esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Electrical Resistivity Responses  
Real-time electrical resistance for different kinds of sandwiched coal-rock specimens was monitored throughout the unaxial compressive experiments. Then, the electrical resistivity was calculated based on Equation (1). The green curves in Figure 6 represent the response of the electrical resistivity for C-R, R-C, R-C-R, and R-C-R-C-R, respectively.  
Before loading, the initial electrical resistivity for C-R, R-C, R-C-R, and R-C-R-C-R was 1.67 M\(\Omega\cdot\)m, 1.67 M\(\Omega\cdot\)m, 1.42 M\(\Omega\cdot\)m, and 1.55 M\(\Omega\cdot\)m, respectively. With the development of compaction deformation, the electrical resistivity of the sandwiched coal-rock specimens decreased gradually although a slight fluctuation was observed during stage I (see Figure 6). The enclosure of natural micro-pores and cracks could explain this phenomenon, which resulted in the generation of new electrical channels [10]. Under the coupled effects of the original and newborn electrical channels, the overall conductive capability of the sandwiched coal-rock specimens was improved.  
At stage II, the declining tendency of the electrical resistivity continued to be monitored for different kinds of sandwiched coal-rock specimens. However, the declining rate weakened gradually. It indicated that the conductive channels in the defective areas were damaged locally during the elastic deformation process.  
The electrical resistivity of the sandwiched coal-rock specimens was maintained at a relatively small value during stage III. An overall decreasing tendency was shown along with the yielding growth of the bearing capacity. A remarkable increase of the electrical resistivity appeared at the end of stage III when the peak bearing stress was captured. This was attributed to the generation of macro-cracks, which further induce serious damage to the electrical conductive channels.  
When the axial stress evolved into stage IV, the electrical resistivity of the sandwiched coal-rock specimens seemed to be chaotic due to the uncertainty of crack propagation and the diversity of the failure mode. As presented in Figure 6a, sudden drops and growth of the electrical resistivity were captured at stage IV for C-R. A general increasing tendency of the electrical resistivity is shown in Figure 6b,c for R-C and R-C-R. As shown in Figure 6d, the electrical resistivity for R-C-R-C-R declined initially with some fluctuations, while it tended to grow at the end of stage IV.
# Nonlinear Dynamics and Machine Learning for Robotic Control Systems in IoT Applications
## 2 Materials and Methods  
This study presents a control system designed for a 20 DOFs robotic platform, integrating nonlinear dynamic equations and machine learning models. The robot is modeled using a set of nonlinear differential equations, and the control system is developed using MATLAB. The dynamic modeling incorporates the Adams-Bashforth-Moulton method for solving the equations of motion, chosen for their balance between computational efficiency and accuracy in predicting dynamic states. The hybrid control strategy combines traditional feedback control and a neural network that dynamically adjusts the control parameters based on real-time sensor inputs. The neural network is trained on historical data collected from the robot's previous trajectories, allowing it to predict necessary adjustments for improved performance in real-world tasks.
# 3-D Printed Fabry-Perot Resonator Antenna with Paraboloid-Shape Superstrate for Wide Gain Bandwidth
## 2 Resonant Condition for FPRA  
As illustrated in Figure 1, a typical FPRA includes an excitation antenna, a superstrate and a ground. The EM energy emerges from the excitation antenna and undergoes multiple reflections between the superstrate and the ground. By using of the ray tracing model [1; 16], assuming the EM ray is with an incident angle of \(\theta\), and denoting the \(i\)th transmitted ray to be _Rayi_ (\(i=0,1,2\)... ), the phase difference \(\varphi\) between two adjacent rays can be expressed as  
\[\varphi=\varphi_{s}-\frac{k\cdot 2H}{\cos(\theta)}+\varphi_{G}-(-k\cdot 2H\cdot \tan(\theta)\sin(\theta)) \tag{1}\]  
where \(k=2\pi f/c\) is the wave number, \(f\) is the working frequency, \(c\) is the light velocity, \(\varphi_{S}\) and \(\varphi_{G}\) are the phases of reflection coefficients for the superstrate and the ground respectively, and \(H\) is the resonant height.  
