esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Exacavation Compensation Effect  
Mining can cause stress concentration in the roadway surrounding rock, deformation, and damage in the surrounding rock, and roadway accidents. This situation is more evident under the influence of secondary mining. According to the numerical modeling results, under the influence of secondary mining, the maximum principal stress can reach 10 times the original rock stress.  
According to the Mohr-Coulomb theory, the failure of a rock mass is mainly determined by its maximum principal stress (\(\sigma_{1}\)) and minimum principal stress (\(\sigma_{3}\)), as shown in Figure 14a. In this case, the rock surrounding the roadway is in the original rock stress state. When mining activities are conducted, the original rock stress state of the surrounding rock is disrupted. The stress concentration caused by the \"excavation effect\" is the fundamental cause of surrounding rock damage; the stress state of the surrounding rock at this time may be as that shown in Figure 14b. Roadway maintenance aims to achieve \"excavation compensation effect\" to offset the \"excavation effect\" through certain means, in order to reduce the stress concentration and area of Mohr's circle of the surrounding rock; in this case, the stress state of surrounding rock may be as that shown in Figure 14c.  
Figure 14: Mohr–Coulomb theory: (**a**) original rock stress state; (**b**) excavation effect; (**c**) excavation compensation effect.  
The most effective method for achieving the \"excavation compensation effect\" is to reduce the maximum principal stress of the surrounding rock and increase its minimum principal stress. Commonly used methods include roof cutting and pressure relief, roadway support technology, shotcrete and grouting, and excavation space filling. In this study, the considered study site had low in situ stress and a simple geological structure. Conventional surrounding rock control methods can meet the requirements of safety. For some roadways with largely buried depths, high stresses, complex structures, or soft rocks, more complex means are needed to achieve the \"excavation compensation effect\". However, this is a challenge that needs to be addressed through future research in the field of underground engineering.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 4 Machine Learning and AI for Network Slicing Beyond 5G Systems
### Unsupervised Learning for Network Slicing  
In unsupervised learning, unlabeled data are provided and the purpose of the algorithm is to find the hidden or missing information from the unlabeled data pattern. Approach of unsupervised learning categorized into classification, anomaly detection, and dimension reduction. The main purpose of clustering is to form a class of data samples into various classes according to their analogy. Dimension reduction compress the data without losing useful information. For example, principal component analysis (PCA) is one of the algorithms for dimension reduction, which helps to compress the transmitted data. By finding the linear relationship between the sensors, the least-variance elements are neglected [30].
## 2 Background and Theory
### Classification Algorithms  
This study incorporates five machine learning models: LinearSVM, CART, GSVM, stochastic gradient descent (SGD), and SVM with a polynomial kernel. A detailed explanation of each classifier is provided in this section.
# Lateral Capacity of URM Walls: A Parametric Study Using Macro and Micro Limit Analysis Predictions
## 2 Limit Analysis Formulation
### Micro LA Formulation  
In the micro LA formulation, the dry-stack assemblage is represented by rigid blocks connected by frictional contact interfaces with a non-associative flow rule, with zero dilation (Figure 2).  
The solution scheme proposed in [52], involving a non-associative frictional flow rule consisting of sequential solutions of linear programs, is adopted (Figure 2b). At each iteration a static LA problem is defined in the form of a linear program (LP) as:  
\[\begin{array}{ll}\text{Maximize}&\lambda\\ \text{Subject to}&\mathbf{Bq}-\lambda\mathbf{f}_{L}=\mathbf{f}_{D}\\ &\mathbf{C}^{T}[\mathbf{q}-\mathbf{c}]\leq 0\end{array} \tag{6}\]  
where \(\lambda\) is the load multiplier and \(\mathbf{q}\) the vector of unknown contact forces, \(\mathbf{f}_{L}\) and \(\mathbf{f}_{D}\) are the live and dead loads, \(\mathbf{c}\) is the cohesion vector, \(\mathbf{B}\) and \(\mathbf{C}\) are the equilibrium and yield  
Figure 1: Kinematic description of the sliding-rocking mechanism for an in-plane shear wall and definition of \(\alpha_{b}\) for quasi-periodic bond patterns.  
Figure 2: (a) Dry-stack masonry wall, (b) yield function for the non-associative solution.  
constraint matrices. The first constraint represents the equilibrium of forces, whereas the second is the condition for yielding (failure) of the contact interfaces.  
