esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
In this review, structural vibration control and wing morphing control were selected as major research items since they are easily integrated with SMA actuators: wire, film, embedded composite, and so forth. It is remarked that various research works are summarized using tables to clearly show the effectiveness, implemented controller, and possible applications. These tables can provide the guideline for the choice of SMA types and geometrical parameters of the structural mechanisms. Magnetic shape memory alloys capable of generating force or motion by applying a magnetic field were introduced in 1996, and various studies have been conducted until recently. Generally, magnetic shape memory alloy is an alloy of nickel, manganese, and gallium (Ni-Mn-Ga). However, magnetic shape memory alloys are different from thermal shape memory alloys in their driving method and are not yet actively applied in the aerospace field, so they are not included in the research scope of this review paper. In addition, various modeling techniques have been proposed to compensate for the hysteresis characteristics of shape memory alloys, and control algorithms have been proposed based on these models. However, this review paper focused on application fields and control algorithms, and modeling techniques were not included in the scope of this review paper.
## 3 Results
### Distribution of Phosphorus  
Figure 2 shows the fractionation of various phosphorus forms (Ca\({}_{2}\)-P, Ca\({}_{8}\)-P, Ca\({}_{10}\)-P, Al-P, Fe-P, O-P, and Org-P) in the soil samples for different particle sizes. There were little differences in contents of P between sample A and B. Among the fractions investigated, the highest TP content was found in fine sand, followed by silt, coarse sand, and clay. Inorganic P (IP) was the main component of TP in all fractions, accounting for 52.48-87.95%, and its proportion increased with particle size.  
Similar to the distribution of phosphorus in other calcareous soils [39], calcium phosphorus (Ca-P) was the main form of IP in this sample, especially in the coarser fractions, accounting for 55% and 49% of TP in coarse sand and fine sand, respectively. The level of Ca-P was relatively lower in silt and clay, accounting for 21% and 14%, respectively, decreasing with particle size. For each particle size fraction, Ca\({}_{10}\)-P was the main form of Ca-P, accounting for 45% and 41%, respectively, of TP in coarse and fine sand, and 16% and 10%, respectively, in silt and clay. The content of Ca\({}_{8}\)-P was lower than that of Ca\({}_{10}\)-P, with approximately one-fifth of the content for sand and one-third of the content for clay and silt. The lowest content was represented by Ca\({}_{2}\)-P, accounting for about 1% of TP in all particle size fractions.  
There was little difference in the contents of O-P and Al-P across the sample, accounting for 12-20% and 5.4-8% of TP, respectively. On the other hand, the content of Fe-P varied greatly, decreasing approximately threefold from clay to coarse sand.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fractions** & **Particle Size/\(\upmu\)m** & **Sample A** & **Sample B** \\ \hline Clay & \textless{}2 & 3.42 \(\pm\) 0.31 d & 3.26 \(\pm\) 0.77 d \\ silt & 2–20 & 37.20 \(\pm\) 1.21 b & 37.13 \(\pm\) 1.34 b \\ Fine Sand & 20–200 & 39.55 \(\pm\) 0.16 a & 39.84 \(\pm\) 0.48 a \\ Coarse Sand & 200–2000 & 19.82 \(\pm\) 1.26 c & 19.76 \(\pm\) 1.27 c \\ \hline \hline \end{tabular} Different letters mean significant differences between sample plots in four particle size fractions (\(p\) < 0.05).  
\end{table}
Table 3: Particle-size distribution of the soil samples.  
Figure 2: Distribution of various forms of P (expressed as percentage of the total) in each particle size fraction (Ca\({}_{2}\)-P: dcalcium phosphate, Ca\({}_{8}\)-P: octacalcium phosphate, Ca\({}_{10}\)-P: decacalcium phosphates, Al-P: aluminum phosphate, Fe-P: phosphate adsorbed on iron oxides, O-P: iron oxide occluded phosphate, Org-P: Organic phosphorus).
# Cooperative Three-View Imaging Optical Coherence Tomography for Intraoperative Vascular Evaluation
## 1 Introduction
With the customized sample arm configuration, we get simultaneous cross-sectional images of the vessel from three different views. With proper image registration, a synthetic image can be produced which is equivalent to circumferential imaging of the vessel. We first test the system on phantom plastic tubes with different outer diameters. Then in vivo rat artery imaging was performed to evaluate our system. We believe this novel cooperative three-view imaging optical coherence tomography can be of great value to intraoperative vascular anastomosis evaluation.
