esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing the Homogeneity of Forage Mixtures Using an RGB Camera as Exemplified by Cattle Ratios
## 2 Materials and Methods
### Use of Fluorescence and RGB Camera to Detect Mixing Quality of Feed Mix Components  
As the hyperspectral camera is an expensive device, it is not practical to equip machines for detecting mixing quality. This option would have a significant impact on the cost of production.  
The proposal was to test the use of the fluorescence phenomenon. Due to the different absorption capacity values of the feed mix components, the reflective effect would also be significantly different.  
Therefore, the second part of the study aimed at testing the possibility of using inexpensive devices such as an RGB camera and a set of filters to increase the contrast of the obtained image and assess the effectiveness of preparing the feed mix by counting pixels.  
To ensure convenient image acquisition, we assembled a frame made of aluminum profiles and capable of changing the focal distance. To provide the required spectral range, LED illumination with a blue light filter and a Basler ace RGB camera-acA5472-17 uc (Basler AG, Ahrensburg, Germany) with a yellow light filter were used:  
* Sensor IMX183;
* Shutter Rolling Shutter;  
Figure 1: Photo taken during the experiment (**left**), functional diagram of the laboratory installation (**right**): 1—illuminator, 2—hyperspectral camera, 3—sample under study, 4—calibration pad, 5—computer for image processing.  
- Sensor Size 13.1 mm \(\times\) 8.8 mm;
- Resolution (H \(\times\) V) 5472 px \(\times\) 3648 px;
- Resolution 20 MP;
- Pixel Size (H \(\times\) V) 2.4 \(\upmu\)m \(\times\) 2.4 \(\upmu\)m.  
We assembled a more compact bench to check the possibility of detecting the homogeneity of feed mixes under real conditions using similar filters (Figure 2). Here an RGB camera replaced a smartphone to ensure video streaming to a personal computer, where MATLAB algorithms processed the image.  
For the laboratory experiment, we assembled a bench that included an LED illuminator with a light filter to excite fluorescence and improve the contrast of the feed mix by increasing the reflectivity, a camera with a lens and a filter mounted on it, a computer to process the information, and a TMR container.  
The bench shown, was fixed to the feed car mixer side, where video images were recorded. Then the broadcasted video image was processed using MATLAB software with the Image Processing Toolbox extension algorithms, namely, the Color Thresholder app and Image Region Analyzer app.  
Color Thresholder app can select areas in an image based on color characteristics of pixels. The app helps create a binary segmentation mask for a color image. Color Thresholder supports four image representation spaces: RGB, HSV, YCbCr.  
Image Region Analyzer app identifies regions in a binary image and determines their characteristics. The output is presented in a table form, which is suitable for quickly sorting and filtering the areas for further analysis.
## 4 Results and Discussions
### The Effect of Void Fraction  
Figure 12 shows the isothermals (on the left) and streamlines (on the right) for \(Ra=10^{5}\), \(Kr=10\), \(\phi=0.6\), \(Nr=10\), \(G=1.2\) and \(Pr=0.71\), with \(\phi=0.1\), \(0.3\), \(0.5\), \(0.7\) and \(0.9\). The isothermals for all the cases are similarly distributed but different in the fine details. From  
Figure 11: Variations of the average Nusselt number as a function of \(Nr\).  
\(\phi=0.1\) to \(\phi=0.9\), the isothermals in the greatest part of the cavity are almost horizontally distributed and the levelness of the isothermals becomes higher with increasing \(\phi\), which indicates that air flow is strengthened. In addition, the isothermals near the interface become more intense. The streamlines turn out to be more chaotic with increasing \(\phi\), and the maximum velocity increases gradually. For \(\phi=0.1\), one vortex in the middle is formed, and for \(\phi=0.3\), \(0.5\), \(0.7\), \(0.9\), two vortexes are formed due to the strengthened air flow. This indicates that the natural convection is strengthened by an increase in \(\phi\).  
Figure 13 shows the evolution of the average convective, radiative and total Nusselt numbers of the hot interface as a function of \(\phi\). It can be seen that \(\overline{Nu}_{\rm conv}\) and \(\overline{Nu}_{\rm rad}\) increases with an increase in \(\phi\). This demonstrates that the increasing \(\phi\) have a positive effect on both natural convection and surface radiation. The reason for this is that increasing \(\phi\) leads to a large temperature difference in the cavity and changes the characteristic length. Both natural convection and surface radiation are driven by temperature difference and characteristic length. \(\overline{Nu}_{\rm t}\) increases with increasing \(\phi\) as a result of \(\overline{Nu}_{\rm conv}\) and \(\overline{Nu}_{\rm rad}\), which represents that the total heat transfer is strengthened in the cavity.  
