esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Field Ground Measurements
#### 2.1.2 Measurement Scheme  
The field experiments were conducted in the city of Baoding, in the province of Hebei, China (39.14455\({}^{\circ}\)N, 115.73785\({}^{\circ}\)E) (Figure 2). There were six plots of the same size of 2 m \(\times\) 4 m. Five of the plots were planted with maize, and only one of the maize plots was properly irrigated. In this study, the relationship between the retrieved SIF and PAR was used to evaluate the quality of SIF since the true value of canopy SIF is difficult to obtain [19]. However, the relationship between canopy SIF and PAR may not be linear under water stress [20; 21]. Therefore, only the data from the plot without water stress were used in this study.  
or the measurements of the QE65 pro spectrometer in the studied plot, one bare fiber was mounted at a distance of approximately 2 m above the maize canopy to measure the L from the nadir. The ground instantaneous field of view of bare fiber is a circle with a diameter of about 0.89 m. A CC-3 cosine-corrector probe was installed vertically at the same height as the canopy to measure incident irradiance. To obtain the matching incident solar radiation for the quantum sensor, it was mounted at the same height as the CC-3 cosine-corrector probe, keeping the sensor horizontal and free of shadow interference. Before the experiment, we performed radiometric and wavelength calibrations. The observations of the canopy were conducted on every no-rain day between 8:00 and 16:00 between DOY  
Figure 1: Schematic illustration of the improved AutoSIF structure. The solid blue lines represent the fibers. The black connecting lines represent the data transmission lines.  
214 and DOY 234 in 2020. In total, the observation data of 17 days were collected. In DOY 214-234, the maize was between the jointing stage and flowering stage. In addition, the LAI of the canopy of maize was about 3-3.5 in DOY 224-230.
# A Lightweight Fault Diagnosis with Domain Adaptation for Defected Bearings
## 3 Experimental Results  
In this section, experiments are conducted to evaluate the performance of the proposed method for bearing fault diagnosis. First, the proposed method is applied to the self-designed test platform dataset and assess its accuracy and robustness under controlled experimental conditions. Next, the publicly available bearing fault dataset from Paderborn University (PU) is used to further validate the method's generalization capability across different fault modes and operational conditions. Finally, the performance of the proposed method is compared with other state-of-the-art fault diagnosis approaches to highlight its strengths and potential limitations. All experiments are conducted using Python 3.13.2, TensorFlow 2.3.0, and CUDA 12.0.139 on an AMD Ryzen 7 5800H (16 GB RAM) (AMD, Santa Clara, CA, USA) with an RTX 3050 GPU (NVIDIA Corporation, Santa Clara, CA, USA).
## 1 Introduction
### Effects on the Musculoskeletal System  
During short-duration spaceflight, low back pain and disk herniation are common due to the presence of microgravity. While the pathogenesis of space-related low back pain and disk herniation is complex, the etiology is likely multifactorial in nature (e.g., microgravity induced hydration and swelling of the vertebral disk, muscle atrophy of the neck and lower back) [19; 214; 215]. Additionally, various joint injuries (e.g., space-suited shoulder injuries) can also occur in space due to the presence of microgravity [216; 217; 218; 16]. Interestingly, one study showed that performing specific exercises could potentially promote automatic and tonic activation of lumbar multifidus and transversus abdominis as well as prevent normal lumbopelvic positioning against gravity following bed rest as a simulation of space flight [219], and the European Space Agency suggested that exercise program could relieve low back pain during spaceflight [220]. Further longitudinal studies are required to develop specialized exercise protocols during space travel.  
During long-duration spaceflight, the presence of microgravity could cause an alteration in collagen fiber orientation within tendons, reduce articular cartilage and meniscal glycosaminoglycan content, and impair the wound healing process [22; 23; 24; 221]. These findings seen in animal studies suggest that mechanical loading is required in order for these processes to occur in a physiologic manner. It is theorized that there is a mandatory threshold of skeletal loading necessary to direct balanced bone formation and resorption during healthy bone remodeling [222; 223]. Despite the current countermeasure programs, the issue of skeletal integrity is still not solved [224; 225; 226].  