# Flaw Size Quantification for Cable Flaw Inspection System with Inductive Search Coil Sensor
## 1 Introduction
The feature of this method lies in its use of a single self-induction coil, which is different from conventional coil-detection configurations that typically rely on separate exciter and receiver coils. This design choice not only reduces data processing requirements but also facilitates a lightweight structure that can be easily carried by robots. During the inspection process, the cable under examination is guided through the self-induction coil. Defects in the cable induce cross-sectional loss, leading to changes in the self-inductance of the coil. Real-time identification and quantification of internal flaws on the cable are achieved by continuously monitoring the self-inductance variation during the scanning process using a search coil sensor. Furthermore, the adoption of a single self-induction coil not only ensures a portable and lightweight system but also simplifies the overall cable flaw detection process. The elimination of separate exciter and receiver coils streamlines the method reduces complexity to a certain extent, which enhances the practicality of the method.
# Heterogeneous Wireless Networks for Smart Grid Distribution Systems: Advantages and Limitations
## 5 Performance Study
### Effect of MPTCP Maximum Congestion Window Size  
The maximum congestion window \(W_{M}\) is an essential parameter for tuning the MPTCP protocol performance. We found that the selection of \(W_{M}\) leads to a significant change in the achievable throughput. Figure 4 shows the MPTCP throughput performance for different combinations of \(W_{M}\) values for the WiFi link and a 70 Mbps 4G link. In fact, as Figure 4 reveals, there is some optimal value of \(W_{M}\) that gives the best throughput. For example, at a 3G speed of 70 Mbps, setting the \(W_{M}\) of both interfaces to 55 segments gives the highest throughput. Determining the right value of \(W_{M}\) for an interface is subject to a further study considering a number of related parameters, such as \(W_{M}\) of the other interface, speed, buffer capacity, and the number of competing devices. The reason for the variation of throughput is that a large \(W_{M}\) allows the congestion window to grow to a large value. This implicitly means sending more segments on each interface, and hence increasing the throughput. However, in our network setup, the primary substation has to send ACK messages to every substation. Therefore, as the congestion window size of a sending interface becomes large, the buffer size of the corresponding receiving interface at the primary substation overflows quickly. As a result, a large number of ACK packets are dropped causing throughput degradation. On the other hand, small values of \(W_{M}\) do not lead to dropping large number of ACK packets at the primary substation but lead to a low throughput.  
Figure 3: Data transfer time and the percentage of WiFi share.
# Automating Visual Blockage Classification of Culverts with Deep Learning
## 3 Dataset
### Synthetic Images of Culverts (SIC)  
This dataset consisted of synthetic images of culverts generated using a three-dimensional (3D) computer application based on a gaming engine (i.e., Unity3D) specifically designed to simulate multiple culvert blockage scenarios. Application has the capability to generate virtually countless blockage scenarios by dragging different debris materials into the scene and placing them in desired orientation/location. Images of different simulated blockage scenarios were captured using batch capture functionality and are referred to as \"Synthetic Images of Culvert (SIC)\". Dataset offered the diversity in terms of debris type (i.e., urban, vegetative, mixed), culvert types (i.e., pipe, single circular, double circular, single box, double box, triple box), camera viewpoints, time of day, and water levels. Limitations of the dataset include single natural background and non-realistic effects/animations. For this investigation, 1289 images were used with 1140 images from the \"blocked\" class while 149 images from the \"clear\" class. Figure 3 shows the sample images of each class from SIC.
# An Efficient Dummy-Based Location Privacy-Preserving Scheme for Internet of Things Services
## 1 Introduction
In this paper, we propose an enhanced greedy manner to improve the existing dummy-based location selection scheme (such as DLP) and improve the efficiency of dummy location generation. Compared with the DLP scheme, the dummy location generation time of our scheme is reduced by 68.14%. 2. The Enhanced-DLP scheme enhances the user's privacy protection, making it difficult for an attacker to distinguish the user's real location from the anonymous set. Compared with the DLP scheme, the probability of our scheme revealing the user's real position is reduced by 33.63% under the same attack conditions.
