esa-sceva/satcom-chunk-collection huggingface dataset:
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 4 Preliminaries
### WiFi Fingerprint-Based Localization  
WiFi fingerprint localization is typically divided into two phases: reference point dataset construction and user localization query. Below, we provide a detailed explanation of the computational process and its formalization.  
In the reference point dataset construction phase, the DO first determines \(n\) fixed access points and measures the received signal strengths at \(m\) RPs. The physical location of each RP is represented by the coordinate \(X_{i}=[x_{i},y_{i}]\) (or \([x_{i},y_{i},z_{i}]\) in a three-dimensional  
\begin{table}
\begin{tabular}{c c} \hline
**Notations** & **Descriptions** \\ \hline \(\lambda\) & Security parameter, =128 \\ \hline \(P_{i}\) & Computing servers, \(i\in\{0,1,2\}\) \\ \hline \(n\) & Number of access points \\ \hline \(m\) & Number of reference points \\ \hline \(m^{\prime}\) & Subset size for two-layer Sillcom\({}_{tnd}\) \\ \hline \(k\) & Number of nearest neighbors as result \\ \hline \(l_{RSS}\) & Bit-length of \(f_{ij}\) \\ \hline \(l\) & Bit-length of \(f_{ij}\) in secure computation \\ \hline \(l_{r}\) & Bit-length for ranks \\ \hline \(\Pi\) & Secure protocol \\ \hline \(k^{<}\) & FSS key for comparison \\ \hline \end{tabular}
\end{table}
Table 2: Terms and notations.  
Figure 1: System model.  
scenario), and its WiFi fingerprint consists of an RSS vector \(V_{i}\) received from \(n\) APs. Finally, the fingerprint database \(\mathcal{D}\) is constructed as follows:  
\[\mathcal{D}=\{(X_{i},F_{i})|i=1,2,...,m\}, \tag{1}\]  
where \(F_{i}=[f_{i1},f_{i2},...,f_{in}]\) records the RSS values received from \(n\) APs at location \(X_{i}\).  
In the user localization query phase, the user device measures the WiFi signal strength from \(n\) APs, forming the fingerprint \(F_{q}=[f_{q1},f_{q2},...,f_{qn}]\) and transmits it to the localization server. The server computes the distances between \(F_{q}\) and all stored fingerprints \(F_{i}\) in the database and employs top-k to find the \(k\) closest reference points \(\{X_{i_{1}},...,X_{i_{k}}\}\). Finally, the user location is determined by computing the average coordinates of these reference points.  
Various distance metrics have been studied and applied in WiFi-based indoor localization, including Manhattan distance, Sorensen distance, and others [13]. To optimize performance, this paper adopts Euclidean distance as the distance metric for comparison.
## 5 Conclusions  
The mangrove soils of Qi'ao Island were heavily contaminated by trace elements transported from the Pearl River. These soil-borne trace elements pose a high risk to the mangrove ecosystem. The whole intertidal zone received relatively constant amounts of trace elements from the Pearl River catchment in the past two decades. Zn showed common sources with many other trace elements while some trace elements were not closely related to each other. The findings obtained from this study have implications for understanding the terrestrial inputs of trace elements into part of the Pearl River estuary. Although a firm conclusion cannot be drawn from the data obtained at this study site alone, the special location of Qi'ao Island does allow some level of confidence to suggest that Pearl River is amongst the heavily contaminated rivers in the world. This will provide useful information for  
