esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Provable Data Possession (PDP) protocol makes it possible for cloud users to check whether the cloud servers possess their original data without downloading all the data. However, most of the existing PDP schemes are based on either public key infrastructure (PKI) or identity-based cryptography, which will suffer from issues of expensive certificate management or key escrow. In this paper, we propose a new construction of certificateless provable group shared data possession (CL-PGSDP) protocol by making use of certificateless cryptography, which will eliminate the above issues. Meanwhile, by taking advantage of zero-knowledge protocol and randomization method, the proposed CL-PGSDP protocol leaks no information of the stored data and the group user's identity to the verifiers during the verifying process, which is of the property of comprehensive privacy preservation. In addition, our protocol also supports efficient user revocation from the group. Security analysis and experimental evaluation indicate that our CL-PGSDP protocol provides strong security with desirable efficiency.  
cloud storage; provable data possession; privacy-preserving; certificateless cryptography +
Footnote †: journal: Future Internet
## Acknowledgment to Reviewers of _Applied Sciences_ in 2020
Rifat Hmelzeyer, Michael Hazelwood, Richard A.
# PLORC: A Pipelined Lossless Reference-Free Compression Architecture for FASTQ Files
###### Abstract  
The rapid growth of genomic sequence datasets in a FASTQ format calls for efficient storage and transmission solutions. Compression-decompression algorithms for streaming applications offer a promising potential to address these challenges. In this paper, we present a novel Pipelined Lossless Reference-free Compression (PLORC) architecture designed specifically for streaming genomic data in a FASTQ format. The proposed PLORC architecture consists of several submodules optimized for the structure of FASTQ files, maintaining the balance between the compression ratio (CR) and throughput rate (TPR). To verify the PLORC architecture in hardware, we implemented the PLORC compressor and decompressor in FPGA (field-programmable gate array). The experimental results across various open-source genomic datasets reveal that our PLORC compressor achieved about a 440 MB/s throughput rate, which was higher than the tested Gzip, LZ4, and Zstd compressors. In addition, the PLORC decompressor achieved a throughput rate matching that of the compressor. Additionally, the PLORC achieved competitive compression ratios with some well-known non-streaming compression algorithms.  
Keywords:lossless compression; genomic data; FASTQ compression; reference-free compression; streaming architecture; field-programmable gate arrays +
Footnote †: journal: Applied sciences
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 4 Preliminary Concepts
### Internet of Things  
In a presentation describing how RFID may be used for supply chain management, [PERSON] [21] introduced the phrase \"Internet of Things\". The phrase \"Internet of Things\" today is used to refer to a network of devices and gadgets--including various kinds of sensors and actuators, mobile phones, and wearable technologies--that communicate with one another over the Internet [22]. These devices establish connections with servers so that information can be retrieved and transmitted between them, successfully providing essential services. This ground-breaking technology can be seen as a paradigm change in patient health-focused low-cost healthcare applications. The Internet of Things connects patients and clinicians in a setting that incorporates sensors, sophisticated algorithms, cloud interfaces, and communication interfaces. Technology advancements in sensor, RFID, and WSN networks support data collection infrastructure as shown in Figure 8. Advanced algorithms are then used to analyze the data [23]. Cloud services can be utilized to upload medical data and lessen the complexity of the scenario due to the resource limitations of IoT devices. Healthcare applications cannot effectively use the delays provided by cloud computing due to low latency, great dependability, and other criteria for healthcare. With real-time analysis and efficient decision-making tools, healthcare applications are run anywhere near users to overcome these limitations. To overcome these restrictions, fog, and edge computing run healthcare apps close to IoT devices with real-time analysis and efficient decision-making tools [24]. Despite all its advancements, the IoT continues to be in its infancy. Numerous issues--such as the heterogeneity of various devices, scalability, security, and privacy--are still being researched.  
