esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State of the Art
### Technologies  
To address all the strategies imposed by the social and environmental requirements, novel manufacturing technologies have rapidly emerged over the last decade, establishing their position in the manufacturing sector [66]. Prior publications have focused on the enabling technologies in the concept of Industry 4.0 (or even Industry 5.0) and the subcategories of these. The current work expands on this approach by investigating the integration of the nine attributes, namely the processes, the cell, the auxiliary functions, monitoring/control, manufacturing functionalities, ICTs, Industry 4.0, materials and policies on a small-medium enterprise (SME) or original equipment manufacturer (OEM) environment with respect to the environmental footprint.  
The existing research does not fully address classification, while the current paradigm is adaptive enough to cover it as a whole; as an example, [PERSON] and [PERSON] [67] briefly presented the enablers of the Industry 4.0 such as IoT, additive manufacturing (AM), Big Data and so on. A similar brief investigation was conducted by [PERSON] et al. [68] who presented some of the applications of the concept of Industry 4.0. A more thorough research regarding the Smart Factory in the context of Industry 4.0 and the structure of itself was conducted by [PERSON] et al. [69]. Furthermore, a recent review by [PERSON] and [PERSON] [70] outlined the related technologies in the context of Industry 4.0., and the same authors structured a classification in four different pillars such as the strategic, managerial, technical and human resource view [71]. Moreover, [PERSON] et al. [72] integrated the enabling technologies into the social manufacturing context, meaning that clients/customers can interfere directly with the production. Similar keywords of the enabling technologies of Industry 4.0 may be found in the article from [PERSON] et al. [73], who discussed the key technologies for \"Smart and Optimal Manufacturing\". It is pointed out, however, that business models, in addition to technologies, are also essential to make everything work. In addition, it is a fact that various technologies found in the literature, cannot be implemented in a straightforward way. There are challenges pertaining to cyber-physical cloud manufacturing [74], challenges linked to management and standards [75], and technology limitations (i.e., related to augmented and virtual reality (AR/VR) [76]).
## 1 Introduction
Compared with the Butterworth filter, the PSMF has a larger amplitude-frequency gain as well as a smaller phase lag, so it  
Figure 1: Principle of the opto-electric servo platform (OESP). can achieve a better speed estimation. However, the accuracy of PSMF will be affected when the SNR of the speed signal is low [15]. Moreover, the complicated structure of the PSMF results in a difficult realization, and increases the computational costs of personal computers(PCs) evidently. In order to get a better estimation of the torque disturbance as well as the rotational speed, this paper proposes a wrap-down stability control scheme in combination of a PI controller and a state-augmented Kalman filter (SAKF). In this scheme, the PI is taken to regulate the linear part of the OES and the SAKF performs torque disturbance observation and speed estimation. Through the controller parameters tuning method provided in the paper, the OES achieves the satisfactory inertial stability accuracy and low-speed performance. The sections of this paper are organized as follows: Section 2 gives the dynamic model of the OES, which provides the basis for the design of the control scheme. Section 3 introduces the principle of the control algorithm and the structure of the main functional modules. Considering the fact that the control algorithm runs in a digital signal processor (DSP), the discretization representation of the algorithm is solved in Section 4, and the implementation steps of the algorithm are also provided in this section. In Section 5, this algorithm is evaluated based on OESs with different reducers, and in the last section, Section 6, the conclusions of this paper are presented.
# Fine-Grained Boundary Conditions in Field-Based Routing
## 6 Performance Evaluation
### Simulation Results
#### 6.3.2 Inner Boundary Conditions  
As previously mentioned, our evaluation of inner boundary conditions focuses exclusively on the non-grid regular topology as a representative case that best demonstrates their impact. To purely assess the effects of the proposed inner boundary conditions, we eliminate other factors in our simulations. Specifically, we assume a network topology with no broken links and apply the existing Dirichlet-based outer boundary conditions. In this configuration, we assign a 20% offered load to sub-domain 1 and an 80% offered load to sub-domain 2 by randomly selected nodes, thereby creating distinct loads for each sub-domain. Additionally, we set the potential value of the gateway in the sub-domain 1 to 0, while in the sub-domain 2, it is set to 0.5. This adjustment considers the difference in the number of nodes between the two sub-domains, ensuring that the expected load difference also is twice. By doing so, we aim to demonstrate the performance difference between ALFA models using conventional coarse-grained Dirichlet-based and advanced fine-grained Dirichlet-based inner boundary conditions.  
