esa-sceva/satcom-chunk-collection huggingface dataset:
# Characteristic Volatile Composition of Seven Seaweeds from the Yellow Sea of China
## 2 Results and Discussion
### Characteristic VOC Molecules and Potential Application in Chemotaxonomy
#### 2.2.2 PCA on Aldehyde Variables  
Using all the VOC data as PCA variables, _E. prolifera_ was found to be an outlier, as shown in Figure 1(a) (DVB/CAR/PDMS fiber), but not in Figure 1(b) (PDMS fiber). Considering that aldehydes are the most abundant VOC categories (Figure 1), aldehydes were used as variables in PCA to reveal chemical differences between species. The results show that _E. prolifera_ was separated well from other seaweeds (Figure 3). The aldehydes that contribute the most to species differences are shown in Table 4. There are six aldehydes characteristically distributed in _E. prolifera_.  
Figure 2: Principal component analysis (PCA) of VOC compositions in seven seaweeds determined by HS-SPME/GC–MS; score plot for (**a**) divinylbenzene/carboxen/polyddimethylsiloxane (DVB/CAR/PDMS) fiber; (**b**) polydimethylsiloxane (PDMS) fiber.  
Aldehydes are substances released by algae under biotic or abiotic stress and some aldehydes may induce the synthesis of a series of oxylipins in algae and therefore act as inducers of metabolic responses [29]. In view of the limited species and numbers of seaweeds in the present study, our data can only suggest that aldehydes may be of important value for the chemotaxonomic significance of _E. prolifera_ and that the DVB/CAR/PDMS fiber is more suitable for headspace solid phase microextraction for aldehydes.  
The volatile components of red algae _Bangia fuscoppururea_, _Gelidium latifolium, Calithannion granulatum_, _Ceranium elegans_, _Laurencia papillosa_ and _Laurencia coronopus_ from Black sea was detected and found that hydrocarbons can be used as chemotaxonomic markers of the two classes Bangiophyceae and Florideophyceae [30]. The chemotaxonomic significance was also discussed according to VOCs detection in two brown algae: _Taonia atomaria_ and _Padina pavonica_[6]. The VOCs were used in evolutionary relationship discussion between the algae and liverwort _Fossombronia angulosa_ due to the similarity [31].
## 5 Implementation  
The environment of the system proposed in this study is based on the Ubuntu operating system and runs on Hyperledger Fabric version 1.4. HTML, CSS, JavaScript, and Node.js are used as development languages for network implementation. World State DB is used to exchange application and blockchain data, and Next.js is used for SPA.  
A virtual scenario was simulated to operate the proposed blockchain system with theoretical background knowledge for fine dust management in actual construction sites. The detailed assumptions for running the simulated scenario are as follows:  
1. The peers currently participating in the X construction site (channel 1) are the IoT Node (IoTID:1-1), contractor (GCID:2-1), inspection agency (RegulationID:3-1), and civic group (Civigroup ID: 4-1). These peers are configured as virtual nodes.
2. Methods for acquiring fine dust concentration data at construction sites include dynamic measurement using robots, drones, etc., and static measurement via the installation of sensors in each section. In this scenario, the static measurement using a light scattering digital dust sensor was considered for real-time measurement of the fine dust concentration in a construction site. It was assumed that the sensor performed the measurement in intervals of 10 min, and the GP2Y1010 AU0F module was used to acquire the sensor data [21].
3. Fine dust regulations vary by country or region. This scenario was simulated by referring to the \"Special Act on Fine Dust Reduction and Management of Korea.\" Inspection agencies set the fine dust concentration value to 300 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\).
## 5 Implementation: Deep-learning in IoMT Edge Devices
### Platforms to Execute DNN Applications  
There are several popular platforms with which to execute DNNs such as Central Processing Units (CPUs), Graphics Processing Units (GPUs), and Field Programmable Gate Arrays (FPGAs) [85]. Application-Specific Integrated Circuits (ASICs) are also experiencing increasing popularity in DNN computation as they can offer optimized performance and power efficiency. Many-core systems-on-chip (MPSoCs) are another platform providing flexibility for the communication among neurons. These platforms can be embedded into a system, called embedded systems, to perform a specific task. Various attempts have been conducted so far to improve the efficiency of such platforms for DNN applications, either targeting the training or inference phase [86]. Concerning IoT devices, the inference phase is of our interest, where the trained model is deployed on the device. The computations in the inference phase can be optimized using frameworks such as Tensorflow Lite, TensorRT, XLA, AMP, and Arm NN. These frameworks try to bridge the gap between the DNN models and the underlying hardware platforms. Among them, TensorflowLite and TensorRT are the engines targeting IoT and embedded devices.