The void fraction is a significant parameter that affects the thermal protection performance of the intumescent coatings. With increasing \(\phi\), thermal conduction is weakened due to low thermal conductivity, whereas natural convection and surface radiation are strengthened. As a consequence, an optimal \(\phi\) exists to guarantee the best thermal protection performance. This is valuable for materials scientists to design better intumescent coatings.  
Figure 12: _Cont._  
Figure 12: Streamlines and isothermals obtained for (**a**) \(\varphi=0.1\), \(W_{\max}=59.963\), (**b**) \(\varphi=0.3\), \(W_{\max}=63.882\), (**c**) \(\varphi=0.5\), \(W_{\max}=66.759\), (**d**) \(\varphi=0.7\), \(W_{\max}=70.289\) and (**e**) \(\varphi=0.9\), \(W_{\max}=74.105\).
## 2 Data Collection  
The 2015 Mw 7.8 Gorkha, Nepal, earthquake took over 9000 lives and injured more than 23,000 people. According to the U.S. Geological Survey (USGS) ([[http://earthquake.usgs.gov](http://earthquake.usgs.gov)]([http://earthquake.usgs.gov](http://earthquake.usgs.gov))), the origin time of this event was 06:11:26 (UTC), on 25 April 2015; its epicenter was located at 28.147\({}^{\circ}\) N, 84.708\({}^{\circ}\) E; and its hypocentral depth was 15 km. It was the worst natural disaster to strike Nepal since the 1934 Nepal-Bihar earthquake. Continued aftershocks occurred throughout Nepal at the intervals of 15-20 min. The country also suffered from continuous risk of landslides. The temblor was caused by a sudden thrust, or release of built-up stress, along the major fault line where the Indian plate, carrying India, is slowly diving underneath the Eurasian plate, carrying much of Europe and Asia. Kathmandu, situated on a block of crust approximately 120 km wide and 60 km long, reportedly shifted 3 m to the south in a matter of just 30 s.  
The BDS and GPS data were collected and processed to validate the benefit of high-rate satellite clocks for BDS PPP to the measurements of seismic displacements of this earthquake event.
# Radiofrequency Ablation of Painful Spinal Metastasis: A Systematic Review
## 1 Introduction  
Spinal metastases represent a significant global health burden, affecting up to a third of cancer patients and causing severe pain and neurological complications [1]. While systemic therapies improve survival, pain management, neurological function preservation, and spinal stability remain crucial palliative goals [2]. Spinal metastases can lead to a variety of debilitating complications, including cord compression, pathological fractures, and radioculopathy, significantly impacting patients' quality of life. Managing these complications often requires a complex, multidisciplinary approach involving oncologists, neurosurgeons, radiation oncologists, pain specialists, and rehabilitation professionals. Timely intervention is crucial to prevent irreversible neurological damage and improve patient outcomes [3].  
Traditional management strategies, such as surgery and radiotherapy, while effective in certain cases, are associated with substantial morbidity, including infection, bleeding, and neurological deficits. These complications can significantly impact patients' functional status and quality of life, often requiring prolonged hospital stays and rehabilitation.  
Radiofrequency ablation (RFA) has emerged as a minimally invasive complementary technique to surgery and radiotherapy, as outlined by CIRSE guidelines [4], which offers a less invasive approach for the management of spinal metastases that can potentially reduce the need for more invasive procedures and their associated complications.  
While individual studies have explored RFA, the existing literature still lacks a comprehensive, up-to-date synthesis that consolidates findings on its overall analgesic effectiveness and safety profile, particularly concerning short- and mid-term outcomes across diverse patient populations and tumor types.  
Therefore, the object of this study was to systematically evaluate the effectiveness and safety of radiofrequency ablation (RFA) for managing pain caused by spinal metastases.  
This review aimed to consolidate evidence on RFA's analgesic efficacy and associated risks to inform clinical practice in palliative cancer care, addressing the aforementioned gap by providing a robust overview of the current evidence.
