esa-sceva/satcom-chunk-collection huggingface dataset:
# CoSiWiNeT: A Clock Synchronization Algorithm for Wide Area IIoT Network
###### Abstract  
Recent advances in the industrial internet of things (IIoT) and cyber-physical systems drive Industry 4.0 and have led to remote monitoring and control applications that require factories to be connected to remote sites over wide area networks (WAN). The adequate performance of remote applications depends on the use of a clock synchronization scheme. Packet delay variations adversely impact the clock synchronization performance. This impact is significant in WAN as it comprises wired and wireless segments belonging to public and private networks, and such heterogeneity results in inconsistent delays. Highly accurate, hardware-based time synchronization solutions, global positioning system (GPS), and precision time protocol (PTP) are not preferred in WAN due to cost, environmental effects, hardware failure modes, and reliability issues. As a software-based network time protocol (NTP) overcomes these challenges but lacks accuracy, the authors propose a software-based clock synchronization method, called CoSiWiNeT, based on the random sample consensus (RANSAC) algorithm that uses an iterative technique to estimate a correct offset from observed noisy data. To evaluate the algorithm's performance, measurements captured in a WAN deployed within two cities were used in the simulation. The results show that the performance of the new algorithm matches well with NTP and state-of-the-art methods in good network conditions; however, it outperforms them in degrading network scenarios.  
clock synchronization; industrial automation; industrial internet of things; wide area networks; NTP; Kalman filter; RANSAC  
11
# Spectral Coexistence of QoS-Constrained and IoT Traffic in Satellite Systems
## 1 Introduction
### Related Work  
The first research direction considered spectrum coexistence for geographically spaced systems [25; 26]. More recently, the idea of sharing bandwidth among non-homogeneous services has started to receive some attention in the context of 5G cellular systems, aiming to go beyond the inefficiencies of orthogonal slicing. In [27], the possibility to multiplex eMBB and ultra-reliable low-latency communications (URLLC) was studied following an information theoretic approach, pinpointing the potential of the idea in a multi-cell cloud radio access network (C-RAN). Additional insights are provided in [28], where the two aforementioned services are flanked by mMTC traffic. Among other scenarios, the work studied a heterogeneous non-orthogonal multiple access solution for the OFDM 5G uplink, where resources are shared between eMBB and mMTC with different reliability requirements. Assuming packets that are long enough to justify an asymptotic information-theoretic analysis, the authors explored via numerical solutions some key trade-offs in terms of rates that can be granted to the distinct services, showing how an overlay allocation can be beneficial in certain regimes. Providing efficient connectivity while guaranteeing the target QoS among several concurrent services was also thoroughly investigated above layer two, e.g., [29]. Finally, a survey on inter-system spectrum sharing focusing on services with equal access rights to the resources can be found in [30].
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 2 Candidate Wireless Technologies
### Popular Low-Power Wireless Technologies
#### 2.1.2 IEEE 802.15.4 and ZigBee  
IEEE 802.15.4 [55] and ZigBee [56] are two widely used radio standards in BAN applications. IEEE 802.15.4 technology includes physical (PHY) and Medium Access Control (MAC) layer protocols focusing on low data rate and medium-range wireless communications which makes it an appropriate solution for health-monitoring applications.  
Similar to IEEE 802.15.4, ZigBee is an enhanced version which provides additional layer protocols such as network, security and application layers that reside on top of physical and MAC layers defined by IEEE 802.15.4. The main purpose of both standards is to provide low power solution for battery-powered devices. The physical layer utilizes Direct Sequence Spread Spectrum (DSSS) modulation technique for interference mitigation and the MAC layer also utilizes Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) for channel access. The ZigBee standard provides support for flexible network topology. Devices in a ZigBee network are distinct between Reduced Function Device (RFD) and Full Function Device (FFD). FFDs are able to set up a mesh network where low duty cycle reduced function devices join the network as leaf nodes. In addition, ZigBee fully supports the low duty cycle operation of nodes (sensor nodes turn off their radios most of the time to reduce energy expenditure).  