The yield conditions are updated at each iteration based on the normal forces at the previous iterations:  
\[\begin{split}& s_{i,j}\leq c_{i}+\alpha\cdot\mu_{i}\cdot n_{i,j}\\ & c_{i,j+1}=c_{i}^{0}+(1+\alpha)\cdot\left(\beta\cdot n_{i,j}+(1- \beta)\cdot n_{i,j-1}\right)\cdot\tan(\varphi_{i})\end{split} \tag{7}\]  
Here, \(s_{i,j}\) and \(n_{i,j}\) are the shear and normal forces of the \(i\)-th interface at the \(j\)-th iteration. \(\alpha\) and \(\beta\) are algorithm parameters set to 0.01 and 0.6, respectively.  
Finally, the steps of the iterative algorithm are the following [52]:  
1. Set up the micro LA problem, according to Equation (6) with associative-frictional yield condition.
2. Solve the LP and save the load multiplier \(\lambda_{0}\) and the normal forces at each interface \(\mathbf{n}_{0}\).
3. Modify the shear failure condition based on the previous iteration, according to Equation (7).
4. Solve the LP with the modified yield conditions in Step 3 and save the load multiplier \(\lambda_{j}\) and normal contact forces \(\mathbf{n}_{j}\).
5. If the exit condition \(\left(\frac{\left|\lambda_{j}-\lambda_{j-1}\right|}{\lambda_{j}}\leq\text{ tolerance}\right)\) is true, the algorithm terminates. Else, repeat from Step 3.
6. Calculate the kinematic variables (displacement rates) from the dual linear program.  
The algorithm has been implemented in a custom computer code in the JAVA programming language, and the interior point LP solver of the MOSEK optimisation software ([[https://www.mosek.com/](https://www.mosek.com/)]([https://www.mosek.com/](https://www.mosek.com/)) (accessed on 28 January 2021)) has been used for the subsequent numerical studies.
# EconLedger: A Proof-of-ENF Consensus Based Lightweight Distributed Ledger for IoVT Networks
## 5 Experiment and Evaluation
### Performance and Security Analysis
#### 5.4.4 Analysis of Possible attacks  
_1. Double spending attacks_: In a double spending scenario, an adversary attempts to revert a transaction that has been finalized on the distributed ledger. In Econledger, a voting-based chain finality mechanism ensures the total order and persistence of data recorded on the distributed ledger. Thus, once a transaction is finalized in the checkpoint block, all other honest validators will work on the finalized main chain and disregard any double spending transactions from attackers.  
_2. Free-riding attacks_: There is a possibility of free-riding attacks that some lazy nodes only gain benefits by using the security service without fulfilling their responsibilities in the EconLedger network, such as forwarding messages or submitting ENF proofs. The punishment strategies can prevent against free-riding attacks by reducing credit stake of dishonest nodes or even isolating them from the entire network.  
_3. Selfish-mining attacks_: In a selfish-mining attack, the adversary tries to withhold blocks and release them strategically to reduce chain growth and increase the relative ratio of his proposed blocks. In PoENF consensus, only valid blocks generated in the current round can be accepted by honest validators, while those outdated blocks are discarded. Moreover, withholding blocks is a type of misbehavior in PoENF, and it decreases both profits and credit of a dishonest node. Therefore, selfish-mining is unprofitable for rational validators according to reward and punishment strategies.  
_4. ENF-proof replay attacks_: The adversary can launch replay attacks by sending duplicated ENF proofs. As ENF fluctuations of power grid vary as time changes, the duplicate ENF proofs generally output large ENF scores. As a result, these Byzantine validators have marginal chances to propose valid blocks. Furthermore, ENF-proof replay attacks can be detected by analyzing ENF proofs on EconLedger. Thus, identifying misbehavior and isolating suspicious nodes can improve system robustness, while we leave ENF-based detection topics to future work.
