esa-sceva/satcom-chunk-collection huggingface dataset:
# Screen-Based Sports Simulation Using Acoustic Source Localization +
## 2 Impulsive Sound Estimation  
### Direction-Based DSBF  
Figure 1 shows an example model of the planar wavefront to estimate the direction of the acoustic source using a microphone array that is arranged in a 2D \(x\)-\(y\) plane. Sound has the property of spreading spherically from the originally generated position. However, when \(|\mathrm{r_{s}}|\), the distance between the acoustic source and the microphone array, is far away as compared with the distance between two adjacent microphones, it is assumed that the wavefront of the sound is planar, as shown in Figure 1.  
To utilize the planar model, \(|\mathrm{r_{s}}|\) should be satisfied as follows:  
\[|\mathrm{r_{s}}|>\frac{\left(d\times M\right)^{2}}{\lambda}, \tag{1}\]  
where, \(d\) is the distance between two adjacent microphones, \(M\) is the number of microphones in a single axis, and \(\lambda\) is the wavelength of the sound. The time, \(\tau\), which the time it takes for sound waves to pass through microphones separated by \(d\), is defined as \(\frac{d\times\mathrm{osc}\theta}{c}\), where \(c\) is the speed of the sound waves. The measured signal at each microphone in the free acoustic field, where \(i=[1,\ \ldots\,\ M]\), is defined as follows:  
\[p(t)=s[t-(i-1)\tau], \tag{2}\]  
Figure 1: Planar wavefront model on a uniformly arranged microphone array in the \(x\)-\(y\) plane.  
where, \(s\) is the source location in 3D space. If Inequality (1) is satisfied, the planar model-based beamforming output for a single axis at \(t\) with respect to candidate steering angles \(\theta\) is defined as follows:  
\[bf(\theta,t)=\frac{1}{M}\sum_{i=1}^{M}p_{i}[t-(i-1)\tau]. \tag{3}\]  
For the impulsive sound samples at a sampling frequency, \(f_{s}\), Equation (3) is rewritten as follows,  
\[bf(\theta,k)=\frac{1}{M}\sum_{i=1}^{M}s[s_{k}-(i-1)\tau f_{s}], \tag{4}\]  
where, \(\rm{s}_{k}\) is the \(k\)th sound sample measured at \(t\). The direction-based DSBF for valid impulsive sound samples with length \(L\) is expressed as follows:  
\[DSBF(\theta)=\frac{1}{L}\sum_{k=1}^{L}bf(\theta,k). \tag{5}\]  
The steering angle, \(\theta_{s}\), for \(n\) candidate \(\theta\) between the acoustic source and the microphone array is defined as follows:  
\[\theta_{s}=\max_{n}[DSBF(\theta)]. \tag{6}\]  
Finally, the unit vector, \(\overrightarrow{v}\), is expressed as follows:  
\[\left\{\begin{array}{c}v_{x}=\cos\theta_{xz}\times\sin\theta_{yz}\\ v_{y}=\cos\theta_{yz}\\ v_{z}=\sin\theta_{xz}\times\sin\theta_{yz}\end{array}\right., \tag{7}\]  
where, \(\theta_{xz}\) and \(\theta_{yz}\) are the direction between the acoustic source and the microphone array in the \(x\) and \(y\) axes, respectively.
## 3 Results and Discussions
### Analysis of Experimental and Numerical Data Comparisons
#### 3.1.1 Utilization of Distilled Water as a Cooling Fluid  
A numerical model was developed using the finite element method with ANSYS Fluent software, with initial and boundary conditions based on experimental data for inlet temperature, flow velocity, and applied heat flux, and the resulting numerical outcomes were compared across all scenarios with the experimental findings. Figure 9 presents the temperature distributions obtained at three different flow rates for the Al TPMS structure using distilled water. The numerical results obtained using ANSYS Fluent 2023 R2 agreed with the experimental measurements. This agreement proves the reliability and accuracy of the developed numerical model. The reasons for the small deviations observed between the two data sets can be listed as heat fluctuations, heat losses, and inlet flow variations. As seen in the distribution curve, the difference between the experimental and numerical data sets being less than 1 \({}^{\circ}\)C is within acceptable limits for thermal management systems. Figure 10 compares the numerical and experimental results of temperature distributions using the Ag TPMS block and distilled water in the experimental setup. It was observed that the Ag TPMS results were similar to the Al TPMS data. However, lower temperatures were achieved for the Agyroid structure.  
Figure 10: Temperature comparison between the numerical and experimental analysis using distilled water for Ag TPMS at different flow rates.  
