esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results  
As explained in a previous section, two datasets are available from wireless environmental sensors that send data periodically to a server at the University of Ioannina campus in Arta. The Port dataset consists of fewer data than the Town Hall dataset. More specifically, the Port dataset contains 794,522 lines of data and the Town Hall dataset has 1,048,575 entries of data. The dataset features were normalized between 0 and 1 and were clustered to identify the optimal number of clusters to include them as a column of the two datasets.  
We set up the experiments using the two aforementioned methods in order to define the % fault that is identified. As we can see in Figure 5, the grammatical evolution solution outperforms the other four approaches in both areas that are examined. In particular, in the greater Port area, the GENCLASS exhibits 5.65% fault as opposed to the LBFGS method which exhibits 9.52%, ADAM with 14.26%, GENETIC with 8.21%, and BAYES with 11.53%.  
With regard to the Town Hall dataset, the fault is larger compared to the Port area. Moreover, in order to be more specific, it can be seen that the fault of the GENCLASS is 7.75%, the LBFGS is 17.23%, the ADAM is 27.27%, the GENETIC is 10.50%, and the BAYES is 14.00%, as can be seen in Figure 6.  
In order to gain an overall picture of the experimental results, Table 3 is introduced. It lists the name of each method used, if it is a global or local minimization method, and the average error in the two datasets.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline N\_G & 500 \\ \hline N\_C & 500 \\ \hline P\_S & 0.90 \\ \hline P\_M & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters in methods used.  
Figure 5: Average classification error measured on Port dataset.  
The two global optimization techniques generally had the best results, although they require significantly more computational time than the other techniques since a high number of chromosomes are required to be used in both. Of course, the additional computing time could be significantly reduced by using modern parallel computing techniques.
# An Overview on Management and Valorisation of Winery Wastes
## 5 Winery Wastes as Precursors for Sustainable Energy  
The increasing interest in using biomass as an energy source has resulted in the use of winery wastes for thermal decomposition [27; 96]. The potential energy content of residual biomass resulting from a grapevine hectare was approximated at 19 GJ [97]. It was demonstrated that pyrolysis was more feasible than combustion from an economic point of view, resulting in minimal amounts of residue. From one tonne of grape marc, 150 kg of biochar and 140 kg of biofuel were produced [96].  
One disadvantage of the winery wastes is the humidity, which affects energy productivity. In this respect, other processes, such as hydrothermal carbonization, which requires milder operation conditions (180-250 \({}^{\circ}\)C and 20-40 bar), must be applied [98]. The resulting products mainly consist in a liquid phase with dissolved organics, a solid phase rich in carbon (hydrochar), as well as a small amount of gases [98]. Higher energy yield in hydrothermal carbonization was achieved by grape pomace processing [99]. A recent investigation confirmed that grape marc hydrothermal treatment constitutes an efficacious way to obtain CO\({}_{2}\) neutral solid fuels directly in the wineries [100].  
The winery waste removal and valorisation of carbons obtained with high efficiency would be an important advance, transforming an environmental problem into an economic advantage [100]. There are few studies regarding the transformation of winery wastes into carbons, with the majority treating the potential use of biomass as raw material for porous carbons [101]. One of the more important research projects involved the valorisation of winery wastes by one-pot activation [101]. Stalks, bagasse, and oil-free seeds were used as precursors for economically obtaining porous carbons, with increased efficiency in CO\({}_{2}\) capture and electric energy storage in supercapacitors [101].  
The use of anaerobic digestion for winery waste removal is an appropriate option, resulting in lower energy costs and improved efficiency [102]. The valorisation of vineyard residues by anaerobic co-digestion was investigated using activated sludge, resulting in 65% methane and a yield of 0.4 Nm\({}^{3}\)/kg COD [103]. The transformation of sludge with low organic matter concentrations led to a 25-30% biogas production [104]. Grape pomace, pulp, and seeds were processed by anaerobic digestion for methane production, with the substrates being ground to increase their maximum degradability up to 22% [105]. The biogas and methane generation from organic wine wastes was also investigated, with the one with the highest potential being grape must, resulting in 1.152 and 838 m\({}^{3}\)/tonne volatile solids of biogas and methane, respectively [106].
