esa-sceva/satcom-chunk-collection huggingface dataset:
# A Noncontact Method for Calibrating the Angle and Position of the Composite Module Array
## 3 Calibration Method
### Angle Calibration Experiments  
The experiments were performed on the CMM, and the composite module array was placed along the X direction of the CMM. The angle sensor was installed on the spindle of the CMM and moved along the X direction of the CMM. At the same time, the Renishaw laser interferometer was used to compensate the spindle angle error. Calibration experiments were performed five times in total, and the results are shown in Figure 9.  
The standard deviation of the data was calculated according to the Bessel formula, and the calculation results are shown in Table 13. Standard deviation was used to evaluate repeatability error. Therefore, the angle repeatability error was within 0.4'' around the X-axis and within 0.3'' around the Y-axis.  
Figure 8: Experimental system.  
Figure 9: Repeatability of the calibration experiment: (**a**) angle around the X-axis; (**b**) angle around the Y-axis.  
The average of five experimental results was used as the final calibration result in Table 14. It can be seen that due to the effect of the three fine threaded threads, the relative angle of each composite module was within \(\pm\)180\(\arcsec\).
## 3 Research Methodology  
### Data Collection  
In order to evaluate energy potential and find solar panel defects, gathering data is an essential part of this study process. The energy data are sourced from Kangwon National University's Samcheok campus, with a particular focus on the Green Energy Building as the sample site. These data encompass metrics related to solar energy production, including historical weather patterns, solar irradiance levels, temperature, and humidity. These variables are vital for predicting energy output using advanced machine learning models such as the Transformer model. Historical weather data offer insights into seasonal trends while solar irradiance data reflect the available sunlight, both essential for accurate energy forecasting.  
For solar panel fault detection, the study collects data in the form of images and sensor readings. This includes thermographic images that reveal temperature variations on the solar panel surfaces, which can indicate potential issues like hotspots or degradation. Furthermore, sensor data--such as voltage and current measurements--assist in locating abnormalities in performance. Convolutional Neural Networks (CNNs) and ResNet-50 are two machine learning models that are trained with these photos and sensor data in order to automatically identify and categorize solar panel defects. The university's solar-hydrogen system analysis and problem detection procedures depend critically on the integration of these datasets.
## 2 Literature Survey
The technology showcased its promise for practical CV-QKD applications in short- to medium-range communication, as the test findings demonstrated its ability to reach safe key rates of 3 Mbits/s over a 30 km transmission distance. In spite of these encouraging outcomes, the system was not without its flaws; namely, its extreme susceptibility to jitter and noise, which may diminish its performance, particularly in contexts where communication is lengthy or extremely noisy. The need for additional tuning to ensure improvement in stability and resistance to noise for the system was acknowledged. The CV-MDI-QKD system, which stands for Continuous-Variable Measurement-Device-Independent Quantum Key Distribution, can handle complicated communication situations [24]. Parameter estimate is the first step in the process to evaluate quantum channels for better key distribution reliability. In addition, the system was fortified against possible assaults using Gaussian post-selection technology, which secured the quantum states and filtered out noise, especially when the attack was on the target quantum channel. The experimental results showed the suggested framework enabled sustainable enhancement of the CV-MDI-QKD system's security when compared to prior approaches. The total efficiency of the system was reduced due to the loss of important information during the filtering process, which is a major constraint that requires addressing. The requirements for parameter estimation and post-selection methods to achieve a better balance between security and performance, considering the trade-off between enhanced security and lower efficiency, could be a subject for future study. Table 1 presents details of the CV-QKD protocols that were analyzed, with the optimization approaches, quantitative performance outcomes, and limitations of each protocol.
## 3 Results and Discussion
### Effect of Breed and Finishing Diet on Volatile Compounds
#### 3.2.2 Acids  
In this study, three distinct acids were identified in our samples (Table 3). As can be seen, the breed significantly (\(p<0.05\)) affected both the total amounts of acids and each individual compound values. In particular, BU foals reported significantly greater (\(p<0.001\)) total content than JN foals (11.72 _vs._ 8.30 AU \(\times\) 10\({}^{4}\)/g fresh meat). Unsurprisingly, this trend is also reflected in the singular acids identified, where the BU group showed significantly higher (\(p<0.05\)) amounts, apart from butanoic acid, 4-hydroxy-. This latter compound, in fact, represented the most abundant acid identified in JN foals (\(p<0.05\)), while the hexanoic acid is the predominant in BU group (\(p<0.001\)).  
