esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Causality Inference Using Imess
### Improved Multivariate Effective Source Selection (IMESS)
```
0: time series \(\textbf{X}=(\textbf{x}_{1},\textbf{x}_{2},...,\textbf{x}_{n},\textbf{x}_{n+1},..., \textbf{x}_{N})\), target parameter \(X^{T}(T\in\{1,2,...,n\})\)
0:\(\textbf{V}_{\chi^{T}}\)(source parameters of \(X^{T}\))
1:functionIMESS(\(\textbf{X},X^{T}\))
2:\(\textbf{V}_{\chi^{T}}\leftarrow\emptyset\)
3:repeat:
4:for\(Z\in\textbf{D}\backslash\textbf{V}_{X}\)do
5: calculate \(NETE_{Z\to X^{T}|\textbf{V}_{\chi^{T}}}\)
6:endfor
7: Add \(Z=\arg\max(NETE_{Z\to X^{T}|\textbf{V}_{\chi^{T}}})\) in \(\textbf{V}_{\chi^{T}}\)
8:until:
9: No more information in \(X^{T}\) left to account for OR no source \(Z\) provides a statistically significant information contribution \(NETE_{Z\to X^{T}|\textbf{V}_{\chi^{T}}}\) which could account for part of the remaining uncertainty in \(X^{T}\)
10:repeat:
11: for\(\textbf{Z}\)= \(\{Z_{a},Z_{b}|Z_{a},Z_{b}\in\textbf{D}\backslash\textbf{V}_{X^{T}}\}\)do
12: calculate \(NETE_{Z\to X^{T}|\textbf{V}_{\chi^{T}}}\)
13:endfor
14: Add \(\textbf{Z}=\arg\max(NETE_{\textbf{Z}\to X^{T}|\textbf{V}_{\chi^{T}}})\) in \(\textbf{V}_{X^{T}}\)
15:until:
16: no source \(\textbf{Z}\) provide a statistically significant information contribution \(NETE_{Z\to X^{T}|\textbf{V}_{\chi^{T}}}\) which could account for part of the remaining uncertainty in \(X^{T}\)
17:for\(Z\in\textbf{V}_{X^{T}}\)do
18:if\(Z\) does not add a statistically significant amount of information \(NETE_{Z\to X^{T}|\textbf{V}_{\chi^{T}}}\) given all of the other sources selected for \(\textbf{V}_{X^{T}}\)then
19: Remove \(Z\) from \(\textbf{V}_{X^{T}}\)
20:endif
21:endfor
22: return \(\textbf{V}_{X^{T}}\)
23:endfunction
```  
**Algorithm 2** Improved multivariate effective source selection (IMESS)
# Communication Manager for Hyper-Connected RPAS Environments
## 3 Communication Infrastructure Manager: Design and Deployment
### Reference Scenario  
To study the feasibility and functionality of the CIM in multi-RPA environments, we propose a reference scenario (depicted in Figure 3). The reference scenario includes the three domains present in the Labyrinth project (RPA, RPS, and UTM) for a correct operation. Additionally, it includes the external services domain required for enabling communications using the public Internet.  
As it can be appreciated, the CIM entity (shadowed in blue) is located within the RPA and RPS domains since both domains may include several communication alternatives. The figure indicates that each domain includes four communication alternatives in this particular case. However, the number of interfaces may vary depending on each case. The figure also shows the SDN controller (located in the RPS) that acts as the network's brain.  
The following subsections detail the required steps to properly configure the reference scenario for further experimentation.
## Adaptive-Compression Based Congestion Control Technique for Wireless Sensor Networks
Thereafter, it assigns priorities to these data groups in an inverse proportion to the respective frequencies of the data groups and defines the quantization step size of ADPCM in an inverse proportion to the priorities. RLC generates a less number of packets for a packet with a low priority. In the relaying node, the ACT reduces the number of packets by increasing the quantization step size of ADPCM in case of congestion. Moreover, in order to facilitate the back pressure, the queue is controlled adaptively according to the congestion state. In the next section, we review related studies. Section 3 describes the ACT, and Section 4 provides a performance evaluation. We conclude the paper in Section 5.
