esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Works and Baselines
Our environment involves using a portable NIR device to capture 1D NIR signals, which are then processed to predict the nutritional content of food. One of the most comparable models is NIRSCam [30], which uses several traditional machine learning methods such as K-Nearest Neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RT), and XGboost (XGB) [38; 39; 40; 41; 42] to analyze food composition. Additionally, we incorporate two deep learning-based models: CNN [28] and Multi-Modal [31]. The CNN model utilizes 1D convolution layers to analyze food components, while the Multi-Modal (MM) model comprises two distinct components that are nutrient-prediction nodes and reconstruction nodes. The reconstruction nodes are responsible for reconstructing the provided input, and the model calculates the difference between the input and the reconstructed value to determine whether to reject the result based on this difference. We conducted evaluations using the model denoted as MM, which employs the estimated results from the given model without any additional post-processing. Additionally, we named and evaluated the optimized model as MM*, which removes uncertain results from the estimated outputs. Similarly, for our proposed model, we performed evaluations by distinguishing between the Proposed model and the optimized Proposed model as Proposed and Proposed* respectively. While there are other models available, they either handle different types of data or share similar characteristics with the representative models mentioned above. Therefore, we chose these models as representative baselines for our evaluation. Furthermore, the Multi-Modal model was chosen as a comparative target for reliability assessment, as it includes a mechanism to discard uncertain predictions, which aligns with our proposed idea. This comparison ensures a robust comparison of reliability and performance across various modeling approaches. Recent studies [43; 44; 45] have explored sensor fusion approaches that incorporate RGB, NIR, and other modalities for food classification and caloric estimation, achieving significant success in nutrient analysis. While these methods rely on features captured through multispectral imaging, combining both visual and near-infrared data, our approach focuses exclusively on NIR spectroscopy. By integrating an uncertainty-driven deep learning model, we aim to improve both the accuracy and reliability of nutrient estimation, distinguishing our solution from others that solely pursue accuracy. Additionally, most models utilize a deep learning architecture based on CNNs, which is closely related to the architecture presented in the study [28]. Therefore, we evaluated our NIR-based analysis using a CNN framework, with [28] considered as a reference. Sensor fusion approaches, however, remain a promising solution for enhancing accuracy and may continue to gain attention for their ability to leverage diverse data sources effectively. Our proposed method has the potential to further contribute to advancements in this field by integrating sensor fusion solutions in future iterations.
# Adaptive Wavelet Coding Applied in a Wireless Control System
## 4 Obtained Results
### Analysis of the Impact of Wavelet Coding Processing Time on a Control Loop  
In order to verify the impact of the use of wavelet coding in a control system, two communication scenarios were evaluated. In the first one, the control loop with wired communication was simulated computationally, in order to tune the controller embedded in the microcontroller. In the second one, experimental tests involving the analysis of a control system using the wavelet coding were carried out in order to investigate the impact of the coding processing time on the controller performance. In both scenarios, the absence of errors caused by the communication channel was admitted.  
Figure 8 shows the performance curves of the control system obtained for the two scenarios. It can be observed in this figure that the addition of the wavelet coding does not generate delay in this control system, therefore leading the system to an instability condition. For a better analysis of the two results, it is important to mention that the two scenarios adopt the same configuration: a plant of second order, in which the pump transfers the liquid to Tank 1. The orifice at its base allows this liquid to flow into the Tank 2, whose level must be controlled, and the controller used is a PID, with \(K_{p}=1.5\), \(k_{i}=0.04\) and \(k_{d}=0.05\). Tests were carried out with different periods of sampling with the objective of evidencing the impact of processing time on the control system.  
In addition, the processing time of the encoder and decoder embedded in the ATMEGA 328P microcontroller with a clock of 16 MHz was also measured. The obtained values were around 450 \(\upmu\)s and 180 \(\upmu\)s, respectively. From Figure 8, it can be observed that the time scale of the wavelet coding  
Figure 8: System response for both scenarios.  
in \(\upmu\)s does not generate delay in a control system and therefore does not harm the controllability of the system. The encoder and decoder processing times were measured with timers present on the microcontroller used.
