esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Wind-Probability-Distribution-Function-Based Wind Speed Prediction Model
#### 2.2.1 Weibull-Distribution-Based WSP (WEB)  
The Weibull probability density function is a two-parameter distribution with a dimensionless shape parameter \(k\) and a velocity scale parameter \(c\) in m/s [26].  
\[f(u)=\frac{k}{c}\left(\frac{u}{c}\right)^{k-1}\exp\left[-\left(\frac{u}{c} \right)^{k}\right] \tag{2}\]  
where \(f(u)\) denotes the probability distribution of wind speed \(u\). The quality of wind resources can  
Similar to the PM method, the Weibull-distribution-based wind speed prediction model (WEB) assumes that wind speed follows the same distribution for the next time period. So, the mean speed (\(u^{\prime}\)) can be used as a point forecast in the WEB [25].  
\[u^{\prime}=c\ \Gamma\bigg{(}1+\frac{1}{k}\bigg{)} \tag{5}\]  
where \(\Gamma\) is the gamma function, defined as: \(\int_{0}^{\infty}\exp(-u)(u)^{x-1}dx\).  
Median and mode can also be used for forecasting purposes [22]. However, mean speed might not provide accurate predictions due to the skewness in the Weibull probability density function.  
\[median=c\ \left(\ln 2\right)^{1/k} \tag{6}\]  
\[mode=c\bigg{(}1-\frac{1}{k}\bigg{)}^{1/k} \tag{7}\]  
where \(mode=0\) when \(k\leq 1\). It is practically not possible to have a scale factor less than 1 at commercial wind farms [22]. In our study, we used mean speed as point forecast in WEB.  
The cumulative distribution \(F(u)\) is an integral of the probability distribution function given by Equation (8), which gives the probability of getting wind speed \(u\) or less.  
\[F(u)=1-\ \exp\bigg{[}-\Big{(}\frac{u}{c}\Big{)}^{k}\bigg{]} \tag{8}\]  
\[\text{Assume},\ R=F(u)=1-\ \exp\bigg{[}-\Big{(}\frac{u}{c}\Big{)}^{k}\bigg{]} \tag{9}\]  
Using inverse transform, we get:  
\[u=c\Big{[}-ln(1-R)^{\frac{1}{k}}\Big{]} \tag{10}\]  
where \(R\) is a random variable with values between 0 and 1 and, as shown in Equation (9), representing the cumulative distribution function [21]. The values of random variable \(R\) between 0 and 1 should be uniformly distributed. Hence, in this way, we have used Equation (10) to simulate a wind speed using parameters of the Weibull distribution function and the method is represented as WEBS.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 4 SMTrust Design and Analysis
### Trust Index Calculation  
Trust metrics are aggregated according to the assigned weights, and trust index is calculated. Current trust values are given more weightage (0.18) than historical observations (0.10). The success rate, energy, location, mobility, and recommended trust are given equivalent weightage of 0.18 because these are based on the current observations of the nodes. Whereas the historical observation is given less weightage of 0.10 because it is based on past behaviour but still is essential to take into consideration to develop the trust relationship gradually and not spontaneously. The aggregated trust values are ranging from 0 to 1. SMTrust uses the notion of a fuzzy threshold-based mechanism for the evaluation of trust ratings [64]. As evaluated by fuzzy judgment, the degree of trust index is five-tuple and is presented in Table 3 below. The tuple \(t3\) is used as the trust threshold.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Trust Index** & **Trust Rating** & **Trust Value Range** \\ \hline t1 & No Trust & 0.0–0.20 \\ t2 & Poor Trust & 0.21–0.45 \\ t3 & Fair Trust & 0.46–0.70 \\ t4 & Good Trust & 0.71–0.90 \\ t5 & Full Trust & 0.91–1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Five-Tuple Trust Rating.  
SMTrust does not consider the nodes that lie in _t1_ and _t2_ tuple for routing decisions and secure communication. The nodes in tuple _t4_ and _t5_ are considered as reliable and trustworthy to be forwarded for routing decisions. However, the nodes included in _t3_ are in the middle of _\"No Trust\"_ and _\"Full Trust\"_. Hence, SMTrust tends to consider the nodes in _t3_ when there is a shortage or no nodes available in the range of _t4_ and _t5_ tuple for communication, following the strict calculation of the trust index. This is also to overcome the non-availability of nodes for routing. So, the trust threshold is kept at 0.46, which is tuple _t3_. Moreover, the nodes with higher trust values are selected first for routing.