Space radiation could also impact bone remodeling, though the net effect differs based on the amount of radiation involved [6]. In summary, high doses of space radiation lead to bone destruction with increased bone resorption and reduced bone formation, while low doses of space radiation actually have a positive impact with increased mineralization and reduced bone resorption. Most importantly, space radiation, particularly solar particle events in the case of a flare, may induce acute radiation effects, leading to hematopoietic syndrome [7]. This risk is highest for longer duration missions, but can be substantially minimized with current spacecraft shielding options.  
Longitudinal studies are required to develop special exercise protocols and further assess the aforementioned risk of space radiation on the development of musculoskeletal malignancies.
## 3 STRP-DBSCAN
### Preprocessing of Spatial-Temporal Trajectory Data  
Each spatial-temporal trajectory data contains information in multiple dimensions, such as object ID, latitude and longitude, velocity, direction, time, etc. Due to the presence of noise and error in the acquired raw spatial-temporal data, the data should be preprocessed to ensure the rationality of the data, which can effectively reduce the impact of the noisy data on the subsequent calculations of the algorithm.  
Taking ship trajectory data as an example, the ship class spatial-temporal trajectory dataset mainly relies on the collection of data reports continuously sent by shipboard AIS equipment. The time interval when shipboard AIS equipment continuously send data reports will be different, i.e., in the same period of time, the different ship AIS reports are prone to redundancy, so for the same ship, one data point can be selected within each minute for approximation.  
In this paper, the main process of the preprocessing of spatial-temporal trajectory data is as follows:  
1. First, removing the points that do not conform to regular speeds, have missing values for certain attributes, and have irregularities in latitude and longitude;
2. Constructing key-value pairs with ID as the key and time as the value in the dataset. For different categories of trajectory data, setting the smallest time measurement unit according to the specific situation;
3. Removing data elements in the dataset where both ID and time are the same, i.e., removing the redundant data;
4. Normalizing the data in terms of latitude, longitude, precision, and time, i.e., eliminating the effect of magnitude on subsequent clustering.  
The normalization can limit the preprocessed data to a certain range, thus eliminating the adverse effects caused by singular sample data. Since normalization only scales the data and does not change the original information of the data, there is no loss of information. In this work, we use the most commonly used max-min normalization, as shown in Equation (1).  
\[x^{\prime}=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{1}\]  
Figure 6: Overall execution flow of STRP-DBSCAN.
## 1 Introduction
The attenuation range can be controlled by the control voltage (\(V_{\mathrm{C}}\)) and incorporates Lange couplers [29; 30; 31] at its input and output. The \(V_{\mathrm{C}}\) was set to 0 V to ensure maximum attenuation. This work presents the characterisation of the circuit at cryogenic temperatures to be able to use it in an astrophysical instrumentation application with MKIDs. The paper is structured as follows: First, in the introduction, the MKIDs and the requirement of working under cryogenic conditions are described, and the proposed attenuator is presented. Subsequently, in Section 2, the chip testing platform used to characterise the circuit is explained in detail. In the third section, measurements of the attenuator are conducted at the temperatures of interest, and the experimental results are presented. Finally, Section 4 concludes with the key findings of this work. Figure 1: Proposed MKID readout configuration.
## 3 Materials and Methods
### Lightweight U-Net Model Design with Embedded Gabor Kernel
#### 3.2.3 Lightweight Design of U-Net  
In the course of downsampling in U-Net, different backbone networks may be employed as encoders, among which VGG16 is considered to be an exceptional model for feature extraction [31]. However, despite its efficacy in feature extraction, the VGG16 network's abundant channels may lead to an excessive number of parameters. It is necessary to reduce the network's weight while preserving its capacity for accurate image segmentation.  
In this paper, the channel numbers of each network layer have been reduced to decrease the number of parameters. However, reducing the channel numbers may lead toinformation loss, therefore, we adopted the inverted residual block instead of the convolution method to mitigate this issue during downsampling. Unlike the residual block, the inverted residual block has a small head and a large middle structure. The ReLU activation function may cause a significant loss of low-dimensional feature information [32], therefore, the inverted residual block increases the dimension of the feature map before applying the ReLU activation function, thereby reducing information loss. The structure of the inverted residual block and residual block [33] are shown in Figure 4, respectively.  