# ISAR Imaging Analysis of Complex Aerial Targets Based on Deep Learning
###### Abstract  
Traditional range-instantaneous Doppler (RID) methods for maneuvering target imaging are hindered by issues related to low resolution and inadequate noise suppression. To address this, we propose a novel ISAR imaging method enhanced by deep learning, which incorporates the fundamental architecture of CapsNet along with two additional convolutional layers. Pre-training is conducted through the deep learning network to establish the mapping function for reference. Subsequently, the trained network is integrated into the electromagnetic simulation software, Feko 2019, utilizing a combination of geometric forms such as corner reflectors and Luneberg spheres for analysis. The results indicate that the derived ISAR imaging effectively identifies the ISAR program associated with complex aerial targets. A thorough analysis of the imaging results further corroborates the effectiveness and superiority of this approach. Both simulation and empirical data demonstrate that this method significantly enhances imaging resolution and noise suppression.  
deep learning; inverse synthetic aperture radar (ISAR); CapsNet; AKConv; GSConv
## 3 Verification and Validation
Figure 5a compares the mean pressure coefficients along the roof centerline, as obtained with four subgrid-scale models. The experimental data are from the literature [19], and these subgrid-scale models include the Smagorinsky-Lilly (SL) model, kinetic energy transport (KET) model, wall-adapting local eddy-viscosity (WALE) model, and wall-modeled LES (WMLES) model. The differences among the computational results of the four subgrid-scale models are small, and the computational results are in good agreement with the experimental data. This indicates that all subgrid-scale models can obtain accurate roof pressures. Figure 5b compares the mean pressure coefficients along the roof centerline, as obtained by the application of five discretization schemes to the diffusion and convection terms. The bounded central differencing scheme exhibits the highest level of performance among all discretization schemes, since the result obtained with the bounded central differencing scheme is the closest to the experimental data from the literature [19]. To validate the numerical method, the mean pressure coefficient change rates from the present simulation under varying wind speeds are compared with the experimental results [19]. The mean pressure coefficient change rate \(\eta_{p,mean}\) along the roof centerline for all cases is shown in Figure 6. When the wind speed is small, there is an obvious difference between the experimental data and the simulation results at some data points. The reason for this difference may be that the heating effect and chemical reaction of the active actuators are not considered in the present simulation. In summary, the numerical results agree with the experimental data, indicating that the numerical method adopted by this paper is reasonable and reliable. The wind speed of 7 m/s is selected as the object of subsequent researches. It can also be noted that the control effect of the plasma actuators continues to decrease as the wind speed increases. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Grid No. **} & \begin{tabular}{c} **Mesh Numbers** \\ **(Million)** \\ \end{tabular}  & \multirow{2}{*}{\(\Delta\)_z_/_h_} & \multirow{2}{*}{\(\Delta\)_y_/_h_} & \multirow{2}{*}{**Mean \(C_{d}\)**} & \multirow{2}{*}{**Mean \(C_{l}\)**} & \multirow{2}{*}{**RMSE \(C_{d}\)**} & \multirow{2}{*}{**RMSE \(C_{l}\)**} & \multirow{2}{*}{\(S_{t}\)**} \\ \hline Grid-1 & 2.78 & 0.0006 & 0.02 & –2.19
# Hard SyDR: A Benchmarking Environment for Global Navigation Satellite System Algorithms +
## 3 Hard SyDR  
The _Hard SyDR_[24] project is based on the _SyDR_[20] and _C SyDR_[23] projects (outlined in Figure 3), sharing their goals of implementing a modular SDR for benchmarking GNSS algorithms. While trying to minimize redundant developments, our goal is to mix programming languages to have the best of both worlds. For instance, while Python is known to have high overheads stemming from being interpreted rather than compiled and executed [21], its versatility and native support for numerous open-source libraries make it attractive for the first stages of algorithm development and file I/O and analytics (e.g.,  
Figure 3: Conversion workflow for SyDR from pure Python to partial hardware implementation enabled by PYNQ and HLS.  
databases, plotting). Achieving similar functionality in lower-level languages like C/C++, or at the hardware level, is much more challenging.  
With this in mind, we retain a large part of the tool in Python to deal with most of its user, file, and analysis features and port only the functions relevant for benchmarking outside of Python. As outlined in Section 2.1, these functions are encapsulated within channels. Yet, while C SyDR retains the control of these structures in Python, interfacing compiled C/C++ code with ctypes, this style of processing would require too many PS-to-PL data transfers in Hard SyDR. Instead, we port this control to the FPGA and enclose it in a new _\"channel manager\"_ module, which we describe in more detail in the following.
