esa-sceva/satcom-chunk-collection huggingface dataset:
# A Feasibility Study for a Hand-Held Acoustic Imaging Camera
## 8 Hardware Development Considerations  
Constructing optimized microphone arrays to realize portable acoustic cameras presents additional implementation challenges including:  
* Fabricating irregular array geometries with a large number of elements;
* Microphone calibration and mismatch compensation;
* Embedded platform with multichannel digitization and processing;
* Robust beamforming algorithms executable in real time;
* Packaging, power, and interfacing for field deployment.  
The printed circuit board population provides a potential fabrication approach for unconventional layouts. The layout can be rendered as copper traces linking microphone footprints. Micro-electromechanical system (MEMS) technology enables compact sen  
Figure 24: _Dual Cam 2.0_ cted beam pattern power at different frequencies: (**a**) 500 Hz, (**b**) \(\approx\)2.5 kHz, (**c**) \(\approx\)6 kHz.  
Figure 25: _Dual Cam 2.0_ with larger audio bandwidth [0.5, 6.4] kHz: effect of the FIR length on the metrics of the evaluation. (**a**) Directivity with K = 7 (red) vs K = 21 (blue); (**b**) WNG with K = 7 (red) vs. K = 21 (blue).  
sors [46]. Calibration tools measure each microphone response to derive compensation filters. FPGAs offer parallelism for multichannel acquisition and beamforming [47; 48]. Robust and adaptive algorithms help counteract model errors. Energy-efficient architectures would enable battery-powered operation. A USB- or WiFi-linked interface with a smartphone or tablet app could provide deployment flexibility. Ongoing research is focused on addressing these areas to translate the simulated performance gains into practical miniature acoustic cameras for expanded applications. In addition to hardware, robust calibration procedures and beamforming software refinement would help approximate idealized models. User studies assessing video augmented with acoustic imaging for tasks like machine diagnostics can quantify real-world benefits. Developing the signal processing, microphone technologies, and system integration techniques to realize compact acoustic cameras would represent a breakthrough for broader adoption in noise monitoring, condition-based maintenance, virtual reality, and other fields constrained by large form factors today.
# On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
## 5 Simulation Results  
This section shows numerical results using realistic parameters. We will first describe system setup, and then show results to corroborate the above theoretical analysis.
## 3 Results and Discussion
### Phenolic Compounds at the End of Gastric and Intestinal Phases of Digestion  
Figure 1A,B shows the amounts of total phenolic compounds and phenolic subgroups released at the end of gastric and intestinal phases of digestion. They were compared to the amounts before the digestion to see possible differences. At the end of the gastric phase, amounts of total released phenolics from peel and flesh were significantly lower than before the digestion (\(p<0.05\)) and additionally decreased in the intestinal digestion, but statistically significant only for the flesh (\(p<0.05\)). The same could be seen for phenolic subgroups. Total anthocyanins, flavan-3-ols, dihydrochalcones, phenolic acids, and flavonoids were released in the gastric phase in significantly lower amounts than before the digestion (\(p<0.05\)). An exception was flavon-3-ols from apple flesh, whose amounts in natural apples were similar to the amounts after gastric digestion. Anthocyanins, flavan-3-ols, and dihydrochalcones were not identified at the end of the intestinal phase. However, phenolic acids and flavonoids showed stability, and the amount of flavonoids even increased during intestinal digestion (significant in the apple flesh (\(p<0.05\))).  
Figure 1C,D shows the percentage recovery of phenolic compounds. Only 35% and 67% of total phenolics were recovered during the gastric phase (peel and flesh, respectively), and that percentage decreased to 26% and 27% in the intestine (respectively). Recoveries of different subgroups were 21% to 52% (peel) and 36% to 109% (flesh) in the gastric phase. In the intestinal phase, the recovery of phenolic acids was 21% and 31% (peel and flesh, respectively), and that of flavonoids 59% and 99% (peel and flesh, respectively).  