The inverted residual block effectively addresses the issues of gradient disappearance and information loss. However, it is also more complex compared to the regular convolutional structure, which also elevates the computational burden on the model. The essence of this venous segmentation is semantic segmentation [34], and the processing of semantic information is mainly concentrated in the deep network. Hence, to minimize the loss of semantic information and reduce the processing time caused by complex structures, we exclusively employed an inverted residual block in the last two blocks to substitute regular convolutions. This approach ensures that the model maintains its non-linear expressive capability while not increasing the image segmentation time.
## 5 Conclusions  
This work presents an integrated redesign of a module that is part of a satellite platform employing PBF-LB/M and lattice structure. The method considers all the fabrication steps and provides a selection for lattice structures as well as the skin. The final design includes features that can be manufactured only by AM technologies. The following conclusions can be drawn:  
* The proposed design optimization allowed us to find a solution complying with the first frequency request which was not satisfied by the original design fabricated via traditional manufacturing.
* The selected fabrication parameters, in particular the high layer thickness, allow a fast fabrication. However, the resulting strength is lower than expected for this Aluminum alloy. Furthermore, it is lowered by the thermal treatment selected for the lattice structure cares.
* The modal analysis demonstrated that satisfying the frequency requirement is most challenging if a lightweight structure must be attained. For this purpose, a particular framework was developed and adopted for locally thickening the critical zones of the lattice. The adoption of a particular cell complied with the specification with a good margin also in the case where the simulation was provided with a very low Young modulus value.
* The total estimated weights of the parts are 2.66 Kg for Module 1 and 3.8 Kg for Module 2, that lead to a weight saving, respectively, equal to 0.12 Kg (4.3%) and 1.46 Kg (27.8%).  
Conceptualization, L.M. and A.B.; methodology, A.B. and L.B.; software, L.M.; validation, A.B.; formal analysis, A.B.; investigation, L.B.; resources, L.M.; data curation, L.B.; writing--original draft preparation, S.V. and L.B.; writing--review and editing, A.B. and L.B.; visualization, S.V.; supervision, A.B. and L.M.; project administration, L.M.; funding acquisition, L.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Not applicable here.  
The authors would like to thank [PERSON], [PERSON], [PERSON] and [PERSON], for their help and effort in designing and prototyping this challenging project.  
The authors declare no conflict of interest.
## 2 Wind Data in Taizhou
### Wind Speed Data from Early Meteorological Stations
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Wind Angle** & **(0-5] m/s** & **(5-8] m/s** & **(8-11] m/s** & **(11–14] m/s** & **(14–17] m/s** & **(17–20] m/s** & **x > 20 m/s** & **Total** \\ \hline N & 0.00 & 1.46 & 0.84 & 0.00 & 0.00 & 0.00 & 0.00 & 2.30 \\ NNE & 0.00 & 1.67 & 1.25 & 0.00 & 0.21 & 0.00 & 0.42 & 3.55 \\ NE & 0.00 & 0.63 & 1.46 & 0.42 & 0.84 & 0.00 & 0.42 & 3.76 \\ ENE & 0.00 & 5.85 & 4.59 & 1.46 & 0.00 & 0.00 & 0.21 & 12.11 \\ E & 0.00 & 3.97 & 2.30 & 0.21 & 0.00 & 0.00 & 0.21 & 6.68 \\ ESE & 0.00 & 0.63 & 2.09 & 0.63 & 0.00 & 0.00 & 0.00 & 3.34 \\ SE & 0.00 & 0.84 & 0.42 & 0.21 & 0.21 & 0.00 & 0.00 & 1.67 \\ SSE & 0.00 & 0.00 & 0.63 & 0.00 & 0.00 & 0.00 & 0.00 & 0.63 \\ S & 0.00 & 2.51 & 4.18 & 0.00 & 0.00 & 0.00 & 0.00 & 6.68 \\ SSW & 0.00 & 3.13 & 5.22 & 0.21 & 0.42 & 0.00 & 0.00 & 8.98 \\ SW & 0.00 & 0.21 & 0.21 & 0.00 & 0.00 & 0.00 & 0.42 \\ WSW & 0.00 & 0.21 & 0.00 & 0.00 & 0.00 & 0.21 & 0.00 & 0.42 \\ W & 0.00 & 0.42 & 0.00 & 0.21 & 0.00 & 0.00 & 0.00 & 0.63 \\ WNW & 0.00 & 6.05 & 4.59 & 0.84 & 0.42 & 0.00 & 0.00 & 11.90 \\ NW & 0.00 & 8.56 & 14.20 & 4.38 & 0.63 & 0.42 & 0.00 & 28.18 \\ NNW & 0.00 & 1.25 & 6.68 & 0.63 & 0.21 & 0.00 & 0.00 & 8.77 \\ Total & 0.00 & 37.37 & 48.64 & 9.19 & 2.92 & 0.63 & 1.25 & 100 \\ \hline \end{tabular}
