esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling DGNSS Pseudo-Range Correction Messages by Utilizing Satellite Repeat Time
## 1 Introduction
In our implementation, a DGNSS server produces modelling parameters based on repeating patterns of PRCs. The user needs to access those parameters only once and can produce reliable PRC values when PRCs are not available. In this paper, we present new algorithms to generate real-time PRC predictions for GPS and the BeiDou navigation satellite system (BDS), and we conducted validation tests. First, we briefly introduce the repeating patterns of PRC, and we show how to obtain their repeat times. For repeat-time estimation, rapid or ultra-rapid GNSS orbits in Standard Product 3 (SP3) format are used. PRC parameters per satellite are calculated using past PRC data and the orbital period of the satellite. Using these parameters, we predict PRC values for a single day, and we then analyze their accuracy by comparing them with real values generated at the reference station. In addition, DGNSS positioning accuracy was evaluated using predicted PRC.
# An Improved Method Based on EEMD-LSTM to Predict Missing Measured Data of Structural Sensors
## 3 Experimental Design and Evaluation Criteria
### Evaluation Criteria  
In order to quantitatively evaluate and compare the accuracy of the proposed EEMD-LSTM model for missing data completion, mean squared error (_MSE_), root mean squared error (_RMSE_), and mean absolute error (_MAE_) were used. The four evaluation indicators and the coefficient of determination/R squared (\(R^{2}\)) were used to compare the accuracy of the models.  
Figure 9: Decomposition of the RG dataset.  
(1) _MSE_  
\[MSE(y,\hat{g})= \frac{1}{n}\sum_{i}\|y_{i}-\hat{y}_{i}\|_{2}^{2} \tag{8}\]  
The _MSE_ is the mean value of the sum of squared errors at the corresponding points of the predicted and measured values. The smaller the _MSE_ value, the better the accuracy of the missing data-completion model.  
(2) _RMSE_  
\[RMSE(y,\hat{g})= \sqrt{\frac{1}{n}{\sum_{i}}{\|y_{i}-\hat{y}_{i}\|_{2}^{2}}} \tag{9}\]  
_RMSE_ is the square root of the squared and means values of the errors at the corresponding points of the predicted and measured values. It is susceptible to significant or minor errors in a set of measurements such that it can reflect the accuracy of the prediction well. The smaller the _RMSE_ value, the better the accuracy of the missing data-completion model.  
(3) _MAE_  
\[MAE(y,\hat{g})=\frac{1}{n}{\sum_{i}}|y_{i}-\hat{y}_{i}| \tag{10}\]  
This indicator is the mean value of the absolute sum of the errors at the corresponding points of the predicted and measured values. The smaller the _MAE_ value, the better the accuracy of the missing data-completion model.  
(4) _R\({}^{2}\)_  
\[R^{2}(y,\hat{g})=1-\frac{\sum_{i}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i}(y_{i}-\bar {y}_{i})^{2}} \tag{11}\]  
The denominator is the sum of the differences between the measured and mean values. The numerator is the sum of the squares of the errors at the corresponding points of the predicted and measured values. _R\({}^{2}\)_ is a measure of the model's fit, and the closer it is to 1, the higher the accuracy of the missing data-completion model, where \(\hat{y}_{i}\) is the prediction of \(y_{i}\); \(y_{i}\) is the measured value; \(\overline{y}_{i}\) is the mean of the measured value; n is the total number of test samples. It is important to note that the data used are highly random, so the _R\({}^{2}\)_ values are only used to compare the fit of the methods.
## 2 Field Test
### Monitoring Results  
#### 2.2.1 Settlement  
The variations of settlement with the number of passes are shown in Figure 7 (including ESD-L, ESD-C, ESD-R, and the average value). Settlement at all measurement points gradually increased with the increase in the number of passes. The increments of settlement were generally greater in the first 10 passes. The maximum settlement reached 159.6 mm after 10 passes. This was due to the compaction of the gravel bedding and a certain depth of soft soil layer under impact loading. Under the action of impact, the pore water pressure of shallow soft soil dissipated rapidly, and the soil consolidated rapidly to form a dense, reinforced layer. After that, the increments of settlement gradually decreased as the number of passes increased. As shown by the variation of the average value with the number of passes, the last 20 passes induced only 17.1% of the total settlement. This was  
Figure 5: Schematic diagram of embedded settlement device: (**a**) initial state, (**b**) monitoring state.  
