esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
###### Abstract
We present an appropriate framework on image stitching to analyze the performance of the chosen detector and descriptor combination. Our aim is to present an assessment of all detectors and descriptors for image stitching applications. For future research in image stitching area this framework can assist as a guideline. 4. We present a comparison and performance analysis of detectors and descriptors for different types of datasets for image stitching applications. 5. Finally, we obtained a rank for the detector--descriptor pairs in terms of individual datasets. This paper compares several feature detectors and descriptors for image stitching applications. It may assist researchers in selecting a suitable detector and descriptor combination for image stitching applications. First, all popular detectors and descriptors are presented. Next, each combination of detector and descriptor is evaluated for stitching the images. For the evaluation, a variety of datasets including various possible scenarios are considered. The outcomes of this work, as presented above, add various new contributions to this domain. These contributions are outlined after a brief overview of earlier studies. The paper is structured as follows: A brief overview of feature detectors and descriptors used in this study is presented in Section 2. Section 3 provides details about the datasets and methodology used in the study. Section 4 presents results and discusses the outcomes. Finally, the paper is concluded in Section 5.
# Comparative Analysis of Skew-Join Strategies for Large-Scale Datasets with MapReduce and Spark
## 4 Evaluation
### Experiments
#### 4.2.2 Data Description  
We generated experimental datasets with _scalar skew distribution_. The term \"scalar skew\" was introduced by [PERSON] and his colleagues [19] when developing a taxonomy of skew effects. Scalar skew distribution was later used by other researchers for skew handling [6; 23; 24; 25]. In this work, we generated six datasets, in which each had 100,000,000 records. The idea of scalar skew distribution is that, in 100,000,000 records, the skew-join key with a value of 1 appears in some fixed number of records. The remaining records contain randomly appearing join key values from 2 to 100,000,000. This will help us to easily understand which experiments are performed while keeping the output size constant over varying amounts of skew. The format of the datasets was plain text consisting of three fields separated by commas, the primary key, the join key, and other text (pk, jk, others). The six created datasets had different skew ratios for the experiments. The details of the datasets are presented in Table 2.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **Number of Records** & **Number of Skewed Records** & **Skew Ratio** \\ \hline
**DSKew100** & 100,000,000 & 100 & 0.000001 \\
**DSKew1K** & 100,000,000 & 1000 & 0.0001 \\
**DSKew100K** & 100,000,000 & 100,000 & 0.001 \\
**DSKew1M** & 100,000,000 & 1,000,000 & 0.01 \\
**DSKew10M** & 100,000,000 & 10,000,000 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset description.
# Frequency Selective Surfaces: A Review
## 1 Introduction
Next, we present a classification of the FSSs, focus on the importance of their functionalities, and categorize FSS on the basis of nature of array element used, structure design, and applications. 3. This paper targets to link among their crucial theoretical, structure geometry, and significant performance parameters. 4. For every functionality, the polarization state of the array elements and the physical mechanism of their functions must be related to the frequency responses provided by FSSs. By knowing the polarization characteristics of FSS periodic elements for a specific function, the implementation of required FSS response in certain frequency band becomes straightforward and therefore, we elaborate all types of FSS from this perspective. 5. We illustrate an extensive survey of most recent researches on FSSs and also include their fabrication techniques, experimental verifications of FSSs design. Prospects and challenges, particularly in the microwave regime, are discussed focusing on how the innovation in this field could smooth the progress in advanced electromagnetics. The article is organized, as described below. In Section 2, we overview the concept of periodic surfaces with some of their types. In Section 3, we introduce FSSs in detail and its theoretical background, along with some  
Figure 1: Comparison of the presented survey with previous review articles on FSSs. history and significant advances in recent years. In Section 4, we review different types of FSSs, their related properties, implementation, and fabrication techniques. Section 5 presents future challenges, trends, and potential applications of FSSs. In the last section, we provide concluding remarks and a point of view about future research directions.
