esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Method for Spectrometry Based on Imaging Systems
## 3 Results and Discussions
### Spectrometry Proof of the N/I Parameter
356 keV peak integral for the three depth regions resulted, respectively, in 875, 893 and 879 counts (\(\sim\)33% for each one) proving a close agreement with the expected interaction probability (i.e., following the attenuation law). Figure 12: Gamma-ray spectra resulting for the next three depth regions identified from the \(N/I\) parameter distribution. (**a**) Spectrum resulting from the selection of events in the second depth region identified from the \(N/I\) parameter distributions (\(N/I\) from 15 to 25). A zoom on the 356 keV peak is reported, too. (**b**) Spectrum resulting from the selection of events in the third depth region identified from the \(N/I\) parameter distributions (\(N/I\) from 12 to 15). (**c**) Spectrum resulting from the selection of events in the fourth depth region identified from the \(N/I\) parameter distributions (\(N/I\) from 5 to 12). By simple qualitative observations based on the obtained spectra, the \(N/I\) parameter appears to be strictly related to the depth of interaction of events occurring inside the crystal and offers the possibility to conduct deeper analysis on spectrometry leading to more reliable identification of gamma emissions revealed by the detector.
## 3 Results and Discussion
### Created VR Application for Rehabilitation
#### 3.1.4 Minimization of Cybersickness  
During the game, the patient moves neither in the real world or in the virtual world. The stationarity of the patient in the real world allows the game to be operated in a sitting position, for example, on a hospital bed or chair, which minimizes the risk of injury or falls. In addition, using the game in a seated position reduces the fatigue caused when the patient has to maintain a standing position and thus allows for longer rehabilitation time using virtual reality. Given that the effects of stroke affect people of all ages but with a predominance of people older than 65, the lack of mobility in the virtual world is intended to simplify the game, as well as minimize the effects of cybersickness.  
In addition, cybersickness minimization strategies described in the literature were used. During the development of the application, parameters dedicated to the Meta Quest 2 headset used were set. Among them, first of all, the design was created using Universal Render Pipeline technology, which allowed the creation of optimized graphics in the application. After the application was developed, the number of frames per second (FPS) was also checked, which for the designed application was approximately 400, but for the headset it lowered to around 120, because of the equipment limitation. The frequency should be as high as possible [20], in order to ensure the smoothest possible image and minimize the effects of cybersickness. Referring to the literature data, it was assumed that the frequency obtained was sufficient for the designed application. In addition, part of the strategy for minimizing the negative effects of being in the headset is included in the device itself. For this reason, the developed application is dedicated exclusively to the headset, rather than the more economical option, the use of the phone. The correctness of the identification of the position of the controllers results in a smooth and realistic reproduction of hand movements. It should also be noted that the technologies used in VR hardware are constantly being developed and upgraded. Through the use of more modern displays, better mounts, and more accurate controllers, they have the potential to improve the user experience while reducing the impact of cybersickness.