Transportation, smart cities, monitoring, healthcare, and other industries are among the applications covered by the IoT concept. Applications for the IoT can be used in the healthcare sector to monitor patients at the hospital or, more precisely, at home for elderly patients with chronic illnesses. This results in an earlier diagnosis, better treatment, lower healthcare costs, and a longer life expectancy [25].  
Figure 8: The IoT architecture consists of sensors and actuators for the perception layer, network layer protocols, and application layer components [25].
## 3 Materials and Methods
### Data Analysis Techniques  
The data were coded and analyzed using IBM SPSS Statistics (version 30.0.0). Descriptive statistics, including frequencies and percentages, were used for data analysis. Mann-Whitney U test [45], which generalizes the Wilcoxon method, was used to determine significant differences between the distributions of independent groups. The test was found to be appropriate as it does not require assumptions about population parameters and can be used when the type of distribution is unknown and when the dependent variable is ordinal. Moreover, if the distribution shape is similar in both groups, the test allows verification of whether there is evidence of a statistically significant difference between the medians of the two groups. The categorical variables used to create the groups were company size and the introduction of new technology in the last 5 years. A \(p\)-value of less than 0.05 with a confidence level of 95% was considered significant.
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 3 Background
### Unmanned Aerial Vehicles  
UAVs are described with different terms, such as drones and Unmanned Aerial Systems (UAS). These terms vary according to the research field, often describing the same thing though [1]. In this work, UAV is defined as an aerial vehicle, with an embedded computer platform, capable of flying with no human pilot on-board.  
UAVs can be remotely controlled and/or autonomous, and they can perform a variety of tasks. Although initially purposed for military tasks, there is an increasing growth in civilian, commercial, and scientific applications (e.g., traffic surveillance, communication relays, disaster management, data and image acquisition, etc.) [16].  
As shown in Table 1, UAVs can be classified according to their weight, altitude and endurance [9]. Figure 1 shows an example of a mini UAV, the Inspire 2, by DJI, as categorized by Table 1[17]. This UAV weighs around 3.3 kg and its flight reaches up to 27 min.  
Although there is a wide variety of UAVs, this work focuses on their communication, considering micro and mini UAVs battery, space, and weight constraints.  
For mini and micro UAVs, the battery represents an important portion of their weight. The DJI Inspire 2, for example, typically works with a pair of 4280 mAh batteries, weighing 515 g each. Although over 30% of this UAV weight is assigned to batteries, it is only able to fly 27 min per charge.  
\begin{table}
\begin{tabular}{c c c c} \hline
**UAV** & **Weight (kg)** & **Altitude (km)** & **Endurance (h)** \\ \hline Micro & 0.1 & 0.25 & 1 \\ Mini & \textless{}30 & 0.15–0.3 & \textless{}2 \\ Short range & 200 & 3 & 2–4 \\ Medium range & 150–500 & 3–5 & 30–70 \\ Long range & - & 5 & 6–13 \\ Endurance & 500–1500 & 5–8 & 12–24 \\ Medium altitude, & 1000–1500 & 5–8 & 24–48 \\ long endurance & & & \\ High altitude & 2500–12,500 & 15–50 & 24–48 \\ long endurance & & & \\ \hline \end{tabular}
\end{table}
Table 1: Unmanned aerial vehicles (UAVs) classification. Adapted from the work in [9].  
Figure 1: DJI Inspire 2, a mini UAV weighing around 3.3 kg and capable of a flight time up to 27 min [17].
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 3 Methods and Materials
### Mathematical Preliminaries
#### 3.2.5 Chirikov Standard Map  
The Chirikov standard map is a mathematical model that illustrates the transition from stability to chaos in dynamical systems. It is widely used in studying chaos theory and nonlinear systems [27].  
\[\theta_{n}+1=(\theta+Pn)mod2\pi\] \[P_{n}+1=(P_{n}+k/(2\Pi)sin(2\Pi\theta_{n}))mod2\Pi\]  
where \(p_{n}=0.1\), \(\theta=5.24\), and \(K=0.98\). This map has the following features:  
Phase space, in which the map conserves area, making it simplistic;  
Lyapunov exponents are used to measure sensitivity to initial conditions, indicating chaos [27].