Our findings, depicted in Figure 9, reveal that the new version of ALFA with advanced fine-grained inner boundary conditions outperforms the existing ALFA with the coarse-grained inner boundary conditions in terms of the overall packet delivery ratio and aggregate throughput. We observe an approximate increase of 130 Kbps in aggregate throughput and a 10% boost in overall packet delivery ratio, on average. Additionally, approximately two times better results are observed for both metrics in terms of standard deviation. This demonstrates that the advanced inner boundary conditions enable ALFA to better adapt to the surrounding traffic load levels. Fundamentally, the routing field directs a portion of the traffic from sub-domain 2 towards sub-domain 1. It is worth noting that the observed improvement, while not dramatic, is indeed significant, highlighting the impact of modifying the inner boundary conditions within the same protocol.  
Figure 9: \(Cont.\)
## 4 Applicability of the Practical Criterion
### Application Scope of the Practical Criteria  
According to the results shown in Figure 5, when the voltage drops seriously, the motor will become unstable only for a short sag duration. When the voltage drops slightly, a long sag duration is needed to cause motor instability. This shows that the practical criteria cannot give an accurate judgment in all situations. Therefore, the application scope of the practical criterion needs to be discussed.  
We assumed that the practical criterion was that the voltage could not be lower than \(U_{\text{s}}\) for a time exceeding \(T_{\text{s}}\). The voltage magnitude \(U_{\text{s}}\) and the duration \(T_{\text{s}}\) form a point (\(T_{\text{s}}\), \(U_{\text{s}}\)) in Figure 6. The application scope of the practical criterion varies with the location of the point, as indicated below.  
1. If point (\(U_{\text{s}}\), \(T_{\text{s}}\)) falls in the red region, the system can be determined to be unstable when the voltage drops below \(U_{\text{s}}\) and remains low for longer than \(T_{\text{s}}\).
2. If point (\(U_{\text{s}}\), \(T_{\text{s}}\)) falls in the grey region, the system can be determined to be stable when the voltage is larger than \(U_{\text{s}}\) or the sag duration is less than \(T_{\text{s}}\).
3. If point (\(U_{\text{s}}\), \(T_{\text{s}}\)) falls in the white region, the stability cannot be determined only by the voltage magnitude \(U_{\text{s}}\) and the duration \(T_{\text{s}}\).  
The WSCC stipulates that the voltage cannot remain below 0.8 pu for longer than 0.33 s. In China, the SGCC stipulates that the voltage magnitude shall not be lower than 0.8 pu for more than 10 s. Comparing the two practical criteria with the stability boundaries in Figure 5, we can draw the following conclusions:  
1. It can be approximately considered that the system will be stable if the voltage is higher than 0.8 pu or the sag duration is less than 0.33 s.
2. It can be approximately considered that the system will be unstable if the voltage drops below 0.8 pu and remains low for more than 10 s.
3. If the voltage drops below 0.8 pu and remains low for 0.33-10 s, the system stability cannot be determined.  
Obviously, the three conclusions are not applicable to all power systems and all working conditions. However, the goal of this paper is mainly to clarify the applicability of the practical criteria. The specific values of voltage magnitude and sag duration are obtained by engineers through their experience. Therefore, the above conclusions can still be applied in practical engineering.
# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 2 Satellite Systems in Remote Sensing
### Resolutions of Satellite Instruments  
Satellite imagery is a satellite product used by businesses and governments. Its resolution varies based on the instruments used and the altitude of the satellite's orbit and can be split into five types:  
**Spatial** resolution is a satellite image's pixel size representing the size of the surface area measured on the ground. This resolution refers to the smaller discernible feature of a satellite image. A spatial resolution of 10 m means that a pixel of the image represents a ground area of \(10\times 10\) m.  
**Spectral** resolution is determined by the size of the wavelength interval and the number of intervals measured by the sensor. In other words, it refers to the sensor's capacity to detect specific wavelengths of the electromagnetic spectrum. Higher spectral resolution segments the electromagnetic spectrum into finer wavelength ranges, allowing the identification of more specific classes (i.e., rock type vs. vegetation).  
**Temporal** resolution is defined by the time interval between imagery collection for the same surface location. Satellites' ability to take photos of the same geographic region more regularly has drastically improved over the years, offering more accurate data.  
**Radiometric** resolution is the capacity of a sensor to capture a variety of brightness levels, such as contrast, and its actual bit depth. i.e., the number of gray-scale levels.  
**Geometric** resolution defines a sensor's capability to successfully display a patch of the earth's surface within a single pixel.
## 3 Base Station Deployment and Self-Optimization Positioning Method
### Trilateration Positioning
This paper, based on the principles of the trilateration positioning algorithm, primarily explores how to select distance information with relatively higher precision through a base station self-optimization method when four known nodes' distances to the target node are obtained, in order to perform positioning calculations and achieve higher precision in the positioning coordinates.