Figure 3: Comparison of ERI among the 17 sampling locations in the investigated area.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Study Area** & **Cr** & **Ni** & **Cu** & **Zn** & **As** & **Cd** & **Hg** & **Pb** \\ \hline Sanya, China [35] & 61.5 & - & 21.0 & 126 & 14.3 & 0.09 & - & 42.3 \\ \hline Luoyang Bridge, China [35] & 18.6 & - & 34.0 & 106 & 5.31 & 0.06 & - & 167 \\ \hline Dongzhai Harbor, China [36] & 75.8 & 31.0 & 19.6 & 44.8 & 8.67 & 0.62 & - & 20.7 \\ \hline Yingluo Harbor, China [37] & 56.7 & - & 7.76 & 31.9 & - & 0.11 & 0.04 & 14.2 \\ \hline Shenzhen Bay, China [7] & 96.2 & 44.6 & 88.8 & 358 & 172 & 0.94 & 0.14 & 72 \\ \hline Zhangjiang River Estuary, China [38] & - & - & 21.04 & - & - & 0.33 & 0.03 & 63.2 \\ \hline Sunderban mangrove, India [39] & 41.8 & 47.4 & 60.06 & 88.3 & - & 0.48 & 0.24 & 52.9 \\ \hline Guanabara Bay, Brazil [40] & 42.4 & - & 98.6 & 483 & 1.32 & 1.32 & - & 160 \\ \hline Kottul, India [41] & 0.26 & 0.38 & 69.3 & 384 & - & 0.03 & - & 6.91 \\ \hline Hong Kong, China [11] & 40.0 & 3.00 & 240 & 40.0 & - & 3.00 & - & 80 \\ \hline Pichavaram, India [42] & 530 & 253 & 150 & 108 & - & 34.5 & - & 133 \\ \hline Brisbane River, Australia [43] & 7.6–116 & 2.4–57.6 & 3.1–34.1 & 40–144 & 0–13.0 & 0–2.0 & - & 7.7–84.7 \\ \hline This Study & 99.5 & 29.2 & 40.8 & 116 & 30.0 & 1.16 & 0.41 & 44.0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Levels of heavy metals in mangrove wetland soils.  
decision-makers to better control the discharge of trace elements from the catchment area and manage the aquatic ecosystems in the Pearl River Estuary.  
Conceptualization, S.X. and A.N.; methodology, A.N., J.M.; investigation, A.N., J.M., and Y.G.; resources, S.X.; data curation, A.N. and Y.G.; writing--original draft preparation, A.N.; writing--review and editing, C.L.; visualization, Y.G. and A.N.; supervision, S.X. and C.L.; project administration, S.X.; funding acquisition, S.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 41877411.  
The authors declare no conflicts of interest.
# Performance Evaluation of a Quadcopter by an Optimized Proportional-Integral-Derivative Controller
## 2 The Statement of Methodologies  
It is believed that the critical research points addressed in UAVs include many issues. For practical reasons, a common speed control structure is applied in the PID controller. The mathematical model and speed control of the BLDC (brushless DC) motor have been proposed and validated using fuzzy logic and PID controller [24; 25]. Specifically, in order to achieve better performance the BLDC motor requires a control drive that facilitates the control of its speed and torque. Therefore, a suitable control driver system is required to control the speed and torque to obtain better performance for the BLDC motors. Under the considered condition, an optimized PID controller is probably the best candidate. It is a complex task to design the structure of a BLDC motor as it depends on many issues such as project selection, modeling, simulation, etc. In terms of the speed frame of the BLDC motor, a variety of modern control solutions have been proposed [26]. The main features of a conventional PID controller algorithm are that it is easily adjustable, has steady operation and has a simple design, which makes it widely used for controlling systems. In most cases, a different finding is seen in terms of practical experience where the volatility of well-structured prototypes, and different units of nonlinearity and low variability have been at work. In general, the parameters are not easily tuned for a PID controller; hence, obtaining the optimal position under the examined circumstances is challenging [27]. This study proposes a PID controller by making some changes to it, which can increase the control speed of the BLDC motor.  
For example, there are different types of motors, different requirements for power supply, and even different controllers that are included in the areas of the hardware [25]. Moreover, there are the events of the control method, the different application scenarios, operating modes, etc. These are included in the software aspects. Thereafter, in the following subsection, both the hardware and software key issues are going to be addressed and explored in advance.
