esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
## 4 Learning Strategies and Network Architectures
### Regular Strategy  
The estimation is treated as a classification task. The whole space is divided into different classes and the emitted signal detects which class the signal belongs to. The elevation space, ranging from \(30^{\lx@math@degree}\) to \(150^{\lx@math@degree}\), is divided into 13 different classes with the interval of \(10^{\lx@math@degree}\). Similarly, the azimuth space, ranging from \(0^{\lx@math@degree}\) to \(360^{\lx@math@degree}\), is divided into 36 classes with the interval of \(10^{\lx@math@degree}\). In total, 468 different classes can be obtained. Moreover, each class is granted a label to distinguish it. For each class, a large number of samples are generated for the network to learn and give the network the ability to localize the sound source. For the classification, a convolutional neural network is utilized for the task. The network is composed of three convolutional layers and two fully connected layers. For the convolutional layers, the size of the convolution cores in these three convolutional layers is 2 \(\times\) 2. Moreover, the number of filters in all layers is fixed at 8. All the convolutional  
Figure 2: The flowing chart of obtaining the input features. Cov represents the operation to acquire the covariance matrix of the signal.  
layers are activated by the rectified liner unit (ReLU) activation [32]. A flattened layer follows the last convolutional layer to reshape the output of the convolutional layers. The following components are two fully connected (FC) layers. The first layer with \(N_{H}\) nodes is activated by the ReLU activation. The second layer with \(N_{C}\) nodes uses softmax for the activation. \(N_{C}\) is the number of classes for the task. The softmax function [33] helps to obtain the posterior probabilities of all the candidates. Finally, according to the principle of maximum posterior probability, the position where the maximum probability occurs is the estimated DOA. Figure 3 demonstrates the details of the network for the estimation task. During learning, the cross-entropy [34] is chosen as the loss function because its derivation is simpler and it is only related to the probability of the correct class. Furthermore, the loss is able to conveniently derive the input of the softmax activation layer. Adam [35] is adopted for the stochastic optimization. The details of the CNN are shown in Figure 3.
# Solid-Phase Microextraction Techniques and Application in Food and Horticultural Crops
## 5 Applications of SPME and HPLC in Agricultural Products
### Analysis of Phenolic Acids in Orange Juice Using SMPE and HPLC  
[PERSON] et al. used in-tube solid-phase microextraction for the analysis of five phenolic acids (Figure 9) present in orange juice samples. This involved using in-tube solid-phase microextraction, with subsequent analysis using a HPLC equipped with a DAD [54]. The adsorbent coating on the inside of the capillary consisted of a poly (1-allyl-3-methylimidazolium bis [(trifluoromethylomethyl)] sulfonyl] imide-co-ethylene dimethacrylate) polymer embedded with Fe\({}_{3}\)O\({}_{4}\) nanoparticles. The extraction of the phenolic acids from the juice sample was enhanced via the use of a magnetic field around the capillary.  
Juice samples were first centrifuged, filtered, diluted, and adjusted to pH 6, before being pumped through the capillary. The adsorption phase consisted of pumping a 4.5 mL prepared orange juice sample into the capillary at 100 uL/min. The desorption of phenolic acids involved pumping 120 uL of methanol though the column at 40 uL/min. The methanol sample was then injected into a HPLC for analysis. The method showed good linearity (0.10-300 ug/L) and a good limit of quantitation (0.040-0.19 ug/L) for the five phenolic acids investigated.  
Figure 8: Triazine herbicides extracted using a chromium terephthalate porous solid chitosan absorbent.  
Figure 9: Extraction of phenolic acids using an adsorbent of poly (1-allyl-3-methylimidazolium bis [(trifluoromethylomethyl)] sulfonyl] imide-co-ethylene dimethacrylate) polymer embedded with Fe\({}_{3}\)O\({}_{4}\) nanoparticles.
# A Cumulant-Based Method for Acquiring GNSS Signals
## 4 Simulation and Experimental Results
### Indoor GPS Signal Acquisition Results  
As a final experiment in this paper, we assess the capability of the proposed algorithm in acquiring weak signals. For this test, the receiver was placed inside a building, within one meter of a window on the local campus. Figures 16 and 17 display the acquisition results using the traditional method and the proposed method, respectively. As seen in Figure 16, the traditional method struggles to correctly identify the code phase delay, with the peak occurring at the wrong code-phase position. In contrast, Figure 17 shows that the  
Figure 14: (**a**) GALILEO PRN 19 signal acquisition results using the traditional method; (**b**) GALILEO PRN 19 signal acquisition results using the proposed method.  
