esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Application of Hyperspectral Analysis to Rock Art Panels from El Castillo Cave (Spain)
## 2 Materials and Methods
### Visualization Improvement
#### 2.6.4 Independent Component Analysis  
ICA transformation is used as a tool for blind source separation. The fundamental objective of independent component analysis (ICA) is to provide a method for finding a linear representation of non-Gaussian data so that the components are statistically independent or as independent as possible [41; 42]. Such representation makes it possible to obtain the fundamental structure of the data in many applications, including feature extraction and signal separation.  
Compared to principal component analysis, ICA analysis offers some unique advantages:PCA is an orthogonal decomposition. It is based on the analysis of the covariance matrix, which is based on a Gaussian distribution assumption. ICA analysis is based on the assumption of non-Gaussian distribution from independent sources. PCA analysis uses only second-order statistics, while ICA analysis uses higher-order statistics. Higher order statistics are a more robust statistical assumption, revealing interesting features in generally non-Gaussian hyperspectral datasets.  
If the characteristic of interest (such as an anomaly) occupies only a small part of all pixels, which makes the contribution to the entire image covariance matrix negligible; in ICA analysis, the characteristic of interest will be considered as the noise bands. In this method, the characteristics are distinguished from the noise bands.
## 2 Materials and Methods
### Composition Analysis of Crude Cellulose and Lignin  
The chemical compositions analysis of ML straw, separated crude cellulose, and crude lignin were measured by the National Renewable Energy Laboratory (NREL) standard analytical procedure (NREL/TP-510-42618, Standard two-step acid hydrolysis) [24]. Briefly, the sample (300 mg) was hydrolyzed with 3 mL of 72% (_m/v_) sulfuric acid at 30 \({}^{\circ}\)C for 60 min. After hydrolysis, the obtained acid sample was diluted until a final sulfuric acid concentration of 4% (_w/w_) was obtained by adding distilled water (84 mL), and then it was allowed to react at 121 \({}^{\circ}\)C for 60 min in a GI54 DWS automatic pressure steam sterilizer (Zealway Instrument Inc., Xiamen, China).  
The reaction mixture was filtrated, and the residue was dried at 105 \({}^{\circ}\)C to a constant weight in an aluminum box and weighed to determine the acid-insoluble lignin (AIL) content. Acid-soluble lignin (ASL) was determined by the BlueStar A Spectrophotometer (Beijing LabTech Instrument Co., Ltd., Beijing, China). Glucose, xylose, and formic acid in supernatants were analyzed on an UltiMate-3000 HPLC (Thermo Fisher, Waltham, MA, USA) equipped with an Aminex HPX-87X column (300 mm \(\times\) 7.8 mm) and a RefractoMax521 refractive index detector. The mobile phase was 5 mM H2 SO4 at a flow rate of 0.60 mL/min, and the column temperature was kept at 55 \({}^{\circ}\)C. The contents, puri  
Figure 1: Schematic diagram of the experimental procedure of the formic acid method.  
ties, and recoveries of cellulose, hemicellulose, and lignin were calculated according to Equations (1)-(8):  
\[\text{{Cellulose content}}\ (\%)=\frac{W_{Glc}\times 0.9}{W_{ODS}}\times 100 \tag{1}\]  
\[\text{{Hemicellulose content}}\ (\%)=\frac{W_{Xyl}\times 0.88}{W_{ODS}}\times 100 \tag{2}\]  
\[\text{{Lginin content}}\ (\%)=\frac{W_{ASL}+W_{AIL}}{W_{ODS}}\times 100 \tag{3}\]  
\[\text{{Cellulose purity}}\ (\%)=\frac{Weight_{Cellulose in crude cellulose}}{Weight_{Croude cellulose}}\times 100 \tag{4}\]  
\[\text{{Lginin purity}}\ (\%)=\frac{Weight_{Total lignin in crude lignin}}{Weight_{Croude lignin}}\times 100 \tag{5}\]  
\[\text{{Cellulose recovery}}\ (\%)=\frac{Weight_{Cellulose in crude cellulose}}{Weight_{Cellulose in feedstock}}\times 100 \tag{6}\]  
\[\text{{Hemicellulose recovery}}\ (\%)=\frac{Weight_{Hemicellulose in extract}}{Weight_{Hemicellulose in feedstock}}\times 100 \tag{7}\]  
\[\text{{Lginin recovery}}\ (\%)=\frac{Weight_{Lginin in crude lignin}}{Weight_{Lginin in feedstock}}\times 100 \tag{8}\]  
where \(W_{Glc}\), \(W_{Xyl}\), \(W_{ASL}\), \(W_{AIL}\), and \(W_{ODS}\) are the mass weight (g) of glucose, xylose, Acid-Soluble Lignin (ASL), Acid-Insoluble Lignin (AIL), and oven-dry sample, respectively. The numbers of 0.9 and 0.88 are the correction factor, which were used to estimate the cellulose and hemicellulose content.
