esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study combines remote-sensing technology and model simulation methods to provide a comprehensive quantitative assessment of NPP. The spatial and temporal distribution of NPP and its driving factors are explored, and various factors are combined to  
Figure 10: Spatiotemporal distribution in mean precipitation and mean temperature of Ordos City.  
analyze and predict NPP, providing an important reference basis for ecological environmental protection and sustainable development in Ordos City. Combining the needs of socioeconomic development and ecological protection, a series of ecological restoration and protection measures with high feasibility are proposed, which provide a reference for the ecological environmental management and sustainable development of Ordos City. The following conclusions can be drawn:  
1. The NPP in Ordos City mainly exhibited a distribution of low values in the northwest and high values in the southeast, with the low-value areas mainly concentrated in Hanggin Banner, the west of Otog Banner and the west of Otog Front Banner, and the high-value areas were in Ejin Horo Banner, Dongsheng District, Jungar Banner, Kangbashi District and Uxin Banner. The NPP showed an obvious upward trend.
2. Usable grassland area and annual mean precipitation were significantly positively correlated with total NPP, whereas the other factors were more significantly negatively correlated.
3. The degree of influence, in descending order, is: usable grassland area > non-agricultural population > emissions of CO\({}_{2}\) from fossil fuel combustion > annual mean precipitation > total population > raw coal production > fixed asset investment.
4. A total NPP-anthropogenic factor regression model and a mean NPP-natural factor regression model were constructed, which can predict NPP to a degree. The R-value of the total NPP-anthropogenic factor regression model was higher.
5. Human activities such as fossil and raw coal burning, electricity production, population growth and the area of available grassland were the main causes of change in NPP. Measures such as the planting and conservation of green plants and scientific and effective energy extraction plans can enhance the carbon sequestration level in Ordos City.  
Conceptualization, L.Z. and W.S.; methodology, L.Z. and W.S.; software, L.Z., J.W. and D.F.; writing--original draft preparation, L.Z. and W.S.; writing--review and editing, W.S., J.W. and D.F. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Ordos City Landmark Team Project (2022).  
Not applicable, this study does not involve humans or animals.  
Not applicable.  
The authors would like to thank the editors and the anonymous reviewers for their constructive comments and suggestions, which greatly helped to improve the quality of the manuscript.  
The authors declare no conflict of interest.
# Development of Radio-Frequency Sensor Wake-Up with Unmanned Aerial Vehicles as an Aerial Gateway
## 7 Conclusions and Remarks
It is arguable that a mesh topology with communication relays can be used. Potentially, we believe that this decision depends on application context. In our context the UAV had two roles: the gateway to wake up sensors and collect data, and a remote sensing platform to collect geospatial images of the ground. Given this mobility and its dual role, a star topology is considered more appropriate. Nevertheless, if a mesh or other dynamic topology is considered in conjunction with the UAV-based sensor activation, this will demand a new research endeavor. A different scenario that uses the star topology for collecting data in a large geospatial area was discussed in our recent work [36]. In this case, the whole WSN can be segmented into a few subsets with a star topology, and the UAV will stay hovering in each subset for both wake-up and data collection procedures. Once the task is performed, the UAV can fly to the next subset. This practical treatment is believed to be a feasible solution to use the UAV with the aforementioned dual roles in an aerial-ground network in larger outdoor environments. [PERSON] conducted the design and experiment of the research work as a doctoral student researcher, and prepared the manuscript; [PERSON] contributed to the experimental setup and data collection during his visit at UMKC as a visiting scholar and assisted the preparation of the manuscript; and [PERSON] is a principal investigator of the research project and led the overall efforts of the research and development and manuscript preparation. This material is partially based upon work supported by the National Science Foundation (NSF) under Award Number IIA-1355406 and work supported by the United States Department of Agriculture's National Institute of Food and Agriculture (USDA-NIFA) under Award Number 2015-68007-23214. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of NSF or USDA-NIFA. The authors declare no conflict of interest.