Figure 15: (**a**) Partial enlarged view of Figure 14a; (**b**) Partial enlarged view of Figure 14b.  
proposed method successfully identifies the correct code-phase position, with a clear and distinct peak.
## 4 Experimental Validation
### Damping System Effectiveness: Dynamic Behaviour of Active Boring Bar When Control Is On
#### 4.2.1 DVF Implementation  
DVF control logic was implemented in Matlab(r) Simulink v14 and integrated using VeriStand 2014 in a National Instruments PXIe 1071 controller equipped with a BNC-2110. Velocity signal is acquired by the controller, and the actuation signal is generated by multiplying the velocity of a defined control gain. In order to protect the actuator by overvoltage (\(\pm\)100 V that corresponds to \(\pm\)3 V on the command signal), a saturation block (set at \(\pm\)2.5 V) was added before the PXI output.
# Evaluating the Quality of Light Emitted by Smartphone Displays
## 2 Materials and Methods
### The Laboratory Tests
#### 2.2.3 Spectral Properties Measurements  
The GL Optic (Puszczykowo, Poland) Spectis 4.0 device was used with a dedicated measurement probe and appropriate software for spectral measurements. It was assumed that the average distance from which a user observes the smartphone display during the day is about 30 cm, and measurements of the spectral power distribution (SPD) emitted by the screen and the correlated color temperature (CCT) were taken at this distance. The probe and smartphone were positioned opposite each other to align their centers in a straight line. The displays of the tested phones were set to the maximum brightness level for the white test screen (R = 255, G = 255, and B = 255). The SPD and CCT for each smartphone sample were measured under three different screen setting variants. The first option was the screen with no additional filters. In the second variant, the built-in blue light filter was turned on. In the third variant, one of the free, publicly available apps that reduce blue light was enabled.
## 6 Conclusions  
To improve erosion ability of the SC-CO\({}_{2}\) jet for better practical applications, effects of nozzle configuration on rock erosion were experimentally investigated with respect to the macroscopic appearances and erosion volumes of eroded specimens. The main results are described as follows:  
1. The macroscopic appearances of specimens eroded by SC-CO\({}_{2}\) jets discharging from different nozzles display similar typical \"drilling type\" damage. Small pieces of rock breakage can be observed around the erosion pits caused by the Laval nozzles, while the pits eroded by the convergent nozzle are relatively flat and smooth.
2. Compared with the convergent nozzle, the Laval nozzles can enhance the erosion ability of the SC-CO\({}_{2}\) jet more and more significantly with increasing inlet pressure; whilst at relatively lower inlet pressure of 20 MPa, they reduce the erosion capacity.
3. Under constant inlet pressure, the maximum increments of erosion capability caused by the Laval nozzles are reduced by the increasing ambient pressure. Besides, at ambient pressure of 26 MPa, the Laval nozzles turn to weaken the erosion ability. When \(\Delta P\) is constant, the enhancements caused by the Laval nozzles decrease with growing ambient pressure.
4. As the fluid temperature rises, the enhancements of erosion capacity produced by the Laval nozzles increase. Moreover, the Laval nozzles weaken the erosion intensity at a temperature of 290 K, which puts CO\({}_{2}\) in liquid state.
5. The SC-CO\({}_{2}\) jet issuing from the Laval-2 nozzle with a smooth inner profile always has a greater erosion ability than that discharging from the Laval-1 nozzle.  
This research is financially supported by the National Key Basic Research Program of China (No. 2014 CB239203), the National Natural Science Foundation of China (No. 51474158) and the Hubei Provincial Natural Science Foundation of China (No. 2016 CFA088).  
