esa-sceva/satcom-chunk-collection huggingface dataset:
Winemaking is traditionally based on using grapes as the fruit of choice, although there are also traditional examples of fermented drinks made from rice, honey, and other fruits, such as persimmon and kiwi [3; 4]. Several fruits are grown in huge quantities around the world to obtain alcohol in the fermenting process. The process is similar to grape-based winemaking through alcoholic fermentation using yeasts, _Saccharomyces cerevisiae_ as the predominant species, obtaining ethanol, CO\({}_{2}\), and other secondary metabolites that increase the aroma profile such as esters, and higher alcohols [5]. Fruits other than grapes such as apples, berries, cherries, wild apricots, kiwifruit, plums, peaches, and strawberries are used in winemaking in many parts of the world. Cider, or apple wine, is obtained from fermented apples and is one of the most popular non-grape fruit wines [6]. In the US and Canada, some examples of fruit-based winemaking include strawberries, plums, peaches, blackberries, etc. Europe predominates in the use of apples and peaches, whilst Asia includes tropical and subtropical fruits such as mango, cocoa, pineapple, etc. [7]. The main components of theses fruits wines (alcohols, monoterpenic compounds, and ethyl esters) resulted in the production of fruity, green apple, banana, sweet, citrus, roses, and honey aroma, whereas aromas from typical white wines by grapes are described as fruity, floral, green, sweet, and fatty [7]. Melon (_Cucumis melo_ L.) is a fruit with a great commercial value that is cultivated in different parts of the world due to its adaptability to many soil types and temperatures. Melon production has risen by 20 million tons in the world [8]. Spain was the world's foremost melon exporter in 2020, producing more than 600,000 tonnes and exporting 440,000 tonnes, approximately 20% of total global exports [9]. _C. melo_ cultivars belonging to different botanical varieties such as cantalupensis, reticulatus, inodorus etc., are highly valued for their sweetness, aroma, and pulp texture. Some authors studied the physicochemical analysis of melon-based wine [10] and evaluated the majorities of volatiles in melon distillates [11]. In this research, we study two melon cultivars for fruit-based winemaking: Jimbee(orange flesh) and Okashi(r) (pale green flesh); no references for these fermented wines are available. The objective of this study is to develop and characterize a fruit-based wine from two melon cultivars with different flesh colors, when those fruits are discarded by the supermarket as fresh fruits for not meeting the required aesthetic standards, despite still having a high sensorial quality that could provide a newly fermented wine with commercial potential.
## 3 Experimental Results
### Software Solution  
As a result of this research and development project, a fully-fledged Python software package was developed for Reach-U Ltd. that could be used to generate the data for training and inspect them; annotate (digitize) the images as needed for creating defect masks and also updating the initial image masks; train the deep learning ConvNets and apply them to arbitrary new images. The package comprised back-end functionality in two separate Python libraries and also had graphical user interfaces developed using PyQt5. The intended end-user application entitled nnapply included a graphical user interface and allowed processing arbitrary road images using the trained ConvNets, showing the detected and suspected defects. It also generated a report in Microsoft Excel format. Examples of both types of output from this application are depicted in Figures 11 and 12.  
The implementation of the back-end described in [2] is now being updated to use the newly introduced deep learning libraries, but thanks to the proper separation of back- and front-end functionality, this is a relatively straightforward process.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **MCC** \\ \hline VGG16 net fine-tuned & 0.95 & 0.90 & 0.79 & 0.82 \\ \hline ResNet34 fine-tuned & 0.96 & 0.92 & 0.82 & 0.84 \\ \hline
**ResNet101 fine-tuned \({}^{*}\)** & **0.97** & **0.90** & **0.87** & **0.87** \\ \hline \hline \end{tabular} \({}^{*}\) Validation set figures are 0.96 for precision and 0.93 for recall.  
\end{table}
Table 7: Performance metrics of the obtained ConvNets computed over the test set discussed in Section 3.1.  
Figure 10: A selection of outputs from the ResNet101 classifier.  
Figure 11: Example image with defect location suggestions generated by the nnapply application. The highlighted area is unmasked, and therefore, only segments fully belonging to this area are considered during partitioning of the image. The segments are extracted at 75% overlap to provide more detail and color coded as red where the intensity of the color corresponds to classifier certainty to having discovered a defect. The regions of the orthoframe having a defect probability over 0.6 are displayed at a higher zoom level.  
