esa-sceva/satcom-chunk-collection huggingface dataset:
# Image-Based Dedicated Methods of Night Traffic Visibility Estimation
## 4 Experiment  
### Laser Transmission System  
The laser transmission system consists of a laser, a data acquisition, and the control unit (photodiode and peripheral circuitry). We chose a point-like near-infrared laser with a wavelength of 905 nm and a power of 50 mW as the emitter. The parameters are shown in Table 2. The data acquisition and control unit consists of an avalanche photodiode (APD), several amplifiers, several relays, and a STM32. The transmitted laser signal, which is collected by an avalanche photodiode, then amplified and A/D sampled by the data acquisition and control unit, is the output as a high signal to noise ratio signal [17]. During the measurement process, a visibility meter is placed at the same time for data calibration.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Range** \\ \hline Wavelength & 905 nm \\ Power & 50 mW \\ Voltage & DC2.8–5.0V \\ Working current & \(\leq\)180 mA \\ Size & \(\phi 22\times 110\) mm \\ Divergence angle & 0.5–1.0 mrad \\ \hline \end{tabular}
\end{table}
Table 2: Main parameters of the laser.  
Figure 4: Images in the same visibility (10,000 m) at different time. (**a**) daytime image; (**b**) evening image; (**c**) late-night image.  
The avalanche photodiode measures the photocurrent according to the amount of laser irradiation received. Response r = \(\frac{1}{\text{p}}\), received optical power \(P_{\lambda}=\frac{1}{\text{r}}\), APD(AD500-9) response r is 52 A/W, and P\({}_{\lambda 0}\) is the input power of the laser. The distance between the laser and the receiver is 10 cm. During this process, the atmospheric transmissivity of the system is:  
\[\tau=\frac{P_{\lambda}}{P_{\lambda 0}} \tag{23}\]
## 3 Results and Discussion
### Filtration Efficiency
found that the quality factor of commercially available melt-blown fabrics was 0.033 Pa\({}^{-1}\)[31]. In comparison, the proposed electret melt-blown fabrics outperform the commercially available ones in terms of filtration efficiency. In particular, the quality factor reaches 0.083 Pa\({}^{-1}\) when TiO\({}_{2}\) is 3 wt% and the electric field intensity is 2.5 kV/cm.
## 3 Test Results and Analysis
### Results of the CT
#### 3.3.4 Changes in Pore Fractal Characteristics at Different Soaking Times  
By examining the horizontal cross-section pore characteristics of the samples subjected to different corrosion durations and integrating this with the box dimension fractal theory, we determined the fractal dimension of the damaged pores, as illustrated in Figure 9. The calculation results indicate that the correlation coefficient R\({}^{2}\) exceeds 0.96, demonstrating the high accuracy and reliability of these findings.  
Figure 9: Change trends for the connected and non-connected porosity of samples after soaking in different months.  
The relationship between the soaking time and fractal dimension is derived from time and is expressed as  
\[D=0.78+0.27t-0.24t^{2}+0.06t^{3} \tag{5}\]  
In this study, \(D\) represents the two-dimensional fractal dimension of the sample at time \(t\). As illustrated in Figure 10, the fractal dimension exhibits a trend of initially increasing, followed by a decrease, and then a subsequent increase as the soaking time extended. This phenomenon indicates that the immersion effect of brine-CO\({}_{2}\) initially enhanced the complexity of damaged pores within the sandstone material, which then diminished over time before ultimately increasing again. This process reflects a transformation in the degree of damage within the sample, characterized by an initial increase, followed by a reduction and finally stabilizing. These findings align with the analytical results derived from the three immersion stages outlined in this article.
# Voxel-Based Navigation: A Systematic Review of Techniques, Applications, and Challenges
## 4 Key Techniques for Voxel-Based Navigation
### Voxel Storage and Management
#### 4.4.1 Static Grids  
Static grids, once generated, typically do not change or only undergo minimal updates. They are suitable for scenarios requiring fixed spatial partitioning and are appropriate for handling static or minimally changing environments. The regular grid structure [66] is straightforward, storing voxels in a three-dimensional array. The topological relationships between voxels enable rapid queries and operations. However, regular grids are memory-inefficient, particularly for large scenes or high-resolution models, requiring substantial storage space. Sparse Voxel Octrees (SVOs) [67] enhance memory efficiency by recursively subdividing space into smaller blocks and storing only occupied voxels. SVOs also support Level of Detail (LoD), allowing flexible rendering at various resolutions. While SVOs have been applied successfully across different fields [27; 85; 86; 87], they still demand significant memory and bandwidth for huge scenes. Sparse Voxel Directed Acyclic Graphs (SVDAGs) further reduce storage by merging identical subtrees, significantly compressing voxel data while maintaining performance. Unlike traditional SVOs, an SVDAG shares pointers to identical subtrees, cutting down memory usage. It also eliminates decompression, providing compact and efficient voxel representation. This method is ideal for large  
Figure 8: The agent’s trajectory is colored by visibility degrees, with gray values representing negative visibility degrees due to obstructions (from [64]).  
scale, high-resolution voxel data applications, such as city-scale 3D models and complex volumetric rendering. Several improvements have been made to SVDAGs, such as [70; 71; 72], making voxel data structures more effective in managing large-scale models.