Figure 1: Phenolic subgroups and total phenolic compounds from apples before and at the end of two digestion phases (simulated gastric and intestinal digestion phases). Amounts (mg kg\({}^{-1}\) FW) released from (**A**) peel and (**B**) flesh; percentages recovered from (**C**) peel and (**D**) flesh. Different letters above columns represent differences according to post-hoc Tukey test (\(p<0.05\)).  
In general, phenolic compounds are released in the gastric phase but in lower amounts than naturally present in apples, and their amounts additionally decrease in the intestine. Flavonols might be an exception since their amount increased from the gastric to the end of the intestinal digestion.
###### Abstract  
We performed Computational Fluid Dynamics (CFD) modeling, and simulated a people evacuation process from a tunnel in the event of a fire, for evaluating the potentialities of using, as a temporary safety measure, an emergency vehicle equipped with a micronized water system for contrasting the fire growth phase. The structure investigated is a one-way road tunnel with only natural ventilation, and with a length less than 1000 m. The tunnel is assumed at present to be affected by refurbishment works for making it comply with the minimum safety requirements of the European Directive 2004/54/EC. In particular, it is considered that it has not yet been provided with hydrants, and with the sidewalks and the emergency exit which are still under construction. This means that users are forced to use the road carriageway for escaping from the tunnel if a fire occurs. The CFD findings have shown that the use of the micronized water system might lead to a significant improvement in the environmental conditions along the escape route since the tenability limits of temperature, radiant heat flux, CO and CO\({}_{2}\) concentration were found to be better satisfied. Additionally, the visibility distance was shown to increase, even though it was found to be higher than the acceptable threshold value only in a few cases. However, the quantitative risk analysis based on a probabilistic approach, which was combined with a method currently used in Europe for assessing the risk due to the transit of only dangerous goods, shows that the final cumulative F-N curves related to the micronized water system always lie below those without the mentioned system, and in addition, they are always contained within the limits of the ALARP region. It is to be stressed that our paper might represent a reference in showing the effectiveness of the micronized water system as a temporary safety measure. However, it is desirable that the Tunnel Management Agencies accelerate the refurbishment works for making road tunnels definitively safer for users in a short period of time.  
computational fluid dynamics; temporary safety measures; emergency vehicles; micronized water system; road tunnels; quantitative risk analysis; dangerous goods vehicles; F-N curves +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Data Sources and Preprocessing  
#### 2.2.1 Night-Time Light Data  
We used night-time light data from the Visible Infrared Imaging Radiometer Suite (VIRS) onboard the United States National Polar-orbiting Partnership Satellite (NPP/VIIRS), provided by the Earth Observation Group (EOG). These data have a spatial resolution of approximately 500 m. We selected the February 2024 VIIRS_vcmslcfg data, which have been corrected for scattered light to enhance the dynamic range. This correction helps to better capture the variations in night-time light intensity, which is crucial for our analysis of urban areas. The data can be accessed at [[https://eogdata.mines.edu/products/vnl/](https://eogdata.mines.edu/products/vnl/)]([https://eogdata.mines.edu/products/vnl/](https://eogdata.mines.edu/products/vnl/)) (10 June 2024).
## 4 Experiments and Results  
To optimize the training and evaluation of the model, three distinct configurations were employed:  
1. Generalist model (GM): This model is designed to predict systolic and diastolic values using data not encountered during the training phase.
2. Specialist model with retraining (SMR): Built upon the generalist model, this approach involves retraining on a subset of data from a specific user for evaluation during the test phase.
3. Specialist model without retraining (SM): This model starts from a random point in its weights, without leveraging pre-trained weights.  
For the GM approach, data partitioning consisted of 1,000,000 samples divided into 7 s segments extracted from 977 individuals for training, from 326 individuals for validation, and from another 326 individuals for testing.  
