esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Discussion  
The computational efficiency was compared considering the analysis conducted in Table 6. The D-LSEs were evaluated for practical computational overhead with 50 trials on an Intel Xeon(R) Gold 3.3 GHz system with 63.7 GB RAM for all test cases present in Tables A1 and A2. The execution time was calculated using (12). \(j\) is either the substation level, area level, or network level. It is important to note that this analysis did not account for communication latency or other processing delays. The computational time within the PMU data rate window as a percentage is shown in the last column of Table 6.  
\[LSE\ Time(T_{LSE\_j})=T_{MF}+T_{MS}\ ;\ where\ T_{MS}=T_{MS}\ or\ (T_{MS1}+T_{MS2}) \tag{12}\]  
Figure 13: The topology change detected by H-TNTP-PMU Level 2-Area and H-TNTP Level 3 [28]. (**a**) Topology S2A (fully connected) to Topology S2B. (**b**) Topology S2B to Topology S2C. As a system, the modified two-area four-machine power system is small, and the computation time was less compared to the IEEE 68 bus system. Furthermore, the substation-level and the area-level computational times demonstrate the value of the distributed architecture. Since these two levels can be processed in parallel, the overall computational time can be minimized compared to the network level. This is important for new applications requiring PMU-based state estimation, where the smaller computation overhead is taken by TNTP [28] and LSE. Thus, it allows for higher computational flexibility for the new applications. H-TNTP-PMU updates the network model in every PMU measurement frame, and SOTA-TNTP updates the network model in every SCADA measurement frame. The PMU and SMRS sampling at 30 Hz (every 33.33 ms) and every 2 s, respectively, is presented in a timeline as shown in Figure 13(a). Due to the uncertainty of the instance, the network topology change occurred with respect to the PMU and SCADA samples collected; two possible scenarios in the topology identification-based flow can be defined as discussed in [28]. An extension to the same timeline interpretation, including three levels, is shown in Figure 13(b),c. The delay between the instance the topology change occurred, and the next sample collected by the PMU and SCADA are defined as \(T_{PMU}\) and \(T_{SMRS}\), respectively. \(T_{SOTA-TNP}\) is the time taken to complete SOTA TNTP, while \(T_{HT\_k}\) refers to the H-TNTP-PMU completion time, where \(k\) refers to either the substation level (SB), area level (A), or network level (N). \(T_{LSE\_s}\) refers to the time it takes to complete LSE in each level, where \(s\) is either the substation level (SB), area level (A), or network level (N). The time analysis shown in Table 7 was conducted for 50 trials for the Topology S1A to Topology S1B change discussed in Section 3.1.1. It is important to state that out of the 50 trials, 14% of the time the \(T_{SMRS}\) was less than 33.33 ms.
## 3 Results
### Comparison of RP-MPSK with Other LPWAN Modulations  
RPW operates on sub-gigahertz and other lower ISM bands. ISM band along with a few parts of licensed spectrum, are the bands of interest for most of the LPWANs that have gained global attention. Therefore, we have compared the performance of RP-MPSK with other LPWAN modulations. Table 2 lists different uplink modulation schemes and LPWANs in which they are used [5]. Figure 7 shows a comparison of their BER performance.  
Figure 6: BER Performance of RP-BPSK, RP-QPSK, RP-8-PSK and RP-MPSK at \(N_{p}=3\).  
64-QAM has the highest degradation in BER while RP-16-PSK with \(N_{p}=3\) offers the best BER. It can be observed that BERs of 8-PSK, 16-QAM, BFSK and CSS are similar therefore the curves overlap. It is important to mention here that BER curve of CSS holds for all values of SF when plotted against \(\alpha\)[26]. BPSK and QPSK outperform all other modulations except RP-16-PSK. Hence, we infer that RPW with RP-MPSK modulation is a highly reliable LPWAN solution, and therefore it can be used for industrial communication. The results also show that it offers a higher energy efficiency than other LPWAN technologies. RPW with RP-16-PSK is also a good trade-off between high data rate and high energy efficiency, as a reliable high data rate up to 2 Mbps is realizable.