# Broadband Plasmonic Metamaterial Optical Absorber for the Visible to Near-Infrared Region
## 3 Results Analysis
### Performance Evaluation for Solar Application
So, the calculated \(A_{AM1.5}\) implies that efficiency of solar cell photon conversion would be high by considering the proposed meta surface design. A solar absorber must be optically thick to allow near-unity light absorption and photocarrier current collection [34]. That's why thickness of the traditional wafer-based solar cells is typically ranged from 180 to 300 \(\upmu\)m, which increase the overall size of the cell and makes the structure oblique angle sensitive. Nanostructured metallic particles (Plasmonic structures) absorbers can address those conflicting demands. A thin absorber like the proposed plasmonic MA sandwiched in semiconductor material can take advantage of the strong local electrical field enhancement around the metal nanoparticles to increase absorption. In this sandwich structure, nanoparticles act as an antenna to store incident energy as localized surface plasmon mode for the sunlight. Besides, the photocarriers must be generated near to the collection junction area and the carrier diffusion lengths should be smaller to achieve higher energy conversion efficiency. Also, the absorption rate in the semiconductor must be larger than the reciprocal of the typical plasmon decay time, which can be seen for the proposed MA in the Figure 14. In the Figure 14, the metal nanoparticlesshow an intense electrical field close to the surface for TE wave propagation for with a wavelength \(\lambda\) = 800 nm in normal incident angle.
## 2 Materials and Methods
### Recombinant Expression  
DNA coding for both rChit62J4 (resulting product sequence, tag and cleavage site residues are underlined: MGSSHHHHHHSSGENLYPQGGTHMEAQSL... WQKQ, 598 amino acid residues, theoretical MW 65.222 kDa) and rChit62J4 cat (MGSSHHHHHHSSGENLYPQGGTHMEAQSL... LTPV, 357 a.a., 39.395 kDa) was amplified by PCR using primers incorporating _Xhol_ and _BamHI_ restriction sites (Supplementary Table S1) and subcloned into a pET-15 bTEV expression vector. The DNA sequences were verified by sequencing using vector-specific primers. Proteins were heterologously expressed in _E. coli_ BL-21 (DE3) in LB medium induced by the addition of 0.5 mM IPTG (isopropyl \(\beta\)-d-1-thiogalactopyranoside) at 37 \({}^{\circ}\)C. Cells were disrupted using sonication after 4 h of target protein expression. The proteins were purified using a Ni-NTA Superflow(tm) gravity column (QIAGEN, Hilden, Germany), washed with 50 mM imidazole and eluted with 200 mM imidazole. The last step in protein purification was size exclusion chromatography using Superdex 200 (buffer 0.5 M H\({}_{2}\)KPO\({}_{4}\)/HK\({}_{2}\)PO\({}_{4}\), pH 7), and isofocusing using a MonoP column equilibrated in 0.025 mM BisTris, pH 7.0 pH and eluted with a pH gradient by Polybuffer75 pH 4.0. The quality of the resulting protein samples was monitored using SDS-PAGE (Supplementary Figure S1).
# Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
## 3 Classifications of Cyber-Attacks in SGs
### Packet Sniffing Attacks  
This type of attacks is designed to capture information packets in the network and read their content. The term of sniffing is to listen to data traffic. An attacker aims to capture and store all data between two entities by monitoring the network traffic. It is one of the most used methods, and connections must be encrypted for protection [81].
# Synthetical Modal Parameters Identification Method of Damped Oscillation Signals in Power System
## 2 Synthetical Modal Parameters Identification (SMPI) Method
### Empirical Mode Decomposition (EMD) Method  
In essence, the EMD method [13] is widely used in signal pre-processing, which eliminates high-frequency noise from the raw signal by filtering. The procedure of EMD functioned in denoising the oscillation signal is described as follows:  
Step 1: Derivative the input signal \(x(t)\) and extract its extreme value (minimum value and maximum value).  
Step 2: Fit the upper and lower envelopes of \(x(t)\) with cubic spline interpolation function, and get the maximum value \(e_{max}(t)\) and minimum value \(e_{min}(t)\) of the envelope curve.  
Step 3: Calculate the mean value \(m(t)\) of the upper and lower envelope curves as Equation (1), and then subtract \(m(t)\) from \(x(t)\) to obtain the residual \(r(t)\) as Equation (2).  
\[m(t)=\frac{1}{2}(e_{max}(t)+e_{min}(t)) \tag{1}\]  
Figure 1: Flowchart of modal identification using the SMPI method.  
\[r(t)=x(t)-m(t) \tag{2}\]  
Step 4: Determine whether \(r(t)\) meets IMF conditions: (a) The number of local extrema, that is, the total number of local minima and local maxima, and the number of zero crossings differ by at most one; (b) The mean value of the upper and lower envelopes constructed from the local extrema is zero [13].  