# Multi-Color Channels Based Group Sparse Model for Image Restoration
## 1 Introduction
These good characteristics make the GSR model and its improved models very successful in various image processing tasks. However, these methods only extract the Y channel, and cannot be loyal to each color channel of the color image. In this paper, the color channels refer to red (R), green (G), and blue (B) channels in RGB space. In order to exploit all the three channels, we propose a new GSR model based on multi-color channels. Compared with the traditional GSR model, which only extracts the Y channel in YCbCr space, the proposed model preserves as much image information as possible. The main contribution of the paper is to propose the multi-color-channels-based GSR model for image restoration. In this proposed model, three channels are exploited simultaneously, and hence as much image color information as possible is retained. The paper is organized as follows. Section 1 introduces briefly the image restoration models and the development of the sparse-representation-based methods. Section 2 describes the traditional GSR model. The proposed multi-color-channels-based GSR model is provided in Section 3. Section 4 provides the visual and quantitative experimental results. Finally, conclusions are given in Section 5.
# Data-Driven Redundant Transform Based on Parseval Frames
###### Abstract  
The sparsity of images in a certain transform domain or dictionary has been exploited in many image processing applications. Both classic transforms and sparsifying transforms reconstruct images by a linear combination of a small basis of the transform. Both kinds of transform are non-redundant. However, natural images admit complicated textures and structures, which can hardly be sparsely represented by square transforms. To solve this issue, we propose a data-driven redundant transform based on Parseval frames (DRTPF) by applying the frame and its dual frame as the backward and forward transform operators, respectively. Benefitting from this pairwise use of frames, the proposed model combines a synthesis sparse system and an analysis sparse system. By enforcing the frame pair to be Parseval frames, the singular values and condition number of the learnt redundant frames, which are efficient values for measuring the quality of the learnt sparsifying transforms, are forced to achieve an optimal state. We formulate a transform pair (i.e., frame pair) learning model and a two-phase iterative algorithm, analyze the robustness of the proposed DRTPF and the convergence of the corresponding algorithm, and demonstrate the effectiveness of our proposed DRTPF by analyzing its robustness against noise and sparsification errors. Extensive experimental results on image denoising show that our proposed model achieves superior denoising performance, in terms of subjective and objective quality, compared to traditional sparse models.  
parseval frame; transform; sparse representation +
Footnote †: journal: Applied Sciences
## 4 Results and Analysis
### Predictive Modelling of Material Removal Using ANFIS
#### 4.2.2 ANFIS Rules Employed in Model  
The fuzzy modelling of Abrasive Belt Grinding uses five input parameters and one output parameter. The five input parameters include cutting speed, shore A hardness, feed, force, and  
Figure 11: Adaptive Neuro-Fuzzy Inference System (ANFIS) model for belt grinding showing inputs and output.  
gri size, and the output parameter is the material depth of cut. Each parameter corresponds to six linguistic variables. These variables can generate a number of rules in the designed control rules of the system. The topology of ANFIS architecture that implements the sigmoidal membership function designed with 243 fuzzy rules for depth of cut prediction used in this research is illustrated in Figure 12. Figure 12 present an ANFIS architecture that is equivalent to a five-input first-order Sugeno fuzzy model. The ANFIS used contains 243 rules, with sigmoidal membership functions assigned to each input variable. The total number of fitting parameters is 303, including 60 premise (nonlinear) parameters and 243 consequent (linear) parameters. The applicable control rules formulated along with the membership function for the model are shown in rule viewer of the fuzzy model, as presented in Figure 13. These rules were implemented in a MATLAB environment using a Sugeno-type fuzzy inference system in the fuzzy logic toolbox.  
Figure 12: Topology of adaptive neuro-fuzzy inference system architecture.  
Figure 13: A part of the rule viewer in the proposed fuzzy model.  
Figure 14 presents the initial and final membership functions of the five belt grinding input parameters derived by sigmoidal membership function training. It can be seen from Figure 14 that the changes between the initial and final membership functions hardness and feed parameter are insignificant.  
However, on comparing the shape of initial and final membership functions of belt grinding parameters such as grit size, RPM, and force, a considerable change in the final membership function after training can be seen. Analysis of the sigmoidal membership function after training indicates that the factor in the belt grinding parameters that has the most impact on material removal is grit size, followed by RPM and force, confirming the results of the ANOVA performed in Section 4.1.
## 3 Results and Discussion
### The Selection and Fusion of Data
#### 3.3.2 Extraction of Texture Data  
The physical structure of the muscle mainly determines the WHC of the chicken meat, and the texture features can represent these structures effectively [2; 4; 6]. Each file had a total of seven key wavelength band images for all three WHC traits. Six texture variables were extracted by GLCM from each image, and a total of 42 texture variables (6 variables \(\times\) 7 bands) were obtained for each file in the end. Seven key wavelength band images were shown in Figure 5, and the mean value of the 42 texture variables of all 90 fillets were listed in Table 4.  