In order to obtain high gain at \(\theta\) direction, all transmitted rays should be in phase, and hence the phase difference \(\varphi\) between two adjacent rays should be \(2n\pi\) (\(n=0,1,2\)... ). For most practical applications, the FPRAs are required to have maximum gain at the broadside direction (\(\theta=0\)), and hence the resonant height \(H\) should satisfy the resonant condition as  
\[H=\frac{c}{4\pi f}(\varphi_{s}+\varphi_{G}+2n\pi),\ n=0,1,2\ldots, \tag{2}\]  
where \(n\) is usually chosen as 0 to realize a low profile for the FPRA.  
For a perfect electric conducting (PEC) ground plane, its reflection phase \(\varphi_{G}\) is a constant of \(\pi\). The reflection phase of a superstrate \(5\varphi_{S}\) normally varies with the frequency. Hence for a FPRA with a fixed resonant height \(H\), the resonant condition illustrated by Equation (2) is satisfied only within a narrow frequency band. This is why most FPRAs have a narrow gain bandwidth. Moreover, it can be expected that the FPRA is able to achieve a wide gain bandwidth if the FPRA's configuration provides multiple resonant heights.
## 5 Conclusions  
This study proposed a novel method for the classification of epileptic seizures using RNN. The proposed method was compared with traditional machine learning models such as ANN and SVM, using data acquired from two widely used publicly available datasets, CHB-MIT and BONN. The proposed model presents a novel technique by integrating several stages, including EEG signal filtering, data segmentation, pre-processing, feature extraction, and classification. The data were segmented into epochs of 3 s each. The EEG signal was first pre-processed using DWT-based decomposition into five frequency sub-bands, and statistical features were extracted from each sub-band to obtain an input vector for training the classification models, including RNN. Training and testing of the classification models were performed multiple times with various parameter sets such as learning rate, training/testing ratios, and frequency analysis techniques using 10-fold cross-validation. The results conclude that the proposed RNN-based model demonstrated the highest classification accuracies of 93.27% on the CHB-MIT dataset and 99.84% on the BONN dataset. In the future, ensemble methods will be applied by combining RNN with other machine learning algorithms such as ANN and SVM to enhance classification accuracy.  
Conceptualization and writing of original draft, S.Y.S.; review and editing, H.L., R.M.G. and D.L. All authors have read and agreed to the published version of the manuscript. This research was supported in parts by The Data Lab.  
Not applicable.  
The authors used two publicly available datasets: the CHB-MIT Scalp EEG dataset ([[https://physionet.org/content/chbmit/1.0.0/](https://physionet.org/content/chbmit/1.0.0/)]([https://physionet.org/content/chbmit/1.0.0/](https://physionet.org/content/chbmit/1.0.0/)) (accessed on 1 June 2023)) and the BONN EEG dataset ([[https://repositori.upf.edu/handle/10230/42894](https://repositori.upf.edu/handle/10230/42894)]([https://repositori.upf.edu/handle/10230/42894](https://repositori.upf.edu/handle/10230/42894)) (accessed on 1 June 2023)).  
The authors declare no conflicts of interest.
# Experimental Study on Preparation and Characteristics of Concrete Modified by Construction Waste
## 5 Conclusions  
This article uses orthogonal experiments to conduct modification experiments on recycled concrete and develops recycled concrete materials. The following conclusions are drawn:  
1. The water absorption rate of recycled aggregate shows a decreasing trend with the dosage of modifier and modification time, which is also reflected in the slump test of fresh concrete, indicating that the modification of recycled aggregate is effective.
2. Targeting the 28 d strength of recycled concrete, the optimal factor level combination is A3B3C1, with a water cement ratio of 0.29, a modifier dosage of 12%, a modification time of 90 min, and a recycled aggregate dosage of 20%.
3. In the interface transition zone between modified recycled fine aggregate and cement stone, only small size tabular CH crystals can be seen, and the hydration products C-S-H gel, ettringite, etc. are interspersed with each other, with a dense structure and no obvious pores.
## 4 POMDP Model Formulations
### CSPOMDP Model
#### 4.1.3 Observation  
The true degradation state of the pipelines is hard to determine due to several factors. However, various methods can be implemented to estimate the current states which were used to formulate the model. ILI using different tools has commonly been used in the industry. The tools included magnetic flux leakage, ultrasonic, electromagnetic, and acoustic technology, and eddy current testing [14]. Mathematical models can also be used to estimate the degradation based on the progression behavior of corrosion or the operational parameters of the pipeline.  
