esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Implementation
### Storage Layer
#### 3.1.3 Data Router  
It is expected that all data inflows and outflows will pass through the data router on the controller node. The data router transforms raw data, chunks it, and dispatches the chunks to different storage nodes. A diagram of the upload data flow in F3 BFS is shown in Figure 2, and the download process is quite the opposite.  
When a user performs an upload operation, the uploaded data will first be processed by the encoder chain consisting of a series of encoders. Encoders take the output of previous encoders in a chain as their input, and convert the input data into their output, which is the input of the next encoder. A F3 BFS encoder allows F3 BFS to achieve a wide range of features, such as transparent compression, automatic extraction of sequence metadata, automatic creation of BLAST alignment databases, etc. Through the processing of the encoder chain, the raw uploaded data is finally transformed into the internal stored format.  
The mechanism of the encoder and decoder makes it easier for researchers to add their own functions. F3 BFS is designed to be extensible by mechanism. Since biomedical analysis is developing very fast, this mechanism is very important to prevent F3 BFS from becoming obsolete. The encoders and decoders in chain are highly configurable. By default, F3 BFS only enable the compression encoder. Other available encoders are shown in Table 1. Each encoder has a corresponding decoder, which has the opposite function and is responsible for retrieving the original data from the internal stored format.  
Figure 2: Data flow during the upload procedure.  
Chunking is a method of dividing large files into smaller files--called chunks. For the purpose of data deduplication, achieving better compression quality and improving the parallelism, the output data of the encoder chain will then be chunked into smaller chunks by using the content aware chunking (CAC) algorithm. In order to ensure that chunks can be properly recreated when the user requests to retrieve the file, the data router will send chunk information to the metadata database once it confirms that the uploaded data has been successfully stored in the cluster.  
As a final step, the data router will utilize the hash function to determine which storage nodes are being used to store the chunks. Specifically, the hash function is used to resolve the target storage node to which the chunk will be sent to and stored on. In order to transmit data between the data router and the storage node more efficiently, F3 BFS will make use of remote-direct-memory-access (RDMA) whenever it is possible to do so, given that the underlying network hardware supports it. Network communication within a data center can be enhanced through the use of RDMA, which provides low-latency network communication.
## 3 Results
### Test of SF Algorithm with PTB Diagnostic ECG Database
#### 3.2.3 Test 3: PLI Constant (Common-Mode Phase Test)  
The design of PLI constant (common-mode phase test) in Table 2 simulates eight different common-mode phase shifts in the range of 0-360\({}^{\circ}\) (step of 45\({}^{\circ}\)) introduced to five different PLI frequencies (48-52 Hz, step of 1 Hz). Similarly to the previous test, the maximal effect is reported for the extreme PLI amplitude of 1000 \(\upmu\)V r.m.s. added to lead II as the representative lead of the worst case with maximal errors in the comparative study of all 12 leads (Figure 10).  
The statistical results in Figure 12 show that MAXE, RMSE, and \(SNR_{imp}\) are periodically synchronized to the common-mode phase with a maximal median value span of 1 \(\upmu\)V (RMSE), 2 \(\upmu\)V (MAXE), and 15 dB (\(SNR_{imp}\)) for \(f_{PLI}\) = 50 Hz (the exact multiple of the sampling rate where the rounding errors are normally minimal). Nevertheless, considering that other PLI frequencies produce an even smaller span of min-max performance metrics, our general conclusion is that the common-mode phase has a negligible influence on SF algorithm performance. In practice, the common-mode phase could be any and cannot be controlled due to the unknown phase shift in the common-mode channel of AFE; therefore, these results are considered for test purposes but cannot be used as a variable setting for optimization of the SF algorithm performance.
