esa-sceva/satcom-chunk-collection huggingface dataset:
# Possibilities of Powering Military Equipment Based on Renewable Energy Sources
## 1 Introduction  
All NATO armies, including Poland, are looking for innovative solutions in terms of supplying weapons and military equipment. One of the considered technologies involves the use of renewable energy sources. The current research is conducted towards the possibility of applying them together with the modern military technology. It is an innovative approach, as used generators powered with gasoline and diesel are mainly in use. The current state of affairs forces the necessity of ensuring timely deliveries of large amounts of fuel, which may cause numerous logistic problems. The analysis conducted so far indicates that electricity obtained mainly from wind power and solar energy will be the likely direction of the development of alternative power supply for troops in the areas where soldiers are stationed (and also during the implementation of combat tasks).  
Nowadays various power devices can be used, If only they are based on renewable energy sources derived from water, wind or sun. These solutions have advantages and disadvantages. The practice of military units shows that the requirements related to mobility are severely limited by the use of turbines powered only by water or wind. Therefore, a wider range of focus is dedicated for solutions that allow for powering weapons and military equipment through mobile solar and wind-solar power plants as well as solar panels.  
The geographical location of troops determines the possibilities of using renewable energy sources. If the number of sunny or windy days is over 200, there are conditions for using solar and wind power plants in Middle Europe. The intention of the authors is topresent selected possibilities of using renewable energy sources for the Polish army. There are similar solutions that the authors encountered in Russia and China, or in the Middle East, but they are not taken into account as they will not be bought by NATO countries.  
Technical details of the solutions constitute the area of military analysis and may only be presented as a publication with a non-confidential clause. The study is of an overview nature. One novelty of this study is the concept of using a personal photovoltaic power supply device for soldiers.
## 4 Simulation Results
### Nonlinearity Compensation  
The time domain amplitude outputs of the proposed scheme and the original received \(y(n)\) are illustrated in Figure 6. For a fair comparison, the amplitude outputs of the ideal equalization case are also depicted here, where the channel information is perfectly known to the receiver. From the figure, it can be observed that the amplitudes of the original received \(y(n)\) are severely distorted as compared with the ideal case. However, with the help of an effective feature learning, the proposed scheme can exactly predict a similar amplitude output as the ideal case.  
The corresponding power spectral density (PSD) performance comparison is demonstrated in Figure 7. As shown in the figure, the overall nonlinearity of the IM/DD channel is manifested in the manner of in-band distortion and out-band spectral regrowth. However, the PSD of the proposed scheme shows a similar curve as compared with that of the ideal case. Although the sideband level at a high frequency is slightly inconsistent with that of the ideal case, it does not affect the signal demodulation because the power of useful information is mainly located at the in-band PSD. Therefore, the detrimental distortions can be well compensated by the proposed DL model.  
Figure 5: The cost performance comparison under different time steps.  
Figure 6: Amplitude comparison of the proposed scheme with the ideal case.  
The corresponding BER performance is illustrated in Figure 8 as the testing SNR \(\zeta\) varied from 0 to 36 dB. In addition, the other three schemes in terms of the DNN [13], the LSTM [19], and the CNN compensator [21] are also presented here. In general, the proposed scheme has the closest BER performance to the one for the ideal case, and the BER accuracy of the DNN scheme tends to be saturated when \(\zeta\) is over 27 dB. As for the same BER of \(1\times 10^{-3}\), the proposed scheme can reduce the required SNR at least by 2.5 dB as compared with the LSTM approach and nearly by 14.2 dB as compared with the DNN method, which shows the superiority of the proposed scheme for nonlinearity mitigation. The channel characteristics can be perfectly revealed and then learned by the proposed scheme, and it can still work effectively even though the testing conditions are not exactly the same as the channel noise used in the training stage, which shows the good generalization ability of the proposed model. Besides, the CNN scheme can also provide an excellent BER performance as compared with the DNN and LSTM methods, because the CNN offers dilated convolutions, in which the convolution layer could handle the spatial information and store the memory. However, there will be more computation time in the training phase to achieve the convergence, which is analyzed next.