## 3 Results and Discussion
### Physico-Chemical Properties of Tomato Pomace Suspensions
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **pH** & **Acidity (\% Citric Acid)** & **Soluble Solid Content (\({}^{\circ}\)Brix)** \\ \hline MT1 & 4.23 \(\pm\) 0.01 \({}^{\mathrm{e}}\) & 0.352 \(\pm\) 0.01 \({}^{\mathrm{a}}\) & 1.22 \(\pm\) 0.03 \({}^{\mathrm{a}}\) \\ MT2 & 4.22 \(\pm\) 0.01 \({}^{\mathrm{c}}\) & 0.357 \(\pm\) 0.01 \({}^{\mathrm{b}}\) & 1.77 \(\pm\) 0.06 \({}^{\mathrm{c}}\) \\ MT3 & 4.22 \(\pm\) 0.02 \({}^{\mathrm{b}}\) & 0.366 \(\pm\) 0.00 \({}^{\mathrm{c}}\) & 1.97 \(\pm\) 0.06 \({}^{\mathrm{d}}\) \\ CT1 & 4.21 \(\pm\) 0.01 \({}^{\mathrm{a}\mathrm{b}}\) & 0.374 \(\pm\) 0.01 \({}^{\mathrm{d}}\) & 1.13 \(\pm\) 0.06 \({}^{\mathrm{a}}\) \\ CT2 & 4.21 \(\pm\) 0.01 \({}^{\mathrm{a}\mathrm{b}}\) & 0.381 \(\pm\) 0.00 \({}^{\mathrm{e}}\) & 1.52 \(\pm\) 0.08 \({}^{\mathrm{b}}\) \\ CT3 & 4.20 \(\pm\) 0.00 \({}^{\mathrm{a}}\) & 0.391 \(\pm\) 0.01 \({}^{\mathrm{f}}\) & 2.20 \(\pm\) 0.05 \({}^{\mathrm{e}}\) \\ HT1 & 4.23 \(\pm\) 0.02 \({}^{\mathrm{d}}\) & 0.413 \(\pm\) 0.00 \({}^{\mathrm{g}}\) & 1.23 \(\pm\) 0.03 \({}^{\mathrm{a}}\) \\ HT2 & 4.22 \(\pm\) 0.02 \({}^{\mathrm{b}}\) & 0.422 \(\pm\) 0.01 \({}^{\mathrm{h}}\) & 1.60 \(\pm\) 0.10 \({}^{\mathrm{b}}\) \\ HT3 & 4.20 \(\pm\) 0.01 \({}^{\mathrm{a}\mathrm{b}}\) & 0.426 \(\pm\) 0.00 \({}^{\mathrm{i}}\) & 2.28 \(\pm\) 0.03 \({}^{\mathrm{e}}\) \\ \hline \hline \end{tabular}  
* Tomato pomace powder suspension with 15, 20 and 25% tomato pomace at 20 \({}^{\circ}\)C (MT1, MT2 and MT3); thermal treated at 60 \({}^{\circ}\)C (CT1, CT2 and CT3) and thermal treated at 95 \({}^{\circ}\)C (HT1, HT2 and HT3). Values presented as mean \(\pm\) standard deviation and different letters in the same column indicate that there are significant differences (\(p\) < 0.05).  
\end{table} 
Table 1: The physicochemical characteristics of tomato pomace suspension.
# Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
## 3 Experimental Setup  
This section briefly describes the experimental setup for comparing the performance of the considered MPTCP schedulers and CCAs. We briefly describe the testbed setup process combining NS3-DCE [24], MPTCP Linux Kernel v0.90 [25], and NS-3.33 mmWave [26] module. Then, we discuss the considered experimental scenario.
## 1 Introduction
query region tree_ (_BQR-tree_), which stores the additional bit-vector information required to describe the non-spatial query values. However, the BQR-tree is a naive form of the enhanced spatial query indexing structure, where tree construction is based mostly on the spatial information. In this paper, we propose a new query indexing structure, called the _group-aware query region tree_ (_GQR-tree_) for efficient evaluation of CM range monitoring queries. For the tight integration of the spatial and the non-spatial specifications of the queries, the GQR-tree groups the queries according to their non-spatial query values (_i.e._, non-spatial information) when being built on their spatial query ranges (_i.e._, spatial information). Similarly to the BP-tree, the QR-tree, and the BQR-tree, the main role of the GQR-tree is to index queries and to search the resident domains of moving objects in order for cooperative evaluation of CM range monitoring queries between the server and moving objects. The remainder of this paper is organized as follows. In Section 2, some related work is reviewed. In Section 3, the system overview is provided. In Section 4, the problem is formally defined. In Section 5, we present the details of the GQR-tree. In Section 6, we provide the performance evaluation and verify superiority of the proposed GQR-tree method as compared with existing methods. Finally, in Section 7, we present our conclusions.
# Short-Term Wind Speed Prediction Based on Principal Component Analysis and LSTM
## 3 Case Study
### Discussion  
In the above experiments, the BPNN, GPR, LSTM, RNN, and SVR methods were selected in Case I to predict wind speed using only historical wind speed data. Among them, the comprehensive performance of LSTM was the best, which reflects the strong fitting ability of the LSTM model to the nonlinear problem. By comparing Case I with Case II, the use of meteorological characteristics related to wind speed during wind speed prediction can improve the prediction results. In the comparison between Case II and Case III, the PCA algorithm was used to reduce the dimension of meteorological characteristics related to wind speed, and the prediction results were further improved in all prediction models.
