esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient and HRA Secure Universal Conditional Proxy Re-Encryption for Cloud-Based Data Sharing
## 2 Preliminaries
### Hidden Secret Decryption
By performing the algorithm \(\mathrm{Setup}\big{(}1^{\lambda}\big{)}\) to generate the public parameter \(\mathrm{Par}=(\mathbb{G},p,g)\), where a generator \(g\) of a multiplicative cyclic group \(\mathbb{G}\) of prime order \(p\). To set the secret key \(sk\) and the public key \(pk\), we chose random \(x,y\leftarrow\mathbb{Z}_{p}^{*}\) and compute \(f=g^{x}\) and \(h=g^{y}\), and then set \(\mathrm{sk}=(x,y)\) and \(\text{pk}=(f,h)\). To encrypt a message \(m\in\mathcal{M}\), we select \(r,s\leftarrow\mathbb{Z}_{p}^{*}\) and set the ciphertext to be \(\text{ct}=(u,v,w)=(f^{\prime},h^{s},g^{r+s}\cdot m)\). Decryption is executed by computing \(m=w/u^{x^{-1}}v^{y^{-1}}\). **The BBS Encryption with Hidden Secret Decryption**. Setup, KeyGen, and Enc algorithms are the same as the above description of the BBS encryption. Then, the user generates the hidden secret \(\text{hsk}=u^{x^{-1}}v^{y^{-1}}\) by the algorithm \(\text{SecretHidden}(\text{sk},\text{ct})\) and sends it to the third party. Finally, decryption of the ciphertext \(\text{ct}=\langle u,v,w\rangle\) is performed by computing \(m=w/u^{x^{-1}}v^{y^{-1}}\). **The RSA Encryption**. The RSA encryption [33] is a multiplicative homomorphic encryption scheme. In Section 5, we will instantiate our generic construction UCPRE between RSA encryption schemes. Here we show the implementation of this scheme. By executing the algorithm \(\text{KeyGen}\big{(}1^{\lambda}\big{)}\) to generate the public key \(pk\) and the secret key \(sk\), we firstly choose two big prime \(p,q\) and set \(n=p\cdot q\), and compute \(\varphi(n)=(p-1)\cdot(q-1)\). Next, we choose a random \(e\), and generate \(d\), where \(1<e<\varphi(n)\) and \(de\,\text{mod}\,\varphi(n)=1\). Finally, we set the public key \(pk=(e,n)\) and the secret key \(sk=(d,n)\). Encryption is executed by setting the ciphertext \(\text{ct}=m^{e}\,\text{mod}\,n\). Decryption of a ciphertext \(\text{ct}\) is performed by computing \(m=(ct)^{d}\,\text{mod}\,n\). **The RSA PKE with Hidden Secret Decryption**. Key generation, and encryption algorithms are the same as the above description of the RSA encryption. In our system, the user generates the hidden secret \(\text{hsk}=(ct)^{e^{-1}d}=m^{d}\,\text{mod}\,n\) by performing the algorithm \(\text{SecretHidden}(\text{pk},\text{sk},\text{ct})\) and sends it to the semitrusted third party secretly. Finally, decryption of a ciphertext \(\text{ct}\) is executed by computing \(m=(\text{hsk})^{e}=m^{de}\,\text{mod}\,n\).