## 7 Potential Future Research Direction  
In the future, this work shall extend to multiple receiving antennas, as it can increase receive diversity, which will eventually enhance the performance of data rate and coverage, and reduce noise, interference, and call dropout. Additionally, optimizing detection, such as utilizing maximum likelihood with necessary modifications, could enhance the receiver detection process. This will add some computational complexity compared to the proposed techniques. GQSM-QOSTBC methods can be adapted for terahertz or millimeter-wave (mmWave) bands by using hybrid beamforming, which is effective for sparse channel characteristics and antenna arrays that are suitable for spatial modulation. To better represent real-life wireless situations, it can study and simulate GQSM-QOSTBC systems using different fading models (like Nakagami-m, \(\kappa\)-\(\mu\), or \(\eta\)-\(\mu\)), which affect how reliable and diverse the system is. To scale the system to 6G network scenarios with high user density, it can investigate multi-user MIMO or massive MIMO variants of GQSM-QOSTBC and create user scheduling and precoding algorithms. AI can assist in overcoming analytical issues in complicated or time-varying channels by using deep learning to dynamically adjust GQSM-QOSTBC parameters (such as antenna activation patterns and coding rate) under changing channel conditions.
## 2 Materials and Methods
### ML Model Setting
#### 2.4.2 Decision Tree (DT)  
A Decision Tree is a non-parametric supervised learning method that repeatedly divides a set of training data into smaller subsets based on tests of one or more variables to create a prediction model for the target value [39]. Unlike many other statistical approaches, the DT (regression tree) does not have the requirements of value distribution or the independence of the variables from one another [40]. The model is trained by learning based on decision rules from features. The collection of nodes intends to decide on values connected to estimate a numerical target value (regression approach). The selected variable will optimally separate to reduce the error following the criterion. The new nodes are repeatedly built until the estimated numerical value meets the target (criteria).  
The sampling of new nodes is repeated until the stopping criteria are met. A prediction for the class label \"attribute\" is determined depending on the majority of \"examples\". The criteria reach this leaf during generation, while an estimation for a numerical value is obtained by averaging the values in a leaf.  
Our DT model was built by determining optimal parameters using threefold cross-validation. We then trained by setting the splitting criterion as Least Square (LS) error, which minimizes the squared distance between the average values in the node regarding the actual value. The LS is one of the standard methods to build a regression tree. It can improve the computational efficiency result that deals with the nominal and numerical variable tree [41]. The least square error criterion can be expressed as follows:  
\[\frac{1}{n}\sum_{i}^{n}\left(y_{j}-r\left(\beta,x_{i}\right)\right)^{2} \tag{4}\]  
where \(n\) is the sample size, \(x_{i}\) is a data point, and \(r(\beta,x_{i})\) is a regression model for each pair of \((x_{i},y_{i})\). The model criterion was set for 20 maximum depths. We also applied auto-prepruning to control the parameters of minimal gain = 0.05, minimal leaf size = 2, minimal size for split = 4, and a number of prepruning alternatives = 3 were used as stopping criteria.
# Monitoring Perishable Commodities Using Cellular IoT:
## 2 Materials and Methods
### Autonomy Test  
Given the importance of achieving the maximum possible autonomy for the system to operate continuously, a test was carried out to measure the current consumption of the datalogger during normal operation, applying all of the previously mentioned battery consumption optimization strategies.  
For the test, a National Instruments (North Mopac Expressway, 11500, Austin, TX, USA) USB-6008 data acquisition card [67] was used to measure the voltage across a 0.22 \(\Omega\) shunt resistor (\(R_{shunt}\)) placed in series with the battery. The resistor used has a very small value so that there is the smallest possible voltage drop, powering the rest of the circuit. The card was used with a differential mode analog input with a range of \(\pm\)1 V and a resolution of 12 bits, which is equivalent to a resolution of 0.488 mV. Furthermore, the card used a data buffer size of 3000 samples and a sampling rate of 1 kHz, obtaining averages every 0.25 s.  
