esa-sceva/satcom-chunk-collection huggingface dataset:
### Energy Efficiency Based Routing  
In this subsection, the energy efficient routing protocols are discussed with their achievements, challenges and limitations. In [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21], the energy efficient routing protocols are proposed. In the papers [8; 10; 11; 16; 17; 18; 19; 21], authors worked on reliability of Data Packets (DP) from the source node to the destination node. Moreover, for efficient routing, packet size is kept small. Additionally, authors in [10; 17] worked on packet size management for reliable and efficient data delivery. The small packet size during data transmission results in fewer collisions during transmission. In addition, mobility management is also an important parameter while designing a routing protocol. This important parameter is focused on by the authors in [8; 11; 17; 19; 20; 21], respectively. Considering these important parameters, authors in [10; 11; 16; 17; 21] performed single hop routing. Meanwhile, multi-hop routing is also considered in [8; 9; 16; 17; 18; 19; 20; 21].  
The energy effective routing is enhanced by the authors in [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. In return, the lifetime of the network is improved. PDR is focused in [8; 11; 17]. In addition, load traffic is balanced by the authors in [10]. Moreover, the protocol proposed in [16] is efficient in handling a large amount of DPs (during dense network). The efficient path selection routing protocol is proposed by the authors in [18]. Aforementioned routing protocols outperformed to achieve their objectives.  
However, these routing protocols have some challenges and limitations, i.e., the dense deployment of nodes in [8] and attenuation problem in [9]. Moreover, nodes have to face E2E delay during data delivery. Furthermore, network has to face path loss because of water currents [10; 11]. To overcome these issues, additional energy is required which makes the network energy deficient. The authors in [10; 11] have not focused on additional EC problem in UWSN, which degrades the network performance. Extra resource allocation and network security are the key challenges in [16; 17], respectively. However, the network is facing a high E2E delay. Additionally, both protocols are not performing effectively in the sparse network. Complexity and data loss are the serious challenges in [18; 19]. Moreover, extra memory is needed to compute extra calculations in [18; 19]. Overhead in routing and security issues with the complex networks are the important challenges in [20; 21]. In the end, packet E2E delay and network complexity is increased in the aforementioned papers.
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 4 Edge Node Deployment Algorithm Design
### Improved Sand Cat Swarm Optimization Algorithm
#### 4.2.2 Spiral Exploration Strategy  
During the exploration phase, the sand cat identifies prey by considering the optimal location, current position, and sensitivity range. This phenomenon is mathematically described by Equation (22):  
\[pos(i+1)=r(pos_{bc}(i)-rand(0,1)pos_{c}(i)) \tag{22}\]  
where \(pos_{bc}(i)\) is the optimal solution, \(pos_{c}(i)\) is the current position, and \(r\) is the sensitivity range.  
Spiral exploration within the algorithm facilitates the discovery of multiple search paths and assists individuals in making more effective adjustments to their positions. Following the integration of spiral exploration, individuals perform search operations within  
Figure 6: The flowchart of the ISCSO.  
the search space in a spiral pattern. Expanding their exploratory capabilities enhances the algorithm's potential to escape local optima and improves its overall global search performance. The update formula is represented by Equation (23):  
\[pos(i+1)=o*r(pos_{bc}(i)-rand(0,1)pos_{c}(i)) \tag{23}\]  
Equation (24) signifies the computation of the spiral exploration factor, denoted as \(o\):  
\[o=exp(bg)cos(2\pi g) \tag{24}\]  
where \(b\) represents the spiral shape constant and \(g\) represents the path coefficient, where \(g\in[-1,1]\).