Step 5: If the IMF condition is met, take \(r(t)\) as one of the IMF components decomposed from \(x(t)\), and go to Step 6; If the condition is not met, take \(r(t)\) as the new input signal and repeat Step 1 to Step 5 until the condition is met.  
Step 6: Determine whether the stop screening condition is met, that is, the residual \(r(t)\) is monotonous or a constant. If the condition is met, go to Step 8. If the condition is not met, go to Step 7.  
Step 7: Take the difference between \(x(t)\) and \(r(t)\) as the new input signal, and repeat Step 1 to Step 6.  
\[x_{new}(t)=x(t)-r(t) \tag{3}\]  
Step 8: Reshape the original signal into a new signal \(c_{EMD}(t)\) which consisted of total \(n\) IMFs \(c_{i}(t)\) and the last residual \(r_{n}(t)\) as Equation (4).  
\[c_{EMD}(t)=\sum_{i=1}^{n}c_{i}(t)+r_{n}(t) \tag{4}\]
# Application of ANN in Predicting the Cantilever Wall Deflection in Undrained Clay
## 4 Practical Tool for Predicting the Cantilever Wall Deflection  
### ANN-Based Formulation  
For the application of the developed ANN model to practical problems, the current form of ANN can be converted into an explicit mathematical equation. Based on the procedures discussed in the above sections, the formula for calculating the cantilever wall deflection \(\delta\) can be obtained directly from the proposed ANN model by utilizing its weights, biases, activation functions, and normalization factors. The normalized cantilever wall deflection \(\delta_{N}\) is a function of six considered input parameters presented in Figure 7. The above procedures are further illustrated in the Equations (8) and (9) as below:  
\[\delta_{N}=p_{0}+\sum_{i=1}^{T=6}p_{i}G_{i} \tag{8}\]  
\[G_{i}=\text{LOGSIG}(C_{i0}+C_{i1}X_{1}+C_{i2}X_{2}+C_{i3}X_{3}+C_{i4}X_{4}+C_{ i5}X_{5}+C_{i6}X_{6}) \tag{9}\]  
where \(t=6\) is the number of the hidden neurons. Table 2 shows the other coefficients, i.e., \(p_{0}\) to \(p_{n}\) and \(C_{i0}\) to \(C_{i6}\), for the formulation of the cantilever wall deflection.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(i\) & **Standard Deviation (StD)** & \(p_{i}\) & \(C_{\text{th}}\) & \(C_{\text{th}}\) & \(C_{\text{th}}\) & \(C_{\text{2}}\) & \(C_{\text{th}}\) & \(C_{\text{14}}\) & \(C_{\text{15}}\) & \(C_{\text{16}}\) \\ \hline
0 & 0.0151 & 1.3064 & - & - & - & - & - & - & - \\
1 & - & 4.4400 & \(-\)0.6637 & 0.1058 & \(-\)3.4106 & 0.6422 & 0.6699 & 2.9597 & 2.5766 \\
2 & - & \(-\)1.1517 & \(-\)0.0507 & \(-\)0.4111 & \(-\)3.1252 & 1.0541 & 4.4313 & \(-\)2.8763 & 0.8767 \\
3 & - & \(-\)0.7667 & \(-\)0.0094 & 2.1242 & \(-\)0.0013 & 1.2719 & \(-\)1.5536 & 2.8314 & 0.5952 \\
4 & - & 1.5632 & 0.0504 & 3.9952 & \(-\)1.3110 & \(-\)0.9595 & 4.8911 & \(-\)3.0350 & \(-\)2.5302 \\
5 & - & 12.7459 & \(-\)1.5609 & 0.0537 & \(-\)7.9093 & 0.8502 & \(-\)0.9578 & 7.5918 & 2.0398 \\
6 & - & 3.6588 & \(-\)0.0772 & 1.2851 & \(-\)4.3867 & 3.1687 & \(-\)1.4389 & 1.1998 & \(-\)2.9990 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coefficients for formulation of the cantilever wall deflection \(\delta_{N}\).  
Figure 16: Sensitivity analysis of input parameters.  
It is noted that the output in Equation (8) of the cantilever wall deflection \(\delta_{N}\) is a normalized value ranged between \(-1\) and \(1\). Hence, it needs to be transformed into the real value, and the cantilever wall deflection \(\delta\left(\text{m}\right)\) can then be further calculated as:  
\[\delta=4.48\times 10^{-4}+(\delta_{N}{+}1){\times}3.75\times 10^{-1} \tag{10}\]  
where \(\delta_{N}\) and \(\delta\) stand for the normalized and real value of the cantilever wall deflection, respectively.