The error in the estimation can be used to formulate the observation function. For an ILI, equipment errors emanate from four sources: detection, sizing or measuring, false call, and reporting threshold [33]. Detection errors arise from the inability to detect the due corrosion size. Smaller-sized defects have a low probability of detection. Sizing is a random error due to the intrinsic nature of the equipment. The normal distribution function is used to estimate this error [30]. Equipment creates a false call error when it reports a non-existing defect in the pipeline. This error occurs mostly to large-sized defects using poor-quality inspection equipment. Threshold reporting refers to the detection range of equipment. Any defect beyond this range will not be detected, creating a detection error. [PERSON] and [PERSON] [30] and [PERSON] and [PERSON] [33] have proposed several mathematic formulations for these errors. In addition to the equipment errors, the observation formulation can incorporate inspection personnel errors (reading errors) and errors emanating from the pipeline operating environment. Historical or empirical data or mathematical models can be used to compute the errors and formulate the observation function.  
Continuous observation functions would have to be discretized for a discrete model before integrating them into a discrete space POMDP. The observation range can be divided into equal intervals to create discrete values. Alternatively, an advanced discretization mathematical method can be applied to construct the values. When the observation matrix is generated, the pipeline state can take multiple observation values (expressed as a probability function) or a specific value. It is important to note that this discretization step is skipped for a continuous observation POMDP model to incorporate the function directly.
## 4 Mean Outage Duration Estimation  
In the context of outage performance estimation, another metric, relevant to outage probability at least as important, and very practical, is the mean outage duration (MOD) of the FSO system. Indeed, the value of MOD is a significant figure of merit, because in combination with the throughput of the communication system, it relates to the percentage of information loss over a properly selected reference time. In fact, this metric denotes the average duration over the reference time interval that the examined communication system fails to operate properly. Thus, MOD indicates the average period of time that an outage occurs, i.e., the average duration that the system is practically unavailable over the determined reference time. Consequently, it becomes evident, that MOD is of particular practical use for FSO systems design, since its value directly reveals whether the investigated system can meet the required outage specifications or not. More precisely, the MOD, \(T_{od}\) for a specific period of time is calculated as [29; 36]:  
\[T_{od}=P_{out}T_{R} \tag{20}\]  
with \(T_{R}\) being the appropriately chosen reference time, e.g., \(T_{R}\) = 3600 s for the case where the reference time is one hour. Additionally, the coherence time of the atmospheric turbulence, \(\tau_{0}\), is in the range of milliseconds [37; 38], and for \(T_{od}\), it should be verified that \(T_{od}\geq\tau_{0}\). Furthermore, note that for the simulation experiments conducted in the next section we assume a reference time of one hour.  
In view of the above, by substituting either (18) or (19) into (20) we obtain the closed-form expression for the MOD valueof the total FSO system, \(T_{GG,od}\), for G-G:  
\[T_{GG,od}=T_{R}\left\{\prod_{i=1}^{N}\left\{1-\prod_{j=1}^{H}\left[1-\frac{ \psi_{\{i,j\}}^{2}}{\Gamma(\alpha)\Gamma(\beta)}G_{2,4}^{3,1}\left(\sqrt{ \frac{\gamma_{\{i,j\},th}}{\mu_{\{i,j\}}}}\frac{\alpha\beta\psi_{\{i,j\}}^{2} }{\psi_{\{i,j\}}^{2}+1}\left|\begin{array}{cc}1,&\psi_{\{i,j\}}^{2}\\ \psi_{\{i,j\}}^{2},&\alpha,&\beta,&0\end{array}\right.\right)\right]\right\}\right\} \tag{21}\]  
or, \(T_{NE,od}\), for NE turbulent channels, respectively, as:  
\[T_{NE,od}=T_{R}\left\{\prod_{i=1}^{N}\left\{1-\prod_{j=1}^{H}\left[1-\psi_{\{ i,j\}}^{2}G_{2,3}^{2,1}\left(\sqrt{\frac{\gamma_{\{i,j\},th}}{\mu_{\{i,j\}}}} \frac{\psi_{\{i,j\}}^{2}}{\psi_{\{i,j\}}^{2}+1}\left|\begin{array}{cc}1,& \psi_{\{i,j\}}^{2}+1\\ \psi_{\{i,j\}}^{2},&1,&0\end{array}\right.\right)\right]\right\}\right\} \tag{22}\]
# Hyperspectral Python: HypPy
## 1 Introduction  
Image processing packages have been around since the creation of the first computers. However, at the start of this century, not many packages were capable of handling hyperspectral image data. Hyperspectral images tend to be huge, and standard operations such as image classification are unfit for handling the high dimensionality of hyperspectral data. Traditionally, hyperspectral images are processed using proprietary packages such as ENVI (ENVI and IDL are trademarks of NV5 Geospatial Solutions, Inc.) (Environment for Visualizing Images) [1] and others. However, with the advent of free open-source software [2], the need was felt to be able to use and process hyperspectral images independently of proprietary packages.  