## 1 Introduction
The increase in the number of available images at a high spatial resolution from 2016 with Sentinel-1 (S1) and Sentinel-2 (S2) allows the use of temporal analysis of reflectance [20] and backscatter [18] for dry forest monitoring. While, for optical data, the increased image acquisition brings the availability of a greater number of cloud-free images and the possibility of temporal analysis of phenology, for SAR data it provides an opportunity to remove the variations in the recorded radar signal due to viewing angles and soil moisture [18]. The potential of combining optical and SAR data has been demonstrated for the mapping and monitoring of the tropical savanna in the Sahel [21; 22; 23] and it opens  
Figure 1: Challenges of tropical dry forests monitoring (**A**) deciduous vegetation—same forest area in the humid (May) and the dry (July) season, (**B**) Fires—same forest area affected (2010) or not (2011) by fire, (**C**) Four different areas illustrating the variety of tree cover. “Maps data: Google, © 2021 Maxar Technologies”. new avenues to map and monitor tropical dry forests [24]. It also paves the way for the combined use of S1 and S2 or Landsat-8 time series through data fusion [25]. The exploitation of these large data volumes has been made possible by the development of cloud-based computing capacities with a range of classifiers, allowing rapid processing [26]. Cloud computer processing and machine learning techniques are now becoming the norm for extracting meaningful information from terabytes of satellite images [27; 28]. However, there is a learning and technological gap for many of the users in the field [29]. A transition from image interpretation methods to more advanced big data processing is needed. A successful way to operate that transition is to create user-flexible methods with different levels of uptake from the users [30]. The main objective of this present study is to demonstrate the potential of open Copernicus data, namely S1 and S2, for the national-scale mapping of dry tropical forests in the framework of REDD+. First, we explored what is the best period in which to handle the seasonality and the availability of the S2 data and we evaluated the benefit of S1 data. Second, an expert-based data collection was performed that resulted in a training dataset that was suitable for dry forest monitoring applications with Earth observation (EO) data. Third, different classification models and approaches to incorporate the spatial information into the models were tested. In order to support the work that is done by NFMS, we share the scripts for processing the satellite imageries, the resulting maps and the expert-based reference dataset.
# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 2 Classification of Research Perspectives
### Type of Surface Taxi Time
#### 2.1.2 Actual Taxi Time  
Using departing aircraft as an example, when an aircraft is unable to taxi unimpeded for takeoff, it typically encounters two situations that affect the taxi time. Firstly, due to the high number of aircraft operating on the surface, the aircraft must maintain a certain safety interval with other operating aircraft during taxiing. This safety measure impacts the taxiing speed, leading to prolonged taxi times. Additionally, the aircraft may encounter conflicts during taxing, necessitating stops as per the controller's instructions, and waiting for the conflicts to be resolved. The combined effect of taxing control restrictions and conflicts, and the time spent waiting in this queue at the end of the runway result in additional time (T\({}_{\text{additional}}\)). Therefore, as shown in Figure 5, the total taxi time of an aircraft can be broken down into reference time and additional taxi time [30], as illustrated in Equation (1) below:  
\[T_{actual}=T_{reference}+T_{additional} \tag{1}\]  
The variance between the actual taxi time and the reference time of an aircraft is referred to as the additional taxi time, which is also commonly known as additional time in taxi-phase. Researchers have used calculations based on probability distributions of takeoff delays and takeoff times to predict the taxi time. [PERSON] et al. [19] employed accurate ground monitoring data to mitigate additional time in taxi-phase and offered optimization suggestions for runway allocation and departure sequencing to reduce taxi-out time. [PERSON] et al. [20] developed a queuing model for aircraft departures and utilized a quantization matrix to compute taxi time by predicting takeoff time. Experimental results demonstrated that the application of this quantization matrix reduced the error in takeoff time estimation. [PERSON] et al. [21,22] focused on estimating the degree of air traffic congestion and established a departure delay distribution model based on a genetic algorithm. They utilized a non-parametric estimation method to compute daily and quarterly trends of departure delays. [PERSON] et al. [23] developed a stochastic model to examine the relationship between flight delay components and the factors influencing delays. Using a Bayesian network approach, they predicted flight delay components, including departure delays.