## 3 Quantification of Risk Index
### Loss Degree  
Taking into account the influence of wind power output and load uncertainty on power grid operation, the degree of loss is analyzed and quantified from the perspective of the power grid cutting load caused by equipment fault. In order to simplify the calculation of the cutting load, the DC cutting load model is adopted, and the specific formula is as follows:  
\[P_{Loss}=min\sum_{i\in ND}C_{i} \tag{7}\]  
\[T(S)=A(S)(PG-PD+C) \tag{8}\]  
Power balance constraint:  
\[\sum_{i\in NG}PG_{i}+\sum_{i\in ND}C_{i}=\sum_{i\in ND}PD_{i} \tag{9}\]  
Unit operation constraint:  
\[PG_{i}^{min}\leq PG_{i}\leq PG_{i}^{max}\ \ (i\in NG) \tag{10}\]  
Cutting load constraint:  
\[0\leq C_{i}\leq PD_{i}\ (i\in ND) \tag{11}\]  
Line transmission power constraint:  
\[|T_{k}(S)|\leq T^{max}\ \ (k\in L) \tag{12}\]  
where \(ND\) is the number of load nodes, \(C_{i}\) is the cutting load of each node, and \(P_{Loss}\) is the total cutting load of the system; the meanings of other variables can be found in [8].  
When the cutting load \(P_{Loss}\) is certain, the loss degree caused by transformer fault is:  
\[Loss=\theta\times P_{Loss} \tag{13}\]  
where \(\theta\) is the risk value of the unit load and \(\theta\) = China's GDP in a certain year/electricity consumption in the corresponding year. In this paper, China's GDP in 2020 was CNY 101,598.62 billion and its electricity consumption was CNY 75,110 billion kWh, so \(\theta\) = 13,527 RMB/(MWh).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Voltage Grades (kV)** & **Capacities (MVA)** & **Value Range** \\ \hline \hline  & 120 & 4 \\
220 & 150 & 5 \\  & 180 & 6 \\ \hline \hline  & 20 & 1.5 \\
110 & 31.5 & 2 \\  & 40 & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different voltage grades and capacities corresponding to the reference value of equipment importance \(E\).  
Considering the uncertainty of wind power output and load fluctuation, a probabilistic power flow model based on three-point estimation is introduced to calculate the probability distribution of the loss degree.
# The Current Development Status of Agricultural Machinery
## 5 Summary and Future Perspectives
### Innovative Multi-Modal Mobility Mechanisms
#### 5.1.2 Intelligent Mobility Mechanisms and Soil-Friendly Design  
The thin and erosion-prone soils commonly found in hilly and mountainous regions necessitate the development of intelligent locomotion mechanisms as a fundamental component of soil-conservative agricultural designs. By integrating multi-source terrain modeling--specifically LiDAR elevation mapping combined with multi-spectral soil hardness detection--a dynamic surface friction model is established to enable autonomous switching strategies for wheel-track chassis systems. This dual-mode system operates in wheeled configuration on low-slope rigid surfaces (friction coefficient \(\mu>0.6\)) to maintain operational efficiency while automatically transitioning to tracked mode on steep clay slopes (\(\mu<0.3\)) to suppress slippage. This approach significantly improves terrain adaptability while simultaneously reducing the risk of soil compaction associated with conventional wheeled systems. Based on this framework, a hybrid-model-driven contact pressure optimization system has been developed. The system integrates physical models--particularly [PERSON]'s track-soil vertical stress distribution model--with real-time soil moisture data to dynamically predict areas at high risk of soil compaction. Using model predictive control (MPC), the hydraulic suspension output is adjusted in real time. The iterative coordination between terrain modeling and physical-based mechanisms further refines the control parameters of the locomotion system, thereby enhancing both operational stability on slopes and energy efficiency. As a result, this integrated methodology effectively achieves the dual objectives of high-efficiency machinery operation and sustainable soil ecosystem conservation.
# Human Activity Recognition Based on Deep Learning Regardless of Sensor Orientation
## 3 Methodology
### Datasets
#### 3.1.3 UCI HAR [17]  
In an experiment involving 30 volunteers aged between 19 and 48 years, six activities (walking, walking_upstairs, walking_downstairs, sitting, standing, laying) were performed while a Samsung Galaxy S II smartphone (Samsung Electronics, Suwon-si, Republic of Korea) was worn on the waist. The embedded accelerometer and gyroscope in the smartphone captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50 Hz. The dataset obtained from the experiments was randomly divided into two sets, with 70% of the volunteers used for generating the training data and 30% for the testing data.  
