esa-sceva/satcom-chunk-collection huggingface dataset:
# Cell Response to Oxidative Stress in Antarctic Filamentous Fungi
## 7 Cold Stress Response of Antarctic Fungi and Adaptation  
We have summarized a compendium of research reports on the antioxidant cell response data for fungi with a geographical origin of Antarctica (Table 3). \begin{table}
\begin{tabular}{l l l l} \hline
**Strain** & **Sources** & **Response** & **References** \\ \hline \multicolumn{4}{c}{**Morphological adaptation**} \\ \hline _Cryomyces antarcticus_ & \begin{tabular}{l} McMurdo Dry Valleys, \\ Southern Victoria Land, \\ \end{tabular} & Meristematic growth & [75] \\ \hline _Cryomyces minteri_ & Alatna Valley, Victoria Land & Meristematic growth & [75] \\ \hline _Friedmanniomyces_ & \begin{tabular}{l} Victoria Land \\ \end{tabular} & \begin{tabular}{l} Meristematic growth, \\ Melanization \\ \end{tabular} & [185] \\ \hline _Friedmanniomyces_ & Northern Victoria Land & Meristematic growth & [75] \\ \hline _Penicillium_ sp. 1-6-4 & Livingston island & \begin{tabular}{l} Intercalary growth \\ chlamdospores \\ \end{tabular} & [47] \\ \hline _Friedmanniomyces_ & \begin{tabular}{l} Northern Victoria Land \\ \end{tabular} & \begin{tabular}{l} Morphological changes \\ \end{tabular} & [155] \\ \hline _Mrakia blollopis_ & \begin{tabular}{l} Vestfold Hills area of Davis \\ \end{tabular} & \begin{tabular}{l} Morphological changes of cells, \\ hyphae, colonies \\ \end{tabular} & [186] \\ \hline _Penicillium commune_ 161 & Livingston Island & Ultrastructural changes & [187] \\ \hline _Penicillium grisefoulvum_ & Terra Nova Bay & Ultrastructural changes & [171] \\ \hline _Cryomyces antarcticus_ & McMurdo Dry Valleys & Ultrastructural changes & [188] \\ \hline \multicolumn{4}{c}{**Physiological Changes**} \\ \hline _Penicilliumgrisefoulvum_ & Terra Nova Bay & Decrease in biomass content & [171] \\ \hline _Penicillium olsonii_ p14 & Casey Station & \begin{tabular}{l} Decrease in biomass content \\ Terra Nova Bay \\ \end{tabular} & [38; 52; 87; 170] \\ \hline _Pichia pastoris_ & Cold-adapted & Decrease in biomass content & [189] \\ \hline _Pseudogymtoascus roseus_ & Mars Oasis, Alexander Island & Decrease in biomass content & [190] \\ \hline _Volvariella volvacea_ & Cold-adapted & Decrease in biomass content & [191] \\ \hline _Mortierella elongata_ & Signy Island & Trehalose accumulation & [168] \\ \hline _Volvariella volvacea_ & Soil samples & Trehalose accumulation & [192] \\ \hline _Penicillium olsonii_ p14 & Casey Station & \begin{tabular}{l} Trehalose and \\ glycogen accumulation \\ \end{tabular} & [52; 87; 170] \\ \hline _Aspergillus fumigatus_ I-9 & Livingston Island & \
\begin{tabular}{l} Trehalose and \\ glycogen accumulation \\ \end{tabular} & [167] \\ \hline _Aspergillus glaucus_ & Livingston Island &
\begin{tabular}{l} Trehalose and \\ glycogen accumulation \\ \end{tabular} & [163] \\ \hline \end{tabular}
\end{table} 
Table 3: Antioxidant cell response of Antarctic fungi.
# Impact and Stability of mandibular Setback after Intraoral Vertical Ramus Osteotomy
## 3 Results
### Stability  
The measurement of skeletal stability was performed by calculating the changes between the SNB angle and the distance between the Co and B points based on lateral cephalograms at T1 (3 days postoperative) and T2 (8 months postoperative). At T2, the mean SNB angle was 79.99 degrees (SD = 4.39), while at T1, the mean SNB was 81.71 degrees (SD = 3.97) with significant differences (\(p<0.01\)). The mean distance between the Co and B points at T2 was 104.89 mm compared to 106.50 mm at T1; the changes from T1 to T2 were not statistically significant (\(p=0.07\)), representing skeletal stability with a mean backward relapse of 1.6 mm (as presented in Table 1).  