Figure 6: Installation of various instruments: (**a**) embedded settlement device, (**b**) in-place inclinometer, (**c**) pore water pressure piezometer.  
because the overlying crust formed by the adequately compacted topsoil had the effect of spreading the stresses, making the impact loads less influential.  
Figure 8 shows the test data for settlement induced by RDC observed in this study as well as in other studies by other researchers [35; 36; 37; 38]. The trend of the settlement with respect to the number of passes in this research is basically the same as it is in other studies. However, the settlement of this test is higher than that obtained from other studies with the same number of passes. This indicates that the deformation of soil caused by RDC is more significant in soft clay than in coarse-grained materials such as sand and gravel soils under the same conditions. During the compaction of the large-grained refuse fill materials [37], the surface developed a heave, which may be related to the lower leveling of the compacted materials.  
The maximum difference in settlement between measuring points was calculated as an uneven settlement value. The variation of the uneven settlement value with the number of passes is shown in Figure 9. At the beginning of rolling, the uneven settlement increased rapidly with the increase in the number of passes. The uneven settlement value reached its maximum, close to 48.2 mm, after the sixth rolling. This was caused by the uneven distribution of the thickness of the soft soil layer in the rolling area. Due to the limitations of the site, the compaction work of the impact mill at different locations of the embankment was different. With the increase in the number of passes, the embankment was consolidated and settled under the action of impact rolling. Part of the uneven settlement was eliminated.  
Figure 8: Settlement curves in different soil types [35; 36; 37; 38].  
Figure 7: The variation of ground settlements with the number of passes.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 8 Simulation
### Wi-Fi Wireless Network
#### 8.1.4. Secondary Tank Wi-Fi Network  
Utilizing a single omnidirectional antenna to serve all three remote wells. Figure 17 shows wells w-658 and ww7 successfully achieved the RSSI margin at around 12 dBm. This can be attributed to their proximity to the tank, with distances just under 1 km. Conversely, well ww3, being the farthest at a distance of 4.02 km, necessitated a higher Tx power of 17 dBm to achieve an acceptable RSSI margin.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sector/Link** & **Distance** & **Antenna Type** & **Antenna Type** & **TX Power** & **Antenna Height** & **RSSI** & **Fresnel** & \\
\begin{tabular}{c} Height \\ adjusted, \\ feasible \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 6: Link Parameters for Sector 3.  
Figure 16: Transmit Power vs. RSSI for wells connected to Sector 3 with improved heights.  
Given that the Fresnel zone in the ww3 link is free from obstructions, raising the antenna height did not improve the signal for well ww3. The alternative options to improve the link's RSSI include installing a repeater antenna midway between the BS and ww3 or utilizing higher gain antennas to establish a dedicated point-to-point link. However, the ww3 link is considered viable at 17 dBm with the current antenna configuration. The final link parameters are summarized in Table 7.
# Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach
## 4 Datasets  
VoIP suffers from a severe shortage of publicly available traffic datasets, and there is no benchmark dataset [30]. The two publicly available datasets are used to ensure the validity of the proposed model.  
The INRIA dataset [16] was developed in 2010. The authors prepared a network consisting of two computers that have one hundred instances of VoIP bots used to generate the normal traffic, a server that contains SIP server software (i.e., Asterisk and Opensips), and a computer that contains the attack tools to produce the attack traffic.  
This dataset contains two types of VoIP attacks: INVITE flooding and SPIT. In addition, attack messages were generated at different intensities. The most important advantage of this dataset is the utilization of different data sources: server logs stored as.txt files, CDRs stored as.csv files, and network traffic stored as.cap files. Every type of these data sources is vital for the detection of VoIP attacks; for example, the logs of the Opensips server have different probes that can be utilized for detecting unusual memory consumption and processing.  
Recently, a new VoIP traffic dataset (i.e., RIT dataset) was generated to help in developing machine learning models for detecting VoIP attacks [31]. A test bed consists of four computers to generate normal traffic, an Asterisk server, and a computer to act as an attacker.  
This dataset contains various attacks (i.e., REGISTER hijacking, RTP flooding, REGISTER flooding, BYE, and SPIT) with different scenarios. In addition, the authors utilized attack tools that are usually used to exploit weaknesses in VoIP networks to produce useful attack traffic data. Although this dataset has VoIP traffic from attacks larger than the INRIA dataset, it does not contain CDRs or server logs.  