These compounds were not detected in raw meat from GM \(\times\) HB foals [25; 27], so they could be considered characteristic of these autochthonous Navarre foal breeds. However, deeper investigations are necessary to define these volatiles compounds as potential breed biomarkers. The contribution of acids on the total volatile compounds was very low. Indeed, this family showed to be the second with the least presence in BU foals and the least plentiful in JN group. More concretely, total acids represented 0.28 and 0.24% in foals from the BU and JN breeds, respectively (Figure 1).  
Furthermore, statistical analysis revealed that acid compounds (both total and singular compounds) were strongly (\(p<0.05\)) influenced by the type of finishing diet (Table 3). Actually, D1 foals showed a total content significantly (\(p<0.001\)) higher than D2 ones, and individual acids followed the same trend. The D2 group displayed the lowest (\(p<0.001\)) amounts of the singular compounds detected in comparison with D1 foals. In particular, hexanoic acid showed to be the most abundant compound in the D1 group, representing 54.88% of the total acids. This outcome could be justified by the fact that D1 foals also reported the highest concentrations of C18:1\(n\)-9 (Table 2). A recent investigation actually stated that hexanoic acid could be originated by lipid degradation, and especially by this fatty acid [29]. Considering the total volatile compounds, also in this case, this class of family represented a low percentage. In fact, D1 and D2 generated only 0.32 and 0.19% of total acids, respectively.  
Therefore, statistical analysis showed significant differences among the four groups, where the BU-D1 group generated the major amounts of acids (15.48 AU \(\times\) 10\({}^{4}\)/g fresh meat), while JN-D2 foils reported the lowest values (5.20 AU \(\times\) 10\({}^{4}\)/g fresh meat). However, significant interactions among the main categories (breed \(\times\) finishing diet) were not found (\(p>0.05\)).
# Cloud-Edge Collaborative Model Adaptation Based on Deep Q-Network and Transfer Feature Extraction
## 4 Experiment
### Ablation and Baseline Comparison Experiments
#### 4.4.1 Analysis of the Impact on Performance After Removing the Reinforcement Learning Mechanism  
In the absence of the reinforcement learning mechanism, the triggering of category transfer relies on manual configuration. This experiment aims to verify the effectiveness of introducing a reinforcement learning strategy in improving the model's adaptability. Table 5 presents the inference performance comparison of different types of aircraft targets with and without the reinforcement learning mechanism.  
The experimental results validate the effectiveness of introducing the reinforcement learning mechanism. As a migration trigger strategy, reinforcement learning plays a positive role in the detection of different target categories, improving the model's adaptability to uncertain targets and enhancing detection accuracy to a certain extent. After removing this mechanism, the model loses its ability to automatically adjust inter-class migration strategies, resulting in a slight decline in inference performance.
## 3 Methodology  
First, reference data were prepared for the LC classification of the Kaski district during the years 2020 and 2010. For this purpose, unsupervised K-means classification was performed on the Landsat 8 and Landsat 5 imagery. A total of 50 clusters was made for the classification. The spectral similarity between the classes was observed. With the help of unsupervised clusters and the respective year's google imagery, the reference data were prepared. Later the number of reference data was adjusted in proportional to the area of the ten unsupervised training classes. In GEE, a cloud-free composite of the Landsat 5 and Landsat 8 surface reflectance (SR) of the whole year was taken using the median filter for the composite. Cloud-free composites were made by masking out the clouds from the image. Snow masking was also performed to remove the void pixels from the image. The true- and false-color composites of the median Landsat SR are shown in Figure 5.  
Figure 4: Distribution of training data: (**a**) for the year 2010; (**b**) for the year 2020.  
Figure 5: Median composite of the clipped Landsat scenes of Kaski District for the year 2020. (**a**) True-color composite; (**b**) false-color composite.  