# Structure-Enhanced Prompt Learning for Graph-Based Code Vulnerability Detection
## 4 Experiments
### Result Analysis
#### 4.3.2 RQ2: Syntax-Aware Embedding Effectiveness  
The CPG representation of code combines multiple structural elements, including the AST, providing a solid foundation for vulnerability detection. However, traditional GNNs struggle to adapt to the tree structure. Our analysis shows that directly applying unified graph neural architectures (e.g., GCN) to hybrid code leads to suboptimal performance in vulnerability detection. To overcome this, we propose a hierarchical learning framework. First, we use syntax-aware embedding techniques to model AST nodes in the CPG. Then, we apply a GCN to extract features closely related to security vulnerabilities, improving the model's ability to detect potential vulnerabilities.  
To systematically evaluate the impact of syntax-aware encoding on vulnerability detection capabilities, we designed a controlled ablation study to compare two architectures: (1) the baseline GCN and (2) the GCN enhanced with a syntax-aware embedding module. Both configurations were implemented as binary classifiers and evaluated on the three datasets mentioned earlier. To ensure statistical reliability, we conducted 20 independent trials for each configuration, maintaining identical hyperparameter settings (as detailed in Table 2).  
In the experimental results shown in Table 3, similar trends are observed across the three datasets. Here, we focus on the FFmpeg+Qemu dataset for a more detailed discussion. The baseline GCN model exhibits relatively weak overall performance. In contrast, CNN-GCN outperforms the GCN across all evaluation metrics, including accuracy  
Figure 10: Performance under varying batch sizes and prompt dimensions on three datasets.  
(58.05% vs. 57.14%), precision (56.95% vs. 55.69%), recall (58.05% vs. 54.60%) and F1 score (55.47% vs. 53.52%), demonstrating that incorporating CNN-based syntax-aware feature extraction enhances the model's discriminative ability to some extent. However, CNN-GCN also shows significantly higher standard deviations (up to \(\pm\)1.44), indicating that the model's performance is more unstable and sensitive to variations in the experimental setup. This implies that while CNN-based syntax-aware feature extraction improves representation capacity to some degree, it does not fundamentally overcome the modeling limitations of GCNs.  
In comparison, the Text Prompt method exhibits clear advantages over CNN-GCN across all metrics--accuracy, precision, recall and F1 score. Notably, the median accuracy and precision both exceed 60%, and the distribution is more concentrated. These results suggest that leveraging natural language prompts can effectively enhance the model's understanding of task objectives, leading to more robust and efficient feature learning.
# Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN)
###### Abstract  
Although many deep-learning-based methods have achieved considerable detection performance for pedestrians with high visibility, their overall performances are still far from satisfactory, especially when heavily occluded instances are included. In this research, we have developed a novel pedestrian detector using a deformable attention-guided network (DAGN). Considering that pedestrians may be deformed with occlusions or under diverse poses, we have designed a deformable convolution with an attention module (DCAM) to sample from non-rigid locations, and obtained the attention feature map by aggregating global context information. Furthermore, the loss function was optimized to get accurate detection bounding boxes, by adopting complete-IoU loss for regression, and the distance IoU-NMS was used to refine the predicted boxes. Finally, a preprocessing technique based on tone mapping was applied to cope with the low visibility cases due to poor illumination. Extensive evaluations were conducted on three popular traffic datasets. Our method could decrease the log-average miss rate (\(MR^{-2}\)) by 12.44% and 7.8%, respectively, for the heavy occlusion and overall cases, when compared to the published state-of-the-art results of the Caltech pedestrian dataset. Of the CityPersons and EuroCity Persons datasets, our proposed method outperformed the current best results by about 5% in \(MR^{-2}\) for the heavy occlusion cases.  
pedestrian detection; feature extraction; computer vision; image processing +
Footnote †: journal: Applied Sciences  
_Article_  
Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN).