\end{table} 
Table 3: Occurrence frequency of monthly extreme wind samples in each direction and speed range (\(\%\)) from 1978 to 2021 at Hongjia station. Figure 2: Monthly extreme wind rises at Hongjia station from 1978 to 2021.  
taller and denser, which will weaken the wind speed magnitude at that location to some extent [27]. Therefore, it can also be seen that the reference wind pressure obtained from the existing meteorological stations has reduced its significance as a guide to the wind resistance design of the actual structure. The wind speed and direction data from these five meteorological stations are quite complete, which is very useful for studying the design wind pressure in other regions of Taizhou. There are only 95 meteorological stations in the 44 streets and 85 towns in Taizhou. Most sites were set up between 5 and 20 years ago. In the past 19 years, few typhoons have made landfall directly in Taizhou. As a result, the data of extreme wind speed collected by the stations are quite limited, and the basic wind pressure calculated by the extreme I probability model is also too small. It is obviously unreasonable to use these underestimated reference wind pressures for the structural design of buildings. Therefore, we proposed to use the wind speed and direction data of these five reliable meteorological stations as a reference to derive the basic wind pressure of other towns and villages in Taizhou by a meso-scale CFD simulation.
# A Multi-Objective Bio-Inspired Optimization for Voice Disorders Detection: A Comparative Study
## 2 Related Works
Evolutionary multi-objective optimization (EMO) techniques have also been successfully applied in various domains, including voice disorder detection. [PERSON] et al. [25] utilized the NSGA-II algorithm to select optimal wavelet features from spontaneous speech for early Alzheimer's disease detection, achieving 98.33% accuracy. Additionally, [PERSON] et al. [26] applied a multi-objective evolutionary association-rule mining approach to acoustic voice characteristics for diagnosing PD, where the NICGAR algorithm performed the best among the methods tested. [PERSON] et al. [27] proposed a multi-objective Java algorithm for PD detection, formulating the problem as a weighted sum fitness function for feature reduction and accuracy maximization, achieving 92.26% accuracy with a KNN classifier. Finally, [PERSON] et al. [28] demonstrated the effectiveness of MOEA/D for optimized feature selection in medical diagnosis. Recently, despite the widespread use of machine learning methods in various fields, and specifically in voice disorder detection, there are some limitations. For instance, many research studies used machine and deep learning algorithms to detect pathological voices, a high proportion of them targeted [PERSON] patients, disregarding other diseases. Nonetheless, many of the studies which used evolutionary optimizers formulated the optimization problem as a single-objective optimization problem to reduce the high dimensionality of the problem. Very few research studies examined evolutionary multi-objective optimization for pathological voice detection, where they emphasized [PERSON]'s disease. This study attempts to address the limitation by employing multi-objective evolutionary optimization with various voice disorders that eventually reduce the number of features and power consumption. Table 1 concludes related studies for voice disorder detection. As can be noticed in the table, most of the studies rely on accuracy to evaluate proposed methods. In this study, the generational distance and hypervolume metrics are employed solely to evaluate the effectiveness of optimization in the context of multi-objective evolutionary algorithms, and not to compare the classification performances among different models.