Further, several indices like NDVI, NDBI and MNDWI were derived from the primary bands of satellite imagery. These bands were used to accurately classify the Landsat imagery eliminating shadows of hills and buildings. Since Kaski has a snow-covered Himalayan range and hills, topographical factors like slope and elevation obtained from SRTM DEM were also used for classification. The total reference data were randomly divided into a test set and a validation set. The yearly composite data were downloaded and further processed using the R programming language. The hyperparameter tuning was performed for both algorithms for both years, and the best hyperparameter was used for the LC mapping. Later, RF and XGBoost based classification was executed for the supervised classification. RF uses various carts as a single unit to give the best result. LC maps were generated for each test area using both compositing methods, and the classification accuracy of each map was computed using the corresponding validation data sets.  
After preparing the LC classification in the R, postprocessing was done in ArcGIS Pro. The majority filter of eight neighborhood pixels was applied to remove the salt-and-pepper effect on the obtained maps. The changes were computed between RF-derived categorical classes for two years. Finally, post-classification change was detected and mapped. The change map was analyzed using [PERSON]'s diagram.
## 3 Instrument Development
### Optical Setup  
The AMULSE laser is a GaSb-based DFB semiconductor diode in a butterfly package (nanoplus GmbH, Gerbrunn, Germany). The optical power at the output of the single mode fiber is approximately 1 mW at a current of 95 mA. A fiber-based collimator in a pigtail style (LPC-01-2004-7/125-S-1-2.61 GR-40-3A-3-1, Oz Optics, Carp, ON K0A 1L0, Canada) with a GRIN lens (2.61-mm focal length) is connected to the output of the laser optical fiber in order to collimate the light (1-mm beam diameter). This light is directed to a home-made open-path multipass cell configured as Herriott type. The focal length of the used mirrors (2-inche diameter) is equal to 150 mm. Small (2-Watt) heaters are attached on the edge of each mirror in order to prevent condensation during  
Figure 2: Simulated absorption spectra ranging from 4989 cm\({}^{-1}\) to 4994 cm\({}^{-1}\) for an optical path-length of 6 m at ground level (1050 mbar, 20 \({}^{\circ}\)C), 5 km altitude (660 mbar, 0 \({}^{\circ}\)C) and 10 km altitude (275 mbar, \(-\)45 \({}^{\circ}\)C) for the carbon dioxide absorbing at 400 ppm and the water vapor absorbing at natural concentrations [19]. The selected carbon dioxide transition (in the dashed-line box) is sufficiently isolated for atmospheric sensing (at 4991.26 cm\({}^{-1}\)) and has no interferences with other CO\({}_{2}\) transitions nor H\({}_{2}\)O transitions.  
balloon descent due to the low temperature of the high troposphere. One hole on each mirror is drilled to attach the optical-fiber collimator on the input mirror side, and the 1-mm diameter InGaAs photodetector (J23-18D, Judson, Montgomeryville, PA, USA) on the output mirror. The effective optical path length is 6 m for a base length of 19.5 cm. The tunable diode laser is set to emit near the selected CO\({}_{2}\) absorption line by controlling its temperature and the injected DC current (resulting in a 1.95 cm\({}^{-1}\) spectral interval). A sinusoidal modulation current at 4.5 kHz is introduced superimposed to 5 Hz saw tooth current ramp (very slow compared to the modulation frequency) which sweeps the laser wavelength across the absorption feature.