# The Self-Calibration Method for the Vertex Distance of the Elliptical Paraboloid Array
## 4 Conclusions  
The present study was designed to research a self-calibration method with respect to the vertex distance of the elliptical paraboloid array, which solves the benchmark problem in long-range displacement measurements. The self-calibration method, which was based on the geometric relationships between elliptical paraboloids, was verified by experiments using a Renishaw XL80, Hexagon CMM, and optical slope sensor designed by ourselves. The results of these experiments show that the calibration results are consistent with the design values, and the repeatability was within 3 \(\upmu\)m in the X direction and within 1 \(\upmu\)m in the Y direction. In addition, the comparison experiment proved that the displacement measurement system error was reduced from 100 \(\upmu\)m to 3 \(\upmu\)m after calibration. The results show that the self-calibration method can meet the calibration requirements of the elliptical paraboloid array, which lays a solid foundation for subsequent experiments related to displacement measurement. In order to improve the accuracy of the calibration results, it would be necessary to select high-precision motion guides and ensure that their motion direction is parallel to the elliptical paraboloid array. Further studies need to be carried out in order to validate the application value of the elliptical paraboloid array in the displacement-related measurement field, such as by focusing on the positioning error, straightness error, and perpendicularity error.  
[PERSON], [PERSON], and [PERSON] proposed the method and modified the paper; [PERSON] designed the experiments and wrote the paper; [PERSON] and [PERSON] developed the system software and processed the data; [PERSON] and [PERSON] designed the mechanical and optical structure.  
This research was financially supported by the National Natural Science Foundation of China (NSFC) (No: 5175378); the Science Foundation Ireland (SFI) (No.15/RF/B3208); the National Key R&D Program of China (No.2017 YFF0108102); and the Natural Science Foundation of Shanxi Province, China (Grant No. 201801D121180).  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Effect of the Cluster Diameter on the Accuracy of Peak Analysis Method  
Since the purpose of peak analysis is to predict the average radius of clusters, it is important to study the accuracy of estimation for different sized clusters. As membrane protein clusters are often smaller than 100 nm in diameter [28], we varied the set radius of clusters from 20 nm to 100 nm while holding the cluster separation constant at 1000 nm, for each simulation. [PERSON]'s H function is calculated in Figure S4. From the estimated radius of the cluster and the standard deviation of 100 point patterns in Figure 3a, we found that the predicted cluster radius by peak analysis (dashed black, \(n=100\)) corresponds well to the actual radius (dashed gray), but the standard deviations (SD) are greatly increased as the radius varies from 20 nm to 100 nm. The major reason for this interesting [PERSON] may increase the cluster diameter equivalent to reduction of the 'buffer zone, bringing more errors in peak analysis. To evaluate further, the relative error defined as the SD over the actual radius is calculated in Figure 3b; we found that the relative errors were always close to 30%[PERSON] This result is consistent with the above result, up to 1000 nm cluster separation, and we suggest that the peak analysis method may predict the cluster radius within a factor of 1.3 of actual radius. Moreover, these findings suggest that although the relative errors do not depend on the cluster radius, for a large cluster, a single peak analysis may provide an estimated radius with unacceptable errors. To have an accurate prediction of the cluster radius, it is essential to estimate the cluster radius by averaging at least ten from the peak analysis.  
Figure 2: (**a**) Peak analysis results for three modeled single molecule localization microscopy (SMLM) images containing 10 randomly distributed clusters of set radius \(\text{R}_{\text{cluster}}^{\text{set}}\ =\ 50\text{nm}\) with different average distances of cluster separations (100 nm, 400 nm and 800 nm). (**b**) The cluster radius predicted by peak analysis as a function of the cluster separation, the \(\pm\)SD represents the standard derivation of peak analysis for 100 independent point distributions for each cluster separation.
## 2 Materials and Methods  
A typical charging process with CC stage and CV stage is illustrated in Figure 1. The variations of output current \(I_{o}\), output voltage \(V_{o}\) and output power \(P_{o}\) during the procedure can be viewed. The resistance \(R_{L}=V_{o}/I_{o}\) is the equivalent load resistance, which is used to reflect the instantaneous charging power. In the CC stage, the output current is constant, and the output voltage increases continuously, which indicates an increasing equivalent load resistance. The output power increases in this stage too. In the CV stage, the output current decreases continuously and the output voltage is constant, which means that the equivalent load resistance keeps increasing in the stage. The output power is decreasing in this stage. At the boundary point C, the converter has maximum output power, voltage and current.  