Figure 9: Temperature comparison between the numerical and experimental analysis using distilled water for Al TPMS at different flow rates.
## 3 Software Receiver Implementation
### Front-End  
Figure 4 describes the architecture of the implemented front-end section.  
The organization of these modules slightly differs from the general architecture given in Figure 2, to be adapted to the SW implementation. The front-end modules need to be executed in a chain, where the result of each module is given to the subsequent module. For this reason, the parallelism of the front end is constrained, and it is necessary to optimize each single module to speed up this part of the receiver. Nonetheless, the front-end part of the system is the less expensive one in terms of processing power; hence, the GPU parallelism has been limited to the data parallelism of each single block. The studies conducted on the performance of the overall system have identified in the back end the bottle-neck of the system, as will be made clear in the next Section. For this reason, the implementation of the single blocks of the front end part of the system will not be discussed in this scope. Overall, to develop the front end part of the receiver, a hybrid paradigm has been used, exploiting the GPU when working on large amounts of data in parallel (i.e., when working on the whole PL frame) and exploiting the CPU when higher frequency is needed and less data are processed (i.e., when working on frame header and descriptor).
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
###### Abstract  
Industry 5.0 is transforming energy demand by integrating sustainability into energy planning, ensuring market stability while minimizing environmental impact for future generations. There are several patterns for calculating energy consumption depending on whether it is measured daily, monthly, or annually through the integration of artificial intelligence approaches, particularly Artificial Neural Networks (ANNs) and Random Forests (RFs), and within the framework of Industry 5.0. This study employs machine learning techniques to analyze energy consumption data from two distinct buildings in Spain: the LUCIA facility in Valladolid and the FUHEM Building in Madrid. The implementation was conducted using custom MATLAB code developed in-house. Our approach systematically evaluates and compares the predictive performance of Artificial Neural Networks (ANNs) and Random Forests (RFs) for energy demand forecasting, leveraging each algorithm's unique characteristics to assess their suitability for this application. The performances of both models are calculated using the Root Mean Square Percentage Error (RMSPE), Root Mean Square Relative Percentage Error (RMSRPE), Mean Absolute Percentage Error (MAPE), Mean Absolute Relative Percentage Error (MARPE), Kling-Gupta Efficiency (KGE), and also the coefficient of determination, R\({}^{2}\). Training times are validated using ANN and RF models. Lucia RF took 2.8 s, while Lucia ANN took 40 s; FUHEM RF took 0.3 s, compared to FUHEM ANN, which took 1.1 s. The performances of the two models are described in detail to show the effectiveness of each of them.  
Keywords:industry 5.0; sustainability; nZEB; AI integration; optimization; ANN; random forest; smart grids; energy forecasting +
Footnote †: journal: Applied sciences
## 5 AGEI Application to a Case Study
### AGEI Procedure Calculation for Configuration A  
Configuration A, which is the original system of the village designed and built to meet the water and HVAC needs, consists of the following components, whose characteristics were collected on-site [23]:  
* Forty-eight electric boilers, with an efficiency of 0.95;
* One LPG boiler, characterised by an efficiency of 0.7;
* An Air Treatment Unit (ATU), with an average EER of 3.5.  
The drinking water needs are covered by bottled water, supplied by means of a transport system that connects the worker village to a local factory, which treats seawater and bottles it.  
Configuration A is schematised in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Energy Input** & \(f_{i}\) & **Reference** \\ \hline Electricity & 2.13 & [45] \\ LPG & 1.07 & [43] \\ \hline \hline \end{tabular}
\end{table}
Table 4: UAE primary energy conversion coefficients \(f_{i}\).  
Figure 4: Worker village, desalinisation plant, and airport localisation [44].  
As mentioned above, drinking water is supplied to the worker village in bottles. To assess the energy efficiency of the \"provided water\" as a useful effect, by means of a desalination plant combined with a bottling system, the current authors propose, for the first time, to use the WET metric. Its value is calculated by using as the denominator all the energy consumed during the various steps needed to provide the bottled water on-site, starting from seawater.
# Virtualized MME Design for IoT Support in 5G Systems
## 5 Virtualized MME Design for M2M
### Proposed Schemes
#### 5.3.2 Traffic Shaper Scheme  
The second proposed scheme adds a traffic shaper after the front end tier to control the traffic of each class to be processed by the SL and SDB tiers. This can be considered as a middle approach between the overdimensioned and the traffic separated schemes; see Figure 8.  