To preprocess the sensor signals, noise filters were applied and the signals were sampled using a fixed-width sliding window of 2.56 s with a 50% overlap (128 readings per window). The sensor acceleration signal was then separated into body acceleration and gravity components using a Butterworth low-pass filter, assuming that the gravitational  
Figure 5: A participant’s running waveform, including accelerometer (**top**) and gyroscope (**bottom**).  
force has only low-frequency components. A filter with a cutoff frequency of 0.3 Hz was employed for this purpose. Finally, a feature vector was generated from each window by calculating variables from the time and frequency domains.  
The details of the datasets and related settings are presented in Table 1.
# A Gentle Introduction to Applications of Algorithmic Metatheorems for Space and Circuit Classes
###### Abstract  
Algorithmic metatheorems state that if a problem can be described in a certain _logic_ and the inputs are _structured_ in a certain way, then the problem can be solved with a certain _amount of resources_. As an example, by Courcelle's Theorem, all monadic second-order (\"in a certain logic\") properties of graphs of bounded tree width (\"structured in a certain way\") can be solved in linear time (\"with a certain amount of resources\"). Such theorems have become valuable tools in algorithmics: if a problem happens to have the right structure and can be described in the right logic, they immediately yield a (typically tight) upper bound on the time complexity of the problem. Perhaps even more importantly, several complex algorithms rely on algorithmic metatheorems internally to solve subproblems, which considerably broadens the range of applications of these theorems. This paper is intended as a gentle introduction to the ideas behind algorithmic metatheorems, especially behind some recent results concerning space and circuit classes, and tries to give a flavor of the range of their applications.  
algorithmic metatheorems; Courcelle's Theorem; tree width; tree depth; monadic second-order logic; logarithmic space; circuit classes
## 5 Simulation Results
Since the correlations are beneficial for the system, the system performance is enhanced. Figure 7 shows the upper and the lower bounds on the system outage probability and their asymptotic approximations given in Equations (25) and (26) for several values of \(K\). It is noteworthy that the curves generated by Equations (25) and (26) represent the system DO. We can see that the DO curves converge to their corresponding lower and upper bound curves in the high SNR region, which confirms that the considered system can achieve DO equal to the number of available relays. In addition, the figure indicates that, by implementing a greater number of relays, the system can obtain  
Figure 6: System outage probability versus the number of relays at 20 dB average SNR. Figure 7: Upper and lower bounds on the system outage probability and their asymptotic approximations with \(\rho=0.9\). a much better outage performance. The reason is that with a greater number of relays, the system has more freedom to select the best helper for its communication. In Figure 8, we present a comparison among the effects of the correlations between a channel and its estimate \(\{\rho_{1},\rho_{2}\}\), correlations among the first-hop channels and correlations among the second-hop channels \(\{\rho_{SR},\rho_{RD}\}\), and the correlation between two channels of each relay \(\{\rho_{k}\}\) on the considered system performance. The 'independent curve' also represents the case with perfect channel estimation. It is again confirmed that among the three correlation forms, only the correlation between the two channels of each relay has a positive impact on the system performance, even though the improvement of the system outage probability is diminished in the high SNR region. We observe that the dash curve with circle mark and the dash curve with asterisk mark are parallel with the solid curve in the high SNR region. It means that the last two aforementioned correlation forms do not affect the system achievable DO [21]. On the other hand, it is shown that the two dash curves with diamond and triangle marks are parallel with the dash curve in the high SNR region. This observation infers that the first aforementioned correlation form significantly degrades the system DO, i.e., from four to one [18; 19]. It is also worth mentioning that even with \(\hat{\rho}_{1}=\hat{\rho}_{2}=0.99\) (this corresponds to the case that the estimate channels are only very little outdated compared to the actual channels), the system performance as well as the system DO are seriously deteriorated. This observation implies that the full benefit of single relay selection scheme is difficult to achieve in practise since perfect channel estimation is not an easy task to be done.