# An Intelligent TCP Congestion Control Method Based on Deep Q Network
## 2 Related Works  
### Traditional Congestion Control Protocols  
Improving network performance is a hot research topic [12; 13]. Over the years, researchers have proposed various congestion control algorithms. They can be roughly divided into four types according to design principles: loss-based, delay-based, capacity-based, and hybrid.  
Reno, NewReno, and BIC are representatives of loss-based congestion control mechanisms. They both use packet loss to detect congestion and reduce the congestion window (CWND) to reduce network congestion when congestion is detected, where Reno and NewReno halve CWND only when three duplicate ACKs are detected, while cubic uses the cubic function to adjust CWND. Loss-based congestion control mechanisms use packet loss as the only signal to detect congestion. When the receive buffer is large, the data will not be lost until the buffer is full. During this period, the sender continues to increase its sending rate. In this case, there is a long time difference between the occurrence of congestion and the awareness of network congestion by the congestion control mechanism.  
Delay-based congestion control mechanisms can solve the above problems by using delays instead of packet loss as the congestion signals. Vegas [14] is the representative of delay-based congestion control protocol. When Vegas detects that RTT exceeds the threshold value, it starts decreasing CWND.  
Capacity-based congestion control mechanisms use the estimation of link sending capability as the basis for congestion control. Westwood [15] is improved from NewReno, which measures ACK packets to determine an appropriate send speed and adjusts CWND and slow start thresholds.  
The Hybrid congestion control mechanism combines the above two congestion control mechanisms to get their own advantages and further improve the congestion control. Compound [16], BBR [17] belong to Hybrid congestion control mechanism.  
Each of the above congestion control mechanisms has its own unique attributes, advantages, and disadvantages. However, since traditional congestion control mechanisms use a set of rules to control CWND and other related parameters, it is difficult for traditional congestion control protocols to adapt to the complexity and rapid development of modern networks.
## 2 Materials and Methods
### Data on Meteorological Variables  
The meteorological variables chosen for this study (relative humidity, precipitation, temperature, wind, radiation, and insolation) have been divided into several subtypes, provided by the AEMET, in order to provide more precise information about the possible interactions with the concentration of pollen grains in the atmosphere of Madrid.  
A double study was carried out on the meteorological variables, one with monthly values and the other with daily values. For this reason, 2 sets of subtype designations of meteorological variables were established. Thus, those corresponding to monthly values are detailed below:  
1. Relative humidity: monthly average humidity (HMed), monthly average of the maximum daily humidity (HMax), and monthly average of the minimum daily humidity (HMin).
2. Precipitation: monthly total precipitation (PT) and maximum daily precipitation (PMax).
3. Temperature: monthly mean of the maximum daily temperature (TMax), monthly mean of the minimum daily temperature (TMin), and monthly mean temperature (TMed).
4. Wind: maximum monthly gust speed (VMax) and number of days with wind speed \(\geq\)36 km/h (V36).
5. Radiation: monthly average of daily diffuse radiation (DR), monthly average of daily global radiation (GR), and monthly average of daily ultraviolet radiation (UVR).
6. Insolation: monthly total insolation (IT) and average daily insolation (IMed).  
And the subtypes of the meteorological variables, corresponding to daily values, are shown below:  
1. Relative humidity: daily maximum humidity (dHMax), daily minimum humidity (dHMin) and daily mean humidity (dHMed).
2. Precipitation: daily total precipitation (dPT) and daily maximum precipitation intensity (dPMax).  
Temperature: daily maximum temperature (dTMax), daily minimum temperature (dTMin), and daily mean temperature (dTMed).
* Wind: maximum daily wind gust speed (dVMax).
* Radiation: total daily diffuse radiation (dRD), total daily global radiation (dRG) and total daily ultraviolet radiation (dRUV).
* Insolation: total daily insolation (dIT) and daily insolation percentage (dIP).
## 3 Results and Discussion
### Adaptive Data Fusion Analysis of Table Grapes' Cold Chain Logistics  
The optimal weighted data fusion effective and error are analyzed to evaluate the accuracy of the sensor data acquired by sensor nodes under the whole table grapes' cold chain logistics. The mean square error and weight coefficients were calculated at the sensor node every data acquisition interval and then transmitted to the remote control center via the GPRS in the network coordinator according to the scenario's setup. Figure 6 describes the temperature and relative humidity with surface fitting by the MATLAB software (MathWorks Incorporated, Natick, MA, USA) after the data fusion.  
