esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### ActiGraph Activity Monitor  
ActiGraph has been used in large-scale field studies and has become the de facto standard device for objective physical activity monitoring [16]. It is particularly recommended for examination of children and adolescents because it allows for detection of acceleration in three planes of motion, which provides more accurate analysis of the movement relative to pedometers. This is especially important in the case of children's examination because the device records all forms of physical activity, such as doing push-ups or climbing. Many publications describe the advantages of using accelerometers in scientific research, such as objectivity, non-invasiveness, and accuracy, while maintaining the comfort of the user [15].  
Published findings related to the application of ActiGraph concerned with exploring differences in daily physical activity profiles among individuals with mild Alzheimer's disease were compared to a control group [17]. Features that can be derived from the accelerometer have been also used to recognize the presence and severity of motor fluctuations in patients with Parkinson's disease [18]. It has been also used with measurements of physical activity to evaluate the effectiveness of surgical and therapy-based interventions in children with cerebral palsy or to derive diurnal rest-activity patterns from actigraphy in adolescents and to analyze associations with adiposity measures and cardiometabolic risk factors [19; 20].  
However, ActiGraph activity monitors have limited memory and battery capacity to store raw signal data and are additionally quite expensive. One of the current models, ActiGraph wGT3X-BT, currently sells for 225 USD [21]. The costs of devices may vary if bought separately, as compared to bulk orders.  
Due to memory limitations, information about movement is read by the accelerometer in the form of the number of pulses (named counts), which are added up in the designated time unit [22]. A count is a unit aimed to be proportional to the average overall acceleration of the human body in a specified period of time. The sum of the received counts is converted into the intensity of physical activity, categorized as sedentary behaviors, light physical activity (LPA), moderate PA (MPA), and vigorous PA (VPA).  
There are commonly-used regression equations named as cut points for the ActiGraph accelerometers in predicting energy expenditure (EE) in children and adolescents [23]. The cut points are derived as a part of published research aimed at quantifying activity levels using ActiGraph products. All cut point sets are scaled to 60-s epochs.  
In this study, the parameters of physical activity are calculated according to the [PERSON] (2005) model. Definitions of the cut point levels for this model are given in Table 1.
# Synthesis of Star Nanoparticles and Their Applications for Bioactive Compound Encapsulation
## 3 Encapsulation Methods
### Potato Starch  
[PERSON] et al. used potato starch for nanoparticles mediated delivery of docetaxel through the microemulsion method. The docetaxel in ethanol and the potato starch in ethyl acetate were prepared and mixed homogeneously. These two solutions were mixed to obtain the organic phase that was poured into an aqueous solution of PVA and subsequently the mixture was emulsified with the help of a high-speed homogenizer. To facilitate the complete diffusion of the organic phase in the aqueous phase, purified water was added to increase the volume. Finally, the organic phase was completely evaporated [30].  
Finally, when it comes to bioactive compounds, curcumin is one of the most studied due to its chemopre and therapeutic properties against many tumors [83]. [PERSON] et al. studied synthesized bovine serum albumin (BSA) based SNPs for encapsulation of curcumin using an ultrasonic atomization method for the synthesis of the SNPs. BSA binary compounds including BSA/capsule starch, BSA/national starch, BSA/chitosan were applied as carriers of the model drug. Curcumin was dissolved in ethanol and this solution was added to the aqueous biopolymer solution with stirring in a sealed beaker to form a thick emulsion. A high-frequency piezoelectric oscillator aerosolized the solution and the generated atomized droplets were blown through the hot glass drying chamber where the solvent was evaporated. The BSA SNPs loaded with curcumin were finally collected in the stainless-steel electrostatic collector [68]. Also, SNPs loaded with curcumin were synthesized using oxidized starch and simultaneously modified with cholesterol and imidazole (Cho-Imi-OS) by a simple dialysis method previously used by [PERSON] et al. Cho-Imi-OS was dissolved in dimethyl sulfoxide (DMSO) under stirring at high temperature in an oil bath. The model drug was dissolved in DMSO and this was added dropwise to the starch solution under constant stirring. Then the mixture was transferred to a dialysis bag and dialyzed against PBS solution. Finally, the SNPs were washed with deionized water [41].
