esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Data Sparsity Solutions  
Obviously, it is important to exploit the users' and locations' embeddings well, which is not only limited by the models' representation ability, but is also influenced by the sparsity of user-location interactions [26]. A user usually visits few locations in a city during their daily routine. However, if they have a short travel time, the location visiting will become more random, meaning it is hard to make recommendations. The sparse interactions and random behaviors inspire us to overcome the data sparsity and acquire more information, whether explicit or implicit, to improve recommending results. To overcome the extreme sparsity of user-location interactions, the canonical method is to introduce external sources as auxiliary information to explore latent user interests or location features. In [18], users' information are supplied, such as age, gender, etc., which believes that users with similar attributes may have similar location preferences. Social relationships such as Facebook friendship [19; 43] are also considered as an important role in recommendations owing to the fact that people in an intimate relationship often share interests and travel together. However, these information about users are too private to collect. In order to extract the location characteristics, POIs are often considered. [PERSON] et al. [20] use POIs categories and geographic proximity to improving location recommendations under data sparsity, because POIs often reveal the similarity between two places.  
In fact, many studies [44; 45] have shown that app usage records in telecommunications can reflect users' personal interests, because app usage patterns of different users are quite different. Furthermore, there is also a strong correlation between the users' app usage and location attributes [46; 47]. For example, [PERSON] et al. [47] find that the usage frequency of music apps in sport venues is higher, and educational apps are used more frequently in the school. [PERSON] et al. [23] introduce app data into location recommendations and prove that the cold-start problem can be mitigated a lot, but they do not discuss whether it is useful in solving data sparsity. Therefore, we consider utilizing app usage data because it can not only indicate user interests but also reflect functional attributes of locations, which provides an opportunity to learn user preferences and location features with less information collected. To our best knowledge, this is the first time that app usage records have been utilized to solve data sparsity in personalized location recommendations, and our experimental results show that this method is quite effective and feasible.
## 3 Methods
### Feature Selectors
#### 3.4.1 Minimum Redundancy Maximum Relevance Feature Selector  
The methodology behind mRMR is based on two main rationales. The first rationale is that highly correlated features should not be used, i.e., the redundancy between features should be taken into account, thus keeping features that are maximally dissimilar to each other [36]. A possible solution to determine redundancy among the variables in \(S\) is  
\[W_{I}(S)=\frac{1}{|S|^{2}}\sum_{X_{i},X_{j}\in S}MI\big{(}X_{i},X_{j}\big{)} \tag{15}\]  
where \(MI\big{(}X_{i},X_{j}\big{)}\) gives the measure of mutual information between the variables \(X_{i}\) and \(X_{j}\).  
The second rationale of mRMR is that minimum redundancy should be complemented by a maximum relevance criterion of the features in regard to the class variable. A measure of global relevance of the variables in \(S\) in terms of \(C\) is  
\[V_{I}(S)=\frac{1}{|S|}\sum_{X_{i}\in S}MI(C,X_{i}) \tag{16}\]  
A straightforward but efficient formula of combining redundancy and relevance to obtain a good subset of features is  
\[S^{*}=argmax_{S\subseteq U}(V_{I}(S)-W_{I}(S)) \tag{17}\]  
Because it is not practical to find the best subset using an exhaustive search, the selected subset is obtained in an incremental way, starting with the feature having maximum value of \(MI(C;X_{i})\big{(}S_{0}=\big{\{}X_{i_{0}}\big{\}}\big{)}\) and gradually adding, to the current subset \(S_{m-1}\), the feature that maximizes  
\[\begin{split}\underset{X_{j}\in U}{max}\Bigg{(}MI\big{(}C,X_{j} \big{)}-\frac{1}{m-1}\sum_{X_{i}\in S_{m-1}}MI\big{(}X_{j},X_{i}\big{)}\Bigg{)} \end{split} \tag{18}\]
## 3 Results and Discussion
### Extraction of Cd and Zn from Gazania after Phytoremediation  
#### 3.4.1 Effect of the Extractant Solvent on the Extraction of Cd and Zn from _Gazania_  
For the effective reuse of absorbent materials, an extraction experiment of Cd and Zn from _Gazania_ after phytoremediation was conducted. The amounts of Cd and Zn extracted from the exhausted plants by using acetic acid (CH3 COOH), nitric acid (HNO3), sulfuric acid (H2 SO4), phosphoric acid (H3 PO4), aqueous ammonia (NH3), and sodium hydroxide solution (NaOH) are shown in Figure 12. In Figure 12, the extraction by HNO3 shows the best extraction efficiency for Cd, while H2 SO4 was most effective for Zn, although HNO3 was also good. Furthermore, the extraction efficiency of the acid solvent was larger than that of the base solvent, regardless of the concentrations, which may be related to the differences in the solubility of the metal salts. In addition, it was found that a low concentration of 0.18 mol/L was more effective in terms of extraction efficiency than 3.6 mol/L solvent for both Cd and Zn. Thus, we used a concentration of 0.18 mol/L; for example, 0.18 mol/L nitric acid showed more effective extraction efficiency than 3.6 mol/L of solvent for both Cd and Zn. Therefore, we regarded 0.18 mol/L nitric acid as the most appropriate extraction solvent of metal from _Gazania_.