# EliNAC: Autoencoder Approach for Electronic Invoices Data Clustering
## 4 Performance Evaluation
### ELINAC's Performance Evaluation versus Other Literature Approaches
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Clusters** & **Completeness** & **Homogeneity** & **Mutual Info** & **Fowlkes–Mallows** \\ \hline ELINAC & Specific & 95.051\% & 95.708\% & 87.070\% & 77.675\% \\ K-means & Specific & 96.630\% & 97.228\% & 91.416\% & 82.712\% \\ DBSCAN & Specific & 96.945\% & 94.883\% & 89.088\% & 74.909\% \\ Hierarchical & Specific & 96.928\% & 95.578\% & 89.883\% & 78.547\% \\ ELINAC & Generalist & 80.885\% & 86.699\% & 68.903\% & 40.518\% \\ K-means & Generalist & 88.383\% & 96.906\% & 85.134\% & 55.684\% \\ DBSCAN & Generalist & 97.223\% & 76.523\% & 76.938\% & 19.109\% \\ Hierarchical & Generalist & 95.731\% & 97.204\% & 93.653\% & 84.831\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance evaluation of ELINAC compared to other literature approaches. of extensive clustering NF-e data. We observed that ELINAC is the most appropriate approach to identifying outliers, significantly outperforming the second-best algorithm (i.e., DBSCAN) in the clustering time. Table 7 presents the values of the pair confusion matrices for specific and generalist clusters in each algorithm. The values represent the number of paired records for each matrix coordinate (True Negatives, True Positives, False Negatives and False Positives). Analyzing specific clusters in Table 7, all four methods are very similar, with more noticeable variations in false negatives and false positives. K-means had the better result for the false positives and true negative pairings, while DBSCAN had the best result for false negatives and true positives. ELINAC's results were close to k-means, with slightly more false negatives. This means that ELINAC is very consistent when compared to the other algorithms. Although the Hierarchical algorithm had similar values, there were more false positives and fewer true negatives when compared to ELINAC. The variations are clearer for generalist clusters in Table 7, where the most noticeable difference was between ELINAC and DBSCAN, which presented more false positives. While ELINAC performed worse for generalist clusters compared to its results for specific clusters, it still managed to be very consistent and reliable, in addition to being faster in clustering time (Figure 10). Contrary to that, DBSCAN showed a great deal of inconsistency, having many more false positives than true positives. For the other methods, K-means and Hierarchical performed similarly for true negatives and false positives but very different for true positives and false negatives.
# Effect of Polycarboxylate Admixture on the Performance of Fluorogypsum-Based Self-Leveling Material
## 4 Conclusions  
This study investigated the effects of PCE on the setting time, fluidity, and mechanical properties of FSLM. The hydration products, pore structure, and microstructure of FSLM were examined at different PCE doping levels, leading to the following conclusions:  
1. Results showed that the optimal PCE doping level was 0.14%. By adding an appropriate dose of PCE, FSLM exhibits increased fluidity, and the 30 min slump of 142 mm and slump loss was reduced even at a lower water-cement ratio. FSLM prepared with 0.14% PCE demonstrates good performance and meets the main performance requirements outlined in JC/T 1023-2021 (gypsum-based self-leveling mortar).
2. Optimal PCE doping leads to gypsum crystals with a larger aspect ratio and tighter intercrystalline lapping, improving the structural density and reducing porosity. This results in an optimized pore size distribution. However, excessive PCE hinders the above performances of fluorogypsum and thereby has a negative impact on strength.  
According to the conclusions, these findings contribute to the comprehensive utilization of fluorogypsum. It has the potential to replace cement-self leveling materials, reducing the costs, carbon emission, and risk of cracking during large-scale use. However, it is well known that the water resistance of gypsum is poor, so it is also an important aspect in future research to investigate and improve the water resistance of FSLM and determine whether PCE will have a positive or negative effect.  
**Author Contributions:** Conceptualization, Methodology, Investigation, Writing--Review & Editing, L.F.; Conceptualization, Methodology, Investigation, Data curation, Visualization, Writing--Original Draft, S.X.; Investigation, Validation, Resources, W.W.; Data curation, Investigation, J.Z.; Conceptualization, Writing--Review & Editing, Supervision, Funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by fundamental research funds for the central universities, grant number 2023 CDJX7-020 and Chongqing performance incentive projects, grant number CQHKY2023 ZX00007 and CQHKY2023 CZ001.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the privacy that this research has related to our later experiments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Influence of the Foliar MeI and ACP-MeI Treatments on Must Volatile Compounds
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Weight of** \\ **100** \\ **Beries (g)** \\ \end{tabular} & \begin{tabular}{c} **Probable** \\ **Alcohol** \\ **(\% w/r)** \\ \end{tabular} & \begin{tabular}{c} **Total** \\ **Alcohol** \\ **(\% w/r)** \\ \end{tabular} & \begin{tabular}{c} **Glu \(\ast\)** \\ **(g/L)** \\ \end{tabular} & \begin{tabular}{c} **Flu** \\ **(g/L)** \\ \end{tabular} & \begin{tabular}{c} **Ratio** \\ **(g/L)** \\ \end{tabular} & \begin{tabular}{c} **Flu** \\ **(g/L)** \\ \end{tabular} & \begin{tabular}{c} **Mulic** \\ **A Acid (g/L)** \\ \end{tabular} & \begin{tabular}{c} **Total** \\ **Phenols** \\ **(mg/L)** \\ \end{tabular} & \begin{tabular}{c} **Anmonium** \\ **Nitrogen** \\ **(mg/NL)** \\ \end{tabular} & \
