esa-sceva/satcom-chunk-collection huggingface dataset:
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 6 Enablers for 6G and Relevant Technical Preparation and Implementation
### Intelligent Reflecting Surface (IRS)  
IRS (intelligent reflecting surface), a new concept in wireless communication, is expected to be an extension of mMIMO [99]. It has been considered in recent times as a promising technology that is highly capable of decreasing the energy utilization of wireless networks and also of attaining theoretically unmatched and mMIMO increments [100; 101]. IRS involves a large surface area that is placed over a wall or a flat surface. It comprises a significant number of low-cost reflecting electronic components that have reconfigurable variables, such as conventional reflect-arrays as well as varactor diodes or micro-electrical-mechanical systems, the resonant frequency of which can be regulated electronically.  
Since IRS is comparatively simple to install and uses low energy, IRS-assisted communications may be used to bring about a significant increase in the coverage of 6G networks in various possible situations [102; 103; 104]. For instance, IRS can be used to coat the external wall of buildings in the outdoor urban environment. This provides an opportunity to improve the coverage and enhance the energy efficiency of 6G networks in cities. IRS can particularly be used on the surface of high buildings, which produces a virtual line-of-sight (LoS) link among the AP and IRS, which is especially favorable when the direct path is extremely shadowed [105]. In addition, because of the extensive deployment of IRS, there may be a significant decrease in the electromagnetic radiation produced by network infrastructures within the city. IRS can also be used to coat the interior walls in the indoor environment to improve the local connectivity of different types of devices, such as mobile phones and tablets, that depend on wireless connectivity for their operation. IRS can be specifically used in ceilings and underground walls to offer the required connectivity to several users simultaneously [102]. Figure 8 provides an IRS illustration.  
Figure 8: Intelligent Reflecting Surface.
# Ring-Overlap: A Storage Scaling Mechanism for Hyperledger Fabric
## 6 Simulation Experiment
### Analysis of Experimental Results  
From Equation (1), it is known that the smaller the \(s\), the lower the storage rate; however, the number of tolerable failure nodes becomes smaller, and the system reliability decreases. This requires a careful choice of \(s\) versus \(n\) when designing the scheme to compromise between the storage rate and system reliability.  
Figure 8: Storage space occupied by Ring-Overlap with Hyperledger Fabric and scalable model.  
Figure 7: The statistic result of query efficiency.  
Take a network of 100 nodes with 25 nodes per cluster as an example. Let the number of transactions be large enough to calculate the storage rate in terms of \(s/mn\). Table 3 lists the storage ratios and the number of tolerant failure nodes of the system corresponding to different \(s\) values. For clarity purposes, the \(n\) is omitted in calculating the result, which means that we only focus on the storage ratio on a single node.  
As can be seen from Table 3, in a cluster of 25 nodes, if \(s=2\), a block is repeatedly kept in 2 copies within a cluster, only 1 node failure in the system can be tolerated; when \(s=3\), 2 nodes can be tolerated to fail at the same time, and such a result is already able to provide high system reliability.  
Assuming that the reliability \(\lambda\) of nodes in the blockchain network is 0.9, 0.95, and 0.99, the probability of node failure is 0.1, 0.05, and 0.01. In a network of 100 nodes divided into four clusters, when \(s\) takes different values, the reliability \(q\) of the system can be calculated according to Equation (2), and the system reliability is shown in Figure 9.  
By analyzing the experimental results in Figure 9, the following can be concluded. When \(\lambda\) is the same, the larger the value of \(s\), the higher the reliability of the system. When \(\lambda\) = 0.95, the number of single block storage copies in a single cluster needs to be greater than eight for the system to reach 99.9% reliability. When \(\lambda\) = 0.99, the number of single block storage copies in a single cluster only needs to be greater than four for the system reliability to reach 99.9%. This indicates that the higher the node reliability, the more applicable the scheme is. Combining Table 3 and Figure 9, we know that when \(\lambda\) = 0.95, the storage rate only needs to be 32% for the system reliability to reach 99.9%. Almost 2/3 of the storage space is saved compared with the original Hyperledger Fabric storage. This experimental result demonstrates that the scheme is capable of effectively scaling Hyperledger Fabric storage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Overlapping Block Number s** & **Storage Rate** & **Number of Failure Tolerant Nodes** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 3: Storage ratios and the number of tolerant failure nodes.