## 5 Evaluation  
In this section, we introduce the experimental platform and evaluation of QZRAM. Additionally, we use the traditional file read/write micro-benchmark tool FIO (Flexible I/O) and the distributed memory database Redis [30]. The former is used to test various performance improvements of QZRAM compared to ZRAM. The latter shows whether QZRAM can result in performance improvements in memory-intensive applications in real big-data application scenarios.
## 2 Overview of the IEEE 802.15.4e Protocol
### General Superframe Structure  
In a beacon-enabled network, the PAN coordinator periodically broadcasts the beacon that contains the information about the superframe structure. The length of a superframe is known as Beacon Interval (\(BI\)) and is defined by the parameter Beacon Order (\(BO\)).  
\[BI=a\;base\;superframe\;duration\times 2^{BO}symbols,\;0\leq BO\leq 14\]  
The \(BI\) is divided into active and inactive parts as shown in Figure 1. The active part duration is called the Superframe Duration (\(SD\)) and is defined by the parameter Superframe Order (\(SO\)).  
\[SD=a\;base\;superframe\;duration\times 2^{SO}symbols,\;0\leq SO\leq BO\leq 14\]  
In Figure 1, the beacon duration, Contention Access Period (CAP) and CFP are present in the active part of the superframe. The superframe contains 16 superframe slots of equal duration out of which the first slot is reserved for a PAN coordinator to broadcast the beacon frame. It is used for the synchronization with the PAN coordinator and re-synchronization of the devices that go to power saving or sleep mode. In the CAP period, the devices access the channel by using the  
Figure 1: Superframe structure of IEEE 802.15.4e-enabled devices.  
slotted CSMA/CA, whereas in CFP, the devices assess the channel by the reservation-based channel access mechanism.
## 4 Results and Discussion
### Comparison of Evaluation Results  
In the prevention and control of water disasters through grouting, numerous uncertainties exist, including the complexity of geological conditions, variations in the performance of grouting materials, and disturbances during the construction process. To better promote this approach, we aim to establish a comprehensive indicator system encompassing all facets of water hazard grouting prevention and control. Beyond traditional metrics such as grouting material performance, grouting pressure, and grouting volume, factors like geological conditions, hydrogeological parameters, construction technology, engineering costs, and environmental impacts must also be taken into account. For instance, regarding geological conditions, indicators like rock permeability, porosity, and strength can be considered; for hydrogeological parameters, indicators such as groundwater level, water flow velocity, and aquifer thickness can be taken into account; and for construction technology, factors like the layout of grouting holes, grouting sequence, and grouting time can be taken into consideration. Through case analysis of actual water hazard grouting prevention and control projects, we continuously verify and refine the TOPSIS and RSR evaluation methods. A comprehensive evaluation model for grouting effectiveness was developed by integrating subjective and objective weighting methods, the TOPSIS approach, and the RSR sorting and grading method. This model was applied to assess the grouting works within the study area, and the evaluation results were used to delineate the grouting effect zones, as shown in Figure 6.  
The analysis indicates that relatively weak grouting areas are primarily located along the edges of the study area and in the central and western parts of the 110504 working face. Most of the grouting works in the study area fall within the qualified zone, while a smaller portion belongs to the relatively weak zone. A segment of the study area is classified as excellent, mainly situated in the northern region and near the cutting eye of the 110504 working face. The evaluation results were cross-referenced with actual  
Figure 6: Evaluation of grouting effect zoning map.  
water inflows from the coal seam's bottom plate, confirming the scientific reliability of the evaluation model.