With packages like numpy [3], scipy [4], and matplotlib [5], the Python language [6] has become a data-processing and prototyping environment, rivaling systems (proprietary and free) such as MATLAB (MatLab is a registered trademark of The MathWorks, Inc.), IDL (ENVI and IDL are trademarks of NV5 Geospatial Solutions, Inc.), Octave (Octave is a trademark of Octave Technologies, Inc.), and R.  
Python, in addition the aforementioned packages numpy and scipy, is free and open-source. Users can access the source code, make modifications, and distribute their own versions. In principle, any code written in Python can be distributed freely.  
We use Python for the implementation of the package Hyperspectral Python, HypPy (pronounced [hip-ee], like the subculture in the 1960s), which is a software package for reading and writing, visualizing, processing, and analyzing hyperspectral data.  
HypPy started as a number of image classes and factory functions enabling the reading and writing of ENVI format images. The creation of more tools in HypPy gained momentum when our research group became involved in the Mars Planetary Mapping Pilot Project [7] in 2009 and 2010. Subsequently, various general tools and specific tools for handling Mars hyperspectral data were written. At the same time, HypPy offered aplatform for implementing experimental ideas. Gradually, the package was extended to what it is today. This paper describes the design considerations and implementation of HypPy providing a number of examples.
## 3 The Communication Program for the Semi-Trailer ECPB In-Loop Testing System
### Data Flow Implementation  
The ECPB hardware test bench for semi-trailer transport is located in a WIFI-covered site. The pressure sensors collect the brake valve pressure, which can be uploaded to the cloud server. The in-loop testing system imports the driving information of the twin test vehicle into the upper controller through the wireless network and sends it to the lower controller after processing. The driving information of the twin test vehicle is monitored by various sensors, including a pressure sensor, GPS, six-axis sensor and wheel speed sensor. These sensors communicate through different protocols, such as RS232, TTL and CAN. The wireless network module facilitates data interaction between the twin test vehicle and the cloud server through a 5G network.  
To guarantee the stability of the data flow between the ECPB hardware test bench and the twin test vehicle, MQTT is implemented for data interaction. Figure 4 shows the specific implementation process. Emqx serves as the MQTT server, while the hardware test bench and the twin test vehicle act as clients to publish and subscribe to the service.  
Figure 3: Data flow of the ECPB semi-trailer in-loop testing system.  
Assuming the client and the messaging server establish a connection via the subscription function, when a client publishes real-time data on a topic, another client that subscribes to the same topic can obtain the corresponding data through its load information. When the data change, the control status of the bench or test vehicle should be updated, and the updated status should be sent to the MQTT server, so as to update the status repeatedly. This mechanism enables the interaction of real-time data between the hardware test bench and the twin test vehicle, providing stable and reliable data transmission for real-time data interaction. The transmission mechanism is highly stable and meets the requirements for real-time data interaction.
# On Edge Pruning of Communication Networks under an Age-of-Information Framework
## 8 Simulation
### Simulation Summary  
After running the simulation that shows the effect of pruning the least significant \(k\) links from the network under various different settings, we see in effect the usefulness of our algorithm and its impact on the repetitiveness of the messages delivered at the destination. At the same time, we see the impact of the pruning process on the total AoI of the network and we observe the trade-off between those two characteristics of the network under the different settings.  
