esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Comparison Results with State-of-the-Art Models
#### 5.4.2 Comparison Results from WISDM Dataset  
We perform comparison research using the WISDM dataset to evaluate the generalization capability and robustness of the ConvResBiGRU-SE model. Table 9 displays this comparative analysis, which includes several deep learning architectures.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Reference** & **Year of Publication** & **Model** & **Accuracy (\%)** \\ \hline [PERSON] et al. [42] & 2018 & Residual BiLSTM & 93.60\% \\ [PERSON] et al. [43] & 2020 & LSTM-CNN & 95.78\% \\ [PERSON] and [PERSON] [44] & 2020 & Hierarchical Deep LSTM & 91.65\% \\ [PERSON] et al. [45] & 2020 & CNN & 91.98\% \\ [PERSON] et al. [46] & 2021 & iSPILnception & 95.09\% \\ [PERSON] et al. [47] & 2022 & Ens-HAR & 95.05\% \\ The proposed model & - & ConvResBiGRU-SE & 99.18\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance comparison of state-of-the-art model on the UCI-HAR dataset.  
Table 9 provides a comparative analysis of the ConvResBiGRU-SE model we propose against several leading methods for human activity recognition on the WISDM dataset. The proposed model attains an accuracy of 98.78%, showcasing competitive performance and slightly surpassing existing methods.  
Examining the chronological development of HAR models, [PERSON] et al.'s U-Net architecture [48] set a strong baseline with 97% accuracy, while [PERSON] et al.'s KNN approach [49] achieved 96.20% accuracy. The evolution continued with [PERSON] et al.'s RNN-LSTM model [50], which showed relatively lower performance at 94%. This indicates that not all architectural advancements necessarily lead to improved results on this specific dataset.  
A notable recent development is [PERSON] et al.'s Ens-HAR [47], which achieved an impressive accuracy of 98.70%. Our ConvResBiGRU-SE model builds upon this progress, achieving a marginal improvement with 98.78% accuracy. Although this 0.08% improvement may seem modest, it represents a significant advancement in a field where performance gains become increasingly challenging to achieve at higher accuracy levels.
# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 7 Further Probabilistic Forecasting and Applications
### Probabilistic Forecast of Other Uncertain Parameters  
In [136], the authors developed a probabilistic forecasting model for the current rating of transmission lines using QRF in order to solve the dynamic line rating problem with a focus on the reliability of the distribution network's lower part. In the second stage, the results were employed to conduct a cost benefit analysis using a bi-level stochastic problem. The problem considered two aspects of costs: (1) the reduced generation costs due to the higher power transfer capacity and (2) the increased reserves' adoption resulted from forecast errors.  
As another application, ref. [137] investigated the probabilistic forecast of low-voltage states (voltages as well as active and reactive power) for effective operation of distribution networks. In this regard, it tested two quantile forecasting methods considering different levels of distributed renewable generation injection. The probabilistic forecasting results were then integrated with an optimization problem to avoid over voltages within the networks.
## 3 Methodology
### Problem Formulation  
We represent an urban road network \(G\) as a weighted graph of the form \(G=(\mathcal{V},\,\mathcal{E},\,A)\), where \(\mathcal{V}\) is a set of nodes and \(|\mathcal{V}|=N\), corresponding to \(N\) roads, and \(\mathcal{E}\) is a set of edges and \(|\mathcal{E}|=E\), reflecting connections between roads. The \(A\in\mathbb{R}^{N\times N}\) represents an adjacency matrix of traffic flow data. After observations at time step \(t\) in a road network, a traffic signal matrix \(X_{t}=\left(x_{t}^{1},x_{t}^{2},\dots,x_{t}^{N}\right)\in\mathbb{R}^{N\times C}\) can be obtained, where \(C\) represents the number of features. The goal of traffic flow prediction is to learn a function \(f\) that can predict the feature matrix for \(T^{\prime}\) future time steps, given a graph \(G\) and a feature matrix of historical \(T\) steps. The function mapping relationship is shown in Equation (1).  
\[[X_{t-T+1:t},G]\overset{f}{\rightarrow}X_{t+1:t+T^{\prime}} \tag{1}\]  
Here, \(X_{t-T+1:t}\in\mathbb{R}^{T\times N\times C}\) and \(X_{t+1:t+T^{\prime}}\in\mathbb{R}^{T^{\prime}\times N\times C}\), we assume that \(G\) is fixed and independent of time.