## 5 Conclusions  
Based on signals that can be readily obtained from a force plate, we introduced several postural stability features by quantifying the coupling strengths between the COP and its GRF components. The efficacy of the proposed features was demonstrated by comparing their performances in detecting the effects of aging on the quiet standing posture. Using statistical and binary classification tests, we showed that two of the proposed features outperformed one of the most effective conventional COP features.  
Based on the values of the proposed features obtained from our experiments, we made the following observations, which elucidate the effect that the GRF components have on stabilizing the quiet standing posture. First, the COP was largely determined by the vertical GRF components in quiet standing. Second, despite the dominant role of the vertical GRF components, the contributions of the horizontal GRF components were not negligible. Our experimental results demonstrated that the AP-direction GRF component of the younger group played a more active role than that of the older group in positioning the COP. In fact, this difference was not only statistically significant but also very effective in differentiating postural performance between the younger group and the older group. Third, by having a weaker coupling strength with COP, the AP-direction GRF components of the younger group were more flexible in maneuvering the motion of the COM and in controlling the FM. Considering the simplicity and efficacy of the proposed features, future studies can extensively test the effectiveness of our proposed features in characterizing impairments to postural stability from many health-related conditions.  
All authors participated in the research. Conceptualization, J.-L.S., C.-W.Y. and L.-J.L.; data curation, C.-H.L. and P.L.; formal analysis, J.-L.S., C.-W.Y. and L.-J.L.; investigation, L.-Y.G., P.L. and L.-J.L.; methodology, J.-L.S., L.-Y.G. and C.-W.Y.; Software, J.-L.S. and C.-H.L.; writing--original draft, C.-W.Y.; writing--review & editing, C.-W.Y. and L.-J.L. All authors discussed the results and contributed to the final manuscript. All authors have read and agreed to the published version of the manuscript.  
Ministry of Science and Technology of Taiwan funded this research under the grant numbers MOST-106-2221-E-110-044 and MOST 107-2221-E-110-073.  
This research was partly supported by the Ministry of Science and Technology in Taiwan (R. O. C.), under grants 106-2221-E-110-044 and MOST 107-2221-E-110-073.  
The authors declare no conflict of interest.
## 4 Experiment and Evaluations
### Data Processing  
A lack of sufficient data can lead to overfitting during model training. To prevent overfitting and enhance the recognition performance of the convolutional neural network on images, it is imperative to supply an ample number of samples for the network to acquire a broader range of image features. Image augmentation operations were performed on the dataset, which included image rotation, flipping, adding Gaussian noise, and enhancing contrast. Each of the four image augmentation methods was applied to 25% of the original dataset, ensuring a balanced and diverse augmentation process to enrich the training data and improve model robustness. After augmentation, a total of 4580 tea plant disease images were obtained. Taking tea brown blight as an example, Figure 7 presents the images in their original form alongside their augmented counterparts. The augmented dataset was annotated using the visualization annotation tool, LabelImg. The annotation results generated a series of corresponding txt files, which included details such as the disease type and location coordinates for each image. These files, along with the tea plant disease images, formed the final dataset.  
Figure 6: Tea disease images under various situations.  
To optimize the use of the dataset, it was randomly split in a 9:1 ratio. The training set included 4124 images for network model training, while the validation set contained 456 images used to evaluate model convergence during training. The dataset split details are presented in Table 1. This approach ensures optimal use of the dataset, advancing the model's generalization and performance, thereby improving its applicability to real-world scenarios. The model is trained primarily on data from a specific crop. To generalize the model to other crops, we propose that future work includes expanding the dataset to encompass a broader variety of crops with different characteristics. This helps to enhance the model's ability to adapt to the unique features and disease manifestations of different plant species.
