esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Acquisition and Pre-Processing  
#### 2.2.1 Remote Sensing Data Sources  
The dataset used in this study was obtained from an airborne integrated sensor system that is capable of collecting multispectral imagery, LiDAR data, and IMU and GPS data. LiDAR data were acquired by a Leica ALS80-HP sensor (manufactured by Leica Geosystems AG, Heerbrugg, Switzerland), with an acquired point cloud density of 2.47 points/m\({}^{2}\). The simultaneous acquisition of multispectral data was carried out with the Leica DMC III multispectral image, with a sensor size of 26,112 (across) \(\times\) 15,000 (along) and an average spatial resolution of 0.2 m. The dataset was collected in the Shenomigia forest area from 10 to 16 August 2020. The absolute altitude of the flight was 4000 m., with an along overlap rate of 60% and an across overlap rate of 25%. The data were acquired with clear skies and few clouds, and the main parameters of the two sensors of the airborne integrated sensor system are shown in Table 1.
## 4 Numerical and Experimental Investigation of MR Semi-Active Control
### Acceleration Response  
Figures 11 and 12 shows the peak acceleration of each layer. The acceleration time history response of the fifth floor of the structure under two control conditions is given in Figure 13. As shown in these figures, the control effect of continuous control under different intensities is better than that of switch control. The seismic isolation layer performed better under earthquakes where the maximum acceleration was 0.72 g than that in earthquakes where the maximum acceleration was 0.126 g. Similarly, the damping effect of isolation layer under El Centro earthquake and Taft earthquake was better than that of Tianjin ground motion. In earthquakes where the maximum acceleration was 0.72 g, the peak acceleration of the fifth layer under El Centro earthquake and Taft earthquake in continuous control was reduced by half relative to the input ground motion. Shock absorption effect was obvious in these figures. The peak acceleration of the fifth layer under the ground motion where the maximum acceleration was 0.126 g also decreases by 1/3. Top acceleration is slightly enlarged under frequent intensity earthquake.  
Figure 10: The displacement response of isolation layer. (**a**) El Centro earthquake—0.36 g; (**b**) El Centro earthquake—0.72 g; (**c**) Taft earthquake—0.36 g; (**d**) Taft earthquake—0.72 g.  
Figure 11: The peak acceleration (0.36 g). (**a**) Continuous control; (**b**) switch control.  
Figure 12: The peak acceleration (0.72 g). (**a**) Continuous control; (**b**) switch control.  
Figure 13: The displacement response of isolation layer. (**a**) El Centro earthquake—0.36 g; (**b**) El Centro earthquake—0.72 g; (**c**) Taft earthquake—0.36 g; (**d**) Taft earthquake—0.72 g.  
The reason why the continuous control was better than the switch control can be obtained by the analysis of the displacement response. The displacement of the isolation layer under the switch control was much less than that of the continuous control. The displacement of the isolation layer decreased so that the energy dissipation of the damper was relatively small, and the energy transferred to the superstructure increased, so the acceleration response became larger.  
It is worth noting that the peak acceleration of the top layer in various conditions relative to all other floors have greatly enlarged, which is inconsistent with the overall control effect. It could be due to the fact that the top of the structure was damaged in the previous test which caused significant stiffness reduction. Therefore, whiplash effect occurred during the test. The hypothesis was also verified by the observations in the next experiment.
## 2 Related Work
### Suarm Intelligence  
Swarm intelligence network recovery refers to the use of swarm intelligence methods to repair faults or restructure networks in order to restore normal network operation and optimize network performance. [PERSON] [22] designed a topology reconstruction algorithm for node self-repair and a topology reconstruction algorithm for the dynamic repair of redundant nodes. [PERSON] [23] proposed a damage recovery strategy based on swarm intelligence for unmanned aerial vehicle swarm networks, which has good performance in terms of recovery ability, convergence time, and communication overhead. [PERSON] [24] proposed a multi-unmanned ship network topology optimization control algorithm based on improved particle swarm optimization, taking into account factors such as network connectivity, communication link quality, and network connection cost. [PERSON] [25] proposed a distributed solution for multi-channel wireless sensor network connection recovery to address the issues of network failures and network function recovery [26]. This scheme only uses neighborhood information when performing channel reallocation and does not consider the routing followed by the data toward the sink [27].  
