esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Work and Results
### Characterization  
A characterization model is required for each of the classifiers and datasets. This model aims to identify the best model settings in terms of neuron numbers, and the value of the regularization factor. To address this aim, a mesh surface was generated. Every point in the mesh represents the testing accuracy with respect to a certain value of neuron numbers and regularization factor. Figure 4 exhibits the mesh generated from each of the three datasets. Moreover, every point in the surface of the mesh had a different accuracy according to the number of hidden neurons and regularization value. The aim of this study was to select the point with the best accuracy. The regularization parameter was based on the relationship between accuracy and regularization factor (C). The number of hidden neurons was selected on the basis of their relationship with (L), which represents the number of hidden neurons.  
To extract the values of C and L, the mesh was mapped to C versus the accuracy curve, or L versus the accuracy curve, respectively. The three dataset curves are shown in Figure 5, while the results for L and C that achieved the best accuracy for each of the three models are given in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **C** & **L** & **Accuracy** \\ \hline
**TampereU** & \(2^{-6}\) & 750 & 0.8501 \\ \hline
**UJIndoorLoc** & \(2^{-9}\) & 850 & 0.6841 \\ \hline
**KDD99** & \(2^{-14}\) & 600 & 0.9977 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected values for regularization factor regularization factor (C) and number of hidden neurons (L), and their corresponding accuracy for the three datasets.  
Figure 4: Characterization model represented in mesh for the accuracy relation between the number of hidden neurons and regularization factor. (**a**) TampereU; (**b**) UJIndoorLoc; and (**c**) KDD99 datasets.  
Figure 5: Projections of the mesh on accuracy vs. regularization factor, and accuracy vs. number of neurons in hidden layers for the three datasets TampereU, UJIIndoorLoc, and KDD99. (**a**) Mesh representation of the relation between the accuracy, L, and C for ELM classifier based on TampereU dataset (**b**) relation between the accuracy and C for ELM classifier based on TampereU dataset. (**c**) relation between the accuracy and L for ELM classifier based on TampereU dataset. (**d**) relation between the accuracy, L, and C for ELM classifier based on UJIIndoorLoc dataset. (**e**) relation between the accuracy and C for ELM classifier based on UJIIndoorLoc dataset. (**f**) relation between the accuracy and L for ELM classifier based on UJIIndoorLoc dataset.
# Data Knowledge Dual-Driven Rate of Penetration Prediction Method for Horizontal Wells
## 4 Case Study  
### Data-Set Attributes  
As illustrated in Figure 11, this is the horizontal projection map of a multi-well pad targeting area in a shale gas reservoir in Sichuan, China. The platform accommodates a total of four horizontal wells, namely A1 to A4, each with a horizontal section length of 1500 m. The spacing between adjacent wells is less than 400 m. The actual drilling trajectories are confined within a 3.5 m box, and all wells penetrate the high-quality Longmaxi Formation shale reservoir. Therefore, during the modeling process, the impact of geological properties on the mechanical drilling rate model can be disregarded. In this study, we conducted mechanical drilling rate prediction experiments using drilling data from each well's horizontal section on this platform as an example. The simulation includes data from real-time mudlogging data, drill bit records, drill string description, survey data, drilling logs, and more. Notably, for the testing dataset, all four wells (A1 to A4) are equipped with the same type of drill bit. Consequently, the influence of drill bit types was omitted during the modeling process.  
Figure 10: Conventional data-driven ROP model.  
Figure 9: Data knowledge dual-driven ROP model in horizontal well.  
The root mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R\({}^{2}\)) are introduced to measure the prediction performance.  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{(\hat{y}_{i}-y_{i})}^{2}}} \tag{5}\]  
\[\text{MAE}=\frac{1}{n}{\sum\limits_{i=1}^{n}{|\hat{y}_{i}-y_{i}|}} \tag{6}\]  
\[\text{R}^{2}=\frac{\left(n\sum\limits_{i=1}^{n}{\hat{y}_{i}y_{i}-\sum\limits _{i=1}^{n}{\hat{y}_{i}\cdot\sum\limits_{i=1}^{n}{y_{i}}}}\right)^{2}}{\left(n \sum\limits_{i=1}^{n}{\hat{y}_{i}^{2}-\left(\sum\limits_{i=1}^{n}{\hat{y}_{i} }\right)^{2}}\right)\cdot\left(n\sum\limits_{i=1}^{n}{y_{i}^{2}-\left(\sum \limits_{i=1}^{n}{y_{i}}\right)^{2}}\right)} \tag{7}\]  
where \(\hat{y}_{i}\) and \(y_{i}\) are the predicted value and actual value of the \(i\)-th sample, respectively; and \(n\) is the number of samples.