## 3 Results
### Bacterial Survival at Free Chlorine Concentration of 0.5 mg/L for 30 min
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Bacterial Isolates**} & **Surviving Population** & **Log Reduction (log\({}_{10}\)** & **Residual Chlorine** \\  & **(CFU/mL)** & **CFU/mL)** & **(mg/L)** \\ \hline AEC1 & \(3.0\pm 0.14\times 10^{3}\) & 4.88 & 0.29 \(\pm\) 0.01 \\ AEC8 & \(4.0\pm 0.36\times 10^{3}\) & 5.75 & 0.39 \(\pm\) 0.01 \\ AEC11 & \(1.70\pm 2.6\times 10^{3}\) & 5.12 & 0.41 \(\pm\) 0.01 \\ AEC14 & \(3.10\pm 1.6\times 10^{3}\) & 4.86 & 0.35 \(\pm\) 0.01 \\ AEC16 & \(1.10\pm 1.3\times 10^{3}\) & 5.3 & 0.33 \(\pm\) 0.04 \\ _E. coli_ SAMRC-1 & \(3.23\pm 0.55\times 10^{3}\) & 4.84 & 0.44 \(\pm\) 0.01 \\ AEC17 & \(1.0\pm 0.1\times 10^{3}\) & 5.75 & 0.36 \(\pm\) 0.01 \\ AEC18 & \(3.30\pm 0.06\times 10^{3}\) & 6.0 & 0.34 \(\pm\) 0.01 \\ AEC24 & \(3.17\pm 0.12\times 10^{3}\) & 5.04 & 0.35 \(\pm\) 0.01 \\ DEC 2 & \(3.00\pm 0.2\times 10^{3}\) & 4.88 & 0.41 \(\pm\) 0.01 \\ DEC3 & \(3.3\pm 0.21\times 10^{3}\) & 5.04 & 0.35 \(\pm\) 0.01 \\ DEC4 & \(3.0\pm 0.17\times 10^{3}\) & 4.88 & 0.42 \(\pm\) 0.02 \\ DEC12 & \(4.0\pm 0.1\times 10^{3}\) & 5.0 & 0.41 \(\pm\) 0.01 \\ DEC 18 & \(2.67\pm 0.3\times 10^{3}\) & 4.93 & 0.42 \(\pm\) 0.04 \\ DEC 19 & \(2.50\pm 0.3\times 10^{3}\) & 5.0 & 0.31 \(\pm\) 0.01 \\ DEC 20 & \(2.5\pm 0.2\times 10^{3}\) & 5.0 & 0.42 \(\pm\) 0.01 \\ DEC 23 & \(5.67\pm 0.31\times 10^{3}\) & 5.0 & 0.39 \(\pm\) 0.01 \\ DEC 26 & \(5.5\pm 0.21\times 10^{3}\) & 5.0 & 0.40 \(\pm\) 0.01 \\ _E. coli_ SAMRC-2 & \(9.67\pm 6.03\times 10^{3}\) & 4.37 & 0.37 \(\pm\) 0.01 \\ _E. coli_ SAMRC-3 & \(3.03\pm 1.8\times 10^{4}\) & 3.88 & 0.42 \(\pm\) 0.04 \\ \hline \hline \end{tabular}  
* AEC: _E. coli_ isolates recovered from wastewater treatment plant A; DEC: _E. coli_ isolates recovered from wastewater treatment plant B, \(n=20\).  
\end{table} 
Table 1: Bactericidal activity and residual chlorine at free chlorine concentration of 0.5 mg/L on _E. coli_ isolates for 30 min.
# Introduction to Radar Scattering Application in Remote Sensing and Diagnostics: Review
## 4 Radar and Remote Sensing Applications
### Alternative Radar and Imaging Systems  
A passive radar is an inherently unique case of bistatic radar. It uses a non-cooperative source to illuminate the objects; the passive radar then detects targets through the reflection of the energy generated by such a source and caused by the targets themselves [79; 80]. Passive radar has been extensively used to describe the observation of geophysical and aerospace targets. Those radars can use commercial broadcast communication systems, such as Radio FM, analog TV, Digital Television (DTV), and navigation systems (GPS) [81; 82; 83; 84; 85; 86]. Lidar is the acronym for Light detection and ranging, and it constitutes an alternative to the active remote sensing technology [87]. Lidar technology can measure 3D images of forest canopies accompanied with detailed topographic maps and accurate assessment of vegetable crown. Lidar is also used to estimate accurately high-biomass ecosystems and to predict forest standard structures [88]. [PERSON] et al. described the cloud decomposition obtained by using imaging radar polarimetry technique for interpretation of the scattering from vegetation [60]. The recent development of polarimetric radar imaging and remote sensing can provide information regarding the Earth environment, damage assessment of the Earth, forest volume and height evaluation, glacier monitoring and mapping, and snowfall analysis [89]. Advanced polarimetric analysis techniques can also be used for speckle statistics, scattering polarization, and speckle filtering [90]. Synthetic-aperture radar (SAR) is an alternative and useful tool for processing coherent optical systems. This type of radar can provide finer-resolution images than conventional beam-scanning radars [91]. SAR is also considered an advanced tool to deal with the categorization of scattering from radar targets [92]. Wall imaging radar is an advanced Ultra Wideband (UW) communication tool that can be used to retrieve high-resolution images of detected objects, and it is based on short-pulse radar penetration through a wall surface [93].
