esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments Results and Analysis
### Discussion
#### 4.3.1 Effect of Different Features on the Model  
In order to improve the recognition effect of the model, we introduce various features, including character-level features, word vector features, word length features, and pos features, to address the problem that the credit entities of shipping enterprises are long, complex in composition, and have the phenomenon of nested entities. In order to study the degree of influence of different features on the entity recognition effect in the credit domain of shipping enterprises, different features were added to the BiGRU-CRF model for experiments, respectively, and the entity recognition effect of the model is shown in Table 6.  
According to the experimental results in Table 6, it can be seen that by adding the word length feature, pos feature, and word vector feature to the BiGRU-CRF model, respectively, although the effect of shipping enterprise credit entity recognition is similar, there are subtle differences, but the effect is not significant, so these three features have approximately the same degree of influence on the model. These three word-level features were spliced and input into the BiGRU-CRF model to form the MF-BiGRU-CRF model, and their precision, recall, and F1 values reached 79.65%, 77.93%, and 78.42%, respectively, all of which were higher than the recognition results achieved by incorporating any single feature in the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline Word length Features-BiGRU-CRF & 72.15 & 67.92 & 69.39 \\ POS Features-BiGRU-CRF & 73.35 & 71.09 & 72.21 \\ Word vectors-BiGRU-CRF & 74.42 & 74.50 & 74.33 \\ MF-BiGRU-CRF & 79.65 & 77.93 & 78.42 \\ BERT-BiGRU-CRF & 84.51 & 82.17 & 83.36 \\ BERT-MF-BiGRU-CRF & 92.85 & 90.64 & 91.73 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experiment results of NER based on different features.  
model, indicating that using the spliced features in the model is better than a single feature and more suitable for the NER task of shipping enterprise credit.  
In order to compare the impact of character-level features with the spliced word-level features on the model, the experimental results of the BERT-BiGRU-CRF model are compared with the MF-BiGRU-CRF model in this paper, and the F1 value of the former is 4.94% higher than that of the latter; considering that the performance of the model incorporating multiple features is better than that of the model incorporating single features in the shipping enterprise credit NER task, the three features obtained at the word level and the character-level features obtained by BERT are spliced and input into the BiGRU-CRF model to form the BERT-MF-BiGRU-CRF model, the accuracy, recall and F1 values of which are higher than 90%, which is the best among all models for entity recognition. Therefore, for the characteristics of shipping enterprise credit text, we choose several suitable features to join the NER model and can obtain better recognition effect.
## 3 Method
### Developing the Simplified PL Model
However, in the case of the propagation of waves in a multilayered lossy medium, reflection and transmission at the interfaces have to be taken into account. Thus, Equation (3) is here modified to:  
\[\frac{P_{r}}{P_{t}}=\left(\frac{(1-|S_{11}|^{2})(1-|S_{22}|^{2})\;\lambda_{ med}^{2}\;G_{t}\;G_{r}}{(4\pi R)^{2}}\right)\]  
\[\left(\frac{E_{inc}^{2}}{2}\;\prod_{i=1}^{n}\frac{T_{i}\;exp(\alpha_{i})l_{i} )}{\eta_{i}}\right)\left(\frac{T_{n}\;exp(-\alpha_{n}(d-\sum\limits_{i=1}^{n- 1}\;l_{i}))}{\eta_{n}}\right) \tag{4}\]  
\[\left((1+R_{n+1}\;exp(-2\alpha_{n}\sum\limits_{i=1}^{n}\;l_{i}-d)\right)\]  
\[T_{i}\;=\;\frac{2\cdot\eta_{i+1}}{\eta_{i}+\eta{i+1}} \tag{5}\]  
Figure 3:0  
where \(T_{i}\) = transmission coefficient, \(R_{i}\) = reflection coefficient, \(\alpha_{i}\) = attenuation constant of the medium and \(\eta_{i}\) = characteristic impedance of the medium (see Figure 4). \(E_{inc}\) is the incident electric field, and, for example, in the first layer, we have, \(E_{0}=E_{inc}\;T_{1}\;exp(\alpha_{1}l_{1})\), as shown in Figure 3:1 The Poynting vector is then calculated for all layers, and for the first layer, we obtain, \(P_{1}=\frac{E_{1}^{2}}{2\eta_{1}}\), where, \(E_{1}\) = electric field in Layer 1 and \(\eta_{1}=\) characteristic impedance in Layer 1. Substituting all of the parameters in the Friis transmission equation, we obtain a simplified PL model, as shown in Equation (4). This PL model only takes into consideration the transmitted wave in the layered medium, because the medium is lossy, and it has been observed that taking into account the reflected wave does not provide any added value to the PL model due to tissue attenuation and absorption [2]. However, we do retain a reflection coefficient parameter in the last layer, where the antenna is placed, to take into account any changes that might be introduced in case there is high variation of the medium properties between the layer where the antenna is placed and the proceeding layer. The scattering parameters and the gains of the Tx and the Rx mentioned in Tables 2 and 3 are then used for the PL calculation. In Table 3, \(S_{22_{Med}}\;[dB]\) is the reflection coefficient and \(G_{r_{Med}}\) is the gain of the insulated dipole antenna in the medium and is calculated using the method from [8]. Thus, to summarize, we start developing a PL model by using a plane wave in a layered medium and calculating the Poynting vector, then the antenna characteristics are introduced in the Friis transmission formula to develop a PL model for layered tissues to calculate the PL in it.