## 2 Materials and Methods
### Research Methods
#### 2.3.4 Selection of Drought Periods and Corresponding SPEI Timescales  
Drought impacts vary across temporal scales, requiring appropriate SPEI timescales to capture short-term to long-term dynamics. This study selected SPEI-03, SPEI-06, SPEI-12, and SPEI-24 to represent climate drought conditions in Xinjiang over different periods, focusing on cumulative water balance. SPEI-03 reflects short-term drought conditions over three months, capturing rapid changes in water availability during critical growing seasons. SPEI-06 represents intermediate droughs spanning six months, suitable for assessing cumulative seasonal water deficits. SPEI-12 reflects annual drought patterns, aligning with vegetation growth cycles and ecosystem processes. SPEI-24 captures multi-year drought conditions, providing insights into persistent water deficits and their long-term impacts on vegetation in arid and semi-arid regions.
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 4 Thermal Analysis
### Results of Thermal Analysis  
Analysis is carried out according to the thermal control measures and defined workloads including the high and low temperature conditions. Temperature variation curves of the camera, battery and solar panels are obtained and shown in Figures 7-12 and temperature variation conditions of instruments inside and outside the satellite cabin is shown in Table 12.  
Figure 6: Thermal analysis model of the Luojia 1-01 satellite.  
Figure 8: Analysis result of the temperature variation curve of the camera under low temperature conditions.  
Figure 7: Analysis result of the temperature variation curve of the camera under high temperature conditions.  
Figure 9: Analysis result of the temperature variation curve of the battery under high temperature conditions.  
Figure 11: Analysis result of the temperature variation curve of the solar panels under high temperature conditions.  
Figure 12: Analysis result of the temperature variation curve of the solar panels under low temperature conditions.  
Figure 10: Analysis result of the temperature variation curve of the battery under low temperature conditions.  
From the above figures and tables, it can be concluded that during long term working shifts from high to low temperature conditions, every instrument is working under the required temperature by the present quasi-all-passive TCS even with some design margin. The temperature of the camera stables at 12 \({}^{\circ}\)C and battery stables at the range from 18 \({}^{\circ}\)C to 20 \({}^{\circ}\)C. Temperature of instruments inside the satellite cabin is ranging from 5 \({}^{\circ}\)C\(\sim\)30 \({}^{\circ}\)C and the fluctuation is better than 5 \({}^{\circ}\)C.
## 4 Experimental Results
### Feature Extraction Result  
A 3D steerable wavelet transform to calculate the Riesz coefficients, which consist of the composition of two operators, is executed. The first one is the 3D isotropic wavelet transform, which performs multiscale decomposition and gives vanishing moments. The next is the 3D Riesz transform, which brings directionality and steerability. The texture features of the segmented images are extracted using the steerable Riesz wavelet transforms and are shown in Figure 6. The steerable Riesz wavelet functions are steering in the direction of the local gradient energy, which is more suitable for analyzing and processing the texture of the CT images.  
The proposed classifier is trained using the features extracted and validated to classify the pulmonary nodules as either benign or malignant.
# Internet of Drones Intrusion Detection Using Deep Learning
## 3 The Proposed Drone Intrusion Detection Framework
### Data Collector  
The data collectors' primary functions are to handle the data before feeding it to the RNN-LSTM module. This module also has the responsibility to stripe the data packets and extract the necessary features, such as transmission rate, reception rate, transmission to reception ratio, source IP, destination IP, transmission mode, and activity duration. Since our framework is designed to work in both data modes, batch and stream, as mentioned earlier, the data collector is assigned this task. Therefore, as seen in Figure 1, two collector modules are proposed in our framework; one of the data collector modules exists in each drone's component, and another resides at the base station component. Therefore, in batch data processing, the collector is configured to keep the data in a buffer before passing it to the RNN-LSTM module. The buffer size depends on the batch size to be processed, keeping in mind the drone limitations. In the case of the data stream mode, which is the case of our work in this paper, the data collector will be responsible for feeding the data as stream to the RNN-LSTM module. In other words, since we are simulating the drone's activities, the data collector emulates the real-time data processing and prepares it accordingly. However, in physical drones, which is not the case in this paper, the data collector will be responsible for intercepting the communication module's data and preparing it to fit the RNN-LSTM module requirements. In addition, the drone's data collector module is also responsible for transferring the collected data to the base station collector module along with the drone's RNN-LSTM module decision.  