[PERSON], [PERSON], and [PERSON] conceived and designed the facilities and the experiments; [PERSON], [PERSON], [PERSON], and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data and made the analysis; [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
###### Abstract  
The identification of urban functional zones (UFZs) is crucial for urban planning and optimizing industrial layout. Fusing remote sensing images and social perception data is an effective way to identify UFZs. Previous studies on UFZs recognition often ignored band information outside the red-green-blue (RGB), especially three-dimensional (3D) urban morphology information. In addition, the probabilistic methods ignore the potential semantic information of Point of Interest (POI) data. Therefore, we propose an \"Image + Text\" multimodal data fusion framework for UFZs recognition. To effectively utilize the information of Gaofen-7(GF-7) stereo images, we designed a semi-transfer UFZs recognition model. The transferred model uses the pre-trained model to extract the deep features from RGB images, and a small self-built convolutional network is designed to extract the features from RGB bands, near-infrared (NIR) band, and normalized digital surface model (nDSM) generated by GF-7. Latent Dirichlet allocation (LDA) is employed to extract POI semantic features. The fusion features of the deep features of the GF-7 image and the semantic features of POI are fed into a classifier to identify UFZs. The experimental results show that (1) The highest overall accuracy of 88.17% and the highest kappa coefficient of 83.91% are obtained in the Beijing Fourth Ring District. (2) nDSM and NIR data improve the overall accuracy of UFZs identification. (3) POI data significantly enhance the recognition accuracy of UFZs, except for shantytowns. This UFZs identification is simple and easy to implement, which can provide a reference for related research. However, considering the availability of POI data distribution, other data with socioeconomic attributes should be considered, and other multimodal fusion strategies are worth exploring in the future.  
data fusion; GF-7 image; POI; 3D urban morphology; urban functional zones 2023
# Optimal Clustering in Stable Instances Using Combinations of Exact and Noisy Ordinal Queries
## 5 Conclusions, Extensions, and Open Questions
### Error Model  
* Our error model assumes constant (and independent) error probability across all comparisons. Other error models are also possible and they typically incorporate the \"distance values\" in the error probabilities (very different values are easier to detect correctly than very close ones). Examples of such models can be found in, e.g., [6; 9].
* Different error models may result in different (perhaps better) dislocation bounds on the approximate sorting problem (Lemma 2). This may directly improve some of our bounds, where the maximum dislocation is the bottleneck for finding the minimum (or top-\(k\)-elements) with high probability (if the maximum dislocation becomes \(D^{\prime}\ll\log n\), then we need \(O(D^{\prime})\ll\log n\) high-cost operations for the latter problem, which is used as a subroutine in most of our algorithms).
## 5 Results of Experiment 2: Precision and Alarm Rate
### Cooperative Decision Performance  
In the previous section, we showed that combining the alerts generated by various detectors can improve the detection capability. Now, we want to study examine how such combination impacts _TP_, _FP_ and precision of the detectors. The configurations used in this case for low and med. are the same as in the previous experiment, but, given that ModSecurity's PL3 and PL4 modes have been discarded in the second experiment., the max. configuration in the ensemble module will be given by the max. configuration of InspectorLog and the med. configuration of ModSecurity.  
The results obtained are shown in Table 6. The union of the alarms from different SIDS (i.e., at least one detector flags the attack) provides a fair precision and a manageable number of _FP_ with the least-sensitive (low) configuration. According to Table 4, this ensemble scheme and configuration also provides a minimally acceptable detection capability (73.5%). The intersection provides no _FP_ and a precision of 1 but, as seen earlier, does not provide an acceptable detection rate. A voting majority provides a good precision with the least sensitive configuration (0.85) but, unfortunately, this configuration provides an unacceptable detection rate according to Table 4.  
Therefore, it can be concluded that combining the output of various detectors with predetermined configurations can improve either the detection rate or the precision with respect to running a standalone SIDS. Unfortunately, both improvements seem to be mutually exclusive, so the extent of the benefit remains unclear. With respect to Nemesida, the benefit is in the detection capability (from 63.9% to 73.5%, union scheme). The question of cost-benefit trade-off also needs to be analyzed in light of the computational cost of running several SIDS, which is out of the scope of this paper.
## 4 Results
### Myocardial Scar Segmentation and Quantification  
#### 4.2.1 Ensemble Size Selection  
The idea of training an ensemble of classifiers using 7 CNNs is based on a comparative analysis conducted for different ensemble sizes. Results obtained for different ensemble models (with k = 1, 3, 5, 7, 9 components) are reported in Table 3. The coarse segmentation by itself achieved an overall Dice index of 73%. When the segmentation refinement was introduced, results improved until reaching a mean Dice index of 77.22% for the ensemble using 7 CNNs. It is noticeable that using an ensemble with more CNNs did not improve the segmentation performance. Besides, the ensemble of 7 CNNs performed closely in terms of Hausdorff distance (\(<\) 1 mm difference) to the best performing model (ensemble with 1 CNN). Consequently, after this experiment the number of CNNs was fixed to seven and from here on, all presented results are obtained under this chosen configuration.