# LoRaWAN Base Station Improvement for Better Coverage and Capacity
## 2 Overview of LoRaWAN
#### Spreading Factor  
As mentioned, LoRaWAN uses CSS technology to cope with interference and to provide long-range. CSS is a modulation method where frequency modulated signals, also known as chirps, are used to encode the data. Chirp with increasing frequency is called upchirp, and with decreasing frequency is called downchirp. On the physical layer, one LoRa packet consists of:  
* 8 upchirps for preamble,
* 2.25 downchirps for synchronization symbols, and
* \"choppy\" upchirps for physical payload.  
Each of the \"choppy\" upchirps starts with frequency between \(f_{min}\) and \(f_{max}\) (\(BW\) = \(f_{max}\) - \(f_{min}\)) that are minimum and maximum frequencies of CSS and define input information. Number of bits in the input information and chirp's length are dependent on a spreading factor (SF) which represents number of bits per symbol (one symbol has \(SF\) bits) and number of _chips_ per symbol (there are \(2^{SF}\)_chips_ in one symbol) and can be any number between 7 and 12. The correlation between spreading factor and symbol duration is given in Figure 1. Since the _chip_ duration is equal to 1/_BW_, symbol duration with 2\({}^{SF}\)_chips_ is 2\({}^{SF}\)/_BW_. Therefore, the symbol rate is _BW_/2\({}^{SF}\) which leads to the data rate:  
\[DR=SF*\frac{BW}{2^{SF}}*CR \tag{1}\]  
where _CR_ is the code rate.  
Lower spreading factor increases the data rate but decreases range and vice versa. For example, in conditions with constant bandwidth, when the distance between end device and base station is challenging, the use of spreading factor 12 is suggested to enable communication. In the opposite situation, when the distance between them is short, higher data rate is possible using spreading factor 7. Adapting the spreading factor and bandwidth to conditions in the communication channel is one of LoRaWAN advantages. Network server adapts the parameters based on the value of SNR (Signal to Noise Ratio) to provide the best data rate in the given situation. This feature is called _Adaptive Data Rate_ (ADR).  
Spreading factor also has an impact on Time on Air (ToA) defined as elapsed time for a LoRaWAN packet to transmit between end device and base station. It is given with:  
\[ToA=(n_{preamble}+2+2.25+n_{data})*T_{symbol} \tag{2}\]  
where \(n_{preamble}\) is the number of symbols in preamble, \(n_{data}\) the number of symbols in data and \(T_{symbol}\) the aforementioned symbol duration (2\({}^{SF}\)/_BW_). Since LoRaWAN operates in unlicensed spectrum, its devices have duty cycle limitation which means that there is a maximum percentage of time during which a device can occupy a channel. Exact time a device has to wait after sending a message can be calculated for each subband using ToA:  
\[TimeOff_{subband}=\frac{ToA}{DutyCycle_{subband}}-ToA \tag{3}\]  
Duty cycles are usually regulated by governments and are dependent on subband [27].
## 1 Introduction
Reference [17] first employed a pre-trained model to learn the power and error characteristics of a source-domain photovoltaic power station, then applied the learned features during model fine-tuning to achieve rapid transfer and to compensate for the insufficient data volume of newly established photovoltaic power stations. Reference [18] proposed a two-stage transfer learning method for short-term wind power forecasting. By constructing temporal features and scenario matching, the forecasted wind speed was preliminarily corrected from both spatial and temporal dimensions, improving prediction accuracy. However, most of the existing research has focused on transfer learning among wind and solar stations that are geographically close and limited in historical power data. Moreover, these studies have been confined to a single type of power station, lacking investigation into the features of combined wind and solar power output curves. When there is a considerable geographical distance between source and target domains, it results in differences in meteorological data distribution. To ensure the quality of the transfer, it becomes necessary to extract comprehensive meteorological information features from both domains during the transfer process. To address the above issue, this paper proposes a TCN-ATT-LSTM transfer learning method for wind and solar power forecasting that incorporates sensitive meteorological feature selection. This approach resolves the challenge of rapidly constructing adaptive models in data-scarce target wind and solar power sites while significantly improving prediction accuracy. The remaining structure of this paper is organized as follows. In Section 2, the overall framework of the proposed model is introduced. In Section 3, the selection method for sensitive meteorological feature factors and feature extraction by TCN-ATT-LSTM pretrained network are demonstrated. Case studies are given in Section 4, and the conclusions are presented in Section 5.