# A Distributed RF Threat Sensing Architecture
## Abbreviations  
The following abbreviations are used in this manuscript:  
5G 5 th generation  
AoA  Angle of arrival  
AI  Artificial Intelligence  
BER  Bit error rate  
C/No  Carrier-to-Noise density power ratio  
CRN  Cognitive radio networks  
DL  Deep learning  
DoA  Direction of arrival  
DoS  Denial of Service  
ECC  Error-correcting code  
GNSS  Global Navigation Satellite System  
GPS  Global Positioning System  
GUI  Graphical User Interface  
IoT  Internet of Things  
IoE  Internet of Everything  
JI  Jamming index  
LNA  Low-noise amplifier  
LOS  Line-of-sight  
MDPI  Multidisciplinary Digital Publishing Institute  
MaMIMO  Massive MIMO  
MIMO  Multiple-input multiple-output  
ML  Machine learning  
mmWave  Millimeter Wave  
MQTTs  Secure Message Queuing Telemetry Transport  
NLOS  Non-line-of-sight  
NOMA  Non-orthogonal multiple access  
OFDM  Orthogonal frequency division multiplexing  
RSS  Received signal strength  
RSSI  Received signal strength indicator  
SDR  Software-defined radio  
SDN  Software-defined networking  
SNR  Signal-to-noise ratio  
SoI  Signal of interest  
SOC  Security operations center  
TAL (RSS) Threshold Alert Level  
UAV  Unmanned aerial vehicle  
WLAN  Wireless Local Area Network  
WSN  Wireless Sensors Network
# Investigation of Nanocutting Characteristics of Off-Axis 4H-SiC Substrate by Molecular Dynamics
## 3 Results and Discussion
### Influence of Cutting Depth  
#### 3.2.1 Structure  
Figure 7(a)-c present the structure transformation of the off-axis 4H-SiC workpiece after cutting with a cutting depth of 0.5, 1.0, and 1.5 nm respectively. Atoms on the surface are changed into amorphous phase. With a larger cutting depth, more atoms are changed and material remove rate is higher. It can be seen that there is no defect in the workpiece in Figure 7(a). The number of defects in Figure 7(c) was also more than that in Figure 7(b). The deepest defect in Figure 7(c) almost reached the thermostat layer. The quality of the substrate was worse when cutting depth was larger.
# Enabling Trustworthy Multicast Wireless Services through D2D Communications in 5G Networks
###### Abstract  
Device-to-device (D2D) communication is considered as one of the key enabling technologies for fifth-generation (5G) networks as it allows data offloading generated by the huge number of connected devices. In this respect, group-oriented services are among the most interesting usage scenarios. Indeed, D2D can improve the performance of the conventional multicast scheme (CMS) in cellular networks, which is known to suffer from low spectral efficiency. Security is a further key field of investigation for 5G systems, as any threat to privacy and security may lead to both deteriorated user experience and inefficient network resources' utilization. Security issues are even more in focus for D2D connections between devices that are in mutual proximity. To improve the CMS performance and also sustain security requirements of the 5G network, this work proposes a secure D2D data transmission algorithm. Making use of mechanisms such as encryption and signature, this algorithm aims to protect the exchanged data and the privacy of the devices involved in the communication. A simulation campaign conducted using MATLAB shows the ability of the proposed solution to take advantage of the establishment of secure D2D communications and efficiently utilize network resources.  
Keywords:device-to-device (D2D) communication; 5G networks; security; multicasting +
Footnote †: journal: Future Internet
# Pattern and Analysis of Corrugation-Sand Retaining Seals for
## 3 Structural Design
### Key Structural Parameters and Dimensions  
The research object is an 8 1/2 inch (215.9 mm) drill bit. The cross section of the corrugated main seal is arc-shaped, which is based on the improvement of the O-ring seal. Therefore, refer to the traditional O-ring compression rate selection rule, as shown in Table 1.  
The new type of seal ring is a rotary seal with a compression rate of 5-10%. The initial diameter of the main seal arc is 5.7 mm, and the initial thickness of the seal base is 4 mm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Seal Form** & **Sealing Medium** & **Stretching Rate/\%** & **Compression Rate/\%** \\ \hline \multirow{2}{*}{Static seal} & Hydraulic oil & 1.03–1.04 & 15–25 \\  & Air & \textless{}1.01 & 15–25 \\ \multirow{2}{*}{Reciprocating seal} & Hydraulic oil & 1.02 & 12–17 \\ \multirow{2}{*}{Rotating sealed} & Air & \textless{}1.01 & 12–17 \\ \multirow{2}{*}{Rotating sealed} & Hydraulic oil & 1 & 5–10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selection range of compression rate and elongation rate of O-ring.  
Figure 3: Overall structure design. (**a**) Option 1, (**b**) Option 2, (**c**) Option 3; 1—cone, 2—steel ball, 3—tooth palm, 4—new type of sealing ring, 5—sand retaining flap.  
The key structural parameters that affect the sealing performance of the special-shaped sealing ring are shown in Figure 4.  
In order to meet the compression rate and elongation rate of the seal ring, the approximate value ranges of each structural size are set as shown in Table 2. The inner diameter of the cone groove directly determines the compression rate of the seal ring, and other structural parameters are determined on the basis of satisfying the basic structural characteristics of the sealing ring.
