esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Statistics Analysis Results  
Table 3 illustrates the main statistics corresponding to the results of the test evaluation applied during the third week, and the final test in the fifth week to perform an Analysis Of Variance, determining significant differences between the acquired knowledge of both groups. The main results obtained out during the 3 rd week illustrates no significant difference between the two groups (\(p\) = 0.532 > 0.05). Therefore, the students had equivalent knowledge before the use of the proposed tool. Once the experiment is carried out, it is worth noting that the experimental group VRAT completed the task in less time than the participants in the TT group, and the statistical analysis shows a significant difference (\(p\) = 0.008 < 0.05) illustrating the benefits of the teaching methodology based on virtual reality. One of the main advantages of using the VR system is that the participant can interact with the objects as in the real world, and provides a platform to learn by interacting in a visual, auditory, and kinesthetic way. Then, not only better knowledge is acquired, but the effectiveness of the use of time spent teaching is gained. Furthermore, Figure 9 depicts the increasing average on grades for both groups, emphasizing the effectiveness in the use of the VR tool.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Instructions** & **Score** & **Comments** \\  & **0 = worst; 10 = best** \\ \hline
1 & Select the capacity and quantity of the solar panel \\
2 & Choice of the inverter according to electrical characteristics \\
3 & PV arrays design showing the Voc and Isc \\
4 & Illustrate Vin and Iin for the inverter \\
5 & Calculate the capacity of the electrical fuses \\
6 & Determine the capacity of the DC and AC switches \\
7 & Obtain the surge protections \\
8 & Calculate electric conductor gauge \\
9 & Obtain the monthly generation produced by solar PV system \\
10 & Mention of personal protection needs \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation checklist.  
Figure 8: The experimental teaching process.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**3 th Week Activity**} & \multicolumn{4}{c}{**Final Test**} \\ \hline  & **Average** & **F** & \(\mathbf{p}\) & **Average** & **F** & \(\mathbf{p}\) & **Time** \\ \hline TT group & 6.74 & 0.40599977 & 0.53202831 & 7.35 & 8.66918919 & 0.00801796 & 90 \\ VRAT group & 6.34 & & & 8.7 & & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Participant students.
# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 4 Keystroke Recognition Using Wi-Fi Sensing
### Keystroke Recognition Model  
It has been shown in prior work [3] that a Wi-Fi sensing apparatus can be used to detect which key is pressed on a keyboard by computing features on CSI time series data. This flavour of the attack is particularly concerning since it extends the domain of privacy risks beyond physical attributes towards cyber information that may be entered through a computer keyboard. We implemented the algorithm initially proposed by WiKey as demonstrated by Figure 3 below.  
In Figure 3, we illustrate the algorithm for CSI-based keystroke recognition. As before, Tx propagates a Wi-Fi signal (red) which incurs time-varying oscillations due to the typing hand. This time varying Wi-Fi signal is then received by Rx. The CSI time series is first processed with a lowpass filter to remove noise above the cutoff frequency of 5 Hz. Then, the 56 time series (one for each of the 56 OFDM subcarriers) are collectively processed via PCA. The 2 nd, 3 rd and 4 th PCA vectors are used in parallel for all subsequent computations.  
Encapsulated within Figure 3, we present a plot of a CSI time series after lowpass filtering and PCA. The CSI data was captured whilst a human repeatedly pressed the 'Q'  
Figure 3: WiFi-based keystroke detection model.  
key on a keyboard. The x-axis represents time, and the y-axis represents the amplitude of the processed CSI. We observe clearly that the CSI amplitude varies in sync with the repeated keystrokes. To extract this keystroke information, we find the local minima in the time series to isolate each of the 'crevices' at the end of a keystroke. The samples between these minima are then considered to be keystrokes, and we perform segmentation of the time series to extract the detected keystrokes. Each segmented keystroke is then subject to the DWT, which characterises the shape of the keystroke segment by a frequency decomposition using Daubechies Wavelet 4. These shape features are then used as inputs to 3 KNN's, which utilise DTW distances to calculate the distance between points and their neighbours. The decisions from the 3 KNN algorithms are then multiplexed in order to make an overall classification of the keystroke.