Based on the previous review, in this paper, for the air-sea cross-domain communication network, in the prediction stage, the whale optimization algorithm is used to optimize the gray model to solve the problem of fixed time response function structure and poor adaptability to the original data. In the reselection link stage, aiming at the problem of excessive network load for minimum communication cost recovery, a link selection scheme combining improved Dijkstra and load balancing is proposed to realize the recovery of the network. The proposed algorithm is simulated in this paper. The results show that the proposed algorithm can effectively restore the network and maintain the network connectivity better than traditional recovery methods.
# Vibration Suppression of Collaborative Robot Servo with Dual Encoders
## 7 Control Principle of Dual Encoder Vibration Suppression
### Closed-Loop Control with Dual Encoder  
Figure 8 shows the structure of the dual encoder control scheme utilized in this study, which distinguishes itself from the single encoder control scheme in Figure 4. The speed on the reducer side effectively mirrors the speed fluctuations on the load side. The dual encoder scheme necessitates the simultaneous acquisition of data from the motor-side encoder and the reducer-side encoder within a single control cycle, along with the conversion of the reducer-side speed to the motor-side speed. By employing the formula \(\omega\) = k1\(\cdot\omega\)1 + k2\(\cdot\omega\)2, where \(\omega\)1 represents the speed derived from the motor side encoder, and \(\omega\)2 denotes the speed obtained from the reducer side encoder, with k1 and k2 serving as adjustment coefficients within the range of 0 to 1. By continuously adjusting the proportional coefficients k1 and k2, the optimal proportional coefficient can be found to minimize the speed fluctuation on the load side, thereby suppressing the vibration of the collaborative robot. The calculated \(\omega\) will be added to the speed loop control. The selection of k1 and k2 is related to the stiffness of the reducer, where k1 and k2 represent the proportional contribution of feedback speed to the speed loop. In the case of a purely rigid reducer, \(\omega\)1 equals the product of the ratio and \(\omega\)2 (\(\omega\)1 = ratio \(\times\)\(\omega\)2). Both k1 and k2 can take any value between 0 and 1, provided that their sum equals 1 (k1 + k2 = 1). This implies that for a purely rigid reducer, there is no distinction between feedback speeds sampled entirely from the motor encoder and those sampled entirely from the reducer encoder. However, experimental observations indicate that as stiffness decreases, the proportion of k2 should increase. Typically, the optimal values of k1 and k2 can be determined through sampling tests on a batch of reducers. The motor-side speed sampling weight k1
# Comparison of the Heights of Sporadic E Layers and Vertical Ion Convergence Parameters
###### Abstract  
Sporadic E (Es) layers are thin layers of enhanced electron density that commonly appear at altitudes of 90-130 km, often impacting radio communications and navigation systems. The wind shear theory posits that the vertical ion drift, influenced by atmospheric neutral winds and the magnetic field, serves as a significant dynamic driver for the formation and movement of Es layers. In current studies, both the heights of ion vertical velocity null (IVN) and the maximum vertical ion convergence (VICmax) have been proposed as the potential height of Es layer occurrence. In this study, utilizing the neutral atmospheric wind data derived from the WACCM-X (The Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension), we computed and compared these two parameters with the observed Es layer heights recorded by the FORMOSAT-3/COSMIC (FORMOsa SATellite-3/Constellation Observing System for Meteorology, Ionosphere, and Climate) radio occultation (RO) observations. The comparative analysis suggests that IVN is a more likely node for Es layer occurrence than VICmax. Subsequently, we examined the height-time distributions of IVN and Es layers, as well as their respective descent rates at different latitudes. These results demonstrated a notable agreement in height variations between IVN and Es layers. The collective results presented in this paper provide strong support that the ion vertical velocity null plays a crucial role in determining the height of Es layers.  
sporadic E layer; WACCM-X; COSMIC; wind shear theory; ion vertical velocity null 2023
## 5 Conclusions  
Cognitive Radio Network (CRN) is an innovative technology that utilizes free spaces in the authorized spectrum by SUs without interfering with PUs. CRN depends on spectrum sensing to look for the free spaces in the authorized spectrum that PUs are not using for their transmissions. This process of spectrum sensing, however, is interrupted by some malicious entities. These malicious entities copy the signal features of PUs and cause PUEAs, limiting the SUs from using the free band.  