## 5 Conclusions  
In hospitals, some unattended patient bed-exiting events might result in falls, increasing the burden on medical staff. At present, commonly used means of physical or chemical restraint might harm the physical and mental health of patients. Ordinary RGB camera monitoring systems involve privacy concerns. Therefore, our virtual monitoring system based on depth cameras has been developed for preventing patients' movements and fall risk.  
We demonstrated that the eNightTrack system had a convincing sensitivity of 96.8% for detecting bed-exiting events, making it a potential effective tool to prevent falls. Moreover, it offers several advantages, including the avoidance of privacy issues and could serve as an alternative to current restraint measures. The system is robust to disturbances caused by bed height variations, furniture changes, and medical personnel entering the predefined region. However, there are still several limitations and concerns that should be focused on during future developments. A further modified eNightTrack system could be installed in hospital wards to support nurses in monitoring at any moment.  
Conceptualization, Y.-P.Z. and J.C.-W.C.; methodology, Y.-P.Z. and J.C.-W.C.; software, Y.-J.M. and A.Y.-C.T.; validation, A.Y.-C.T. and Q.T.-K.S.; formal analysis, Y.-J.M. and A.Y.-C.T.; investigation, A.Y.-C.T. and Q.T.-K.S.; resources, Y.-P.Z. and J.C.-W.C.; data curation, A.Y.-C.T. and Q.T.-K.S.; writing--original draft preparation, Y.-J.M.; writing--review and editing, J.C.-W.C.; supervision, Y.-P.Z. and J.C.-W.C.; project administration, Q.T.-K.S. and J.C.-W.C.; funding acquisition, J.C.-W.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University Grants Committee of Hong Kong under the General Research Fund (GRF), grant number: PolyU15223822.  
This study was approved by the Human Subjects Ethics Sub-committee of Hong Kong Polytechnic University (No. HSEARS20210127007).  
The data presented in this study are available on request from the corresponding author.
## 5 Performance Evaluation Metrics
### Root Mean Square Error (RMSE)  
RMSE measures the square root of the average squared deviations between predictions and observations [49]:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{7}\]
## 4 Semantic Understanding of Object Layer  
Object layer semantics mainly describe the logical concepts of scenes in images, usually based on a large number of visual layer descriptors. Compared with the visual layer, the object layer is closer to the human understanding of the geographic scene. For instance, in the process of geographic scene understanding, we rely more on the houses and roads in the images, rather than recognizing that there are small dense and regular highlighted areas, narrow and long gray-banded areas and so on in the image. Houses, roads, sky and grass, which conform to human cognition, constitute the object layer semantics of geographic scenes. In addition, object layer semantics can also be abstract local areas, such as visual words generated by feature detection algorithms. There is also a certain context structure between different objects, forming a corresponding spatial relationship. Thus, a geographic scene is a combination of a set of specific objects. According to the different semantic forms of objects, there are three types: target object semantics, local area semantics and spatial structure semantics.