# An Overview of Terahertz Imaging with Resonant Tunneling Diodes
###### Abstract  
Terahertz (THz) imaging is a rapidly growing application motivated by industrial demands including harmless (non-ionizing) security imaging, multilayer paint quality control within the automotive industry, insulating foam non-invasive testing in aerospace, and biomedical diagnostics. One of the key components in the imaging system is the source and detector. This paper gives a brief overview of room temperature THz transceiver technology for imaging applications based on the emerging resonant tunneling diode (RTD) devices. The reported results demonstrate that RTD technology is a very promising candidate to realize compact, low-cost THz imaging systems.  
terahertz; terahertz imaging; non-destructive; resonant tunneling diode; RTD 20221 12 2022  
and high power conversion efficiency [17]. They also offer a coherent source and coherent detection; this is hugely important, especially for imaging layered structures.  
The paper is organized as follows: the THz imaging system is first briefly introduced in Section 2. Then the two key components of the system, the source and detector based on RTD technology and recent breakthroughs, are described in Section 3. Examples of RTD based THz imaging systems with non-destructive testing (NDT) applications and experimental results are described in Section 4. Conclusions are given in Section 5.
## 4 Simulation Results and Performance Analysis
### Existing Algorithms Used for Comparison  
Our proposed algorithm CDS_SC was compared with recent protocols DTA (Density and Topology-based Approximation) [22] and DWCM (Distributed and Weighted Clustering based on Mobility Metrics) [23]. DTA uses a hybrid approach, a combination of the topology and density-based conditions for computing dominating set members. A vehicle with the highest degree among its  
Figure 8: Clustering of vehicles in dense traffic scenario.  
Figure 7: Clustering of vehicles in an average traffic scenario.  
Figure 9: Clustering of vehicles in sparse traffic scenario.  
neighbors was selected as a cluster head. Gateways were selected as the set of vehicles that had at least two neighbors those were not directly connected to each other or if it had at least one neighbor that was not covered by the other neighbors or if it had at least one neighbor that was not covered by a pair of gateway vehicle's neighbors where these two vehicles were neighbors of each other. DTA introduces a major bottleneck in sparse and average traffic scenarios due to the selection of a higher number of gateway nodes. On the other hand, DWCM constructed a d-hop dominating set by considering the relative velocity and relative acceleration with the neighborhood. The vehicle will act as a cluster head if it has the highest priority among its d-hop neighborhood. The vehicle will act as a cluster gateway if it can hear from more than one cluster head. DWCM has the problem of choosing multiple gateways between the same cluster heads when the traffic density increases. Hence, the proposed scheme alleviated these problems by selecting a minimum number of gateways based on the set cover.
# Application of Soil Moisture Active Passive (SMAP) Satellite Data in Seismic Response Assessment
###### Abstract
The GPM monitored precipitation, which is a pivotal factor in soil moisture content, throughout the study period (time interval between pre- and post-event SMAP data). The GLDAS model data are generated from a climatologically consistent atmospheric forcing dataset and soil moisture variations are simulated based on satellite and ground-based observational meteorological data products. The main objective of this paper is to evaluate the potential application of SMAP to quantify surface soil moisture before and after earthquakes. The paper is organized as follows: First, the target earthquakes and the data used in this study are identified. Then, the soil moisture and rainfall data products and methods are described. The findings from the integration of soil moisture variations with seismic data are presented. Finally, the conclusions and some recommendations for future investigations are given.
## 5 Model Evaluation
### Single ML Models
Meanwhile, the coefficient of variation (CoV) shows a CoV of 0.014 for the DT training set and 0.226 for the test set, reflecting the low volatility in the training set and high volatility in the test set, while the CoV of KNN is 0.262 for the two sets of data, which demonstrates the consistency of the model. In summary, DT has excellent performance on the training set but is not stable enough on the unknown data and needs to be optimized; KNN, although the overall error is slightly higher, shows good generalization ability and consistency and is suitable for application in a variety of scenarios. Figure 5: Comparison of predicted and experimental values of each RCSW specimen obtained by the single ML models. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**CoV**} \\ \cline{2-10}  & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** \\ \hline DT & 2.39 & 279.4 & 5.7 & 78,078.9 & 0.3 & 116.2 & 1 & 0.94 & 0.014 & 0.226 \\ KNN & 269.7 & 281.8 & 71,374.4 & 79,421.5 & 103.4 & 137.9 & 0.91 & 0.94 & 0.262 & 0.262 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Predictive performance metrics for single ML models.