## 2 Materials and Methods
### DIMONA Platform and Model
#### DIMONA Platform Overview
Figure 4: The entrance screen for machine learning (ML) modelling. Figure 3: Part of the entrance screen for Internet of Things (IoT) modelling and/or exploring weather data at any geographic location around the world. Tier II serves the back end on the hosting server (Apache) with its database, dealing with user sign up and log in. Tier II creates and maintains users' personal accounts on the _DIMONA_ server, allowing each user to remotely create, rename, copy, transfer, and delete different files and folders on their personal accounts. Tier II manages the upload of model input, drivers, parameters, and other files from the users' PC to the _DIMONA_ server and download of model output from the _DIMONA_ server to the users' PC after running the PB, IoT, or ML model executables. Tier III deals with interactions between _DIMONA_'s back end and the Developer's PC, where the programming and compiling of _DIMONA_ model executables are performed. Tier III accommodates the deployment of compiled model executables on the _DIMONA_ server and executing the.exe and.py files for remote model runs. Tier IV refers to other websites with API end points and external databases to/from which _DIMONA_ requests/receives available.json files with model parameters, drivers, digital maps, or other data. The.json files received from the API websites are parsed by _DIMONA_ for obtaining the data of interest. Through Tier IV, _DIMONA_ has access to any geographic coordinates in the world via API websites, such as OpenWeatherMap for current weather data every few minutes, Agro Dashboard for Agricultural Monitoring, referred to as Agro API, for soil near-surface water contents and temperature every 12 h, SoilGrids REST API for soil physical and chemical properties, texture, organic matter, nutrients, and classification, and the Global Biodiversity Information Facility (GBIF) for plant, animal, and fungi species and ecological habitats. Tier V allows users to upload, via the FTPS method with SSL or TLS protocols, model input and output, data, and parameter files from their personal _DIMONA_ accounts directly to external websites, to which they have secured access, especially when the hard drive capacity allotted to each model user exceeds the limits of the pilot version. Figure 5: Data exploration screen for soil properties at preset geographic locations around the world.
# CCAIB: Congestion Control Based on Adaptive Integral Backstepping for Wireless Multi-Router Network
## 4 Performance Evaluation
### Network Topology and Parameters  
The network topology is shown in Figure 1. For simplicity, multiple devices and sessions are represented by one source in the figure.  
In the comparison between CCAIB and Hoo, four cases are investigated and the network parameters are shown in Table 2. The desired queue length is 100 packets for all four cases. We set the link capacity to 1000 packets/s and 3000 packets/s to represent low-speed and high-speed transmission scenarios, respectively. In the real wireless networks, for example, mobile network and Wi-Fi, the wireless packet loss ratios are usually lower than 5%. Thus, 0.05 and 0.005 are chosen to be the packet loss ratios for high quality link and low quality link, respectively [57]. It is worth noting that the wireless packet loss ratios are unknown for the system and to be estimated in CCAIB. In the evaluation of impact of network parameters, one network parameter varies and the other parameters are set according to Table 3. The controller parameters of CCAIB are set as Table 4. Parameters of Hoo are set the same as [53]. The three key performance indicators (KPIs) used to compare CCAIB and Hoo algorithm are the convergence performance of the queue, the window size and packet loss ratio in the queue.
## 2 Materials and Methods
### Data
#### 2.2.3 Ground-Borne Radar Precipitation Product (RAR)  
The Radar-AWS Rainrates (RAR) is a data product developed by the Korea Meteorological Administration (KMA) with the objective of utilizing precipitation forecasts. It provides quantitative estimates of real-time rainfall by applying the Z-R relationship, which is derived from radar reflectivity (Z) and rain gauge measurements (R). The RAR synthesis used in this study incorporates the algorithm of RAR version 2.0, which was enhanced and implemented from October 2015 onwards [47]. This version includes the Local Gauge Correction (LGC) technique, which is an enhancement of the Inverse Distance Weighting (IDW) method. The LGC approach applies weights that account for discrepancies between measurements from Automated Weather Stations (AWSs) and values derived from radar, allowing for the correction of ground radar-based gridded precipitation [48]. Detailed methodology regarding this technique can be found in [PERSON] et al. [49], and Figure 3 illustrates the comprehensive process of the RAR algorithm, incorporating the Local Gauge Correction method [47; 50]. Additionally, Table 2 presents specific details regarding the RAR data, including file names, resolution, data structure, and storage format. As indicated in the table, RAR data is primarily provided in a binary format, which requires conversion into a human-readable format (e.g., ASCII) for applications such as flood analysis and hydrological modeling. The RAR data are produced at 10 min intervals and represent a grid-based precipitation synthesis with a spatial resolution of 1 km. For reference, the \"Lambert Conformal Conic Projection\" is adopted as a Projected Coordinate System (PCS) for RAR, which minimizes distortion when representing areas, and it finds extensive usage in hydrological modeling and watershed studies.
# Formal Analysis of Ratchet Protocols Based on Logic of Events
## 2 Logic of Events Theory
### Fundamental Concepts  
In the Logic of Events Theory (LoET), protocol interactions are modeled as _threads_--ordered sequences of actions performed by protocol participants--and _matching relations_ that capture both message consistency and causal dependencies. The definitions that follow formalize these concepts using set theory and first-order logic, with relevant terms introduced below.  