# ClipQ: Clipping Optimization for the Post-Training Quantization of Convolutional Neural Network
## 4 Experiments
### Ablation Study
We evaluate the performance of Max, ACIQ, Percent, KL, and ClipQ in activation clipping, as shown in Table 5. Affected by outliers in the activation, Max causes a large loss to the network performance. Percent only considers the first 99.99% of the activation and uses its maximum and minimum value as clipping thresholds. Compared with Max, the quantization accuracy is significantly improved. ACIQ and Percent are six of one and half a dozen of the other. As the core method of TensorRT, KL is widely used in activation quantization. In our experiments, it outperforms Max, ACIQ, and Percent. As one can see, ClipQ achieves the best quantization results. It achieves 1.02% higher accuracy as compared to KL on average. ClipQ's quantization accuracy is 1.77% and 1.7% higher than KL for MnasetV1 and SSD, respectively. If the importance of activation is not considered, the quantization accuracy of ClipQ will be degraded to some extent. In fact, ClipQ is equivalent to using the MSE for activation clipping. Overall, it is slightly better than KL. In addition, we also added standard deviation to the experimental results to more comprehensively evaluate the stability and performance fluctuations of different quantification methods. Compared with methods such as ACIQ and MSE, the standard deviation of ClipQ is smaller on multiple models. For example, in the MnasNetV1 task, the mean accuracy of ClipQ is 69.82 %, with a standard deviation of only \(\pm\)0.3 %. In contrast, the standard deviation of the ACIQ method is larger, indicating that its fluctuations are more pronounced under different experimental conditions. In other models and task performances, the ClipQ method maintains a low standard deviation, and the results have high stability and repeatability.
# Predicting the Effect of Processing Parameters on Caliber-Rolled Mg Alloys through Machine Learning
## 2 Materials and Methods
### Machine Learning  
An ANN can be constructed with hidden layers as well as input and output layers. The numbers of hidden layers and units are determined by the researcher, which directly affects the prediction of the performance of the ANN model. In this work, these hyperparameters were optimized using an algorithm called 'hyperband' [28] in the range of 1-10 hidden layers and 4-32 hidden units per layer. The optimum ANN architecture for this study consisted of 224, 200, 192, 72, 184, 72, 152, and 176 units in the first to eighth hidden layers, respectively. In addition, the learning rate was also optimized using the same method between \(10^{-5}\) and \(10^{-3}\), for which the hyperband yielded an optimum value of \(10^{-3.865}\). The LeakyReLU [29] and mean square error were chosen as the activation and loss functions, respectively. The adaptive moment estimation [30] was employed for the stochastic optimization.  
The GAN [31] generated fake data similar to the genuine ones using two competing functions called a discriminator and generator. Both functions used a multilayer perceptron model similar to an ANN, which consisted of two hidden layers. The size of latent space was determined as 1 \(\times\) 64 by trial and error. The discriminator had 128 hidden units, while adopting the LeakyReLU and sigmoid for the activation functions. The generator required more complex architecture than the discriminator as it generated fake data from the noises. Accordingly, 256 hidden units were allocated to the generator, which doubled those of the discriminator. The generator also used the LeakyReLU for the activation function. Binary cross entropy was employed as the loss function for both models. Only the training dataset was provided for the learning of the GAN model at a rate of 3 \(\times\) 10  
Figure 1: Scheme of the finite element model used in this study and meshes for the cross-section.  
for 5000 epochs. The mini-batch method was adopted to make the machine learning stable and robust through iterations. The batch number was set to be 41, which was an aliquot part of 1230 instances; note that one of 1231 instances was arbitrarily excluded to avoid the overfitting of the last batch. As a result, the training dataset doubled to 2462 instances through the GAN approach. The architecture of the final model, GAN-aided ANN, is presented in illustration (Figure 2).