The mean overbite before surgery (T0) was \(-0.16\) mm (SD), and the overjet was \(-5.76\) mm (SD = 2.72). At day 3 postoperatively (T1), the mean overbite was \(\pm 1.78\) mm,  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **N** & **Mean (+SD)** & **Mean Relapse** & \(p\)**Value (\(t\)-Test)** \\ \hline T1 SNB (degrees) & & 81.71 + 3.97 & & \\ \hline T2 SNB (degrees) & & 79.99 + 4.39 & 1.72 & \(p<0.01\) \\ \hline T1 Co–B (mm) & & 106.15 + 9.08 & & \\ \hline T2 Co–B (mm) & & 104.89 + 8.16 & 1.60 & 0.07 \\ \hline \end{tabular}
\end{table}
Table 1: Skeletal stability; SNB angle and Co-B measurements.  
Figure 2: Lateral cephalometric X-ray measuring points. Abbreviations: OB—Overbite; OJ—Overjet.  
and overjet was 1.73 mm (SD = 0.78, SD = 0.57), with statistical significance (\(p<0.01\)) representing the surgical setback. At T2 (8 months postoperatively), the mean overbite was 1.8 mm (SD = 0.56), and the mean overjet was 1.60 mm. (SD = 0.78). The changes from T1 to T2 were not statistically significant for either overbite (\(p\) = 0.19) or overjet (\(p\) = 0.46), representing dental stability with a mean relapse of less than 2 mm (OB \(\pm\) 0.17, OJ \(\pm\) \(-\)0.06) (as presented in Table 2).
## 5 Conclusions  
This study employed state-of-the-art techniques to measure the student activity (SA) level based on audio monitoring. Simultaneously, variations in temperature and relative humidity were recorded in school environments that experienced significant fluctuations in student presence between lectures.  
The results showed a partially uncorrelated behavior between the average temperature recorded inside the lecture hall during each session and the corresponding occupancy level.  
A result of particular interest contextualized within the specific scenario of environments in sub-optimal conditions was that the SA level appeared to negatively correlate with occupancy. In other words, in highly attended lecture halls, the higher the occupancy,  
Figure 7: Comparison between measured SA levels and predicted values.  
the lower the SA level. A somewhat consistent behavior emerged: above a certain SA level threshold, students tended to speak less. In other words, the relationship between SA level and occupancy density did not correlate as one might have expected based on the concept of group size typical of cocktail party effects.  
Conceptualization, G.F., D.D.S. and D.D.; methodology, G.F., D.D.S. and D.D.; software, D.D.S.; validation, G.F. and D.D.S.; investigation, G.F.; data curation, D.D.S.; writing--original draft preparation, G.F., D.D.S., V.T. and D.D.; writing--review and editing, D.D.; supervision, M.G., C.B. and P.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
GMMGaussian Mixture Model  
PAPublic address  
RHRelative humidity  
RTReverberation time  
SAStudent activity  
TTemperature
## 4 Optimal Energy Control System  
### OECS Design  
In this study, an OECS has been proposed to form the aircraft longitudinal flight control system and to control the airspeed and altitude of the aircraft. There are two parameters, specific energy distribution rate, \(\dot{L}_{\mathrm{s}}\), and total specific energy rate, \(\dot{E}_{\mathrm{s}}\), utilized to characterize the energy of an aircraft or UAV. The block diagram of the proposed OECS is shown in Figure 9. The block diagram of the total energy loop is used to perform the tracking of aircraft altitude. Altitude tracking begins with the commanded altitude, \(h_{\mathrm{c}}\). The difference between the commanded altitude and the current altitude of the aircraft, \(h\), is the altitude error, \(h_{\mathrm{e}}\) which in turn will be scheduled by gains, \(K_{h}\). The values of \(K_{h}\) were decided according to the tests of flight simulations. Table 3 shows the gains used in the gain scheduling. This simple gain scheduling is necessary to guarantee the throttle response is small, and thus meet the linearized total energy model, which stated that the increment of total specific energy rate, \(\dot{E}_{\mathrm{e}}\), is proportional to \(\Delta\delta_{\mathrm{T}}\). The result of gain scheduling is the climb rate or descent rate of an aircraft, which in turn will be divided by airspeed to obtain the flight path angle error, \(\gamma_{\mathrm{e}}\). By assuming that the airspeed is almost constant, thus the longitudinal acceleration will be neglected.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{\(\Delta\delta_{\mathrm{T}}\left(PWM\right)=15\)} \\ \hline
**Flight Tests** & \(V\) **(m/s)** & \(h\) **(m)** & \(\Delta\dot{E}_{\mathrm{s}}\) \\ \hline _f33 run10_ & 30.0 & 150.5 & 0.0050 \\ _f33 run11_ & 30.2 & 152.6 & 0.0045 \\ _f33 run12_ & 30.0 & 150.7 & 0.0050 \\ _f33 run13_ & 30.0 & 151.0 & 0.0050 \\ _f33 run14_ & 30.1 & 153.0 & 0.0050 \\ \hline Average & **-** & **-** & 0.0049 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The average value of \(\Delta\dot{E}_{\mathrm{s}}\) from positive throttle input. PWM, Pulse Width Modulation.  