## 3 Results
### Risk of Bias within Studies  
One RCT was included and was assessed as having some concerns of risk of bias [6] (Figure 2). Table 2 (NOS scale scores) shows the risk of bias assessment for the included studies and all four studies were considered of low risk of bias [31; 32; 33; 34].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Country**} & \multirow{2}{*}{**N (f/M)**} & \multicolumn{3}{c}{**Participants**} & \multicolumn{2}{c}{**Outcome**} & \multirow{2}{*}{**Funding**} \\ \cline{5-8}  & & & **CA** & & & & & & \\ \hline \hline \multirow{9}{*}{[PERSON] (2007) [31]} & \multirow{9}{*}{USA} & \multirow{9}{*}{60 (43/17)} & \multirow{9}{*}{33 27} & \multirow{9}{*}{Adults treated with Invisalign} & \multirow{9}{*}{} \\  & & & & & & aligners experienced less pain & & \\  & & & & & and fewer negative impacts on & & & \\ \cline{1-1}  & & & & & their lives during the first week & \multirow{9}{*}{NR} \\ \cline{1-1}  & & & & & of orthodontic treatment than & & \\ \cline{1-1}  & & & & & did those treated with & & \\ \cline{1-1}  & & & & & fixed appliances & & & \\ \hline \multirow{9}{*}{[PERSON] (2012) [32]} & \multirow{9}{*}{USA} & \multirow{9}{*}{68 (45/23)} & \multirow{9}{*}{21} & \multirow{9}{*}{47} & \multirow{9}{*}{The Invisalign patients} & \multirow{9}{*}{} \\  & & & & & complained of relatively high & & \\ \cline{1-1}  & & & & levels of pain in the first days & & & \\ \cline{1-1}  & & & & & after insertion; however this & & \\ \cline{1-1}  & & & & & group was characterized by the & & \\ \cline{1-1}  & & & & lowest level of oral symptoms & & & \\ \cline{1-1}  & & & & & and by a similar level of & & \\ \cline{1-1}  & & & & & general activity disturbances & & \\ \cline{1-1}  & & & & & and oral dysfunction compared & & \\ \cline{1-1}  & & & & & to be buccal appliance. & & \\ \hline \multirow{9}{*}{[PERSON] (2017) [6]} & \multirow{9}{*}{USA} & \multirow{9}{*}{41 (24/17)} & \multirow{9}{*}{23} & \multirow{9}{*}{18} & \multirow{9}{*}{Patients treated with traditional fixed appliances reported greater discomfort and consumed more analgesics than patients treated with aligners.} & Partially funded by the [PERSON] Chair in Orthodonics and by Align Technology \\ \cline{1-1}  & & & & & & During the first week of & \\ \cline{1-1}  & & & & & & treated with Invisalign aligners & & \\ \cline{1-1}  & & & & & reported lower pain than did & & \\ \cline{1-1}  & & & & & those treated with passive & & \\ \cline{1-1}  & & & & & self-ligating fixed appliances. & & \\ \hline \multirow{9}{*}{Pergentelli (2019) [34]} & \multirow{9}{*}{Italy} & \multirow{9}{*}{40 (23/17)} & \multirow{9}{*}{20} & \multirow{9}{*}{20} & \multirow{9}{*}{Therapy with traditional fixed orthodonics appliances caused more discomfort to the patients, than clear aligner therapy} & \\ \cline{1-1} \cline{5-
## 2 Materials and Methods  
### Materials  
Three nanoporous alumina structures with similar thickness (~60 \(\upmu\)m) were studied: an asymmetric commercial membrane (AnodiscTM 25 from Whatmann, Maidstone, UK, sample NPAS-And) and two symmetric structures manufactured in the Nanoporous Membranes Laboratory of the Nanotechnology Division, Universidad de Oviedo, Spain (samples NPAS-Sf and NPAS-Ph).  
NPAS-Sf and NPAS-Ph samples were fabricated by the two-step aluminum anodization method [1] using an electrochemically polished high purity Al foil (Al 99.999%, Goodfellow, Huntingdon, UK), which was cleaned by sonication in isopropanol and ethanol prior to the first anodization process (see Figure 1a). The NPAS-Sf sample was obtained by establishing a constant anodization potential of 25 V between the Al foil and a Pt counter-electrode in a 0.3 M sulfuric acid solution, while these parameters in the case of the NPAS-Ph sample were 0.1 M phosphoric acid and 195 V. The electrolyte temperature was maintained around 1 \({}^{\circ}\)C by an external recirculation bath. The first anodization step lasted for 24 h, guaranteeing the development of highly ordered nanopore domains following a close-packed hexagonal arrangement. The aluminum oxide layer during the first anodization step was then removed by selective chemical etching in an aqueous solution of 0.18 M CrO\({}_{3}\) and 0.61 M phosphoric acid, respectively. The as-obtained nanostructured Al substrate was exposed to a second anodization step until a nanoporous structure with a thickness of around 60 \(\upmu\)m was obtained (Figure 1b). The remaining un-oxidized Al substrate at the bottom of the samples was selectively dissolved by submitting it to a wet chemical etching step (0.14 M CuCl\({}_{2}\) and 4 M HCl aqueous solutions). The etching was stopped when the samples became transparent, thus evidencing the complete removal of the Al substrate. This procedure was carried out in an area of approximately 1 cm\({}^{2}\) placed at the center of the sample, in order to keep a surrounding Al ring that allowed for straight-forward handling of the 60 micron thick samples (Figure 1c). Finally, the alumina barrier layer that occluded pore bottoms was dissolved by floating the samples in a 10 wt % phosphoric acid solution at room temperature under static conditions, which allowed for opening of the nanopores by the two sides. The complete opening of the barrier layer (Figure 1c) was indicated by the fact that the etching solution was able to flow through the nanopores, which wets the upper surface of the samples.