## 2 Results
### Dynamic Adsorption and Desorption  
When adsorption reaches the breakthrough point, the adsorption effect decreases and even ceases [28]. Hence, it is important to establish the breakthrough point to calculate the appropriate sample feed concentration and bed volume (BV) of the sample solution. The effect of feed concentration (0.657 mg/mL, 1.009 mg/mL, 1.502 mg/mL) at a flow rate of 2 BV/h (1 BV = 15 mL) on the adsorption capacity of PGMA-III was studied. As the feed concentration increased, the volume of adsorption solutions (eluate) at the 10% breakthrough point decreased; this also indicated that the process time decreased as the feed concentration increased. However, the adsorption capacity increased and reached its peak value (64.02 mg/g) at a feed concentration of 1.502 mg/mL. Therefore, based on the process time and the adsorption capacity, a feed concentration of 1.5 mg/mL and flow rate of 2 BV/h were selected for the following experiments.  
To optimize the elution of glucoerucin from PGMA-III, the effects of different concentrations of aqueous desorption solvents (ammonia 5%, 10%; KCl 1.0, 2.0 mol/L) were evaluated at a flow rate 2 BV/h and 30 \({}^{\circ}\)C. Figure 8 shows that the concentration glucoerucin increased in the eluate with increasing concentrations of aqueous ammonia or KCl. Maximum desorption occurred using 10% aqueous ammonia or 2.0 mol/L KCl solution, equivalent to desorption ratios of \(>\)75% or \(>\)82%, respectively; both dynamic desorption curves showed that glucoerucin eluted in a lower volume of eluate over a narrow range. Therefore, both 10% aqueous ammonia water and 2.0 mol/L KCl solution could be used as dynamic desorption solvents.  
Figure 7: Effect of desorption solvent concentration on the static desorption ratio of glucoerucin from PGMA-III adsorbent at 30 \({}^{\circ}\)C. (**a**) 5% ammonia water solution; (**b**) 10% ammonia water solution; (**c**) 18% ammonia water solution; (**d**) 25% ammonia water solution; (**e**) 0.5 mol/L KCl solution; (**f**) 1 mol/L KCl solution; (**g**) 2 mol/L KCl solution.
## 3 Results and Discussion
### Membrane Characterization
#### 3.1.2 Infrared (IR) Spectrophotometry  
In order to confirm incorporation of the Aliquat-336 carrier in the membrane matrix, an infrared analysis was performed for the virgin membrane and those containing the carrier at different amounts.  
The results of the different absorption bands are presented in Figure 3.  
With regard to the absorption bands of the PVC polymer, no modification in the polymer chain after the addition of the carrier was noticed. On the other hand, on the membranes containing the carrier, a new absorption band located between 3000 and 3500 cm\({}^{-1}\) was observed. By increasing the content of Aliquat-336, the intensity of the C-N band increased mainly at 3450 cm\({}^{-1}\), at which no band exists in pristine PVC. This can be attributed to the CN group, which is a characteristic group of the Aliquat-336 carrier and, therefore, confirms its physical immobilization in the membrane matrix without the modification of the main absorption bands characteristic of the PVC polymer. Moreover, a distinct peak at 1600 cm\({}^{-1}\) appeared, increasing in intensity at higher Aliquat-336 contents in the membrane. This was attributed to the vibration of ammonium groups of Aliquat-336. A weak band at 2250 cm\({}^{-1}\) was also observed for the PVC/Aliquat membranes, confirming the existence of quaternary ammonium groups of Aliquat-336. Similar results were reported by [PERSON] et al. [41], confirming the formation of positive charges (NH\({}_{3}\)\({}^{+}\)) on the chitosan matrix. The comparison of FTIR spectra of the produced membranes showed no band displacement, assuming that Aliquat-336 is still free, and no interaction between the additive and the polymer occurs.
## 3 Results and Discussion
### Parametric Analysis
#### 3.2.4 Enhanced Solution  
To improve the thermal performance of low thermal inertia constructive solutions, presenting a consequential risk of overheating (as noticed in the results of Scenario C), an enhanced solution containing PCM was tested. At this level, the strategy includes the use of PCM incorporated into the walls and ceilings of the constructive solution of Scenario C. PCM were applied behind the plasterboard for both constructive solutions (exterior walls and ceiling), expecting these materials to be able to buffer the temperature swing in the cooling season during the day to prevent overheating.  