## 3 HKE Spectra and Spectral Energy Budgets  
### Methodology  
In WZ2018, they calculated the energy spectra using the two-dimensional (2D) discrete Fourier transform (DFT). However, using the DFT on regional domains with an aperiodic structure will result in the aliasing of large-scale variance into shorter scales; thus, destroying all usefulness of spectra at large wavenumbers; while the method of discrete cosine transform (DCT) can solve this problem effectively [30]. Thus, in this study, HKE spectra and various spectral budget terms are calculated based on the 2D DCT of fields over the inner domain. Let \(\phi(\mathbf{k})\) be the DCT of field \(\phi(x,y)\), where \(\mathbf{k}=(k_{x},k_{y})\) is the horizontal wave vector. The HKE spectrum per unit volume is defined as  
\[E_{h}(\mathbf{k})=\overline{\rho}\Big{[}\overset{\alpha}{\mathbf{u}}(\mathbf{ k})\cdot\overset{\alpha}{\mathbf{u}}(\mathbf{k})\Big{]}/2=\overline{\rho}[\dot{a}( \mathbf{k})\dot{a}(\mathbf{k})+\dot{\sigma}(\mathbf{k})\dot{\sigma}(\mathbf{k })]/2 \tag{1}\]  
where \(\overline{\rho}\) is the height-variant basic-state density and \(\mathbf{u}=(u,v)\) is the horizontal velocity vector. Figure 2: (**a**) Profile of the basic-state Brunt-Vääisälä frequency and (**b**) time-height distribution of the diabatic heating rate averaged over the main convective region (K h\({}^{-1}\)). The HKE spectrum can be further decomposed into horizontally rotational and divergent components [\(E_{R}(\mathbf{k})\) and \(E_{D}(\mathbf{k})\)], respectively [18], which are defined as  
\[E_{R}(\mathbf{k})=\overline{p}\hat{\zeta}(\mathbf{k})\hat{\zeta}(\mathbf{k})/ \big{(}2|\mathbf{k}|^{2}\big{)}\text{ and }E_{D}(\mathbf{k})=\overline{p}\delta(\mathbf{k})\hat{\delta}(\mathbf{k})/ \big{(}2|\mathbf{k}|^{2}\big{)} \tag{2}\]  
where \(\zeta=\partial v/\partial x-\partial u/\partial y\) is the vertical vorticity and \(\delta=\partial u/\partial x+\partial v/\partial y\) is the horizontal divergence. Following [PERSON] et al. [31], the spectral HKE budget formulation is given by  
\[\partial E_{h}(\mathbf{k})/\partial t= t_{h}(\mathbf{k})+\partial_{z}F_{h\uparrow}(\mathbf{k})+ \partial_{z}F_{p\uparrow}(\mathbf{k})+C_{A\to h}(\mathbf{k}) \tag{3}\] \[+\text{Div}_{h}(\mathbf{k})+H_{h}(\mathbf{k})+J_{h}(\mathbf{k}) +D_{h}(\mathbf{k})\]  
where \(\partial E_{h}/\partial t\) is net tendency term of HKE; \(t_{h}\) is spectral transfer term due to nonlinear interactions; \(F_{h\uparrow}\) and \(F_{p\uparrow}\) are HKE vertical flux and pressure vertical flux, respectively, and \(\partial_{z}F_{h\uparrow}\) and \(\partial_{z}F_{p\uparrow}\) are the vertical divergence of the corresponding vertical flux; \(C_{A\to h}\) represents the spectral conversion of available potential energy (APE) to HKE; \(\text{Div}_{h}\) is the spectral tendency due to the three-dimensional (3D) divergence; \(H_{h}\) is the spectral tendency due to diabatic processes; \(J_{h}\) is the adiabatic nonconservation term; and \(D_{h}\) is the dissipative term.
## 2 Materials and Methods
### Evaluation and Calculation  
The main assessment criteria are the mass loss of the tested specimens calculated according to Formula (1) and the relative mass loss rate calculated according to Formula (2).  
\[\delta_{mp}(\tau)=\frac{m-m(\tau)}{m}\cdot 100 \tag{1}\]  
where \(\delta_{mp}(\tau)\) is the mass loss at time (\(\tau\)) (%), \(m\) is the original specimen mass before the experiment (g), and \(m(\tau)\) is the specimen mass at time (\(\tau\)) (g).  
\[v_{r}=\frac{m(\tau)-m(\tau+\Delta\tau)}{m(\tau)\cdot\Delta\tau}\cdot 100 \tag{2}\]  
where \(v_{r}\) is the relative mass loss rate (%\(\cdot\)s\({}^{-1}\)), \(m(\tau)\) is the specimen mass at time (\(\tau\)) (g), \(m(\tau+\Delta\tau)\) is the specimen mass at time (\(\tau+\Delta\tau\)) (g), and \(\Delta\tau\) is the time interval at which the masses are read (s). The time interval for reading the mass is 10 s in our case.  