## 2 Materials and Methods
#### 2.1.1 Use of the Universal Soil Loss Equation to Evaluate Soil Degradation  
Techniques for the direct or indirect measurement of water erosion, such as Geographic Information Systems (GIS), erosion nails, erosion plots, sediment traps, etc., and the universal soil loss equation have been widely applied worldwide. Their practical use shows that they require a lot of effort and time and that not all the techniques provide information on the spatial distribution of erosion. The universal soil loss equation (USLE) and its family of later models, the revised equation (RUSLE) [33] and the modified equation (MUSLE) [34; 35], use default values to determine rainfall erosivity and soil erosion susceptibility factors. This leads to estimation errors that make it necessary to adjust the equation according to the actual state of the soil under study [36]. However, using these equations, such as RUSLE, is now commonplace. Among many other works that apply these equations can be mentioned the research by [PERSON] in 2021 [37; 38], who used the RUSLE equation to understand the dynamics of soil loss and sediment production from uncalibrated basins and thus design flood and land resource management strategies in regions where rivers cause extensive land degradation and frequent flooding. The works of [PERSON] in 2021 [39] use the RUSLE equation by integrating remote sensing images to explore temporal changes in cultivated land, soil erosion, and the loss of its organic carbon. Recent works [40] use the RUSLE with GIS to examine the dynamics of soil loss and the potential for sediment production and identify critical erosion points. Also, [PERSON], in 2022 [41], implemented the soil erosion prediction model with RUSLE using free-access geospatial data and computing processes to model the rate of soil loss concerning species richness, habitat types, and their conservation status in protected areas. Most recent works by [PERSON] in 2023 [42] apply the RUSLE model and GIS to quantify and analyze the spatial redistribution of water erosion in different land cover levels in Ecuador's mid-upper Basin of the River Mira. Also, in Ecuador, [PERSON] in 2023 [43], based on the USLE method and using GIS, modeled the erosion rates in the River Muchacho. The latest works by [PERSON] and [PERSON] in 2023 [44] apply the Soil and Water Assessment (SWAT) model to identify critical areas of soil erosion and sediment production in order to select effective watershed intervention techniques for environmental protection.
## 3 Results
### Load-Bearing Capacity  
The bending tests are illustrated in the graphs, which show the loading force and deflection of the beam at mid-span. Figure 6 illustrates the force-deflection relationship for unreinforced beams. The results for reinforced beams are presented in Figure 7.  
Figure 6 illustrates a nearly linear correlation between load and deflection for unreinforced beams from the beginning to the end of the test. Some nonlinearity, however, is visible for the A2 and A3 beams in the final phase of the test. This was caused by the plasticization of the compressive zone. Generally, fractured wood resulted in a significant drop in loading force--greater than 60% of the maximum value. That drop was treated as the failure moment of the beam.  
Figure 7 shows the smaller decreases occurring when the timber breaks in the tension zone and the larger decreases occurring at failure. The B-series beams demonstrate a more distinct transition into the plastic working range.  
Tables 2 and 3 present the values for the failure force, bending moments, and deflection of each beam.  
A comparison of the average deflection and maximum loading force values are shown in the graphs below (Figure 8).  
Figure 5: Views of unreinforced (**A**) and reinforced (**B**) testing elements.  
Figure 6: Force–deflection relationship for unreinforced beams (A-series).
## 3 Results and Discussion
### Photoelectrochemical Testing
#### 3.4.2 Electrochemical Impedance Studies  
Electrochemical impedance analyses (EIS) of the as-prepared samples were conducted to qualitatively examine the charge transfer resistance (_Rct_) through Nyquist plotting, as shown in Figure 7a. The pre-annealed sample had high resistance compared to the annealed samples at 450 \({}^{\circ}\)C and 550 \({}^{\circ}\)C. The _Rct_ was evaluated based on the size of the semicircle, wherein the smallest size of the semicircle represented the lowest _Rct_. The effect of electrolyte on the _Rct_ was further conducted to the annealed 450 \({}^{\circ}\)C sample, where the smallest semicircle was produced when EIS testing was conducted in H\({}_{2}\)SO\({}_{4}\) solution. Correspondingly, the extent of _Rct_ evaluated herein explained the results obtained in the measurement of photocurrent generation, as shown in Figure 6. In this case, the lowest _Rct_ improved the efficiency of charge transfer from the semiconductor to the electrolyte and vice versa, consequently yielding the highest photocurrent.  