In the present step, the conduction transfer function (CTF) model for the algorithm of surface heat balance calculation methodology was used [46]. Figure 14 displays the simulation results for the different PCM.  
Due to the similar results, as well as the density of the points depicted in the plot, only BioPCM(r) M91/Q29_0.037 is represented in Figure 14. However, in terms of effective discomfort values, the different melting points have different effects, as shown in Table 9. Running the simulations with PCM with different melting point values revealed that the overheating rate during the summer season decreases about 7% in all thermal zones. The results of the BioPCM(r) M91/Q29_0.037 are slightly better and, therefore, this material is a suitable option to be applied to control the overheating risk in construction associated to a low thermal inertia. Despite the improvements in overheating, the effects in underheating are almost negligible.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Thermal Zone**} & \multicolumn{2}{c}{**Thermal Discomfort (\%)**} \\  & & **Winter** & **Summer** \\ \hline BioPCM® & 01 & 34.37 & 19.52 \\ M91/Q25\_0.037 & 02 & 30.26 & 14.64 \\ \hline BioPCM® & 01 & 34.44 & 18.50 \\ M91/Q27\_0.037 & 02 & 30.49 & 12.68 \\ \hline BioPCM® & 03 & 35.67 & 7.64 \\ \hline BioPCM® & 01 & 35.30 & 16.58 \\ M91/Q29\_0.037 & 02 & 30.95 & 10.15 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results obtained for PCM solutions with different melting point values.  
Figure 14: Thermal comfort analysis using EN16798-1 for the enhanced solution with BioPCM® M91/Q25_0.037.
# An Effectively Finite-Tailed Updating for Multiple Object Tracking in Crowd Scenes
## 3 Problem Formulation and the Approach
### Revisiting FairMOT
#### 3.1.1 Backbone Network  
FairMOT adopts ResNet-34 as a backbone to strike a good balance between accuracy and speed. An enhanced version of Deep Layer Aggregation (DLA) [28] is applied to the backbone to fuse multi-layer features. In addition, the convolution layers in all up-sampling modules are replaced by deformable convolution such that they can dynamically adjust the receptive field according to object scales and poses. These modifications are also helpful to alleviate the alignment issue.
# BICM-ID with Physical Layer Network Coding in TWR Free Space Optical Communication Links +
## 2 System Description
### Channel Model
#### 2.2.3 Atmospheric Loss  
The atmospheric channel attenuates the signal traversing it as a result of absorption and scattering processes, which can be described by the exponential Beers-Lambert law [22] as:  
\[h_{l}=\exp(-\delta L_{d}), \tag{11}\]  
where \(\delta\) is the weather attenuation coefficient, which can be determined from the visibility data through [PERSON]'s model [23] as:  
\[\delta= \bigg{(}\frac{3.91}{V}\bigg{)}\bigg{(}\frac{\lambda}{550}\bigg{)}^{-q}, \tag{12}\]  
where \(V\) in kilometres (km) represents the visibility and \(q\) is the particle size distribution coefficient, which can be defined from [24] as:\[q=\begin{cases}1.6,&V>50\text{ km}\\ 1.3,&6\text{ km}<V<50\text{ km}\\ 0.16+0.34,&1\text{ km}<V<6\text{ km}\\ V-0.5,&0.5\text{ km}<V<1\text{ km}\\ 0,&V<0.5\text{ km}\end{cases}. \tag{13}\]