## 4 Experiments
### Implementation Details  
#### 4.2.1 Network Details  
The spatial encoder consists of a preprocess module and five basic modules, and the preprocess model includes a convolutional layer and max pooling. Each basic module contains two convolution layers with batch normalization and ReLU activation function. The epsilon and momentum of batch normalization are set to \(1\times 10^{-5}\) and 0.1, respectively. A max pooling operation is applied between the two convolution layers for downsampling. All convolutional layers are set with F3-S1-P1, where F, S, and P denote the filter size, stride, and padding size, respectively. We increase the channels module by module, and the output channels is unified to 256.  
The fusion module is two vanilla Transformer encoder layers based on [PERSON] et al. Specifically, each layer is set as 8 heads and 2048 hidden units, and the dropout rate is 0.1. As illustrated in Figure 3, we design one fusion module to process the different feature maps. In addition, the config of MSF-ET is similar to the fusion module.  
In the inference stage, we utilize the CTC beam search algorithm to decode the sequence, and we set the beam size as 5.
## 4 Emerging Biogas Upgrading Technologies
### Separation via a Solid Agent
#### 4.2.3 Polymer Resins  
Polymer resins are studied as a novel technique for upgrading biogas. The polymer resins used consist of beads of about 0.6 mm in diameter, which are functionalized with CO\({}_{2}\) selective groups and used in bulk [126]. The polymer resins are impregnated with amino functional groups, which selectively adsorb the CO\({}_{2}\) from the biogas [127]. For their regeneration, heat and/or a purge gas are used [126; 128]. It is expected that this technique will entail reduced energy demand, lower CAPEX, and lower OPEX compared to conventional technologies [126]. Final methane concentrations of 98-99% were achieved with continuous lab-scale units [126; 127].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Biochar Type** & **Biogas Composition (\%Vol CH\({}_{4}\))** & **Case/(Reference)** \\ \hline \multirow{2}{*}{Corn stover} & 90 & \multirow{2}{*}{1/[123]} \\  & & \\ \cline{3-3}  & & \\ \hline \multirow{2}{*}{Pine} & 92.3 (mesophilic T) & \multirow{2}{*}{2/[119]} \\  & 79 (thermophilic T) & \\ \hline \multirow{2}{*}{White oak} & 89.8 (mesophilic T) & \multirow{2}{*}{3/[119]} \\  & 78.5 (thermophilic T) & \\ \hline Walnut shell & 77.5-98.1 & 4/[125] \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of biochar utilization inside a biodigester.  
Polymer resins with a higher capacity and lower adsorption enthalpies should be analyzed to achieve longer cycles and reduce energy demand [126].
## 3 Mathematical Approach Using Homomorphic Encryption and Zero-Knowledge Proofs  
Split Learning is one approach to dividing a neural network between the clients and a server for which the raw data of the clients is not shared with the server. A backdoor attack happens at a point in time when a malicious client modifies model parameters to insert unwanted behaviors undetected by the server. Homomorphic encryption enables the processing of encryption, while zero-knowledge proof ensures that one party is able to prove that a correct operation has been performed without revealing detailed information. In this mathematical proposal, we will use HE and ZKP to ensure data and training parameters remain private and secure; second, the integrity of the training process is also verified.
## 4 Experimental Results and Analysis
### The Settings of Clustering Center and Privacy Budget Parameters
#### 4.2.2 UCI Datasets
On the other hand, for high-dimensional datasets like ecoli, movement, dermatology, and abalone, the clustering performance is better when using the  
Figure 3: Clustering effect of UCI dataset varying with \(\varepsilon\): (**a**) seeds; (**b**) ecoli; (**c**) movement; (**d**) dermatology; (**e**) banknote; and (**f**) abalone. similarity measured by cosine distance. These findings suggest that the choice of similarity measure depends on the dimensionality of the dataset. Cosine distance appears to be more effective for high-dimensional datasets, while Euclidean distance is more suitable for low-dimensional datasets.