# Do Heavy Vehicles Always Have a Negative Effect on Traffic Flow?
## Impact of Heavy Vehicles on Traffic Flow  
### Heavy Vehicles That Determine the Characteristics of Traffic Flow  
Heavy vehicles are generally larger in size than passenger cars. Therefore, it is more difficult to pass a heavy vehicle than a small vehicle. In other words, as the length of the section increases when passing, and the visibility is limited, the driver pays more attention when passing a heavy vehicle than a small car. Therefore, it is highly likely that a heavy vehicle will be located at the front of the vehicle group in a general road driving pattern in which the vehicles form a platoon of vehicles on the road.  
Figure 14: Relationship of ratio of heavy vehicles-speed 5 (flow rate: \(\geq\)1000 veh/h/ln).  
Figure 12: Relationship of ratio of heavy vehicles-speed 4 (flow rate: 600–800 veh/h/ln).  
Figure 13: Relationship of ratio of heavy vehicles-speed 5 (flow rate: 800–900 veh/h/ln).  
The relationship between heavy vehicles and platoon formation has also been addressed in some previous studies [9; 19]. According to [PERSON] and [PERSON] [19], the flow of vehicles in expressways is in the form of a vehicle group, and it is highly likely that a heavy vehicle will be at the front of the vehicle group. Therefore, the mean and variance of the speeds of small and heavy vehicles were analyzed, and used to interpret the results obtained from the analysis of speed changes according to the ratio of heavy vehicles. They suggested that as the proportion of heavy vehicles increases, the probability that they will become the leader of the vehicle group increases; as such, the traffic flow can be divided into two platoons centering on the heavy vehicles, leading to possible changes in the average speed of the traffic flow. Table 12 shows the probability of each vehicle type becoming the leader of a vehicle group. According to the presented values, the probability that a truck will become the leader of the vehicle group is approximately 1.8 times that of a passenger car.  
The leading vehicle determines the characteristics of the traffic flow of the vehicle group. If the leading vehicle cannot be overtaken, the rest of the group will continue to move at a speed similar to that of the leading vehicle. Thus, the characteristics of traffic flow of the vehicle group become homogeneous. When a heavy vehicle is leading the vehicle group, it becomes difficult for passenger cars to get a chance to pass, so the rest of the vehicles move at the same speed and driving pattern as that of the heavy vehicle.
# Solving Task Scheduling Problems in Dew Computing via Deep Reinforcement Learning
## 2 Edge and Dew Computing  
Mobile Edge Computing is a paradigm that seeks to solve latency and network traffic problems found in Mobile Cloud Computing environments [23]. [PERSON] et al. [24] define Edge Computing as _\"a model that allows a cloud-based computing capacity providing services making use of the infrastructure that is on the edge of the network.\"_ In this way, Mobile Edge Computing allows the use of servers or workstations that are within a computer network, thus ensuring low latency and at the same time enabling the efficient processing of information, which permits the deployment of more robust applications. Furthermore, this paradigm also lets Edge servers work with other nodes in proximity or collaborate with Cloud services, thus deploying much larger and more efficient applications [25; 26]. However, while Edge computing helps to reduce the network's problems, it still depends on the network backbone that may not be available or reachable in certain situations (such as working with IoT devices in mines and on ships, in deserts, or moving vehicles).  
Dew computing is a new paradigm where connected devices offload jobs to nearby devices in the same network. This paradigm proposes an architecture that tries to reduce network latency, the energy cost of remote data communication, and the cost inherent to Cloud infrastructure usage [27]. Through this, Dew computing optimizes the usage of mobile and IoT devices in two manners. First, it treats mobile devices as clients in the network infrastructure to offload their work to other devices located in the same network [28]. Second, Dew computing considers mobile and IoT devices as resources to increase the available computational power from an existing system. In this approach, one device can offload its work onto another available device in the network (including other mobile and IoT devices) [29; 30].  