In this research, a TDOA based localization methodology using DE is proposed to mitigate the PUEA. The proposed CRN consists of CRBS, one PU, 50 random SUs, and a PUEA. The position of the PUEA is fixed and it can be positioned inside or outside the network. The network is designed in such a way that both the SUs and the attacker know the position of the PU. All the SUs cooperate during spectrum sensing and send the sensing information to the CRBS. The CRBS gathers all the information and applies cross-correlation to find the TDOA measurements. Afterward, these values are used to find the location of the sender, whether it is a PU or a PUEA. The DE algorithm is used for the correct estimation and to minimize the MSE so that convergence occurs faster.  
The performance of the proposed algorithm is evaluated using metrics such as CDF, MSE, and the correct and false detection probabilities. The MSE is checked against three parameters: iterations, number of SUs, and SNR. As these parameters increase, the MSE decreases. Simulation results show that the proposed method increases the efficiency and the accuracy of the correct estimation of the location of the attacker. It is also obvious from the results that the proposed approach is more enhanced, precise, and effective than the techniques presented in the literature. The proposed approach is applicable only when PUs, SUs, and a PU attacker are fixed, so in the future, we will explore this technique for mobile setups with more than one attacker.  
Conceptualization, R.B. and N.B.; methodology, R.B. and N.B.; software, R.B.; validation, R.B., N.B. and N.M.; formal analysis, R.B., N.B., N.M. and S.A.; investigation, R.B., N.B., N.M. and S.A.; resources, R.B.; data curation, R.B.; writing--original draft preparation, R.B., N.B., N.M. and S.A.; writing--review and editing, R.B., N.B., N.M. and S.A.; visualization, R.B., N.B., N.M. and S.A.; supervision, N.B. and S.A.; project administration, N.B.; funding acquisition, S.A. and N.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to thank Fatima Jinnah Women University, Pak-Austria Fachhochschule Institute of Applied Sciences and Technology and Saudi Electronic University.
# Simulation of a Narrowband Power Line Communications System over Medium Voltage
## 3 Noise in PLC Transmission  
### Noise Considerations in PLC Channels  
In general, PLC channels are affected by strong, time-varying, non-white, non-Gaussian noise [8]. Measurements of the background noise level in an MV power line test bed [11; 12] have shown that, in the frequency range 1-20 MHz, the noise levels observed are approximately 20-30 dB higher than the noise levels observed in in-house LV lines. In addition, the noise in power lines exhibits periodicity synchronous to the main voltage due to the relevant noise generated by the various electric devices connected. This periodic noise cannot be ignored since, due to the relatively narrow operational bandwidth exploited by NB-PLC transmission, the symbol duration and, consequently, the packet length, tend to be long. As the noise at higher frequencies is attenuated more, the noise affecting PLC transmission exhibits a higher PSD at lower frequencies. Furthermore, many noise sources are active mainly in the lower PLC frequency range. In conclusion, the noise PSD affecting NB-PLC transmission in the 3-500 kHz band is not Gaussian and exhibits an approximately exponential decay with frequency.  
The noise encountered in MV power lines is a mix of periodic impulse noise, background noise and narrowband noise [12]. The background noise is primarily dependent on the weather conditions, the terrain profile and conditions, _etc._[7]. Moreover, as PLC systems share frequency bands with primary wireless services, the latter cause interference to PLC channels on a local basis, which is perceived as narrowband noise [4]. Finally, background noise in MV lines is caused by corona discharges, especially under humid and heavy weather conditions [13]. When referring to narrow frequency ranges such as the NB-PLC frequency range under consideration, the corona noise is perceived as white noise.
# Robustness of Cyber-Physical Supply Networks in Cascading Failures
## 4 Modeling Cascading Failures in Cyber-Physical Supply Networks
### Cascading Failure Model
#### 4.1.2 Overload Cascading Failure in the Cyber Network  
When one node in a complex network fails, entirely or partially, and shifts its load to nearby nodes in the system, overloading could occur, leading to the failure of further nodes [36]. If this continues, overloading could fail the whole system.  