Contrary to ZigBee, classic Bluetooth does not support low duty cycling operation. In Bluetooth devices, a slave node must be kept synchronized to the master node for data transmission. As a result of that, there is an increase in 'radio on' period which in turn leads to increased energy consumption. The ZigBee Alliance incorporates several public profiles which simplifies distribution of systems with interoperable multi-supplier ZigBee-based devices. For instance, ZigBee has recently developed a profile termed Personal Health and Hospital Care (PHHC) [57]. The aim of this profile is to provide reliable and secure monitoring of non-invasive, non-critical healthcare applications mainly focused on physical fitness, chronic disease and aging. The PHHC profile also fully supports ISO/IEEE 11073 standard [58] and utilizes the ISO/IEEE 11073 protocol for exchange of medical information.  
Moreover, the ZigBee Alliance has recently introduced an optional feature in the ZigBee 2012 specification. This feature is termed ZigBee PRO Green Power [59]. Green Power provides adequate power for battery-less devices with harvested energy and allows them to actively join ZigBee PRO 2012 networks. Furthermore, IEEE 802 has recently introduced the first international WBAN standard called IEEE 802.15.6 [60]. The main purpose of this standard is to provide a short-range, low-power and extremely reliable communications in the vicinity of or inside the human body. IEEE 802.15.6 is targeted to serve a range of medical and non-medical applications.
# Mineral Identification Based on Deep Learning Using Image Luminance Equalization
###### Abstract  
Mineral identification is an important part of geological research. Traditional mineral identification methods heavily rely on the identification ability of the identifier and external instruments, and therefore require expensive labor expenditures and equipment capabilities. Deep learning-based mineral identification brings a new solution to the problem, which not only saves labor costs, but also reduces identification errors. However, the accuracy of existing recognition efforts is often affected by various factors such as Mohs hardness, color, picture scale, and especially light intensity. To reduce the impact of light intensity on recognition accuracy, we propose an efficient deep learning-based mineral recognition method using the luminance equalization algorithm. In this paper, we first propose a new algorithm combining histogram equalization (HE) and the Laplace algorithm, and use this algorithm to process the luminance of the identified samples, and finally use the YOLOv5 model to identify the samples. The experimental results show that our method achieves 95.6% accuracy for the identification of 50 common minerals, achieving a luminance equalization-based deep learning mineral identification method.  
deep learning; image enhancement; mineral identification; convolutional neural networks +
Footnote †: journal: Applied sciences
## 2 Case Study--Arsenic Contamination of Groundwater in Bangladesh
### Mobilization of Arsenic in Groundwater in Bangladesh
#### 2.3.1. Pyrite Oxidation Hypothesis  
Studies have shown that there is a high level of arsenopyrite (FeAsS) in the alluvial regions of Bangladesh [31; 35]. The basis of the pyrite oxidation hypothesis is that the oxidation of arsenopyrite resulted in the release of arsenic into groundwater [36; 37; 38; 39]. This process is understood to be aided by the invasion of the aquifers by atmospheric oxygen when the water table is lower than these deposits, resulting in its diffusion into  
Figure 4: Arsenic contaminated areas in Bangladesh [24]. Reproduced with permission from BGS.  
Figure 3: Surface geological units of Bangladesh [27]. Reproduced with permission from BGS.  
the pore space and groundwater. Consequently, the reaction between the arsenopyrite and available oxygen resulted in a water-soluble form of arsenic which was consequently released into the groundwater [30].  
The associated reactions for this process are:  
\[4\text{FeAsS}+11\text{O}_{2}+6\text{H}_{2}\text{O}\to 4\text{ FeSO}_{4}+4\text{ H}_{2}\text{AsO}_{3}{}^{-}+4\text{H}^{+}\]  
\[4\text{FeAsS}+13\text{O}_{2}+6\text{H}_{2}\text{O}\to 4\text{ FeSO}_{4}+4\text{ H}_{2}\text{AsO}_{4}{}^{-}+4\text{H}^{+}\]  
The above hypothesis is supported by the absence of arsenic-affected people before irrigation became more intensive at the beginning of the 1980s. Consequently, the total irrigated area which used groundwater increased significantly from 41% to 71% within 1982/1983 and 1996/1997, respectively, while in the same period the use of surface water decreased considerably from 59% to 29% [30].  
