esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Deck-roof Building
#### 3.2.3 Turbulence Intensity and Flow Acceleration  
The measurements of turbulence intensities in the stream-wise (\(l_{U}\)) and vertical (\(l_{W}\)) directions over the deck roof are shown in Figure 12 for two wind angles of 0\({}^{\circ}\) and 45\({}^{\circ}\), at four heights above the measurement points on the alignment \(x/D=0.5\), together with the free-stream values marked with black circles. The figure also includes the normalised velocity profiles of the mean value of the stream-wise component in order to examine the speed-up effect above the roof.  
Considering the results for both wind angles, the turbulence intensity profiles present favourable conditions for the installation of wind turbines over the deck roof. Namely, measurements of the stream-wise turbulence intensity at the majority of heights are comparable to the free-stream ones, not exceeding 13%. The only exception is the value at height z/D = 0.075 above point 58 at 0\({}^{\circ}\), which reaches 19%, indicating that it is affected by upstream separation. Vertical turbulence intensity values are even lower than the reference ones for all cases, particularly close to the roof, as they are in the range of 5-10%.  
Figure 11: Mean surface pressure coefficient (\(C_{p,mean}\)) distributions on the deck roof for different approaching flow angles: (**a**) \(0^{\circ}\), (**b**) \(15^{\circ}\), (**c**) \(30^{\circ}\) and (**d**) \(45^{\circ}\). Black dots on the surface plot represent the measurement taps.  
Figure 12: Profiles of stream-wise (\(I_{U}\)) and vertical (\(I_{W}\)) turbulence intensities (with the reference profile from the free-stream, values marked with black circles), and normalised stream-wise velocity \(U_{mean}\)/\(U_{ref}\), with uncertainty limits, measured over the deck roof for two different wind angles of \(0^{\circ}\) and \(45^{\circ}\), above points 50 and 58 (\(\chi/D=0.5\)).  
In the vicinity of the roof, for both wind angles, the wind speed accelerates with a rate of more than 20%. Moreover, a larger area seems to be affected at 45\({}^{\circ}\) with this favourable flow condition. As the height increases, the speed-up effect gradually decreases.  
Presented results for the deck-roof building show that the wind direction has a small effect on both the mean wind velocity and turbulence intensity above the roof. In addition, although the deck roof generates a velocity amplification of similar magnitude compared to the flat roof at 0\({}^{\circ}\), this occurs at notably lower positions (closer to the roof surface). Moreover, the turbulence intensity in that zone is equal or even lower compared to the free-stream at most measurement points. Similar flow conditions are observed over the deck roof at 45\({}^{\circ}\) compared to the flat roof case.
# Compressive Sensing-Based IoT Applications: A Review
## 4 CS-Based IoT Applications
### Application Layer
#### 4.3.3 Mobile Crowd Sensing  
CS can be deployed efficiently for applications related to smart cities in order to monitor environmental and urban conditions using the so-called mobile crowd sensing (MCS) approaches. MCS explores the user's mobility; the sensors embedded within the users smart-phones and the existing wireless infrastructures are used to sense and collect environmental data. The collected data can relate to a number of phenomena, including air quality, noise level, street surface and pavement conditions. MCS consists of dividing a specific area to \(N\) different cells, where, at each cell, single or multiple users are allocated with different sensing tasks. The main drawback of conventional MCS approaches is that they incur a high sensing cost in term of energy consumption to achieve a high sensing quality. In addition, many participants are required to collect and transmit data which puts high constraints in term of bandwidth occupancy. Thus, sparse MCS presents an alternative solution to the conventional methods. Sparse MCS consists of allocating only a small number of cells (\(M\ll N\)) with participants to collect data. The aim of this approach is to estimate the data of the all \(N\) cells from the collected data from \(M\) cells. This can be seen as a CS basic problem by estimating an \(N\)-length data from an incomplete set of measurements. Sparse MCS approaches face two key challenges, first, how to select the cells that provide the optimum coverage, second, how to assess the estimated data quality without apriori knowledge of the data of the non-sensed cells.  
To address these challenges, wang et al. [129] presented a framework for sparse MCS application with an iterative task allocation process. In this framework, cells allocation operation is determined using uncertainty-based approach [130]. After transmitting data from the selected cells, the whole area conditions are estimated using spatiotemporal CS recovery algorithms [131]. The obtained results show a significant reduction in the sensing cost while still guaranteeing the overall data quality for urban sensing. Thereafter, several efforts have been made to further enhance the performance of sparse MCS in terms of privacy [132], cell allocations [133; 134] and reducing the number of participants required for data collection [135].