The cyber node failure will lead to larger-scale data collection and processing failures, so control signals will not be sent to the physical network in real time. In the cyber network, when a router fails, the untreated information will be redistributed to its neighboring nodes, and the information flow tends to choose routers with high processing capacity to maintain the smooth operation of the whole network [37]. Therefore, the load of the failed node will be redistributed to its nearest neighbor, depending on the probability:  
\[\Delta{L_{\text{is}}^{c}}^{+}=L_{v_{i}^{c}}(0)\frac{C_{v_{i}^{c}}-L_{v_{i}^{c} }}{\sum_{\tau\in\Gamma_{i}}(C_{v_{\bar{\tau}}}-L_{v_{\bar{\tau}}})}, \tag{11}\]  
\[\Delta{L_{\text{ih}}^{c}}^{+}=L_{v_{i}^{c}}(0)\frac{C_{v_{h}^{c}}-L_{v_{h}^{c} }}{\sum_{\tau\in\Gamma_{i}}(C_{v_{\bar{\tau}}}-L_{v_{\bar{\tau}}})}, \tag{12}\]  
where \(\Delta{L_{\text{is}}^{c}}^{+}\) and \(\Delta{L_{\text{ih}}^{c}}^{+}\) are the increased load for \(v_{s}^{c}\) and \(v_{h}^{c}\), respectively, and \(\Gamma_{i}\) is the set of neighboring nodes directly connecting to \(v_{i}^{c}\).  
If the load of \(v_{s}^{c}\) and \(v_{h}^{c}\) are more than the upper node capacity  
\[L_{v_{s}^{c}}(t)>C_{v_{s}^{c}}, \tag{13}\]  
\[L_{v_{h}^{c}}(t)>C_{v_{h}^{c}}, \tag{14}\]  
then the successive failures will occur on \(v_{s}^{c}\) and \(v_{h}^{c}\) at time \(t\), and its load will be redistributed to other functional nodes. All the remaining functional nodes will get an additional load from the failed nodes, which may lead to overloading and failure of the other nodes. The process takes place until no further failure is possible and the cyber supply network is considered stable.  
Figure 2: Schematic diagram of load redistribution after a node failure.
## 4 Discussion
### Irregularities Generated by the Geomagnetic Storm  
A large scale of irregularities have been produced by the event at high and middle latitudes. The results in Figure 7 were in accordance with the observations reported by Reference [45], through the investigation of CHAIN (Canadian High Arctic Ionospheric Network) data. The characteristics of the irregularities generated this time were different from those described in the previous studies, which was dominant in the scales and occurred on the June solstice, the rarest period for large-scale plasma bubbles. The ionosphere irregularities were discovered mainly after the commencement of the storm, with two sub-phases, as Figure 8 showed. The simultaneous behaviors from different locations further verified the storm influences on the ionosphere irregularities generations, in addition to the general ionosphere irregularity morphology indicated by [7; 8]. With spaceborne observations from SWARM satellites launched by ESA, [PERSON] demonstrated the changes of equatorial electrojet (EEJ) under the main phase of the storm [25]. With similar observations, Reference [10] proved that a super-bubble located between 30\({}^{\circ}\) N and 40\({}^{\circ}\) N in Europe was generated by the storm and severe ROTI enhancements were found from 23:00 UT on 22 nd to 02:00 UT on 23 rd. Moreover, through the investigation in Section 3.4, the multi-GNSS derived ROTI showed intensive appearances of large-scale irregularities, and other concentrating ROTI enhancements were discovered from 02:00 UT to 06:00 UT on the 23 rd at REYK, HOFN, KIRU, and METG in the high latitudes of the northern hemisphere, and CZTG, KRGG, and DUND in the middle latitudes of the southern hemisphere (for the winter hemisphere, refer to Reference [25]). In the second sub-phase of the storm, both the north and south hemispheres suffered from ionosphere irregularities at high latitudes, demonstrated in Figure 8b. The large-scale ionosphere irregularities were considered to be generated by prompt penetration of the electric field during the storm [46]. As explained in previous studies, PPEFs affects the ionosphere by lifting the ionosphere up to a higher latitude, producing a decrease of the plasma density at the bottom-side of the ionosphere. From this work, the occurrence of large-scale ionosphere irregularities had mainly two influential factors: (1) the expansion of the oval at high latitudes, accompanied with particle precipitation, which can be verified by results in the REYK, HOFN, and KIRU stations, as well as the enhancements of AE during the storm; (2) PPEFs affects produced by the storm and propagatingthrough low latitudes to middle latitudes, which can be seen from the results of the CZTG, KRGG, and DUND stations.