# Nitrate-Polluted Waterbodies Remediation: Global Insights into Treatments for Compliance
## 4 Nitrate Removal Technologies in Waterbodies
### Transformation-Based Technologies  
These technologies, applying different procedures depending on the specific process, are to convert nitrates normally into N\({}_{2}\) gas, to be released into the atmosphere without side effects. Technically, it is a reduction process that can be performed by microorganisms or by chemical, electrochemical, or photochemical reactions.
## 1 Introduction
[28] compared high-pressure sodium with LED street-lighting systems based on drivers' impressions. A subjective approach requested the drivers to instantly fill in the questionnaire survey about the luminance after driving through both types of roads in Belgrade. [PERSON] et al. [11] evaluated drivers' preference for street-lighting color using a questionnaire survey, in which the drivers selected the more appropriate installation amongst 3000 K and 4000 K LED lighting systems based on lighting design (e.g., detection of light- and dark-colored obstacles) and overall visibility. A study in Stockholm (Sweden) evaluated the impact of electronic billboards on drivers' visual distraction (after looking at the billboard for over 2 s, using eye-tracking measures) and performance in terms of speed, lateral position, and minimum time headway [10]. The review of the literature highlights a lack of studies on performance appraisal of USLS, which (i) aggregate driver opinion on design, visibility, and satisfaction using a fuzzy-based (suitable for subjective evaluations) multicriteria approach and (ii) highlight problems in the Gulf region, including the Kingdom of Saudi Arabia (KSA), with one of the highest crash rates in the world. The Ministry of the Qassim Region (MoQ) of the KSA found lighting conditions as one of the main factors affecting crash frequency [29]. Consequently, the MoQ determined to improve the region's traffic lighting system by changing the existing road lights with LED technology. The present research targeted to appraise the performance (in terms of design, visualization, and driver satisfaction) of existing USLS based on drivers' opinions to offer more feasible solutions to current road safety issues faced in Buraydah, the capital of Qassim. The performance evaluation criteria were established in the form of a questionnaire survey to assess compliance with KSA's road safety codes for lighting design and placement of electronic advertisement boards. The main objectives of this research are to: (i) develop a questionnaire survey to obtain drivers' responses on the performance of existing USLS, (ii) identify important criteria for performance evaluation, and (iii) develop a fuzzy-based evaluation method for information aggregation and decision-making. The methodology developed in the present work is a step forward to giving appropriate attention to public opinion in decision-making for infrastructure enhancement. Moreover, the present work will help the Electricity Division of MoQ in its ongoing endeavor to determine whether light-emitting diode (LED) streetlamps were a viable solution for reducing energy consumption and maintenance costs and improving the area of Buraydah's visual appeal. The drivers' opinions and responses helped the team to evaluate whether excitant light by street-lighting is a desirable possibility to reduce crashes due to advertisement boards and lighting appearance. Furthermore, the research findings will support the Ministry of Transport and MoQ in defining baseline standards for the installation of new or upgradation of street-lighting systems to ultimately have a fully LED livelihood.