The inner loop is energy distribution and pitch stabilization loop where the \(\dot{L}_{\rm s}\) is regulated to zero. The inner loop, controlled by elevator, is responsible to distribute the energy of the aircraft between airspeed and altitude. The outer loop is total energy loop, which corresponds to the total energy of the aircraft. Total energy loop, controlled by throttle, is responsible to drive the total specific energy rate error \(\Delta\dot{E}_{\rm s}\) to zero.
# Effect of the Insulating Layer on the Properties of SMC Inductors
###### Abstract  
In inductor applications, different soft magnetic materials are used depending on the frequency range. Owing to powder metallurgy technology and to the increase in the implementation of innovative multifunctional materials, it is possible to find an alternative to the traditional magnetic materials of the inductance application sector. This study concerns a deep analysis related to soft magnetic composite materials. The insulating layer's effect is investigated to explore the applicability of such materials to the inductor sector. Four coatings systems are selected and two types of samples are prepared in the shape of a toroid and a rod, which are tested in different operating conditions. The rod inductors are also compared with a traditional one, based on soft ferrite materials. This work aims to integrate data coming from different measuring devices: the useful small-signal measurements of an RLC meter are completed by large-cycle data measured through a hysteresisraph. Different parameters are considered for the investigation: magnetic permeability (maximum and initial), iron losses at different induction peak values, and inductor quality factor are the most important. The obtained results prove that each analysis type is not fully reliable without the other in determining the coatings' effects on the SMC inductors' performance. In the end, it is demonstrated that SMC inductances can be successfully applied in a particular frequency range.  
soft magnetic; soft magnetic composite; inductance; Q factor; magnetic permeability; iron losses; magnetic measurements; epoxy resin; soft ferrites +
Footnote †: journal: Applied Physics Letters
# Joint Diagnosis of RIS and BS for RIS-Aided Millimeter-Wave System
## 3 Proposed Algorithm  
From Equation (6), we find that the received signal \(\mathbf{y}(t)\) is affected by the RIS blockage and the BS blockage simultaneously. We call this effect the blockage coupling. Due to the effect of blockage coupling, the blockage estimation method proposed in [10; 11] for the scenario where the RIS is blocked and the BS is free cannot be applied for the scenario considered in this paper. In this paper, we assume that the cascaded channel information \(\mathbf{G}^{\text{BR}}\text{diag}(\mathbf{g}^{RU})\) is known.
# A Raisin Foreign Object Target Detection Method Based on Improved YOLOv8
## 2 Materials and Methods
### YOLOv8  
YOLO's fundamental concept is to approach object detection as a direct, end-to-end regression problem, with the algorithm involving a single neural network that predicts object bounding boxes and class probabilities directly from the input image. YOLOv8, introduced in 2023, builds on the YOLOv5 framework, significantly improving its performance. It integrates the capability to classify, detect, and segment objects within a single model. The comprehensive structure of YOLOv8 is illustrated in Figure 4.  
Figure 3: Images after data augmentation.  
Figure 2: Raisin images to be detected.  