Both of them are relatively stable during the cold chain logistics transportation. According to Figures 5 and 6, the relative humidity rises from the normal relative humidity to the refrigerated relative humidity at about 92% as the temperature decays from the normal temperature to the refrigeration temperature at about \(-2\)\({}^{\circ}\)C. Then, they keep the relative stable condition in the preservation storage process until the table grapes' loading. The relative humidity decays from the refrigerated relative humidity to the normal relative humidity at about 64% as the temperature rises from the refrigerated temperature to the normal temperature at about 25 \({}^{\circ}\)C for the table grapes' unloading.  
The performance results of the fused temperature and relative humidity in the cold chain, compared to the arithmetic mean, are shown in Table 2. The mean absolute error and mean relative error of the temperature data are 0.06 \({}^{\circ}\)C and 8.61% and the relative humidity data are 0.12% and 0.23%, respectively, is more accurate than the arithmetic mean. However, the run time of AOW is longer than that of arithmetic mean, and the mean battery charge status is 93.2% of the arithmetic mean and 91.5%  
Figure 6: Temperature and relative humidity in the cold chain logistics after data fusion.  
of the AOW, respectively, after 15 days' transportation in the cold chain. These may be caused by the adaptive weight coefficients' calculation. However, the run time and mean battery charge status meet the actual real-time application requirements in the cold chain logistics [6,34].  
The results suggest that the adaptive optimal weighted data fusion could fuse the sensor data, accurately, efficiently and adaptively, which reflects the real-time data dynamic characteristic in the refrigeration truck during the whole cold chain logistics of table grapes.
# A High-Resolution SAR Focusing Experiment Based on GF-3 Staring Data
## 4 Discussion and Conclusions  
This paper introduces an experiment regarding GF-3 ST mode. After data acquisition and pre-processing, some special aspects such as model error correction, stop-and-go correction, and antenna pattern demodulation are executed with a two-step processing algorithm, and the focused image shown in Figure 10 is produced. The interferogram shown in Figure 11 demonstrates that the data processing presented in this paper keeps the phase fidelity very well. This ST focusing experiment not only provides the highest-resolution image of GF-3, but also lays the foundation for the development of new higher-resolution Chinese SAR in the future.  
Because the ST mode introduced in this paper is not a regular working mode of GF-3, users did not do a ST experiment in the calibration field at the commissioning phase, and we cannot get an image with ideal corner reflectors. The scene of the only attainable ST image shown in Figure 10 is very complicated, and it is difficult to find an ideal corner reflector. In order to obtain more precise results, another ST experiment needs to be applied at the calibration field.  
ST is different from SL. The existing methods to remove SL cubic phase error in the range history is not suitable for ST. In this experiment, we calculated the cubic phase error for every pixel in azimuth, but this is time consuming. If the resolution becomes higher, the higher-order phase error will also influence focusing. Therefore, it is necessary to develop a new technology that can compensate cubic and high-order phase error efficiently.  
In summary, there are still a lot of difficulties to be overcome in the future, and we will focus our efforts on solving these problems, especially the atmosphere's influence on high-resolution images, at a later stage.  
The authors wish to acknowledge the support of the Gaofen-3 mission, and especially [PERSON] and [PERSON], who conceived of and performed the GF-3 staring data acquiring experiment. This work was jointly supported by the National Science Foundation of China under Grant No. 61331017. And this work was partly supported by major project of high resolution earth observation system under Grant No. 41-Y20A13-9001-15/16.  
[PERSON], [PERSON], and [PERSON] initiated the research. [PERSON] and [PERSON] performed the data processing experiments and analysis. [PERSON] conducted the interferometry experiment. [PERSON] wrote the manuscript, [PERSON], [PERSON], [PERSON], and [PERSON] reviewed the paper and submitted comments, and [PERSON], [PERSON], and [PERSON] revised the manuscript. All authors read and approved the final version of the manuscript. The authors declare no conflict of interest.