## 2 Push-Pull Converter: Operating Principle and Non-Ideal Electrical Model
### Non-Ideal Push-Pull Model  
The non-ideal model of the converter is obtained from the analysis of the non-ideal equivalent circuit shown in Figure 2 for the operating sequences of the power transistors. Thus, following the modelling proposed in [17] and considering as state variables the inductor currents (\(i_{L_{P_{1}}},i_{L_{P_{2}}},i_{L_{M}},i_{L_{S_{1}}},i_{L_{S_{2}}},i_{L_{F}}\)) and the capacitor voltages of the circuit (\(v_{C_{P_{1}}},v_{C_{P_{2}}},v_{C_{OSS_{1}}},v_{C_{OSS_{2}}},v_{C_{S_{1}}},v_{C _{S_{2}}},v_{C_{F}}\)), the input voltage of the converter as the input of the model (\(v_{in}\)) and the converter output voltage and current as the output variables (\(v_{R},i_{R}\)), the complete non-ideal model of the push-pull converter calculated in [17] can be written in its state-space representation according to (1):  
\[\begin{split}\mathbf{x}(k+1)&=\mathbf{A}\mathbf{x}(k)+\mathbf{B}u(k )+\mathbf{E}\\ \mathbf{y}(k)&=\mathbf{C}\mathbf{x}(k)+\mathbf{D}u(k)\end{split} \tag{1}\]  
where \(k\) is the sampling time, the matrices \(\mathbf{A}\), \(\mathbf{B}\), \(\mathbf{C}\), \(\mathbf{D}\) and \(\mathbf{E}\) can be found in [17], and  
\[\begin{split}\mathbf{x}=\left[i_{L_{P_{1}}},i_{L_{P_{2}}},i_{L_{M}}, i_{L_{S_{1}}},i_{L_{S_{2}}},i_{L_{F}},v_{C_{P_{1}}},v_{C_{P_{2}}},v_{C_{OSS_{1}}},v_{C_{OSS_{2}}},v_{C_{S_{1}}},v_{C _{S_{2}}},v_{C_{F}}\right]^{T}\\ u(k)=v_{in}(k)\\ \mathbf{y}(k)=[v_{R}(k)i_{R}(k)]^{T}\end{split}\]  
Figure 2: Non-ideal electrical circuit of the push–pull converter of Figure 1.
# Day-Ahead Dispatch Model of Electro-Thermal Integrated Energy System with Power to Gas Function
## 2 The Concept of P2G
### Hcng  
HCNG, a mixture of compressed natural gas and hydrogen by energy, was studied in detail by the European project called NATURALHY in the period from June 2004 to 2009. The study shows that the natural gas pipeline not only provides a large transport channel for hydrogen, but also makes the loss of hydrogen very small in the transmission process. Moreover, the utilization of hydrogen can reach more than 90% in the pipeline terminal [12]. The Dutch study shows that the volume fraction of hydrogen mixed with natural gas can reach 12% [13]. The GRHYD project supported by the French Environment and Energy Control Agency shows that if hydrogen is supplied to the gas stations or the users, the volume fraction of mixed hydrogen will be as high as 20% [14].  
The application of HCNG involves transportation fuel, civil clean gas, residential heating and so forth. The gas network as an example is mainly used for daily heating demand. If the hydrogen can be transferred by the gas network and used for heating, the consumption of natural gas will reduce on a large scale.  
Figure 1: Diagrams of (**a**) alkaline electrolyzer, (**b**) Proton exchange membrane electrolyzer and (**c**) Solid oxide electrolyzer.
## 5 Conclusions  
In this study, MODIS NPP data were combined with temperature and precipitation measurements from meteorological stations to reveal the spatial and temporal changes of grassland NPP on the Tibetan Plateau from 2001 to 2022, as well as to investigate the effects of temperature and precipitation on NPP variation and NPP sensitivity. From 2001 to 2022, the trend of grassland NPP on the TP varied spatially from \(-30.2\) gC/m\({}^{2}\)/yr to \(15.8\) gC/m\({}^{2}\)/yr, with a mean value of \(1.01\) gC/m\({}^{2}\)/yr. The trend of NPP was unimodal with elevation, and 91% of grassland exhibited an increase in NPP. Temperature and precipitation were not the main factors contributing to changes in grassland NPP, with their combined contribution to NPP change being 0-87% and significantly decreasing with elevation. It was also demonstrated that temperature was the dominant climatic factor for the variation of grassland NPP compared to precipitation. Temperature trend, precipitation trend, and elevation combined to influence NPP sensitivity, and elevation was the primary factor for the sensitivity of grassland NPP on the TP. In conclusion, our study contributes to a deeper understanding of the macro-spatial variations of alpine grassland NPP as influenced by climate, and supports site-specific policies for grassland management.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs16244754/s1](https://www.mdpi.com/article/10.3390/rs16244754/s1)]([https://www.mdpi.com/article/10.3390/rs16244754/s1](https://www.mdpi.com/article/10.3390/rs16244754/s1)).  