## 8 Conclusions  
A parametric optimization of a single-stage EHD thruster was investigated. Based on an argon propellant EHD thruster developed by [PERSON] [24], the parameters of the geometry of the cylindrical thruster, of the external control circuit, and the secondary electron emission at the cathode's thruster were used for optimization. Adjusting the discharge current with the ballast resistor swapping from 2500 M\(\Omega\) to 500 M\(\Omega\), and keeping the applied voltage constant at 3 kV, the thrust output increased 5 times; with a fixed ballast resistor of 500 M\(\Omega\), and altering the applied voltage from 0.9 kV to 40 kV, a maximum thrust of about 0.6 uN is attained for 20 kV, while in the range 20-40 kV, there is a stagnation of the thrust, whereas the T/P ratio decreased.  
For argon and xenon, at fixed ballast resistor and applied voltage of 500 M\(\Omega\) and 20 kV, respectively, the most performant geometrical dimensions were determined. The secondary electron emission coefficient appears to play a significant role, and for low values of \(\gamma_{\rm i}\), thrust and thrust-to-power ratios would significantly escalate. As it is expected, xenon is more efficient than argon, due to its higher atomic mass, and in the best cases of our work we obtained a thrust with xenon of 4 uN, while argon reaches 2 uN.  
It is found that the flow velocity profile of the two gases, in the respective thrusters, as well as the distributions of charged particles (ions and electrons) are correlated with the behavior shown by the electric field/potential in the corona discharge region.  
Although we have no information in the literature that permits us to verify and validate our model for the two noble gases studied, our results point to a promised future of the EHD thrusters in the astronautical industry, fundamentally for space missions. However, more fundamental research is necessary in the study of the geometry of the thruster, and in the understanding of its coupling with the physics of the electric field lines morphology inside the ion thruster discharge.  
Conceptualization, P.A.S. and M.J.P.; methodology, P.A.S., M.J.P. and E.M.C.; software, E.M.C.; formal analysis, P.A.S., M.J.P. and E.M.C.; investigation, P.A.S., M.J.P., and E.M.C.; resources; P.A.S. and E.M.C.; data curation, P.A.S., M.J.P. and E.M.C.; writing--original draft preparation, P.A.S., M.J.P. and E.M.C.; writing--review and editing, P.A.S., M.J.P. and E.M.C.; visualization, E.M.C.; supervision, P.A.S. and M.J.P.; project administration, P.A.S.; funding acquisition, P.A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no specific grant from any founding agency in the public, commercial, or not-for-profit sectors.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
We acknowledge the Department of Engineering Physics and the Transport Phenomena Research Center of the Faculty of Engineering, University of Porto.  
The authors declare no conflict of interests.
# Research on the Predictive Algorithm of Wear Degree Based on Adaptive Feature Learning
## 2 Establishment of the Model  
The neural network and linear autoregression combined prediction model proposed in this paper (Figure 1) is based on the encoder-decoder structure [26]. The encoder-decoder is an abstract concept for deep learning models. The encoder is responsible for transforming the input into features, while the decoder transforms the features into targets. This structure was first applied to machine translation and achieved excellent results. Later, researchers began to consider its application to time series prediction and conducted a large amount of research and experimentation, making significant progress. In the wear prediction model in this article, the encoder represents the input data as a fixed-length vector, and the decoder represents the vector generated by the encoder as the corresponding output result, which is the predicted wear degree. However, a challenge with the encoder-decoder structure arises as the length of the input sequence increases, causing the dilution of previous information by subsequent inputs and significantly degrading its performance. To address this issue, the feature attention mechanism and the temporal attention mechanism are introduced to integrate with this structure.  