# Photonic Integrated Circuits for NGPON2 ONU Transceivers (Invited)
## 3 Passive Optical Networks
The latter has limited high-frequency performance and tough mechanical design challenges to meet the right soldering robustness, with an expected high impact on the electrical performance of assemblies and sub-assemblies. Among PON future technologies, high interest has been given to the research/development of long-reach/high-speed transmission, where optical amplifiers, electrical domain digital signal processing (DSP) to enhance the link budget, and/or transceiver technology are predicted to be key factors [73]. Generically, for N subscribers, a PON requires a single transceiver at the central office, which results in a total of N + 1 transceivers overall [67]. For NGPON2, the OLT transceiver should contain four transmitters (Tx) and four receivers (Rx) with an internal wavelength mux/demux optical module. In the ONU, a 10 Gbps transceiver with both tunable Tx and Rx is required [70]. By employing wavelength-selective branching devices in ODN, losses can be diminished, nevertheless at the cost of wavelength transparency waiving. Furthermore, by increasing the number of wavelengths, a high number of transceivers at the OLT is expected, raising power consumption, port density, and cost [73]. Similar to what happened with integrated circuits' contribution to electronics development, PIC technology is one of the most attractive solutions to tackle the referred issues. Thus, integrated photonics can have a key role in the deployment of more flexible PON networks, as they can incorporate different optical components with various functions in a potentially cost-effective way without increasing the assembly process complexity [7, 8].
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 4 Implementation and Analysis of Real-Time SR Algorithms in IoT Context
### Challenges in Hardware Deployment  
Deploying SR algorithms on IoT devices poses numerous obstacles. One significant challenge is the low computing capability of IoT devices, which sometimes lack the processing capabilities needed for complicated SR algorithms. This constraint involves the creation of lightweight models or the use of optimization techniques like pruning and quantization. Another issue is memory limits, since many IoT devices have limited RAM and ROM. This limits the size and complexity of deployed SR models. For example, creating S-Boxes with combinational logic rather than look-up tables can drastically reduce memory needs [86].  
Furthermore, energy efficiency is crucial for battery-powered IoT devices, which necessitate solutions with low power consumption. For example, NeuriCam's [85] dual-camera hardware prototype produces 640 \(\times\) 480 color wireless video at 15 fps while consuming just 46 mA, demonstrating the promise for energy-efficient SR in IoT applications.  
Subsequently, hardware heterogeneity in IoT networks [87] is a significant barrier. These ecosystems frequently comprise disparate hardware platforms with differing capabilities, making it challenging to provide universally compatible SR solutions. Addressing these problems is critical to the effective implementation of SR algorithms on IoT devices.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SR Method** & **Latency (ms)** & **Energy (mW)** & **PSNR** & **SSIM** \\ \hline Bicubic Interpolation & 50 & 400 & 24.3 & 0.821 \\ \hline SRCNN & 120 & 750 & 25.6 & 0.833 \\ \hline ESPCN & 80 & 750 & 26.9 & 0.852 \\ \hline [PERSON] et al. [84] & 95 & 700 & 25.6 & 0.869 \\ \hline NeuriCam [85] & 200 & 850 & 26.5 & 0.872 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of SR methods on Raspberry Pi 3 for the scale factor of x4.
# Computationally Efficient Channel Estimation in 5G Massive Multiple-Input Multiple-output Systems
## 4 Proposed Algorithm
### Complexity Analysis
#### 4.3.1 Initial Value and First Iteration Calculation  
Equation (15) requires \(\mathbf{2K}\) multiplications. The first iteration is mainly to calculate \(\mathbf{r^{(0)}}=\hat{\mathbf{y}}-\mathbf{W_{S}}^{(0)}\), \(\mathbf{p^{(0)}}=\mathbf{Wr^{(0)}}\), and scalar. Obviously, the respective \(\mathbf{4K^{2}}\), \(\mathbf{4K^{2}}\), and \(\mathbf{4K}\) sub-multiplications are required. Combining the first iteration of Equation (18), a total of \(\left(\mathbf{2K^{2}}+\mathbf{10K}\right)\) multiplications are required.