## 3 Experimental Results and Analysis  
### Hyperparameter Setting and Evaluation Metrics  
The PPO with an adaptive KL penalty algorithm controls the distance between the updated policy and the old policy in order to avoid noise during a gradient update. Hyperparameter tuning was used to select the proper variables for the training process. Hence, PPO hyperparameter initializations improve the effectiveness of RL at various tasks. In this study, we propose the set of PPO hyperparameters for mixed-autonomy traffic at a non-signalized intersection shown in Table 2. The time horizon per training iteration is calculated by multiplication between the time horizon of a single rollout and the number of rollouts per training iteration. The time horizon of a single rollout is 600 and the number of rollouts per training iteration is 10. Therefore, the time horizon per training iteration is 600. The \"\(256\times 256\times 256\)\" means that we set up 3 hidden layers, and each layer has 256 neurons at all. In addition, based on our experiment, the agent performs well at the number of training iteration of 200.  
The training policy's performance was verified by the maximum reward curve over 200 iterations. A flattening of the curve indicates that the training policy has completely converged. Furthermore, the simulation performance was evaluated by measures of effectiveness (MOE), which are designed to analyze traffic operations. Such an evaluation can help to predict and address traffic issues. In this study, we adopted the following MOE to evaluate the simulation's performance:  
* Mean speed: the average speed of all vehicles at a non-signalized intersection.
* Delay time: the time difference between real and free-flow travel times of all vehicles.
* Fuel consumption: the average fuel consumption value of all vehicles.
* Emissions: the average emission values of all vehicles, including nitrogen oxide (NOx) and hydrocarbons (HC).
## 4 Experimental Results
### Ball  
In this section, the bearing ball fault signal measured from the test rig is analyzed and the corresponding results are shown in Figure 7.  
Figure 5: Results of experimental outer race fault signal.  
The time domain waveforms of the noisy ball fault signal measured are shown in Figure 7a. The length of this signal was 76,800. The envelope spectrum analysis of the noisy bearing fault signal is shown in Figure 7b. The fault feature frequency also cannot be obtained directly. The result of the estimated transient components in the time domain is shown in Figure 7c. The envelope spectrum analysis of the estimated transient components is shown in Figure 7d. From Figure 7d, it can be obtained that the fault feature frequency of the ball was \(f_{3}=132.7\) Hz, which is nearly equal to the theoretical value (132.6 Hz). Since there are always unavoidable errors in the measurement and calculation process, 132.7 Hz should be the correct required fault feature frequency.  
Figure 6: Results of experimental inner race fault signal.  
Figure 7: \(Cont\).  
Therefore, the proposed method can effectively extract the fault feature frequencies of three different types of faults from the corresponding noisy experimental signal. The aforementioned simulated and engineer tests were all implemented in MATLAB2016b on a computer whose operating system was Windows 10. The CPU parameter of the computer was Intel(R) Core(TM) i7-4790 at 3.60 GHz and the RAM was 12.0 GB, and the execution time of both simulated and engineering signals were less than 1 s. It can be seen that the time consumption of the proposed method is very low and stable.
## 1 Introduction
In [31], the authors proposed a dual-hop cooperative relaying system with an energy-constrained DF relay that harvests energy from the source information stream. The exact closed-form OP equations are generated using special functions. Recently, NOMA and cooperative relaying NOMA (C-NOMA) combined with EH have been hotly studied [32; 33; 34; 35; 36; 37; 38]. In [32], the authors investigated spatial modulation-based CNOMA for the BS downlink using a full-duplex near user that might collect energy and transmit data to a far user. Ref. [33] investigated collaborative user pairing and resource allocation for WPT-based CNOMA with EH near users. In [34], EH is carried out with CNOMA, and it is taken into consideration by choosing the optimal (decode-and-forward) DF relay. Only a two-user downlink NOMA and the dual-hop scenario are taken into account in the system model and analysis. When there are many EH-based amplify-and-forward (AF) relays between the base station and the far user, the best one can be chosen for data forwarding [35]. In [36], a two-user CNOMA network is explored, with the source node's power consumption lowered to meet the needed transmission rates using both TS relaying and PS relaying strategies. The Lyapunov optimization methodology is utilized by the authors in [37] in order to maximize throughput or restrict transmit power levels. For a wireless-enabled IoT network, the author in [38] proposes a fairness-aware NOMA-based scheduling system. Additionally, they suggest boosting network through throughput-aware NOMA-based scheduling. Recent research has demonstrated that deep learning (DL) methodologies can help with a variety of real-world challenges in wireless communication networks, such as resource allocation, congestion control, and queue management [39]. In [40], a deep neural network (DNN) was utilized to boost a cell-edge user's productivity under both perfect SIC and imperfect SIC in wireless-powered CR-NOMA-based IoT relay networks. In [41], a DNN was developed to tackle classification and regression issues in cognitive two-way relay networks for a relay. The authors of [42] investigated a DNN incorporated into a NOMA system and achieved outstanding performance in terms of channel encoding, decoding, and detection. Ref. [43] described a unique way to improve service distribution in IoT networks that employed DL to discover the best distribution strategy.