As shown in Figure 5, the images of the same file in different bands are distinctly different, and those key wavelength band images can reflect texture information in different ways. In Table 4,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Key Wavelength**} & \multicolumn{6}{c}{**Texture (Mean \(\pm\) SD)**} \\ \cline{2-7}  & **Mean** & **Homogeneity** & **Contrast** & **Entropy** & **Energy** & **Correlation** \\ \hline
1079 & 2.12 & 0.81 & 0.35 & 0.66 & 0.58 & 0.54 \\
1138 & 1.55 & 0.81 & 0.37 & 0.63 & 0.59 & 0.57 \\
1272 & 1.04 & 0.81 & 0.43 & 0.61 & 0.61 & 0.60 \\
1414 & 0.31 & 0.84 & 0.39 & 0.36 & 0.74 & 0.75 \\
1896 & 0.28 & 0.84 & 0.34 & 0.36 & 0.74 & 0.74 \\
2110 & 0.43 & 0.82 & 0.38 & 0.46 & 0.69 & 0.66 \\
2180 & 0.54 & 0.83 & 0.33 & 0.46 & 0.69 & 0.67 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Extraction of texture data.  
Figure 4: Selection of key wavelengths. (**a**) Drip loss. (**b**) Expressible fluid. (**c**) Salt-induced water gain.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**WHC Traits**} & \multicolumn{6}{c}{**Key Wavelength**} \\ \hline Drip loss & 5 & 1079 & 1272 & 1414 & 1896 & 2180 \\ Expressible fluid & 5 & 1138 & 1272 & 1414 & 1896 & 2110 \\ Salt-induce water gain & 4 & 1079 & 1272 & 1414 & 1896 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Selected important wavelengths of WHC traits.  
the mean value of the texture variables varied with the changing of wavelength bands. Mean and entropy had the same change trend as the reflectance of the spectral curves (shown in Figure 3), and the variation of mean was greater. Meanwhile, the change trend of correlation, energy, and homogeneity was the same, which was opposite to the reflectance of the spectral curves. Contrast was different from the other five variables, and it increased to the maximum at the band of 1272 nm and then decreased gradually. In summary, the total information and complexity of the image increased with the reflectance and gray scale of the image, but the uniformity and regularity of image decreased at the same time. This result is in agreement with previous studies [36; 37].
## 2 Derivation of the Physical Model of the Set of Performance Curves
### Derivation of the Physical Model Equations
[PERSON] (2015) formulated the model for \(Q_{Rez}\) using an PT1-Controller [2]. This approach can effectively emulate the behaviour, but it is a discontinuous approximation. The sigmoid function \(s(x)\) (Equation (15)) was inserted into the physical model to model the recirculation efficiency. \[u_{i}:=\pi\;n\;d_{i} \tag{3}\]4  
The sigmoid function is defined as \(s(x)\in[0;1]\), which makes it appropriate for modelling purposes. The sigmoid function is modified by the tuning parameters \(t_{3}\) and \(t_{4}\). For the variable \(x\), \(x=Q/Q^{*}\) applies. Equation (16) represents the functional approach for the recirculation efficiency. \[u_{i}:=\pi\;n\;d_{i} \tag{3}\]5  
The physical model, incorporating the tuning factors \(t_{i}\in t\), \(0\leq i\leq 4\), is expressed through the following system of equations:  
\[ \begin{gathered} u_{2}=\pi\;n\;d_{2}\\ \tilde{Y}=\Big{(}\gamma+t_{0}-\frac{Q+t_{2}\sqrt{H}}{\pi\;(B\;d \;u)_{2}}\text{cot}(\beta_{2B})\Big{)}u_{2}^{2}\\ P_{K}=\tilde{Y}\;\rho\;\Big{(}Q+t_{2}\;\sqrt{H}\Big{)}+t_{1}+P_{m}+P _{K,exp}\Bigg{(}1-\Bigg{(}\frac{1+t_{4}}{1+e^{-t_{3}\frac{Q}{Q^{*}}}}-t_{4} \Bigg{)}\Bigg{)}.\end{gathered}  \tag{17}\]  
For the variable speed calculation, it should also be noted that \(t_{1}\propto n^{3}\), \(t_{2}\propto n\), and \(Q^{*}\propto n\). It is assumed that the mechanical power loss \(P_{m}\) is known.