The base station data collector module receives all of the drones' data and their decisions. It processes all received data and passes them to the centralized RNN-LSTM module on the base station for decision verification. Then, it will forward the final decision to the decision-maker module for further processing.
# Memory-Efficient Batching for Time Series Transformer Training: A Systematic Evaluation
## 5 Experimental Setup and Evaluation
### Dataset Generation  
We evaluate our approach using both synthetic and real-world time series datasets. Synthetic data were generated to systematically control dimensionality and sequence characteristics. For real-world validation, we utilized publicly available electricity transformer temperature (ETT) datasets (ETTh1, ETTh2, ETTm1, ETTm2), widely recognized benchmarks for multivariate forecasting. These datasets vary in sequence length, seasonal patterns, and feature dimensionality, ensuring comprehensive evaluation of the proposed batching method's effectiveness. Detailed dataset statistics and source code are provided in our public GitHub repository for reproducibility.  
For synthetic data, we employed a standard Python random floating point number generator to simulate multivariate time series datasets with controllable characteristics. This approach allowed us to tailor aspects like the number of features (dimensionality) and sequence length for a diverse experimental benchmark. Our simulations ensured dataset sizes were representative of real-world time series problems.
## 3 Test Results
### Failure Mode
#### 3.3.3 Failure Mode of SRRAC Beams  
Failure modes of SRRAC beams after exposure to elevated temperature is the same as those at room temperature, which reflects shear-baroclinic failure, bond failure and bending failure. SRRAC beams with shear span-to-depth ratios of 1.2 show shear-baroclinic failure. SRRAC beams with shear span-to-depth ratios of 2.0 show shear-baroclinic, bond failure and bending failure.  
Shear-baroclinic failure is as follows: Vertical cracks can be seen in the mid span and develop slowly upward when the external load is between 0.13 Pu and 0.35 Pu. The oblique crack appears between the loading point and the support when the external load is between 0.3 Pu and 0.6 Pu. And then, oblique cracks increase and widen, the oblique concrete zones crushed, then the specimens were destroyed.  
The bending failure process is as follows: Vertical cracks can be seen in the mid span when the external load is between 0.23 Pu and 0.3 Pu. And vertical cracks increase and developed when the external load is between 0.45 Pu and 0.5 Pu. The oblique crack appears between the loading point and  
Figure 8: The failure modes of column specimen.  
the support when the external load is between 0.5 Pu and 0.6 Pu. Vertical cracks develop faster when the external load is at 0.75 Pu. Finally, the concrete is crushed, then the specimen is destroyed.  
Bond failure process is as follows: Longitudinal bonding crack was found near the compressive zone of section steel on the outside flange when the external load is at 0.65 Pu. Finally, the concrete to the outside of the bonding crack is split, then the specimen is destroyed. The failure modes of some specimens are shown in Figure 9. The BBi-j, SAi-j and SBi-j are used to represent corresponding pictures. And SA is shearing beam, and its span-to-depth ratio is 2.0; BB mean bending beam, and its span-to-depth ratio is 1.2. And \(i\) and \(j\) are replacement percentage and temperature, respectively.
## 4 Discussion
### The Role of the Cryosphere in SDGs in the SAM  
The cryosphere, an emerging element within Earth system science, interacts strongly with the atmosphere, land, ocean, and lithosphere via physical, chemical, and biological processes, and has a significant impact on humans through the direct transfer and transformation of mass and energy [14]. To advance global sustainable development integrating both socio-economic and environmental concepts, the United Nations developed the 2030 Agenda for Sustainable Development in 2015 [17]. This included 17 Sustainable Development Goals (SDGs), and 169 specific targets [82]. Cryospheric changes are projected to pose challenges to livelihoods and other economic activities in mountain regions, including hydropower, agriculture, and tourism, particularly under future warmer climate scenarios [51]. This is likely to make the implementation of sustainable development in mountainous areas much more difficult. Defining a set of functions of the cryosphere, associated risks, and potential adaptive strategies will be required to guide the realization of mountain sustainable development goals [82].  