The expression relating the voltage drop measured by the card (\(\Delta V_{shunt}\)) to the current consumed by the system (\(I_{bat}\)) is shown in Equation (9), applying Ohm's Law, which was used to perform the voltage-to-current conversion and to obtain the datalogger consumption data shown in the Results section in Section 3.2.  
\[I_{bat}=\frac{\Delta V_{shunt}}{R_{shunt}}=\frac{\Delta V_{shunt}}{0.22} \tag{9}\]  
Figure 8: Structure of the database in InfluxDB.
# Code Smell Detection Using Ensemble Machine Learning Algorithms
## 3 Proposed Research Framework
### Dataset Choice and Illustration  
The previous literature [20; 22; 36] used a code smell dataset from [PERSON] et al. [7] and obtained the best accuracy. They also examined systems from the Qualitas Corpus [42], release 20120401r, one of the most comprehensive compiled benchmark datasets to date, explicitly created for empirical software engineering research. Therefore, to conduct the experiment, we used four code smell datasets: DC, GC, FE, and LM [7]. [PERSON] et al. [7] selected 74 systems out of 111 of various dimensions and computed a large set of object-oriented metrics. For the 74 software systems, they calculated 61 metrics for class-level code smells (DC and GC) and 82 metrics for method-level code smells (FE and LM). They used various tools and approaches to detect code smells. Table 2 explains the automatic detection tools and techniques they used. Each dataset they created has 140 smells and 280 no-smells.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Code Smells** & **Reference, Tool/Detection Rules** \\ \hline GC & iPlasma (GC, Brain Class), PMD [43] \\ \hline DC & iPlasma, Fluid Tool [44], Anti-Pattern Scanner [15] \\ \hline FE & iPlasma, Fluid Tool [44] \\ \hline LM & iPlasma (Brain Method), PMD, Marinescu detection rule [45] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Automatic Detector Tools and Techniques (ADVISORS) [23].  
Figure 1: Proposed research scheme.  
Table 3 shows the class level and method level code smells datasets. Sixty-one metrics are computed for DC and GC at the class level code smells. Eighty-two metrics are computed for FE and LM at the method level code smells. These datasets can be downloaded from [[http://essere.disco.unimib.it/reverse/MLCSD.html](http://essere.disco.unimib.it/reverse/MLCSD.html)]([http://essere.disco.unimib.it/reverse/MLCSD.html](http://essere.disco.unimib.it/reverse/MLCSD.html)) (accessed on 2 August 2022).  
The code smells datasets are defined below.  
DC: Classes that do not have enough functionality are called data classes. It refers to those classes that keep data with simple functionality and have other classes that strongly depend on them. It exposes data through accessor methods [30].  
GC: It refers to those classes that have many functionalities. It can be referred to as a huge class with a large number of lines. It causes problems connected with big code size, coupling, and complexity [30].  
FE: These methods use a lot of data from other classes rather than theirs. They prefer to use the features of other classes, taking into account features entered via accessor methods [30].  
LM: These methods are the results of a human tendency to write a new code instead of reading an existing code. An LM has an excessive amount of code, is complex, tough to recognize, and makes extensive use of data from other classes [30].
# FLATSIM: The ForM@Ter LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry Service
## 3 FLATSIM Architecture  
The goal of the FLATSIM project was to integrate the NSBAS chain in the CNES high performance computing (HPC) infrastructure to benefit from its computing power and from the fact that CNES mirrors locally the Sentinel-1 data from ESA. FLATSIM is thus able to process up to 6 years of Sentinel-1 data over large geographical areas (up to 7 million km\({}^{2}\) per year in total). FLATSIM is designed to automatically access and process Sentinel-1 radar images and to distribute interferometric products which cover worldwide areas of interest. As shown in Figure 7, FLATSIM is composed of two main components: a production module and a delivery module.