## 1 Introduction
In most of the available research, a two-step process for ECAS disinfection of eggs was followed. In the initial washing step, dirt and debris are washed off the egg surface with water or alkaline detergent, followed by the ECAS disinfection. [PERSON] et al. [19] reported that immersion washing of eggs with acidic ECAS significantly reduced \(S\). Enteritidis and _Escherichia coli_ from the egg surface but also damaged the egg cuticle layer. On the other hand, the spray washing of eggs with slightly acidic EO water reduced total aerobic bacteria without negatively affecting the cuticle layer [22]. In other studies, an immersion washing with pH-neutral ECAS was not effective in reducing the total bacterial load [23], while a spray wash significantly decreased the level of _Listeria monocytogenes_ without affecting the egg cuticle layer [20]. More recently, [PERSON] et al. [24] reported that pH-neutral EO spray treatment for 30 s significantly reduced \(S\). Enteritidis and _E. coli_ loads on the egg surface without adversely affecting egg cuticle integrity. In this study, we explored the potential of pH-neutral ECAS (150 mg/L of free available chlorine (FAC)) as spray and aerosol fog for the sanitisation of unwashed, visibly clean eggs, assessing the reduction in total bacterial counts and _Salmonella_ Enteritidis, and its effects on the cuticle layer. ECAS with neutral pH still contains hypochlorous acid (HOCI) as the main oxidising component (active chlorine compounds also include hypochlorite ions and dissolved gaseous chlorine) [25; 26; 27] but is less corrosive and more durable than the acidic and slightly acidic forms.
# A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs
## 4 Proposed Method
### On the Receivers' Side  
The operations done by receivers rely on secret keys that contain generation information of matrix \(A\) and matrix \(H\). The reconstruction processes of original signals with and without critical information are carried out, respectively, by full-authorized receivers and restricted-authorized receivers. Restricted-authorized receivers could merely obtain the data without critical sections, whereas the full-authorized receivers could recover the complete data.
# The State of Ethereum Smart Contracts Security: Vulnerabilities, Countermeasures, and Tool Support
## 4 Smart-Contracts Vulnerabilities and Countermeasures
### Vulnerabilities and Countermeasures
#### 4.2.10. Transaction Order Dependence  
Since transactions change the world state from one to another, the world state or state of contracts depends on the transaction execution order. The miner arbitrarily decides the execution order, and this non-deterministic feature of the execution order makes it difficult to forecast the state before transactions are submitted. In a real-world situation where a transaction depends on the state of the contract, a different order may cause severe problems such as buying or selling items at unexpected prices [28].  
_Countermeasures_. Transaction order is affected by accounts that pay the higher gas price or miners who decide the order. If an upper bound is put on the gas price, a proportion of transactions will not be exploited by attackers willing to pay more gas prices.
## 6 Analysis of Experiment Results
### TWNFI Hyperparameters and Their Effect on Performance  
The quality and computation time of TWNFI may greatly depend on the amount of samples chosen to build the local model. Several parameters specific to the algorithm affect its performance, and therefore require further examination. One of such parameters is the number of samples TWNFI uses to build a local model, the influence of which can be seen in Table 7.  
The results show that a larger amount of samples may increase accuracy but also is guaranteed to increase computation time. The accuracy, however, does not necessarily increase with a larger number of samples, which could be due to the fact that a larger amount of samples allows less relevant data vectors to be selected. As an example, from the table, we can see that the accuracy percentage peaks at 25 similar samples and decreases at higher values. Meanwhile, the time steadily increases from 5845.22 ms to 15,024.77 ms.  