## Appendix A Fourier Transform of the Mass Fluxes Fluctuation Correlations
Now, we can repeat the same procedure over the frequency \(\omega_{2}\)  
\[\frac{4(ze)^{2}cDe^{i\mathbf{k}_{2}\cdot\mathbf{A}\mathbf{r}}}{(2\pi)^{5/2} \varepsilon\epsilon_{0}}\int_{\infty}^{\infty}\frac{d\omega_{2}}{[\omega_{2}^ {2}+D^{2}(k_{2}^{2}+\kappa^{2})^{2}]}=\frac{4(ze)^{2}ce^{i\mathbf{k}_{2}\cdot \mathbf{A}\mathbf{r}}}{(2\pi)^{5/2}\varepsilon\epsilon_{0}}\frac{\pi}{k_{2}^{2 }+\kappa^{2}}. \tag{11}\]  
Finally, we integrate over the wave vector \(\mathbf{k}_{2}\), which leads to  
\[\langle\delta\psi(\mathbf{r}_{1},t_{1})\delta\rho_{e}(\mathbf{r}_{2},t_{2}) \rangle=\frac{4\pi(ze)^{2}c}{(2\pi)^{4}\varepsilon\epsilon_{0}}\int_{-\infty} ^{\infty}d\mathbf{k}_{2}\frac{e^{i\mathbf{k}_{2}\cdot\mathbf{A}\mathbf{r}}}{k _{2}^{2}+\kappa^{2}} \tag{12}\]  
This is easy to accomplish if we select a coordinate system where the vector \(\mathbf{\Delta r}\) is directed along \(k_{2z}\) axis (see Figure 11). Then, \(\mathbf{k}_{2}\cdot\mathbf{\Delta r}=k_{2}r\cos\theta\) and \(d\mathbf{k}_{2}=k_{2}^{2}\sin\theta d\theta d\phi\), and the integral in (12) becomes  
\[ \begin{split}\int_{-\infty}^{\infty}d\mathbf{k}_{2}\frac{e^{i \mathbf{k}_{2}\cdot\mathbf{\Delta r}}}{k_{2}^{2}+\kappa^{2}}=\int_{0}^{\infty }\int_{0}^{\pi}\int_{0}^{2\pi}dk_{2}d\theta d\phi\frac{k_{2}^{2}\sin\theta e^{ ik\cos\theta}}{k_{2}^{2}+\kappa^{2}}=\\ 2\pi\int_{0}^{\infty}dk_{2}\frac{k_{2}^{2}}{k_{2}^{2}+\kappa^{2 }}\frac{e^{ikr}-e^{-ikr}}{ikr}=\frac{4\pi}{r}\int_{0}^{\infty}dk_{2}\frac{k_{2} \sin(k_{2}r)}{k_{2}^{2}+\kappa^{2}}=2\pi^{2}\frac{e^{-kr}}{r}\end{split}  \tag{13}\]  
Inserting (13) in Equation (12) leads to the final results for the potential-charge correlation (see Equation (33)). \[\langle\delta\psi(\mathbf{r}_{1},t)\delta\rho(\mathbf{r}_{2},t)\rangle=\frac{k _{2}T\kappa^{2}}{4\pi}\frac{e^{-kr}}{r} \tag{14}\]
# Computer Modelling of Heliostat Fields by Ray-Tracing Techniques: Simulating the Sun
###### Abstract  
To computer-simulate solar-concentrating facilities, an accurate knowledge of the Sun's position as a function of latitude, longitude, time and date is required. In this work, it is reported first a simplified description of a general algorithm, developed by the astronomy community to accomplish that. Our implementation of this algorithm (included in our Light Analysis Modelling package) has been successfully validated against well trusted astronomy data. The software was then used to produce a wide range of results for 2024, for two well-known research facilities, the most northern (Julich, Germany) and the most southern (Protaras, Cyprus) heliostat fields listed in the official SFERA-III EU project. This includes altitude and azimuth data, sunrise and sunset data, analemma curves, angular speed data and geocentric Sun trajectories around the observer's position. Other ray-tracing techniques are also reported to help simulate the Sun vectors reaching the solar devices. The truly inspiring results obtained show how important this type of software is, from the scientific and industrial point of view, to better understand our relationship with our neighbor star, the Sun.  
computer ray tracing; horizon; equatorial and ecliptic coordinates; true; eccentric and mean anomaly; sun position algorithm; sun intensity; sun angular speed Article