\begin{tabular}{c} **Anmion** \\ **Nitrogen** \\ **(mg/NL)** \\ \end{tabular} &
\begin{tabular}{c} **VAN** \\ **(mg/NL)** \\ \end{tabular} \\ \hline \multicolumn{11}{c}{**Treatment (T)**} \\ \hline _Control_ & 156.63 a & 23.50 a & 13.80 a & 3.79 a & 4.37 a & 233.14 a & 113.74 a & 119.39 a & 1.73 a & 863.47 a & 99.58 a & 135.52 a & 235.10 a \\ _Md_ & 174.74 a & 22.20 a & 12.91 a & 3.74 a & 4.87 a & 217.06 a & 104.48 a & 112.58 a & 2.04 a & 954.82 b & 104.00 a & 170.87 a & 274.87 a \\ _ACP-Md_ & 155.92 a & 22.87 a & 13.36 a & 3.77 a & 4.58 a & 227.37 a & 109.75 a & 117.62 a & 1.95 a & 967.05 b & 108.03 a & 163.97 a & 272.00 a \\ \hline \multicolumn{11}{c}{**Session (G)**} \\ \hline _2019_ & 124.14 a & 23.43 a & 13.75 a & 3.81 a & 4.98 b & 232.25 a & 113.32 a & 120.94 b & 2.43 b & 1281.10 b & 95.25 a & 165.44 a & 206.68 a \\ \multicolumn{11}{c}{**2020**} \\ \hline _2020_ & 200.71 b & 22.28 a & 12.96 a & 3.73 a & 4.23 a & 219.46 a & 107.33 a & 112.12 a & 1.38 a & 575.79 a & 112.49 b & 148.13 a & 260.63 a \\ \hline \multicolumn{11}{c}{**Interaction**} \\ \hline \(T\times S\) & N.S. & N.S. & N.S. & N.S. & N.S. & N.S. & N.S. & N.S. & \(\ast\) & \(\ast\) & \(\ast\) \\ \hline \multicolumn{11}{c}{**Clic glucose, Fire fructose, YAN: yeast assimilible nitrogen. For each parameter and factor, different letters indicate significant differences between samples (\(p\leq 0.05\)). Interactions: \(\alpha\), \(p\leq 0.05\), N.S., not significant (\(p>0.05\)).**} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Multifactor analysis of variance of general parameters of the musts with the two factors studied: treatment (control, Mel, ACP-Mej) and season (2019 and 2020).
# Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong--An Exploratory Study
## 3 The Pilot Study--ML-Powered Natural Terrain Landslide Susceptibility Analysis
### Feature Selection
#### 3.5.3 Landslide Data  
For a landslide susceptibility analysis treated as a binary classification problem, landslide occurrence is the variable to be predicted by the ML models (i.e., dependent variable). In this study, landslide data as recorded in the ENTLI (see Section 2.3) was adopted. As compared with the reported landslides or field-mapped landslides which are commonly adopted in the landslide susceptibility studies by others, landslide data based on the ENTLI provided a more complete picture of landslide occurrence over the study area in the past which was not biased by the accessibility of the landslide locations. On the other hand, the temporal resolution of the data was limited by the frequencies of aerial photography. A year-based landslide susceptibility study was considered as a result.  
In forming the landslide dataset, grids containing the crowns of the landslides were identified as the landsl area and denoted as '1' in the dataset. The remaining grids were considered as non-landsliding area and denoted as '0' in the dataset. The identification of landsl and non-landsliding grids were undertaken on a yearly basis from year 1985 to 2008.
# Spatial Shape-Aware Network for Elongated Target Detection
###### Abstract  
In remote sensing detection, targets often exhibit unique characteristics such as elongated shapes, multi-directional rotations, and significant scale variations. Traditional convolutional networks extract features using convolution kernels and rely on predefined anchor boxes and sample selection to frame the targets. However, this approach leads to several issues, including imprecise regional feature extraction, the neglect of object shape information, and variations in the potential of positive samples, all stemming from shape variations, ultimately impacting the detector's performance. To overcome these challenges, we propose a novel Spatial Shape-Aware Network for Elongated Target Detection. Specifically, we introduce three key modules: a Boundary-Guided Spatial Feature Perception Module (BGSF), a Shape-Sensing Module (SSM), and a Potential Evaluation Module (PEM). The Boundary-Guided Spatial Feature Perception Module adjusts the sampling positions and weights of convolution kernels, aligning the feature maps produced by the backbone network to the actual shape and location of the target, while reducing feature responses to irrelevant noise. The Shape-Sensing Module incorporates shape information into the sample selection process, allowing high-potential anchor boxes--which may have low IoU but capture critical target features--to be temporarily retained for further training. The Potential Evaluation Module integrates the potential information of positive samples into the loss function, providing stronger training feedback for high-potential positive samples. Experiments demonstrate that, compared with existing detection networks, our proposed network structure achieves superior detection performance on two widely used datasets, UCAS-AOD and HRSC2016.  