To achieve the required CC and CV charging operation, a DBRC is adopted in this work. Shown in Figure 2, the DBRC has two active full bridges isolated by a HF transformer and a series resonant tank including \(L_{\text{s}}\) and \(C_{\text{s}}\). Each full bridge is implemented by four power switches (\(S_{1}\)\(\sim\)\(S_{4}\) on the primary side and \(S_{a}\)\(\sim\)\(S_{d}\) on the secondary side) with their anti-parallel body diodes. \(V_{i}\) is the input dc voltage, and \(V_{o}\) is the output dc voltage. \(C_{1}\) and \(C_{o}\) are filter capacitors on two sides respectively. \(n_{t}:1\) is the turns ratio of the HF transformer. Half-bridge, push-pull or three-phase bridge topology can also be used to implement the two bridge-type inverters for different applications. Thanks to the structure, there are multiple control options available for power manipulation, including varying switching frequency, phase-shift and pulse-width modulation, etc. In the following parts, two modulation strategies with related design procedure would be proposed to achieve both CC and CV charing stages with the considerations of low conduction and switching losses.
## 5 Numerical Results
### Results for a General Wideband Signal Detection
As mentioned in [2], the performance ranking of AND, OR and majority-voting can vary with different system parameters or scenarios. Figure 1: ROCs for sample fusion, decision fusion and eigenvalue fusion using GLRT for sensing a general wideband signal. Figure 2: ROCs for sample fusion, decision fusion and eigenvalue fusion using MMED for sensing a general wideband signal. When \(N=300\) samples per carrier in the sample fusion scenario, the performance is better than the performance of the other fusion schemes, except in the case of ED (for the same number of samples per carrier, computing the signal energy using sample values is equivalent to computing the energy using  
Figure 4: ROCs for sample fusion, decision fusion and eigenvalue fusion using ED for sensing a general wideband signal. Figure 3: ROCs for sample fusion, decision fusion and eigenvalue fusion using MED for sensing a general wideband signal. the average of the eigenvalues). However, when \(N=300\), the number of samples sent to the FC is six times the number of samples when \(N=50\). Moreover, the sample matrices used for covariance matrices computations have order \(6\times 300\) for \(N=300\) and \(6\times 50\) for \(N=50\). Although the resulting covariance matrices have the same order (\(6\times 6\)), it was expected that more samples led to better performances. This scenario is clearly favoring the sample fusion, resulting in unfair comparisons. Still referring to the results in Figures 1-4, surprisingly the ED tests overcome the MED tests for eigenvalue combining and for the decision fusion schemes with majority-voting, AND and OR rules, inverting the behavior obtained with the use of sample fusion. In other words, the test statistics proposed in Equations (17) and (21) unveiled more statistical power than the test Equations (16) and (20), respectively, indicating that the latter ones have margin for further improvements in their expressions.
# Numerical Analysis of Bicycle Helmet under Blunt Behavior
## 3 Results
### Influence of Velocity in Cyclist Safety  
#### 3.3.1 Energy Absorbed by the Helmet during Impacts  
The helmet absorbs partial energy that the head otherwise would, reducing the probability of head injuries. To assess the percentage of energy absorbed, and the influence of velocity, the EN 1078 flat anvil test with velocity variation is implemented with eight simulations shown in Figure 9.  
Figure 9 shows that with increasing velocity, the helmet tends to absorb a higher percentage of energy, the upmost at 8.46 m/s, absorbing 50.15%. These results suggest that the helmet is a key element of safety during accidents. Furthermore, it shows that even at low impact velocities (less than 10 km/h), the helmet is effective, absorbing up to 30% of the energy. For a standardized 5.42 m/s test, the helmet absorbs 44.7%.
# HGTMFS: A Hypergraph Transformer Framework for Multimodal Summarization
## 5 Experiments
### Implementation Details  
#### 5.2.1 Benchmark Models for Comparison  
The efficacy of our proposed model is assessed through a comparative analysis with a set of baseline models, which are grouped into two separate categories.  
* **MSMO**[44]: Pioneering the field, this model is the initial framework for multimodal output summarization, employing an attention mechanism to craft textual summaries.
* **MOF**[45]: Building upon MSMO, this enhanced version introduces an additional loss function focused on image accuracy, thereby elevating the quality of the generated textual summaries.
* **MAtt**[4]: This model leverages an attention-based approach, integrating visual features into the textual domain through the utilization of an image content filtering mechanism.