## 5 Results
### Generalization Experiment  
To further verify the model's generalization, we finally chose to conduct experiments on 200 real satellite images that came from various institutions including NASA, and had never been used during the model's training or validation phases. By carrying out testing on these new datasets, we can more accurately assess the model's efficiency and stability when handling unknown data. The results of the experiment will help us understand the reliability of the model in real-world applications, thereby providing guidance for further optimization and application of the model. Moreover, this will also demonstrate the model's adaptability in diverse environments and verify whether its performance remains consistent under various conditions.  
The P-R curves allow for a visual comparison of the performance and accuracy of the YOLOv8s and LTSCD-YOLO models on real satellite images. The detection results of YOLOv8s are shown in Figure 17a, while the detection results of LTSCD-YOLO are shown in Figure 17b. From these figures, it can be seen that the overall mAP50 of LTSCD-YOLO is 91.2%, which is 2.13% higher than YOLOv8s. In solar panel detection, LTSCD-YOLO achieved a mAP50 of 92.7%, 0.22% higher than YOLOv8s; a mAP50 of 85.6% in antenna detection, 5.68% higher; and a mAP50 of 95.4% in main body detection, 1.06% higher.  
Figure 18 visually demonstrates the detection performance of the LTSCD-YOLO and YOLOv8s models on real satellite images. Figure 18a represents the Ground Truth, Figure 18b represents the detection results of YOLOv8s, and Figure 18c represents the detection results of LTSCD-YOLO. The results indicate that YOLOv8s had one missed detection, while LTSCD-YOLO had no issues with missed detections and demonstrated higher detection accuracy. This further proves the effectiveness of the improved LTSCD-YOLO model in detecting real satellite images and its generalization ability.  
Figure 17: Comparative images of the detection results: (**a**) YOLOv8s detection results and (**b**) LTSCD-YOLO detection results.
## 3 Architecture of the Satellite-Enabled IoRT Network
### IoT Satellite Constellation  
A key aspect of the satellite-enabled IoRT network is remote data transmission via satellites. China's first IoT satellite constellation has greatly motivated this study of the IoRT for in situ data transmission from remote areas. The Xingyun satellites can encourage stakeholders to support remote monitoring in remote areas across the TP. The satellites of the Xingyun IoT satellite constellation communicate in the L frequency band, and these satellites can transmit sensor data and realize bidirectional short-message communication. Hence, the satellite-enabled IoRT can facilitate data retrieval in remote areas arbitrarily yet efficiently.  
At present, the two Xingyun satellites pass over the same observation station 4 times a day (2 times per satellite), with each pass lasting 5-10 min. In addition, the Xingyun satellites are equipped with an intersatellite laser communication capability, which can reduce the downlink time. These characteristics enable rapid monitoring data collection.
## 5 Discussion
### Limitations of the Experiment  
Although this experiment included extensive quantitative and qualitative analysis of Sentinel\(-\)1A and ALOS/PASAR\(-\)2 data and analyzed their potential for adaptation to in landslide detection in detail, the following deficiencies remain:  
* Due to the different revisiting times of the two data sources (three days apart), it is impossible to maintain strict consistency in the date selection of the two data sources, which will lead to a degree of deviation in the final result.