# Wireless Accelerometer Architecture for Bridge SHM: From Sensor Design to System Deployment
## 1 Introduction
### Wired Monitoring Systems  
Traditional wired systems are highly effective in ensuring reliable data transmission and synchronization. However, their deployment on bridges presents practical challenges:  
* **High Installation Costs and Time**: Permanent installation of wired sensors often requires extensive infrastructure modifications. For example, bridge decks necessitate specialized ducts and fittings to protect communication cables, leading to significantly increased installation times and costs [12].
* **Maintenance Complexity**: Over time, signal cables degrade, necessitating periodic maintenance. Additionally, diagnosing issues, such as identifying malfunctioning transducers within a network, can be complex and resource-intensive [13].
* **Logistical Challenges on Long Bridges**: On large-scale structures, the process of laying cables can dominate the installation timeline, further escalating project costs and delays.
## 10 Conclusions  
In rolling element bearings, especially high-speed bearings, higher temperatures can be expected than in the case of lower rotational speeds. This is often the case in the spindles of HSC machines. High temperatures affect not only the thermal deformation of the spindle, but also the resistance to movement in the bearing. The latter issue is not included in the available literature. Therefore, the article presents the results of simulation tests of temperatures and the resulting thermal deformations of bearing elements. They allow for the following final conclusions:  
1. The method of modeling heat sources in a bearing has no significant effect on the temperature distribution. Therefore, it is suggested to place heat sources only on the raceways of both rings, proportionally to the resistance to motion on these raceways. It is technically the simplest way of modeling heat sources, which does not introduce any significant error to the final results.
2. The contact model for balls and raceways has a significant influence on the temperature distribution in the bearing. By default, in FEM systems such as Ansys, the Bonded model dominates, and the default contact area includes raceway surfaces and ball surfaces. This method of modeling contacts gives the best compliance with the results of experimental research. Modeling the contact area, e.g., in the form of a contact ellipse, leads to high temperatures on the bearing elements.
3. Convection of heat to the environment has a significant influence on the temperature distribution in the bearing. The constructor can shape the surfaces of the bearing seat that emit heat to the environment to minimize temperature differences between the outer and inner rings. Then, smaller differences in thermal deformation between these elements can be expected, and consequently, smaller contact forces.
4. FEM simulation tests show that the influence of temperature on the contact loads of the bearing may be greater than the influence of mechanical loads, and because of this, the resistance to motion may be greater.
5. Research on the influence of the temperature on the thermal deformation of the bearing elements has to be continued, as the thermal deformations are much larger than the mechanical ones, and thus may have a greater impact on the resistance to motion of the bearing.  
This research was funded by Silesian University of Technology, Gliwice, Poland, project number BK-243/RMT-7/2021; 10/070/BK_21/0052.  
Not applicable.  
The data supporting record are not archived to publicly dataset.  
The author declares not conflict of interest.
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 3 Architecture Proposal
### DTN Module
#### 3.1.2 Routing Function  
The primary goal of the Routing Function is to provide forwarding (or storage) decisions when requested by the DTN Controller, abstracting the routing algorithm specifics from it. It should implement algorithms specifically designed for routing in DTN networks. Among these, the most promising candidate is the CGR algorithm [27], which has been extensively studied and is available in multiple implementations. The Routing Function must interface seamlessly with the DTN Controller by receiving route requests and responding with the computed forwarding decision and the timing of the next available contact. This functionality is critical for ensuring efficient store-and-forward operations in DTN environments, as it allows the node to make informed decisions about whether to store a packet temporarily or forward it immediately. By leveraging advanced routing strategies, such as congestion-aware routing, this function plays a key role in optimizing network performance in DTN scenarios.