# Efficient Deep Learning-Based Detection Scheme for MIMO Communication Systems
## 1 Introduction  
Wireless communications is a pervasive technology whose design involves many distinct challenges for researchers [1, 2], such as channel modeling, hardware imperfections, and better signal detection schemes for reliable data reception [3]. Multiple input-multiple output systems (MIMOs) have received special attention as a key enabler technology for 5G/6G wireless communications due to their BER performance gains and higherachievable data rates [4]. As a fundamental part of a MIMO system, signal detection is a procedure that faces high-complexity challenges as the number of transmitter antennas increases. Several researchers have proposed a variety of solutions for MIMO detection systems when computational resources are limited [5; 6; 7; 8; 9; 10]. Most of strategies achieve near-optimal detection with close performance to that of the maximum likelihood (ML) approach. However, their complexity increases exponentially as the number of transmitter antennas increases. On the other hand, deep learning (DL) is a research area that has gained interest in the last few years. DL is derived from machine learning but is focused on the development of neural network (NN) architectures to solve a variety of problems in engineering, physics, social and economic fields, among others [11; 12; 13]. As a result of recent advances in big data, optimization techniques and the stronger capacity in computational and hardware resources, DL is seen as an efficient way to design newer solutions for the optimal detection of signals in MIMO systems [14; 15].
# Using Markov Random Field and Analytic Hierarchy Process to Account for Interdependent Criteria
## 3 MRF-Based AHP
For each criterion, \(i\), update its priority \(p_{i}{}^{\prime}\) based on the sum of the incoming and outgoing pairwise potentials multiplied by their corresponding priorities. Mathematically, this can be represented as: \[p^{\prime}{}_{i}=\sum W_{ij}p_{j}+\sum W_{ji}p_{j},\forall i\
eq j.\] (7)
3. Normalize the updated priorities to ensure they sum up to 1: \[p^{\prime}(t+1)=p^{\prime}(t)/\sum p^{\prime}{}_{i}(t)\] (8)
4. Repeat steps 1-3 for a specified number of iterations (e.g., 10,000). Store the priorities obtained in each iteration. 5. Calculate the mean of the priorities across all iterations to obtain the adjusted priorities \(\mathbf{P^{\prime}}=[p_{1}{}^{\prime},p_{2}{}^{\prime},p_{3}{}^{\prime},p_{4}{}^{ \prime}]\). The adjusted priorities are derived from the Gibbs sampling as \(\mathbf{p^{\prime}}=[0.200,0.292,0.230,0.278]\). Step 4: Compute the global priorities of the alternatives  
Using the overall priorities of the criteria obtained from the MRF model, we can now compute the global priorities of the alternatives by aggregating the local priorities of the alternatives with respect to each criterion, taking into account the overall priorities of the criteria. \[\text{Adjusted priority}=(\frac{1}{N})\times\sum_{t=1}^{N}p^{\prime}(t) \tag{9}\]  
Note that the method for obtaining adjusted priorities via the mean of priorities across all iterations, instead of just using the last iteration, is chosen to ensure stability and robustness in the decision-making process. This approach mitigates the risk of outliers or anomalies in any single iteration heavily influencing the final decision. Using the mean of all iterations provides a more representative and balanced view of the priorities, reflecting a consensus over the entire process. This method, while more complex than ANP, aims to accurately capture the nuances of interdependencies in criteria, offering a comprehensive and reliable decision-making framework. Step 5: Select the best alternative  
Finally, we can select the best alternative based on the information in the decision table, as shown in Table 2, and choose the highest global priority. Next, we give a numerical example to demonstrate the proposed method with different graph models and compare the results with the AHP and FCM. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Decision Table** & \(\mathbf{C_{1}}\) & \(\mathbf{C_{2}}\) & \(\mathbf{C_{3}}\) & \(\mathbf{C_{4}}\) & **Total** \\ \hline \(A_{1}\) & 0.5 & 0.4 & 0.6 & 0.3 & 0.4383 \\ \(A_{2}\) & 0.3 & 0.1 & 0.2 & 0.4 & 0.2464 \\ \(A_{3}\) & 0.2 & 0.5 & 0.2 & 0.3 & 0.3153 \\ \hline Adjusted weights & 0.2000 & 0.292 & 0.230 & 0.278 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Decision table for the best alternative.