## 3 Analysis of the Selected Case Studies
### Computer Simulation Analysis
#### 3.4.2 Shading Hours on Facades and Shading Levels on Streets and Urban Spaces  
Analysis of the shading levels on urban spaces showed that the urban formation strategy of the traditional city's urban fabric in West Asia featured shading considerations of high significance. As shown in Figures 9 and 10, the shading ratio of the urban spaces in the Al-Kadhimiya district in Baghdad was the most significant, followed by Old Damascus, the old city of Sana'a, and, finally, the city of Yazd. As for the shading by the facades, especially the main facades, the shading percentage was semi-full in all of the studied cities, with a high shading rate during the hot summer season.
# Enhancing Sensor Data Imputation: OWA-Based Model Aggregation for Missing Values
## 2 OWA Methodology
### Learning Algorithm  
This section provides a description of the learning method and its data-driven learning. Consider \(\{b_{1},\ldots,b_{n}\}\) to be a set of finite elements, i.e., the observation data. In other words, \(\mathbf{b}\) can be sensor measurements, experts, or attributes in decision-making problems. Let the training dataset \(O\) consist of \(M\) pairs of both the labels and the observations dataset such that \(O=\{(b_{i},y_{i})\},i=1,2\ldots,M\), \(b_{i}\) represents the \(i^{th}\) observation, and \(y_{i}\) represents the corresponding label. Furthermore, \(o_{i}(b_{k})\) is the observed value for the input of the observation data from the instance \(i^{th}\) with the input of \(k^{th}\). Considering \(\mathbf{w}=[\mathbf{w}(\{b_{1}\}),\mathbf{w}(\{b_{2}\}),\ \ldots,\mathbf{w}(\{b_{n}\})]^{T}\) is considered to be the weight value vector; the discrete OWA on \(o_{j}\) with respect to \(\mathbf{w}\) can be rewritten as follows:  
\[C(o_{j})=\mathbf{b}_{j}^{T}\mathbf{w} \tag{4}\]  
where \(j^{th}\) represents the index of the observation vector in the dataset, and \(\mathbf{b}\) represents the input column vector. The sum of squared error (SSE) of the proposed approach can be expressed as  
\[E_{j}(O,\mathbf{w}) =\sum_{j=1}^{M}(C(o_{j})-y_{j})^{2}=\sum_{j=1}^{M}(\mathbf{b}_{j} ^{T}\mathbf{w}-y_{j})^{2}\] \[\qquad=\sum_{j=1}^{M}(\mathbf{w}^{T}\mathbf{b}_{j}\mathbf{b}_{j} ^{T}\mathbf{w}-2y_{j}\mathbf{b}_{j}^{T}\mathbf{w}+y_{j}^{2}). \tag{5}\]  
where \(E_{j}(O,\mathbf{w})\) is the error. The least-squares minimization formulation can be expressed as follows:  
\[\text{(OP1)}\ \min_{\mathbf{w}}f_{\text{O}}(\mathbf{w})=\mathbf{w}^{T} \mathbf{H}\mathbf{w}+\mathbf{d}^{T}\mathbf{w}, \tag{6}\]  
Subject to \[\sum_{j=1}^{n}\mathrm{w}_{j} =1 \tag{7a}\] \[0\leq\mathrm{w}_{j}\leq 1, \tag{7b}\]  
where  
\[\mathbf{H}=\sum_{j=1}^{M}\mathbf{b}_{j}\mathbf{b}_{j}^{T}\text{, and }\mathbf{d}=-2\sum_{j=1}^{M}y_{j}\mathbf{b}_{j}.\]  
\(\mathbf{H}\in\mathbb{R}^{n\times n}\); standard QP solvers can minimize the problem error. In this case, OP1 is used as a model for training according to Equation (6) to determine OWA's operators when inputs exist.
## 3 Hardware Development
### Impact Detection  
In many applications, the full waveform of the impact response signal can provide more information about the impact. To keep the full impact information, the charge amplifier rather than the voltage comparator is employed. The essence of that is to measure charges of the PZT sensor. As the charge of PZT sensor tends to be quickly lost due to input resistance, it is necessary to design a charge amplifier that can effectively maintain the charge.  