Another significant parameter is the ratio of the maximum value of the mass loss rate and the time to reach this value. As the value grows, the rate of flame spread increases [40]. This value is determined as an average only for the first peak of the mass loss rate according to Formula (3).  
\[R_{fs}=\frac{v_{r}}{\tau(v_{r})} \tag{3}\]  
where \(R_{fs}\) is the fire spread rate (%\(\cdot\)s\({}^{-2}\)), \(v_{r}\) is the relative mass loss rate (%\(\cdot\)s\({}^{-1}\)), and \(\tau(v_{r})\) is the time to reach the first peak of the maximum mass loss rate (s).  
The results in the graphs and tables are specified as average values.  
For the mass loss parameter, several comparisons were subjected to one- and two-factor analysis of variance (ANOVA) using the statistical software R (The R Project for Statistical Computing) version 4.1.2.  
Figure 2: Test equipment scheme (legend: 1—gas bomb, 2—gas supply to the burner, 3—Bunsen burner, 4—burner holder, 5— specimen holder, 6—scales, 7—connection of the scales to the computer, 8—computer).
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 1 Introduction
We propose a method based on bit plane separation and cross-reorganization to protect sensitive information in medical images. The bit plane separation method is based on image segmentation, and the reorganization method protects the high-bit sensitive pixels in the ROI. 3. We propose a method for medical image tampering recovery by compressed data repeated filling. In the authentication stage, if the authentication fails due to tampering, the data of the untampered area can be directly used for tampering recovery. The structure of the rest of this paper is as follows. Section 2 describes the basic theory related to the proposed scheme. In Section 3, the hiding and authentication process based on double POB system in medical images is detailed. The experimental results and analysis are given in Section 4. Finally, this paper is concluded in Section 5.
# Multi-User Linear Equalizer and Precoder Scheme for Hybrid Sub-Connected Wideband Systems
## 1 Introduction
### Organization and Notation  
This paper is organized as follows: Section 2 describes the transmitter, channel, and receiver system model. In Section 3 the analog precoder employed at each UT is described, while in Section 4 the sub-connected hybrid analog-digital multi-user equalizer is derived. In Section 5, the main performance results are presented. Finally, the conclusions are discussed in Section 6.  
The following notation is used in this paper: boldface uppercase letters, boldface lowercase letters, and italic letters denote matrices, vectors and scalars, respectively. The operations \((.)^{T},(.)^{H},(.)^{*}\), and \(tr(.)\) represent the transpose, the Hermitian, the conjugate, and the trace of a matrix, respectively. The operator \(\mathrm{diag}(\mathbf{A})\) corresponds to the diagonal entries of the matrix \(\mathbf{A}\). The identity matrix of size \(N\times N\) is denoted \(\mathrm{I}_{N}\). \(\mathbb{E}[.]\) and \(\left\{\alpha_{l}\right\}_{l=1}^{L}\) represent the expectation operator and an \(L\) length sequence, respectively. \(\left|a\right|\) denotes the absolute value of a. \(\left[\mathbf{A}\right]_{n,l}\) represents the entry of the \(n\)th row and \(l\)th column of the matrix \(A\). The indices, \(t,k\), and \(u\) represent the time domain, subcarrier in the frequency domain, and user terminal, respectively.