Another important parameter is the _Dthr_ parameter in the ECM algorithm, which plays an instrumental part in clustering according to radius length by serving as a comparison. Due to TWNFI relying on ECM for clusterization and creation of rules, this parameter can greatly affect the amount of rules created, which in turn can affect the accuracy. Table 8 demonstrates how it affects the overall performance.  
One thing to note is that increasing the _Dthr_ parameter decreases both prediction and fitting time, with a larger _Dthr_ parameter reducing the fitting time by more than two times (from 20,404.17 ms to 9877.32 ms in this case), and the prediction time by more than 20 times (from 6.1129 ms to 0.3771 ms here). This is likely caused due to a smaller amount of clusters created by ECM with a larger _Dthr_, and, by extension, a smaller amount of rules, reducing the amount of extra variables in the program. The same, however, cannot be said about the accuracy percentage, which appears to fluctuate through different parameter numbers. The reason for such behavior remains unknown for now.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Number of Similar Samples** & **Accuracy Percentage (ms)** & **Avg. Fitting Time (ms)** & **Avg. Prediction Time (ms)** & **Mean Square Error (for 50 Experiments)** \\ \hline
15 & 64\% & 5845.22 & 0.3516 & 0.36 \\ \hline
20 & 76\% & 8022.30 & 0.3315 & 0.24 \\ \hline
25 & 78\% & 9807.30 & 0.3514 & 0.22 \\ \hline
30 & 66\% & 11,973.35 & 0.3356 & 0.34 \\ \hline
35 & 72\% & 15,024.77 & 0.3408 & 0.28 \\ \hline \hline \end{tabular}
\end{table}
Table 7: TWNFI evaluation with a different number of local model samples, with _Dthr_ = 0.2, on a dataset of 250 samples.
# Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks +
## 5 Solution of Dynamic Computation Offloading Optimization Problem  
The optimization problem \(OP1\) is a non-convex mixed integer nonlinear programming problem due to the binary constraints and, therefore, hard to solve in polynomial time [64]. To facilitate an efficient solution, it should first be transformed to an equivalent quadratically constrained quadratic programming (QCQP) problem. Then, the binary offloading decisions can be recovered using a semidefinite relaxation (SDR) and stochastic mapping method. The QCQP can be efficiently solved, e.g., with conic optimization solvers [65], especially with accelerators [66, 67]. On a contemporary personal computer with a moderate performance level, the QCQP solution takes on the order of 1 second for the typical problems considered in the evaluation in Section 6. With the appropriate computing hardware at BSs and suitable accelerators, solution times on the order of 0.1 seconds or less are anticipated, making the online solution of the optimization feasible for scenarios with pedestrian-level mobility. We outline additional strategies for speeding up the offloading decision making in future research in Section 7.2.
# A Hybrid Linear Quadratic Regulator Controller for Unmanned Free-Swimming Submersible
## 3 Mathematical Structure of UFSS  
A general representation of the UFSS vehicle is shown in Figure 1. The control scheme of the UFSS is as follows: An elevator surface of the UFSS is diverted by a selected amount during forwarding motion. This diversion results in a force to move the UFSS about the pitch axis. The pitch of the UFSS generates a force to submerge or rise in a vertical axis. The heading control system is responsible to steer the UFSS as shown in Figure 1. The head is a combinational effect of yaw and roll which creates a rudder command to steer the UFSS. The head and pitch controls are modeled in the forthcoming subsections. After deriving the transfer function of the head and pitch control systems, we have converted these transfer function into state space representation. This state space model is utilized for HLQR controller, as described in Section 5. The modeling of head and pitch utilizes different notations. Therefore, the mathematical notations that have been used in this work are given in Table 1.
## 6 Simulations and Testbed Experiments  
To verify our CDSAs, firstly, we carry out some simulations and show that the approaches perform much better than the non-scheduling approach. Secondly, we present results from experiments on a 15-node WSN testbed.