Snow cover is the key parameter that determines the cryospheric function, service, and risk in the SAM, and influences the various hazards and water availability through hydraulic connectivity (Figure 8). Snow drought will lead to a decline in water availability in the mountain regions, which then induces a series of cascading effects on clean energy, agriculture, lake ecology, and fisheries. In contrast, extreme winter snowfall will increase the risk of snowstorms and subsequent spring snowmelt flooding. Meltwater changes that are induced by winter snow accumulation fluctuations (Figure 3a) have resulted in spring snowmelt floods and summer droughts (Figure 6). Cryospheric changes in the SAM directly affect SDG6 (Clean Water and Sanitation), with snowmelt also having a significant role in SDG7 (Affordable and Clean Energy), SDG14 (Life Below Water), and SDG15 (Life on Land), which then induces cascading effects into SDG1 (No Poverty), SDG2 (Zero Hunger), and SDG11 (Sustainable Cities and Communities), highlighting that cryospheric processes can be linked to various components in the regional socio-economic system, such as regulation, society and culture, and habitat. The 2019 Intergovernmental Panel on Climate Change Special Report on Ocean and Cryosphere in a Changing Climate (SROCC) highlighted that integrated water management approaches are needed to sustain clean energy production, agriculture, ecosystems, and drinking water supplies, and raise the potential for water reservoirs to meet the challenge of climate change in high-mountain regions [52]. Optimization of the storage and release of mountain meltwater by increasing the reservoir capacity in the SAM may be an effective way to mitigate the fluctuations in snow drought and flooding caused by interannual cryospheric changes. Transboundary cooperation between China, Kazakhstan, and Mongolia will further support efforts that address the potential risks to water resources in terms of their availability, access, and distribution governance.
## 2 Antenna Structure  
Figure 1a shows the overall structure of the proposed antenna. It comprises a conventional hybrid coupler that is engraved on the top side of the first layer (substrate 1), as exhibited in Figure 1b. A 3 dB hybrid coupler is used to feed the antenna due to its simplified structure. It simply generates two orthogonal modes equal in magnitude and a 90\({}^{\circ}\) out-of-phase mode to obtain dual circular polarization, and it provides good isolation between the two input ports. The circular patch with slits is placed on the top side of the second layer (substrate 2) as in Figure 1c. The side view of the antenna is displayed in Figure 1d, which consists of a double-layer FR4 dielectric substrate with a height of 1.6 mm, dielectric constant of \(\varepsilon_{r}\) = 4.3, and loss tangent of tan\(\delta\) = 0.025. The feeding technique of the antenna is the proximity-coupled structure to improve the antenna impedance bandwidth by providing electromagnetic coupling between the patch and feedline. Additionally, this method has lower spurious radiation due to the large isolation between the patch and feedline [24]. This method is optimized for LTE 43 of 5G applications in this antenna design. Therefore, this technique might have limitations in avoiding impedance mismatch when applied to other operating frequency bands due to changes in the requirements of the intended antenna. It is evident that the ground plane completely covers the proposed antenna to minimize back radiation [12]. Standard LTE 43 of the 5G mid-band has a range of (3.6-3.8 GHz) and a center frequency of 3.7 GHz. Therefore, the antenna should be designed with a \(-\)10 dB bandwidth that covers this range. Additionally, the axial ratio should be below 3 dB to obtain circularly polarized radiation within this frequency range to ensure radiation with circular polarization along the intended band. The overall dimensions of this design were 0.81\(\lambda_{o}\)\(\times\) 0.43\(\lambda_{o}\)\(\times\) 0.019\(\lambda_{o}\), where \(\lambda_{o}\) is the free-space wavelength at a center frequency of 3.7 GHz. The presented antenna was simulated with the aid of the CST microwave studio suite.