## 3 Scheme Design
### Iterative Algorithm Based on FPP-SCA
#### 3.1.2 Optimization of Source Beamformer \(\mathbf{F}_{i}\)  
Similarly, \(\mathbf{F}_{i}\) can be optimized given \(\mathbf{F}_{r}\) and \(\mathbf{W}_{i}\). According to Equation (13), the original problem in Equation (11) can be transformed into Equation (16)  
\[\min_{\mathbf{f}_{i}} \mathbf{f}_{i}^{H}\mathbf{Q}_{i}\mathbf{f}_{i}-\mathbf{q}_{i}^{H }\mathbf{f}_{i}-\mathbf{f}_{i}^{H}\mathbf{q}_{i}+\mathbf{C}_{i}+\lambda\left\| \mathbf{s}\right\|\] (16a) s.t. \[\mathbf{f}_{i}\mathbf{f}_{i}^{H}\leqslant p_{i} \tag{16b}\] \[\mathbf{f}_{i}^{H}\mathbf{Q}_{1i}\mathbf{f}_{i}\leqslant p_{r}- Tr(\mathbf{Q}_{f_{i}})\] (16c) \[\mathbf{f}_{i}^{H}\mathbf{Q}_{2 iD}^{{}^{\prime}}\mathbf{f}_{i} \leqslant\Delta_{iD}\] (16d) \[\mathbf{f}_{i}^{H}\mathbf{Q}_{2 iD}^{{}^{\prime}}\mathbf{f}_{i} \leqslant\Delta_{i\D}\] (16e) \[s_{m}\geqslant 0,\;\;m=1,2, \tag{16f}\]  
where \(\mathbf{f}_{i}=vec(\mathbf{F}_{i})\), \(\mathbf{Q}_{i}=\beta_{\tilde{1}}\left(\mathbf{I}_{M_{T}}\otimes\mathbf{A}_{i }\mathbf{A}_{i}^{H}\right)\), \(\mathbf{q}_{i}=\sqrt{\beta_{\tilde{1}}}vec\left(\mathbf{A}_{i}\right)^{H}\), \(\mathbf{A}_{i}=\mathbf{W}_{\tilde{1}}\mathbf{H}_{RS_{\tilde{1}}}\mathbf{F}_{r} \mathbf{H}_{S_{i}R}\), \(D=1\), \(\Dcal{D}=2\), \(\mathbf{Q}_{2 iD}^{{}^{\prime}}=-\mathbf{I}_{M_{T}}\otimes\mathbf{Q}_{iD}^{H} \mathbf{Q}_{iD}\), \(\mathbf{Q}_{iD}=\mathbf{H}_{RS_{D}}\mathbf{F}_{r}\mathbf{H}_{S_{i}R}\), \(\Delta_{iD}=Tr\left(\mathbf{Q}_{SiD}+\sigma_{S_{D}}^{2}\mathbf{I}_{M_{R}} \right)-\frac{\tilde{\epsilon}_{D}}{(1-\beta_{D})}+s_{D}\), \(\mathbf{Q}_{SiD}=\mathbf{H}_{RS_{1}}\mathbf{F}_{r}\mathbf{Q}_{Ci}\mathbf{F}_{r }^{H}\mathbf{H}_{RS_{1}}^{H}\), \(\mathbf{Q}_{Ci}=\mathbf{J}_{i}\mathbf{C}_{i}^{H}+\sigma_{T}^{2}\mathbf{I}_{N_{ R}}\), \(\mathbf{Q}_{1i}=\mathbf{I}_{M_{T}}\otimes\mathbf{H}_{S_{i}R}^{H}\mathbf{F}_{r}^{H} \mathbf{F}_{r}\mathbf{H}_{S_{i}R}\), \(\mathbf{Q}_{f_{i}}=\mathbf{F}_{r}\mathbf{J}_{i}\mathbf{J}_{i}^{H}\mathbf{F}_{ r}^{H}+\sigma_{r}^{2}\mathbf{F}_{r}\mathbf{F}_{r}^{H}\), \(\mathbf{C}_{i}=Tr\left(\mathbf{C}_{iD}+\mathbf{C}_{iD}+\sigma_{S_{i}}^{2} \mathbf{W}_{i}\mathbf{W}_{i}^{H}+\sigma_{S_{i}}^{2}\mathbf{W}_{i}^{*}\mathbf{W} _{i}^{H}+2\mathbf{I}_{M_{T}}\right)-\sqrt{\beta_{i}}\mathbf{Tr}\left( \mathbf{R}_{W}\right)-\sqrt{\beta_{i}}\left(\mathbf{R}_{W}^{H}\right)+Tr\left( \mathbf{R}_{W}\mathbf{R}_{W}^{H}\right)\), \(\mathbf{R}_{W}=\mathbf{W}_{\tilde{1}}\mathbf{J}_{i_{B}}\), and \(\mathbf{C}_{iD}=\sigma_{r}^{2}\beta_{D}\mathbf{W}_{D}\mathbf{H}_{RS_{D}} \mathbf{F}_{r}\mathbf{F}_{r}^{H}\mathbf{H}_{RS_{D}}^{H}\mathbf{W}_{D}^{H} \mathbf{I}_{M_{R}}\).  