## 3 Results and Discussion
### Oxide Surface Characterization
#### 3.1.2 Oxide Surface Topographies  
Figure 2 shows representative oxide surface topography results for each oxide group. Optical microscopy images are shown in the left column, and micro- and nano-scaled SEM images are shown in the middle and right columns. Optical images revealed a relatively uniform distribution of darker and lighter areas for the CPTi, TiMo \(\beta\), and TAV oxide groups. Contrastingly, the TiMo \(\alpha+\beta\) oxide showed localized areas with a white patchy appearance surrounded by darker areas. Micro-scaled SEM images revealed a similar rough surface appearance on each oxide group, while nano-scaled SEM images revealed white deposits surrounded by nano-scale surface porosity. However, no compositional differences were observed between the chemistries of the surface white deposits and the surrounding darker areas in subsequent EDS spot analyses at high magnification. Previously, anodized oxide surfaces with micro-scale roughness topographies have shown improved mechanical bone interlocking [23; 54], while surfaces with nano-scale roughness features have shown increased protein absorption, osteoblast cell attachment, differentiation, and ultimately faster osseointegration [21; 22]. More recent anodization studies have revealed surfaces exhibiting complex hierarchical surface topographies integrating micro- and nano-scale surface features to promote mechanical interlocking, cell proliferation, differentiation, and extracellular matrix formation [21; 22].  
Figure 1: _Cont._
# A Real-Time Dynamic Gesture Variability Recognition Method Based on Convolutional Neural Networks
## 5 Discussion  
The results presented in the text show that the proposed model for gesture recognition is effective for recognizing different groups of gestures with a high degree of accuracy. The analysis of the different groups of gestures showed that certain factors, such as the orientation of the palm and the trajectory of movement, play an important role in the accuracy of the model.  
The high validation accuracy of 0.93 indicates that the model performs well and can be trusted to make accurate predictions on new data. This is an important metric for any machine learning model, as it shows how well it generalizes to new, unseen data. The results also suggest that the proposed model has the potential to be a useful tool for gesture recognition in sign language and can be applied to multiple sign languages, indicating its multilingualism.  
Furthermore, the experiment conducted in the study was compared to other works on a test set from the AUTSL and LSA64 datasets, and the results showed that the proposed method had a higher precision, recall, F1 score, and accuracy.  
This further supports the effectiveness of the proposed method for gesture recognition in sign language. Further development of the issues discussed in the article may include the refinement of the proposed method and experimental setup to improve its performance and accuracy. In addition, the system's applicability in real-world scenarios and its ability to work with various sign languages and dialects should also be studied.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy** \\ \hline STF+LSTM [31] & 0.9856 \\ \hline
3D CNN, CGN, RGB-D [32] & 0.9853 \\ \hline
**authors’ method** & **0.98** \\ \hline ResNet. Transformer [33] & 0.9292 \\ \hline CNN+FPM+BLSTM+Attention (RGB-D) [26] & 0.6203 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the authors’ method with other work (only RGB) on a test set from the AUTSL dataset.
# Spiral Mobility Based on Optimized Clustering for Optimal Data Extraction in WSNs
## 5 Proposal of Spiral Mobility Based on Optimized Clustering Routing Protocols
### Data Transmission Phase  
In the transmission phase of SMOC and M-SMOC, communication between the nodes, CH and sink takes place. Nodes transmit their sensed data to the specific CH using their assigned TDMA slots; these CHs wait for the mobile sink until the sink reaches a specific cluster, and the CH forwards these data to the mobile sink.  