# Wireless Data Acquisition System with Feedback Function
## 2 Materials and Methods
### Analysis of Existing Concepts for Building a Wireless DAS for Low-Power GEPPs
This problem is highlighted particularly clearly in DAS variants that record data on SD cards and transmit them via the Bluetooth interface [27]. Such a delay is acceptable provided that all GEPP equipment is in good working order and is set up correctly after commissioning. If at least one of these conditions is not met, then equipment failure during automatic monitoring without constant or at least regular monitoring by people can be prevented only by adding to the DAS the function of automatically responding to problems and abnormal conditions.
# Traceability of Surgical Instruments: A Systematic Review
## 4 Results
### Medium-to-Long-Term Benefits: Economy and Environment  
#### 4.2.1 Economic Benefits  
Optimizing resource use through traceability systems can significantly reduce healthcare costs by extending the lifespan of instruments and improving inventory management.  
This impact is crucial in the medium and long term, as surgical trays can hold up to 188 instruments per procedure [36].
* At the Japanese Red Cross Wakayama Medical Center (2019) [22], traceability systems predicted instrument service life, enabling better resource planning and cost savings.
* Duke University Hospital [18] reported that logistical improvements, such as reduced setup time, contributed to a more cost-effective workflow.
## 3 Material and Methods
### Layers of the Architecture
#### 3.3.2 Max-Pooling and Dropout Layer  
The pooling layer collects the feature maps from convolutional layer and generates a reduced size of feature maps to emphasize more on important features. An example of how a Max-Pooling layer generates feature maps from the provided input feature map is presented in Figure 7. A kernel size of 2 \(\times\) 2 matrix is used for the max-pooling layer in the CNN architecture which finds the maximum value from the 2 \(\times\) 2 location on feature map 1 generated by convolutional layer and removes the rest of the values. An example of the max-pooling layer's algorithm is also shown in Algorithm 2. The Ri \(\times\) Rj (Line 4) container in the input feature map pools out each value from the output feature map, with Ki \(\times\) Kj (Line 4) indicating the pooling kernel size. The amount of output function maps grows as the number of pooling kernels grows.  
The number of max-pooling layers used in the architecture is three. It occurs after the convolution layer to decrease the size of feature maps so the model can execute and process  
Figure 7: Example of a max-pooling layer which takes feature maps of previous layers as input and generates its own reduce sized feature maps.  
images in less time as an architecture with fast diagnosis and light weight was considered. The first Max-Pooling layer consists of 64 filters which generated the feature maps of size 110 \(\times\) 110. The second max-pooling layer occurs after the third convolution layer as mentioned in Figure 5. The second max-pooling layer generates the feature map of size 54 \(\times\) 54; it uses 64 filters to shrink the input feature maps. The third and last max-pooling occurs after fourth convolution layer to generate smallest feature maps of 26 \(\times\) 26 making the work flow of dense layer to predict and classify easily. The third max-pooling layer has the greatest number of filters which were 128. At the end of each max-pooling layer a dropout technique was carried to prevent overfitting of the CNN model. At each update of the training process, dropout works by setting the outgoing edges of hidden units to 0 at random.  