Figure 6: Photocurrent density curve for samples annealed at (**a**) Pre-annealed, (**b**) 450 \({}^{\circ}\)C, (**c**) 550 \({}^{\circ}\)C using different electrolytes, and (**d**) The chronopotentiometric stability of annealed 450 \({}^{\circ}\)Cand 550 \({}^{\circ}\)C samples in H\({}_{2}\)SO\({}_{4}\) electrolyte.  
Figure 7: The derivation of charge transfer resistance (_Rct_) from Nyquist plot through EIS analysis for (**a**) 450 \({}^{\circ}\)C in different electrolytes, (**b**) the evaluation of different samples prepared at 450 \({}^{\circ}\)C and 550 \({}^{\circ}\)C using H\({}_{2}\)SO\({}_{4}\) at pH 1, and (**c.d**) Mott–Schottky plots of the thin films annealed at 450 \({}^{\circ}\)C and 550 \({}^{\circ}\)C in 0.5 M H\({}_{2}\)SO\({}_{4}\)
# Mouth Sounds: A Review of Acoustic Applications and Methodologies
## 6 Conclusions  
The current work aimed to identify trends in mouth sound applications. First, the physiology and acoustic mechanisms of mouth sounds were explained. Secondly, the methodology to review research was described. Further, the most common trends and limitations were discussed. The findings of the review indicated that vowels, speech, breathing sounds, and crying were the most researched sounds across studies. The most analyzed features were ZCR, PEBF (STE, volume or loudness), and ABF (shimmer and amplitude descriptor) in the time domain; on the other hand, TBF (jitter, HNR, NHR, f0, and formants), SBF (spectral centroid, spectral roll-off, and spectral spread), and CF (MFCC, CPP, and CPPS) were the most employed features in the frequency domain. Regarding acoustic feature analysis, t-test, variations of ANOVA, and Pearson's correlation tests were the most common statistical tests used. SVM and GMM were the most-used ML methods. NNs were employed according to data availability. The main applications of mouth sound research were physical monitoring for disease detection and mental condition monitoring to assess  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mouth Sounds**} & \multicolumn{2}{c}{**Acoustic Features**} & \multicolumn{1}{c}{\multirow{2}{*}{**Applications**}} \\ \cline{2-3} \cline{5-5}  & **Time** & **Frequency** & \\ \hline Vowels & ZCR & MFCC & Clinical diagnosis \\ Speech & Volume or loudness & CPP & Respiratory complications \\ Breathing sounds & STE & CPPS & Neurological disorders and conditions \\ Crying & Shimmer & Jitter & Emotional wellbeing and disorders \\  & Amplitude descriptor & HNR & Emotion recognition \\  & & NHR & Mental health \\  & & f0 & Sleep disorders \\  & & Formants & Speech and communication/expression \\  & & Spectral centroid & \\  & & Spectral roll-off & \\  & & Spectral spread & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of important aspects regarding mouth sounds, analyzed acoustics features, and applications.  
emotions. Nonetheless, other applications, such as communication, have shown favorable results. Finally, the lack of recording condition specifications (recording environment, signal acquisition parameters, and audio devices), sample size, and database selection indicate the need for standard procedures for mouth sound acquisition and analysis. Future research is encouraged to improve acoustic pattern interpretation of mouth sounds.
# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
## 2 Related Works
### Classifier Construction  
Classifier construction involves using extracted features from data to perform fault diagnosis. In the past research literature, several effective classification algorithms have been proposed, including KNN [16], SVM [17], and ANN [18]. These traditional algorithms have been widely used and studied for fault diagnosis tasks. With the advancement of deep learning technology in recent years, new types of neural network-based fault diagnosis classifiers have emerged:  
1. RNN-based sequence data fault diagnosis classifiers: These classifiers are specifically designed to handle sequential data, such as time-series data from sensors in manufacturing processes. RNN and its variants, like LSTM and GRU, are utilized to capture temporal dependencies in data, which are crucial for diagnosing faults that evolve over time.
2. CNN-based image and signal data fault diagnosis classifiers: CNNs are particularly effective for image and signal data processing. They excel in extracting spatial and temporal features from raw data, making them suitable for tasks where fault patterns are visually or structurally discernible in data.  