# Fault Location in Double Circuit Medium Power Distribution Networks Using an Impedance-Based Method
## 1 Introduction
### Approach and Contributions  
In this paper, an improved impedance-based method is proposed for fault location in double circuit power distribution networks. In the suggested method, a collection of equations are derived to prove a new quadratic equation for locating fault in double circuit lines. The method is applied using only the recorded voltage and current at the beginning of the feeder. In this method, after distinguishing the short circuit fault, the voltage and current of each section of the distribution network is calculated through [PERSON]'s voltage law KVL and [PERSON]'s current law (KCL) equations, and the location of the fault is determined using the proposed method. In the proposed method, the capacitor effect and the mutual effect of the lines on each other is considered. The suggested method is tested in a thirteen-node system using MATLAB simulation and the results confirm the method's accuracy and validity. The obtained results show that the accuracy of the proposed method is very high and the sensitivity is very low in different fault conditions.
## 2 Methodology
### Unsupervised Anomaly Detection Using Spherical Detectors and Local Neighborhood Sampling
#### 2.4.5 Algorithm Steps  
1. Initialize the anomaly counter: \[N_{\text{anomaly}}\gets 0.\]
2. For \(i=1\) to \(N_{\text{rand}}\): (a) Generate a random point, \(\tilde{x}_{i}\), in the neighborhood of \(x\): \[\tilde{x}_{i}=x+(\text{rand}(n)\times 2-1)\odot\text{bounds}\times\text{perc}.\] (b) Determine if \(\tilde{x}_{i}\) is detected as an anomaly: \[\bullet\quad\text{Set detected}\leftarrow\text{False}.\] \[\bullet\quad\text{For each detector}\;(c_{j},r_{j})\in D\] \[\bullet\quad\text{If}\;\|\tilde{x}_{i}-c_{j}\|\leq r_{j}\text{:}\] \[\bullet\quad\text{Detected}\leftarrow\text{True}.\] \[\bullet\quad\text{Break the inner loop}.\] \[\bullet\quad\text{If detected}=\text{True}:\] \[\quad\text{-}\quad\quad N_{\text{anomaly}}\gets N_{\text{anomaly}}+1.\]
3. Calculate the anomaly detection percentage: \[P=\left(\frac{N_{\text{anomaly}}}{N_{\text{rand}}}\right)\times 100\%.\]
# Inverse Kinematics of a Serial Manipulator with a Free Joint for Aerial Manipulation
## 5 Simulation Results
### Redundant Manipulator on the UAV  
Finally, the inverse kinematics approach was tested on the redundant 4-DOF manipulator. The stroboscopic views for the trajectory tracking task are shown in Figure 16, joint angles and torque applied by the manipulator on the UAV CoG are shown in Figure 17, and UAV rotation and translation are shown in Figure 18.  
The torque applied by the manipulator on the UAV is comparable with the non-redundant case (see Figure 11), and the UAV rotation is negligible.  
The trend of the UAV translation is similar to the non-redundant case (see Figure 12); however, the translation is larger because of the increased inertia of the manipulator.  
The position error of the end-effector remained below \(1\cdot 10^{-4}\) m during the simulation.  
Figure 16: Stroboscopic view of the 4-DOF manipulator attached to the UAV executing the trajectory tracking task. **(left)** First stage (time \(<\) 5 s). **(right)** Second stage (time \(>\) 5 s). The green marker represents the UAV’CoG, the red marker represents the end effector, and the black circles represent the manipulator’s revolute joints.  
Figure 17: Trajectory tracking for the 4-DOF manipulator attached to the UAV. **(left)** Joint angles and (right) torque applied to the UAV during the task.  
The load picking task was solved with the 4-DOF UAM. In this task, the damping of the free joint was considered. The stroboscopic views for this case are shown in Figure 19, resulting joint angles and torque applied by the manipulator on the UAV CoG are plotted in Figure 20, and UAV rotation and translation are shown in Figure 21.  