The charge amplifier, also referred to as the first-order integrator, is illustrated in Figure 4. Traditionally, the non-inverting input of a charge amplifier is connected to ground, but in this paper, it  
Figure 3: Hardware description of the scheduling module.  
Figure 2: The hardware frame for the scheduling module.  
is connected to a non-zero and constant input to introduce a bias voltage to the output. This design can make the system more robust and universal because it can work over a greater range of power supply. Moreover, this design will not affect the performance of the charge amplifier due to the superposition theorem for electrical circuits. Then, the amplified charge signal will be measured by an analog-to-digital converter (ADC).
# Efficient Dissemination of Safety Messages in Vehicle Ad Hoc Network Environments
## 3 The Proposed Safety Message Dissemination Scheme
### V2I Communication
#### 3.4.2 RSU Scheduling
The proposed scheme unconditionally assigns a score of 2 to the emergency safety message if there are clusters that have not received it. The \(BCT\) priority score of general safety messages is the sum of the holding rate and request rate. \[BCT\ Score_{(D_{(a)})}=\left\{ \begin{array}{c}2\\ HR\Big{(}D_{(a)}\Big{)}+RR\Big{(}D_{(a)}\Big{)}\end{array} \right.\text{, }if \ EM\\ \text{, }if\ SM\end{array} \tag{8}\]Algorithm 5 shows the RSU scheduling algorithm. The RSU receives the cluster information and CRD from each CH. If a new safety message is received, the RSU calculates the storage priority and performs \(BCT\) management, which includes inserting new messages and deleting unimportant messages. If there is no new message received or \(BCT\) management is completed, then the formula table is updated. Updating the formula table includes calculating the holding rate and request rate for each message. The priority of each message is calculated based on the updated formula table, and the \(BCT\) is updated accordingly. The RSU broadcasts safety messages based on the updated \(BCT\). If there are still vehicles within the RSU's communication range after broadcasting, the RSU receives the vehicle information and safety messages and repeats the process. If there are no more vehicles within the RSU's communication range after broadcasting, the RSU waits until new vehicles connect. ```
1:RSU scheduling algorithm (with broadcast). 2:Notation
3:lifetime = Survival time that must be guaranteed for EM
4:BCT = Broadcast candidate table
5:BCTscore(D(a)) = BCT priority score for a message
6:Input:cluster information and requests, sorted_BCTscore_list
7:Output:broadcast
8:def FT_update(cluster information and requests, BCT):// update formula table
9:calculate length rate
10:returnHR(D(a)),RRD(D(a))
11:defBCT_update(HRD(D(a)),RR(D(a)))
```
12:BCTscore(D(a)) = HR(D(a)) + RR(D(a)) //priority score
13:BCTscore_list = BCT[BC.score(D(a)),... BCTscore(D(c))]
14:sorted_BCTscore_list = BCTscore_list.sort(reverse = True) // sort priority scores in descending
15:order
16:returnsorted_BCTscore_list
17:defRSU_Broadcast(cluster information and requests, sorted_BCTscore_list):
18:ifa new message is received:
19:BCT management// insert new messages, delete low-importance messages
20:FFL update(cluster information and requests)
21:BCT_update(HR(D(a)),RR(D(a)))
22:ifthere is a safety message to request to vehicle:
23:add safety message request
24://send priority messages corresponding to the packet size RSU can send at once
25:for in len(max(RSUPacketsize)):
26:broadcast(sorted_BCTscore_list[i])
27:whileifa vehicle is within communication range:
28:receive cluster information and requests
29:RSU_Broadcast(cluster information and requests, BCT) ```  
**Algorithm 5** RSU scheduling algorithm (with broadcast).
## 8 Conclusions  
The data presented in this review indicate that _A. foeniculum_ and _H. officinalis_ EOs exhibit antimicrobial activity, particularly against Gram-positive bacteria, and demonstrate antifungal efficacy against a wide range of strains. Oxygenated monoterenes and monoterepne hydrocarbons, represented by isopinocamphone, pinocamphone, and \(\beta\)-pinene, are the main classes found in _H. officinalis_ EO. Estragole and methyl eugenol (phenylpropanoids), and oxygenated monoterepne menthone are the main compounds found in _A. foeniculum_ EO. These chemical compounds are responsible for the bioactive proprieties of these EOs. These findings highlight their potential as natural food preservatives, offering a promising alternative to synthetic additives. The antifungal activity of _H. officinalis_ EO during the cheese ripening process and having no effect against _Lactobacillus_ strains, but inhibiting the growth of _S. aureus_, makes it suitable for utilization in cheese factories. Additionally, its antifungal effects on mold strains on fruits make it suitable for preventing fruit spoilage. Moreover, the bitter and minty aroma of _H. officinalis_ EO, along with the anise-minty flavor of _A. feniculum_ EO, can enhance the taste and overall acceptability of food products that incorporate these EOs. A key gap identified in this literature review is the lack of studies specifically investigating the application of these findings into the food industry. While various research efforts have explored the potential of bioactive compounds and antimicrobial properties, their direct implementation in food systems remains underexplored. Future studies should focus on evaluating these EOs in food matrices by assessing their performance in food preservation, sensory impacts, and regulatory compliance.  
Conceptualization, A.L.N. and A.M.R.; methodology, A.L.N., A.M.R. and C.R.P.; software, L.M.; investigation, A.L.N., A.M.R., C.R.P., L.M., G.M.C., F.D.L., C.C.R. and R.V.; writing--original draft preparation, A.L.N.; writing--review and editing, A.M.R., C.R.P., L.M., G.M.C., F.D.L., C.C.R. and R.V.; supervision, A.M.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This work was supported by a grant from the Ministry of Research and Innovation, CNCS-UEFISCDI, Romania (PN-IV-P2-2.1-TE-2023-1083). The authors declare no conflict of interest.
## 5 Results and Discussion
### Parameter Values  
The values of all parameters regarding the MODE algorithm are given in Table 2. In our Matlab(r) implementation, the generation size and processor number are adjusted according to the literature. Population size and generation size are two effective parametersinfluencing time complexity. The values of these parameters are set similarly to the state-of-the-art methods to make the comparison fair. As it can be seen in Table 2, the population size and generation size are set to 200 and 300, respectively. It is worth mentioning that the VNS technique does not add much additional time complexity to the hybrid method because the small and fast version of VNS is applied. As a result, the suggested hybrid strategy is more effective considering the quality of the Pareto front.
## 3 Results
### Metal Mesh  
Figure 9 shows an image of the time-averaged velocity field in the channel with the metal mesh frame, without the nanofibrous filter. These measurements were carried out for reference. The velocity field in Figure 9a is the magnitude of the velocity vector in the color scale. The color scale bar is at the bottom of the diagram. Figure 9b shows the velocity field in vector representation. The measurements were carried out for a volume flow rate of 162 L/h. The time-averaged flow velocity in the channel was about 0.2 m/s, and the Reynolds number for this velocity is \(Re\) = 212, at normal temperature and pressure.  
Figure 10 presents the velocity vector component perpendicular to the plane of the metal mesh, and at six cross sections, at different distances from the mesh plane, determined from the flow field shown in Figure 9. The distribution of the component of the velocity vector shown in Figure 10, at distances to 3 mm downstream of the mesh upwards, is slightly uneven, and varies between about 0.1 and 0.2 m/s. At a distance of 5 mm, this component of the velocity vector becomes more uniform, and is about 0.15 m/s, but it starts to vary from \(-\)0.1 to 0.2 m/s at a distance of 7 mm due to a large vortex produced in the channel. Although the Reynolds number in this case is \(Re\) = 212, which is much lower than that for which the turbulent flow occurs (usually \(Re\) = 2340), a single vortex can be noticed at a distance of about 1/3 of the hydraulic diameter of the channel downstream of the mesh (\(>\)5 mm).  
Figure 9: Time-averaged flow velocity field in the channel (in m/s) with stainless steel mesh without nonwoven fibrous filter. Time between laser pulses 200 μs: (**a**) velocity vector modulus (in color); (**b**) vector representation.  
The flow field upstream of the mesh is also turbulent, but this turbulence results from the differences in the hydraulic diameter of the channel and the inlet connector. At the cross section close to the mesh (\(-1\) mm distance), the velocity vector was measured to be positive and negative, which indicates the existence of small local flow vortices. Small-scale vortices of a size of double the pitch of the mesh can also be noticed upstream of the mesh in Figure 9a. Although the aerosol flow in the channel is turbulent at the inlet, the flow downstream of the metal mesh is stabilized, and the vortices are suppressed close to the mesh, which is confirmed by Figure 9b.  
Figure 10: Velocity component (in m/s) perpendicular to the mesh plane at different distances (in mm) from the mesh plane determined from the velocity field shown in Figure 9. Steel mesh without nonwoven fibrous filter.