## 2 Background
### Wind Turbine Alarms  
Wind alarm systems vary widely between manufacturers but generally share the same broad functionality. They monitor wind turbines' operational variables and trigger alarms when the signals exceed threshold limits. A sample of an alarm list is shown in Table 1. Alarms are recorded continuously in chronological order. The alarm records contain turbine number, triggering time, alarm types, alarm codes, alarm flags, and description. Amongthem, the alarm code is the unique code of an alarm. The alarm flag represents the start or the end of each alarm. Hence, each alarm has two records.  
When a wind turbine experiences a fault, it can result in alterations to multiple variable values and the subsequent generation of multiple alarms. Nevertheless, these alarms, occurring in a short time frame, are not indicative of the specific fault type. As such, further analysis of the alarms is necessary to identify the underlying cause of the fault.  
Furthermore, it can be observed that the alarm data are in non-numerical form. To efficiently analyze and process this data, it is necessary to convert these non-numerical data into numerical form. Finding a reasonable and effective transformation method is one of the problems addressed in this paper.
# DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios
###### Abstract  
Accurate long-term cloud demand forecasting is critical for optimizing resource procurement and cost management in cloud computing, yet it remains challenging due to dynamic demand trends, limited historical data, and the poor generalization of existing models in few-shot scenarios. This paper proposes DimAug-TimesFM, a dimension-augmented framework for long-term cloud demand forecasting, which addresses these challenges through two key innovations. First, Delivery Period Extracting identifies critical resource delivery phases by analyzing smoothed utilization trends and differencing thresholds, enabling focused modeling on periods reflecting actual demand. Second, Dimension-Augmented TimesFM enhances the pretrained TimesFM model by integrating cross-pool data via Dynamic Time Warping based similarity matching, enriching training data while mitigating distribution discrepancies. Experiments on real-world cloud resource utilization data demonstrate that DimAug-TimesFM significantly outperforms SOTA baselines (e.g., TimesFM, DLinear, PatchTST) in both short-term (16-day) and long-term (64-day and 128-day) forecasting tasks, achieving average reductions 72.9-81.7% in RMSE. DimAug-TimesFM also exhibits better robustness in scenarios where TimesFM fails, attributed to its synergistic integration of temporal feature enhancement and cross-pool data augmentation. This work provides a practical solution for few-shot cloud demand forecasting, enabling enterprises to align resource allocation with dynamic usage patterns and reduce operational costs.  
time series prediction; data augmentation; finops; cloud demand forecasting; large time series models +
Footnote †: journal: Applied sciences
## 2 Related Works
### Available Transitional HAR Datasets  
Although there are few datasets, specific transitional HAR datasets are publicly accessible and can be used to train and verify HAR classifiers. The HAPT dataset [31] comprises data from three-dimensional accelerometers and three-dimensional gyroscopes for six fundamental behaviors (stepping, going up and down stairs, sitting, standing upright, and lying) and six transitional actions (sit-to-stand, stand-to-sit, sit-to-lie, lie-to-sit, lie-to-stand, stand-to-lie). Sensor data were collected at the waist of 30 participants using IMU sensors placed in a smartphone (Samsung Galaxy II). Individuals performed the predetermined 12 categories of tasks. The HAPT dataset is the most often utilized standard dataset in transition-based HAR investigations [31; 41; 42]. The MobiAct v2.0 HAR dataset [43] was employed in our study since it has been extensively used in HAR studies [38; 40; 44]. The dataset was created using information from a smartphone's 3D accelerometer, 3D gyroscope, and 3D orientation (Samsung Galaxy S3, Samsung Electronics, Seoul, South Korea). This dataset contains 11 distinct tasks of daily life and five fall-related actions from 67 subjects. Seven fundamental activities (walking, going up and down stairs, jogging, leaping, seating, and standing upright) and four transitional activities comprise the ADL activity categories (sit-to-stand, stand-to-sit, car-step in, and car-step out). Additionally, this dataset contains five daily-life situations containing numerous ADLs lasting between two and three minutes each, which could also be utilized to develop and evaluate HAR models. Along with significant everyday actions, this study discusses transitory actions. According to assessments of the literature, a few available public datasets feature transitional activities captured using wearable sensors. As a result, this study conducts tests on the HAPT and MobiAct v2.0 datasets. Table 2 compares the two datasets, with the primary purpose of this study being to separate fundamental and transitional tasks correctly.