# Can Biochar Improve the Sustainability of Animal Production?
## 6 Land Application
### Land Application of Biochar and Manure Separately  
[PERSON] et al. [123] conducted a five-crop season field test to evaluate the impacts of land application of biochar addition to cattle manure on the soil CO\({}_{2}\), CH\({}_{4}\), and N\({}_{2}\)O flux. Seasonal CO\({}_{2}\) variation was consistent between the control and biochar treatments. Overall, the differences in N\({}_{2}\)O and CH\({}_{4}\) emissions were negligible between the treatments and control. Although biochar treatments increased the crop yields in certain plots, as well as increased carbon storage, this was not a major point of interest in the study.  
Biochar did not have negative effects on the gaseous emissions; however, it did not show significant results regarding emission reduction. The comparison of these two studies raises questions about the validity of lab-scale experiments when translated into full-scale operation. While biochar has proven impacts on soil microbial communities, water-holding capacity, and aeration, actual environmental considerations, such as weather patterns and types of crops add a variety of factors to study.  
[PERSON] et al. [124] conducted a study to compare dairy effluent saturated biochar (SBC) and unsaturated wood-derived biochar (UBC) on Bermuda grass growth and soil fertility. Both treatments were applied at 1%, 2%, 4,% and 8% loading rates, and the performance was evaluated for 10 weeks in a greenhouse pot study. The SBC significantly outperformed UBC in N and P retention in the soil at 2%, 4%, and 8% loading rates. Specifically, SBC and UBC increased the total N by 36% and 20%, 74% and 34%, and 74% and 56%, at 2%, 4%, and 8% loading, respectively.  
Likewise, SBC and UBC increased the total P by 28% and 10%, 34% and 21%, and 75% and 39%, at 2%, 4%, and 8% loading, respectively. In terms of plant growth, SBC outperformed UBC in stem dry weight, root dry weight, number of leaves, and total biomass by more than double for each parameter and loading rate. Both biochar treatments showed increased populations of N-fixing bacteria due to better aeration of the biochar-amended soils. Overall, the saturation with nutrient-rich dairy effluent was absorbed by the biochar and contributed to the retention and slow release of valuable, bioavailable nutrients for plant and soil health.
# DenseZDD: A Compact and Fast Index for Families of Sets +
## 8 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Data Set**} & \multicolumn{3}{c}{**Traverse Time (s)**} & \multicolumn{3}{c}{**Search Time (s)**} \\ \cline{2-7}  & **Z** & **DZ** & **DZ\({}_{dc}\)** & **Z** & **DZ** & **DZ\({}_{dc}\)** \\ \hline rect1x10000 & 0.000 & 0.002 & 0.002 & 4.563 & 0.012 & 0.014 \\ rect5x2000 & 0.000 & 0.002 & 0.002 & 2.082 & 0.014 & 0.015 \\ rect100x100 & 0.000 & 0.001 & 0.002 & 0.092 & 0.009 & 0.011 \\ rect2000x5 & 0.001 & 0.002 & 0.003 & 0.006 & 0.009 & 0.021 \\ rect10000x1 & 0.001 & 0.003 & 0.015 & 0.002 & 0.009 & 0.070 \\ randomjoin256 & 0.001 & 0.004 & 0.005 & 0.470 & 0.013 & 0.013 \\ randomjoin2048 & 0.021 & 0.057 & 0.065 & 3.772 & 0.014 & 0.015 \\ randomjoin8192 & 0.088 & 0.176 & 0.201 & 14.568 & 0.019 & 0.020 \\ randomjoin16384 & 0.144 & 0.269 & 0.306 & 25.244 & 0.016 & 0.016 \\ bddqueen13 & 0.013 & 0.054 & 0.237 & 0.014 & 0.005 & 0.007 \\ bddqueen14 & 0.068 & 0.259 & 0.998 & 0.015 & 0.005 & 0.006 \\ bddqueen15 & 0.420 & 1.421 & 4.778 & 0.016 & 0.005 & 0.006 \\ T40I10D100K:0.001 & 0.054 & 0.222 & 0.298 & 0.003 & 0.002 & 0.003 \\ T40I10D100K:0.0005 & 0.314 & 1.210 & 1.606 & 0.003 & 0.002 & 0.002 \\ T40I10D100K:0.0001 & 11.615 & 42.730 & 55.085 & 0.004 & 0.001 & 0.002 \\ accidents:0.1 & 0.002 & 0.007 & 0.028 & 0.003 & 0.000 & 0.000 \\ accidents:0.05 & 0.011 & 0.038 & 0.150 & 0.003 & 0.000 & 0.000 \\ accidents:0.01 & 0.369 & 1.165 & 4.507 & 0.003 & 0.000 & 0.000 \\ chess:0.1 & 0.075 & 0.251 & 1.000 & 0.003 & 0.000 & 0.000 \\ chess:0.05 & 0.218 & 0.707 & 2.640 & 0.003 & 0.000 & 0.000 \\ chess:0.01 & 0.394 & 1.276 & 3.911 & 0.003 & 0.000 & 0.000 \\ connect:0.05 & 0.022 & 0.069 & 0.169 & 0.003 & 0.000 & 0.000 \\ connect:0.01 & 0.169 & 0.492 & 1.219 & 0.003 & 0.000 & 0.000 \\ connect:0.005 & 0.316 & 0.906 & 2.250 & 0.003 & 0.000 & 0.000 \\
16-adder\_col & 0.090 & 0.340 & 2.054 & 0.053 & 0.002 & 0.018 \\ C1908 & 0.007 & 0.030 & 0.174 & 0.169 & 0.221 & 1.851 \\ C3540 & 0.085 & 0.358 & 2.162 & 0.072 & 0.123 & 1.017 \\ C499 & 0.007 & 0.031 & 0.171 & 0.101 & 0.145 & 0.