Figure 19: Stroboscopic view of the 4-DOF manipulator attached to the UAV with a damper while it executes the load picking task. **(left)** First stage (time \(<\) 6 s). **(center)** Second stage (time 6 s \(<\) time \(<\) 16 s). **(right)** Third stage (time \(>\) 16 s). The green marker represents the UAV’CoG, the red marker represents the end effector, and the black circles represent the manipulator’s revolute joints.  
Figure 20: Load picking for the 4-DOF manipulator attached to the UAV with a damper. **(left)** Joint angles and (right) torque applied to the UAV during the task.  
Figure 18: Trajectory tracking for the 4-DOF manipulator attached to the UAV. **(left)** UAV rotation and **(right)** translation during the task.  
As for other load picking cases considered in this paper (see Sections 5.1 and 5.2), the grasping of the load causes a sharp increase in the torque and triggers damped vibrations.  
The trend of UAV rotation and translation is similar to the non-redundant case, but the redundant manipulator enables the reduction in the maximum \(x\) position (\(5.5\cdot 10^{-3}\) m against \(10.9\cdot 10^{-3}\) m).  
Position error of the end-effector was below \(5\cdot 10^{-5}\) m. Again, the position error is comparable with the one obtained in [6].
###### Abstract  
This study investigates the probabilistic stability of embankment slopes subjected to water level drawdown using the random field finite element method (RFEM) with strength reduction technology. The shear strength of soil properties was controlled by cohesion and internal friction angle for the slope shear failure. The cohesion and internal friction angle were modeled by a random field following the log-normal distribution. The factor of safety (FOS) for the embankment slope with random soil is calculated by strength reduction technology. During the numerical simulation, the limit analysis upper bound and lower bound method are applied to the finite element method, respectively, to obtain the upper bound and lower bound value of the FOS. Seepage action is also considered during the water drawdown by setting five different water levels (WLs). A total of 1000 Monte Carlo simulations are performed for each work condition, resulting in histograms of the FOSs. The results show that the FOSs obtained by the random field model are all lower than those by the deterministic method. Even if the FOSs obtained by the two methods are close, there still exists the possibility of slope failure. Compared to the deterministic results, the RFEM method is more reasonable for evaluating slope stability.  
random field; slope stability; seepage; spatial variability; limit analysis Article
## 3 Results and Discussion  
### Cell Viability  
The viability of _Lactobacillus plantarum_ ATCC 14917 as well as possible spoilage by yeasts and fungi or coliforms were recorded after juice fermentation and during the four weeks of storage at 4 \({}^{\circ}\)C (Table 1). According to the results, cell viability of the probiotic _Lactobacillus plantarum_ strain was maintained at high levels throughout three weeks of cold storage (above 10 log cfu/mL), while decreased during the last week of storage (statistically significant). Specifically, viable probiotic cell counts were decreased to 8.83 log cfu/mL at the last week of storage (4 th). However, even in this case, the recorded viability value was above the limit of 6-7 log cfu/mL, which is required for probiotic products [9]. At this point, it should be underlined that, the initial pH value of the freshly prepared pomergnate juice used in this study was approximately 3.0. Likewise, before fermentation, a slight increase of the substrate pH (with NaOH 4N) was made to a value of 3.5, so as to make the pomergnate juice more fermentable by _Lactobacillus plantarum_ ATCC 14917 (Table 1).  
A possible explanation of the high levels of _Lactobacillus plantarum_ ATCC 14917 viability during storage is that lactic acid fermentation might have increased the bio accessibility of phenolic compounds. There are reports from the literature claiming that phenolic compounds may act as prebiotics [37]. Likewise, possible prebiotic activity led to the amelioration of the growth of _Lactobacillus plantarum_ ATCC 14917. In addition, it has been noted that some strains of _Lactobacillus plantarum_ can grow in fruit matrices due to their tolerance to acidic environments [38]. Indeed, _Lactobacillus plantarum_ ATCC 14917 has been reported to exhibit high acid resistance ability [29]. Furthermore, no spoilage of the fermented pomegranate juice, by yeasts, fungi and coliforms was observed even after the 4 th week of storage at 4 \({}^{\circ}\)C (Table 1). It seems that lactic acid fermentation of pomegranate juice could provide a protective effect from microbiological spoilage as also reported by previous studies [39; 40].