## 4 Results, Comparison, and Limitations
### Limitations  
One potential limitation of the proposed system for identifying and registering animals is the availability of data for all livestock animals. To train the object detection model and extract the features and descriptors of the animals' noses, a sufficient amount of data is needed. If data are not available for all of the livestock animals that the system is intended to identify, the model may not be able to accurately detect and recognize these animals. This could result in lower overall accuracy of the system and a reduced ability to identify and register all of the animals.  
Another potential limitation of the system is the reliance on manual annotation of the data to label the animals' faces and noses. This process can be time-consuming and may  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Title** & **Technique** & **Accuracy \%** \\ \hline \hline Arabian Horse Identification and & & \\ Gender Determination System & Fusion of LBP and SURF, SVM with & \\ Based on Feature Fusion and Gray & gray-wolf optimization & 99.00 \\ Wolf Optimization [14] & & \\ Muzzle Point Pattern Based & & \\ Techniques for Individual Cattle & & \\ Identification [15] & & \\ Individual Beef Cattle Identification & & \\ Using Muzzle Images and Deep & & \\ Learning Techniques [16] & & \\ A Preliminary Investigation on & & \\ Horses Recognition Using Facial & & \\ Texture Features [17] & & \\ Real-time recognition of cattle using & & \\ animal biometrics [18] & & \\ Sparse Neural Network for Horse & & \\ Face Detection in a Smart Riding & & \\ Club Biometric System [19] & & \\ DTL-AFI-VHA (proposed) & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the proposed system with other studies.  
not be feasible for large datasets. In addition, manual annotation is subject to human error, which could also impact the accuracy of the model.  
Finally, the use of approximate nearest neighbor matching with FLANN to identify the animals may also have limitations, as the method is not able to provide exact matches and is subject to a certain level of error. While the use of a threshold distance and a minimum number of good points can help to reduce the likelihood of incorrect matches, there is still the possibility of misidentification or false positives.  
Overall, the proposed system for identifying and registering animals may be limited by the availability of data for all livestock animals, the reliance on the manual annotation of the data, and the potential for error in the approximate nearest neighbor matching process.
# Underwater Electromagnetic Sensor Networks--Part I: Link Characterization +
## 2 Experimental Section
### Simulation Procedures  
Measurements aimed at the validation of simulation tools and their setup parameters, which are carried out using a commercial MoM (Method of Moments) solver: FEKO. This solver supports the main characteristics required for this analysis: planar green functions for multilayered media, dielectric coated wires and special basis functions for low frequency analysis. Once measurements agree with simulation results, some measurements in the harbour can be avoided, as FEKO will simulate environmental and technical features precisely.  
Carrying out simulations for RF underwater communications is solving an electromagnetic problem. Therefore, the first things to decide are frequency bands and distances of interest. Typically, simulations are carried out from 10 KHz to 1 MHz varying distance from 2 to 13 m in 1 m steps. Regarding simulations setup, the most simple case is when the medium is homogeneous, that is, sea water or free-space to start with. This is explained in previous papers [12] where its inaccuracy was proved. In order to get close to a real environment, a layered medium was also simulated: sea water with seabed. According to [6; 16], sea water can be modelled as a dielectric with conductivity \(\sigma=4.5\,\mathrm{S/m}\) and permittivity \(\epsilon_{r}=81\). Seabed, fine sand, is modelled as a dielectric with permittivity \(\epsilon_{r}=3.5\) (the effect of the value of permittivity on our simulations is negligible since it is a magnetoquasistatic problem) and conductivity \(\sigma=1\,\mathrm{S/m}\). The purpose of this last simulation was to determine the influence of the sea water-seabed interface in the wave propagation.