# A Novel Routing Scheme for Creating Opportunistic Context-Virtual Networks in IoT Scenarios +
## 5 SACAR Algorithm Description
### SACAR Specific Algorithms
```
1:An IoT scenario: \(\mathcal{D}\), simulation time: \(T\), frequency \(\omega_{t}\), encounter list: \(\mathcal{L}\)
2:do
3:for all node \(d_{i}^{x;y_{i}}\in\mathcal{D}\)do
4:\(d_{i}^{x;y_{i}}\_listen()\)
5:if\(\omega_{t}\) is triggered then
6:if\(G_{i}\
eq\emptyset\)then
7:\(\mathcal{N}_{i}\gets d_{i}^{x;y_{i}}\_getNeighbours()\)
8:for all neighbor node \(d_{j}^{x;y_{j}}\in\mathcal{N}_{i}\)do
9:if\(d_{j}^{x;y_{j}}=\) dest. node OR dest. node \(\in d_{j}^{x;y_{j}}\_\mathcal{L}\) OR \((G_{i})\cap(G_{j}\cup(S_{j})\
eq\emptyset\)then
10:\(\triangleright\) If the selected node is the destination or has common interests
11:\(\triangleright\) or is in the encounter list of the intermediate node
12:\(m_{i}\leftarrow\{G_{i},S_{i}\}\)\(\triangleright\) The message is created and sent
13:\(sendMessage(m_{i},d_{j}^{x;y_{j}})\)
14:endif
15:endfor
16:endif
17:endfor
18:while\(t<T\)
```  
**Algorithm 6** SACAR-Hybrid pseudocode.
## 4 Artificial Soiling Test with Pollen
### Results  
The detrimental effect of the accumulation of cypress pollen on the normalized transmission spectra is shown in Figure 10. While \(T_{\text{NORM}}\) dropped by \(\sim\)5% and \(\sim\)12% for pollen mass densities of 1.4 g/m\({}^{2}\) and 3.1 g/m\({}^{2}\), respectively, it further decreased for higher soiling levels, particularly in the ultraviolet (UV) range. For a given pollen mass density, \(T_{\text{NORM}}\) remained constant within a broad wavelength interval, but it decreased at shorter wavelengths. In summary, the transmittance worsened steadily both as the pollen mass density increased and as the wavelength approached to the UV range. Within the visible (VIS) range, cypress pollen exhibited non-selective light scattering. That is, as the pollen particle size (i.e., 29-38 \(\upmu\)m in diameter within the genus _Cupressus_[78]), exceeded the incident wavelength many times, a constant light scattering occurred within the VIS range. An analogous optical behavior was observed for desert dust not exceeding our particle size [97]. Conversely, the observation of a strongly wavelength-dependent transmittance towards the UV range was the reason to complementarily perform some hemispheric reflection measurements. Only a few reflection spectra were measured to prevent an accidental contamination by pollen inside the integrating sphere.
## 1 Introduction
### Problem Background and Motivation  
A problem variant that has been studied especially in the context of online social networks is the minimum positive influence dominating set (MPIDS) problem in which a social network is modeled by a simple, connected undirected graph where vertices represent a group of individuals (people) and edges indicate relationships and interactions between them. The problem was first introduced by [PERSON] et al. [6], based on the following motivation. With the explosive growth of online social networks, the need for social network analysis tools in order to study the social influences and interactions between individuals within groups and organizations has become a primary concern. As an example, one of the most popular online social networks worldwide is Facebook. In January 2021, Facebook had approximately 2.74 billion users [7], more than any other social network. In addition, ideas and information propagated in social networks can have a significant impact on society (negative or positive) and on various aspects of the life of people. Social norms theory has shown that the behavior of individuals can be affected by perceptionsof others' thoughts and behaviors [8]. Thus, exploiting the relationships among people in social networks can provide great benefits to both economy and society. The aim of the MPIDS problem is to identify a small subset of key influential individuals to speed up the spread of positive influence. It can be applied in viral marketing, which is an advertising strategy that utilizes social relationships, such as friendships, professional interactions and families to spread the awareness about a promoted product among individuals in a given social network [9; 10]. The idea is to identify a limited number of customers that can quickly lead to the entire network being influenced to adopt the product. Other applications of the MPIDS problem can be found in e-learning software [11], online business [12], drinking, smoking, and drug related problems [6].