In the specialist model training scenario, holdout validation was employed, with 80% of data from an individual being used in the training phase, 10% for validation, and the remaining 10% for testing. This model type is justified by the unique frequency of the biological signal emitted by each individual, making it challenging for a model to distinguish among hypertension categories in different individuals.  
By using the same testing environments, the proposed model, NeuBP, was compared to the Spectro-Temporal model as proposed by [PERSON] et al. [19].
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 7 Open Challenges
### Variability in Collected Data  
The first step in any data-centric IoMT analysis involves collecting a vast volume of data, which greatly impacts subsequent stages such as network access, storage, energy consumption, and system architecture. Various sensors generate different formats of data; for example, light or temperature sensors produce numerical data, while proximity sensors generate categorical data. Smart cameras produce video along with audio, while sensors such as ECG and EMG generate time series data. This variability in data necessitates different data processing methods to handle the data effectively.
## 3 Results and Discussions  
### Examples of Fabricated Membranes  
Examples of selected cast PES membranes with 1 h, 4 h, 8 h, and 24 h solvent evaporation times are shown in Figure 3a. These time points were chosen because salinity reduction performance of PES membranes significantly decreases beyond 8 h, making longer evaporation durations less practical. As shown in the figure, the PES membrane with 1 h solvent evaporation time has no transparency, but with the increase of solvent evaporation time, the PES becomes more translucent. When the time reaches 24 h, the PES becomes transparent. For sPES membranes, examples with 0.5 h, 8 h, 16 h, and 24 h evaporation times are shown in Figure 3b, reflecting their extended performance up to 20 h, with an even distribution of selected times for comparison. From the figure, 0.5 h sPES has no transparency. With the increased solvent evaporation time, the sPES membranes become more translucent. When the time reaches 24 h, the sPES membranes become transparent.  
Both membranes exhibited a similar trend of becoming more translucent with the increase of the solvent evaporation time but with a slightly different timeline. As time increased within 24 h, the sPES membranes progressively became more translucent. The  
Figure 3: (**a**) PES membrane examples with 1 h, 4 h, 8 h, and 24 h solvent evaporation time. (**b**) sPES membrane examples with 0.5 h, 8 h, 16 h, and 24 h solvent evaporation time.  
differences in the intermediate transparency stages between PES and sPES membranes might be attributed to the distinct chemical and physical properties of the base polymers, that after sulfonation, the sPES membranes may have a more open or porous structure facilitating faster solvent evaporation. However, transparency is an indication of excessive density, which while enhancing mechanical stability, inhibits ion exchange and diminishes salinity reduction performance, making such membranes unsuitable for electrodialysis applications.
# Assessing Browser Security: A Detailed Study Based on CVE Metrics
## 1 Introduction  
Web browsers, used by more than 5.35 billion internet users [1], are essential tools for accessing information through websites and facilitating communication on social media platforms [2]. They play a crucial role in modern digital life, enabling activities such as online banking, shopping, and accessing cloud-based services [3]. However, their extensive use and the sensitive information they handle make them prime targets for cybercriminals [4, 5].  
Over the past decade, browser-related security incidents have shown a concerning upward trend in both frequency and severity [6]. For instance, in 2021, Google Chrome faced CVE-2021-4102 [7], a critical zero-day vulnerability actively exploited in the wild, prompting emergency updates. In 2024, the type confusion vulnerability CVE-2024-1238 [8] once again forced Google to patch billions of devices within days to prevent widespread exploitation. Similar threats have impacted other major browsers. For example, Mozilla Firefox experienced CVE-2022-26485 [9], a use-after-free vulnerability enabling remote code execution, which was also exploited before its discovery. In 2024, Mozilla patched critical zero-day vulnerability CVE-2024-9680 [10] in its Firefox browser, which was actively exploited in the wild. These incidents underscore how rapidly evolving attack techniques, particularly zero-day exploits and targeted malware injections, continue to compromise sensitive data [11].  