The INRIA dataset consists of 63,584 messages, with 3584 of them being SPIT messages and the remaining messages considered normal. On the other hand, the RIT dataset comprises 64,000 messages, with 4000 of them classified as SPIT. In order to allocate the available SPIT messages for validation and testing purposes, an equal division was employed.  
The RIT dataset, which is a recent publication, has not yet been utilized as a benchmark for VoIP classification systems. In contrast, the INRIA dataset, published in 2010, has been extensively employed in the detection of VoIP attacks [15; 24; 32].
# MZAP--Mobile Application for Basketball Match Tracking and Digitalization of Endgame Reports
## 5 MZAP Android Application--Match Report Preview and Digital Signing
### Security Mechanisms for PDF Match Endgame Reports
#### 5.2.6. Legal Compliance and Data Protection  
Since the application is currently used exclusively within Serbia and pertains specifically to the Basketball League of Serbia, the primary regulatory framework governing personal data processing is the Law on Personal Data Protection of the Republic of Serbia. Digital signatures are employed to ensure data integrity, thus protecting personal data from unauthorized alterations and to provide non-repudiation for audit purposes.  
To mitigate the risk of private key exposure, the application implements in-app security measures. Trust in the digital signatures is strictly confined to the League's internal systems, which are explicitly configured to recognize and accept the League's self-signed certificate. This approach satisfies the accountability and trust requirements under Serbian data protection law.  
If the application is expanded for use outside Serbia, it will be necessary to ensure compliance with the General Data Protection Regulation (GDPR) as part of the internationalization process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Threat** & **Description** & **Mitigation** \\ \hline PDF Tampering & Attacker modifies signed PDF & Modification invalidates \\ Key Extraction & & signature \\ via Reverse & Attacker decompiles APK to & \
\begin{tabular}{c} Obfuscated app, \\ use server-side validation and \\ signing \\ Server-side checks for \\ checksums and PDF passwords \\ \end{tabular} \\ Vertificate Trust & PDF readers show warnings for & Educate users to accept \\ Failure & self-signed certificates & certificates internally and use \\ Brute Force on & Weak passwords guessed to &
\begin{tabular}{c} Cuustom MZAP viewer \\ Enforced strong passwords and \\ AES-256 encryption \\ \end{tabular} \\ PDF Password & view content & AES-256 encryption \\ \hline \hline \end{tabular}
\end{table}
Table 1: MZAP Android application threat model.  
Figure 23: PDF custom properties (metadata) used for version control.
# Simulation and Experimental Study on the Shrub-Cutting Performance of Quasi-Planetary Cutter
## 1 Introduction
To study cutting performance, finite-element analysis of cutting can be conducted using software such as ABAQUS, ANSYS/LS-DYNA, and ADAMS in conjunction with actual cutting tests. This combination ensures the authenticity and reliability of test data by providing complementary insights. [PERSON] et al. [11] employed numerical finite-element simulation and experimental design to investigate the influence of internal and external factors of sugarcane on cutting quality. They utilised an orthogonal test method to determine the optimal combination of cutting system parameters and subsequently verified the cutting quality through experimental tests. Following the principles of orthogonal testing, [PERSON] et al. [12] used ANSYS/LS-DYNA finite-element simulation software to analyse stress variations during the cutting of mulberry branches with circular saws. They identified the optimal working parameters for circular saws, constructed a mulberry branch-cutting test bench, and compared the resulting surface quality of the cut branches. Similarly, [PERSON] et al. [13] used ADAMS software to establish a virtual prototype of a sugarcane-cutting system. They examined the effects of different harvester forward speeds and cutting disc speeds on sugarcane stalk cutting time, deriving an empirical regression equation linking the cutting tool speed, harvester forward speed, and cutting time. Through optimisation, they determined the ideal speed range and forward speed range. However, shrubs differ from other agricultural and forestry crops in several ways. There are many shrub species, their growing environments are complex, forest roadsare often muddy, and the tool's working parameters may vary accordingly. In order to evaluate the cutting performance of quasi-planetary cutting tools and to optimise their operating parameters in bush-cutting applications, three representative shrub species from Zhangguangcai Ridge were selected as test materials. To improve the efficiency of tool-cutting performance testing, LS-DYNA simulation and a cutting test bench were employed. A single-factor simulated cutting test was conducted, considering tool speed, feed speed, and shrub diameter as variables. Additionally, an orthogonal cutting test was designed using tool speed, feed speed, and shrub moisture content as factors. By comparing the results of the single-factor cutting test and the orthogonal test, the effects of these parameters on peak cutting force and peak cutting power were analysed. A regression model was then established to optimise the minimum peak cutting force and minimum peak cutting power. This optimisation process yielded a reasonable matching relationship between tool speed and feed speed as well as the best timing and season (based on optimal moisture content) for shrub cutting.