The feature attention mechanism is first introduced to analyze the input data sequence, and the weights of each feature under the temporal condition are set based on the contribution of the prediction results, thereby obtaining the LSTM hidden state at the current time. Then, the temporal attention mechanism is introduced to perform weighted calculations on the hidden state information, analyze the correlation of the long time-series data on the wear degree, decode the analysis results, and output them. Finally, considering the low sensitivity of the input signal to the output feedback of the neural network model [27], Arima is used to linearly correct the prediction result and optimize the wear degree prediction result.
## 6 Experimental Results and Analysis
### Discussion  
In the literature, we identified works that have developed similar proposals. Many of the articles that were analyzed seem to assume that the terrain orography does not have much importance in the implementation of the network; these studies are focused on greenhouses. In [2; 34; 36; 38; 47], the type of orography of the site is not detailed. The different authors propose novel systems of intelligent agricultural management and monitoring based on LoRa, without considering the orography of the terrain in mountainous areas.  
Another important factor that is not taken into account when applying the network is the altitude of the locations, since these studies are located below 1700 m above sea level, being a factor that affects the transmission of data packets according to [46]. The distance between the gateway and the growing site of a LoRa network in rural areas according to [38] is 700 m; reaching a spreading factor of seven at 400 m and a spreading factor of nine reaching 500 m, respectively. In this context, it is explained that as the propagation factor increases, which is the communication delay between the client and the servers, an increase in the communication delay was obtained. The proposed system in Andean areas reduces this problem by maintaining optimal communication with SF settings of seven and nine in different weather conditions as explained in Section 6. According to the results found in [2] where they evaluate the performance of a LoRaWAN network, they confirm that the performance of the network is influenced by the sending of messages with or without acknowledgement, the size of the network, the increase in the number of gateways and the distance of placement of the gateway nodes, not to mention the weather conditions  
Figure 17: LoRa data packet transmission time.  
and even more the terrain orography. Therefore, the study focuses on the influence of the packet transmission interval together with the number of nodes on the packet delivery rate.  
Therefore, the importance of this study lies in the implementation of a monitoring system based on the LoRa network in mountainous areas of Ecuador.
## 1 Introduction  
With the proliferation of various wireless devices and wireless communication services, the demand for wireless spectrum is constantly increasing and the available wireless spectrum becomes scarce. Therefore, the study on the effective and sufficient channel allocation schemes of the wireless spectrum has received extensive attention from both academia and industry. However, most of the current studies on the channel allocation schemes are focused on wireless _ad hoc_ networks with _omni-directional antennas (OMN)_, which radiate wireless signals in all directions and consequently lead to high interference to other concurrent transmissions. As a result, an omni-directional antenna has low spectrum reuse. We call such wireless _ad hoc_ networks with omni-directional antennas as _OMN_ networks. Most of the current wireless sensor networks (_WSNs_) are _OMN_ networks.  
Compared with an omni-directional antenna, a _directional antenna (DIR)_ can concentrate wireless signals on the desired direction and lead to low interference to other current transmissions. Potentially, a directional antenna can improve the spectrum reuse and consequently improve the network performance. Specifically, it is shown in [1, 2] that using directional antennas in _WSNs_ can significantly improve the network capacity and reduce the end-to-end delay. We name such wireless networks with directional antennas as _DIR_ networks. In this paper, we study the channel allocation of _DIR_ networks. s In the following, we first survey the related work and then summarize our research contributions.
# Multimode Guided Wave Detection for Various Composite Damage Types
## 4 Detection of Various Damage Types Using ABT-ABT on a 3-mm Quasi-Isotropic Composite Plate
### Multimode Guided Wave Detection
#### 4.3.2 Damage Detection Using Pure SH0 Mode  
SH0 mode was used to detect delamination and impact damage. The incident angle was set to 48\({}^{\circ}\) to generate the pure SH0 mode. A signal comparison between the pristine area, the delamination simulated by the Teflon insert, and the impact damage using SH0 mode is shown in Figure 24. Strong wave packets of SH0 mode were observed in all cases. It was found that significant amplitude drops were clearly observed for both the Teflon insert and the impact damage. The frequency spectrums were calculated by using time windowing on the acquired signals to obtain the main wave packet and remove the trailing waves. Similarly, the amplitude drops were noted in the frequency spectrums. This was related to the through-thickness mode shape of SH0 mode. However, the SH0 mode could not separate the two different damage types, which agreed with the results of the 2-mm quasi-isotropic composite plate in Section 3.3.2. Therefore, pure S0 mode and SH0 mode had to be used together to separate the damage types. The experimental results demonstrated that various composite damage types, such as delamination and impact damage, were successfully detected and separated by using the proposed multimode guided wave detection.