## 4 Augmented Reality Devices
### AR Contact Lenses  
AR contact lenses are a vision for the future of wearable technology. They would function by superimposing digital images or information onto the wearer's field of vision directly on the lens. This technology is still in its early stages of development and presents a number of technological and physiological challenges that need to be addressed before it becomes commercially viable [59]. Technically, AR contact lenses would need to have the ability to project digital images onto the retina, track eye movement, adjust optics for variations in distance, and potentially even monitor health indicators. They would need to be powered in a way that is safe and doesn't interfere with vision, and the technology would need to be miniaturized enough to fit within a contact lens [60].  
There have been several attempts to develop AR contact lenses [60]:  
* **Google's Smart Contact Lens Project**: In 2014, Google announced a project aimed at creating a contact lens with an integrated glucose sensor for people with diabetes. While not an AR lens, this project demonstrated the potential for integrating technology into a contact lens.
* **Innovega's iOptik system**: This system uses contact lenses to create a much larger field of view for AR and VR applications than would be possible with glasses or headsets alone.
* **Mojo Vision's Mojo Lens**: This company is developing a contact lens with an integrated display that can provide information like navigation instructions or health data.  
It's important to note that any AR contact lens will have to meet stringent safety and comfort standards before it can be brought to market. This technology is still in its early stages, but if these hurdles can be overcome, AR contact lenses could provide a seamless and highly integrated form of AR [59].  
As with the types of AR, something similar occurs with the devices, that is, the sectors in which each of the different types of devices can intervene are various, and applications from the same sector can be implemented with various types of AR devices. In this sense, the next section presents how AR technologies (algorithms and devices) have influenced the development of different domain areas in recent years.
## 4 Results: Analysis of the Integration of VR, AR, and MR in the Design Process
### Sensitivity Analysis  
To verify the robustness of the results obtained, a sensitivity analysis was conducted comparing the effectiveness, efficiency, and satisfaction scores between the original scenario (S1) and two alternative weighting configurations: uniform weighting, assigning equal weight to all indicators (S2), and inverse weighting, assigning higher weights to less dominant or more interpretive indicators (S3). For each scenario, the usability results were recalculated. Figure 11 shows the progression of effectiveness, efficiency, and satisfaction values for the seven Design Units analysed under the three scenarios, S1, S2, and S3; all graphs show consistency in the overall trend, with isolated low-magnitude differences between scenarios. Although there are slight variations in absolute values, particularly in intermediate Design Units, the curves maintain a very similar shape, indicating that the behaviour of the indicators is not significantly affected by the changes in applied weights.  
This conclusion is supported by the numerical results obtained through the calculation of the Root Mean Square Error (RMSE) and the Relative Standard Deviation (%RSD) metrics. The results are presented in Table 7:  
Figure 11: Comparison of usability results across the three weighting scenarios S1, S2, and S3: (**a**) effectiveness; (**b**) efficiency; (**c**) satisfaction (X-axis: Design Units; Y-axis: dimension results).  
The %RSD and RMSE results obtained under the different weighting scenarios (S1, S2, and S3) do not show significant deviations. Although a degree of sensitivity is expected when modifying the values, the %RSD results remain below the 25% threshold, which is considered acceptable when human judgement or subjective metrics are involved. This indicates an acceptable level of variability across the evaluated scenarios.  
These results support the robustness of the proposed indicator system under different weighting schemes. They also confirm that the methodology used to calculate usability exhibits a high degree of stability when the weights assigned to each indicator are modified; that is, the conclusions of the study are not critically dependent on any single weighting configuration.
# Autonomous Active Tag Using Energy Harvesting Strategies
## 2 Materials and Methods
### Processing Module
#### 2.1.2 Transceiver  
We select the S2 LP for its low power consumption and its radio configuration parameters. This transceiver works in the following bands: 413-479 MHz, 452-527 MHz, 826-958 MHz, and 904-1055 MHz. The band selected is the 868 MHz frequency, as we have explained in the previous section, if we need to communicate with another tag or a tag reader. The distance achieved is closely related to the adaptation stage. The best performance is traduced in a tradeoff between communication range and binary rate.  