# Design and Optimization of Lattice Structures: A Review
## 2 Lattice Structures
### The Definition and Classification of Lattice Structure
For example, the step-wise gradient lattice structure includes three floors (as shown in Figure 3) [76], each step of lattice structure has the same cell topology and regular arrangement, but the cell topology and arrangement between steps are different. In this way, it is difficult to classify the lattice structure as periodic or aperiodic cellular structure. In addition, with the research of positive Poisson's ratio (PPR) and negative Poisson's ratio (NPR) materials, many scholars have begun to study lattice structures with different Poisson's ratio, which is usually named as non-uniform lattice structures [77; 78; 79; 80; 81]. In short, lattice structures with gradient and non-uniform PR unit cells should be referred to as non-uniform structures. For the purpose of this paper, based on the research of lattice structure in the cited literature, the following is the definition and classification of lattice structure in this paper. Lattice structure is a porous three-dimensional spatial structure formed and tessellated by unit cells with different topological geometries, and belongs to cellular structures (including foam structure, honeycomb structure and lattice structure). According to the uniformity of cells distribution, the lattice structures are classified into uniform lattice structures and non-uniform lattice structures. Uniform lattice structures refer to the porous structures with uniform distribution of unit cells, which are formed by the same topological shapes and geometric sizes of unit cells repeatedly arranged in space. The non-uniform lattice structures refer to the porous structures in which unit cells have different topological shapes or geometric sizes and the unit cells are not arranged repeatedly, i.e., in the uneven distribution of materials. Figure 2: Classification of lattice structures: (**a**) random lattice structures; (**b**) periodic lattice structures; (**c**) periodic lattice structures [23].
# Actuation Mechanisms and Applications for Soft Robots: A Comprehensive Review
## 2 Actuation Materials and Mechanisms for Soft Robots
### Fluid
#### 2.2.2 Origami-Based Pneumatic  
Conventional pneumatic actuation-based soft robots are made of soft materials (e.g., silicone), which expand and elongate themselves after being inflated. If the action of intaking air is continued, such a soft robot will deform excessively, which will affect the robot's movement. Origami is made of folding papers or polymers. Owing to bidirectional stability, origami is a structural innovation of pneumatic soft robots. Origami itself is not flexible and can not deform, but after being folded, it will contract like a spring to avoid unconstrained deformation.  
[PERSON] et al. devised a pneumatic origami muscle actuator (POMA) for a soft robotic hand [39]. The chamber of the robot is an origami structure using a 0.2 mm polyethylene  
Figure 5: (**a**) A pneumatic robot [93]. (**b**) The movement mechanism. (**c**) The moving states of each robot’s part in the time intervals.  
film, which can withstand air pressure of 100 kPa. The chamber's outer layer is a spandex fabric that can be stretched to 200% of its original length, which helps the inner chamber bend. The overall weight of the soft glove is about 200 g, and it can generate a constant driving force and transmit it in various grasping states. [PERSON] et al. presented a pneumatic crawling soft robot based on Miura-Ori [100]. It consists of two pneumatic foldable actuators that act as legs. Each actuator is made of soft materials and a paper skeleton, the total weight is about 2.52 g, and the load can reach 10 g. The working pressure of the PFA is from \(-\)10 kPa to 6 kPa, and the maximum strain under positive and negative pressure is 0.19 and 0.43. By controlling the unfolding and folding sequences of the two PFAs, the linear and turning motions of the crawling robot are realized, and the speeds are about 5 mm/s and 15\({}^{\circ}\)/s.  
Since the origami robot is actuated by using the crease memory of a paper, the movement of the robot can only depend on the folding mode and cannot be changed. Thus, it has low utilisation. Moreover, repeated folding can also make origami vulnerable.
## 2 Spatiotemporal Characteristics of Diversified Loads in Multiple Functional Areas
### Comparisons of Spatiotemporal Characteristics of Diversified Loads in Multiple Functional Areas
#### 2.3.4 Charging Loads of EVs  
The typical predicted charging loads curves of EVs of multiple functional areas are shown in Figure 4, in which the number of EVs is assumed as 2000. The \(x\)-axis represents 24 h of the day. The Monte Carlo simulation method is adopted to simulate EVs parking, driving, and charging behavior at different times and different areas for predicting the spatiotemporal characteristics of charging loads of EVs [27].  