Keywords:remote sensing; elongated target detection; content within anchor box; boundary feature guidance +
Footnote †: journal: _Algorithms_
# Positioning Information Privacy in Intelligent Transportation Systems:
## Attacks Related to Distributed Operation  
Congestion attacksGenerally, urban traffic congestion is considered as one of the major problems from the vehicular systems' operation perspective. Modern cars are already available to report the congestion-related data either with integrated wireless modules or via hand-held user devices. Focusing on the first case, Vehicular Ad-Hoc Networks (VANETs) can provide timely information to the surrounding nodes which result in more effective route selection (around a sudden traffic jam) using the information obtained from other cars. From the information security perspective, the V2V radio connectivity of autonomous vehicles will offer attackers opportunities to combine multiple compromised vehicles into botnets of cars, which will lead to other serious security consequences [104].  
From the infrastructure perspective, such attacks may potentially lead to incorrect traffic light signaling. As a result, the optimization algorithm running in the ITS cloud may operate based on the incorrect data, leading to congestion [95].  
Repudiation attacksThis attack is an example of adequately tracking and recording the user actions, which can maliciously manipulate or fake the identification of new nodes [105]. This attack is used to modify the user's information about the actions performed by the malicious and to register false data in log files. It can also be used to manipulate the master data in a similar pattern involving the messages. If this attack takes place, the data stored in the log files may be considered invalid or misleading.  
Routing Loop AttackIn this type of attack, an internal attacker receives a message, updates it and sends it back to one of the previous forwarders (or the source) even if there is a better node in its routing table that is available to be the next forwarder according to the routing strategy. This attack aims to delay or prevent the delivery of a message [106]. The primary countermeasure against routing-related attacks is by utilizing Intruder Detection techniques and excluding the node from the network [107].  
Sinkhole AttackIn this type of routing attack, the attacker (already being in the system, i.e., an internal one) attempts to announce the nodes in the network with a different location in its beacon messages followed by drops of any other packets [108]. It results in the poisoning of the routing tables and packet loss. The attacker may also be intellectual and analyze the captured packets while conventional one just affects the overall system operation in a harmful way.  
Wormhole AttackThis type of routing attack corresponds to cases of two terminals having a link in between and they resend messages received by one of them to another [109]. Therefore, they can dominate on the routing path by replaying the valid beacons from other nodes. Attackers tend to dominate the connection so they can eavesdrop or share the network.
# Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications
## 3 Proposed FIRL Resource Allocation
The process then enters an iterative loop containing three main components: LTE packet generation, a reinforcement learning sub-process (highlighted in a dashed box containing PRR input, Q-learning, and modulation and coding scheme MCS index output), and a fuzzy inference system sub-process (containing vehicle distance and channel state information CSI input, fuzzy inference system FIS, and probability resource keep \(P_{rk}\) output). The simulation continues with PRR calculation, followed by a decision point checking if \(t<T\). If this condition is not met, the cycle continues with time increment \(t=t+t_{r}\); otherwise, the cycle stops with \(t=T\), leading to overall PRR statistic output and simulation end. The FIRL sequence plays a crucial role in the generation of LTE packets, with its parameter adaptation function optimizing the resource keep probability (\(P_{rk}\)) and MCS index values to suit the network's environmental conditions. This optimization significantly influences the PRR performance calculation. The integration of these components facilitates efficient data transmission in vehicular networks, making it a critical aspect of modern transportation systems. The FIRL approach in vehicular networks involves the real-time collection of current network parameters and continuous PRR performance calculations for each LTE packet generation, ensuring comprehensive coverage of the entire communications system. This simultaneous and ongoing data collection and analysis process enables the FIRL approach to provide an accurate and holistic view of network performance, allowing for continuous adaptation and improvement to meet user needs. By leveraging this adaptive methodology, the system can maintain optimal performance in the dynamic and challenging environment of vehicular communications.
## 4 Experimental Results and Analysis  
This section establishes a Request-Response Latency mathematical model and derives the Average Latency delay model using mathematical expectation. Experimental analysis based on both models demonstrates the following: (1) A comparative analysis of the Request-Response Latency between network-side TCP and UDP transmissions reveals comparable real-time performance under non-degraded network conditions, while TCP exhibits significantly higher latency than UDP in weak network conditions. (2) In-depth analysis of Modbus AUDP data transfer processes under weak network conditions, leveraging the Average Latency model and Markov Chain, determines the optimal value of \(N_{max}\). (3) Comparative analysis of AUDP and UDP transmissions under weak network conditions provides systematic validation of Modbus AUDP's real-time responsiveness and transmission reliability. Figure 18 is the photograph of the designed gateway prototype used for experimental testing.  
Figure 17: Flowchart of the host computer program.