In the backbone network algorithm, the initial convolutional layer has transitioned from the original 6 \(\times\) 6 convolution to a 3 \(\times\) 3 convolution. Additionally, the C3 module has been substituted with the C2f module. These modifications have decreased the complexity and parameter count of the model, resulting in a more streamlined design that improves the richness of gradient flow information. In the neck layer algorithm, the 1 \(\times\) 1 convolutional reduction layer has been eliminated, with the C3 module being replaced by the C2f module. The detection head has been equipped with a decoupled architecture, which separates the classification and regression tasks. Additionally, the anchor-based approach has been replaced with anchor-free, which removes the anchor boxes, simplifying the model design and enhancing adaptability to targets of varying shapes and sizes. This approach diminishes the necessity for extensive hyperparameter adjustment while enhancing the model's capacity for generalization. In the algorithm for the loss function, Binary Cross-Entropy (BCE) Loss serves as the metric for classification loss, whereas Combined Intersection over Union (CIOU) Loss, in conjunction with Distance Factor (DFL) Loss, is employed for regression loss. BCE Loss optimizes classification performance, CIOU Loss improves the accuracy of bounding box localization, and DFL Loss enhances the model's focus on difficult examples during training, achieving comprehensive optimization for object detection tasks, particularly improving the detection capability for small objects and class imbalance issues. For the training strategy algorithm, the addition of Mosaic data augmentation in the final 10 epochs has effectively improved accuracy.
# Lightweight Multiscale CNN Model for Wheat Disease Detection
## 3 Results
### Exploring the Impact of the Inception Module on the Model  
In order to explore the influence of the Inception module on the model, we conducted four sets of experiments using the wheat disease dataset and tested different models with Inception modules. The results are shown in Table 4. It can be seen from the table that only the Inception-1 module was added to the model, and the accuracy, precision, recall, and F1-score of the model increased by 0.76%, 0.75%, 0.83%, and 0.78%, respectively, while Param slightly increased, and FLOPs increased by 0.9 G. The model with Inception-1 and Inception-2 had a lower performance improvement than that of adding only the Inception-1 module. The model with the three Inception modules achieved the greatest improvement in performance but also increased Param by 1.18 M, and FLOPs by double. Based on the comparison, we conclude that adding more Inception modules improves the detection accuracy of the model for wheat diseases but also increases the number of parameters and computations.  
Figure 9 shows the feature maps of layer 1, layer 2, and layer 3 of the model. The first row of the feature maps does not have any Inception module, the second row contains the Inception-1 module, the third row contains both the Inception-1 and Inception-2 modules, and the fourth row contains all three Inception modules. The shallow layers of the model mainly extract features such as image texture, edge, and color. The deep feature maps of the model reduce the visual information of the image and increase the abstract information. The model without any Inception module loses a lot of disease features. Adding more Inception modules enhances the model's ability to integrate the features in the shallow feature maps with the deep semantic information, highlighting the characteristics of the diseased area. The feature map of layer 3 for the model without any Inception module in Figure 9c still preserves clear visual information of the image, which may be caused by the insufficient depth of the model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Inception-1** & **Inception-2** & **Inception-3** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Param** & **FLOPs** \\  & & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(M)** & **(G)** \\ \hline \(\times\) & \(\times\) & \(\times\) & 95.40 & 95.53 & 95.52 & 95.53 & 3.06 & 0.42 \\ \(\surd\) & \(\times\) & \(\times\) & 96.16 & 96.28 & 96.35 & 96.31 & 3.07 & 0.51 \\ \(\surd\) & \(\surd\) & \(\times\) & 96.35 & 96.32 & 96.36 & 96.33 & 3.23 & 0.62 \\ \(\surd\) & \(\surd\) & \(\surd\) & 98.76 & 98.77 & 98.81 & 98.79 & 4.24 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The impact of the Inception module on the model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Optimizer** & **Average Accuracy** \\  & **(\%)** \\ \hline SGD & 75.97 \(\pm\) 0.21 \\ RMSprop & 97.18 \(\pm\) 0.15 \\ AdaGrad & 89.56 \(\pm\) 0.13 \\ Adam & 98.64 \(\pm\) 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test results for different optimizers.
## 3 Preliminaries
### Geospatial Division Model  
As discussed earlier, in proposed schemes network filed is logically divided into \(C_{n}\) cubes through geospatial division method. The following relationships between two cubes are:  
* Two cubes are adjacent to each other at common vertex, that is vertex adjacent.
* Two cubes are neighbor with common one edge, that is edge adjacent.
* If two cubes have adjacent surface to one another, that is surface adjacent.