## 4 Discussion
### Performance Metrics for Evaluating the Platform's Effectiveness
#### 4.3.1 Qualitative and Quantitative Insights from User and Stakeholder Research
Non-disclosure agreements (NDAs) were identified as the most important, endorsed by 64.3% of respondents, followed by confidentiality agreements (53.6%) and secure document sharing (35.7%). These findings strongly support our platform's integration of NDAs as a primary mechanism for safeguarding intellectual property, reflecting community preferences for structured legal protection. During our research, we also conducted interviews with five ambassadors from organizations such as Rubik Hub, Launch Community, Innovation Labs, and Orange Fab, known for their exceptional work in supporting startups in Romania. Based on their input, we designed a SOTA analysis of our system, the baseline being the systems currently in use in their ecosystems. The results are presented in Table 1.
# Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios
## 2 Background and Related Work
### Zurich Festival Study  
In [2], the authors propose an opportunistic protocol based on the Wi-Fi hotspot capability of smartphones. The researchers have built an application that measures some crowd parameters at mass events (e.g., crowd density) and allows the propagation of messages by switching the role of a device between client mode and access point mode. Relying the entire strategy on the Wi-Fi hotspot functionality of smartphones was a choice motivated by compatibility reasons, as this feature is considered universal in devices nowadays. An essential aspect of this work was considering the human body to have an impact on the Wi-Fi signal strength.  
NetLogo ([[https://cl.northwestern.edu/netlogo/](https://cl.northwestern.edu/netlogo/)]([https://cl.northwestern.edu/netlogo/](https://cl.northwestern.edu/netlogo/)), accessed on 10 May 2021) was used as a test environment and was configured using real data collected during the Zurich festival. It is worth mentioning that the Zurich festival is a street festival, so the mobility pattern may differ from that of a music festival in terms of the average standing time for a participant and the time between standing and walking.  
Test results showed that, for two clients per access point and with 40% of the nodes assuming the access point role, a message reached all 1000 users in around 33 min. For a ratio of 20-25% nodes acting as access points at any given time, the convergence time value was 40 min. The conclusion was that Wi-Fi hotspot technology is a viable option for message propagation at large-scale events, while carefully considering the role switching parameters and crowd dynamics.
## 3 Experimental Investigation
### Dynamic Properties
\begin{table}
\begin{tabular}{l c} \hline \hline
**Elements** & **Value** \\ \hline _Load bearing masonry walls (LBMW)_ & \\ \(\ast\) Unit mass, \(\rho_{LBMW}\) [kN/m\({}^{3}\)] & 18 \\ \(\ast\) Modulus of elasticity, \(E_{LBMW}\) [MPa] & 3850 \\ \(\ast\) Poisson coefficient, \(\
u_{LBMW}\) [-] & 0.25 \\ \(\ast\) Shear modulus, \(G_{LBMW}\) [MPa] & 1540 \\ \(\ast\) Characteristic compressive strength [MPa] & 2.66 \\ \hline \multicolumn{3}{l}{\(\ast\)_Partition masonry walls (PMW)_} \\ Unit mass, \(\rho_{PMW}\) [kN/m\({}^{3}\)] & 13 \\ Modulus of elasticity, \(E_{PMW}\) [MPa] & 4600 \\ Poisson coefficient, \(\
u_{PMW}\) [-] & 0.05 \\ Shear modulus, \(G_{PMW}\) [MPa] & 2190 \\ Characteristic compressive strength [MPa] & 1.70 \\ \hline \multicolumn{3}{l}{\(\ast\)_Beams, Staircases, Rigid Elements (S)_} \\ Unit mass, \(\rho_{S}\) [kN/m\({}^{3}\)] & 25 \\ Modulus of elasticity, \(E_{S}\) [MPa] & 33,000 \\ Poisson coefficient, \(\
u_{S}\) [-] & 0.2 \\ Shear modulus, \(G_{S}\) [MPa] & 13,750 \\ \hline \multicolumn{3}{l}{\(\ast\)_Stone columns (SC)_} \\ Unit mass, \(\rho_{SC}\) [kN/m\({}^{3}\)] & 25 \\ Modulus of elasticity, \(E_{SC}\) [MPa] & 31,000 \\ Poisson coefficient, \(\
u_{SC}\) [[FOOTNOTE:]Footnote : footnotemark: [ENDFOOTNOTE] & 0.2 \\ Shear modulus, \(G_{SC}\) [MPa] & 12,917 \\ \hline \hline \end{tabular} Legend: \(\ast\) mechanical properties taken from the literature [37], \(\ast\)—mechanical properties determined as a part of experimental investigation.  