* Many errors will be introduced in data processing, such as registration errors, DEM errors, baseline errors, and unwrapping errors. These errors will ultimately affect the accuracy of the results.
* When selecting the unwrapping reference point, although the selection has been restricted to a certain region as far as was possible, the inconsistent pixel size makes it impossible to maintain the complete unity of the unwrapping reference point. This constraint is also an important factor in the generation of image results.
* This paper uses only IPTA\(-\)SBAS technology to compare the adaptability of the two data sources, and it is impossible to determine whether the results are consistent with those obtained using other methods, which will be a focus of future research.  
Figure 18: InSAR time-series results for the Sanxingwan landslide, (**a,b**) are the time-series results from ALOS/PASAR\(-\)2 and Sentinel\(-\)1A at P1 and P2, respectively; (**c**) is the optical image of the landslide, and the positions of PI and P2 points are marked on the image; (**d**) is the time series of P1 after the time difference was removed.  
* The research area selected in this paper is very typical of the region, but the coverage is small, and it may not fully represent the complex geographic area. More data will be used for analysis and comparison in the future.
## 2 Characteristic Output Behavior of a PV Cell  
The non-linear current-voltage (\(I\)-\(V\)) curve illustrates the characteristic behavior of the output current of a PV cell. The output current varies when the output voltage level is altered [30; 31], as illustrated in Figure 1 for a standard 156 mm \(\times\) 156 mm poly-Si PV cell. Likewise, the power-voltage (\(P\)-\(V\)) curve describes the possible output power (\(P_{out}\)) at different operating voltages (\(V_{\varphi}\)).  
On the \(I\)-\(V\) curve, one point exists at which the product of current and voltage becomes a maximum. This point is commonly referred to as the maximum power point (MPP). The MPP can also be found on the \(P\)-\(V\) curve, as seen in Figure 1. The power of an ideal PV cell or panel (\(P_{ideal}\)) can be derived from multiplying the open-circuit voltage (\(V_{oc}\)) and the short-circuit current (\(I_{sc}\)). The maximum power of a real PV cell, also referred to as the power at the MPP (\(P_{mpp}\)), can be derived by multiplying the current (\(I_{mpp}\)) and voltage (\(V_{mpp}\)) at the MPP.  
The available power at the MPP (\(P_{mpp}\)) greatly depends on the solar radiation level (\(\lambda\)) and PV cell temperature (\(T_{c}\)) [18; 20; 21; 22; 23; 30; 31]. At standard test conditions, with \(\lambda\) = 1000 W/m\({}^{2}\), \(T_{c}\) = 25 \({}^{\circ}\)C and AM1.5, the PV cell produces about 4 watts. However, at lower solar radiation  
Figure 1: \(I\)-\(V\) and \(P\)-\(V\) of a PV cell at STC.  
levels, the power is reduced quickly. For example, with \(\lambda\) = 200 W/m\({}^{2}\), less than 1 watt can be obtained.  
When driving in an urban area, shading from the surrounding environment can represent challenges for the MPPT algorithm [10; 13]. Generally speaking, the MPPT algorithm modifies \(V_{op}\) on a continuous basis in order to stay as close as possible to \(V_{mpp}\); hence, it aims to maximize the possible power and degree of efficiency of PV energy production [32]. For some MPPT techniques, the quickly and rapidly changing environmental conditions during driving can result in a significant reduction of obtained output power [13].
## 3 Experiment
### Result Descriptions of Performance Indicators
#### 3.3.3 Cnr  
The contrast-to-noise ratio (\(CNR\)) is used for the comparison and analysis of the region of interest (ROI). It evaluates the contrast between the selected signal region and the background region and is calculated as follows:  
\[CNR=\frac{1}{m}{\sum\limits_{i=1}^{m}}\left[10\log_{10}\left(\frac{\mu_{i}- \mu_{b}}{\sqrt{\sigma_{i}^{2}-\sigma_{b}^{2}}}\right)\right] \tag{10}\]  
where \(m\) is the total number of selected ROIs, \(\mu_{i}\) and \(\sigma_{i}\) represent the mean and standard deviation of the signal region, while \(\mu_{b}\) and \(\sigma_{b}\) represent the mean and standard deviation of the background region. We manually selected four signal regions and one background region, with the selected signal regions and background region indicated by red and blue boxes, respectively.