* Otherwise, cubes are completely disjoint.  
The first three; cubes have adjacent vertex, edges and surfaces. Moreover, each cube has 8 adjacent neighbor vertex, 12 edges and 6 surfaces. Figure 3 denotes a current cube (CC) with its neighbor cubes (NCs), NC1, NC2 up to NC5. The selection of cubes is discussed in Section 4.
## 3 Materials and Methods
### Processing Techniques
#### 3.3.2 Sentinel-2  
The Sentinel-2 mission comprises two polar-orbiting satellites, Sentinel-2A and Sentinel-2B (launched in June 2015 and March 2017, respectively), placed at 180\({}^{\circ}\) from each other in the same sun-synchronous orbit. They carry a Multispectral Instrument (MSI) providing multispectral data in 13 bands spanning from visible (VIS) to short-wave infrared (SWIR) wavelengths. The SWIR channels in particular, allow for detection of thermal emission produced by hot bodies. Considering a revisit frequency of five days under cloud-free conditions (reduced to 2-3 days at mid-latitudes), and a spatial resolution of 20 m/pixel in the SWIR bands, Sentinel-2 provide important measurements for volcano thermal monitoring. Specifically, analysis of thermal and morphometric features of heat sources facilitates tracking of a series of volcanic processes, including lava flow advancements, lava lake pulses, extrusion phases of lava domes, fumarolic activity, thermal activity at multiple active craters, and rise of magma column in open-vent volcanic systems (e.g., [69; 70]).  
MOUNTS downloads and analyzes Sentinel-2 Level 1C products. We use an approach presented in [55], which is an enhancement of the HOTMAP detection algorithm developed by [71]. It is based on fixed ratios between SWIR bands, and on a contextual threshold derived from a statistical distribution of the thermal anomaly clusters. Images are analyzed considering the TOA (Top of the Atmosphere) reflectance of the 12-11-8A bands (R: 2190 nm, \(\phi\)12; G: 1610 nm, \(\phi\)11; B: 865 nm, \(\phi\)8a). The algorithm detects the number of \"hot\" pixels in each downloaded image, and stores this parameter (SWIR) which can be displayed in time series. This thermal algorithm was already successfully verified on different volcanic cases worldwide, and compared with MIROVA data, showing a strong correlation between the detected number of \"hot\" pixels in Sentinel-2, and the VRP [55].
## 3 Results
### Identify Sources of Uncertainty and Generate Alternatives in Satellite Megacontellation Scenarios
Figure 4: Alternatives to embed flexibility in an EP architecture, in the case of change of type of propellant. Please note that Option 3 represents the case of a single string propulsion system that could mount a xenon tank or a krypton tank. This is symbolized by a red rhombus. Also, the asterisk (*) represents components that are sub-optimized (e.g., reduced efficiency) in order to enable flexibility. krypton option (with performance and reliability levels equal to existing xenon options available today), it impacts substantially the development, test and qualification activities since extensive simulation [51] and physical testing [52] needs to be performed. Option 3 is labelled as _Flexible design_ (single system compatible with either Xe or Kr): in in this option, the possibility to use either xenon or krypton in the same system is enabled by the design a single product compatible for both propellants. Please note that Option 3 represents a single string propulsion system (same as Option 1 and Option 2) that is compatible with either xenon or krypton. This means that the single string propulsion system can be sold by either mounting a xenon tank or by mounting a krypton tank. This is symbolized by a red rhombus in Figure 4. The actual technical means to include this possibility are omitted in this paper to protect company-sensitive information. However, a way of achieving flexibility is to emphasize modularity with standardized interfaces (e.g., [53]). The downside of this option is that it features lower performances and reliability levels lower than the previous options, since it requires \"sub-optimized\" components to be compatible with both propellants. Such sub-optimization of components (e.g., reduced efficiency) is indicated in Figure 4 by an asterisk (*). At the same time, test and qualification activities are also impacted since the compatibility of the hardware with both propellants needs to be tested and qualified. Option 4 is labelled as _Flexible design, reliability improved_: this option allows the use of both propellants, with the additional benefit that the reliability of the system is increased. Reliability is increased by the addition of a redundant thruster and of a thruster switching unit (TSU) which is used to switch the power lines in case of a malfunction in the main thruster. This option requires additional test and qualification of the TSU, as well as additional costs to integrate the redundant thruster and the TSU in the system.