feasible in solving the problem of equipment echelon usage under the conditions of rotation.  
It can be seen from Figure 8 that the I-NSGA-III-VCL could search for four fitness functions at the same time and obtain a Pareto optimal solution set. In addition to the three fitness functions represented by the coordinate system in Figure 8, there also was the number of rotations represented by the color, where rotation \(x\) representing the equipment quantity of rotations is \(x\).  
Using the usage scheme that maximizes the weight formula \(\beta\), it can be seen from Figures 9 and 10 that the remaining working life of the equipment of each unit formed a good echelon, and the average echelon uniformity index \(F_{1}\) reached 86.4%, of which \(Q_{1}\) was 86.4%, \(Q_{2}\) was 86.4% and the average life matching degree \(F_{2}\) reached 94.5%. The average life utilization rate of the remaining life of equipment \(F_{3}\) was 100%, the life waste was the lowest and the total quantity of rotations was 2, which is acceptable.
## 4 Experimental Results Analysis and Discussion
### Impact of Environmental Diversity on Model Performance  
To evaluate the adaptability and robustness of the proposed method under real-world conditions, we define the dataset described in Section 4.1 as Dataset1, and construct five additional datasets (Dataset2 through Dataset6) using signal data collected from the same 10 BDS satellites over a long time span from 2021 to 2024. These satellites were observed under varying temporal and orbital conditions, naturally introducing environmental variability such as changes in satellite hardware states, signal propagation conditions, and ground station configurations.  
It is worth noting that in these supplementary experiments, we retain the model pretrained on Dataset1 and perform fine-tuning using only a small number of labeled samples from each of the new datasets. This setup reflects practical deployment scenarios where a general-purpose feature extractor, once pretrained, can be rapidly adapted to newly collected data without retraining from scratch. Instead, only minimal labeled data are required to fine-tune the model for evolving signal environments. Evaluation metrics, including Accuracy (ACC), Precision (P), Recall (R), and F1-score (F), are used to quantify classification performance across the datasets.  
As shown in Tables 9-12, the proposed model consistently maintains strong performance across all datasets. While a slight reduction in performance is observed under the 10-shot and 15-shot settings, which may be attributed to greater signal variability or environmental noise, the model exhibits rapid adaptation as the number of labeled samples  
Figure 7: Classification accuracy using different VAT parameters during the fine-tuning phase.  
increases. These findings demonstrate that the proposed SSL-SEI framework is capable of learning generalizable features and can be effectively fine-tuned on newly collected satellite observations using only a small amount of labeled data. This confirms the practical applicability of our method in dynamic and diverse environments.
## 5 Experiments and Results  
In this section, the effectiveness of the DLMVO is evaluated on the SDM, DDM, and PV modules. A 57 mm diameter commercial RTC France silicon solar cell with an irradiance of 1000 W/m\({}^{2}\) and temperature of 33 \({}^{\circ}\)C was used for SDM and DDM models, and a solar module named Photowatt-PWP201 (under 1000 W/m\({}^{2}\) at 45 \({}^{\circ}\)C) that consists of 36 polycrystalline silicon cells in series was adopted for SMM models. As shown in Table 1, to ensure the fairness of the comparison, the setting of the upper and lower bounds for each parameter is the same as that of the previous studies [43; 44].  
For this experiment, we designed an excellent algorithm for the same problem to verify the superior performance of the DLMVO algorithm in comparison to the algorithms MVO [31], QMVO [45], Rcr-JADE [46], IWOA [47], CLPSO [48], IJAYA [6], PGJAYA [8], and SATLBO. The parameters of each compared algorithm are configured according to the recommendations in the respective references, shown in Table 2. To avoid the contingency of experiments, the DLMVO and other competitors were all executed in MATLAB 2016a and conducted with 30 independent runs.
## 3 Design of the Learning System and Content Structure
### Question Generation Process  
The question generation process of this system follows the design principles of standardization and automation to ensure the consistency, accuracy, and teaching applicability of the question content. The following steps are used to generate questions:  
1. Definition of Learning Concepts: According to the course requirements, teachers determine the _Verilog_ syntax, digital circuit concepts, or design methods covered by the questions to ensure that students can master the target knowledge points through answering these questions.  
Figure 3: Submission section.  
Figure 2: Answer button to submit answers.  
2. Verilog Code Generation: According to the defined teaching concepts, teachers write _Verilog_ codes to implement the corresponding digital circuit functions and ensure that the codes match the teaching contents.
3. Code Simulation and Verification: Teachers use professional digital circuit simulation tools such as _ModelSim_, _Vivado_, and _Quartus_ to run the _Verilog_ program to confirm that its functions and logic are correct, preventing incorrect programs from affecting students' learning.
4. Question Design: Teachers design targeted test questions based on the code contents or related concepts to ensure that students can strengthen their understanding of grammar and logic through answering the questions.
5. Ensuring Unique Answers: Teachers ensure that all questions have unique answers to avoid students' learning outcomes and automatic assessment accuracy being affected by ambiguous answers.