# Load Balancing Oriented Predictive Routing Algorithm for Data Center Networks
## 1 Introduction
The spatio-temporal residual networks (ST-ResNet) algorithm [18; 19] entails a deep understanding of spatiotemporal data and maximizes the characteristics of spatiotemporal data itself [20]. Each link in the Fat-tree structure has the characteristics of spatiotemporal data in data transmission, and as a result, the link is also spatiotemporal data. Based on the ST-ResNet model [21], the ST-ResNet algorithm is suitable for predicting the link utilization state in the next duty cycle in a DCN. Then, according to the prediction results, a link load balancing oriented predictive routing algorithm (i.e., the LLBR algorithm) is proposed. The LLBR algorithm uses ST-ResNet to train the previous usage of the link [22], thereby predicting the utilization state of network links in the next duty cycle. Therefore, LLBR can optimize the load balancing and improve the network performance [23; 24]. In view of the above introduction of load balancing, this work proposes a method that can predict the occupancy state of the link in the next time slot and make decisions for traffic scheduling by means of design and research. The goal is to realize the load balance of and improve the utilization rate of the link. The difference between this method and the existing methods is that the former can ascertain the state of the link in the next time slot and prepare for traffic scheduling in advance. In realizing this goal, we found that network links and real traffic lines have the same characteristics of spatiotemporal data through research. First, we modified the original residual network algorithm, divided the occupancy state of the network links by time period as the training data, and then combined it with the LLBR algorithm we proposed to make the decision of routing distribution. Finally, the validity of the LLBR algorithm is verified through comparison with RRS and WRRS which are commonly used in load balancing. The rest of this article is organized as follows: Section 2 introduces the typical topology and load balancing algorithm of the DCN. Section 3 presents the design of the LLBR. Section 4 provides the simulation experiments on the designed LLBR and the analysis of the experimental results. Finally, Section 5 gives the conclusions of this work.
# A Segmentation-Based Optimal Seamline Generation Method for SAR Image Mosaic
## 2 Materials and Methods
### Segmentation-Based Optimal Seamline Generation Model
#### 2.3.2 Homogeneous Area Screening
\begin{cases}1,&\text{if }(\omega_{A}(p_{i})=0)\wedge(\omega_{B} (p_{i})=0)\\ 2,&\text{if }(\omega_{A}(p_{i})=1)\wedge(\omega_{B}(p_{i})=1)\\ 3,&\text{if }(\omega_{A}(p_{i})=2)\wedge(\omega_{B}(p_{i})=2)\\ 4,&\text{if }((\omega_{A}(p_{i})=0)\wedge(\omega_{B}(p_{i})=1))\vee((\omega_{A} (p_{i})=1)\wedge(\omega_{B}(p_{i})=0))\\ 5,&\text{if }((\omega_{A}(p_{i})=1)\wedge(\omega_{B}(p_{i})=2))\vee((\omega_{A} (p_{i})=2)\wedge(\omega_{B}(p_{i})=1))\\ 6,&\text{if }((\omega_{A}(p_{i})=0)\wedge(\omega_{B}(p_{i})=2))\vee((\omega_{A} (p_{i})=2)\wedge(\omega_{B}(p_{i})=0))\\ 7,&\text{if }(\omega_{A}(p_{i})=3)\vee(\omega_{B}(p_{i})=3)\\ 8,&\text{if }(\omega_{A}(p_{i})=4)\vee(\omega_{B}(p_{i})=4)\\ \end{cases} . \tag{3}\]  
Finally, a unified stability classification with 8 levels can be derived. In \(\mathbf{\omega}\), a lower value \(\omega(p_{i})\) indicates that parcel \(p_{i}\) has a higher similarity and stronger homogeneity in \(I_{A}\) and \(I_{B}\), suggesting higher stability. The consistency-stable parcels with higher values of \(\omega(p_{i})\) have distinct feature targets in them, so in the optimal seamline generation stage thegeneration is prioritized inside consistency-stable parcels with lower values of \(\omega(p_{i})\), which effectively narrows down the search range and greatly reduces the effect of geometric and radiometric differences and the seamless become more accurate and avoids areas that may reduce the mosaic quality, such as urban built-up areas. Figure 3 shows the classification process and consistency-stable parcels extraction process. The acquisition of \(\mathbf{\omega}\) reflects the level of homogeneous areas, which lays the foundation for the subsequent construction of the cost matrix.