# HPoC: A Lightweight Blockchain Consensus Design for the IoT
## 5 Election Algorithm Based on Proof-of-Capability
### Hierarchical Chain Architecture and Asynchronous PoW  
Hierarchical Chain Architecture: To Decrease the Data  
As a burden on blockchain nodes, the hierarchical chain-architecture design is widely employed. In this study, we propose a hierarchical chain architecture that includes a minute chain, a ten-minute chain, and a day chain, with blocks generated every minute, every ten minutes, and every day, respectively. The non-transaction contents of the blocks, such as the block number and the date, are prefabricated. The LEADER node that generates the block merely aggregates the transactions within that minute and packs them into a premade block for minute blocks. The minute blocks are saved in the LEADER node and the FOLLOWER nodes, but only the minute blocks from the previous seven days are stored on the nodes to decrease the storage burden on the normal nodes, and the minute blocks from earlier than that period are automatically purged. Every ten minutes, the LEADER node merges and packs the minute blocks in order to make a ten-minute block, a type of block with better security for minute blocks. The LEADER node keeps the ten-minute blocks for a long period before uploading them to a cloud server with more computing and storage power, which will combine and construct a daily block, a type of block with increased security for ten-minute blocks. The design of this hierarchical chain architecture is shown in Figure 7.  
**Asynchronous PoW:** The ten-minute block enhances the security of the minute-block, and the day block enhances the security of the ten-minute block. Asynchronous PoW is employed in this architecture, which means that PoW is performed exclusively in the ten-minute and daily blocks, with the process having no effect on the production of the minute-blocks. The tamper-proofness of the block is continually increased as the security of the blockchain is continuously enhanced asynchronously in the followup. The difficulty bits of PoW will be modified in real time, based on the LEADER node's power and computational load as well as the cloud server's. The asynchronous PoW mechanism separates the node's mining and block-generating processes, and minute blocks are issued once every minute, resulting in 1440 min blocks every day, with a PoW time interval of 10 min and one day.  
Figure 7: Hierarchical chain architecture and asynchronous PoW.  
This can increase the blockchain transaction volume while also ensuring security (see Table 1).
## 4 Design of the Platform  
This section elaborates on the detailed design of the proposed platform in terms of the platform architecture, functional components, disease diagnosis process, database, and ML algorithms.  
A software platform is not a specific application; rather, it provides a set of functions and features that are common among potential applications constructed with the platform. Therefore, the reusability of the platform and its configurability for specific applications are key quality requirements. Our approach to meeting these requirements is to design the platform by applying the Open-Closed Principle (OCP) [32]. In this design principle, the common features of a system are designed to be closed to modifications, and the variable features of the system are designed to be open to extension and customization.
# Evaluation of Color Change and Biodeterioration Resistance of Gewang (_Crypha utan_ Lamk.) Wood
## 2 Materials and Methods
### Dry-Wood Termites Resistance Test  
Specimens with a 2.5 cm by 2.5 cm cross section and measuring 5 cm in the longitudinal direction were prepared according to Indonesian National Standard 7207-2014 [18]. This standard has been recognized and improved by researchers from various countries in evaluating the resistance of wood against termite as well as fungi attacks including in Japan [19]. The individual sample was placed in the center of a glass tube (3 cm high by 1.8 cm in diameter), and 50 dry-wood termite workers (_Cryptotermes cynoccephalus_ Light) were introduced into the glass tube. The samples were then placed in a dark room for 12 weeks. At the end of the experiment, wood specimen weight loss and termite mortality percentage were determined with five replications. The wood resistance was classified according to the Indonesian National Standard described in Table 2 [18]. The weight loss and termite mortality percentage were determined using the following equations:  
\[\mathrm{WL}\ (\%)\ =\ (\mathrm{W}_{\mathrm{b}}-\mathrm{W}_{\mathrm{d}})/ \mathrm{W}_{\mathrm{b}}\ \times\ 100\% \tag{3}\]  
\[\mathrm{Mortality}\ (\%)\ =\ (\mathrm{T}_{\mathrm{b}}-\mathrm{T}_{\mathrm{d}})/ \mathrm{T}_{\mathrm{b}}\ \times\ 100\% \tag{4}\]  
where:  
* weight loss percentage of wood specimen.
* gewang wood weight prior to the test at oven-dried condition.
* gewang wood weight after the test at oven-dried condition.
* the number of live termites before the test.