## 2 Materials and Methods  
### Study Area  
Qi'ao Island (113 deg36'41'' E-113 deg39'16'' E; 22 deg23'41'' N-22 deg27'39'' N) has a total area of 23.8 km2. The area experiences a subtropical monsoon climate with annual average temperature and precipitation of 23.5 degC and 1948.9 mm, respectively. It has a mixed semidiurnal tidal cycle and the annual average salinity of the seawater is 1.82%. Major types of rocks in the island include granite and granodiorite. The soils in the investigated area were classified as salic gleyic fluvisols according to the World Reference Base (WRB) soil classification system [13]. Major mangrove species include _Sonneratia apetala_, _Sonneratia caseolaris_ (Linn.) Engl., _Kandelia candel_ (Linn.) [PERSON], _Heritiera littoralis_ Dryand., _Parmentier cerifera_ Seem., _Bruguiera gymnorrhiza_ (Linn.) [PERSON], _Excoecaria agallocha_ Linn., _Thespesia populnea_ (Linn.) [PERSON]. ex Corr. and _Acanthus lilicifolius_ Linn. Sp. [14; 15; 16].
## 2 Materials and Methods
### Evaluation Metrics  
To evaluate the effect of segmentation quantitatively, the values of soil pores are represented as a positive class, while the values of other soil materials are represented as a negative class. The following four situations form the basis for our evaluation metrics: When a pore is predicted to be a pore, it is recorded as a true positive (_TP_). When a pore is predicted to be another material, it is recorded as a false negative (_FN_). Other materials predicted to be pores are recorded as false positives (_FP_). Other materials predicted to be other materials are recorded as true negatives (_TN_).  
Segmentation accuracy is defined as the proportion of correctly segmented pixels among all pixels in a soil image and can be computed as follows:  
\[Acc=\frac{TP+TN}{TP+TN+FP+FN} \tag{2}\]  
Segmentation precision represents the proportion of all correct pore predictions. To some extent, precision can also be defined as the degree of over-segmentation. Precision is computed as follows:  
\[P=\frac{TP}{TP+FP} \tag{3}\]  
Segmentation recall represents the proportion of all real soil pores that are correctly identified. To some extent, recall can also be defined as the degree of under-segmentation. Recall is computed as follows:  
\[R=\frac{TP}{TP+FN} \tag{4}\]  
For a given soil image, there may be differences in precision and recall, making it inconvenient to compare the advantages and disadvantages of different methods. The F\({}_{1}\)-score is the harmonic mean of precision and recall. A higher F\({}_{1}\)-score indicates a better model. The F\({}_{1}\)-score is computed as follows:  
\[F_{1}=\frac{2\times P\times R}{P+R}=\frac{2 TP}{2 TP+FP+FN} \tag{5}\]
# Enhancing Railway Network Safety by Reproducing Wheel-Rail Electrical Contact on a Laboratory Scale
## 2 Bench Conception
### Dimensioning Methodology
#### 2.2.1 Choice of the Roller  
The shape of the roller was chosen to ensure that the contact surface between the wheel and the rail was geometrically similar at both scales. To estimate the wheel-rail contact surface, we relied on [PERSON]'s theory, which is a widely-used reference, although we were aware that its assumptions would only be partially satisfied. [PERSON]'s theory assumes:  
* Perfectly elastic and homogeneous bodies in contact with isotropic mechanical properties.
* Smooth contact surfaces.
* Semi-infinite bodies with contact dimensions very small compared to body dimensions and radii of curvature.  
According to [PERSON]'s theory [11], the contact surface forms an ellipse. The surface and semi-axes (\(a\), \(b\)) are given by the following expressions:  
\[\begin{split} S_{contact}&=\pi*m*n*\left(\frac{3}{2}* F_{N}*\left(\frac{1-p^{2}}{E}\right)*\left(\frac{1}{A+B}\right)\right)^{\frac{2}{3}} \\ a&=m*\left(\frac{3}{2}*F_{N}*\left(\frac{1-p^{2}}{E} \right)*\left(\frac{1}{A+B}\right)\right)^{\frac{1}{3}}\\ b&=n*\left(\frac{3}{2}*F_{N}*\left(\frac{1-p^{2}}{E} \right)*\left(\frac{1}{A+B}\right)\right)^{\frac{1}{3}}\end{split} \tag{1}\]  
\(E\) and \(v\): [PERSON]'s modulus and [PERSON]'s ratio of the steel.  
