esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting the Duration of Forest Fires Using Machine Learning Methods
## 2 Materials and Methods
### The Used Machine Learning Methods
#### 2.2.4 Artificial Neural Networks  
Artificial neural networks (ANNs) are parametric models [74; 75], where a set of parameters, commonly called weights, must be calculated to be adapted to classification or regression data. This machine learning model has been utilized in a variety of scientific and real-world problems, such as physics problems [76; 77; 78], solving differential equations [79; 80], solar radiation prediction [81], agriculture problems [82; 83], problems in chemistry [84; 85; 86], wind speed forecasting [87], economics problems [88; 89; 90], problems related to medicine [91; 92], etc.  
In the area of forest fire prediction and observation, a number of works using artificial neural networks have been published. [PERSON] et al. used ANNs to detect flames and smoke from static image features [93]. [PERSON] and [PERSON] utilized neural networks to predict the risk of wildfires in the city of Cape Town [94]. Also, [PERSON] et al. used neural networks, among other machine learning techniques, for predictive modeling of wildfires from data collected from NASA's Land Processes Distributed Active Archive Center (LP DAAC) [95]. Also, a case study for predicting wildfires in a Chinese province using neural networks was published recently by [PERSON] et al. [96].
# C3 VFC: A Method for Tracing and Quantification of Microglia in 3D Temporal Images
## 3 Materials and Methods
### Imaging Acquisition and Fluorescence Technique  
The multi-cellular dataset consists of 3D images of microglia from living mice using in vivo multiphoton microscopy, see supplementary materials SI2-SI4. Heterozyggous GFP reporter mice expressing GFP under control of the fractalkine receptor promoter (\(CX3 CR1^{GFP/+}\)) were used for the imaging studies. Wild-type C57/B6 mice were crossed with \(CX3 CR1^{GFP/GFP}\) mice from Jackson labs (stock no. 005582) to generate \(CX3 CR1^{GFP/+}\) for these experiments to visualize microglia. For in vivo imaging, mice were implanted with a chronic cranial window as previously described [39]. Briefly, during surgery, mice were anesthetized with isoflurane (5% for induction; 1-2% for maintenance) and placed on a heating pad. Using a dental drill, a circular craniotomy of >3 mm diameter was drilled at 2 mm posterior and 1.5 mm lateral to bregma, the craniotomy center was around the limb/trunk region of the somatosensory cortex. A 70% ethanol-sterilized 3-mm glass coverslip was placed inside the craniotomy. A light-curing dental cement (Tetric EvoFlow) was applied and cured with a Kerr Demi Ultra LED Curing Light (DentalHealth Products). iBond Total Etch glue (Heraeus) was applied to the rest of the skull, except for the region with the window. This was also cured with the LED light. The light-curing dental glue was used to attach a custom-made head bar onto the other side of the skull from which the craniotomy was performed.  
To label microglia in the mouse brain for the dataset in supplementary materials SI1, we used mice with an inducible Cre recombinase under the control of the CX3 CR1 promoter crossed to the A6 fluorescent reporter mouse (Jackson Laboratories, Bar Harbor, ME) to generate \(CX3 CR1^{GFP2/+}\) X A6 zSGreen [40; 41]. At post-natal day (P23) 23 mice were given 10 uL/g body weight of a 20 mg/mL Tamoxifen (Sigma) solution in corn oil to induce recombination of the foxed stop codon leading to ZsGreen expression in microglia.  
All procedures adhered to guidelines of the Institutional Animal Care and Use Committee (ACUC) at the University of Virginia. Microglia of adult mice (7-10 weeks old) were imaged using a Leica TCS SP8 multiphoton microscopy system equipped with a Coherent Chameleon Ti:Sapphire laser and a 25 \(\times\) 0.95 NA immersion lens.
# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
## 4 Target Distortion Optimization  
### Investigation and Motivation  
Each GOP may have a different compressibility level based on the video sequence content (e.g., rate of HP chunks and LP chunks). Given a limited power and bandwidth usage for a video sequence, some GOPs will be more distorted than others under the minimum distortion optimization. Thus, the distortion fluctuations over GOPs reflect a poor viewing experience (even though the overall PSNR might be maximized). As the channel SNR increases, the quality distortion decreases, given the same limiting constraints for that constrained optimization. Nevertheless, this change in quality is visually indistinguishable by the human eye if the PSNR value is high. These observations show that maintaining a relatively more stable distortion over GOPs is desirable. Thus, for high SNR channels, instead of allowing the PSNR to increase constantly, we keep it at a certain high value. At the same time, other users can use the saved constrained resources. Hence, the problem becomes one of reaching a balanced combination of power allocation and chunk selection for a target distortion.
## 2 Concepts and Methods
### HMS System Resolution Extraction Method Concept
It should be noted that system parameters will directly affect the quality of RDG and RFP, but the comparison between the resulting RDG and RFP is not parameter-oriented. In other words, when the results are going to be compared to each other, the effects of the system parameters are neglected. However, these parameters are needed for resolution extraction. The presumed main SAR system parameters are listed in Table 3.
# Simulation of a Narrowband Power Line Communications System over Medium Voltage
###### Abstract  
Narrowband Power Line Communications (NB-PLCs) are investigated as an alternative option for transferring low rate smart grid (SG) data via Medium Voltage (MV) power lines. In this framework, two variants of orthogonal frequency division multiplexing are examined, namely Filtered-OFDM (F-OFDM) and Wavelet-OFDM (W-OFDM), in an attempt to determine which of them is capable of transmitting low rate SG data to greater distances over non-branched MV power lines. The reach of NB-PLC signals via MV power lines is estimated, taking into account the transfer function of the relevant PLC channels and noise mechanisms as well as the specific features of the two modulation options under consideration. Simulations show that NB-PLC transmission constitutes a technically feasible and economically affordable option for exchanging low rate data with remote SG nodes dispersed over the MV grid. Moreover, simulations show that F-OFDM allows low rate data transmission to considerably greater distances compared to W-OFDM.  
Filtered-OFDM; medium voltage; Nakagami; power line communications; smart grid; Wavelet-OFDM +
Footnote †: journal: Applied Sciences
## 3 Results  
### Finite Element Analysis  
To assess the influence of applied force, deformation distribution maps were generated for the male model in the standing case and ascending stairs case, comparing results when the minimum and maximum force was applied to the implant (Figure 9). As anticipated, the  
Figure 8: Flow chart of DL-FEA surrogate modeling process. Workflow for training DL-FEA surrogate model to predict stress patterns across construct.  
resulting deformation was not uniform across the surface of the implant. Due to differences in the X-, Y-, and Z-components of the applied force, the area of greatest deformation shifted in location.  
Further testing revealed that the shear deformation observed in the ascending stairs loading case could potentially affect the contact between the acetabular cup and the liner. In the no-screw configuration, the deformation was concentrated on the outer surface of the acetabular cup. In contrast, the addition of at least one screw helped reduce the effects of applied loads and maintained the structural integrity of the acetabular cup, as demonstrated in Figure 10. Interestingly, the 2-screw model experienced more deformation than the 1-screw model, as indicated by the larger light blue highlighted areas in the figures.  
In summary, FEA findings indicate the following:  
* Deformation distribution varied with screw configuration, with the 2-screw model exhibiting greater stability than the 1-screw model;  
Figure 10: FEA deformation distribution with screw fixation in loading case: (**A**) 1-screw and (**B**) 2-screw model. 2-screw model experienced more deformation than 1-screw model, highlighted in light blue.  
Figure 9: FEA deformation distribution in static and loading cases. Comparison of deformation patterns in (**A**) standing and (**B**) stair ascent scenarios, with minimum and maximum applied forces. Area of greatest deformation (highlighted in red) is due to differences in X-, Y-, and Z-components of applied force.  
* Load simulations confirmed that additional screws reduced acetabular cup displacement but increased stress concentration in adjacent bone structures.
## 4 Results Discussion  
To evaluate the performance of the developed algorithms and the overall system, various tests were conducted to cover as many scenarios as possible. These tests were performed in different spaces using different types of UAVs and at various times of the day to assess the response of the SDR module and the cameras to the presence of an intrusive UAV, thereby determining the system's effectiveness in terms of detection.  