Understanding these risks is crucial, as web browsers are integral to both personal and professional online activities [12]. With billions of users relying on these browsers daily, even minor vulnerabilities can result in widespread consequences [13]. This study contributes to the field by providing a comprehensive analysis of browser-related threats using the security indices CVE, CVSS and EPSS [14] and propose four new, developed for this comparison, \(I_{\text{CVE}}\), \(I_{\text{CVSS}}\), \(I_{R}\) and \(I_{T}\). Unlike previous research, which often focuses on isolated vulnerabilities or specific browsers, this work offers a holistic view of the interconnected nature of browser vulnerabilities across multiple platforms. By investigating these threats in depth, this research lays the groundwork for future development of browser-based penetration testing systems. Such systems could assess user-specific risks [15] by identifying exploitable vulnerabilities, ultimately enabling broader organizational risk analysis and proactive mitigation strategies. This study bridges the gap between theoretical vulnerability assessments and practical, user-centric security solutions, emphasizing the need for scalable frameworks to address evolving cyber threats. This work stands out by providing actionable insights for both individual users and organizations, while also setting the stage for future innovations in browser security.
## 4 Experimental Result
### Qualitative Performance Evaluation
#### 4.2.1 Limitation of the Brightness Histogram in IR Pedestrian Tracking  
Figure 8 shows the image contrast variation as a background temperature change. The brightness value of the pedestrian changes to a considerably large value in an environment where the ambient temperature changes. Panels (c)-(h) show how the distribution of the brightness, 14-bit DC and temperature histogram of the target ROIs in (a) and (b) respectively. The brightness histogram was distributed mainly between 50 and 230 among a total of 256 bin values, as shown in (c), (d). In addition, the distribution of histograms (e) and (f) was different. The brightness histogram shows that the average histogram similarity in meanshift tracking was low because of the background temperature change in the IR pedestrian-tracking environment. On the other hand, (e), (f), (g) and (h) which show the 14-bit DC and temperature histogram bin values, are distributed mainly same shape when extracted in (a) and (b), respectively. In the IR pedestrian tracking environment, the temperature histogram and 14-bit DC values had more stable histogram similarity than the brightness histogram.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**FEATURES** & **FLIR T620** \\ \hline Spectral range & 7.5–14 μm \\ Temperature range & \(-40\,^{\circ}\)C to \(650\,^{\circ}\)C \\ \hline Thermal sensitivity (N.E.T.D) & \(<\)0.04 \({}^{\circ}\)C at 30 \({}^{\circ}\)C \\ Frame rate & 30 FPS \\ Resolution & 640 \(\times\) 480 pixels (14-bit) \\ Field of view (FOV) & Horizontal \(25^{\circ}\times 19^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: IR camera sensor specification.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & **Date** & **Season** & **Total Frame** & **Time** & **Temperature** & **ROI Size** & **Scenario** \\ \hline S1 & 18.08.14 & Summer & 723 & 17 s & 24.6/36.8 \({}^{\circ}\)C & 45 \(\times\) 47 & Image contrast \\ S2 & 19.01.21 & Winter & 390 & 11 s & \(-\)3.4/8.2 \({}^{\circ}\)C & 41 \(\times\) 41 & Image contrast, Background clutter \\ S3 & 19.02.02 & Winter  
Figure 9 shows that the temperature histogram is robust to the target variation problem by comparing the brightness histogram. The brightness, 14-bit DC and temperature histograms of the front, left, and back of the pedestrian head were extracted and compared. In particular, the brightness histogram distribution of (b) was clearly different from (a)-(d). Additionally, the 14-bit DC histograms of (a) and (c) were also different from (b) and (d). Whereas, all temperature histograms showed a similar distribution, i.e., the temperature histogram has a certain degree of histogram similarity, irrespective of the target variation.  