* the number of live termites after the test.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Color Difference** & **Color Change Effect** \\ \hline
1 & \(\Delta\)E \(<\) 0.2 & Invisible changes \\
2 & 0.2 \(<\)\(\Delta\)E \(<\) 2.0 & Very small changes \\
3 & 2.0 \(<\)\(\Delta\)E \(<\) 3.0 & Small changes (color changes visible by high-quality filter) \\
4 & 3.0 \(<\)\(\Delta\)E \(<\) 6.0 & Medium (color changes visible by medium-quality filter) \\
5 & 6.0 \(<\)\(\Delta\)E \(<\) 12 & Big (distinct color changes) \\
6 & \(\Delta\)E \(>\) 12 & Different color \\ \hline \hline \end{tabular}
\end{table}
Table 1: Color change class [16; 17].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Sample Condition** & **Weight Loss (\%)** \\ \hline I & Very resistant & \(<\)2.0 \\ II & Resistant & 2.0–4.3 \\ III & Moderate & 4.4–8.1 \\ IV & Poor & 8.2–28.1 \\ V & Very poor & \(>\)28.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification of wood resistance against dry-wood termites.
## 6 Discussion  
We presented an efficient and robust way to automatically tune stepsize parameters in a Monte Carlo simulation. While for systems involving a few degrees of freedom trial and error approach is usually adequate, for more complex systems it soon becomes an exercise in frustration as the acceptance rates converge much slower and the change of one stepsize may affect more than one acceptance rate.  
Automatic tuning results in several distinct benefits: First, by eliminating the ad-hoc trials the overall computer time use is reduced. Second, by minimizing the need for human interventions, the overall timeframe of the project is reduced. One result of the relentless increase in processor speeds is that the human time spent with the setup of the project becomes the dominant part of the effort, thus savings at this step will have large impact. Last, but not least, automating the tuning ensures the uniform quality of the tuned parameters. Since the efficiency of sampling is the result of a complex interplay among the various parameters, lower-quality tuning of just a few simulation parameters can have a significant impact on the overall sampling efficiency.  
An alternative algorithm for tuning stepsizes [11], based on block averages, has been tested on the alanine dipeptide [8]. Extensive testing resulted in the choice of blocks that included 200 consecutive move attempt of a torsion angle. In our system that would correspond to \(2*10^{5}\) (0.2M) MC steps. However, the acceptance rates for systems with as many torsional degrees of freedom as we have are far form converged during runs of this length. Therefore, the update of the stepsize could easily make a change in the wrong direction, necessitating many more iterations until convergence (such occurrences have indeed been observed in our earlier work tuning the chemical potential parameter). In contrast, the method introduced in this paper robustly approaches the target acceptance rate in a few million MC steps.  
There are two important questions related to the stepsize tuning that the present paper did not address. First of all, it is important to point out that the conformational distribution sampled during the tuning phase of a simulation contains an unspecified bias with respect to the Boltzmann distribution [11]. This is true for any algorithm that adjusts the stepsize during sampling. This is not a problem for simulations that are performed as part of simulated annealing. For simulations that aim to obtain Boltzmann averages, however, the tuning step has to be performed separately from the production run.  
The other question is the selection of optimal target acceptance rate. While in general the sampling efficiency is not too sensitive to the target acceptance rate -- this happens to be the case for the test presented here -- there are examples where the optimized setup resulted in different optimized acceptance rates for different sampling algorithms [24]. The availability of a robust automatic stepsize tuning algorithm, however, is expected to help establishing the optimization protocol for the selection of target acceptance rate.