## 1 Introduction
Moreover, the hydrogen generated may be used as a flexible energy source for a range of campus uses, such as hydrogen fuel for automobiles, offering a real-world illustration of how sustainable energy solutions can be incorporated into regular operations [14]. The current study examines the scientific and financial feasibility of powering Delhi Technological University's Science Block in Delhi, India, utilizing solar photovoltaic energy sources. In a hybrid energy system, the intermittent solar energy is stabilized by using hydrogen energy storage to produce a steady electrical current. The most ideal design, with a net current cost of $1,030,406, has been simulated by HOMER software. With a rated capacity of 240 kW and a mean output of 44.5 kW--or 1068 kWh/day--solar photovoltaic generates 389.865 MWh of energy every year. When operating for 4344 h a year, the PV system can produce up to 253 kW, with a PV penetration of 358% [15]. The major goal of this research is to offer a thorough examination of the solar-hydrogen storage system. Universities can make informed decisions about adopting and integrating this technology into their energy infrastructure if they grasp its complexities. The research intends to go into the practical issues of integrating solar-hydrogen systems into university energy management, in addition to the theoretical ones. This entails investigating universities' existing energy infrastructure, identifying potential obstacles, and suggesting effective integration options. Because colleges shape future leaders and decision-makers, their dedication to sustainable energy practices can have a significant impact on societal norms and practices. We hope to contribute to the discussion of sustainable energy in educational institutions with this study, which focuses on the architecture and integration design of solar-hydrogen storage systems.
# Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
## 2 Methodology  
The goal of this study is to develop an automatic algorithm to extract indoor navigation networks from 3D semantic point clouds. Figure 1 illustrates the pipeline of the proposed method. In the following sections, each step of the proposed method will be introduced in detail.  
Figure 1: The pipeline for extracting JSON-encoded navigation network for indoor path planning from 3D semantic point clouds.
# Interaural Level Difference Optimization of Binaural Ambisonic Rendering
## 4 Perceptual Evaluation
### Test Methodologies
#### 4.1.2 Complex Scenes  
The second listening test used four complex scenes, which were 3-5 second excerpts of soundscape recordings from the open source EigenScape database of fourth-order Ambisonic recordings made using an mh acoustics em32 EigenMike at various locations in northern England [56]. The initial  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\psi\) & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline \(\theta\) (\({}^{\circ}\)) & 180 & 50 & 118 & 0 & 180 & 62 & 130 & 0 \\ \(\phi\) (\({}^{\circ}\)) & 64 & 46 & 16 & 0 & 0 & \(-16\) & \(-46\) & \(-64\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spherical coordinates of test sound locations.  
Figure 12: RMS of 11 measured HpTFs recorded of the Sennheiser HD 650 headphones with the Neumann KU 100 dummy head, with inverse filter and resulting convolved response (left ear).  
format of recordings follows Schmidt semi-normalized (SN3D) normalization, which therefore was converted to N3D normalization by  
\[\beta_{k}^{N3D}=\sqrt{2m+1}\beta_{k}^{SN3D} \tag{19}\]  
The soundscapes used in this listening test, along with a description of the specific excerpt used are as follows:  
1. Beach (waves breaking against the shore)
2. Quiet Street (a single car drives past with birdsong)
3. Pedestrian Zone (pedestrians walking around and talking)
4. Train Station (travel announcement on the station platform)  
The composition of scenes featured mainly horizontal sounds, though elevated sources were present such as the birdsong in scene 2 and travel announcement in scene 4, as well as the room reverberation in scene 3 and 4 due to the recordings having been made indoors.  
The complex-scenes listening test loosely followed the MUSHRA paradigm [54]; however, due to the nature of the stimuli no ideal reference was available. Partly for this reason, the \(M=4\) renders were included in the complex-scenes test which are the highest available Ambisonic order of EigenMike recordings. Lower-order renders were obtained by simply discarding the higher-order channels. An \(M=0\) render was used as an anchor, and test stimuli were binaural Ambisonic renders for orders \(\{M=1,M=2,...,M=4\}\), without and with AIO, totaling 9 test stimuli per trial. For each trial, participants were asked to rate each stimuli with a score between 0 and 100 on plausibility and spaciousness, whereby natural, wide, full and externalized stimuli would be rated higher, and boxed in, lacking lateralization, internalized stimuli would be rated lower. Each trial was repeated once, giving a total of 8 trials. Stimuli and trial ordering was again randomized and presented double blind.