Figure 4: Predicted charging loads of EVs of multiple functional areas.  
Figure 3: Typical electrical load rate curves of multiple functional areas.  
It can be seen from Figure 4 that since residents in the residential area usually go out early and go home late, EVs are charged concentratively at night and the peak period of charging loads occur around 10 p.m. EVs in the office area are charged during working hours, usually from 8 a.m. to 5 p.m. The concentration period of load charging in the commercial area is generally 5 p.m.-9 p.m., and fast charging is taken due to the shorter parking time. Charging loads characteristics of the industrial area are similar to that of the office area, and are concentrated in the working hours from 8 a.m. to 5 p.m. for charging. However, the charging loads from 6 p.m.-9 p.m. in the industrial area are lower than that in the office area, because a small part of the office staff overworks. The number of EVs in the office area is higher than that of EVs in the industrial area during this period.  
The peak of charging loads in each functional area overlaps with the peak period of electric loads of the functional area, but does not match the output curve of renewable energy in this functional area. It will not only impact the distribution network to a certain extent, but also is not conducive to the consumption of renewable energy. Thus, it is necessary to dispatch the charging loads of EVs which have flexibility and energy storage characteristics.
# Advanced Medical Image Segmentation Enhancement:
## 4 Results and Discussion
### Segmentation Results Based on Object Cropping
#### 4.3.1 Quality Enhancement with Preprocessing  
Our exploration begins by emphasizing the critical role of preprocessing for shaping the quality of segmented images. As can be seen from Table 4, images subjected to segmentation without any preprocessing closely resemble their original counterparts. However, the PSO segmentation process introduces scattered noise into the images, potentially obscuring vital image details and diminishing diagnostic accuracy. By contrast, images subjected to preprocessing--notably, employing histogram equalization (HE) and our proposed adjustments--yield substantially improved segmentation outcomes. These preprocessed images exhibit greater clarity and more precise object boundaries.  
The distinctive improvements in image quality, particularly when comparing images with and without HE preprocessing, underscore the significance of employing appropriate preprocessing methods to enhance the quality of medical image segmentation results. The sharper delineation of object boundaries, as demonstrated in segmented and cropped images, holds substantial value for medical practitioners by contributing to more precise diagnoses and well-informed clinical decisions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Medical Images**} & \multicolumn{2}{c}{**Preprocessed**} & \multirow{2}{*}{**Ground Truth**} & \multirow{2}{*}{**Segmented**} & \multicolumn{2}{c}{**Cropped Comparison**} \\ \cline{2-2} \cline{6-6}  & **Yes/No** & & & **Result** & **Ground Truth** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative analysis of medical image segmentation results using PSO segmentation with and without histogram equalization (HE) preprocessing.
## 2 Equivalent Range Model for GEO-LEO BiSAR
### Range Model Analysis for GEO-LEO BiSAR without 'Stop-and-Go' Assumption
The time needed for the signal to travel from the transmitting position to the target \(P\) and then back to the receiving position are denoted as \(\tau_{1}\) and \(\tau_{2}\), respectively. Then, the total time delay without'stop-and-go' assumption is given by \(\tau_{d}=\tau_{1}+\tau_{2}\). Therefore, the real range history of GEO-LEO BiSAR can be expressed as  
\[R_{bi}(t,\tau)=c\tau_{d}=R_{T}(t+\tau)+R_{R}(t+\tau+\tau_{d}). \tag{5}\]  
\(R_{R}(t+\tau+\tau_{d})\) can be approximated as a fourth-order polynomial as follows  