# Particle Swarm Optimization of a Passivity-Based Controller for Dynamic Positioning of Ships
## 4 The PSO Method for DP of Ship  
Similar to most nonlinear control approaches, the gains and parameters of the IDA-PBC control law (10) are generally obtained using trial and error which relies mainly on experience, and usually results in tedious computations. Here, we utilize the PSO algorithm to obtain the optimal gains of this controller which provides an effective way to tune these gains thus improving the control performance of DP system of the ship.
## 5 Evaluation of Local Discomfort Indexes and _Pmv_
### Predicted Mean Vote and Predicted Percentage of Dissatisfied  
Figure 15 illustrates \(PMV\) and \(PPD\) values inside the office for summer-cooling cases. The figure shows that, when the solar load was disregarded, the left zone of the office (where Stations 1 and 2 were located) was rather cool with larger \(PPD\) values compared to those in the right zone. This effect was amplified when the inlet air-flow rate increased, so Case B had the highest dissatisfied percentage. When solar radiation was applied, the situation changed: the minimal air-flow rate was not able to provide the necessary thermal power to balance the heat flux. Therefore, the entire environment was hot (except for the zone adjacent to the fan coil). In this case, the highest \(PPD\) (30%) was near the SE window, so Station 1 was the worst place in terms of thermal comfort. Case D on the other hand, presented a rather low \(PMV\) value in the zone of Station 2, varying from 0.6 to 1.4, representing a cold sensation. In the rest of the domain, a neutral-to-rather-hot sensation prevailed.  
Figure 16 shows the \(PMV\) and associated \(PPD\) distribution in the office during winter-heating conditions. Both cases (minimal and maximal inlet air velocities) ensured a pleasant experience for the occupants, with \(PMV\) values that ranged from \(-0.5\) to \(0.1\). In particular, Case F had the best environmental condition for thermal comfort, with \(PMV\) values close to \(0\) all over the office. For both cases, a slight cold sensation was felt near the windows, especially the one on the northeastern direction; so, it was reasonable to move the working stations, so that they were not too close to them.  
Overall, in both summer and winter conditions, there was strong asymmetry in terms of \(PMV\) due to the low thermal efficiency of the building envelope.  
Figure 14: Winter heating condition. Drought-rate profile on different sections in the z direction obtained with low input air velocity (Case E) and high input air velocity (Case F).
# CNN-Based Crosswalk Pedestrian Situation Recognition System Using Mask-R-CNN and CDA
## 3 Proposed Method
### Stage 2: Crosswalk Detection Using the Crosswalk Detection Algorithm (CDA)  
Figure 4 depicts the process of Stage 2, which uses the CDA to detect crosswalks in original images. Usually, the area of crosswalks is colored white, and several white rectangles are drawn on the road. So, first, the images are made to be black and white. If the white area is more than the specific size pixels, it would be a part of the crosswalk image. Additionally, the CDA draws polylines and connects all crosswalk parts. Finally, we extract the crosswalk area detected by the CDA in black. The color of the crosswalk area is designated uniformly to learn CNN.  
Figure 3: Detecting a pedestrian using Mask R-CNN.  
Figure 2: The Mask R-CNN framework for instance segmentation [14].  
Algorithm 1 presents the CDA, the process of which is as follows:  
1. Thresholding: make the white crosswalk visible by making it clear what is white and what is not;
2. Morphology opening and closing operation: reduce noise by erosion and expansion in areas other than crosswalks. Make the crosswalk area clear;
3. Find contours, filter good contours, and combine good contours: obtain crosswalk contours and extract multiple contents from one image. If the content is larger than a pixel area (e.g.,170, pixels) of a specific size, it is judged by the crosswalk image. Combine the values of the good contours array;
4. Obtain a convex hull, sort the points of contours and draw polylines: convex hull function combines the good contour image with the original image to make a small square into an entire large square. Sort the points of contours combined by x-coordinate (in case of a tie, sort by y-coordinate). Draw polylines in red;
5. Fill the inside of the polylines with black.  
Figure 4: Detecting a crosswalk using the crosswalk detection algorithm (CDA).