The optimized \(\mathbf{f}_{1}\) and \(\mathbf{f}_{2}\) can be separately obtained from Equation (16) using a similar FPP-SCA method foresaid.
## 4 Our Findings
### Probability of Blockchain Attacks  
We attempted to identify the probability of various blockchain attack types, which varies significantly based on the underlying vulnerabilities and the operational design of blockchain systems. Smart contract vulnerabilities exhibit the highest probability compared to other types of vulnerabilities in almost every case, and there are different key factors, including code vulnerabilities, questionable audit quality, and lack of formal verifications. These vulnerabilities often lead to attacks like reentrancy exploits, which are particularly devastating in decentralized finance (DeFi) platforms.  
Table 3 illustrates the analysis of the likelihood of various blockchain attacks, and reveals significant variations in attack probabilities. Man-in-the-middle (MITM) attacks and double-spending attacks also demonstrate relatively high probabilities, at around 35-45%. The prevalence of MITM attacks arises from weaknesses in communication protocols and insufficient encryption mechanisms in blockchain networks. Double-spending attacks, meanwhile, are facilitated by delayed transaction confirmations or inadequate consensus mechanisms in Proof-of-Work (PoW) or hybrid systems.  
Other attack types, such as Sybil attacks, leverage weaknesses in node authentication processes, allowing attackers to disproportionately influence network consensus or disrupt data propagation. Similarly, routing attacks and eclipse attacks exhibit moderate likelihoods, targeting network-layer vulnerabilities to isolate or delay nodes. Lower-probability attacks, like replay attacks, occur in specific contexts such as double spending or hard forks but remain critical threats in those scenarios.  
These probabilities highlight the higher risks associated with certain attack types, such as double-spending attacks and smart contract vulnerabilities. Prioritizing security measures for these high-risk areas can significantly enhance blockchain resilience. These findings provide critical insights for researchers and practitioners, offering a roadmap for addressing blockchain security challenges effectively.
# A Brief Review on the Recent Experimental Advances in Thermal Rectification at the Nanoscale
## 2 Experimental Study
### Thermal Rectification in Suspended Monolayer Graphene
Similarly, Sample 5 has a higher thermal conductivity as heat flows from the wide region to the narrow region, and the measured thermal rectification coefficient reaches 11%. In both cases, the thermal rectification effect is caused by the asymmetric two-dimensional geometries. This results from the strong spatial and temperature dependence of the heat conduction of phonons in graphene. Due to the phonon edge scattering effect [42], the mean free path of the narrow end of graphene is smaller than that of the wide end. At the same time, since the phonon population density on the hot side of graphene is higher than that on the cold side, if the narrow end is placed on the hot side, the edge scattering effect will impose more restrictions on the propagating phonons and cause a bottleneck effect. As a result, the thermal conductivity in the heat flow direction from the narrow end to the wide end is smaller than that in the opposite direction. The thermal rectification effect in Sample 4 follows the similar principle that the carbon atoms deposited on graphene cause stronger phonon scattering, which reduces the mean free path of phonons and plays a similar role to the narrow end of the trapezoidal graphene. Therefore, the thermal conductivity in the heat flow direction from the deposition region to the smooth region is lower than that in the opposite direction. It is worth mentioning that [PERSON] and [PERSON] et al. conducted similar work independently in 2014, and realized a thermal rectification in asymmetric graphene nanoribbons with a rectification ratio up to 10% [8]. Figure 6: Comparison between measured thermal conductivity as a function of sample temperature before and after drilling nanopores. Reprinted with permission from Springer Nature Publishing Group. This work experimentally verified two different physical mechanisms of the thermal rectification phenomenon in suspended monolayer graphene, providing inspiration for the future design of two-dimensional thermal rectifiers.