## 5 Energy Detection Process
### Detection Threshold Estimation
In the case of an ED system based on DT adaptation (\(\lambda_{i}^{\prime DT}\)), the detection probability (from relation (11)) and the false alarm probability (from relation (12)) are given by:  
\[P_{d_{i}}^{DT}=\min_{\lambda_{i}^{\prime DT}\epsilon[\frac{\lambda_{i}^{ \prime}}{\rho^{\prime}},\ \rho^{\prime}\lambda_{i}]}Q\left(\frac{\lambda_{i}^{\prime DT}-\left(P+\sigma_{ n_{i}}^{2}\right)}{\sqrt{\frac{2}{N^{DT}}\left(P+\sigma_{n_{i}}^{2}\right)}} \right)=Q\left(\frac{\frac{\lambda_{i}}{\rho^{\prime}}-\left(P+\sigma_{n_{i}}^ {2}\right)}{\sqrt{\frac{2}{N^{DT}}\left(P+\sigma_{n_{i}}^{2}\right)}}\right) \tag{16}\]  
\[P_{fa_{i}}^{DT}=\max_{\lambda_{i}^{\prime DT}\epsilon[\frac{\lambda_{i}^{ \prime}}{\rho^{\prime}},\ \rho^{\prime}\lambda_{i}]}Q\left(\frac{\lambda_{i}^{\prime DT}-\sigma_{n_{i}}^ {2}}{\sqrt{\frac{2}{N^{DT}}\sigma_{n_{i}}^{2}}}\right)=Q\left(\frac{\rho^{ \prime}\lambda_{i}-\sigma_{n_{i}}^{2}}{\sqrt{\frac{2}{N^{DT}}\sigma_{n_{i}}^{2 }}}\right) \tag{17}\]  
The interdependence between the detection probability and the false alarm probability can be expressed as:  
\[P_{d_{i}}^{DT}=Q\left(\frac{Q^{-1}\Big{(}P_{fa_{i}}^{DT}\Big{)}-\Big{[}\rho^{ \prime 2}SNR+\Big{(}\rho^{\prime 2}-1\Big{)}\Big{]}\sqrt{\frac{ND^{T}}{2}}}{ {\rho^{\prime}}^{2}(1+SNR)}\right) \tag{18}\]  
If the detection threshold is fixed, the factor \(\rho\)\(t=1\), and relations (16), (17) and (18) converge to (11), (12) and (13), respectively. The case when factor \(\rho\)\(t>1\) implies that the ED process is based on the adaptation of the DTs. A higher value of the DT factor results in a greater range of DTs for possible selection.
# Optimal Attitude Determination for the CR200 Underwater Walking Robot
## 2 Problem Definition and Method of Simulation
### CR200 Underwater Walking Robot  
The CR200 is equipped with many exploring devices, as shown in Figure 1.  
The CR200 is equipped with high-resolution scanning sonar and acoustic cameras to capture images of the seafloor. Additionally, the CR200 is fitted with an Ultra-Short Baseline (USBL) system to track underwater positioning, an Acoustic Doppler Current Profiler (ADCP) oriented upward to measure water currents, and multiple optical cameras to monitor the robot's status and location. Further sensors or devices can be added to the robot based on specific exploratory requirements. The CR200 has dimensions of 2.4 m in length and 1.3 m in height. It weighs approximately 700 kg in air and about 200 kg in seawater. The robot's body is streamlined with glass fiber-reinforced plastic (GFRP) for durability and hydrodynamic efficiency. Detailed specifications of the CR200 are provided in Table 1.
# Machine-Learning-Based Side-Channel Evaluation of Elliptic-Curve Cryptographic FPGA Processor
## 5 Experimental Setup
### Step 1--Data Capture  
To conduct our experiments, we must capture the leakage traces, as a power signals database for ECC does not exist. For the hardware setup, we captured the power signals for ECC FPGA (Kintex-7) implementation, operating at 24 MHz. For this research, specialized side-channel analysis board, named as SAKURA-X, is used [35]. On SAKURA-X, for calculating the power being consumed, a resistor is connected in series and a voltage is measured across that. The user does not need to tweak the board, as the connector is available to get the power signal directly. Traces are captured using a Tektronix oscilloscope having a \(5\ GS/sec\) sampling frequency and a 1 GHz bandwidth. We have acquired N = 100 traces for randomly selected ECC points from set M, and for each point \(S_{p}t=10\) traces were captured. Thus, in total \(S_{N}\) = 14,000 traces are collected where each trace has 10 k sampling points.  
For the software side of the data-collection process, we have developed bespoke codes using C# and the MATLAB library to form an automated standalone application which requires little or no intervention from the user. The hardware setup and the application GUI is shown in Figure 7. A few  
Figure 6: Classification process without pre-processing (**left**) and with pre-processing (**right**).  
modules of the C# application provided by SAKURA are used to achieve the purpose [35]. The new bespoke C# application consists of three main units: control unit, data unit, and configuration unit as shown in Figure 8, and an explanation of each is given below.  