The testing process was also crucial for analyzing the system's limitations and identifying which parameters or situations lead to successful or unsuccessful detection. Factors such as the distance to the UAV, its speed, and the electromagnetic interference in the testing environment were considered.  
In most of the tests conducted, the results were analyzed separately, examining the performance of each module individually, as communication between the two modules has never posed a problem. The primary challenge of the project was to ensure that both the SDR module with the autoencoders and the computer vision module accurately detect UAVs, as well as perform localization and tracking functions appropriately.  
Each module presented its challenges. For radio-frequency signals, it was necessary to determine whether the algorithm based on autoencoders is effective and how parameters such as the distance of the UAV, which affects the signal strength received by the SDR, impact performance. Additionally, it was important to examine if the algorithm's effectiveness is compromised when operating in environments with numerous interfering signals in the frequency bands being analyzed.  
For the camera module, the main challenges were related to assessing the detection performance based on the UAV's distance and speed, determining whether the localization system can correctly triangulate the UAV's position and send the precise location to the PTZ camera, and, finally, evaluating if the PTZ camera can successfully track the UAV.
# Assessment of the Potential of High-Performance Buildings to Achieve Zero Energy: A Case Study
###### Abstract  
Buildings that are designed with aggressive energy performance targets are defined broadly in this study as high-performance buildings. As the technology advances, some of these buildings have the potential to become zero-energy ready through the adoption of cost-effective measures, such as retro-commissioning and occupant behavior techniques. This study demonstrated the viability of an office building to achieve the zero-energy goal and intended to engage the owners of similar facilities. The case building was designed as a very low-energy building with an energy use intensity (EUI) goal of 42 kWh/(m\({}^{2}\) a), and the actual EUI was 23.9 kWh/(m\({}^{2}\) a). The calibrated simulation approach was employed in the study, and the results indicated that the case building can achieve the zero-energy goal by optimizing the controls of the HVAC (Heating, ventilation and Air Conditioning) system, changing the occupant behavior and improving the performance of the photovoltaic system.  
high-performance building; zero-energy building; model calibration; energy efficiency measures; case study +
Footnote †: journal: Applied Sciences
## 4 Sampling of Precipitation Conditions for the Constellation Simulation
### Satellite Orbital Separation  
An option to increase the number of P/E events for a fixed number of satellites is to increase the orbital separation \(\Delta t\) between the satellites, thereby increasing the spacing between adjacent ray paths. The larger separation between constellation satellites is more likely to locate an \"outside\" precipitation RO whenever an \"inside\" precipitation RO is captured, relative to the closer satellite spacing. However, the larger spacing also increases the likelihood that the \"outside\" heavy precipitation RO is sampling an air mass that is too distant and not representing the environmental air immediate to the heavy precipitation. In this study, a 50-150 km ray path separation is targeted as the desired range. In Appendix A, Table A1 tabulates the fraction of P/E events that occur within this desired ray path spacing.  
The data collection time is slightly shorter for the 98-degree, synchronous polar orbit, at the expense of a larger fraction exceeding 100-km ray path separations.
## 4 Evaluation and Results
### Dataset  
In this study, we utilize the CICMalDroid 2020 dataset [38], a benchmark dataset for Android malware detection. To ensure the reliability of the dataset, we performed a detailed quality screening phase prior to image conversion and analysis. A total of 993 APKs (5.9% of the original set) were excluded due to structural or extraction-related issues. Specifically, 748 APKs failed ZIP archive validation, indicating corruption or misformatting. Additionally, 31 APKs lacked the essential.dex component, and 3 were missing resources.arsc. The remaining samples were filtered out due to decompression errors (e.g., invalid block types or bad CRC-32 checksums) and unreadable Unicode file paths causing I/O exceptions. These exclusions reduced noise and ensured that only structurally complete and analyzable APKs were retained, resulting in a high-quality corpus of 15,849 samples.  