## 2 Materials and Methods
### Antidiabetic Activity
#### 2.7.4 Dipeptidyl Peptides IV (DPP-IV) Inhibition Test  
The potential inhibitory effect of ethanolic extracts of _C. incana_ on the dipeptidyl peptidase IV (DPP-IV) was assessed using an assay kit (Cayman chemical kit, item number: 700210, Ann Arbor, MI, USA). The chromogenic substrate (gly-pro p-nitroanilide) was cleared by the serine protease DPP-IV, resulting in the release of 4-p-nitroaniline (pNA), a yellow product. Briefly, in a 96-well plate, 35 \(\upmu\)L of the ethanolic extract (20-100 \(\upmu\)g/mL) or positive control (Stigliptin; 20-100 \(\upmu\)g/mL) was added to 15 \(\upmu\)L of dipeptidyl peptidase IV (DPP-IV) solution, 0.1 M Tris-HCl buffer (pH 8.0). The mixture was incubated for 5 min at 37 \({}^{\circ}\)C. Then, 50 \(\upmu\)L of 20 mM of the substrate (gly-pro p-nitroanilide) was dissolved in Tris buffer to commence the reaction, and incubated further for 30 min at 37 \({}^{\circ}\)C. Subsequently, 25 \(\upmu\)L of 25% acetate buffer (pH 4.5) was added to stop the reaction. Absorbance was measured at 405 nm using a microplate reader. Instead of the plant extract, a blank was prepared using 35 mL buffer. [32]. DPP-IV enzyme activity decreased when the synthesis of the pNA yellow product was reduced, owing to enzyme inhibition. The assay was conducted in triplicate. Equation (6) was employed to compute the % inhibition:  
\[\%\text{ Inhibition = (1 - abs of the test well/abs of the untreated (control)) \times 100} \tag{6}\]  
abs: absorbance.
# Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM
## 3 Model Description
### Data Preparation  
For the plant discussed in our work, the radiation angle and location of PV are fixed. Therefore, this information is blended in the historical power output data, with higher self-correlation than the indirect forecasting method. In this paper, the solar power data used in this study has been collected from a 10 MW peak solar power plant located at Jinan, China. The coordinates of this PV power plant in Jinan are 36 degrees 40 s north latitude and 117 degrees east longitude. The inputs of the proposed model were based on numerical weather predictions (NWPs), which were received from the meteorological services of Jinan. The NWPs include the solar irradiance, lowest air temperature, highest air temperature, relative humidity, wind speed, wind direction, cloud amounts, air pressure, and so on. The plant is facing south, the tilt angle is 25\({}^{\circ}\), and the shading angle due to the presence of parallel rows of PV modules is 20\({}^{\circ}\). The 10-MW plant utilizes a centralized inverter connected through a power transformer to the medium voltage grid. The strings are made of 20 series-connected PV modules, while groups of 16 strings are parallel-connected into 16 DC boards where fuses prevent over currents into the strings. The AC sides of the two inverters are connected to a double primary transformer that converts the low voltage output of the inverters (220 V) up to 500 kV, corresponding to the nominal voltage of the electrical grid. The test time period used for prediction is from 1 January 2017 to 31 December 2017. The data interval is 15 min, which is the requirement of grid in China. Figure 6 shows the solar radiation plays a significant role in PV power output forecasting. Relative humidity most strongly affects the production of electricity after the solar radiation parameter. As shown in Figure 7, the daily radiation time depends on different seasons. Therefore, the power of PV power generation is predicted in different seasons. Figure 8 shows the power output data in PV system in different days under four weather types: cloudy day, sunny day, foggy day, and rainy day, and it is obvious that the power output varies greatly under different weather conditions. Therefore, it puts forward higher requirements on the accuracy of LSTM.  
Figure 6: Solar irradiation (SI), air temperature (AT), relative humidity (RH), wind speed (WS), and PV power (P).  
In order to ensure the accuracy and speed of LSTM-models, it is necessary for data pre-processing. One of the well-known solutions is normalization on the basic of eliminating the anomalous data, by which the data can be restricted within the range between 0 and 1 to minimize the regression error, improve precision, and maintain correlation among data set. The process formula is shown as Equation (8)  
\[Y_{n}=\frac{Y_{n}-Y_{\min}}{Y_{\max}-Y_{\min}} \tag{8}\]  
where \(Y_{n}\) is the original input data; \(Y_{\min}\) and \(Y_{\max}\) are the minimum and maximum input data.
# UAV Attitude Angle Measurement Method Based on Magnetometer-Satellite Positioning System
## 3 Magnetometer Calibration Based on Least-Squares Fitting Algorithm  
In a certain region, the magnetic field of the Earth is uniform. If the three magnetic sensitive devices of the magnetometer are installed in three mutually perpendicular directions along the axis of the UAV body, after collecting sufficient data in each azimuth, the spatial distribution of these data should theoretically be such that the center of a sphere is at the origin of the coordinate sphere. However, owing to the influence of factors such as zero drift of the sensitive device and the different sensitivities of each axis, the originally collected data are neither at the center of the coordinate nor the sphere. To synchronize the measured data relative to the theoretical model, calibration of the original data is necessary. Generally, the calibration process includes two aspects: ellipsoid calibration and spherical calibration.