# Distribution System State Estimation Using Model-Optimized Neural Networks
## 6 Conclusions  
Using historical data, several configurations of model optimized neural networks have been developed, trained, and tested against their standard implementations and the conventional state estimation method. As a consequence of the standardized model optimization process, i.e., scheduled weight pruning, the neural network's size and potency is reduced. In spite of this, the neural network still provides satisfactory state estimation results and is more accurate over the conventional WLS-SE. The errors in estimation range from 0.0003 to 0.0087 per-unit for the hybrid AC/DC distribution system composed of the IEEE 34-bus test system and the 9-bus DC microgrid. Furthermore, with the neural  
Figure 16: (**a**) Modified LSTM DC voltage magnitudes and (**b**) 25% Pruned LSTM DC voltage magnitudes; \(y\)-axis corresponds to estimated bus voltage in (p.u.), while the \(x\)-axis corresponds to the bus number.  
Figure 17: (**a**) 50% Pruned LSTM DC voltage magnitudes and (**b**) 75% Pruned LSTM DC voltage magnitudes; \(y\)-axis corresponds to estimated bus voltage in (p.u.), while the \(x\)-axis corresponds to the bus number.  
network's high scalability, this proposal is good for modeling and estimating practical hybrid distribution systems.  
Conceptualization, H.S., D.K. and J.M.D.; methodology, D.K. and J.M.D.; software, J.M.D.; validation, D.K. and J.M.D.; formal analysis, D.K.; investigation, D.K.; resources, J.M.D.; data curation, D.K.; writing--original draft preparation, D.K. and J.M.D.; writing--review and editing, H.S. and J.M.D.; visualization, D.K.; supervision, H.S.; project administration, H.S.; funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Advanced Research Project funded by the SeoulTech (Seoul National University of Science and Technology).  
Not applicable.  
The authors would like to thank Seoul National University of Science and Technology for providing the research funding needed to pursue this topic.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SCADA & Supervisory Control and Data Acquisition \\ AMI & Advanced Metering Infrastructure \\ PMU & Phasor/Phase Measurement Unit \\ SE & State Estimation \\ WLS & Weighted Least Squares \\ WLS-SE & Weighted Least Squares State Estimation \\ BESS & Battery Energy Storage System \\ CUDA & Nvidia Compute Unified Device Architecture \\ API & Application Program Interface \\ MAE & Mean Absolute Error \\ MSE & Mean Square Error \\ RMSE & Root Mean Square Error \\ MLP & Multilayer Perceptron \\ RNN & Recurrent Neural Network \\ LSTM & Long Short-Term Memory Neural Network \\ RAM & Random Access Memory \\ ULTC & Under Load Tap Changer \\ \end{tabular}
# Spatiotemporal Evolution and Impact Mechanisms of Gross Primary Productivity in Tropics
## 1 Introduction
The P model, a LUE-like model, captures deeper mechanisms of photosynthesis and simplifies parameterization [39]. The enhanced P model, integrating the water stress factors, effectively considers the impact of environmental factors such as drought on photosynthesis efficiency [36]. This approach provides new perspectives and methodologies for global ecological studies and the future improvement of GPP models. Consequently, this paper employs the improved P model to estimate GPP since the global application of the P model remains limited. We specifically examine the effects of microclimatic variations and land use changes on GPP. Our methodology not only elucidates the complex contributions and interactions of these factors but also offers crucial guidance for developing targeted strategies to enhance ecosystem sustainability and resilience. The study focuses on the tropics and comprises the following tasks: (1) Calculating the spatiotemporally continuous GPP data in the tropics using the improved P model [39]. (2) Exploring the spatiotemporal evolution characteristics of GPP in the tropics over the past 20 years. (3) Combining climate data (temperature and precipitation data) with land cover data to quantify the impacts of land cover change and climate change on vegetation GPP and analyzing the driving factors of vegetation GPP change.