## 1 Introduction
[14] reported that auditory masking effects [16; 17] of the natural background sounds on the noise could be exploited to improve the quality of the urban soundscape. For this, however, the level of a background sound must be comparable or higher than the level of the noise [16; 17; 18; 19], in the case of which it might be incorrect to label them as \"background\" sounds. Nevertheless, because of its temporal variation, a background sound of singing birds was found to be a less effective masker than broadband natural sounds, such as a water stream (i.e., brook), deciduous vegetation, and coastal breaking waves [13]. On the other hand, bird sounds were found to improve pleasantness more strongly than water features [15]. Furthermore, [PERSON] et al. [12] reported that among nine different background sounds, \"water stream\" and \"waves of lake\" were found to be more preferable than other sounds to be used as mixed background sounds for road traffic noise as well as for construction noise. [PERSON] et al. [15] argued that not only the auditory masking, but also other perceptual processes, such as auditory attention, might affect the perception of noise in a mixture of noise and background sounds. Thereby it is assumed that, e.g., differences in the temporal structure of sounds are associated with different degrees of attention to them. [PERSON] et al. [20] suggested that values of psychoacoustic parameters fluctuation strength [16; 21], sharpness [16; 22], and loudness [16; 23] were the three key indicators for characterization of soundsin soundscape. It can be hence summarized that not only different background sources (water, birds, vegetation, etc.) might influence the perception of the noise differently, but also the temporal structure [13; 24], spectral structure [13; 18; 19], and the level [12; 13; 14; 25] of the background sound could be important parameters for the altered perception of the noise. In accordance with that, [PERSON] et al. [26] suggested a holistic approach when categorizing the soundscape, considering the physical spectro-temporal characteristics, as well as the type and the meaning (i.e., the semantic content) of sounds. With that background, this paper investigates possible effects of background sounds of different types and levels on the perceived annoyance of foreground noise. In particular, sounds of landing helicopters were used as an exemplary noise source. Unlike studies which investigated relatively high-level background sounds--partially masking the noise--in order to improve the urban soundscape [12; 13; 14], the study presented here focused on the usage of background sounds in psychoacoustic experiments and in virtual acoustic applications. Section 2 of this paper introduces briefly the experimental concept. Experimental design and setup follow in Section 3. Experiments 1 and 2 and their results are presented in Sections 4 and 5, respectively. After a discussion of the outcomes in Section 6, Section 7 will conclude the paper.
# Federated Optimization of \(\ell_{0}\)-norm Regularized Sparse Learning
## 4 The FedIter-HT Algorithm
### Sparse Logistic Regression  
can be formulated as follows:  
\[\underset{x}{min}f(x) =\frac{1}{N}\sum_{i=1}^{N}\frac{1}{B}\sum_{j=1}^{B}(\log(1+\exp(z_{ ij,j}^{T}x))-y_{ij}z_{ij}^{T}x)\] subject to \[\|x\|_{0}\leq\tau,\]  
where \(z_{i,j}\in\mathbb{R}^{d}\) for \(j\in[B]\) is a predictive vector and drawn from a sub-Gaussian distribution associated with client \(i\), each observation \(y_{i,j}\) on client \(i\) is drawn from the Bernoulli distribution \(\mathbb{P}(y_{i,j}|z_{i,j},x^{*})=\frac{\exp(z_{ij}^{T}x^{*})}{1+\exp(z_{ij,j}^ {T}x^{*})}\), and \(x^{*}\in\mathbb{R}^{d}\) with \(\|x^{*}\|_{0}=\tau^{*}\) is the underlying true parameter that we want to recover.  