\[R_{R}(t_{s}+\tau_{d}) \approx R_{R0}+k_{R1}(t_{s}+\tau_{d})+k_{R2}(t_{s}+\tau_{d})^{2}\] \[+k_{R3}(t_{s}+\tau_{d})^{3}+k_{R4}(t_{s}+\tau_{d})^{4} \tag{6}\]  
where \(t_{s}=t+\tau\) denotes the transmitting time instant. \(k_{Ri},i=1,2,\cdots,4\) are the expanding coefficients of the receiver range model in (3). Expanding (6) into Taylor series of \(\tau_{d}\) and substituting the results into (5) yields  
\[c\tau_{d}=R_{T}(t_{s})+R_{R}(t_{s})+a_{1}\tau_{d}+a_{2}\tau_{d}^{2}+a_{3}\tau_ {d}^{3}+k_{R4}\tau_{d}^{4} \tag{7}\]  
where \(a_{1},a_{2}\) and \(a_{3}\) are the expanding coefficients given in the Appendix A. In order to obtain an approximate explicit solution with adequate precision, the third- and fourth-order terms of \(\tau_{d}\) are generally small and can be ignored herein to strike a balance between computational complexity and precision. Therefore, \(\tau_{d}\) can be obtained by solving the resulting quadratic equation of (7) as  
\[\tau_{d}(t_{s})=\frac{c-a_{1}-\sqrt{(a_{1}-c)^{2}-4a_{2}[R_{T}(t_{s})+R_{R}(t _{s})]}}{2a_{2}}. \tag{8}\]  
\[R_{R}(t)=\sqrt{R_{R0}^{2}+L_{chord}^{2}-2R_{R0}L_{chord}\sin\theta_{s}}. \tag{3}\]0 Illustration of propagation path for GEO-LEO BiSAR. Please note that in (8), \(\tau_{d}\) is related to \(t\) and \(\tau\). As analyzed in [32], the phase error caused by'stop-and-go' assumption within pulse duration are less than \(\pi/4\) and can generally be ignored for GEO-LEO BiSAR. Hence, the fast time variable \(\tau\) in (8) can be eliminated, yielding the delay \(\tau_{d}(t)\) independent of \(\tau\). Based on the above high-precision bistatic delay in (8), the range error caused by'stop-and-go' assumption can be expressed as a fourth-order polynomial of \(t\)  
\[R_{R}(t)=\sqrt{R_{R0}^{2}+L_{chord}^{2}-2R_{R0}L_{chord}\sin\theta_{s}}. \tag{3}\]1 \[R_{R}(t)=\sqrt{R_{R0}^{2}+L_{chord}^{2}-2R_{R0}L_{chord}\sin\theta_{s}}. \tag{3}\]2  
Therefore, the range model of GEO-LEO BiSAR can be expressed as  
\[R_{R}(t)=\sqrt{R_{R0}^{2}+L_{chord}^{2}-2R_{R0}L_{chord}\sin\theta_{s}}. \tag{3}\]3 \[R_{R}(t)=\sqrt{R_{R0}^{2}+L_{chord}^{2}-2R_{R0}L_{chord}\sin\theta_{s}}. \tag{3}\]4 \[R_{R}(t)=\sqrt{R_{R0}^{2}+L_{chord}^{2}-2R_{R0}L_{chord}\sin\theta_{s}}. \tag{3}\]5  
where \(R_{T08}=R_{T0}+\Delta R_{bi}\). \(k_{Tis}=k_{T\bar{i}}+\Delta k_{i},i=1,2,3,4\) are the sum of the \(i\)-th order coefficients of the GEO transmitter and the'stop-and-go' range error in (9).
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 4 I/O Detection Scheme
### Testing Phase  
Generally, the preprocessing phase improves the quality of the input data; thus, the machine learning models can work effectively with very high accuracy. First, we had a training set with 423 indoor samples, 427 outdoor samples, and a small number of semi-indoor samples that we set as indoor samples. Note that we selected this training set to simulate normal activities (most time is spent in indoor and outdoor environments rather than semi-indoor environments). With the test set with only indoor and outdoor samples, all machine learning models reach very high accuracy (minimum was 96% with the SVM technique, and the maximum was 98% with the KNN technique). To facilitate better evaluation of the data set, we increased the number semi-indoor environment samples as an individual test set to compare the accuracy of all models in a high noise environment. This test set included 170 samples (70 indoor samples, 50 outdoor samples, and 50 semi-indoor samples categorized as indoor samples). We also proposed a unique preprocessing method to improve the final result of this test set.  
We employed an offline learning method for I/O detection. Offline learning uses the trained model for the actual application, and it updates the model based on the programmer's schedule. Offline learning provides a simple model with fast calculations. The application does require a large amount of training data, as well as learning phase calculation. Another advantage of offline training is the uniformity of all applications: note that the model will do the same classification for all GPS sensors.