Figure 12: Example report concerning potential pavement defects generated by the nnapply application.
# Call Model and Test-Verification Methods for PS-LTE Core Equipment
## 4 Measurement of Wireless Coverage and Quality
### Testing Mobile Base Stations
#### 4.3.1 Coverage Test Results of Vehicle-Type Mobile Base Stations  
The vehicle-type mobile base station (PCI: 500, antenna height of approximately 3 m) installed in the parking lot of the KT branch in Gangneung-si, Gangwon-do, has a low antenna height, which causes propagation loss due to neighboring buildings, and which is suitable to be used for traffic dispersion rather than securing coverage in urban areas. The coverage of the vehicle-type mobile base station is valid for up to 245 m in a straight-line distance. The portable mobile base station (PCI: 348, antenna height of approximately 1.5 m) also has a low antenna height and is blocked by neighboring buildings, and the output is also low; therefore, the portable mobile base station with a coverage of approximately 50 m is not suitable for use in urban areas.
# Reliability Analysis of the Main Pier during the Construction Period of HLCR Bridges
## 2 Theoretical Foundation
### Load Probability Modeling
#### 2.4.1 Structural Constant Load  
According to the characteristics of the hanging cradle construction, the structural constant load acting on the main pier in each construction stage mainly includes block gravity, block gravity deviation and block unbalance load. After analysis, the effect of the structural constant load produced at the top (bottom) of the pier is calculated as follows:  
(1) Effect of the action of the cast beam section at the top (bottom) of the pier [1]:  
\[S_{N_{G}}=k_{G}p_{0}+2\sum_{i=1}^{n}k_{G}p_{i}\ \ \ \ S_{M_{G}}=v_{p}\sum_{i=1}^{n}k_{G }p_{i}x_{i} \tag{19}\]  
Here, \(S_{N_{G}}\) is the axial force effect in the axial direction at the bridge pier under the action of cast blocks (kN); \(S_{M_{G}}\) is the along-bridge bending moment effect at the bridge pier under the action of cast blocks (kN\(\cdot\) m); \(k_{G}\) is the statistical parameter of constant load indeterminacy (according to the literature [30] it obeys a normal distribution, and is taken as \(\mu_{k_{G}}=1.0212\), \(\delta_{k_{G}}=0.0462\)); \(p_{i}\) is the weight of the \(i\)-th block (kN); \(v_{p}\) is the relative deviation coefficient of the self-weight of the beam caused by the construction error on both sides of the cantilever (according to the literature [6], it is taken as \(\mu_{v_{p}}=0.025\), \(\delta_{v_{p}}=0.15\)); \(x_{i}\) is the distance of the center of gravity of block \(i\) from the center of the top of the pier (m).  
2. The effect of deviations caused by different overhanging speeds at both ends of the pier top (bottom) [1] is: \[S_{N_{K}}=2k_{G}p_{K}+v^{\prime}_{p}k_{G}p_{K}\] (20) \[S_{M_{K}}=\Big{(}1-v^{\prime}_{p}\Big{)}k_{G}p_{K}x_{K}\] (21) where \(S_{N_{K}}\) is the axial force effect at piers due to beam section deviation (kN); \(S_{M_{K}}\) is the parallel moment effect at piers due to beam section deviation (kN\(\cdot\)m); \(p_{K}\) is the beam section weight (kN); \(x_{K}\) is the distance from the center of the beam section to the center of the top of the pier (m); \(v^{\prime}_{g}\) is the coefficient of imbalance of the pouring speed at both ends, that is, the ratio of the weight of the beam sections being poured on both sides (according to the literature [1], we assume \(\mu_{v_{p}}=0.5\), \(\delta_{v_{p}}=0.075\)); \(K\) is the beam section number being poured. 3. The effect of the self-weight of the bridge pier at the bottom of the pier [1] is: \[S_{N_{q}}=k_{G}qH\ \ \ \ \ S_{M_{q}}=0\] (22) where \(S_{N_{q}}\) is the pier bottom axial force effect caused by the self-weight of the bridge pier (kN); \(S_{M_{q}}\) is the pier bottom bending moment effect caused by the self-weight of the bridge pier (kN\(\cdot\)m); \(H\) is the bridge pier height (m); \(q\) is the average self-weight set of bridge pier (kN\(/\)m).