# Electroencephalogram Emotion Recognition via AUC Maximization
## 5 Numerical Experiments
### Linear Baselines
#### 5.1.2 Support Vector Machine  
Given a dataset \(\{\mathbf{x}_{i},y_{i}\}_{i=1}^{N}\), where \(\mathbf{x}_{i}\in\mathbb{R}^{p}\) are the input predictors and \(y_{i}\in\{-1,1\}\) are the corresponding class labels, the SVM seeks to construct a hyperplane that almost  
\begin{table}
\begin{tabular}{l r r} \hline \hline
**Set** & **Features Included** & **Number of Features** \\ \hline Set 1 & PSD + DE & 112 \\ Set 2 & Set 1 + Segment Range + Segment Moments + Segment Diff & 1008 \\ Set 3 & Set 2 + Channel Range + Channel Moments & 1232 \\ Set 4 & Set 3 + PLV + Corr & 2792 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Feature sets for numerical experiments. Definitions can be found in Table 2.  
separates the classes by using a soft margin. The optimal hyperplane is determined by solving the following optimization problem:  
\[\underset{\mathbf{\beta},\mathbf{\varepsilon},M}{\text{maximize}} M\] s.t. \[\sum_{j=1}^{p}\beta_{j}=1,\] \[y_{i}\cdot\mathbf{\beta}^{\top}\mathbf{x}_{i}\geq M(1-\gamma_{i}), \quad\forall i=1,\ldots,N\] \[\gamma_{i}\geq 0,\quad\forall i=1,\ldots,N\quad\text{and}\quad\sum_{i =1}^{N}\gamma_{i}\leq C,\]  
where \(M\) represents the margin of the hyperplane, \(\gamma_{i}\) are the slack variables that allow for misclassification, \(C\) is the regularization parameter controlling the trade-off between maximizing the margin and minimizing the classification error, and \(\mathbf{\beta}\in\mathbb{R}^{p}\) is the vector of coefficients defining the hyperplane. This optimization problem can be reformulated as a constrained convex optimization problem and solved using methods such as Sequential Minimal Optimization (SMO) or Interior-Point methods. The SVM can also be extended to nonlinear classifiers by incorporating kernel methods, which implicitly map the input features into a higher-dimensional space where a linear separation is feasible.
# Shiga Toxin Therapeutics: Beyond Neutralization
## 3 Current State of Shiga Toxin-Targeted Therapeutic Development
It remains to be seen if immunologic or Gb3-analog toxin neutralizers can be effective in the clinical environment where STEC-infected patients have likely been exposed to Shiga toxins for several days prior to presentation, or if preventing further toxin internalization will improve patient outcomes. The blockade or alteration of the retrograde trafficking system is an alternate strategy for the inhibition of Shiga toxin activity. [PERSON] et al. characterized two small molecule re-localizers of the vesicular transport SNARE protein Syntaxin 5 that were capable of rescuing protein synthesis in cells incubated with ricin and Shiga toxins for 4 h [63]. They named the compounds Retro-1 and 2, and found that the drugs did not impact the localization of several other cargo transport proteins in vitro. Retro-2 was found to be partially protective in a murine model of STEC infection when given orally prior to bacterial toxin induction with mitomycin C and completely protective against nasal ricin challenge if given prophylactically [63; 64]. The metallic cofactor manganese has also been found to protect mice from STX1 toxicity by stimulating the degradation of the endosome-to-Golgi transport protein Gpp130, but doses were given prophylactically every 24 h for 5 days prior to a single bolus injection of toxin [65]. Manganese failed to protect mice from STX2 toxicity due to STX2 transport through proteins other than Gpp130, limiting its usefulness clinically [66]. To date, there have been no published accounts of reduced clinical toxicity when retrograde trafficking inhibitors are given after toxin exposure in vivo, a condition that more closely replicates the likely clinical scenario in diagnosed STEC patients. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
# A Smart Sensing System of Water Quality and Intake Monitoring for Livestock and Wild Animals
###### Abstract  
This paper presents a water intake monitoring system for animal agriculture that tracks individual animal watering behavior, water quality, and water consumption. The system is deployed in an outdoor environment to reach remote areas. The proposed system integrates motion detectors, cameras, water level sensors, flow meters, Radio-Frequency identification (RFID) systems, and water temperature sensors. The data collection and control are performed using Arduino microcontrollers with custom-designed circuit boards. The data associated with each drinking event are water consumption, water temperature, drinking duration, animal identification, and pictures. The data and pictures are automatically stored on Secure Digital (SD) cards. The prototypes are deployed in a remote grazing site located in Tucumcari, New Mexico, USA. The system can be used to perform water consumption and watering behavior studies of both domestic animals and wild animals. The current system automatically records the drinking behavior of 29 cows in a two-week duration in the remote ranch.  