\(A\) and \(B\): combined longitudinal and transverse curvatures.  
\(m\) and \(n\): tabulated coefficients dependent on A and B [12].  
Consequently, the first criterion means maintaining the same \(a/b\) ratio between the long and short axes of the elliptical contact surface on both scales.  
Equation (1) leads to:  
\[\left(\frac{m}{n}\right)_{scalel}=\left(\frac{m}{n}\right)_{reduced\ scale} \tag{2}\]  
It is important to note that at full scale, the wheel can be assimilated to a cylinder when it is in contact with the rail (Figure 2, top). Considering that the curvature radius of the head of the rail \(R_{c\ rail}\) is unchanged and the scaling ratio of the wheel, the shape of the contact surface in the transverse direction would have been a rectangle.  
To satisfy the first criterion, the rolling face of the wheel was chosen to be toroidal, as pictured in Figure 2, at the bottom.  
The transverse radius of curvature \(R_{T\;roller}\) allows us to adjust the shape of the contact surface while maintaining the desired \(a/b\) ratio. Using Equation (2) and [12], the following relation can be derived:  
\[R_{T\;roller}=\frac{1}{\frac{1}{R_{L\;roller}}\left(\frac{1+\left(\frac{\pi}{b} \right)_{\mathit{contact}}}{1-\left(\frac{\pi}{b}\right)_{\mathit{contact}}} \right)-\frac{1}{R_{T\;rail}}} \tag{3}\]  
For design purposes, the final longitudinal radius \(R_{L\;roller}\) value was 30 mm. This value allows for several full revolutions of the roller along the 1 m rail on the bench. The corresponding transverse radius \(R_{T\;roller}\) = 15.4 mm allowing us to maintain the \(a/b\)_ratio_ was determined using (3).
## 4 Proposed Work
In Figure 7, different colors were used to denote the dimension (number of features) of the reduced-feature sets obtained as a result of applying SFHSA on PHOG features extracted from images of dimension \(32\times 32\) in JAFFE database and the corresponding recognition accuracies were presented on the top of each bar, which represents a specific reduced-feature set in HM. Similarly in Figure 8, distinguishable colors were used to specify the dimension (number of features) of the reduced-feature sets in HM obtained as a result of applying SFHSA on PHOG features extracted from images of dimension \(32\times 32\) in RaFD database along with corresponding recognition accuracies, represented in similar fashion as in Figure 7. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Image Size**} & \multicolumn{2}{c}{**No FS**} & \multicolumn{2}{c}{**SA**} & \multicolumn{2}{c}{**GA**} & \multicolumn{2}{c}{**MA**} \\ \cline{3-10}  & & & **Feature** & **Accuracy** & **Feature** & **Accuracy** & **Timeian** & **Accuracy** & **Feature** & **Accuracy** \\  & & **Dimension** & **Score (\%)** & **Dimension** & **Score (\%)** & **Dimension** & **Score (\%)** & **Dimension** & **Score (\%)** \\ \hline \multirow{3}{*}{JAFFE} & \(32\times 32\) & & 57.14 & 2059 & 51.95 & 2613 & 70.13 & 2232 & 70.13 \\ \cline{2-10}  & \(48\times 48\) & & 46.75 & 2011 & 42.86 & 2284 & 61.04 & 2451 & 58.44 \\ \cline{2-10}  & \(64\times 64\) & 4096 & 44.16 & 2132 & 38.96 & 2254 & 57.14 & 2208 & 58.44 \\ \hline \multirow{3}{*}{RAFD} & \(32\times 32\) & & 66.42 & 2024 & 61.19 & 2721 & 70.15 & 2081 & 72.01 \\ \cline{2-10}  & \(48\times 48\) & & 74.07 & 2049 & 68.10 & 2580 & 75.00 & 2584 & 76.49 \\ \cline{2-10}  & \(64\times 64\) & & 69.4 & 2026 & 64.18 & 2457 & 72.76 & 2090 & 74.44 \\ \hline \multirow{3}{*}{**Dataset**} & \multicolumn{2}{c}{**ME-BPSO**} & \multicolumn{2}{c}{**WAO-CM**} & \multicolumn{2}{c}{**LHCMA**} & \multicolumn{2}{c}{**SFHSA**} \\ \cline{2-10}  & & **Feature Dimension** & **Accuracy** & **Feature Dimension** & **Score (\%)** & **Dimension** & **Score (\%)** & **Dimension** & **Score (\%)** \\ \hline \multirow{3}{*}{JAFFE} & \multirow{3}{*}{2118} & 61.04 & 3894 & 61.04 & **2235** & **72.73** & 1380 & 67.41 \\ \cline{2-10}  & & 1975 & 50.65 & 2921 & 51.95 & **2158** & **63.64** & 1416 & 55.84 \\ \cline{2-10}  & & 2595 & 50.65 & 1469 & 54.55 & 2060 & 58.44 & **1433** & **67.42** \\ \hline \multirow{3}{*}{RAFD} & \multirow{3}{*}{2758} & \(66.98\) & 3772 & 70.71 & 2211 & 70.34 & **1513** & **72.39** \\ \cline{2-10}  & & 2615 & 72.57 & **3489** & **77.61** & 2279 & 75.19 & 1493 & 75.74 \\ \cline{1-1} \cline{2-10}  & & 2584 & 69.22 & 3914 & 75.56 & 2383 & 73.69 & **1494** & **75.81** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of SFHSA with respect to No FS, SA, GA, MA, ME-BPSO, WAO-CM and LHCMA for hvnLBP features.