\end{table} 
Table 4: Initial mechanical properties of materials assigned to the elements of the case study’ numerical model. Figure 8: Numerical model of the case study: (**a**) 3D model and (**b**) 3D sub-model of the roof. reactions from the self-weight were obtained, which were transferred to the 3D model of the structure. Staircases are entered into the 3D model of the structure in the same way (Figure 9a). Since all partition walls were damaged in the earthquake, it was assumed that they did not contribute to the structural stiffness and are introduced to numerical model as additional mass (Figure 9b). The mezzanine structure is modeled as a ribbed slab with additional layers.
###### Abstract  
Direct sound that is captured by the upper layer of a three-dimensional (3D) microphone array is typically regarded as vertical interchannel crosstalk (VIC), since it tends to produce an undesired effect of the sound source image being elevated from the ear-level loudspeaker layer position (0\({}^{\circ}\)) in reproduction. The present study examined the effectiveness of band-limited VIC attenuation methods on preventing the vertical image shift problem. In a subjective experiment, five natural sound sources were presented as vertically-oriented phantom images while using two stereophonic loudspeaker pairs elevated at 0\({}^{\circ}\) and 30\({}^{\circ}\) in front of the listener. The upper layer signal (i.e., VIC) was attenuated in various octave-band-dependent conditions that were based on vertical localisation thresholds obtained from previous studies. The results showed that it was possible to achieve the goal of panning the phantom image at the same height as the image produced by the main loudspeaker layer by attenuating only a single octave band with the centre frequency of 4 kHz or 8 kHz or multiple bands at 1 kHz and above. This has a useful practical implication in 3D sound recording and mixing where a vertically oriented phantom image is rendered.  
vertical interchannel crosstalk; vertical auditory localisation; 3D audio; sound recording; microphone technique; mixing; upmixing; psychoacoustics +
Footnote †: journal: Applied Psychoacoustics Lab (APL), University of Huddersfield, Huddersfield HD1 3 DH, UK; [EMAIL_ADDRESS]
# Mambo-DQN: Adaptively Tunes Visual SLAM Parameters Based on Historical Observation DQN
## 3 Method
### Action Space Design
#### 3.5.1 Analysis of Parameters (Action) Selection  
In visual SLAM systems, multi-scale pyramidal feature structures play a crucial role in feature extraction and matching, impacting localization accuracy and computational efficiency. The optimization of scale factors and pyramid levels significantly influences system adaptability. Studies [35,36] have demonstrated that selecting an appropriate scale factor enhances feature stability under varying environmental conditions, while the number of pyramid levels must balance computational cost and feature extraction accuracy [36, 37, 38, 39, 40]. Additionally, the nearest neighbor threshold affects feature matching robustness, requiring adaptive tuning to maintain stability in complex environments [9,41,42]. Collectively, these studies highlight the necessity of adaptive parameter optimization in SLAM, ensuring improved localization accuracy, feature robustness, and computational efficiency.  