# The Optimized Production of 5-(Hydroymethyl)furfural and Related Products from Spent Coffee Grounds
## 1 Introduction
presented a design composed of two initial extractions, one aqueous and one lipophilic, followed by the fermentation of the remaining solid waste [22]. The first extraction was intended to remove high value extracts for upgrading to pharmaceuticals or for the cosmetic industry, while the product of the second extraction were triglycerides, used in the production of biodiesel. Using a similar design, [PERSON] et al. replaced the fermentation process by pyrolysis and torrefaction to produce biochar and bio-oil [23]. While the C6 sugars can be fermented into a range of products, another application is in the acid catalyzed dehydration to produce 5-hydroxymethylfurfural (HMF) [24]. HMF can be used in a wide range of applications such as polymers and biofuels. Two of the most common applications include dimethylfuran (DMF) and 2,5-furundicarboxylic acid (FDCA). While DMF is known for being a potential biofuel with comparable energy density to gasoline, FDCA is a building block used in the production of polyesters, one of which, PEF, is a potential replacement for PET [25; 26]. To date, however, there are no reports of HMF being produced from SCG. In this work, a conceptual HMF biorefinery was investigated with the SCG being fractionated to separate the biomass into three different fractions: a cellulose-enriched fraction (CEF), hemicellulose-enriched fraction (HEF) and lignin-enriched fraction (LEF). This process has been developed for other lignocellulosic biomass, though has again not been demonstrated on SCG, and uses sulfuric acid as a catalyst and a ternary mixture of methyl isobutyl ketone (MIBK), ethanol and water to separate the biomass into the various fractions [27]. More recent work done by [PERSON] et al., demonstrated that the replacement of ethanol in the ternary system with acetone leads to a more effective fractionation process [28]. In this investigation, these solvent systems were then examined for the suitability to produce multiple product streams from SCG, with a focus on the production of HMF.
# Fast Planar Detection System Using a GPU-Based 3D Hough Transform for LiDAR Point Clouds
## 3 Planar Detection System Using GPU-Based 3D Hough Transform
### System Overview  
The flowchart of our proposed fast planar detection system is shown in Figure 1. To overcome the point cloud distribution as parallel and horizontal circles in a plane surface, raw point clouds of a whole scene \(P\) are divided into individual \(N\) subsets \(P_{m}\in P\) based on the storage order. The count \(N\) of sub Hough spaces is equal to the subsets' count of point clouds. LiDAR rotate scans the environment in a sector form for each frame so that the sensed points are stored sector by sector in horizontal orientation. This way, the sensed points in a subset \(P_{m}\) locate in the sector of a certain horizontal angle. After dividing global point clouds into several individual subsets, a series of Hough spacesare generated using the 3 DTH algorithm. Each subset point cloud \(P_{m}\) generates a corresponding sub Hough space \(H_{m}\), shown as the blue block in the middle part of Figure 1. Because of the random distribution and non-ideal shapes of outdoor objects, peaks representing the target plane surfaces always exist in several clusters of the valid cells in the generated Hough space. Traditional peak searching methods therefore are not applicable to simultaneously extract multiple peaks in different clusters. Thus, a 3D flag map is generated and updated using a connected component labelling algorithm for each Hough space to cluster connected cells with valid values. As shown in Figure 1, green pixels in the 3D flag map represent the invalid flag cells, and the pixels in other colors represent valid cells. The description of the 3D Hough space and 3D flag map updating method are detailed in Section 4.1.  
Several cells with peak values are subsequently extracted from corresponding clusters. The coordinate values of extracted cells with peaks in a polar coordinate system of planar slides in each point subset are obtained. The separate detected planar slides of the same parameters are merged into an entire plane. To reduce the time consumption in plane detection, this study uses a parallel computing technology to modify the traditional 3 DHT into the parallel 3 DHT algorithm.
## 3 Materials and Methods
### Transition Design Equations and Parameters
#### 3.1.2 Rectangular Waveguide Calculations  
The following design Equations (6)-(11) were used for the analysis of the rectangular waveguide [15]:  
\[\text{Wavelength; }\lambda=\frac{\text{c}}{\text{f}_{0}} \tag{6}\]  
\[\text{Dominant modes; TE}_{\text{mn}}=\text{TE}_{10}\]  
\[\text{Cut-off frequency; }\text{f}_{\text{c}}=\frac{\text{c}}{2\sqrt{\varepsilon_{ \text{r}}\text{h}_{\text{r}}}}\,\sqrt{\left(\frac{\text{m}}{\text{a}}\right)^{ 2}+\left(\frac{\text{n}}{\text{b}}\right)^{2}} \tag{7}\]  
where, m = 1 and n = 0 for the TE\({}_{10}\) mode,  
a = breadth (broadside) of the waveguide,  
b = width of the waveguide  
\[\text{Guided wavelength; }\lambda_{\text{g}}=\frac{\lambda}{ \sqrt{1-\left(\frac{\text{f}}{\text{f}_{0}}\right)^{2}}} \tag{8}\]  
\[\text{Waveguide impedance; }Z_{\text{g}}=\frac{\text{n}}{\sqrt{1-\left(\frac{ \text{f}}{\text{f}_{0}}\right)^{2}}} \tag{9}\]  
where, \(\text{n}=\sqrt{\frac{\text{n}}{\text{c}}}\)  
\[\text{Waveguide back-short distance; }\text{d}=\frac{\lambda_{\text{g}}}{4} \tag{10}\]  
\[\text{Waveguide length; }\text{L}_{\text{WG}}=\text{N}\frac{\lambda_{\text{g}}}{2} \tag{11}\]  
where, N = any integer value other than zero.