## 3 Problem Formulation
### Dual Decomposition  
The above problem can be expressed as a dual problem by coupling the resource constraints (4b) and (4c) with the objective function by introducing Lagrange multipliers, and keeping the constraints of the association:  
\[\max_{b_{ij}} \left\{\sum_{j=1}^{N}\sum_{i=1}^{M}u_{ij}b_{ij}-\sum_{j=1}^{N} \lambda_{j}\left(1-\sum_{i=1}^{M}\alpha_{ij}b_{ij}\right)-\sum_{j=1}^{N}\mu_{j }\left(1-\sum_{i=1}^{M}\beta_{ij}b_{ij}\right)\right\} \tag{5}\]  
where \(\lambda_{j}\) and \(\mu_{j}\) are the positive Lagrange multipliers values associated with the radio and backhaul constraint on each BS, respectively. Subsequently, from problem (5), the Lagrange function can be obtained as:  
\[L(b,\lambda,\mu)=\max_{b_{ij}} \left\{\sum_{j=1}^{N}\sum_{i=1}^{M}u_{ij}b_{ij}-\lambda_{j}\alpha _{ij}b_{ij}-\mu_{j}\beta_{ij}b_{ij}\right\} \tag{6}\]  
The resulting dual problem for the primal problem is expressed as:  
\[\min_{\lambda\geq 0,\mu\geq 0}g(\lambda,\mu)=\begin{cases}\max_{b}&L(b, \lambda,\mu)\\ s.t.&\sum_{j=1}^{N}b_{ij}=1,\;i=1,\ldots,M\\ &b_{ij}\in\{0,1\}\end{cases} \tag{7}\]In this sense, the maximization of the Lagrange function has the following analytical solution:  
\[b_{ij}^{*}=\begin{cases}1,&\text{if }\ j^{*}=\underset{j}{\text{argmax }}\left(w_{ij}\right)\\ 0,&\text{otherwise}\end{cases} \tag{8}\]  
where \(b_{ij}^{*}\) denotes the assignment solution to user \(i\) and \(w_{ij}\) is the weighted utility that device \(i\) can achieve if it is associated with BS \(j\). It is worth noting that the obtained solution \(b_{ij}^{*}\) obtained in Equation (8) meets the resource constraints at each BS \(j\) and the minimum data rate requirements of each device \(i\), with \(R_{ij}b_{ij}^{*}\geq R_{i}^{\text{min}}\) where \(i\) could be either MTC or HTC device, corresponding to devices \(d\) and \(c\), respectively. Notice that this latter aspect is achieved due to that the minimum rate requirement is involved in the computation of both the radio and backhaul resource costs.  
The weighted utility is defined as the utility minus the price per resource unit on the radio and backhaul resources, which is formally expressed as:  
\[w_{ij}=u_{ij}-\lambda_{j}\alpha_{ij}-\mu_{j}\beta_{ij} \tag{9}\]  
The weighted utility is used in order to steer the distributed BS assignment process, where the main challenge is to efficiently compute the pricing values \(\lambda_{j}\) and \(\mu_{j}\), denoting the Lagrange multipliers associated to radio and backhaul constraints, respectively. Details of the developed pricing update scheme are provided herein.
## 3 Results and Discussion
### Replacement of Model Backbone Networks and Addition of Attention Mechanisms  
The initial step is to register the ShuffleNet module in the 'common.py' file. Subsequently, the module is incorporated into the 'yol.py' file. A duplicate of the YOLOv5 configuration file, designated \"YOLOv5s-ShuffleNet\", was generated, and the underlying network was substituted. The replacement YOLOv5s-ShuffleNet model comprised 193 layers and 849,882 parameters. In comparison to the direct application of the YOLOv5s model, while the number of layers exhibited a slight increase, the number of parameters was merely 12% of that of the original model.  
Figure 8: Convolutional block attention module.  
Subsequently, the \"YOLOv5s-Shufflenet-CBAM\" configuration file was generated based on the \"YOLOv5s-ShuffleNet\" model, resulting in the final model. Figure 9 shows the YOLOv5s-Shufflenet-CBAM network framework. In comparison to the original model, this model comprises 221 layers and 1,066,428 parameters. It is slightly superior to the \"YOLOv5s-ShuffleNet\" model, exhibiting a 15.2% improvement.  
In the context of CNNs, the number of parameters has a direct impact on computational speed, as it represents the number of parameters that the network must calculate. Reducing the number of parameters significantly improves the network computational speed. In terms of memory consumption, memory resources are crucial. During training, a large number of parameters require the network to store weights and intermediate states, which places a high demand on storage space. This is of particular importance for embedded or mobile devices, where memory limitations can impact the viability of the model for implementation. Furthermore, the number of parameters affects the model complexity and expressiveness. Incorporating additional parameters can improve the model ability to learn, but it can also lead to overfitting, which reduces the model accuracy. In conclusion, the substitution of the YOLOv5s backbone with ShuffleNet-CBAM results in a notable reduction in the number of parameters, thereby reducing both the computational and memory requirements. This renders it an optimal choice for deployment on embedded or mobile devices.