# Development of a Digital System for Monitoring Emergency Conditions in 20 kV Distribution Networks
## 3 Results
### Development of the Information Network Structure  
Since LoRaWAN technology was chosen for the organization of the information technology network with the necessary justification, we will now consider the practical aspects of its application.  
The structure of the primary segment of the information technology network includes wireless information transmission systems from zero-sequence voltage monitoring devices, a receiving base station, and an accounting server. When using the Internet, the accounting results can be made available to many interested services (CDS--Central Dispatching Service) (see Figure 20).
# A Carbon Benefits-Based Signal Control Method in a Connected Environment
## 4 Methodology
### Signal Control Optimization Module  
The goal of this module is to reduce carbon emissions at intersections. In this scenario, the additional emissions originate from speed alterations and stops necessitated by red lights while navigating intersections. The lower the speed of vehicles passing through an intersection, the greater the emissions produced as vehicles accelerate to regain their desired speed.  
Building on this analysis, a bi-level optimization model is proposed. At the upper level, signal timing optimization aims to maximize the average speed of vehicles. For computational efficiency, minimizing the vehicles' delay is employed as a surrogate goal for maximizing average speed, which equivalently contributes to reducing carbon emissions. The lower-level model focuses on optimizing speed recommendations for CBCVs, leveraging the guiding efficacy of CBCVs to minimize the number and duration of vehicle stops. This integrated approach, optimizing both signal timing and vehicle trajectory, significantly reduce emissions, aligning intersection control with environmental sustainability goals. Details are illustrated in Figure 4.
###### Abstract  
Liver fluke infections, especially those induced by _Optishorchis viterrini_, pose considerable health and economic difficulties in aquaculture, particularly in Southeast Asia. Traditional approaches for parasite elimination, including chemical treatments and freezing, exhibit constraints regarding efficacy, environmental sustainability, and practicality. This research investigates an improved dielectric heating system utilizing a 2.45 GHz horn antenna for the selective thermal targeting of parasite-associated regions in _Cirrhinus microlepis_ (small-scale mud carp). The dielectric characteristics of fish tissues, encompassing scales, skin, and muscle, were analyzed utilizing an open-ended coaxial probe technique. Simulation and experimental evaluations were performed to improve energy absorption, heating uniformity, and a particular absorption rate to enable precise thermal localization while preserving the integrity of fish tissue. The findings demonstrate that dielectric heating can specifically elevate the temperature of fish scales, where parasites predominantly inhabit, to levels beyond 70 degrees Celsius, while reducing thermal impact on the underlying muscle tissue. The application of a salt coating on fish scales markedly increased their dielectric loss, exceeding that of muscle tissue, thus enhancing selective heating efficiency and supporting targeted thermal treatment. The ideal distance from the antenna to the sample was established as ranging from 6 to 9 cm, ensuring a balance between energy efficiency and homogeneous heating. This work illustrates the efficacy of dielectric heating as a novel and non-chemical approach for thermal management of parasite-prone tissues in aquaculture, providing a sustainable and viable substitute for traditional treatments.  
dielectric heating; liver flukes; _Cirrhinus microlepis_; aquaculture sustainability; horn antenna; electromagnetic heating; parasite thermal targeting; fish tissue dielectric properties +
Footnote †: journal: Applied sciences
## 3 Test System, Description, and Modeling  
The proposed probabilistic framework could be applied to different PEPS. Among others, this work considered the most commonly used structure of LCL-filtered grid-connected VSCs, which revealed the most stability issues that were usually related to the low-frequency oscillations. This was due to lower SCR and PLL and high-frequency oscillations due to LCL-filter resonance and delay.  
The principal scheme of the studied system is depicted in Figure 4. The main aim was to inject the power produced by renewables into the power grid and help the power system keep the voltage at the point of common coupling (PCC) within the allowable ranges. The power system included primary energy sources, the power grid, a three-phase voltage source converter (VSC), an LCL output filter, and a control system.  
The Thevenin model represented the power grid as seen from the PCC. Its impedance could also vary to emulate a weaker or stronger connection point. The control system consisted of five main parts, which included the DC-link voltage controller (DVC), AC voltage magnitude controller (AVC), current controller (CC), highpass filter (HPF), and synchronous reference frame phase-locked loop (SRF-PLL).  