# Cryptographic Key Management in Delay Tolerant Networks: A Survey
## 3 Key Management Taxonomy in DTNs  
As already pointed out, cryptographic key management is the foundation of network security, so it is an indispensable part of the DTN security. Key management schemes in DTNs can be classified into three major categories depending on whether they deal with one or more of the following issues (a) security initialization, (b) key establishment, and (c) key revocation. Key establishment can further be classified into two-party and group communications based on the number of communication parties. To exemplify, Figure 1 depicts a classification of the surveyed key management schemes in such networks based on category, type of communication, and the cryptographic methods used. Note that the majority of works related to security and key management in DTN for the aforementioned three main categories are listed in Tables 1-5 in chronological order. It is to be noted that due to the difficulty of performing tests to a real DTN environment, all the works so far evaluate their scheme either theoretically or via some kind of simulation. Therefore, the performance results are not directly comparable to each other. In fact, only a small fraction of the papers have implemented their scheme. Moreover, the existing results are not comparable to each other because the authors address different kind of DTN and/or the simulation parameters are dissimilar. For this reason, and to avoid any bias, we opt to exclude performance results from the tables. Finally, in the last subsection all the so far standardisation efforts in terms of RFCs and Internet drafts are summarised. Figure 1: Key management Taxonomy. \begin{table}
\begin{tabular}{l l l l l l l l} \hline
**Scheme** & **Cypple-Graphy** & **Methods/Protocol/Schemers** & **DTN Network** & **Evaluation Method** & ** Architecture** & **Year** \\ \hline [PERSON], and [PERSON] [24] & PKC/SSC & 1 HIC, Cority-Shiewring HIRC (OIIEE and HHS), & Rural Area DTN & N/A & Centralised & 2005 \\ [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [1] & wathoff distribution & & & & & \\ [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], P., [PERSON], [1] & PKC/SSC & ITC, cellular authentication infrastructure & Rural Area DTN & Theoretical & Centralised & 2007 \\ [PERSON], [PERSON], and [PERSON] [24] & PKC & HIRC & Network-agnostic & N/A & Centralised & 2008 \\ \hline \(\Pi\) [PERSON], [PERSON], and [PERSON] [29] & PKC & social contact information & Rural Area DTN & Simulation & Deer
# Solving Task Scheduling Problems in Dew Computing via Deep Reinforcement Learning
## 3 Related Work
### RL Methods in Edge and Dew Computing  
To the best of our knowledge, this is the first work exploring the use of RL for distributing jobs in Dew computing. However, RL has been used for task scheduling in closely related environments, such as in Cloud computing [13; 41; 42] and in Edge computing [15; 43; 44; 45; 46; 47; 48; 49].  
In the context of Cloud computing, task scheduling focuses on deciding which of the available resources (servers) should process an oncoming task. In this problem setup, previous works have shown that RL agents can reduce the execution time in distributed systems [41] and avoid overloading (and deadlocking) Cloud servers [13]. Furthermore, [PERSON] et al. [42] showed that deep RL can be helpful for scheduling tasks in large-scale Cloud Service Providers, surpassing traditional methods in terms of energy cost and reject rates.  
As for Edge computing, the task-scheduling problem is similar. The Edge server receives tasks, and it has to decide whether to send those tasks to a nearby server or to the Cloud. Several works have explored how to distribute jobs in Edge computing using RL according to different performance metrics. These metrics include reducing computing times [43], energy consumption [49], latency [44; 45], task failure rate [46], or a combination of the previous [15; 47; 48]. We note that in Edge computing, the action space is usually limited. For instance, sometimes the agent only has to decide whether to send (or not) a job to the Cloud [48]. In contrast, the action space in a Dew environment typically ranges from tens to hundreds (i.e., one action per device)--which makes the decision-making problem considerably harder in Dew computing.  
There are three main differences between our work and the existing work on RL with Cloud and Edge computing. First, we address a different problem. Distributing jobs in a Dew environment has challenges that do not normally arise in Cloud computing or in Edge computing. For instance, in Dew computing, some of the devices might run out ofbattery or users might start interacting with them. Taking these elements into account is a non-trivial task. Second, we explicitly study the generalization performance of the policies that the agent learned. This is a crucial step toward applying RL methods in real systems since, in practice, it is unlikely that the agent would encounter scenarios that it had previously seen during the training. Finally, we are using an RL agent that is better suited for generalization than the agents used in previous works [50,51].
# Region Pixel Voting Network (RPVNet) for 6D Pose Estimation from Monocular Image
## 4 Discussion  
### Comparison between Heatmap and Direction Map  
The Wilcoxon test results show that there is a significant difference between RPVNet and the proposed method using heatmap on both LINEMOD and Occlusion LINEMOD. According to Table 1, on LINEMOD, the average accuracy of RPVNet (86.1%) is 5.3% higher than Heatmap method (81.7%). On Occlusion LINEMOD dataset, as shown in Table 2, the average accuracy of RPVNet, on the other hand, is about 47.6% higher than that of Heatmap method (43.7% vs. 29.6%). These results show that the use of direction map is more robust than heatmap in an occluded environment.  
As explained in Section 2.2.3, the direction map remains relevant to the unobstructed part of the object, when occlusion happens. However, heatmap is located in a region of the image that is not part of the object, which means the network is predicting features that do not belong to the object. Thus, predicting the direction map is more robust than the heatmap.  
A similar phenomenon was found in the study of PVNet [29], when comparing predicting keypoints on object, and predicting bounding-box corners. These results indicate that predicting the visible part of target object in the picture improves the robustness of keypoint predictions.