Based on the pyrite oxidation hypothesis, the lowering of the water table resulted from the considerable use of groundwater, and, in turn, led to groundwater contamination with arsenic. The arsenic mobilization proposed by this hypothesis is widely accepted as the main mechanism for intrusion of arsenic into groundwater in Bangladesh [36; 40; 41]. However, the hypothesis is not yet fully validated as there is a need for further hydrological and geochemical data [42].
# Anti-Recompression Video Watermarking Algorithm Based on H.264/AVC
## 3 Watermark Embedding and Extraction Algorithm
### Video Watermark Extraction Algorithm  
The framework of the video watermark extraction algorithm is shown in Figure 4. Firstly, the video to be detected extracts all I frames in the decoding process, extracts the chroma subblock according to the recorded position in \(D\), demodulates the chroma subblock according to the recorded modulation mode, extracts the watermark sequence, and obtains the watermark image after boosting and inverting the disorder. The pseudo code of watermark extraction is shown in Algorithm 2. The extraction methods corresponding to the four modulation methods are as follows:  
The extraction method for modulation method 1 is:  
\[w(k)=\left\{\begin{array}{l}1\text{ {if }}q3_{k}\
eq 1\\ 0\text{ {if }}q3_{k}=1\end{array}\right. \tag{8}\]  
where \(w(k)\) denotes the \(k\)-th watermark value and \(q3_{k}\) is the third non-zero quantization factor in the \(k\)-th subblock.  
Figure 4: Watermark extraction algorithm framework.  
The extraction method for modulation method 2 is:  
\[w(k)=\left\{\begin{array}{l}1\;\textit{if}\;q3_{k}=1\\ 0\;\textit{if}\;q3_{k}\
eq 1\end{array}\right. \tag{9}\]  
The extraction method for modulation method 3 is:  
\[w(k)=\left\{\begin{array}{l}1\;\textit{if}\;q3_{k}\
eq-1\\ 0\;\textit{if}\;q3_{k}=-1\end{array}\right. \tag{10}\]  
The extraction method for modulation method 4 is:  
\[w(k)=\left\{\begin{array}{l}1\;\textit{if}\;q3_{k}=-1\\ 0\;\textit{if}\;q3_{k}\
eq-1\end{array}\right. \tag{11}\]
# Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks
## 1 Introduction
In such a scheme, both co-tier and cross-tier interferences are perfectly aligned into the orthogonal subspace at each receiver, without causing any performance degradation on the network users;
* as we know, the interference nulling based IA algorithm can achieve its optimum only with negligible noise. By slightly loosening the interference-free constraint on the cognitive femtocell, we also present a partial cognitive IA (P-CIA) scheme that can further improve the network performance under low and intermediate SNR conditions;
* additionally, the feasibility condition, the proof of convergence, and the capacity analyses are derived to demonstrate the effectiveness of the proposed cognitive IA schemes; and
* note that the proposed cognitive IA schemes can be performed in an autonomous way that requires no explicit cooperation between the two tiers. Such schemes can satisfy the low-cost and self-organized features of IoT, and achieves a considerably high network capacity while causing no interference to the macrocell users. Theoretical and numerical analyses are provided to show that the proposed cognitive IA schemes can realize the coexistence between the two tiers, yielding a significant sum-rate and spectral efficiency enhancements for a large range of signal to noise ratio (SNR) values. The rest of the paper is organized as follows. In Section 2, the scenario and system model are introduced. In Section 3, two novel cognitive IA schemes are proposed. The feasibility condition, convergence, and capacity analyses are carried out in Section 4. Numerical simulation results are presented in Section 5, followed by the conclusions and future work given in Section 6. For the notation throughout this paper, we let a lower case italic symbol (e.g., \(x\)) denote a scalar value, a lower case bold italic symbol (e.g., \(x\)) denote a vector, and an upper case bold symbol (e.g., \(\mathbf{X}\)) denote a matrix. \(\mathbf{I}_{N}\) represents the \(N\times N\) identity matrix, and \(\mathbf{0}_{M\times N}\) denotes an \(M\times N\) zero matrix. The eigenvector of \(\mathbf{A}\) corresponding to the \(i\)-th eigenvalue in ascending order is presented as \(\boldsymbol{v}_{\mathbf{A},i}\). \(\text{tr}\{\mathbf{A}\}\), \(\text{rank}\{\mathbf{A}\}\), and \(\mathbf{A}^{H}\) are the trace, rank, and conjugate transpose of matrix \(\mathbf{A}\), respectively. Moreover, \(\mathbb{C}^{M\times N}\) denotes the space spanned by complex \(M\times N\) matrices, and \(\mathcal{CN}(\boldsymbol{a},\mathbf{A})\) stands for the complex Gaussian distribution with a mean, \(\boldsymbol{a}\), and a covariance matrix, \(\mathbf{A}\). \(\mathbb{E}[\cdot]\) denotes the statistical expectation. All vectors are defined to be columns, and in vector and matrix definitions, the subscript \"\(p\)\" refers to the primary tier.