We note that a network topology is needed in order to use mobile and IoT devices as resources in a local network [3]. The _Smart Cluster at the Edge (SCE)_ is a network topology that is commonly used for that purpose in Dew computing. Figure 1 shows how the devices are organized in this type of network. This topology can be established wherever an access point and a group of mobile and IoT devices coexist. The topology's main feature is a central scheduler, which is primarily used to coordinate each task assignment among the network's available resources. This central scheduler can be any capable device in the network [31]. In this work, we address the problem of distributing jobs in a Dew environment under the assumption that the network topology is an SCE.
# Space Charge Effect and Resistance Switching in Doped Monocrystalline Silicones
###### Abstract  
In this work, we report the nonlinear carriers' transport in n-doped monocrystalline silicone with millimeter-scale length. Ohm, effective trap filling, and Mott-Gurney regimes are distinguished from the current-voltage (I-V) curve. Two critical voltages are identified for the lower and upper limitations of an effective trap-filling regime. Meanwhile, the electrode spacing, temperature, and magnetic field dependence of the two critical voltages are demonstrated experimentally. In particular, we propose that the effective trap-filling process is irreversible under electric field. It is observed that the hysteresis of I-V curve initiates from the effective trap-filling regime and extends to the Mott-Gurney regime, forming the resistance-switching loop. In addition, the temperature dependence and the magnetic field dependence of the resistance-switching loop are reported. The above observations may shed light on dopants engineering on carrier dynamics in a space charge regime and further advance resistance-switching devices technology.  
Keywords:resistance; space charge effect; monocrystalline silicones +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Study Limitations and Future Directions  
Despite the results, several limitations of this study warrant discussion to provide a balanced assessment of the framework's clinical applicability. First, data limitations include the reliance on retrospective publicly available datasets, which may not fully represent the diverse spectrum of retinal pathologies encountered in clinical practice, particularly in terms of disease severity gradations and atypical presentations. The datasets also lack comprehensive demographic diversity information, limiting our ability to assess potential biases across age groups, ethnicities, and comorbidity profiles. Second, while our cross-pathology testing demonstrated encouraging generalization, the model's performance in more complex clinical scenarios (such as cases with multiple concurrent pathologies, media opacities, or poor image quality) remains unverified. Third, the current implementation employs a binary classification approach (normal vs. abnormal), which, while clinically valuable as a screening tool, does not achieve the granular multiclass differentiation that would be required for comprehensive diagnostic assistance. Fourth, our framework utilizes transfer learning from VGG16, a somewhat dated architecture that carries increased computational overhead compared to more recent models; this represents a trade-off between established feature extraction capability and efficiency. Finally, despite our emphasis on clinical utility metrics, this study lacks prospective validation in real clinical workflows, where integration challenges and impact on clinical decision-making would become apparent. Future work should address these limitations through multicenter prospective studies with demographically diverse cohorts, expansion to multiclass classification capability, evaluation on cases with coexisting pathologies, and direct comparison against clinical specialists to establish non-inferiority benchmarks for clinical implementation.
## 4 Mitigating Quantization Quality Degradation Based on the Observation
### Quantization-Free Module (QFeM)  
In the full quantization of LLM, all linear layers within the LLM are quantized. Among these linear layers, we propose omitting the quantization of input activations for linear layers where significant quantization errors are caused by activation spikes. To be noted, increasing the number of unquantized modules exhibits a trade-off between the inference latency and the model performance. Thus, determining which module should be quantized (or left unquantized) is crucial to retaining the efficacy of quantization. Here, we use the max-median ratio \(r^{(m)}\) and define a set of unquantized modules, denoted as \(M_{\text{unq}}\), where the ratio \(r^{(m)}\) of each linear layer is larger than the threshold \(\alpha\). For instance, all linear layers in \(M\) are quantized if \(\alpha=\infty\). For clarity, we treat sibling linear layers, such as query-key-value, as a single linear layer. To control the impact of activation quantization only, we leave the weight parameters in unquantized linear layers as INT8 and dequantize them into FP16 during matrix multiplication with the incoming activations, operating as weight-only quantization.  
Optimizing the Threshold \(\alpha\)  
To calculate the activation scale ratio for each linear layer, we first gather token-wise input activation scales from the calibration examples discussed in Section 3.1. Exceptionally, for FFN experts in mixture of experts (MoE) architectures like the Mistral model [6], calibration is performed separately. After determining these ratios, we use binary search to set the threshold value \(\alpha\), balancing inference latency and performance degradation. As a metric, we assess performance through perplexity measured on the same calibration examples. For example, the relationship between the threshold value \(\alpha\) and its impact on performance is depicted in Figure 4, demonstrating how full quantization can degrade performance. Rather than fully quantizing, we identify an optimal threshold by finding the intersection of two performance curves; in Figure 4, this threshold is approximately 16. Details on the QFeM implementation are provided in Table 2.  