* Configuration Unit--The configuration unit uses MATLAB library support for C# and configures the oscilloscope through the C# application. This eliminates setting up the oscilloscope on every start up; the application automatically restores it to the settings required for the data capturing. The configuration unit communicates with the oscilloscope only.
* Control Unit--The control unit has the role of sending the ECC points to the FPGA after taking them from the data unit. When the FPGA receives an ECC point, it starts the process of encryption and sends a trigger signal to the oscilloscope. As soon as the trigger signal is received at the oscilloscope, it will start collecting the leaked information from the FPGA and will transmit it to the control unit. The control unit then stores the information by communicating with the data unit. The control unit communicates with both the oscilloscope and the FPGA.
* Data Unit--The data unit handles the data. It is responsible for storing and retrieving the data in files. The data unit communicates with the control unit only.
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
###### Abstract  
Ultrashort pulses are severely distorted even by low dispersive media. While the mathematical analysis of dispersion is well known, the technical literature focuses on pulses, Gaussian and Airy pulses, which keep their shape. However, the cases where the shape of the pulse is unaffected by dispersion is the exception rather than the norm. It is the objective of this paper to present a variety of pulse profiles, which have analytical expressions but can simulate real-physical pulses with great accuracy. In particular, the dynamics of smooth rectangular pulses, physical Nyquist-Sinc pulses, and slowly rising but sharply decaying ones (and vice versa) are presented. Besides the usage of this paper as a handbook of analytical expressions for pulse propagations in a dispersive medium, there are several new findings. The main findings are the analytical expressions for the propagation of chirped rectangular pulses, which converge to extremely short pulses; an analytical approximation for the propagation of super-Gaussian pulses; the propagation of the Nyquist-Sinc Pulse with smooth spectral boundaries; and an analytical expression for a physical realization of an attenuation compensating Airy pulse.  
ultrashort pulses; dispersion; pulse distortion; optical communications +
Footnote †: journal: Applied Physics Letters
## 2 Preliminaries and Network Model
### Network Model  
We consider a single-sink WSN consisting of \(\boldsymbol{N}\) battery-powered sensors. The sensors are deployed in a square area with a boundary length of 1. We assume all nodes have an identical transmission radius of \(\boldsymbol{r_{t}}\), and that any two nodes can communicate with each other if their Euclidian distance \(\boldsymbol{d}\) satisfies \(\boldsymbol{d}\leq\boldsymbol{r_{t}}\). To guarantee the network connectivity, \(\boldsymbol{r_{t}}\) should also satisfy the following condition [24]:  
\[\boldsymbol{r_{t}^{2}}>\boldsymbol{S}\cdot\text{In}(\boldsymbol{N})/( \boldsymbol{\pi}\boldsymbol{N}), \tag{5}\]  
where \(\boldsymbol{S}\) denotes the deployment area and \(\boldsymbol{S}=\boldsymbol{1}\times\boldsymbol{1}\). Let \(\boldsymbol{X_{N\times 1}}=\left(\boldsymbol{x_{1}},\cdots,\boldsymbol{x_{N}} \right)^{T}\) denotes the \(\boldsymbol{N}\) node readings. Since the readings are spatiotemporally correlative with each other, \(\boldsymbol{X}\) can be compressed on an orthogonal basis \(\boldsymbol{\
abla}=\left(\boldsymbol{\phi}_{i,j}\right)_{\boldsymbol{N} \times\boldsymbol{N}}\). The fast Fourier transform (FFT) orthonormal basis is adopted as the sparse representation basis in this paper. Let \(\boldsymbol{\Phi}=\left(\boldsymbol{\varphi}_{i,j}\right)_{\boldsymbol{M} \times\boldsymbol{N}}\) denote the measurement matrix. The measurement vector \(\boldsymbol{Y}\in\mathbb{R}^{M\times 1}\) can be computed with Equation (2). Furthermore, we introduce the expression of \(\boldsymbol{\Phi}\) in Section 3. Thus, the CS-DDSG network model coincides with the CS model.  
In addition, we define the normalized mean absolute error (NMAE) metric to evaluate the accuracy of reconstruction accuracy:  
\[\text{NMAE}=\frac{\|\boldsymbol{\hat{X}}-\boldsymbol{X}\|_{2}}{\|\boldsymbol {X}\|_{2}}=\frac{\sqrt{\sum_{n=1}^{N}\left(\hat{x}_{n}-\boldsymbol{x}_{n} \right)^{2}}}{\sqrt{\sum_{n=1}^{N}{x_{n}}^{2}}}, \tag{6}\]Equation (6) shows that the smaller the NMAE is, the better performance the algorithm can achieve.