## 5 Case Study
### Estimation of Parameters for the Observed Degradation  
The parameters of the observed degradation \((u,v)\) are estimated via Bayesian approach by considering informative gamma prior distributions for the unknown parameters \((u,v)\), as \(v\sim Ga(\zeta,\eta),u\sim Ga(\delta,\tau)\). Where, the shape parameters are \(\zeta=52.79\) and \(\delta=41.45\), and the scale parameters are \(\eta=0.4257\) and \(\tau=4.13\times 10^{-4}\), for \(v\) and \(u\), respectively. The Markov chain Monte Carlo (MCMC) algorithm is utilized to sample from the joint distribution based on the Gibbs sampler. For this, a code is developed in the software OpenBUGS [53]. A total of 50,000 iterations were considered for burn-in purposes and 100,000 iterations were considered for estimations purposes. The obtained estimations for the mean, standard deviation, Monte Carlo error, and some percentiles for the parameters \((u,v)\) are presented in Table 2. Two sets of initial values are considered in order to assess the convergence of the parameters with the Brooks-Gelman-Rubin (BGR) statistic, the obtained graphs from OpenBUGS are presented in Figure 4. It is considered that convergence is achieved if all the lines in Figure 4 transpose in 1 [54]. It can be noted that convergence is achieved in both parameters.  
The first-passage time distribution of the observed degradation is obtained from (3). By considering the mean estimates from Table 2, and \(z_{0}=0\), the parameters can be obtained from \(\alpha_{0}^{*}=\sqrt{u/(\omega-z_{0})}\) and \(\beta_{0}^{*}=(\omega-z_{0})/w\) as \(\delta_{0}^{*}=0.2039\) and \(\hat{\beta}_{0}^{*}=1.066\). With these estimates, it is easy to compute the mean and variance as \(E(T_{o})=1.088\) and \(Var(T_{o})=0.223\), thus \(CV(T_{o})=0.434\).
## 1 Introduction
The Q system (Q), rock mass rating (RMR), and geological strength index (GSI) are used in preliminary support system designs of rock engineering, which play an important role in determining the unstable structure of rock engineering and overcoming the risk of structural instability [26; 27; 28]. The Q system [23] is essentially a weighting process in which the positive and negative aspects of the rock mass are assessed. The quality of rock masses is described using six parameters, which are rock quality designation (RQD), joint set number (J\({}_{\text{n}}\)), joint roughness (J\({}_{\text{r}}\)), joint alteration (J\({}_{\text{a}}\)), joint water reduction factor (J\({}_{\text{w}}\)), and stress reduction factor (SRF), and the Q rating is derived from the following expression by combining these six parameters:  
\[\text{Q}=\frac{\text{RQD}}{\text{J}_{\text{n}}}\times\frac{\text{J}_{\text{r} }}{\text{J}_{\text{a}}}\times\frac{\text{J}_{\text{w}}}{\text{SRF}}\]  
The RMR has been modified several times; currently, the latest 1989 version [25] is widely used. It employs six parameters: uniaxial compressive strength (UCS) of intact rock material, RQD, spacing of discontinuities, condition of discontinuities, groundwater conditions, and orientation of discontinuities. Each parameter is given a rating of importance for a particular situation. The six input parameters are summed up to yield RMR rating results. The GSI is used as an input parameter for the Hoek-Brown criterion to estimate the reduction in rock mass strength for different geological conditions [26]. This classification is simple and fast, and it is based on the appearance of rock mass and its structure. The GSI values can be obtained from the quantitative GSI chart proposed by [29]. Many researchers [30; 31; 32; 33; 34] had put effort into solving the problem of obtaining accurate mechanical parameters of the engineering rock mass. However, the study of mining engineering drifts under the influence of high plateau-level stress, high ground temperature, and high induced stress, where failure presents diversity, is relatively rare, especially because the acquisition of rock mass parameters of deep mining engineering directly affects the stability of engineering rock mass. The engineering geological properties and geotechnical parameters of rock mass were analyzed in this study using the deep levels of the Sanshandao Gold Mine as an example. Empirical methods and numerical simulations were used to determine the support scheme and reasonable support parameters. This case study is not only rare, but the area is understudied.
# Applied Research of the UAV Illumination Measurement System in Sports Stadiums
## 3 Results
### Illumination Data Processing
On the horizontal plane, the distance from the starting point of the measurement in the eastern and western directions is used as the \(X\) and \(Y\) axes, respectively; the illuminance value is used as the \(Z\) axis, and the illuminance collection data is interpolated using cubic spline interpolation to make it easier to explore the trend of illuminance changes. Illuminance distribution at 4 m height in the stadium is plotted, and the image shows that the illuminance value rises significantly directly below the luminaire and falls significantly between the luminaires as the height increases.