As in the overdimensioned scheme, in SS, all resources are shared among all traffic classes. However, the traffic shaper can smooth traffic peaks and benefit some traffic classes through their shaping criteria. In addition, this scheme avoids the multiplexing loss of processing resources suffered by the separated traffic scheme due to the spare capacity of the dedicated workers that cannot be used for other traffics classes.  
As in the overdimensioned scheme, each tier has to satisfy \(\overline{D}_{j}\) with a target arrival rate. However, for this scheme, the SL and the SDB tiers have the same target arrival rate as they process the same traffic after the shaping of the traffic shaper. The target arrival rate used to design the FE tier is the same as in the traffic separated scheme. Therefore, it can be calculated as:  
\[\lambda_{FE}=\overline{\lambda}_{MBB}+\lambda_{mMZM}^{peak}+\lambda_{1M2M}^{ intsmooth} \tag{14}\]  
Figure 8: Traffic separated scheme block diagram.  
We assume that the traffic shaper is implemented with a token bucket for each traffic class. The token rates of the buckets are set equal to the target arrival rate for each class, respectively. We set the target arrival rate for each class as in the traffic separated scheme. That is, for MBB traffic, the mean arrival rate; for mM2M traffic, we apply moderate smoothing of traffic peaks, \(\lambda_{mM2M}^{smoothpeak}\); and for IM2M, the traffic peak, \(\lambda_{IM2M}^{peak}\).  
After the traffic shaper, the SL and the SDB tiers have a target arrival rate equal to the sum of the target arrival rate of all classes. Then, the target arrival rate can be calculated as:  
\[\lambda_{SL}=\lambda_{SDB}=\overline{\lambda}_{MBB}+\lambda_{mM2M}^{smoothpeak} +\lambda_{IM2M}^{peak} \tag{15}\]  
This design also requires enough memory resources at traffic shaper queues to store all packets while they are queued, as assumed in Section 3.  
Table 2 summarizes the target arrival rate criteria of each baseline and proposed scheme.
# Wearable Voice Dosimetry System
###### Abstract  
The objective quantification of voice acoustic parameters is used for the diagnosis, monitoring, and treatment of voice disorders. Such assessments are carried out with specialised equipment within the doctor's office. The controlled conditions employed are usually not those of the real environment of the patient. The results, although very informative, are specific to those measurement conditions and to the time when they were performed. A wearable voice monitoring system, based on an accelerometer to ensure the message, can overcome these limitations. We present a miniaturised, low-power, and low-cost wearable system to estimate and record voice fundamental frequency (F0), intensity and phonation time for long intervals in the everyday environment of the patient. It was tested on two subjects for up to two weeks of recording time. It was possible to identify distinct periods in vocal activity, such as normal, professional, demanding or hyperfunctional. It provided information on the workload that the vocal cords needed to cope with over time and when and to what extent that workload was concentrated. The proposed voice dosimetry system enables the extraction and recording of voice parameters for long periods of time in the everyday environment of the patient, allowing the objectification of vocal risk situations and personalised treatment and monitoring.  
long-term voice monitoring; phonatory disorders; wearable system +
Footnote †: journal: Medical Engineering Laboratory, School of Medicine, University of Navarra, 31080 Pamplona, Spain; 1 mlorente@unav.  
The variability in F0 is often indicative of an increased vocal load, which occurs when the vocal effort intensifies, potentially leading to organic lesions if not managed with proper technique [3; 4; 5; 6; 7; 8; 9; 10];  
Similarly, an increase in intensity can reflect a prolonged vocal load [3; 4; 8; 9], measurable using sound level meters or accelerometer vibrations [11; 12; 13];  
Phonation time, defined as the duration the vocal cords are in motion, is an important metric, particularly in distinguishing between healthy individuals and those with vocal issues [14; 15; 16].  
This paper introduces a novel wearable device designed to monitor these parameters continuously without external hardware, promoting non-invasive, long-term health monitoring in daily settings. The device features onboard signal processing, a compact size for minimal interference, low power consumption suitable for up to 14 days of continuous use, data storage on removable microSD cards, and affordability. Our aim is to provide healthcare professionals and patients with objective data on voice usage for enhanced diagnostic and therapeutic outcomes.