6. Supplementary Learning Resources: Teachers add relevant supplementary learning resources, such as concept descriptions, images, tables, or sample code, to help students understand the topic backgrounds and core concepts.
7. Automated Conversion to HTML Format: Automated tools are used to convert the contents into HTML formats to generate an interactive web-based quiz environment where students can answer questions directly, ensuring that the platform can provide real-time assessments and visual learning experiences.  
Through this mention systematic question generation process, this system can ensure the accuracy, consistency, and learning effect of the test contents, and improve the efficiency of question generation and management through automation technologies, making _Verilog_ teaching more structured, interactive, and efficient.
## 3 A Proposed Robust Scheme Based on Hop-Count Analysis (MHA)
### Hop-Count Analysis Scheme and Route Selection  
In MHA, a route with too low or too high hop-count is considered unhealthy. A too low hop-count may imply a wormhole attack; while a too high hop-count may decelerate the transmission. Furthermore, a route with a high hop-count has a higher chance to break.  
In MHA, we define random variable \(X\) which represents the hop-count values in the received RREP packets, and \(U=\{x_{I},\ \ldots\,\ x_{i},\ \ldots\,\ x_{j},\ \ldots\}\) is the sample space of random variable \(X\). So we have its cumulative distribution function \(F_{X}(x)\). We further define variables \(\alpha\) and \(\beta\) which represent the lower bound and upper bound of the selected range on \(F_{X}(x)\), then \(m\) and \(n\) will be the corresponding hop-count boundaries. They are defined in Equation (1) as follows:  
\[\begin{split} x_{i},x_{j}\in U\,\ i,j,m,n\in N\\ 0<\alpha<\beta<1\\ m=\sup\Bigl{\{}x_{i}\bigl{|}F_{x}\bigl{(}x_{j}\bigr{)}\leq \alpha\Bigr{\}}\\ n=\inf\Bigl{\{}x_{j}\bigl{|}F_{x}\bigl{(}x_{j}\bigr{)}\leq\beta \Bigr{\}}\end{split} \tag{1}\]  
Finally, we select the route paths from received RREP packets with hop-count \(\text{x}_{t}\) which satisfies Equation (2):\[r\in N\,,\ x_{r}\in U\]  
\[x_{i}\leq x_{r}\leq x_{j} \tag{2}\]  
as legal route paths in MHA protocol. The legal route paths are cached in the source node and given a TTL. Data packets are then transmitted randomly through these routes. If any of these legal route paths are broken, the source node deletes it from the cache. If all legal route paths are broken or vanished, and there is still a requirement for communication, the source node will carry out another route request procedure.  
In addition, the source node unicasts route acknowledgement (RACK) packets along all legal routes to the destination node before the first data packet is sent. The RACK packet contains the intermediate nodes of all legal routes and the expiration time. The destination node then caches them for further transmission. Once this step is completed, the bidirectional routes of a communication pair are established.
# Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study
## 4 Results and Discussion
### Calibration Threshold Technique Results
In order to eliminate this overrepresentation, a layer of only permanent areas was generated. This was created from the RADAR image of 6 April 2018, in which the avenue period had not yet begun. By subtracting the flood layer on 13 April 2018 and the one generated on day 6, with the Band Maths tool, we obtained a display of the temporary flood areas that are represented in Figure 13b. \begin{table}
\begin{tabular}{c c} \hline \hline
**Filter** & **Total Area (ha)** \\ \hline No filter & 1822.01 \\ \hline Lee \(3\times 3\) & 1122.15 \\ \hline Lee \(5\times 5\) & 880.53 \\ \hline Lee \(7\times 7\) & 762.90 \\ \hline Refined Lee & 1115.54 \\ \hline Lee Sigma \(5\times 5\) & 910.17 \\ \hline Gamma Map \(3\times 3\) & 1093.30 \\ \hline Gamma Map \(5\times 5\) & 890.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Water covered area for a specific part of zone 3: Zaragoza–El Burgo de Ebro. Following the same steps for the other two zones gave the total flood map for the calibration threshold technique, as represented in Figure 14.  
Figure 14: General flood map of the entire study area by calibration thresholds created from the RADAR image of 13 April 2018 and a scrubber with the permanently flooded water layer (Source: PNOA base map). Figure 13: **(a)** Map of zone 1: Novillas–Pradilla de Ebro calculated using the calibration threshold methodology, with an intensity limit of \(-21\) dB. **(b)** Representative map only of the flood zones of zone 1: Novillas–Pradilla de Ebro (Source: PNOA base map). This technique applied to dual polarization images, VH in our case, gives good results in the extraction of water bodies, although these are small, but it has a number of limitations. These include, among other things, background noise. The calibration threshold is dependent on the characteristics of the surface to be captured and also on the specific parameters of the capture [65]. This implies that the threshold is not always fixed and cannot be generalized; it will have to be calculated in each study area.