Figure 4: Trade-off between perplexity and \(|M_{unq}|\) for LLMA-2-13B as threshold \(\alpha\) varies. A higher \(\alpha\) quantizes more layers, increasing \(|M_{unq}|\) (left y-axis) but also raising perplexity (right y-axis), where a lower perplexity value indicates better performance. A lower \(\alpha\) restricts quantization, preserving performance but reducing latency gains.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Model** & **Prefix** & \(\alpha\) & \(|M_{unq}|/|M|\) \\ \hline Mistral-7B & [BOS] how\(\backslash\)n & 49.00 & 3/128 \\ Mistral-8x7B & [BOS] \(\backslash\).n & 4.03 & 191/608 \\ SOLAR-10.7B & [BOS] a 1 & 6.48 & 11/192 \\ Gemma-7B & [BOS]. Pi\(\alpha\) & 10.65 & 5/112 \\ LLMA-3-8B & [BOS]-nd & 6.64 & 6/128 \\ LLaMA-3-70B & [BOS] and, & 78.37 & 3/320 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._
# Robotic Button Mushroom Harvesting Systems: A Review of Design, Mechanism, and Future Directions
## 4 Computer Vision System
### Quality and Disease Detection  
[PERSON] et al. developed a vectorial normalization method to improve the detection of bacterial disease-related discoloration in mushroom images. This method distinguished disease-specific color points from healthy ones within the RGB color space, effectively identifying diseased spots without mistakenly detecting healthy or naturally senescent areas and achieved acceptable results in recognizing the two diseases tested [83]. In a study by [PERSON] et al., image processing systems were used to assess the mushroom quality based on color, area, weight, and volume, utilizing artificial neural networks and fuzzy logic. The system evaluated 250 images across three quality categories, achieving a correct detection rate of 95.6 [84].  
[PERSON] et al. explored the use of the L-a-b color model and hyperspectral imaging to differentiate between undamaged (UD) and mechanically damaged (D) white button mushrooms (WBM). Their key findings highlighted the ability to distinguish mushrooms based on the a-values (redness) and b-values (yellowness), with browning and firmness loss more pronounced in D mushrooms. Hyperspectral imaging, particularly at 600 nm, paired with k-nearest neighbors, proved highly effective in accurately classifying mushrooms as UD or D, even on the day of damage induction, indicating potential for online mushroom classification systems [85]. [PERSON] et al. developed an Android app designed to classify mushrooms as poisonous or edible alongside a smart sensor-integrated mushroom chamber that monitors and controls temperature, automating the cultivation process. The system achieved a 72% confidence rate, with plans to enhance its accuracy by incorporating a more diverse dataset in the future [86].
# Geolocation, Calibration and Surface Resolution of CYGNSS GNSS-R Land Observations
## 2 Geolocating GNSS Reflections from Land
### Global Assessment of Land Geolocation Confidence  
GNSS-R observation geometries for which at least one local grid point satisfying the delay, Doppler and Snell criteria is located are defined as valid geolocation while other cases are classified as invalid geolocation. Figure 4 is a map resolved into 0.1 by 0.1 degree pixels of the fraction of valid geolocations using 12 days of data from all 8 CYGNSS observatories. Note that mean validity values between zero and one are possible due to the multiple looks within a map pixel from varying CYGNSS look angles.  
The geolocation confidence flag (which includes the effects of DDM SNR as defined in Table 2) is shown in Figure 5. The results show the frequent high confidence values in most regions except  
Figure 3: Contour maps of the three geolocation checks across local surface grids at second 11 of Figure 2. (**a**) Empirical delay difference (\(\delta\tau\)) contour map, (**b**) Empirical Doppler difference (\(\delta D\)) contour map, (**c**) Geometric Snell reflection angle difference (\(\delta\Psi\)), (**d**) Logical “AND” of the delay, Doppler and Snell area checks, indicating region which satisfies the tolerance limits of all three forward reflection geolocation criteria.  
those areas having high topographic variations. The percentage of the Earth's land surface in each confidence category are also included in Table 2). The global Earth land coverage percentage of observations estimated to be valid (confidence level greater than 2) was found to be 77.2%.  
Figure 4: Map of mean land geolocation validity flag (0 or 1). Areas of lower validity correlate strongly with regions of high terrain variability. CYGNSS is unable to capture DDMS from the Tibetan Plateau and Andes Mountain regions.  
Figure 5: Map of land geolocation confidence flag. Confidence values range between 0 (lowest) and 3 (highest). As expected the general trend is that the estimated geolocation is of higher confidence over lower lying and more moderate terrain. It can also be observed that the geolocation often more difficult in dense forest regions (South America and Central Africa) due to the relatively lower SNR from these surfaces, which make the empirical signal peak checks more difficult.