While CICMalDroid2020 provides a diverse set of malware families, it exhibits notable class imbalance. For example, SMS malware (4505 samples) significantly outnumbers adware (1469), which may bias the model toward overrepresented categories. To mitigate this, we performed an 80/20 stratified split during train-test partitioning to preserve class proportions. Additionally, we reported class-wise metrics in both binary and multi-class classification settings to ensure balanced evaluation despite the skewed distribution.  
To evaluate the generalizability of our approach beyond CICMalDroid2020, we performed cross-dataset validation using two external corpora: the Drebin dataset [39](500 malware APKs labeled via VirusTotal) and CICAndMal2017 [40] (500 benign APKs with verified ground truth).
# Integrated Carbonate-Based CO\({}_{2}\) Capture--Biofixation through Cyanobacteria
###### Abstract  
Microalgae, renowned for their high photosynthetic efficiency and minimal competition with land-based crops, hold great promise in the biofixation of CO\({}_{2}\) from waste sources, making them valuable for diverse applications, including biofuels, food production, and biomaterials. An innovative technology, the integrated carbonate-based carbon capture and algae biofixation system is emerging as an alternative to traditional carbon capture and sequestration (CCS) methods. This closed-loop system utilizes bicarbonates as inorganic carbon sources, which can directly enter microalgae photosynthesis, subsequently regenerating carbonates for another cycle of carbon capture. This system offers significant advantages, including cost savings in carbon supply, simplified photobioreactor development, and reduced labor and energy requirements. Nevertheless, further research is essential to evaluate the suitability of various microorganisms and search for optimal growth conditions. In this study, we assessed the performance of two strains of Spirulina within the integrated system. Employing a Design of Experiments approach, we simultaneously varied temperature, bicarbonate concentration, and light irradiation while operating within a lab-scale photobioreactor. We achieved remarkable results, with a biomass productivity of 875 mg/L-d and an impressive CO\({}_{2}\) utilization efficiency of 58%. These findings indicate a genuine opportunity for further exploration and scaling of this approach in industrial settings.  
microalgae; cyanobacteria; CO\({}_{2}\) biofixation; carbonate CCS; photobioreactor Article
# Influence of Linear Springing on the Fatigue Damage of Ultra Large Ore Carriers
## 5 Results and Analysis
### Influence of Structure Damping  
For the resonance phenomenon, the wave-induced vibration response is closely related with the hull structure damping, which directly affects the peak of the springing response. Figure 6 shows the stress transfer RAOs with a different structural damping of deck longitudinals at midship of the 400,000 DWT OC advancing at 15 kn in head waves. It can be seen that the stress changes significantly with the structural damping. The smaller the structure damping, the larger the stress response in the resonance range.  
Figure 7 shows the ratio of the stress peak of the springing response (\(\sigma_{PS}\)) to the maximum amplitude of low frequency stress response (\(\sigma_{PW}\)). As can been seen, the smaller the damping coefficient and the large the \(\sigma_{PS}/\sigma_{PW}\), particularly, for the smallest \(\delta\) explored, the amplitude of resonant response can be 5.5 times larger than that induced by low frequency waves. As \(\delta\) increases, \(\sigma_{PS}/\sigma_{PW}\) decreases monotonically. As shown in Figure 8, the damage ratio of the upper deck longitudinal drops rapidly as the dimensionless structural damping coefficient \(\delta\) increases from 0.01 to 0.03, after which (i.e., \(\delta>0.03\)), the rate of decease slows down. This is because the stress is amplified to its square when stress transfer function is converted into stress spectrum. For a small structural damping, the energy of wave-induced  
Figure 5: The damage ratio for upper deck longitudinals versus the hull length in full loading condition.  
Figure 6: Stress transfer functions for deck longitudinal with different structural damping ratios.  
vibration response is significant and it plays an important role in the structural fatigue damage. If the damping is large, the wave-induced vibration will reduce rapidly.