# Model Study on the Combination of Operating Parameters of Corn Kernel Harvesters
## 4 Results and Discussion
### Optimization of Operating Parameters
#### 4.3.3 Operating Parameter Optimization
The influence of the feed rate \(x_{1}\) on the impurity rate \(y_{3}\) was relatively large, with the feed rate \(x_{1}\) obtained at an impurity rate of 2% being concentrated below 12 kg/s. The optimum operating speed of the harvester could be selected between 3 and 5 km/h, provided that the impurity rate met the target operating quality index. Figure 8 shows the scatter plot and box plot of the cylinder speed \(x_{2}\) values obtained after the regression model was optimized in conjunction with the actual operating indicators. As shown in Figure 8, the optimized cylinder speed \(x_{2}\) was calculated to be above 360 r/min. In Figure 8a, the values of the engine speed variation rate \(y_{1}\) and the cylinder speed variation rate \(y_{5}\) were mostly concentrated around 360 r/min; the engine worked more smoothly at 360 r/min, and the corn kernels loss rate on the ground was also lower at this rotational speed. It was possible to select 360 r/min as the initial parameter during operation, but it was also possible to adjust it according to the actual situation and make the crushing rate \(y_{2}\) and other indicators lower by adjusting the speed appropriately. Figure 9 shows the scatter plot and box diagram of the distribution of the concave clearance values \(x_{3}\) solved by the model. In the box diagram, it can be seen that the engine speed variation rate \(y_{1}\) and the cylinder speed variation rate \(y_{5}\) were at the optimum value when the concave clearance was around 30 mm. The impurity rate was the least sensitive to the concave clearance \(x_{3}\) and the distribution of the solutions was very wide; the crushing rate \(y_{1}\) was the lowest when the concave clearance \(x_{3}\) was around 35 mm. In the scatter plot, it can be seen that the solutions of other models were distributed around 30 mm,  
Figure 7: Feed rate: (**a**) Scatter plot of feed rate. (**b**) Box graph of feed rate: \(y_{1}\)—engine speed variation rate; \(y_{2}\)—crushing rate; \(y_{3}\)—impurity rate; \(y_{4}\)—corn kernel loss rate; \(y_{5}\)—cylinder speed variation rate. where the data were the most densely distributed. Therefore, the optimum operating parameters were a feed rate \(x_{1}\) of 12 kg/s, a cylinder speed \(x_{2}\) of 360 r/min, and a concave clearance of 30 mm. Therefore, the optimal operating parameters were a feed rate of 12 kg/s, a cylinder speed of 360 r/min, and a concave clearance of 30 mm. In a comparison of the contour plots in Figure 5, it was found that the optimal operating parameters range were a feed rate \(x_{1}\) of 9.8 to 12.25 kg/s, a forward speed of 4 to 5 km/h, a cylinder speed \(x_{2}\) of 330 to 370 r/min, and a concave clearance \(x_{3}\) of 30 mm, while satisfying the engine speed variation rate \(y_{1}\) of less than 5%, a cylinder speed variation rate \(y_{5}\) of less than 10%, a crushing rate \(y_{2}\) of less than 5%, am impurity rate \(y_{3}\) of less than 2%, and a corn kernel loss rate \(y_{4}\) of less than 4%.
## 4 Results and Discussion  
### Impact Performance Comparison  
In this part of the study, we conducted a comparative analysis of the impact performance of the GQ89 and Type B RC-DTH air hammer piston at various inlet pressure settings, as shown in Figure 8a. The Type B RC-DTH air hammer exhibits significant advantages in impact velocity and frequency, indicating a qualitative improvement over the GQ89 model. Increasing the inlet pressure markedly enhances the impact performance of the RC-DTH hammer, notably increasing its final impact speed and frequency.  
Figure 8: _Cont._  
Figure 7: Group of typical sectional pressure contour.  
As depicted in Figure 8b, the Type B hammer exhibits a substantially higher impact power compared to the GQ89 model under all pressure conditions examined. Moreover, as the inlet pressure for the piston is elevated, the Type B hammer demonstrates a steeper growth rate in impact power relative to the GQ89 model. The detailed data comparison indicates that at a piston inlet pressure of 0.6 MPa, the single impact energy of the Type B RC-DTH hammer is 229.8% higher than that of the GQ89 model. Simultaneously, the impact power of the Type B RC-DTH hammer experiences a significant increase of 429.9% compared to the GQ89 model. At a piston inlet pressure of 0.9 MPa, the single impact energy increases by 215%, while the impact power increases by 312%. When the piston inlet pressure is raised to 1.2 MPa, the single impact energy increases by 196%, and the impact power increases by 373%.