# Optimization of Design Parameters in LSTM Model for Predictive Maintenance
## 2 Literature Survey
### Various Approaches for Predictive Maintenance  
#### 2.1.1 Physics Model-Based Approaches  
This category expresses the state of machinery by using mathematical models related to physics, which consist of components defined by characteristics of the material or level of stress and strain. For instance, [PERSON] et al. [4] addressed predictive maintenance of turbine blades by using [PERSON]'s creep model that describes the deformation of material caused by time and temperature, and a Kalman filter for parameter estimation of the model. Paris' law that denotes the growth of cracks in machinery proportional to the stress intensity factor is also widely used to predict machine failure [5]. [PERSON] et al. [6] used [PERSON] model as well as a conventional damage model for fatigue life assessment of metallic material. Additionally, [PERSON] and [PERSON] [7] applied finite element analysis with crack growth represented by Paris' law for the maintenance of aerospace panels. These kinds of approaches for predictive maintenance can achieve high accuracy and fitness, assuming that the user has domain knowledge of physical mechanisms, including fatigue or failure, in order to set proper parameters. In the real world, however, machinery is usually composed of a combination of various mechanical components, which makes it hard to understand the whole system clearly.
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 3 Project Overview and Objectives
### Business and Technical Problem Statement  
Modern software projects face significant challenges in managing cloud infrastructure at scale. Manual provisioning of resources introduces the risk of human error, environmental drift, and inconsistent configurations across development, testing, and production environments. Without automation, deploying infrastructure changes becomes time-consuming, error-prone, and difficult to audit. These challenges are further amplified in teams practicing agile development, where frequent iteration demands infrastructure that is as adaptable and reliable as the application code itself.  
The technical problem addressed by this project is the lack of a standardized, automated, and secure mechanism to deploy the necessary Azure infrastructure for an application. In many organizations, infrastructure definitions are scattered across documents or scripts that lack version control, making them hard to maintain and impossible to reuse. When multiple teams or developers work on the same system, this fragmentation leads to misconfigurations, deployment delays, and increased costs due to resource sprawl or incorrect sizing.  
From a business perspective, these technical issues result in higher operational costs, longer delivery cycles, and an increased risk of service outages. Regulatory requirements may further demand auditable, repeatable deployment processes that many organizations cannot meet without adopting infrastructure as code. For companies aiming to scale their operations or rapidly deliver new features, these inefficiencies become critical blockers.  
The project specifically seeks to solve these problems by defining the entire Azure infrastructure required for the application in Bicep modules stored in a GitHub repository. These modules will be version-controlled, reusable, and easy to audit. Deployment  
Figure 1: GitHub repository structure.  
will be triggered automatically through GitHub Actions workflows, ensuring consistent environments for development, quality assurance, and production [19].