# Evaluation of the User Adaptation in a BCI Game Environment +
## 2 Related Work
Practically, these in-game features are proportional to the attention of the player. Moreover, they used the 3-axis gyroscope of the Emotiv headset to update the white ball's position on the table in order to match the player's position. To test the game, 3 healthy participants played the Mental Pool Game. Before the actual experiment, the subjects trained offline for the neutral and attention states. For the attention level, the subjects were instructed to visualize the movement of a cube. The participants played 1 round of each game until the exhaustion of the balls on the pool table. [PERSON] et al. [18] designed a parkour game based on EEG and electrooculograms (EOG). MindWave Mobile was employed for this BCI application. This headset consists of an earphone, an ear clip, and a sensor arm. The game has four levels, and the in-game avatar can run, jump to avoid obstacles, and collect coins. The avatar has three lives with which to complete one level, and the game rewards the user every time he jumps over an obstacle, collects coins, and finishes a level. More specifically, the attention level is associated with the avatar's speed, meaning that an increase in attention level leads to an increase in the avatar's speed. Additionally, blinking corresponds to in-game jumping. To test their application, 5 subjects played the game. To evaluate their experiment, they used two different algorithms and the following metrics: maximum meditation degree, maximum attention degree, and the time to pass the first stage. The average time to pass the first stage for the first algorithm was 34.6 s, and for the second one, it was 30.2 s.  
[PERSON] et al. [20] developed a serious game to improve the focusing ability of people that suffer from ADHD. To acquire the EEG data, they used the EMOTIV EPOC+ headset, which has 14 EEG channels. The designed 3D game has two different commands, push and neutral state, and the objective of the game is to collect all the available cubes. The push command is related to the attention level, while the neutral command is related to the meditation level of the user. When the user is focused, the avatar is moving forward, and when the user relaxes, the avatar stops moving. Additionally, the user can rotate the avatar using the gyroscope from the Emotiv headset. Four healthy subjects participated in the experiment. In the beginning, they were trained for the 2 mental states, and after completing the training phase, they were introduced to the game. They played the game with 2 protocols: mental commands and a keyboard. It was observed that users had greater engagement and higher attention levels when playing with the mental commands.
## 5 Conclusions  
This study aimed to enhance the stable quality of video streaming within the DASH system by developing a novel deep-reinforcement-learning-based approach. The key contributions and findings of this research are as follows: First, we established a classification of qualities (bitrates) into three distinct classes: Q\({}_{\text{high}}\), Q\({}_{\text{medium}}\), and Q\({}_{\text{poor}}\). Additionally, we introduced a distance factor, \(\Delta\)fact, to quantify the difference between two qualities and defined a reward function that encapsulates the key aspects of quality change, rebuffering events, and segment quality. Second, we formulated the problem of DASH video streaming as a Markov Decision Process (MDP) learning model. In this model, an agent represents client-side adaptation, making bitrate selection decisions based on the current bandwidth measurements, \(Bw_{t}\), buffer states, _buff_state_, and the quality, \(q_{t-1}\), of the previous segment,  
Figure 11: (**a**) Comparison of our approach vs. existing schemes; (**b**) average normalized QoE; (**c**) average switching quality; (**d**) average rebuffering time (s).  
\(s_{\rm i-1}\). Our approach leverages an LSTM neural network for the agent and involves a two-phase training process: first, training the agent alone using the SJTU HDR video sequences dataset under various network conditions (utilizing the Norway Network Dataset); second, training the agent in collaboration with a secondary agent. The secondary agent intervenes only when alerted by the main agent's need for correction. Simulation results demonstrated that our approach outperforms state-of-the-art methods. Notably, it achieved over 98% in average segment quality and QoE value, successfully reducing rebuffering time to less than 1.8 s and limiting switching quality to below 0.21. These outcomes affirm the effectiveness of our approach in delivering stable and high-quality DASH streaming sessions. In future work, we intend to extend our approach to support live video streaming scenarios, further advancing the capabilities and performance of our proposed method. This research represents a significant step toward enhancing the quality of video streaming experiences and contributes to the ongoing evolution of DASH streaming technologies.  
Conceptualization, N.S.; Methodology, N.S.; Software, N.S.; Validation, N.S.; Formal analysis, N.S.; Investigation, N.S.; Resources, N.S.; Writing--original draft, N.S.; Visualization, M.B.; Supervision, M.B. and Y.D.; Project administration, M.B. and Y.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by The General Directorate of Scientific Research and Technological Development, Algeria (DGRSDT).  
Not applicable.  
The authors declare no conflict of interest.