Conceptualization, Z.T., Y.Z. and W.Z.; data curation, Z.T. and M.L.; formal analysis, Z.T. and G.Z.; funding acquisition, Z.T.; methodology, Z.T., Z.L. and Y.C.; resources, Z.T.; software, Z.T. and M.L.; writing--original draft, Z.T.; writing--review and editing, W.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China [Grant No. 42301054].  
The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# A Multi-Mode Recognition Method for Broadband Oscillation Based on Compressed Sensing and EEMD
## 3 Oscillation Signal Decomposition, Based on the EEMD Algorithm  
EEMD is a noise-assisted signal analysis method based on empirical mode decomposition (EMD) [23], which overcomes the mode aliasing problem. EEMD decomposes the original signal with Gaussian white noise many times, so that the signal is automatically decomposed to the appropriate reference scales without subjective calculation [24]. After averaging the decomposition results many times, the noises cancel each other out, satisfying the zero mean of white noise. The final intrinsic mode functions (IMFs) are obtained; the decomposition effect is better than with EMD. EEMD can adaptively decompose signals composed of oscillations at different time scales into modal components of different frequencies, mainly because EEMD has strong robustness to noise, the mode aliasing problem is solved in the signal decomposition process, and the calculation of IMFs does not need to rely on the selection of basis functions. The signal decomposition process of EEMD is as follows.  
1. Given an original signal \(x(t)\), add \(M\) times the different levels of white noise to the original signal; the \(m\)-th added Gaussian white noise is represented as \(w_{m}(t)\), and the signal generated after the \(m\) th added white noise is \(x_{m}(t)\). \[x_{m}(t)=x(t)+w_{m}(t)\ (m=1,2,\cdots,M)\] (5)
2. The signal generated after each addition of white noise is decomposed by EMD to obtain a corresponding set of IMFs, after which the signal after the \(M\) addition of white noise is expressed as follows: \[x_{m}(t)=\sum\limits_{m=1}^{M}d_{i,m}(t)+r_{m}(t)\] (6)  
Adding white noise to the signal multiple times (\(m\) times), \(d_{i,m}(t)\) stands for the i-th IMF generated after EMD decomposition, while \(r_{m}(t)\) is the residual term obtained after decomposition.
3. Because the uncorrelated random sequence statistical mean is 0, the above IMF components are averaged to eliminate the impact of multiple additions of white noise on the real IMF components, i.e.: \[\left\{\begin{array}{l}d_{i}(t)=\dfrac{1}{M}\sum\limits_{m=1}^{M}d_{i,m}(t) \\ r(t)=\dfrac{1}{M}\sum\limits_{m=1}^{M}r_{m}(t)\end{array}\right.\] (7) where \(d_{i}(t)\) represents the \(i\) th IMF after EEMD decomposition. \(r(t)\) is the final residual amount obtained after decomposition.
# Feasibility of LoRa for Smart Home Indoor Localization
## 2 Literature Review
### Bluetooth Low Energy (BLE)  
BLE is a low-cost and high-energy localization technology [55]. The BLE-based system [56] used the received signal strength (RSS) fingerprinting method to measure a distance. It is embedded with k-Means clustering to estimate the indoor position precisely. Yet, the utilization of the fingerprinting algorithm involves an interactive training process which is a barrier to robust implementation of the system. The Bluetooth indoor positioning system (BIPS) [57] is a representative BLE-based localization system, which could track indoor mobile users without training. The BIPS guarantees cost-effective and energy-efficient, but it has a short reception range and the location error above three or four meters.  