**Corollary 6**.: _If all the conditions in Theorem 2 hold, \(\|z_{i,j}\|\leq\mathcal{K}\), \(C_{lower}\leq\exp(z_{i,j}^{T}x)/(1+\exp(z_{i,j}^{T}x))^{2}\leq C_{upper}\) for \(i\in[N]\) and \(j\in[B]\) and \(B\geq C_{7}\tau\mathcal{K}^{2}log(d)\) and with a sufficiently large number of communication rounds \(T\), we have_  
\[E[\|x_{T}-x^{*}\|^{2}]\leq O(\frac{(2N\tau+\tau^{*})\mathcal{B}^{2}\mathcal{K} ^{2}\log(d)}{NB})\]  
_with a probability of at least \((1-\exp(-C_{6}NB)-C_{9}\exp(-C_{10}log(d))+\frac{C_{9}}{\exp(C_{6}NB)\exp(C_{10 }log(d))})\), where \(C_{6}\), \(C_{9}\) and \(C_{10}\) are constants._  
Proof.: If we further assume \(\|z_{i,j}\|\leq\mathcal{K}\) and \(C_{lower}\leq\exp(z_{i,j}^{T}x)/(1+\exp(z_{i,j}^{T}x))^{2}\leq C_{upper}\) for \(i\in[N]\) and \(j\in[B]\), the sparse logistic regression objective function is restricted \(\rho_{s}\)-strongly convex and restricted \(l_{s}\)-strongly smooth with \(\rho_{s}=\frac{4}{5}C_{lower}\) and \(l_{s}=\frac{6}{5}C_{upper}\), respectively, with a probability of at least \((1-\exp(-C_{6}B))\) if \(B\geq C_{7}\tau\mathcal{K}^{2}log(d)\), where \(C_{lower}\), \(C_{upper}\), \(C_{6}\) and \(C_{7}\) are constants. Furthermore, according to Corollary 2 in [46], we have \(\|\
abla f(x^{*})\|_{\infty}\leq C_{8}\mathcal{K}\sqrt{\log(d)/NB}\) with a probability of at least \((1-C_{9}exp(-C_{10}log(d))\), where \(C_{8}\), \(C_{9}\) and \(C_{10}\) are universal constants. Therefore, we can obtain the following corollary. Based on the above result, the estimation error specified in terms of the distances \(x_{T}\) and \(x^{*}\) decreases when the total sample size \(NB\) is large or the dissimilarity level \(\mathcal{B}\) and the dimension \(d\) are small.
# Security Framework for IoT Based Real-Time Health Applications
## 1 Introduction
### Security and Privacy  
In e-health, security, and privacy stand for two different terms according to the context. The security of the patient's essential data refers to the availability, validity, and integrity of data, whereas privacy ensures that the data can only be approached and perceived by the legal owner. Several devices work in a real-time health monitoring system that builds a communication medium between physicians, and patients. This communication medium requires the security of the data which are being exchanged between the health monitoring systems and patients.  
This paper presents a security framework for IoT-based real-time health monitoring applications through the IoT layered architecture. We have defined multiple sections to ensure end-to-end security with communication, and security protocols of constrained application protocol (CoAP) and hypertext transfer protocols (HTTPs). Section 2 describes the related work. Section 3 elaborates the IoT layered architecture, and Section 4 explains the methodology. Implementation is discussed in Section 5 while Section 6 illuminates the conclusion and future direction.
# Fault Detection and Isolation for Redundant Inertial Measurement Unit under Quantization
## 2 Materials and Methods
### The Three-Channel Filter for FDI
#### 2.2.2 The Three-Channel Filter  
For a navigation system, faults of different amplitudes have different fault tolerance capabilities. The inertial sensor is integral and its errors accumulate over time. The hard fault must be detected immediately. The medium fault allows a fault tolerance system to have a short time delay. The soft fault has a slightly larger fault tolerance capability. Therefore, faults of different amplitudes should have different detection strategies. We use the three-channel filter to detect the three categories of fault in order to achieve better performance. The fault detection method based on the three-channel filter is shown in the following figure.  
As is shown in Figure 4, each channel operates independently and does not affect each other. The procedures are classifying the faults according to their amplitude first, and then sending them to different fault detection channels. The original channel filter detects hard fault, the first-order channel detects medium fault, and the second-order channel detects soft faults.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Fault Type** & **Fault Magnitude** \\ \hline Hard fault & \(>\)100\({}^{\circ}/\mathrm{h}\) \\ Medium fault & \(5^{\circ}/\mathrm{h}\)\(\sim\)100\({}^{\circ}/\mathrm{h}\) \\ Soft fault & \(\leq\)5\({}^{\circ}/\mathrm{h}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Categories and amplitudes of fault.