The selected transceiver has a low power consumption mode called the low duty-cycle mode that allows the average power consumption to be reduced during receive operations. This mode builds a synchronized network where both transmitter and receiver can periodically switch to low power mode to reduce the average power consumption. When the transceiver is in reception mode (RX), it waits for specific bytes of the packet heading to continue reading the packet. After that, if a transmission is required, transceiver changes to transmission mode (TX). The configuration of this duty-cycling low power mode will depend on the final application of the device.  
This transceiver has a data rate configuration by default of 38.4 Kbps and a maximum package size of 128 Bytes in our tag for our application. The 2-FSK (Frequency-Shift Keying) is selected because the data that we expect to arrive, which is explained in the description of the reception scenario in Section 2.4.2, can be coded in this way. However, this modulation can be changed if the application requires it.  
The transceiver is connected to the microcontroller through their SPI buses. Thanks to that, we can configure the transceiver with the characteristics that we prefer. Other features selected for our application and which are very influential in the operation of the transceiver are:  
* the power of the output signal: this determines the distance at which the signal is detected. We have chosen 10 dBm because this is the configuration that gives us the greatest communication range;
* the sensitivity for detecting outward signals: in our case, this is around \(-\)109 dBm.  
The antenna selected is external and its central frequency is 868 MHz. The connector is SMA (SubMiniature A) and is welded to the board by one of the edges.
# Dynamic Group Management Scheme for Sustainable and Secure Information Sensing in IoT
## 4 Security and Performance Analysis
### Security Analysis
#### 4.1.3. Error Detection and Forward Security  
The base station, cluster head, and sensor node generate 4\(n\) bits stream and derivate challenge/response values. As a result, the base station, cluster head, and sensor node can validate the location where the ad value is transferred by the lr value during the distance bounding process. That is, the CH, BS, and sensor node can detect errors in the challenge/response process. If the CH, BS, and sensor node use the secret shared substitution box and if the seed value of the filter function is right, error detection and forward security are worked.
# A Diagnosability-Integrated Design Approach Based on Graph Theory
## 3 Diagnosability Integrated Design Method Details
### Diagnosis Method Determination
#### 3.2.2 Feature Cacullation and Feature Selection  
Section 3.2.1 sets the cascade classifier on maximal clique, but the features of the signal need to be extracted and selected before the classification, assuming that the set of features of the signal is \(\mathcal{J}=\{I_{1},J_{2},\ldots,J_{d}\}\), where \(d\) denotes the number of features. The signal features here can use a variety of signal features such as time domain signal characteristics, frequency domain signal characteristics, and time-frequency domain signal characteristics.  
In the feature selection session, the signal features that are beneficial for fault diagnosis are selected based on the Relief_F algorithm. The specific algorithm for feature selection is shown in Algorithm 4.  
```
Input: feature set \(\mathcal{J}=\{I_{1},I_{2},\cdots,I_{d}\}\)  samples \(\{x_{p},y_{p}\}\), \(p=1,\cdots,T,y_{p}\in\mathcal{F}\) Output: \(\mathcal{C}^{*}\)
01. initialize:\(W\leftarrow\varnothing\)
02.for\(i=1\) to \(T\)do
03. select sample \(x_{i}\)
04.for\(j=1\) to \(d\)do
05. find \(k\) neighbors of the same class for \(x_{i}(nearhits)\)
06.for every class \(c\
eq class(x_{i})\)
07. find \(k\) neighbors of different classes of \(x_{i}(nearhisses)\)
08.endfor
09.\(W(j)=W(j)-\frac{\sum\limits_{i=1}^{k}diff(x,nearhits)}{T\times k}+\frac{\sum \limits_{c\in class(x_{i})}\frac{\mu(c)}{1-\mu(c)}\sum\limits_{i=1}^{k}diff(x,nearh its(c))}{T\times k}\)
10.endfor
11.endfor
12.\(sum\gets 0\)
13.\(\mathcal{J}^{*}\leftarrow\varnothing\)
14.while\(sum<0.5\)do
15.\(i=\operatorname*{argmax}(W)\)
16.\(sum\gets sum+\max(W)\)
17.\(\mathcal{J}\leftarrow\mathcal{J}\cup\{f_{i}\}\)
18.\(W\gets W-W(i)\)
19.endwhile
20.Return\(\mathcal{J}^{*}\)
```  
**Algorithm 4** Signal feature screening algorithm based on Relief_F