# Data-Decoupled Scattering Imaging Method Based on Autocorrelation Enhancement
## 3 Results and Analysis  
### Optical Imaging System  
As shown in Figure 6, a green laser with a wavelength of 532 nm, modulated by a half-wave plate (Edmund, HWP, #49-210), was used in combination with a spatial light modulator. The spatial light modulator improved the utilization of the incident light as the polarization state of the incident light can be adjusted. Then, the light was collimated and expanded by combining lenses (fL1 = 35 mm, fL2 = 400 mm), as an illumination source. The spatial light modulator (SLM) (HoloeyePluto-2-VIS-016, pixel size 8 um) loads the virtual target image into the optical path. The laser beam carrying the target information was modulated using two lenses (fL3 = 250 mm, fL4 = 100 mm; diaphragm aperture  
Figure 5: Relationship between complex objectives and essential elements.  
8 mm) and was incident on a static scattering medium (Edmund, frosted glass, 220 and 600 mesh). The speckle image formed after exiting was received by the detector (PYTHON1300 CMOS camera) with a distance (d) of 50 mm between the target and scattering media. Autocorrelation operations were performed using the acquired speckle images as the training and test sets of the model.  
Meanwhile, to address the robustness issue of deep neural networks, 220- and 600-mesh frosted-glass plates with different scattering characteristics were chosen as scattering media. The experimental optical path design was used to obtain the corresponding scattering images of the target. Virtual objects composed of basic elements such as circles, triangles, and squares, as well as their rotational variations and random positions, were used as initial data for the neural network training. The basic element dataset proved to be a simple target dataset due to the lack of apparent detail. The Fashion-MNIST dataset was used as an unknown complex target hidden behind the scattering medium, as the dataset was composed of common everyday clothing targets with more detailed features. Figure 7 shows the loaded basic geometric targets and complex targets obtained through SLM; the received speckle images, which are indistinguishable by the human eye; and the structured results obtained after performing the autocorrelation operation. Moreover, based on the results in Figure 7, the autocorrelation operation results of the same target under different scattering media have similar structures to their truth value autocorrelation results, except they are affected by noise differently. The autocorrelation can be used as prior knowledge to facilitate the optimization of neural networks as well as to improve generalization.  
Figure 6: Experimental optical path design: (**a**) simulated optical path design and (**b**) actual data-acquisition optical path.
# Automated Deep Learning Model for Sperm Head Segmentation, Pose Correction, and Classification
## 5 Results  
Table 2 and Figure 5 compare existing sperm head classification methods across metrics. Our method shows slightly lower performance than SHMC-Net cross-validation results, achieving 97.6% accuracy in cross-validation compared to SHMC-Net's 98.2%. The inclusion of a separate test dataset reduces the number of images available for training and cross-validation, increasing the impact of individual samples on overall accuracy and F1 scores. For instance, in the test set, each sample contributes 2.5% to the overall accuracy (the test set contains 40 images). Despite this, the small standard deviation of our results demonstrates that our method is notably more stable across different folds compared to SHMC-Net, highlighting the consistency of our approach. The macro-specificity of our method on the test set is 99.1%.  
Furthermore, most studies incorporate pose correction, except for one that reports the lowest accuracy, underscoring the critical role of pose correction in achieving reliable  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **PC** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline [PERSON] et al., 2017 [5] & M & 92.2 & 93.5 & 92.3 & 92.9 \\ [PERSON] et al., 2020 [29] & M & 95.7 & 96.1 & 95.5 & 95.5 \\ [PERSON] et al., 2021 [15] & - & 85.2 & 85.2 & 85.3 & 89 \\ [PERSON] et al., 2019 [12] & M & 94.0 & 94.7 & 94.1 & 94.1 \\ [PERSON] et al., 2021 [23] & A & 96.4 & 96.4 & 96.1 & 96.0 \\ [PERSON] et al., 2024 [18] & A & 98.2 \(\pm\) 0.3 & 98.3 \(\pm\) 0.3 & 98.1 \(\pm\) 0.3 & 98.2 \(\pm\) 0.3 \\ Ours (validation) & A & 97.6 \(\pm\) 0.01 & 97.9 \(\pm\) 0.01 & 97.6 \(\pm\) 0.01 & 97.2 \(\pm\) 0.01 \\ Ours & A & 97.5 & 97.7 & 97.5 & 97.5 \\ \hline \hline \end{tabular} PC: Pose correction approach, M: Manual approach, A: Automatic approach, \(\div\) No Correction.  