# A Project Scheduling Game Equilibrium Problem Based on Dynamic Resource Supply
## 3 Model Establishment
### Project Scheduling
#### 3.2.3 Constraints  
In full consideration of the actual situation of the engineering supply chain, the constraints are listed in this section. This will make the model more realistic.  
\[\sum\limits_{j\in R_{s}}\sum\limits_{m=1}^{M_{j}}r_{jmk}\sum\limits_{r^{\prime }=t}^{t+d_{jm}-1}x_{jmt}\leq S_{ks(t-1)}+\sum\limits_{i=1}^{I}v_{isk}(t),\forall k \in K,s\in S,t\in FT \tag{3}\]  
\[\sum\limits_{j\in A_{t}}\sum\limits_{m=1}^{M_{j}}r_{jmk}\leq r_{k},\forall k \in K,t\in FT \tag{4}\]  
\[\sum\limits_{m=1}^{M_{j}}\sum\limits_{t=EF_{j}}^{LF_{t}}tx_{jmt}\leq\sum \limits_{m=1}^{M_{j}}\sum\limits_{t=EF_{j}}^{LF_{t}}(t-d_{jm})x_{jmt},\forall j \in P_{j},j\in J \tag{5}\]  
\[S_{kst}=S_{ks(t-1)}+\sum\limits_{i=1}^{I}v_{isk}(t)-\sum\limits_{j\in R_{s}} \sum\limits_{m=1}^{M_{j}}r_{jmk}\sum\limits_{t^{\prime}=t}^{t+d_{jm}-1}x_{jmt^ {\prime}},\forall k\in K,s\in S,t\in FT. \tag{6}\]  
\[S_{ks0}=0,S_{ksFt}=0,\forall k\in K,s\in S \tag{7}\]  
\[S_{kst}\geq 0,\sum\limits_{k=1}^{K}S_{kst}\leq IC_{s},\forall k\in K,s\in S,t \in FT \tag{8}\]  
\[F_{t}\leq D,F_{c}\leq B \tag{9}\]\[FT_{j}=\sum\limits_{m=1}^{M_{j}}\sum\limits_{t=E_{f}}^{LF_{j}}tx_{jmt},ST_{j}=FT_{j }-d_{jm},\forall j\in J \tag{10}\]  
\[\sum\limits_{m=1}^{M_{j}}\sum\limits_{t=E_{f}}^{LF_{j}}x_{jmt}=1,\forall j\in J \tag{11}\]  
\[x_{jmt}\in\{0,1\},\forall j\in J,t\in FT \tag{12}\]  
\[z_{kt}=\left\{\begin{array}{l}1,\sum\limits_{i=1}^{I}\sum\limits_{s=1}^{S}v_{ isk}(t)>0\\ 0,\sum\limits_{i=1}^{S}\sum\limits_{s=1}^{S}v_{isk}(t)=0\end{array}\right. \tag{13}\]  
Constraint condition Formula (3) represents the resource constraint. Equation (4) represents that the total consumption of resource \(k\) in each time period cannot exceed its maximum supply capacity. Equation (5) is the predecessor constraint. Equation (6) represents the remaining amount of available resources at the end of each time period, which can be regarded as a state transition variable. Equation (7) indicates that, to maximize the utilization of resources, the resource surplus should be zero at the beginning and end of the project. Equation (8) indicates that the resource surplus at the end of each period is greater than or equal to zero, and cannot exceed the inventory capacity. Equation (9) indicates the construction period and budget constraint. Equation (10) represents the start and end times of each activity. Equations (11)-(13) are logical constraints: each activity should be executed within the range of the earliest and latest completion times, and only one activity mode can be executed. Meanwhile, there are also characteristic constraints among the decision variables.
# Multi-Scale CNN-Transformer Dual Network for Hyperspectral Compressive Snapshot Reconstruction
## 5 Discussion
### Ablation Experiments
#### 5.1.1 The Ablation of Complementary Fusion Structure  
This section continues to verify the effectiveness of parallel branches and bidirectional cross-linking interactions in the DualCT module. The bidirectional cross-linking attention is composed of the channel iteration attention and spatial interaction attention. The experimental results are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Baseline**} & \multicolumn{2}{c}{**Parallel**} & \multirow{2}{*}{**Mask Attention**} & \multirow{2}{*}{**PSNR**} & \multirow{2}{*}{**SSIM**} \\  & **Complementary Fusion** & & & \\ \hline ✓ & & & 33.88 & 0.931 \\ ✓ & ✓ & & 34.75 & 0.943 \\ ✓ & ✓ & ✓ & 35.80 & 0.955 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experiments of DualCT-Net. (✓ indicates using this module).  
Figure 9: Reconstructed spectral signatures by five methods of the reconstructed hyperspectral image of scene 8.  
According to the PSNR and SSIM values in Table 3, we can see that the design of parallel branches and bidirectional cross-linking interactions are both positive factors that can improve reconstruction performance.