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 5 Taxonomy for Task Scheduling Algorithms in Fog Computing
### Problem Solution Techniques
#### 5.2.5 Reinforcement Learning-Based Approaches  
Reinforcement Learning is a subset of machine learning that aims to train agents to make decisions that maximize rewards within a given environment. Reinforcement Learning-Based Approaches refer to fog computing task scheduling algorithms that employ Reinforcement Learning principles to optimize the system's performance [70]. These approaches can handle complex scheduling scenarios and are adaptable to the changing conditions of the system.
## 3 Preliminaries and Definition
### Cryptographic Protocol  
#### 3.2.1 Homomorphic Encryption  
Homomorphic encryption represents a group of semantically secure encryption functions that allow certain algebraic operations on the plaintext to be performed directly on the ciphertext. Homomorphic encryption is useful in scenarios where someone who does not have a decryption key needs to perform arithmetic operations on a set of ciphertexts. Thus, homomorphic encryption is usually used for privacy-preserving operations (e.g., voting) in which operations are performed but operands are not disclosed. The most common definition is as follows.  
Let \(\{\cdot\}_{k}\) denote a probabilistic encryption scheme under key \(k\). Let \(M\) denote the message space and \(C\) the ciphertext space. An encryption scheme is said to be homomorphic if for given \(\{m_{1}\}_{k1}=c_{1}\) and \(\{m_{2}\}_{k2}=c_{2}\), there exists a key \(k\) such that  
\[\forall m_{1},m_{2}\in M,c_{1}\odot_{C}c_{2}=\{m_{1}\odot_{M}m_{2}\}_{k},\]  
for some operators \(\odot_{M}\) in \(M\) and \(\odot_{C}\) in \(C\). If \((M,\odot_{M})\) and \((C,\odot_{C})\) are groups, we have a group homomorphism. We say a scheme is additively homomorphic if we consider addition operators, and multiplicatively homomorphic if we consider multiplication operators.  
RSA, El Gamal [30], Paillier [9; 11], Naccache-Stern [31], Boneh-Goh-Nissm [10] are the well-known homomorphic encryption schemes. In particular, Paillier's encryption has been adopted in many current metering data aggregation schemes [3; 5; 6] in smart grid networks due to its additive homomorphic property: \(\{m_{1}\}_{k}\cdot\{m_{2}\}_{k}=\{m_{1}+m_{2}\}_{k}\).
# Preamble-Based Adaptive Channel Estimation for IEEE 802.11p +
###### Abstract  
Recently, research into autonomous driving and traffic safety has been drawing a great deal of attention. To realize autonomous driving and solve traffic safety problems, wireless access in vehicular environments (WAVE) technology has been developed, and IEEE 802.11p defines the physical (PHY) layer and medium access control (MAC) layer in the WAVE standard. However, the IEEE 802.11p frame structure, which has low pilot density, makes it difficult to predict the properties of wireless channels in a vehicular environment with high vehicle speeds; thus, the performance of the system is degraded in realistic vehicular environments. The motivation for this paper is to improve the channel estimation and tracking performance without changing the IEEE 802.11p frame structure. Therefore, we propose a channel estimation technique that can perform well over the entire SNR range of values by changing the method of channel estimation accordingly. The proposed scheme selectively uses two channel estimation schemes, each with outstanding performance for either high-SNR or low-SNR signals. To implement this, an adaptation algorithm based on a preamble is proposed. The preamble is a signal known to the transmitter-receiver, so that the receiver can obtain channel estimates without demapping errors, evaluating performance of the channel estimation schemes. Simulation results comparing the proposed method to other schemes demonstrate that the proposed scheme can selectively switch between the two schemes to improve overall performance.  
IEEE 802.11p; Preamble; Adaptive Channel Estimation; Intelligent Transportation Systems; Vehicular Communications 13 June 2019; Accepted: 3 July 2019; Published: 5 July 2019