# Topology Optimization of Anisotropic Materials with Smooth Fiber Orientation
## 3 Numerical Results
### Parameters and Boundary Conditions  
In this paper, the single-layer graphite/epoxy material with 70% fiber content is used as the optimization object, and the engineering material constants are shown in Table 1.  
This section presents six boundary conditions to be optimized, as shown in Figure 6. Among them, (a) is a cantilever beam with a length-width ratio of 2:1. The left end of the structure is fixed, and the lower part of the right end is subjected to a downward concentrated load. (b) Is a double-load cantilever beam with a length-width ratio of 2:1. The left end of the structure is fixed, and the upper and lower parts of the right end  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Mode** & \(\mathbf{E_{1}/10^{5}}\)**MPa** & \(\mathbf{E_{2}/10^{5}}\)**MPa** & \(\mathbf{v_{12}}\) & \(\mathbf{G_{12}/10^{5}}\)**MPa** \\ \hline graphite/epoxy & T300/5280 & 185 & 10.5 & 0.28 & 7.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Engineering elastic constants of single-layer graphite/epoxy composites with 70% fiber content.  
Figure 5: Topology optimization flowchart of anisotropic materials with smooth fiber orientation.  
bear a downward concentrated load, respectively. (c) Is a double-hole cantilever beam with a length-width ratio of 2:1. Two holes are set in the design domain. The left end of the structure is fixed, and the lower part of the right end is subjected to a downward concentrated load. (d) Is a bridge girder with a length-width ratio of 4:1. The left and right sides are the supporting points, and the middle and lower parts bear the downward concentrated load. (e) Is a double-load bridge beam with a length-width ratio of 2:1. The left and right sides are the supporting points, and the bottom 1/3 and 2/3 are subjected to downward concentrated load, respectively. (f) Is an L-shaped beam whose shape is equivalent to removing a rectangle with a length-width ratio of 1:2 from the upper right of a rectangle with a length-width ratio of 2:3. The upper end is fixed, and the middle of the right end is subjected to a downward concentrated load.  
According to the different boundary conditions, the code can be freely adjusted. For example, in the optimization of the bridge beam, the numerical values of nelx, nely, penal, rmin, angle, volfrac, sig_x, sig_t, and the fcr are set to 80, 20, 3, 1.5, pi/2, 0.3, 5, 20, and 0.98, respectively.
# A Telepresence Wheelchair with 360-Degree Vision Using WebRTC
## 5 Conclusions  
This paper has presented the development of a telepresence wheelchair with 360-degree vision. By exploiting the advancements in the field of emerging information technology and digital cameras, efficient technology integration for developing the telepresence wheelchair with 360-degree vision has been explored. Several experiments were conducted to evaluate the developed telepresence wheelchair system. The outcomes of the experimental results were positive. The practical experiments indicated that the proposed technique is successfully streaming and navigating the wheelchair over wireless communication with excellent performance qualities. The overall performance results convince us that the proposed approach of a 360-degree view of the telepresence wheelchair is feasible. Furthermore, a QoE estimation model for wireless video streaming over WebRTC for a telepresence wheelchair was presented. The model was developed using neural network principles for multiple video sequences streamed and bit rates in different network conditions. The developed model can be used for online QoE estimation given measurable network factors which are necessary for the remote user to control the wheelchair towards reliable wireless signal coverage. Overall, the results confirmed the effectiveness of our approach for the telepresence wheelchair with a full field of view. For future works, the focus work could be on optimization of the hardware and software such that the system can be embedded to accomplish a commercial telepresence wheelchair system.  
[PERSON] investigated the methodologies, developed, implemented, conducted the experiments, analyze the experimental data, and drafted the manuscript. [PERSON] contributed to the technical, scientific aspects and revision of the manuscript. [PERSON] supervised the study and contributed to the overall research and revision of the manuscript. All the authors contributed to the proofreading and approved the final version of the manuscript. All authors have read and agreed to the published version of the manuscript. This work was supported by Australian Research Council under the Discovery Grant DP0666942. The authors declare no conflict of interest.