These new classifier architectures leverage deep learning techniques to handle high-dimensional data effectively and extract more meaningful features. By doing so, they enhance the accuracy and reliability of fault diagnosis compared to traditional methods. The ability of deep learning classifiers to automatically learn representations from raw data contributes significantly to improving diagnostic capabilities across various industrial applications.
## 2 Original Star-Based Configuration
### Worst-Case Battery Life Estimation  
The purpose of this section is to analyze the real energy required by the nodes. Although simulation results are excellent, this step allows us to demonstrate their validity in real situations. This analysis is based on a worst-case scenario to predict the life of the leaf nodes. Tested energy requirements showed life values that greatly exceeded the obtained results using these assumptions. In order to dimension the battery requirements, energy is measured in Ah (amperes \(\times\) hour); this is the unit capacity for batteries. The original star configuration is the following:  
* Three transmissions per day;
* One second per transmission, 10 dBm transmission power;
* No reception capability enabled.  
The energy required for the node is reflected in Table 2. This table includes maximum energy for each part and time in each consumption mode.  
Given these results, we can estimate the life of a given battery. For example, Table 3 shows battery life for the star configuration and different battery models.  
\begin{table}
\begin{tabular}{l|c|c|c} \hline \multirow{2}{*}{**Description**} & **Current** & **Working time** & **Annual requirements** \\  & **(\(\mu\)A)** & **per day (s)** & **(\(\mu\)Ah)** \\ \hline Microcontroller sleep + RTC & 1.0 & 86,313.60 & 8.75 \\ Microcontroller active & 3,000.0 & 86.40 & 26.28 \\ Sensirion SHT1x active & 900.0 & 5.00 & 0.45 \\ Sensirion SHT1x unpowered & 0.0 & & \\ Sensirion SHT1x sleep & 0.3 & & \\ LED active & 1,000.0 & 43.20 & 4.38 \\ TAOS light sensor active & 780.0 & 43.20 & 3.41 \\ TAOS light sensor unpowered & 0.0 & & \\ RF modem CC1101 transmitting + idle & 35,000.0 & 3.00 & 10.64 \\ RF modem CC1101 sleep & 1.0 & 86,397.00 & 8.75 \\ RF modem CC1101 receiving + idle & 19.6 & 0.00 & \\ \hline \end{tabular}
\end{table}
Table 2: Energy requirements for each part. Star configuration. V\({}_{\text{DD}}\) = 3.3 V.  
For the battery originally installed in the nodes, and based on these results, we can guarantee a lifetime of 13 years for the star configuration.
# A Multipath Data-Scheduling Strategy Based on Path Correlation for Information-Centric Networking
## 5 Evaluation  
In this section, we implement our proposal based on NS3, which is a discrete, event-driven, open-source network simulator [39], and conduct a series of simulation experiments to evaluate the performance of our proposal. First, we evaluate the performance of the data-scheduling algorithm in a simple multipath topology. Then, we comprehensively evaluate the performance of the proposed transmission method in real-world topologies.
# 5G-Based Smart Healthcare and Mobile Network Security: Combating Fake Base Stations
###### Abstract  
New mobile network technologies, particularly 5G, have spurred a growth in smart healthcare networks. They enable real-time monitoring, personalized treatments, and more. However, these transformative capabilities have also uncovered potential vulnerabilities, emphasizing the urgency to safeguard patient data and healthcare services. This study analyzes the existing research on 5G-based smart healthcare network security with a specific emphasis on fake base station attacks. The research investigates potential security measures to mitigate the impact of fake base station attacks. And based on those findings, we propose a detection scheme to help combat fake base station threats effectively and to avoid the need to install individual apps on smart devices, providing a foothold for future efforts to develop and deploy better countermeasures. To ensure a secure and resilient ecosystem for 5G-based smart healthcare, continuous research and proactive measures are required. By staying vigilant and committed to research and development, we can protect patient privacy, ensure secure data transmission, and enhance the quality of services within smart healthcare networks and other mobile network applications alike.  
5G; mobile network; smart healthcare; fake base station; information security +
Footnote †: journal:  
existing measures; and to develop effective detection measures that uphold resilience and robustness, safeguarding users' privacy and ensuring secure data transmission within a network.