Figure 8: Comparison of the eight-bit brightness, 14-bit DC and temperature histogram when the background temperature changes: (**a**) target of high temperature blackbody (BB); (**b**) target of low temperature BB; (**c**,**d**) brightness histogram of (**a**,**b**); (**e**,**f**) 14-bit DC histogram of (**a**,**b**); (**g**,**h**) temperature histogram of (**a**,**b**).
## 3 Results and Discussion
### Compression Stress  
The stress-stroke graphs of the compression tests are shown in Figure 4. The near-perfect overlap of the compressive curves proves the stability and repeatability of the tests. In general, for monolithic materials, the absolute values of the tensile and compressive stresses are almost symmetric with respect to the yielding behavior of metals. This condition is also present for ME 316L specimens as the compressive and tensile yield strength values were approximately 150 MPa.  
In addition, the compression load seems to guarantee an improvement in the compressive stress values of the samples. The average compressive stress of the ME 316L specimens was 54% lower than that of the tested bulk sample at 1 mm stroke, whereas the average compressive stress value exhibited a deviation of 26% from the value of the bulk specimen at 8 mm stroke.
# A Point Cloud Registration Algorithm Based on Weighting Strategy for 3D Indoor Spaces
## 4 Experiment and Analysis
### Point Cloud Registration Experiments

Table 10: Groups of importance of labels in 3D indoor spaces. Figure 4: RMS of weighted PP-ICP for different groups of label importance. Figure 6 and Table 13 reveal that Area_6 has the smallest RMS under all three rotation angles, whereas Area_5 has the largest. This corresponds with the results of the PointNet semantic segmentation, where the whole-scene point accuracy is the highest in Area_6 and the lowest in Area_5. Similarly, the whole-scene point accuracy in Area_2 is higher than in Area_4, and the RMS in Area_2 is also smaller than in Area_4 under all three rotation angles. Based on what we have seen in practice, we can say that the proposed algorithm in this paper has a positive relationship between how well it registers and how well it semantically segments point clouds. Correct classifications in semantic segmentation are the only way to assign the correct weights in the registration algorithm's weight distribution process, thereby enhancing the algorithm's accuracy. We conduct comparison experiments after determining the importance of labels in semantic segmentation. We set the translation to [0.1, 0.2, 0.15] for all tests. We set the change in angle in the rotational axis for each small rigid transformation in the weighted PP-ICP to \(1^{\circ}\). Figure 7 and Table 14 display the root mean square error (RMS). Figure 8 and Table 15 demonstrate that in the multi-axis angle tests, the weighted PP-ICP consistently exhibits the smallest RMS, with the exception of the cases at 15\({}^{\circ}\) and 30\({}^{\circ}\). Similar to the single-axis angle tests, the RMS increases as the rotation angle increases. However, we observe that in the multi-axis angle tests, the RMS of all the algorithms surpasses that of the single-axis angle tests, suggesting an increase in registration errors as the complexity of the rigid-body transformations increases. We evaluate the runtime and computation resources used during the multi-axis tests for the four algorithms on a personal computer with a CPU processor (AMD Ryzen 7 4800H) and 16.0 GB of RAM to validate the efficiency. Figure 9 and Table 16 display the results. Figure 9 and Table 16 conclude that the weighted PP-ICP algorithm in this paper exhibits significantly shorter runtimes than the original ICP, NDT, and PointNetLK. However, in comparison to the NDT, the proposed algorithm does not perform better in terms of CPU or memory usage. The main reason for this is that the NDT breaks up the original point cloud into a voxel grid to obtain Gaussian distributions through feature extraction. It then changes these distributions to the reference point cloud's coordinate system [4]. Compared to ICP, which directly matches the original point cloud, the NDT significantly reduces computational complexity, thereby enhancing the hardware efficiency of the algorithm. Figure 8: RMS of multi-axis angle tests. Figure 9: Runtime and computation resource costs during multi-axis tests: (**a**) runtime; (**b**) CPU usage; (**c**) memory usage.