# Acoustic Emission Analysis of the Cracking Behavior in ECC-LWSCC Composites
## 7 Results and Discussion
### Crack Progression Investigation Using b-Value and Ib-Value Analyses  
7.2.1. Calculation of \(b\)-Value and \(lb\)-Value Parameters  
A \(b\)-value analysis was carried out on the AE signals collected throughout the testing. The \(b\)-value parameter was calculated as the ratio of amplitude to the number of hits. A \(b\)-value analysis has been successfully implemented in several previous investigations [42; 43; 44]. The application of the \(b\)-value was originally used in seismic magnitude-frequency equations and later expanded to the analysis of AE data [45]. The \(b\)-value was calculated using Equation (1):  
\[\log N=a-b\log A \tag{1}\]  
where \(N=\) the number of hits having an amplitude larger than \(A\); \(A=\) signal amplitude (dB); \(a=\) the empirically derived constant; and \(b=\) the \(b\)-value [43; 46]; the value '\(a\)' was obtained by plotting \(\log N\) (\(y\)-axis) versus \(\log A\) (\(x\)-axis) for all the six beams. The average value of '\(a\)' for all the beams was then calculated and applied in Equation (1).  
Furthermore, an improved \(b\)-value (\(lb\)-value) analysis was also carried out. The \(lb\)-value, initially proposed by [PERSON] [47], has been successfully utilized in similar previous investigations [48; 49]. The \(lb\)-value is calculated using Equation (2):  
\[\mathrm{Ib}=\frac{\log_{10}N(\mu-\alpha_{1}\sigma)-\log_{10}N(\mu+\alpha_{2} \sigma)}{(\alpha_{1}+\alpha_{2})\sigma} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Beam** & \begin{tabular}{c} **First Crack Load** \\ **(kN)** \\ \end{tabular} & \
\begin{tabular}{c} **Maximum Crack Width** \\ **at Service Load** \\ \end{tabular} &
\begin{tabular}{c} **Failure Load** \\ **(kN)** \\ \end{tabular} & **Failure Mode** \\ \hline ECCP & 27.6 & 0.18 & 349.8 & Flexural \\ ECCS & 23.1 & 0.24 & 358.7 & Flexural \\ ECCP-C & 14.5 & 0.35 & 301.9 & Flexural \\ ECCS-C & 15.1 & 0.40 & 308.0 & Flexural \\ ECCP-T & 28.3 & 0.20 & 313.7 & Flexural \\ ECCS-T & 26.2 & 0.22 & 330.4 & Flexural \\ \hline \end{tabular}
\end{table}
Table 4: Experimental results obtained from the flexural tests.  
where \(N=\) the number of hits having an amplitude larger than \(A\); \(A=\) signal amplitude (dB); \(\sigma=\) the standard deviation of the amplitude distribution of the AE event group; \(\alpha=\) mean values of the amplitude distribution of the AE event group; and \(\alpha_{1}\) and \(\alpha_{2}\) are user-defined constants obtained from the upper and lower limits of the amplitude. All of the previously mentioned constants are calculated based on 100 hits [50].
# Evolution of Access Network Sharing and Its Role in 5G Networks
## 5 Fixed Access Network Sharing Economics  
We have already explored the principles of fixed access network sharing and the technologies that enable it. We have seen that, for example, multi-wavelength systems, such as NG-PON2 can provide both high capacity, isolation and flexibility of operation in shared networks. However, one of its main disadvantages is that it requires CNTs to be equipped with tunable lasers and filters, making them expensive. Indeed, the body of European Regulators for Electronic Communications (BEREC) has published a Report [72] on the New Forms of Sharing Passive Optical Networks Based on Wavelength Division Multiplexing. In a BEREC questionnaire completed by 50 European network operators, more than 20 per cent of the operators have mentioned that the expense of the NG-PON2 equipment was one of the main reasons why it is not likely that the network operators will deploy NG-PON2. On the other hand, only four operators have considered wholesale wavelength unbundled services and the reuse of the passive network infrastructure as primary reasons for the network operators to deploy NG-PON2 [72]. We have seen Section 3 that Time Division Multiplexing is an enabler for network sharing. However, network operators are unlikely to invest in fine-grained sharing models without some economic incentives.  
In this section, we look at some of the economic incentives for Fixed Access Network Sharing (FANS) that involve new ownership models.