## 7 Conclusions  
The results indicate that, in general, the higher the level of privacy, the greater the average distance measured with the Topsoe Divergence. However, the comparison between using HMC alone and combining it with LDPTrace gives several important insights:  
* HMC alone is effective at lowering Reidentified (%) rates, with average distances from 0.05 to 0.10, as shown in Table 12. This indicates that HMC can offer privacy protection while keeping data reasonably useful.
* HMC combined with LDPTrace provides stronger privacy, as shown by the lower Reidentified (%) rates between 0% and 0.37% in Table 13 and larger average distances up to 0.20. This demonstrates that the additional application of HMC further strengthens anonymization.
* While LDPTrace alone increases Topsoe Divergence nearly tenfold, reflecting its strong anonymization effect, adding HMC does not significantly increase this distance further. Instead, it provides a substantial additional drop in reidentification rates, implying that HMC complements LDPTrace effectively by improving anonymization without further distorting the data.  
On the whole, we manage to show that the synthesized trajectories created with the LDPTrace algorithm can be further anonymized and made inaccessible to attacks by adding HMC anonymization.  
To enhance the balance between privacy and data utility, future research should focus on  
* Application-specific studies: Analyzing how different data modification levels impact specific applications or use cases, i.e., environmental pollution or traffic simulations.
* Including other utility metrics: Creating or utilizing other metrics beyond average distance, such as checking for time-based consistency and the accuracy of Points of Interest (POIs), can offer a clearer view of data usability.
* Experiments with other attacks: To strengthen the robustness of privacy protection methods and ensure comprehensive evaluation, future work should focus on testing additional attack scenarios and analyzing their impact on reidentification rates and data utility. This could include exploring attacks such as outlier detection, POI (Point of Interest)-based attacks, and PIT (Point-in-Time)-based attacks; see [2].  
Achieving strong privacy protection while maintaining data utility is crucial for practical applications. The fact that HMC enhances LDPTrace's anonymization without introducing additional distortion makes this combined approach especially promising.  
Conceptualization and writing--review and editing, G.S.G.; software and writing--original draft preparation, C.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the German Federal Ministry of Research, Technology and Space (BMFTR), grant number 16 KISA046K.  
Not applicable.  
The data presented in this study are openly available in _CRAWDAD Eff/Mobility_; IEEE Dataport, [[https://doi.org/10.15783/C7J010](https://doi.org/10.15783/C7J010)]([https://doi.org/10.15783/C7J010](https://doi.org/10.15783/C7J010)).  
The authors declare no conflict of interest.
## 5 Conclusions and Future Work  
In this study, we propose a novel deep-learning-based action recognition method that can automate and accurately recognize manual operations in industrial workflows. Our main contributions are summarized as follows. First, we propose a new framework that uses human detection and pose estimation to obtain the skeleton image. Based on the skeleton image, automatic recognition of the manual operation can be realized. Second, we explore the use of STN to correct skeleton images to avoid the impact of complex real-world production environments on the recognition results. Third, we introduce a GCN to simultaneously extract spatial and temporal information in skeleton images. Fourth, an attention mechanism is added to our method to make the method more applicable to the actual production process and improve the accuracy of the recognition. The results of our method on real production video outperform the existing research, and our study is a kind of next generation industrial and manufacturing technology, which may be significant for the industrial revolution in the coming decades.  
It should be noted that only the actions in 2D RGB video are recognized in this study, but the human operation is done in a complex 3D space. The action recognition method based on the 3D skeleton image is more in line with the actual production requirements, and can be recognized more accurately. How to acquire 3D skeleton images and how to recognize actions based on 3D skeleton images is the main work of the future.  
[PERSON] designed the research method, contributed to the experimental section and wrote the draft. [PERSON] and [PERSON] gave a detailed revision and provided important guidance. 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 71772010) and Technical Foundation Research Project of Ministry of Industry, Information Technology of PRC (Grant number JSZL2016601A004).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Pose Estimation** & **Action Recognition** & \multirow{2}{*}{**Speed (FPS)**} \\  & **Accuracy (\%)** & **Accuracy (\%)** & \\ \hline OpenPose [45] & 89.8 & 91.00 & 9.9 \\ AlphaPose [46] & 95.1 & 91.67 & 20.2 \\ YOLOv3+SHM & 93.6 & 91.17 & 23.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparing the effects of different pose estimation methods.