## 2 Related Work
### Chinese NER Based on Chinese Character Glyph Enhancement
#### 2.2.1 The Decomposition of Chinese Characters  
The decomposition of Chinese characters is to use radicals, wu-bi or structural components to represent Chinese character glyphs. For example, for Chinese character \"\(\mathcal{H}\)\", its radical is \"\(\mathcal{A}\)\"; wu-bi are \"AWSU\" and structural components are \"\(\mathcal{A}\)\". A typical method is ME-CNER [28], which improves the NER performance by using a CNN to capture potential glyph information in a radical sequence. [PERSON] et al. [29] proposed MECT, which introduces the structural components of Chinese characters into FLAT, and designs a Cross-Transformer network to fuse the information of characters, radicals and words. [PERSON] et al. [30] proposed MFE-NER, which uses wu-bi to help generate a glyph embedding, and combines it with character and phonetic features to generate multi-feature fusion embedding, so as to improve the performance of the Chinese NER model and reduce the influence caused by character substitution. [PERSON] et al. [31] proposed StyleBERT to improve the semantic expression ability of a Chinese pre-training language model by integrating word, pinyin, wu-bi and chaizi information. However, the method based on the decomposition of Chinese characters ignores the overall structure and spatial characteristics of Chinese characters, because the structural components of Chinese characters are not all horizontally distributed as with English words, and the distribution of structural components of Chinese characters can be vertical, surrounding, half-surrounding, etc., with obvious spatial characteristics. For example, \"\(\mathcal{H}\)\" (leaf)\" and \"\(\mathcal{H}\) (trap)\" have the same radical \"\(\mathcal{L}\)\" and structural components \"\(\mathcal{L}\)\", but the spatial distribution of the structural components is different.
## 4 Discussion
Specifically: (**a**) boxplot of MSE for low level of degradation (PGA \(\approx\) 1 m/s\({}^{2}\)); (**b**) boxplot of MSE for medium level of degradation (PGA \(\approx\) 6 m/s\({}^{2}\)); (**c**) boxplot of MSE for high level of degradation (PGA \(\approx\) 9 m/s\({}^{2}\)). (**d**) Comparison between the average MSE computed over all the post-predictive model runs for each model (square markers), versus the MSE computed for the MAP estimate (cross markers). The comparison is differentiated into three colored subsets: (i) in black, MSEs computed for a low level of degradation (PGA \(\approx\) 1 m/s\({}^{2}\)); (ii) in blue, MSEs computed for a medium level of degradation (PGA \(\approx\) 6 m/s\({}^{2}\)); (iii) in red, MSEs computed for a high level of degradation (PGA \(\approx\) 9 m/s\({}^{2}\)). correlations between it and the statistical nature of the external force (e.g., amplitude and frequency) and the statistical nature of the modal characteristics (e.g., natural frequencies) of a system, evaluated in operational conditions. For example, for external forces with frequency content close to the natural frequencies of the system, there is a high chance of high degradation to occur, and thus a discrepancy model distribution close to a Gaussian distribution could bring trivial results. This is clearly valid for a given model, since the model selection framework is another research topic that was not evaluated in this study and is complementary to the actual work. This finding could stimulate further research on the statistical correlation of discrepancy models and the magnitude of excitation, helping to calibrate mathematical models that try to emulate real word systems and structures.
# Research on Optimized Energy Scheduling of Rural Microgrid
###### Abstract  
This paper introduces a new rural microgrid model, including residents and agricultural greenhouses. Based on the new model framework, the precise energy scheduling of a rural microgrid is realized by means of load classification and load forecasting. Moreover, we also adopt a new energy-storage mode, cloud energy storage (CES), as the shared energy-storage unit of rural microgrid, and analyze the service and operation mechanism of CES in detail. The shared storage characteristic and adjustable storage capacity of CES are helpful for the precise management of power dispatching. At the same time, in order to accurately implement energy scheduling, we fully consider the load characteristics of rural areas and divide the load into residential load and agricultural load. Then the extreme gradient boosting (XGBoost) algorithm is used to predict the short-term power consumption of the two types of load respectively, which can effectively alleviate the uncertainty of load power consumption and improve the accuracy of scheduling. Finally, an illustrative example of rural energy scheduling is given. The example studies the impact of energy-storage capacity on the cost of the scheduling scheme, and designs a power-dispatching scheme based on load forecasting, which accurately solves the energy charging and discharging planning and grid energy trading planning.  
Keywords:load forecasting; rural microgrid; energy scheduling; cloud energy storage +
Footnote †: journal: Applied Sciences