To evaluate the impact of different parameter settings on SLAM pose trajectory estimation, we conducted experiments and summarized the RMSE results of the ORB-SLAM3 algorithm on selected sequences from the EuRoC dataset in Table 1. The experimental results indicate that, for the same dataset sequences, different combinations of parameters \(P_{1}\), \(P_{2}\), and \(P_{3}\) significantly affect SLAM localization accuracy and exhibit a certain degree of instability. Therefore, employing fixed parameter settings may not always ensure optimal performance across all scenarios, highlighting the potential importance of adaptive parameter tuning mechanisms in SLAM.
## 4 Experimental Results and Discussion
### Subjective Evaluation  
#### 4.5.1 Generation of Testing Stereoscopic Images  
For evaluating the quality of the stereoscopic images generated by different SR methods, a subjective experiment was implemented, and the procedure followed the International Telecommunications Union-Radio Communications Sector (ITU-R) Recommendation BT.500 [37] so that subjective quality of the reconstructed stereoscopic images relative to the original stereoscopic images was obtained. Specifically, this experiment also used the ten aforementioned testing stereoscopic images and adopted a Double-Stimulus Continuous Quality-Scale method [37], which is equivalent to simultaneously scoring two stimuli corresponding to the reconstructed stereoscopic image and the original stereoscopic image. Thus, for ten testing stereoscopic images and three different SR methods, there are a total of 10 \(\times\) 3 = 30 comparison clips produced.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **SC [13]** & **SRCNN [15]** & **Proposed** \\ \hline Average & 2064.80 & 95.05 & 97.54 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Running time of the ten testing stereoscopic images listed in Table 1 (unit: s).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Images** & **Resolution** & **Scale** & **[PERSON] [22]** & **Proposed** \\ \hline Dog & 640 \(\times\) 480 & 2 & 36.54 & 38.33 \\ Pantomime & 640 \(\times\) 480 & 2 & 38.59 & 39.63 \\ Pozan Street & 960 \(\times\) 544 & 2 & 35.78 & 35.90 \\ Ballet & 512 \(\times\) 384 & 2 & 36.34 & 36.83 \\ Breakancers & 256 \(\times\) 192 & 2 & 39.09 & 41.38 \\ Average & - & 2 & 37.27 & 38.41 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with the depth-based Garcia [22] for PSNR (dB). The fourth column data from [PERSON] [22]
## 3 Results
### Intake Recommendations for Total and Specific Fatty Acids
#### 3.1.3 N-6 PUFA--Linoleic and Arachidic Acid Intake Recommendations  
FAO/WHO [17] proposed for LA an estimated average requirement (EAR), i.e., the intake level at which the needs of 50% of the population are met, of 2%E and an AI of 2-3%E after two years of age, based on the fact that the signs of deficiency can be prevented by providing 1-2%E LA in adults. The higher value of the AI (i.e., 3%), as part of a healthy diet, is contributing to long term health by lowering LDL and total cholesterol levels and consequently the risk for CHD. The AI for younger children aged 1-2 years is higher (3-4.5%E). No recommendation was set for ARA.  
EFSA [18; 19] set an AI for LA, because they estimated there was no evidence to support an EAR. The AI for LA for all populations was based on the lowest estimated mean intakes of the various population groups in Europe, where overt LA deficiency symptoms were not present. For children aged 1-3 years old, they noted that LA intake was generally below the adult AI of 4 E%, but that no signs of LA deficiency were observed if LA intakes were >1 E%, so that intake and status of LA in infants and young children living in Europe were of no concern. The adult AI of 4%E was proposed for children. While acknowledging that ARA was present in human milk, EFSA did not identify clinical effects of supplementing infants or children with ARA [26; 27], and thus did not set any recommendation for this fatty acid. This controversial recommendation is not supported by all experts in the field [28].
## 4 Result and Discussion
### Metrics for Evaluation  
The system's performance was assessed using typical statistical parameters such as delay, delivery rate, power, efficiency, and NLT. To evaluate system performance, the findings were compared to existing approaches such as PSO, GA and WOA for various standardized value measures.  