The DVC regulated the active power or DC-link voltage at the desired value, and the AVC controled the reactive power or PCC voltage magnitude. The outer voltage control loops generated the reference for the inner current control loop (CC), and CC provided inverter current control and protection. For PCC voltage synchronization, a PLL was also required. It is worth remarking that all of these employed a proportional-integral (PI) controller to ensure a good dynamic response and zero steady-state tracking errors in the synchronous reference frame. Finally, a voltage feedforward control-based HPF emulated a virtual resistor paralleled with the filter capacitor to dampen the LCL filter resonances. The remaining parameters in Figure 4 are as follows: \(i_{y}\), \(i_{g}\), \(v_{y}\), \(v_{inv}\), and \(v_{dc}\) are converter and grid currents, the voltage across the filter capacitor, inverter output voltage, and DC-link voltage, respectively. In addition, \(Z_{g}\), \(L_{f}\), \(L_{g1}\), \(C_{f}\), \(L_{g2}\), and \(C_{dc}\) represent the seen impedance from PCC, the converter and grid-side filter inductances, filter capacitance, equivalent  
Figure 4: Single line diagram of a grid-connected three-phase voltage source converter (VSC).  
Figure 3: Stopping criterion for the maximum damping factor (SCR = 1.67, \(P\) = \(P_{n}\)).  
grid inductance, and DC-link capacitance, respectively. Additionally, \(\theta\) is the phase angle obtained by the PLL, and the subscript reference gives the reference values for different control loops.
## 3 Results and Discussion
### Development of Optimal Dust Suppressant Formula
#### 3.1.2 Orthogonal Experiment  
Orthogonal experiments concerning moisture retention, hygroscopicity, bonding performance, and infiltration performance were conducted, respectively, with the moisture retention (water loss rate), hygroscopicity (moisture content rate), viscosity, and surface tension of dust suppressant serving as indexes. The results of 16 groups of experiments are shown in Table 3.  
Figure 3: (**a**) Viscosity curve of coagulant solution with concentration; (**b**) mass proportion of dust with large particles; (**c**) change curve of surface tension of surfactant solution with concentration; (**d**) change curve of infiltration time with infiltration length.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \multicolumn{4}{c}{**Factors**} & \multirow{2}{*}{**Water Loss**} & \multirow{2}{*}{**Moisture Content**} & \multirow{2}{*}{**Viscosity**} & \multirow{2}{*}{**Surface Tension**} \\ \cline{2-2} \cline{5-8}
**Group Numbers** & & & & & & & & \\ \hline
1 & 1 & 1 & 1 & 1 & 93.09 & 8.33 &  
According to the range analysis method, the range values of each factor level were calculated; the influence rules of each factor on moisture retention (water loss rate), hygroscopicity (moisture content rate), viscosity, and surface tension were obtained as shown in Figure 4. According to Figure 4, the optimal combinations corresponding to four groups of orthogonal experimental indexes are shown in Table 4.  
According to the analysis principle of \"the secondary submitting to the primary and the minority submitting to the majority\" in the comprehensive balance method, the main factors affecting the suppression effect of the dust suppressant are hygroscopicity, moisture retention, and the bonding effect, followed by surface tension. Therefore, the results of orthogonal experiments on moisture retention, hygroscopicity, moisture retention, and bonding performance were selected as the main optimal combination. The optimal combination was selected as BS4X54 NB4 BZ1,3,4 by applying the principle of the minority submitting to the majority. According to the results of the orthogonal experiment concerning infiltration performance, the preliminary formula of the dust suppressant was determined as BS4X54 NB4 BZ1; namely, 6% of BS + 6% of XS + 0.12% of NB + 0.06% of BZ.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Orthogonal Experiment Category** & **Indexes** & **Optimal Combinations** \\ \hline Moisture retention & Water loss rate & BS4X54 NB4 BZ4 \\ Hygroscopicity & Moisture content rate & X54 BS4 NB2 BZ1 \\ Bonding performance & Viscosity value & NB4 BS4X53 BZ3 \\ Infiltration performance & Surface tension value & BS1 NB1 BZ1X53 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal combinations of orthogonal experiment.  
Figure 4: (a–d) Influence trend of factor levels on water loss rate, moisture content rate, viscosity value, and surface tension, respectively.