RFID; water intake; animal agriculture; motion detector; water quality; watering behavior; rarch; livestock; wildlife; data acquisition 20212885103390/s21082885  
16 March 2021  
Footnote 1: [[https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors)]([https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors))  
1 Kipsch School of Electrical and Computer Engineering, New Mexico State University, 1125 Frenger Mall, Las Cruces, NM 88003, USA; [EMAIL_ADDRESS] (A.B.); [EMAIL_ADDRESS] (R.E.)  
2 College of Agricultural, Consumer, and Environmental Sciences, New Mexico State University, 940 College Dr, Las Cruces, NM 88003, USA; [EMAIL_ADDRESS] (T.P.); [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (M.W.)  
* Correspondence: [EMAIL_ADDRESS]
# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
## 4 Results
### Analysis of Single Evaluation Indicator
#### 4.2.2 Results of Selectivity Indicator  
Figure 5 shows the calculation results of the selectivity values of all the sensors to the experimental gases. Sensors S7 (4.14), S4 (1.63), and S9 (0.69) were the three sensors with the highest selectivity values, while sensors S1 (0.02), S2 (0.03), and S3 (0.06) performed the worst in the selectivity evaluation. From the sensitivity analysis results, the three sensors S1, S2, and S3 had poor abilities in distinguishing target gases, so they were eliminated from the initial sensor array.
## 3 Analytical Methods
### In-Situ Trace Element Analysis by LA-ICP-MS  
In-situ trace element analysis was carried out at the _In-situ_ Laboratory of the Ore Deposit and Exploration Center (ODEC), School of Resource and Environmental Engineering, Hefei University of Technology. The LA-ICP-MS analytical equipment, consisting of an AnalyteHE laser ablation system and an Agilent 7900 ICP-MS, was used for the major and trace element analysis. During the laser ablation process, helium was utilized as the carrier gas while argon was added as a compensating gas to adjust the sensitivity of the instrument. The helium and argon were mixed in the ICP through a T-connector. Prior to sample analysis, the ICP-MS system was optimized to achieve maximum sensitivity and minimum oxide yield, with the index of \({}^{232}\)Th\({}^{16}\)O/\({}^{232}\)Th being less than 0.2%. Each sample point was analyzed for 60 s, with 20 s allocated for background signal acquisition and 40 s for sample signal acquisition. The stripping frequency was 7 Hz, and the stripping energy was 40 mJ. Sample points were selected with care, avoiding any rough or fractured areas, and each point was carefully focused prior to selection. A set of standard samples was inserted every 10 samples to ensure the accuracy and consistency of the analysis. Upon completion of signal acquisition, the data was processed using the ICPMSDataCal 10.2 software [35]. The calibration approach utilized the technique of multiple external standards without incorporating an internal standard for quantitative calculation. The calibration standard references used in this study were NIST610, NIST612 and BCR-2G [36]. The suggested elemental content values for standard samples are sourced from the GeoReM database, which can be accessed via the following URL ([[http://georem.mpch-mainz.gwdg.de](http://georem.mpch-mainz.gwdg.de)]([http://georem.mpch-mainz.gwdg.de](http://georem.mpch-mainz.gwdg.de)), accessed on 31 March 2021). The detailed data processing methods and instrument operation steps are consistent with those described in [PERSON] et al. (2017) and [PERSON] et al. (2018) [18; 37].