## 5 Results
### Results Analysis
In terms of accuracy, the best values obtained by an optimized model were achieved when applying the pruning ratio of 60%, followed by FP16 quantization. When the scenario involves no loss of accuracy in the model, the aforementioned techniques are recommended. To achieve greater compression, it is recommended to use dynamic range quantization (0.16 MB), which presents an accuracy decrease of 0.98%, or the combination of FP16 quantization with a pruning scheme with a ratio of 80% (0.13 MB), which presents an accuracy decrease of 1.18% when compared with GBRAS-Net. Figure 9 presents the results obtained in terms of the memory requirements of all optimization strategies employed in this work. The storage requirements of the original model can be reduced from approximately 17% to, at most, 87% when using the considered strategies. The twelve different employed optimization schemes resulted in models with memory requirements that were one quarter of those of the original model, including dynamic range quantization, pruning, weight clustering, and the combination of both dynamic range quantization and FP16 quantization with pruning.
## 4 Current Issues and Challenges of Realising the Tactile Internet
### Availability and Reliability of Information  
It is expected that control servers must process and execute the end-user information frequently without any breakdown in the communication. Additionally, the communication path opted should ensure a reliable stream of information between the servers and end users. To ensure that there is no corruptive information, the processed data ought to be exemplified to guarantee that any threats or vulnerabilities will not have any access to the information [4].
# Subarctic Vegetation under the Mixed Warming and Air Pollution Influence
## 3 Results
### Trees Growth Dependence on the Eco-Climate Variables  
#### 3.4.1 Partial Correlations  
Within both impact and reference zones, larch growth was positively correlated with air and soil temperatures, whereas soil moisture negatively influenced larch growth (Figure 9). SO\({}_{2}\) emissions strongly (\(r^{2}\) = 0.44) suppress larch growth in the impact zone. A similar correlation for the reference zone is insignificant, although some minor SO\({}_{2}\) influence probably occurred because emissions occasionally covered the reference zone (Figure 5b). In this analysis, the study period was 1993-2021. This period coincided with (i) a time interval of reliable data regarding SO\({}_{2}\) emission values and (ii) with the beginning of the positive trend in thermal variables and negative trend in moisture (Figure 3).  
In the LSB and impact zones, larch similarly responded to the air and soil temperatures, whereas larch response to SO\({}_{2}\) was significantly lower in the LSB zone (\(r^{2}\) = 0.23; Figure 10a,b). Meanwhile, spruce shows a strong (\(r^{2}\) = 0.54) dependence on SO\({}_{2}\) emissions (Figure 10c), whereas the response of birch was significantly lower (\(r^{2}\) = 0.29; Figure 10c). The growth of all species strongly (\(r^{2}\) = 0.38-0.60) responded to the elevated air and soil temperatures (Figure 10).  
Figure 9: In the impact zone (**a**,**b**), larch growth increases with air and soil temperatures increase, and decreases with soil moisture and SO\({}_{2}\) emission increase. In the reference zone (**c**,**d**), similar correlations were observed, except for the GI-SO\({}_{2}\) correlation, which is insignificant. TEMP and ST—air and soil temperatures, SM—soil moisture, SD—sulfur dioxide emission, JUL—July. All trends are significant at \(p<0.05\), except for the GI-SO\({}_{2}\) trend (**c**). The study period is 1993–2021.  
The growth index of willow shows a high response to the elevated temperatures (both soil and air) and a minor (non-significant) response to SO\({}_{2}\) (Figure 11a,b). There is a strong response of willow's GI to the air temperature at the end (September) of the growth period  
Figure 10: In the LSB zone, growth indexes of larch (**a**,**b**), spruce (**c**,**d**), and birch (**e**,**f**) increase with air and soil temperatures increase, and growth indexes decrease with sulfur dioxide emissions and soil moisture increases. TEMP and ST—air and soil temperatures, SM—soil moisture, SD—sulfur dioxide emission, JUL—July. The study period is 1993–2021. All trends are significant at \(p<0.05\).  
(Figure 11a). Alder, similar to the conifer species, responded positively to the thermal variables and negatively to the SO\({}_{2}\) and soil moisture (Figure 11c,d).  
Alongside considered variables, it was found that \"cold period\" (October-May) precipitation stimulated trees' and bushes' growth (\(R^{2}\) = 0.07... 0.27; Table 2).