## 3 Experiment and Analysis
### Sensitivity Analysis of the Voronoi Model's Parameters
#### 3.1.1 Sensitivity Analysis of the Outlier Proportion Parameter  
The sensitivity of the Voronoi model to outlier proportion parameter \(\alpha\) is evaluated using \(\delta=0.110\) m and \(\tau=0.001\). As shown in Figure 17, the analysis indicates that the Voronoi model achieves its highest recall of 84.4% when \(\alpha\) values range between 0.067 and 0.092, with a median of 0.080. Beyond this range, model performance steadily declines, reaching a recall of 70.7% when \(\alpha=1.0\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Segment** & **Features Q** & **Features P** & **Observed Matches** & **New Features** & **Complexity** \\ \hline S1 & 45 & 62 & 54 & 7 & High \\ \hline S2 & 38 & 18 & 18 & 20 & Low \\ \hline S3 & 45 & 33 & 33 & 14 & Moderate \\ \hline S4 & 29 & 15 & 15 & 14 & Low \\ \hline S5 & 31 & 39 & 39 & 4 & High \\ \hline S6 & 51 & 57 & 52 & 0 & Moderate \\ \hline
**Total** & 239 & 224 & 211 & 59 & - \\ \hline \end{tabular}
\end{table}
Table 1: Number of features, observed matches, new features, and complexity of ILI data across the six pipeline segments—case study.
# FusionTrack: Multiple Object Tracking with Enhanced Information Utilization
## 5 Analysis and Discussion
### Joint Track-Detection Decoder  
The joint track-detection decoder is split into detection and tracking branches, which are connected by a shared masked self-attention module. Such a design allows information to flow from the detection branch to the tracking branch, allowing the tracking branch to better classify and localize the objects. We visualize the information flow in the self-attention module in Figure 7 to better understand the module. As shown in the figure, the predictions of detection and tracking branches highly overlap each other. And the tracking branch takes information from the corresponding instance in the detection branch to improve performance, leading to more weights on the diagonal at the top-left corner of the attention map.
## 5 Simulations and Analysis in Battlefields
### The Procedure of Matlab Simulations of UAV Formations in Battlefields
The regression model equation of UAVs out of clusters can be expressed as:  
Figure 28: The regression model of the number of surviving UAVs under 85% fight loss in different-scale formations. Figure 27: The regression model of the number of UAVs out of clusters under 85% fight loss in different-scale formations. \[R_{b}=0.0989M_{n}-1.1704 \tag{19}\]  
\(R_{b}\) is the number of surviving UAVs, and \(M_{n}\) refers to the number of clusters. According to the simulation results, the 95% confidence interval of gradients in the curve model was [0.0987, 0.0992], and the confidence interval of intercepts was [\(-1.9811\), \(-0.3596\)], so the intercepts and gradients of the regression model curve equation satisfied the requirement. The R2 variance explained rate was 0.9999, proving the significance test of the regression equation with excellent fitting. Based on the simulation results, in the 20 different-scale formation clusters based on the mentioned method, even when the fight loss accounts for 85% in each formation, only 5.1-6% UAVs would be out of the formation. In the remaining 15% undestroyed clusters, 54.4-65.7% of the surviving UAVs could continue fighting.
# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## Appendix C Fuzzy Logic  
The fuzzy sets basically represent an extension of the classical notion of set. While in classical set theory, an element either belongs or does not belong to the set, elements of a fuzzy set have degrees of membership. The degrees are defined through membership functions whose values are real numbers in the interval [0, 1]. The application of the membership functions represents the so-called fuzzification, which in AUTOWADE is simply performed using the \(S\) membership function shown in one of the boxes of Figure 11 and defined as \(S(x,x_{1},x_{2})\) where \(x\) is the independent variable and \(x_{1},x_{2}\) are the parameters of the function. For \(x\leq x_{1}\), \(S=0\), while for \(x\geq x_{1}\), \(S=1\). For \(x\) values within the interval between \(x_{1}\) and \(x_{2}\), the membership degree depends on the position in the interval itself. Note that an object-based application of the fuzzy logic is performed by AUTOWADE (Section 3.2.4).  
The final operation of any fuzzy-based approach is the defuzzification that assigns each element of a fuzzy set to a class, according to the final membership degree. In AUTOWADE, the final membership degree is computed as the average of the degrees resulting from the application of the \(S\) function to \(\overline{\Delta\sigma_{VV}^{0}}\), \(\overline{\Delta\sigma_{VV}^{0}}-\overline{\Delta\sigma_{VH}^{0}}\) and \(p\) (Section 3.2.4). A simple threshold-based defuzzification is carried out by labelling a flooded vegetation an object whose final membership degree is larger than 0.5.
## 2 Materials and Methods
### The Equivalent Material Concept under Compressive Loading  
The equivalent material concept (EMC) was proposed by [PERSON] [36] in order to avoid the complexities of elastoplastic analyses when dealing with ductile fracture under tensile loading. Below, this concept is presented in its original form. Then, it is extended to compressive loading, with the aim of obtaining the critical stress of the material being analyzed. This is of practical importance, given that it can be utilized as an essential input in the context of the LEFM criteria when estimating the LCCs of the keyhole notched PMMA and GPPS specimens tested under compression.  
The EMC equates a ductile material having elastic-plastic behavior with a virtual brittle material showing linear elastic behavior until final fracture [36], and having the same elastic modulus and fracture toughness as the real elastoplastic material, but with a different tensile strength. For obtaining the tensile strength of the virtual brittle material, the two materials are assumed to have the same values of strain energy density (SED) until the ultimate point. This means that the shaded areas in Figure 3 must be equal.  
Figure 3: Schematic of the tensile stress–strain curves: (**a**) real ductile material (DM); (**b**) equivalent (brittle) material (EM).  
Figure 2: Two keyhole notched specimens shown during the compressive fracture test: (**a**) PMMA, \(\rho\) = 4 mm; (**b**) GPPS, \(\rho\) = 4 mm.  
Now, given that both materials have the same elastic modulus, and according to [36], by equalizing the expressions of the SEDs of the ductile and the equivalent materials, the following equation can be obtained:  
\[\left(SED\right)_{DM}=\left(SED\right)_{EM}=\frac{\sigma_{f}^{*2}}{2E} \tag{1}\]  
where \(E\) and \(\sigma_{f}^{*}\) are the elastic modulus and the tensile strength of the virtual brittle material, respectively. In accordance with Equation (1), the tensile strength of the virtual material follows Equation (2):  
\[\sigma_{f}^{*}=\sqrt{2E\left(SED\right)_{DM}} \tag{2}\]  
Therefore, by entering the \(SED\) of the ductile material (i.e., \(\left(SED\right)_{DM}\), see Figure 3a) into Equation (2), the tensile strength of the virtual brittle material can be easily calculated.  
Although the EMC was originally formulated for tensile loading conditions, it can also be used under compressive loading conditions. Since in compressive loading both stress and strain have negative values, the sign of \(\left(SED\right)_{DM}\) remains positive, but the equivalent compressive strength will become negative. Hence, the compressive strength follows Equation (3):  
\[\sigma_{f}^{*}=-\sqrt{2E\left(SED\right)_{DM}} \tag{3}\]  
With all this, it can be expected that the critical loads of the keyhole notched GPPS and PMMA specimens tested under compression may be estimated by using the resulting values from Equation (3) in various brittle fracture models, such as those briefly outlined in the following sub-section.