## 5 Conclusions
### Theoretical Advancements  
A multimodal data graded protection system (\(\varepsilon\) = 0.85) was developed based on the dynamic differential privacy model, successfully achieving cross-modal fusion of MOOC platform behavioral data and sports biomechanics parameters. This fills the technological gap between digital education and traditional sports science. Finally, the innovative Tri-Capacity Index (TCI) was proposed, and through structural equation modeling, it was validated for its high alignment with the OECD 2030 Capacity Framework (CFI = 0.93, TLI = 0.91), providing a quantitative evaluation tool for capability-based reforms in smart education systems. These three breakthrough advancements promote the paradigm shift in educational technology from the perspectives of neural mechanisms, data governance, and evaluation systems.
# Effect of SIRRCCs with Varying Steel Fiber Volume Fractions on Flexural Behavior
## 3 Experimental Results and Analysis
### Flexural Strength and Toughness  
Figures 6 and 7 compare the flexural strength and toughness experiment results with respect to the fiber volume fraction. A fiber volume fraction of 6% showed a high maximum flexural strength of 44.7 MPa. This is a desirable flexural strength that corresponds to approximately 50% of the compressive strength, suggesting a desirable resistance to bending (unlike conventional fiber-reinforced concrete).  
The experimental results for the fiber volume fractions of 5% and 4% showed maximum strengths of 33.2 and 24.2 MPa, respectively. The maximum flexural strength tended to decrease with decreases in the fiber volume fraction.  
This study attempted to analyze flexural toughness characteristics based on ASTM C 1609 [21]; however, the flexural toughness limited the area to 1/150 of the span distance in the load-deflection curve, as suggested in ASTM C 1609 [21], which was insufficient as the measured deflection greatly exceeded this value. For this reason, the flexural toughness was compared with an area of 20 mm of deflection (span distance/15). Figure 7 shows the flexural toughness with respect to the fiber volume  
Figure 6: Test results of flexural strength with respect to fiber volume fraction.  
Figure 7: Test results of flexural toughness with respect to fiber volume fraction.  
fraction. The comparison of the flexural toughness until 20 mm of deflection showed an energy absorption capacity of approximately 662 N-m at the fiber volume fraction of 4%; however, the capacity was approximately 1.3 times higher (i.e., 882 N-m) at the fiber volume fraction of 6%. The experimental results based on the fiber volume fraction revealed that the flexural toughness tended to increase with the fiber volume fraction, similar to the results of the flexural strength experiment. Table 6 outlines the calculation results of flexural strength and toughness with respect to the fiber volume fraction.  
The analysis of flexural strength and toughness showed that the behavioral characteristics obtained from the experimental research differed slightly from the general tendency of the fiber volume fraction to greatly affect the fracture toughness after an initial crack or maximum load. This is because the combined action of the high fiber volume fraction and matrix strength of the cement composites in this study not only increased the fracture toughness, but also greatly affected flexural strength. Therefore, to achieve the performance required to increase resistance to impact or explosion loads, it would be advantageous to maintain the fiber volume fraction at the highest level through construction, so as to increase flexural strength and toughness.