```
1. Input: x (X coordinate), y (Y coordinate)
2. Output: sum (sum of product of kernel weight and the value at the corresponding position)
3. x \(\leftarrow\) 0
4. Ri (Input of feature map), Rj (Output of feature map),
5. For (i = 0 to i < Rj-Kj) do
6. y \(\leftarrow\) 0
7. For (j=0 to j < Rj-Kj) do
8. Sum \(\leftarrow\) 0
9. For (ki = 0 to ki < Ki) do
10. For (kj = 0 to kj < Kj)
11. temp \(\leftarrow\) image[i+ki][j+kj]*Kki][kj]
12. var \(\leftarrow\) max(var, temp)
13. Endfor
14. Endfor
15. f1[x][y] \(\leftarrow\) TransformToNonLinear(count)
16. Y++
17. Endfor
18. X++
19. Endfor
```  
**Algorithm 2** Pooling Layer
## 4 Open-Source Libraries
### Summary of Library Features  
Tables 3 and 4 provide a synoptic summary of the methods and relevant features of open-source libraries that solve non-symmetric eigenvalue problems. All of the libraries can handle both real and complex arithmetic types.  
As it can be seen from Table 4, not all libraries provide algorithms that allow for calculating both left and right eigenvectors at once. However, in order to calculate participation factors, which are an important tool of power system SSSA [66; 67; 68], both right and left eigenvectors are required. With this regard, a formula to directly calculate left eigenvectors from right ones was proposed in [69]. In general, which is the most efficient solution for the calculation of the left eigenvalues depends on the library and the eigenvalue problem.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Library** & **Method** \\ \hline LAPACK & QR, QZ \\ ARPACK & IR-Arnoldi \\ SLEPc & Power/Inverse Power/Rayleigh Quotient Iteration, \\  & Subspace, ERD-Arnoldi, Krylov-Schur, \\  & GD, JD, CI-Hankel, CI-RR \\ Anasazi & Block Krylov-Schur, GD \\ FEAST & FEAST \\ z-PARES & CI-Hankel, CI-RR \\ \hline \hline \end{tabular}
\end{table}
Table 3: Methods of open-source libraries for non-symmetric eigenvalue problems.
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 5 Result Analysis and Performance Evaluation
### Under Jellyfish Periodic Drop Attacks
#### 5.1.2 End-to-End Delay  
The end-to-end delay reflects the average amount of time that data packets take to travel from their source to their destination node. As shown in Equation (9), it can be calculated by dividing the sum of the times for all received packets by the total number of packets:  
\[\text{EEDelay}=\frac{\Sigma(\text{Packet}_{\text{received time}}-\text{ Packet}_{\text{sent time}})}{\text{Nbr}_{\text{packets}}} \tag{9}\]  
where \(\text{Packet}_{\text{received time}}\) is the packet received time, \(\text{Packet}_{\text{sent time}}\) is the packet sending time, and \(\text{Nbr}_{\text{packets}}\) is the number of delivered packets.  
Figure 11 shows the end-to-end delay performance comparison results of the SecE-V2X algorithm with the aforementioned security algorithms. As more malicious nodes  
Figure 10: Packet delivery ratio under Jellyfish periodic drop attacks [20; 37].  
are deployed, the end-to-end delay value tends to decrease. According to the results, the proposed SecE-V2X has a lower end-to-end delay value than that of the existing algorithms for any number of attacker nodes. When there are no malicious nodes, we observe that the suggested protocol outperforms the competing approaches and has a reduced end-to-end delay value of almost 6.8 ms. In the presence of three malicious nodes, the results show that the end-to-end delay value of SecE-V2X is 7.48 ms, better in terms of end-to-end delay than [37], 2017 (12.21 ms), and even [20], 2018 (7.94 ms). When the number of attacker nodes increases to six, the end-to-end delay of the proposed protocol increases slightly to 8.31 ms, which is still significantly better than [37], 2017 (16.83 ms) and [20], 2018 (9.64 ms). With nine malicious nodes, the end-to-end delay degrades to 10.48 ms for the proposed protocol, 21.34 ms for [37], 2017, and 13.29 ms for [20], 2018. This can be explained by the fact that the packet delay metric allows our SecE-V2X to favor the selected routes with better end-to-end delays.