* **MSE**[5]: Distinguished by its selective gate network, this model augments the encoder's capability to emphasize pivotal news content, thereby enhancing summarization performance.  
_Traditional Text-only Summarization_:
* **PGN**[46]: This model employs a sequence-to-sequence architecture, enhanced by the integration of an attention mechanism and a pointer network, to facilitate effective summarization.
* **S2S**[47]: Representing a foundational approach, this model utilizes a standard sequence-to-sequence framework, incorporating an RNN-based encoder-decoder structure complemented by global attention mechanisms.
* **TextRank**[48]: A method rooted in graph theory, wherein sentences are depicted as nodes within a graph, with edges assigned weights based on the degree of similarity between sentences.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **CNN** & **Daily Mail** \\ \hline Num. Documents & 1970 & 203 \\ Avg. Words in Article & 951.07 & 970.21 \\ Avg. Sentences in Article & 19.68 & 31.87 \\ Avg. Words in Summary & 59.63 & 29.73 \\ Avg. Objects in Image & 7.16 & 7.32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the datasets.
###### Abstract  
The marine fishery is a high-risk industry, and when a fishing vessel is in distress at sea, it is essential to protect the lives of the fishermen by implementing a fast and effective rescue for the vessel in distress. In this process, quickly retrieving the closest neighboring vessel in distress based on an effective rescue time is critical to the vessel's rescue. In order to improve the time efficiency of retrieving the nearest available rescue vessels in the current sea area, this paper proposes a method based on the Google S2 grid coding for ships in distress at sea. The method is divided into three parts (1) encoding the ship's position based on the Google S2 algorithm, (2) retrieving the set of available ships in the current sea area based on the effective rescue distance and its corresponding coding level, and (3) sorting the set of available ships in the current sea area according to the proximity to the ship in distress by using the method of \"Alternating Grid Sorting Based on Neighborhoods and Different Coding Levels\". The effective rescue distance is set by the rescue time, the type of rescue vessel, and the speed. This paper sets the simulation experiment area as the East China Sea area. Different magnitudes of ship position datasets (\(1\times 10^{2}\), \(1\times 10^{3}\), \(1\times 10^{4}\), \(1\times 10^{5}\), \(1\times 10^{6}\)) are generated by simulating the scenarios where a ship's location is reported or collected by AIS or VMS. The temporal retrieval efficiencies of querying based on the two methods, the Euclidean distance and the Google S2 grid encoding, are compared and analyzed. The experimental results show that the total time consumed by the query method based on the Google S2 grid encoding and the query method based on Euclidean distance is reduced by 37.06%, 29.83%, 72.75%, 94.43%, and 94.53%, respectively, in the process of generating the set of rescable ships retrieved, based on the effective rescue distance. Therefore, the time retrieval efficiency of the maritime vessel search and rescue method based on the Google S2 coding is high, which can effectively improve the query efficiency of rescue vessels in the neighborhood of distressed vessels.  
ship rescue; effective rescue time; gridding; ship position code; neighborhood search; nearest neighbor search; Google S2
###### Abstract  
This paper presents an innovative approach to improving visible light communication (VLC) systems in total shadowing conditions by integrating intelligent reflecting surfaces (IRSs), angle diversity receivers (ADRs), and recurrent neural networks (RNNs). Two ADR configurations (pyramidal and hemispherical) are evaluated, along with signal combination mechanisms: maximum ratio combining (MRC) and select best combining (SBC). The RNN is employed to dynamically optimize the IRS placement, maximizing the signal-to-noise ratio (SNR) at the ADRs and enhancing overall system performance in non-line-of-sight (NLoS) scenarios. This study investigates the spatial distribution of SNRs in VLC systems using RNN-optimized IRSs, comparing the performance of different ADR configurations and signal combination methods. The results demonstrate significant improvements in received power and the SNR compared to non-optimized setups, showcasing the effectiveness of RNN-based optimization for robust signal reception. This article highlights the potential of machine learning in enhancing VLC technology, offering a practical solution for real-world indoor applications. The findings emphasize the importance of adaptive IRS placement and spur further exploration of advanced algorithms and ADR designs to address challenges in complex indoor environments.  
a _Article_  
1