The CH dissipates two-times the energy in one round. First, it needs to receive _pl-bits_ data from the member nodes. The dissipated energy of the CH while receiving data can be calculated as:  
\[E_{CH_{Rx}}=N_{i}E_{Rx}(pl)=plN_{i}E_{ec} \tag{11}\]  
Second, it has to transmit these data to the sink, according to the network topology distance between CHs and the sink being a random variable between \((0,r)\). Thus, the energy needed for transmission of _pl-bit_ data to the sink is:  
\[E_{CH_{Tx}}=\int_{x=0}^{r}\frac{1}{r}E_{Tx}(pl,x)dx=pl\bigg{(}E_{ec}+\frac{E_{ fs}r^{p}}{p+1}\bigg{)} \tag{12}\]  
where \(p\) is the path loss exponent. By replacing the \(E_{Tx}(pl,d)\) in Equation (1), we can calculate the total energy consumption of CH in one round.  
\[E_{Total}=(N_{i}+1)E_{CH_{Tx}} \tag{13}\] \[=pl(N_{i}+1)\bigg{(}E_{elce}+\frac{E_{fs}r^{p}}{p+1}\bigg{)}\]  
CHs utilize a small amount of energy to transmit the data of nodes to the sink, because the sink is moving in a spiral pattern, and when it arrives at the cluster, it will be near the CHs, so the transmission cost from the CHs to the sink is minimized; thus, the bottleneck issue of the network is the working time of the CHs. The network lifetime in terms of the round can be calculated as:  
\[N_{LT}=\frac{E_{o}}{E_{CH_{Rx}}+E_{Total}} \tag{14}\]  
To save the energy dissipation in long-range data transmission, the proposed routing techniques allow member nodes to transmit sensed data directly to the sink if the distance between the member nodes to the CH is greater than the distance between the member nodes to the sink.  
Algorithm 2 shows the communication mechanism between member nodes and the mobile sink.  
```
Input : Location of node \(i\), location of CH, location of mobile sink, Output : Deliver data packets to CH or mobile sink
1 Initialization;
2 Set of nodes in a sensing-field is recognized by \(N_{i}\);
3 Set of member nodes in the cluster is recognized by \(\eta\);
4 ifmember node \(m_{i}\) has to transmit critical datathen
5 Calculate the distance;
6 Check the location of CH and the sink;
7 if\(D_{i,b}>D_{i,CH}\)then
8 Send to cluster head;
9 else
10 Send to mobile sink;
11 end if
12 else
13 Wait for allocated TDMA slot
14 end if
```  
**Algorithm 2**Data transmission between the member node and the mobile sink.
## 1 Introduction
[PERSON] [11] assumed that for the frequency of inspections to be optimal, it had to exactly match the failure rate of the equipment, so he proposed an optimal inspection frequency model to be used as a tool for planning and forecasting maintenance costs that uses a cost rate factor. [PERSON] and [PERSON] [12] evaluated the global cost of inspection planning for offshore structures based on decision and detection theories and included both the probability of false alarms and detection. Revealed and unrevealed failures were tackled by [PERSON] et al. [13] whose objective was to minimize the cost per unit time over an infinite time span by selecting a unique interval for both inspection and maintenance. [PERSON] [14] formulated an optimal inspection and diagnosis policy for a multimode system through the optimization of expected total profit and proposed reliability indices. [PERSON] et al. [15] presented a Markov model for visual inspections of distribution feeders in electric power distribution systems, where the objective was to minimize the total cost of inspection, repair, and reliability. [PERSON], [PERSON] and [PERSON] [16] derived an optimal inspection frequency model that minimizes downtime cost based on a three-layered structure for decreasing, constant or increasing hazard rates. [PERSON] and [PERSON] [17] suggested cost, downtime, or reliability as optimization criteria to determine the optimal critical level and inspection interval, based on a random coefficient growth model where coefficients follow known distributions functions. [PERSON] [18] presented five cost criteria functions in a model for optimal inspection intervals based on failure delay time and conditional residual time concepts. None addressed an method equivalent to the one proposed in this paper [19], which is organized as follows: Section 1 presents a basic introduction and a brief literature review. Section 2 describes how to build a decision model to establish inspection time intervals such that the sum of all pertinent costs is minimized. Section 3 illustrates how the decision model can be applied. Section 4 provides a conclusion of the main results.