Another indoor localization system [58] for smart home adopted Bluetooth technology and RSSI value for positioning. The research team used the range average calculation as the smooth filter to deal with considerable RSSI value fluctuation while using the delta-based sampling to collect persuasive samples. Despite the improved accuracy, several limitations remain unsolved. For example, the accuracy of measurement results depends on the size of valid samples due to delta-based sampling implementation, affecting performance consistency. [PERSON] and [PERSON] [6; 59] used BLE beacons with improved trilateration to enhance the estimated location's outcome. This system is examined in university faculties to locate a professor or staff. The application range of BLE beacon is up to 30 or 70 m depending on the properties of manufacturers. The mean value of measurement error is close to four meters, which does not meet the requirement of accuracy. As a result, the performance of the BLE is not attractive in the indoor localization field.
## 2 Materials and Methods  
In this study, a hydrodynamic module of MIKE 21 FM was employed to investigate the IFCS's effects on the flooding process in Dongting Lake. The hydrodynamic module was used to model the flow in the waterways of Dongting Lake. The required new dike with several hydraulic gates in the lake was designed to regulate the river flows into Dongting Lake. Mike 21 FM is a two-dimensional (2D) depth-averaged hydrodynamic model while using the fine volume method to solve the shallow water equations. The governing equations are:  
\[\frac{\partial h}{\partial t}+\frac{\partial h\overline{u}}{\partial x}+\frac {\partial h\overline{v}}{\partial y}=0 \tag{1}\]  
\[\frac{\partial\overline{u}}{\partial t}+\overline{u}\frac{\partial\overline{u }}{\partial x}+\overline{v}\frac{\partial\overline{u}}{\partial y}+g\frac{ \partial\eta}{\partial x}+g\frac{\overline{u}\sqrt{\overline{u}^{2}+\overline {v}^{2}}}{C^{2}h}=v_{t}\bigg{(}\frac{\partial^{2}\overline{u}}{\partial x^{2} }+\frac{\partial^{2}\overline{u}}{\partial y^{2}}\bigg{)} \tag{2}\]  
\[\frac{\partial\overline{v}}{\partial t}+\overline{u}\frac{\partial\overline{v }}{\partial x}+\overline{v}\frac{\partial\overline{v}}{\partial y}+g\frac{ \overline{v}\sqrt{\overline{u}^{2}+\overline{v}^{2}}}{C^{2}h}=v_{t}\bigg{(} \frac{\partial^{2}\overline{v}}{\partial x^{2}}+\frac{\partial^{2}\overline {v}}{\partial y^{2}}\bigg{)} \tag{3}\]  
in which \(h=\eta+d\) is the total water depth, \(d\) is static water depth, \(\eta\) is water level; \(t\) is the time; \(x\) and \(y\) are in Cartesian coordinates; \(u\) and \(v\) are the corresponding velocity components in \(x\) and \(y\) directions, respectively, \(\overline{u}=\frac{1}{h}\int_{-d}^{\eta}udz\), \(\overline{v}=\frac{1}{h}\int_{-d}^{\eta}ydz\); \(g\) is the gravitational acceleration (m/s\({}^{2}\)); \(C\) is the Chezy coefficient; and, \(v_{t}\) is turbulence viscosity coefficient.  
The hydrodynamic module of MIKE 21 FM has the capability of modeling the process of wetting and drying as water enters and leaves the area, which means that it is suitable for simulating water flow onto the dry land when the water level is relatively low in the lake. Many researchers have used the MIKE 21 FM model to investigate a large lake's hydrodynamic process, like the Poyang Lake (China) [13], Dongting Lake (China) [14; 15], and Vembanad Lake (India) [16], hence, only a brief description of the model is presented here.
# Speech Emotion Recognition through Hybrid Features and Convolutional Neural Network
## 4 Experimental Results and Discussion
### Results of Experiment II  
In this experiment, the performance of MFCCs and t-domain feature was compared to MFCCT feature using the CNN model. To evaluate the performances of the MFCCT feature, 9 SER models were constructed (3 different feature sets \(\times\) 3 different datasets). Figure 6 presents the results of all 9 SER models, where the MFCCT features achieved improved weighted accuracy. In addition, the t-domain features showed the lowest weighted accuracy. The MFCCT features achieved around 50% more weighted accuracy compared to the MFCCs and t-domain features.
# Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition
## 5 Materials and Methods
### Model Architecture  
Three specialized modules are introduced for the classification of tennis strokes based on skeletons. They can be arranged either sequentially or in parallel, to form the basic building blocks of the proposed model. The graph self-attention module captures and extracts spatial as well as long-term temporal information from the skeletal joints. Moreover, information of the relationships between features from different channels is added by the adaptive consecutive attention model. Short-term temporal dependencies are multi-scale modeled by a temporal convolutional network that contributes complementary temporal cues. In addition, the Joint Motion module identifies which joints are in motion versus which remain stationary, guiding the graph self-attention to emphasize \"salient\" joints. Although these three modules function independently, they are closely interrelated. The complete model comprises seven stacked basic blocks, and the model architecture is outlined in Table 1 and Figure 4. In Table 1, each layer describes the input channels and temporal stride, as well as the output channels. Each basic block contains three graph self-attention modules, three adaptive consecutive attention modules, and one temporal modeling module. In blocks 3 and 6, the number of channels is doubled and the temporal dimension is reduced. Each block begins with either a \((1\times 1)\) convolution or a motion fusion operation that increases the dimensionality of the input channel. Three parallel graph self-attention modules connected with adaptive consecutive attention modules are employed to extract joint information at different scales. Their outputs are summed to produce the final feature representation.  
Figure 4: General scheme of the model structure.  
Moreover, the module integrates multi-scale temporal information, and the operation that reduces the time dimension is accomplished via depth-wise convolution. Finally, after traversing all basic layers, average pooling compresses the feature map, followed by classification of the resulting features using a fully connected layer.
# Beneficial Health Potential of Algerian Polysaccharides
## 1 Introduction  
Herbal medicines have attracted considerable interest as alternative anti-inflammatory remedies due to their large therapeutic properties, low toxicity and costs [1]. Today, involved mechanisms of anti-inflammatory remain unclear [2] and potentiating innate immune system has been recognized as a possible way for inhibiting inflammation growth without harming the host [3].  
Polysaccharides from various traditional medicinal herbs have been shown to be immunopotentiating, in vitro as well as in vivo [4]. Many reports on the biological activities of polysaccharides from Plantago highlighted that species belonging to this genus showed anti-inflammatory effects and immune enhancements through specific and non-specificimmunity, anti-peroxidation of lipids, antidiabetic, and reduction in serum lipids [5]. Arabinogalactan Type II were isolated from the leaves of _Plantago major_ and shown anti-complementary activity [6]. A highly branched heteroxylan from _Plantago asiatica_ seeds could induce maturation of murine dendrite cells, promote defecation and have antioxidant activity [7]. Intake of a _Plantago ovata_ husk-supplemented diet prevented endothelial dysfunction, obesity development, and improved dyslipidemia, abnormal plasma concentrations of adiponectin and TNF-\(\alpha\) (tumor necrosis factor-\(\alpha\)) in obese Zucker rats [8]. The polysaccharides from _Plantago palmata_ leaves possessed immunomodulatory properties by stimulating NO (nitric oxide) and TNF-\(\alpha\) production by activated macrophages [9]. A rhamnoarabinogalactan extracted from aerial parts of _Plantago lancelata_ was able to inhibit the peroxidation of soybean lecithin liposomes with OH (hydroxyl) radicals. This polysaccharide showed a high antioxidant activity of 19.1% at 0.23 mM, which represented 45.3% of the activity of \(\alpha\)-tocopherol standard [10].  
Today, identifying, characterizing, and evaluating the potential health benefits of new polysaccharides from African medicinal plants have become a challenge. _Plantago ciliata_, belonging to Plantaginaceae, is a traditional Algerian herbal plant abundantly distributed in the northeast region of Sahara. It has been reported for having many supposed ethnopharmaceutical properties including anti-inflammatory, anti-ulcer, anti-cough, and anti-diabetic activities [11]. To our knowledge, no studies deal with the chemical elucidation and some biological benefits of two water-soluble polysaccharides extracted from _Plantago ciliata_ leaves (L-PSPC) and seeds (S-PSPC).  
Preliminary structural features of both fractions were determined by colorimetric assays, Fourier Transform Infrared (FTIR) spectroscopy, High-Pressure Anion Exchange Chromatography equipped with Pulsed Amperometric Detection (HPAEC/PAD) and Gas Chromatography coupled to Mass Spectrometry with Electronic Impact (GC/MS-EI) analyses. Some biological activities were monitored (anticomplement, anti-inflammatory, anti-diabetic) regarding the structures of polysaccharides (structure/function relationship) and current literature.