\end{table}
Table 2: Comparison of sperm head classification results for existing and our method.  
Figure 5: Performance comparison between existing sperm head classification methods and our proposed method.  
results. While some methods rely on manual correction, this approach is labor-intensive, time-consuming, and impractical for clinical applications. Automated strategies, such as those proposed by [PERSON] et al. and [PERSON] et al., involve complex workflows or additional computational overhead. By contrast, our method integrates segmentation, pose correction, and classification in an end-to-end manner, effectively reducing complexity and cumulative errors while enhancing both efficiency and accuracy.
# Combating Pathogens Using Carbon-Fiber Ionizers (CFIs) for Air Purification: A Narrative Review
## 4 Discussion
### Limitations  
There has been a growing interest in technologies purported to decrease levels of airborne pathogens in indoor spaces. In this study, we thoroughly reviewed the existing literature to explore studies investigating ion emissions in CFI systems, aiming to derive insights into their effectiveness in eliminating microorganisms from the human environment.  
We feel that it is necessary to tackle various issues pinpointed as constraints within specific studies or the broader realm of microbe inactivation induced by CFIs. The studies we examined differed on a number of relevant parameters, such as:  
1. Different particle sizes investigated;
2. Varying degrees of ozone emissions (or simply a lack of O\({}_{3}\) measurement in a particular study);
3. Range of microorganisms studied (some studies report on microbes that are not infectious to humans; many others, although they analyze the effect of ions on human pathogens, do so on only bacteria or viruses; the spectrum of microorganisms analyzed per study is restricted to only several species; ultimately, some microorganisms may be inherently more resistant to ions than others);
4. Vegetative bacterial forms (in this instance, we are still uncertain about how ions impact bacterial spores);
5. Different methods to measure microorganism viability (which can negatively impact interstudy comparisons; viability was also not measured in every study);
6. CFI-related parameters (voltage, material used, and flow velocity);
7. Definition of ion source (an example is a study that does not define whether a CFI unit is utilized);Type of most efficient ions (the optimal type of ions is also disputed, as in several instances a frank contradiction between study results was observed (Cf. Table 1).  
An important issue to note is the lack of real-world data, i.e., conclusions derived from experiments conducted under real-life indoor and/or outdoor conditions (e.g., the CFI effect on removing airborne particulate matter from the breathing zone in offices, storage facilities, education institutions, parks, etc.) where temperature, air currents, and moisture have temporal variations. While creating an appropriate experimental setup to mimic these environments poses challenges, we believe that relying solely on extrapolated data from lab conditions should be approached cautiously. However, it is probable that these limitations are less pronounced for HVAC systems and similar structures.
## 3 Results and Discussion
### EPR Spectroscopy Investigation
#### 3.2.1 The \(g\)-Factor  
The \(g\)-factor (or Lande factor) indicates the magnetic field of resonance (position in the spectrum); it is a parameter sensitive to the chemical environment of the unpaired electron, i.e., to the specific features of the orbital in which it is localized.  
The \(g\)-factor increases in the presence of heteroatoms, owing to their contribution to the electron molecular orbital. In fact, the heteroatoms shift \(g\) to values higher than 2.0023 corresponding to free electron [44]. For all the asphaltene samples, whether unaged or aged, the peak assigned to organic radical gives \(g=2.0027\)-2.0028 (see Table S1 in Supplementary Material) very close to the free electron value, thus indicating that if present in these radicals, heteroatoms are not likely to participate in the molecular orbital of the unpaired electron to any significant degree. Even for maltene samples, the Lande factor remains substantially constant in the range 2.0025-2.0029 (see Table S1 in Supplementary Material) comparable with that obtained for asphaltene. A weak reduction has been found only in the case of maltene fraction isolated from CTRL upon RTFOT treatment (2.0028 \(\rightarrow\) 2.0025). The effects of the oxidation process are in fact more evident on the reference system free of antioxidants, which inevitably favors the aggregation and condensation of adjacent units, forming more complex structures. The latter are accompanied by an increase in the level of aromaticy responsible for the shift of \(g\)-factor towards lower values. Finally, the \(g\)-value for the signal of the vanadyl group does not undergo any variation as a result of the oxidation process for both asphaltene (2.0018-2.0019) and maltene (2.0011-2.0014) fractions analyzed (see Table S1 in Supplementary Material). This condition is in accordance with the fact that in the primary aging process simulated by heat treatment the vanadyl group is not involved in any way.