## Abbreviations  
Some abbreviations and notations are listed below:  
\begin{tabular}{l l} MMR & Method of minimum risk; \\ BMCM & Bayesian Monte Carlo method; \\ ESM & Expert scoring method; \\ SOA & Service-oriented architecture; \\ QoS & Quality of service; \\ AWS & Amazon web service; \\ WSDL & Web service description language; \\ Tc & Traffic control; \\ BMCM & Bayesian Monte Carlo method; \\ ESM & Expert scoring method; \\ \(W_{i}\) & Abstract web service candidates; \\ \(w\) & A specific web service; \\ \(t\) & Duration; \\ \(C_{n}\) & The numbers of invocations of the web service; \\ \(n\) & Failures of the web service; \\ \(F(w)\) & The probability of failure of the web service \(w\); \\ \(r(w)\) & Reliability of the web service \(w\); \\ \(p_{ij}\) & The state transition relations between web services; \\ \(N\) & The number of web services; \\ \(interact\big{(}c_{i},c_{j}\big{)}\) & The number of interactions of web service \(i\) with web service \(j\); \\ \(S_{k}\) & Service composition flow; \\ \(\hat{p}\) & \(n\times n\) matrix; \\ \(I_{n}\) & The identity matrix of order \(n\); \\ \(\hat{Q}\) & The no-absorption transition probability matrix of order \(n\); \\ \(R_{s}\) & Reliability of web service composition; \\ \(\theta\) & Parameter of distribution; \\ \(\pi(\theta)\) & The prior probability density function; \\ \(f(x|\theta)\) & The posterior probability density function; \\ \(L(\theta|x)\) & The likelihood function \\ \((p_{0},c)\) & Reliability requirement; \\ \(p_{s}\) & The probability of failure of the web service composition; \\ H\({}_{0}\) & The test result meets reliability requirement; \\ H\({}_{1}\) & Test result does not meet the reliability requirement; \\ \(G(\theta)\) & Prior distribution of parameter; \\ \(d_{0}\) & Accepted \(H_{0}\); \\ \(d_{1}\) & Rejected \(H_{0}\); \\ \(I(\theta<\theta_{0})\) & The actual failure probability of web service composition being smaller than the \\  & reliability requirement; \\ \(I(\theta>\theta_{0})\) & The actual failure probability of the web service composition being larger than the \\  & reliability requirement; \\ \(L_{0}\) & The custom risk corresponding to cases in which the software is accepted despite the \\  & failure probability being larger than the reliability requirement; \\ \(L_{1}\) & The producer risk corresponding to cases in which the software is rejected despite the \\  & failure probability being smaller than the reliability requirement; \\ \(\delta(x)\) & The risk function of the decision function; \\ \(\pi(\theta)\) & Prior probability density function; \\ \(N\) & The total number of test cases; \\ \(a\), \(b\) & Hyperparameters; \\ \(E\) & Expectation of ESM; \\ \(V\) & Variance of ESM; \\ \(m(x|\pi_{i})\) & Marginal distribution of the prior distribution \(\pi_{i}(\theta)\); \\ \(\varepsilon_{k}\) & Confidence factor. \\ \end{tabular}
# A Design Method of Code Correlation Reference Waveform in GNSS Based on Least-Squares Fitting
## 4 Performance Analysis
### Tracking Jitter
#### 4.2.2 BOC(1,1)  
Figure 42 shows the tracking jitters of simulated S-curves for the BOC(1,1) signal of infinite bandwidth. The performance of the LS coherent S-curve discriminator is basically the same as the LS non-coherent S-curve, but both are still worse than that of the CCRW W2 discriminator and the double-delta correlator discriminator. The reason is the same as for the BPSK signal. The results of the 6.138-MHz bandwidth case are shown in Figure 43. The performances of the fitted S-curves are even better than the W2 discriminator. This implies that the LS method can keep its performance for the band-limited situation, whereas the W2 discriminator experiences the worst loss.  
Figure 41: The tracking jitters for the S-curves of the BPSK(1) signal (4.092-MHz bandwidth).  
Figure 42: The tracking jitters for the S-curves of the BOC(1,1) signal (infinite bandwidth).