* **Act List**: _thr_ is an action list (Act List), representing a sequence of protocol actions (such as _Send_, _Receive_, _Encrypt_, etc.) executed by a single participant. The condition \(\forall i:\text{thr}[i]<_{\text{lock}}\text{thr}[i+1]\) indicates that the events in this list are arranged in order, i.e., local causal ordering holds: each preceding event occurs before the next one and all events belong to the same participant's thread.  
* **Thread (_Thread_)**: An ordered sequence of events executed by a single principal, satisfying intra-participant causal ordering \(e_{i}<_{loc}e_{i+1}\).
* **Weak Match (\(s\sim r\))**: The send and receive events contain identical message content (\(\text{Send}(s)=\text{Rcv}(r)\)), without requiring causal order.
* **Strong Match (\(s\mapsto r\))**: A weak match that additionally satisfies causal precedence (\(s<r\)), ensuring the chronological execution of the events.  
Thereads and Matching Sessions  
Equation (1) formally defines a thread (_thr_) as an _Act List_--an ordered list of protocol actions such as _Send_, _Receive_, and _Encrypt_--executed sequentially by a single protocol participant, preserving local causal ordering.  
\[\textit{Thread}\equiv_{\textit{df}}\{\textit{thr}:\textit{Act List}\mid\forall i: \textit{thr}[i]<_{loc}\textit{thr}[i+1]\} \tag{1}\]  
If there exist two messages \(s\) and \(r\), such that the first message \(s\) is a message on a send event and the second message \(r\) is a message on a receive event with consistent information, then \(s\) and \(r\) are considered to be _weakly_ matched and denoted as \(s\sim r\). If \(s\) precedes \(r\) and there is a direct causal relationship, a strong match \(s\mapsto r\) is formed.The concepts of weak match and strong match are formally captured in Equation (2).  
\[\left\{\begin{array}{c}s\sim r\equiv_{\textit{df}}s\in E(\textit{Send}) \wedge r\in E(\textit{Rcv})\wedge\textit{Send}(s)=\textit{Rcv}(r)\\ s\mapsto r\equiv_{\textit{df}}s\sim r\wedge s<r\end{array}\right\} \tag{2}\]  
If two threads \(\textit{thr}_{1}\) and \(\textit{thr}_{2}\) each contain at least \(n\) messages, and the first \(n\) messages of the two threads match each other such that each message pair \(\langle m_{1},m_{2}\rangle\) satisfies \(m_{1}\mapsto m_{2}\lor m_{2}\mapsto m_{1}\), then threads \(\textit{thr}_{1}\) and \(\textit{thr}_{2}\) constitute a matching session of length \(n\). Moreover, if this matching session can be further subdivided into a strong matching session, it is denoted as \(\textit{thr}_{1}\stackrel{{ n}}{{\approx}}\textit{thr}_{2}\).
# Reversible Data Hiding in Encrypted Color Halftone Images with High Capacity
## 3 Proposed Scheme
### Extraction and Recovery
#### 3.3.2 Image Recovery  
If the receiver has both \(K_{e}\) and \(K_{d}\), he can recover the original halftone image after watermark extraction. By decrypting the cover image \(\mathbf{I}\) we get in Section 3.3.1, the original image \(\mathbf{O}\) is generated. Let the pixel value at position \((u,v,t)\) be \(a^{\prime}_{u,v,t}\), where \(\{1\leq u\leq N_{1}\}\), \(\{1\leq v\leq N_{2}\}\), and \(\{1\leq t\leq 3\}\). To decrypt \(\mathbf{I}\), we calculate exclusive-or operations between \(a^{\prime}_{u,v,t}\) and the pseudo-random bits \(k^{\prime}_{u,v,t}\) as  
\[e^{\prime}_{u,v,t}=a^{\prime}_{u,v,t}\oplus k^{\prime}_{u,v,t} \tag{16}\]  
where \(e^{\prime}_{u,v,t}\) is the pixel value of original image \(\mathbf{O}\), \(k^{\prime}_{u,v,t}\) is generated by the same stream cipher scheme as in the image encryption phase using \(K_{e}\). The diagram of watermark extraction and image recovery is demonstrated in Figure 4.