* Delay: The average time it takes an information parcel to arrive at a goal. This incorporates all deferrals brought about by the support during the course of disclosure delay, and the interface line remains set up. This measurement is determined by subtracting the time transmitted by the principal parcel source from the time the main information bundle arrived.
* Delay Ratio:  
Figure 8: Hop-count-based efficient routing.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter Name** & **Value** \\ \hline Number of nodes & 100 \\ Wireless protocol & 802.11 \\ Area & 1000 \(\times\) 1000 \\ Simulation time & 50 s \\ Packet size & 512 \\ Transmit power & 0.660 W \\ Receiving power & 0.395 W \\ Initial energy & 40 J \\ Transmission range & 250 m \\ Constant bit rate & 500 kbps \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
The packet delay rate is the rate used to calculate the amount of data packages transmitted by the source node. Data obtained by the target node are used to compute the rate of loss of information packages during data transfer over the network. It estimates the rate of loss and measures the equal correctness effectiveness of ad hoc routing protocols. A high packet delivery rate is believed to be high on any network.  
\[PacketDelayRatio=\sum Numberof packetreceive/\sum Numberof packetsend\]  
* Throughput:  
Performance can be defined as how many information packages are established by the recipient during an information transfer or as a successful data transfer over time. It includes the average rate of successful packages delivered after the source hub to the target node in any network project. Performance is specified in bits/bytes per second. High performance is the most important factor in any network.
###### Abstract  
This study proposes a method for detecting internal defects in wind turbine blades using deep learning, eliminating the reliance on inspectors' experiments. To address the class imbalance problem inherent in defect detection environments, synthetic thermographic datasets were generated using a synthetic data generation technique. To minimize the domain gap between synthetic and real thermographic data, preprocessing with a transformation module was employed, enhancing the similarity between datasets. ResNet-50, DenseNet-121, and Vision Transformer (ViT) models were trained on the synthetic dataset, and their defect detection performance was evaluated on real thermographic data. The results validated the effectiveness of the transformation module in improving the similarity between synthetic and real data, particularly enhancing precision and recall.  
wind turbine blade; defect detection; internal defect; thermal inspection; deep learning; synthetic data 2025
## 4 Avinomics Assessment
### Computing  
In relation to the previous control subsystem, the presented algorithms for a UAV's navigation (whether vision or AI-based), as well as for path planning, obstacle avoidance, localization, target tracking, and payload deployment, need physical onboard systems for treatment. In other words, the execution of such algorithms is performed through computerized systems inside the UAV, serving as data processing, data logging, and all forms of diagnostics. Additionally, the raw data provided by the onboard sensors, and communication protocols with external parties, are also processed in the computing subsystem. In this context, a UAV's computing subsystem encompasses two main sets including Single-Board Computers (SBCs) and System on Module (SoM).  
(a) SBCs  
As its name indicates, an SBC represents a complete computer that is built on a single circuit board, including a Central Processing Unit (CPU), Random Access Memory (RAM), storage, Input/Output (I/O) interfaces, and other features similar to a functional computer. Because of the fact that SBCs have smaller form factors, they are found to be compact and suitable for usage in space-restricted applications, such as in a UAV (1977). For UAV process management, the most common SBCs are Raspberry Pi, Odroid XU4, and NVIDIA Jetson (nano, TX2, Xavier).
## 3 Classification
### Role of VRU Devices
#### 3.8.2 Passive  
When the VRU device only 'listens' to the messages from a vehicle and/or when VRU device sends a reply only when it detects the message from a vehicle, this type of participation may be called as passive participation. In a V2P system where the VRU device only listens to the messages from vehicles, vehicles may not be aware of the VRU's presence. This requires the VRUs to be aware of vehicle to avoid potential crashes. Also, in a V2P system where the VRU device replies only when it detects the message from the vehicle, the system completely relies on the reliability and efficiency of the VRU device. Also, performance of such systems in the dense vehicle scenarios remain unseen.