###### Abstract  
With the rapid development of satellite technology and the high transmission efficiency of LEO satellites, LEO satellite communication has received increasing attention. However, the frequent switching of satellite-earth links imposes a great challenge in LEO communication authentication. To tackle this challenge, this paper proposes a Blockchain-based Authentication Protocol Using Cryptocurrency Technology (BAPC), which solves the problem of a long pause time of satellite services caused by user access authentication in a scenario of frequent switching between satellites and ground users. First, we design three stages of the authentication process and introduce the cryptocurrency technology. Using currency transactions as the certificate of authentication improves not only the security of authentication, but also the efficiency of switching authentication. Next, in the network topology, the satellite cluster is divided into multiple regions to improve the efficiency of block consensus. Finally, the protocol is tested through extensive NS2-based simulations, and the results verify that BAPC can greatly shorten the response time of switching authentication and significantly reduce the time of block generation and the network throughput. As the number of users increases, the block generation time and network throughput can be further reduced.  
LEO satellite networks; blockchain; cryptocurrency; switching authentication 2021, 10, 3151. [[https://doi.org/10.3390/electronics10243151](https://doi.org/10.3390/electronics10243151)]([https://doi.org/10.3390/electronics10243151](https://doi.org/10.3390/electronics10243151))
# Fault Detection and VSC-HVDC Network Dynamics Analysis for the Faults in Its Host AC Networks
## 4 Performance Analysis
### Effect of Sudden Load Switching (SLS)  
Sudden load variations in the line may possess detection-related challenges for the protective relay. The relay may maloperate due to such a condition. To further analyze these conditions, a sudden load switching condition was tested for 100% and 125% base loads. The load connected to the PCC was switched on at 2.5 s. The load had a 0.8 lagging power factor. The response of the method for this condition is shown in Figure 11. The DTE index, although changing, remained below the threshold, and thus the proposed method would detect this condition as a non-fault condition. Therefore, the load switching condition is not a problem for the proposed method, and it remains dependable for this condition.
## 3 Results  
In the hyperparameter setting of each algorithm model, the epoch of U-Net and DeepLabV3+ [23] was set to 100, and the epoch of DeepLabV3+ (ResNet50) and SegNet was set to 50. The MiniBatchSize setting was set to 20 for DeepLabV3+ (ResNet50) and 6 for the rest of the algorithms. Adaptive Moment Estimation (Adam) Optimizer was used for U-Net, DeepLabV3+ (ResNet18), and DeepLabV3+ (ResNet50); rmsprop Optimizer was used for SegNet; and training was performed with a constant learning rate of 0.001 (Table 4).  
With the performance metrics results (Table 5), when comparing each algorithm (Table 6), DeepLabV3+ (ResNet50) achieved the best IOU and F1 score among the algorithms. DeepLabV3+ (ResNet50) had the best performance, with an f1-score of 0.742, followed by U-Net with 0.720, and the other two had similar values. For Precision, U-Net had the highest value of 0.776, while SegNet and DeepLabV3+ (ResNet50) had similar values. DeepLabV3+ (ResNet50) showed the best recall, with 0.742, while DeepLabV3+ (ResNet18) and U-Net showed similar values.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **U-Net** & **SegNet** & \
\begin{tabular}{c} **DeepLab V3+** \\ **(ResNet18)** \\ \end{tabular} &
\begin{tabular}{c} **DeepLab V3+** \\ **(ResNet50)** \\ \end{tabular} \\ \hline Optimizer & Adam & rmsprop & Adam & Adam \\ Learning rate & 0.001 & 0.001 & 0.001 & 0.001 \\ Epoch & 100 & 50 & 100 & 50 \\ miniBatchSize & 6 & 6 & 6 & 20 \\ EncoderDepth & 4 & 4 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Setting hyperparameters for each algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **TP** & **FP** & **FN** & **TN** \\ \hline U-Net & 48,767 & 14,111 & 23,739 & 13,016,160 \\ SegNet & 46,213 & 14,568 & 26,293 & 13,017,791 \\ DeepLab V3+ & 48,808 & 18,534 & 23,698 & 13,020,583 \\ DeepLab V3+ & 52,731 & 16,903 & 19,775 & 13,020,126 \\ (ResNet50) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance metrics results.