# Improved Durability of Wood Strand-Based Panels Using Guayule
## 3 Results and Discussion
### Durability and Degradation
#### 3.2.4 Fungal Decay Resistance Percent Mass Loss  
The average fungal decay test specimen results are shown in Figure 7. The SYP control specimens had a mass loss range of 34.02% to 57.89% with an average mass loss of 45.45%. _G. tracheunt_ (brown-rot fungus) should cause weight loss of 40.00% or greater on SYP wood blocks in 12 weeks based on AWPA E10-16, section 18.3 [35]. The control test specimens had >45.00% average mass loss, indicating that the decay fungi were actively degrading the control blocks, showing a valid test. The WSB control specimens had an average mass loss of 16.93%, which was 62.75% lower than that of SYP control specimens. The high temperature during the manufacturing process could be an explanation for such greater mass loss variation in the WSB control specimens. The in-situ specimens had an average mass loss of 6.57%, therefore having a 61.19% lower mass loss compared to the WSB control specimens. However, specimens brushed with guayule resin had an average mass loss of 5.88%, therefore having a 65.27% lower mass loss compared to the WSB control specimens. Specimens sprayed with guayule resin had an average mass loss of 3.24%, which was 80.86% lower than that of the WSB control specimens. There were statistical differences (\(p\leq 0.0001\)) between the specimens of the SYP control untreated specimens, WSB controls, in-situ specimens, and those sprayed or brushed with guayule resin for fungus decay resistance mass loss, as represented by the letters A, B, and C, respectively (Figure 7). Overall, the best solution was the sprayed guayule resin, as indicated by the lowest average percent mass loss compared to any other treatment, despite there being overlaps with brushed and in-situ specimen ranges (Figure 7).  
Figure 6: Photos of in-situ wood strand-based (WSB) specimen (far left) with slight to moderate damage; WSB control specimen (middle image) with slight to moderate damage; and solid southern yellow pine (SYP) wood control specimens (far right) with very severe 50–75% and up to 100% complete failure of termite attack by subterranean termites (_Reticuliternes flavipes_) during a 28-day-long American Wood Protection Association (AWPA) E1-23 termite test.
## 2 Related Work
[PERSON] _et al._[17] suggest a protocol including some techniques such as the sleeping mode of nodes if data in the environment where detection is made is under a certain value and selecting the node with the most energy level as the cluster head in order to enhance energy efficiency. [PERSON] _et al._[18] describe Hierarchical Clustering Routing (HCR) as an extension of LEACH. In HCR, each cluster is managed by a set of associates and energy efficient clusters are retained for a longer period of time. In a variation of HCR, the base station determines the cluster formation. A genetic algorithm (GA) is used to generate energy-efficient hierarchical clusters. The base station broadcasts the GA- based clustering configuration, which is received by the sensor nodes and the network is configured accordingly. In [19], the authors, in their algorithms based on LEACH, propose to transfer the collected data to the nearest node in terms of received signal strength by not allowing the cluster heads to directly communicate with the base station. The selected nearest node compresses the data and forwars it to base station. They state that this shortens the distance between the cluster heads and the base station and reduces energy consumption. In [20] the authors introduce a cluster head election method using fuzzy logic to overcome the drawbacks of the LEACH protocol. They investigate the use of fuzzy variables to prolong the lifetime of a homogenous network system. Many other hierarchical routing protocols have been proposed either in the past [21-23] or more recently [24-26]. Similarly, many alternative approaches have been proposed for energy conservation in wireless sensor networks.
# Indoor Signal Strength Evaluation of the Orbcomm Low Earth Orbit Satellite Constellation +
## 3 Material and Methodology  
We will analyze the signal strength in various outdoor and indoor environments. To evaluate the link quality, two parameters are commonly utilized. The first parameter is the Carrier-to-Noise Ratio \(\left(\frac{C}{N}\right)\). Which is the ratio of carrier power over noise power in the channel [3]. This parameter is a good indicator of system performance and can be calculated as such [11]:  
\[\left(\frac{C}{N}\right)=P_{\mathrm{C}}-P_{N} \tag{1}\]  
with \(\left(\frac{C}{N}\right)\) in dB, \(P_{\mathrm{C}}\) is the carrier power in dBW and \(P_{N}\) is the noise power in dBW [3]. The second commonly used parameter is the \(C/N_{0}\) which is the ratio of carrier power to the noise power per unit bandwidth. To calculate the \(C/N_{0}\) the following formula can be used [3]:  
\[\left(\frac{C}{N_{0}}\right)=\left(\frac{C}{N}\right)+B_{N} \tag{2}\]  
with \(C/N_{0}\) in dB-Hz, \(\left(\frac{C}{N}\right)\) in dB and \(B_{N}\) the bandwidth in dB-Hz.