# Determining the Optimal Positions of Infill Balise Groups for ERTMS/ETCS Level 1 Applications
## 4 Discussion  
With the work presented in this paper, a conclusive, open-source algorithm for the optimization of infill balise groups in ETCS level 1 applications is available now. We achieved this without the need for a complete runtime calculator while still retaining enough leeway to adapt to different problems and train characteristics. However, at this stage, some manual preprocessing is needed to have the data available in the necessary format and granularity.  
Interestingly, when comparing the ratios of infill balise group positions to the IP of the scenario with two infill balise groups from our results, it seems to stabilize around a value of 0.30 for higher speeds. [PERSON] et al. [38], with their mathematically driven model, came to a ratio of around one-third for a comparable scenario. In contrast to [PERSON] [32], we set the objective on minimizing the weighted additional runtime and not on directly quantifying the accompanying capacity effects. This was chosen to reduce the complexity of the algorithm developed, supporting a potential application in planning scenarios. The chosen point of view, however, does not neglect the topic of capacity entirely. The scenarios used to calculate the optimal positions of the infill balise groups mimic a situation where a train must initiate braking due to the track ahead not being free and available, which should only occur in case of delays or disturbances. As the running and clearing times are essential components of the blocking times [50] of a train on a given infrastructure, increasing them leads to longer headway times, higher capacity consumption, and ultimately to a decrease in capacity overall. Therefore, aiming for the lowest weighted additional runtime does in fact have positive effects on the capacity consumption and knock-on delays during operations.  
In a future version, changes in line speed and gradient might be added to further expand the algorithm's usability and adaptability. Parts of the current preprocessing--especially obtaining the deceleration and the IP--could be integrated into the software by implementing parts of the UIC conversion model and the ETCS braking curves calculation. Additionally, a method to perform a comprehensive optimization considering different train types with individual characteristics should be developed. Some methodological approaches were presented in Section 2.4 and could form the foundation for this enhancement. With that, the best equipment for mixed-traffic infrastructures could be derived in one single framework and workflow without the need to manually decide where to place the infill balise groups in case a compromise is needed.
# An Efficient Resource Allocation Algorithm for Device-To-Device Communications
## 2 System Model and Problem Planning
### System Model
Therefore, its SINR is expressed as follows:  
\[SINR_{d}^{n}=\frac{P^{d}G^{d_{t}d_{r}}}{\sum_{c\in M}X_{c}^{n}Pc^{c,d_{r}}+ \sum_{j\in W}X_{j}^{nPj}G^{j,d_{r}}+\sum_{i\in H,j\
eq d}X_{i}^{nPd}G^{i,d_{r }}+N_{0}} \tag{5}\]  
where \(G^{d_{t},d_{r}}\) is the channel gain between the D2D user \(d\) transmitter \(d_{t}\) and the receiver \(d_{r}\); \(G^{c,d_{r}}\) is the channel gain of the macro cell user \(c\) to the D2D receiver \(d_{r}\); \(G^{j,d_{r}}\) is the channel gain of the microcell user \(j\) to the D2D receiver \(d_{r}\); \(G^{j,d_{r}}\) is the channel gain of the D2D user \(i\) to the D2D receiver \(d_{r}\).
# Design Optimization of a District Heating Network Expansion, a Case Study for the Town of Kiruna
## 2 Case Study  
The town of Kiruna holds 18,000 citizens, and is located in the very North of Sweden, 150 km above the Arctic Circle. The climate is frigid in that region, with an annual average temperature well below 0 \({}^{\circ}\)C. The DH network in Kiruna was commenced from the 1960s and is owned by the municipal energy company Tekniska Verken i Kiruna AB (TVAB). It supplies room heating and hot tap water to 1700 customers (30% of the small houses and 95% of the premises), currently delivering 250 GWh/y through 120 km of pipes with a peak demand of 49 MW.  
Figure 3 shows a sketch of the current DH network in which the meshed part of the network is marked in blue and the feeding branches in red. The network has several heat production sites: the larger ones are marked with factory symbols (TVAB is the main one, while LKAB represents the waste heat from the nearby iron mining facility) and the smaller boiler houses are marked with boiler symbols.