From the results in Figures 10 and 11, we observe that pruning a small number of links from the network would not impact the total AoI in a drastic way while the impact on the number of duplicate messages is of the same severity that it would be for a higher number of links to be pruned. These results would make it clear that the number of links to be pruned is a parameter to be tuned independently in a way that would sacrifice the AoI for the sake of getting rid of the duplicate messages.
# Fabric Defect Detection Method Using SA-Pix2 pix Network and Transfer Learning
## 2 Materials and Methods
### Channel Domain Transfer Learning
#### 2.2.1 Channel Weight Estimation  
Each convolutional layer comprises multiple kernels, with each kernel corresponding to a channel. To assess the impact of each channel on defect image reconstruction accuracy, we set all the elements of each kernel to zero separately. The change in the loss function (Equation (1)) is computed to gauge the significance of each channel. This loss can be interpreted as the error between the generator's output and the real labels, indicating the extent to which the images generated by the generator are mistakenly recognized as real images by the discriminator.  
The Pix2 pix network generators are separately trained using source domain data, one with the \(j\)-th channel, and one without. The loss function with the \(j\)-th channel is denoted as \(l_{j}\), and without the \(j\)-th channel as \(l^{\prime}_{j}\). To normalize the change in loss functions, the softmax function is applied, ensuring that the weight for the \(j\)-th channel is non-negative, as presented in Equation (3). From Equation (5), it is evident that if the removal of a specific channel leads to a larger change in the loss function, the channel weight becomes greater, signifying a more substantial impact of that channel on the defect image reconstruction accuracy.  
\[w_{j}=soft\;\text{max}(l_{j}-l^{\prime}_{j}) \tag{5}\]  
\[Z_{\text{j}}=l_{j}-l^{\prime}_{j} \tag{6}\]  
\[\text{soft}\;\text{max}(z_{j})=\frac{\text{exp}(z_{j})}{\sum_{t=1}^{T}\text{ exp}(z_{t})} \tag{7}\]  
In Equation (7), T represents the total number of convolution kernels in the network.
## 3 Results and Discussion
### Results at Near-Neutral pH
Similarly, in the context of dye decolorization, [PERSON] et al. [38] observed that H\({}_{2}\)O\({}_{2}\)/CuII\({}^{-}\)pyridine was more effective than H\({}_{2}\)O\({}_{2}\)/CuII\({}^{-}\) at pH 3 to 9. The authors proposed that hydroxyl radicals were probably involved in the decolorization, since HO\({}^{\bullet}\) scavengers such as superoxide dismutase inhibited the color-stripping. Since H\({}_{2}\)O\({}_{2}\)/Cu-Phen is the most efficient system to produce hydroxyl radicals, its efficiency was compared to a reference system: the Fenton reagent. FeSO\({}_{4}\) was thus used instead of Cu-Phen, under strong acidic conditions, with the same H\({}_{2}\)O\({}_{2}\) and metal concentrations (exp 11, Figure 7). As expected, the Fenton control gives a clear four-line signal typical of DMPO-OH (entry 11 in Table 1a). Yet, it is 25% less intense than for H\({}_{2}\)O\({}_{2}\)/Cu-Phen (total area of 0.43 including 86% DMPO-OH found with H\({}_{2}\)O\({}_{2}\)/Fe, compared to 1.29 including 38% DMPO-OH with H\({}_{2}\)O\({}_{2}\)/Cu-Phen). Assuming that nucleophilic addition of water does not occur neither in the Fenton system nor in the H\({}_{2}\)O\({}_{2}\)/Cu-Phen system (as discussed earlier), the H\({}_{2}\)O\({}_{2}\)/Cu-Phen system is thus a little more active than the Fenton reactant under similar conditions. Still, the HO\({}^{\bullet}\) generation of H\({}_{2}\)O\({}_{2}\)/Cu-Phen is in the same range as that of the Fenton system. Figure 6: Comparison between the X-band EPR spectra of HCOO\({}^{-}\) + DMPO + Cu–Phen + H\({}_{2}\)O\({}_{2}\) (exp 5) and HCOO\({}^{-}\) + DMPO + CuSO\({}_{4}\) + H\({}_{2}\)O\({}_{2}\) (exp 10) - Assignments: \(\mid\) = DMPO–COO\({}^{-}\).