esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Network Model
### RaptorQ Coding  
Fountain code [39] is a kind of forward erasure code, whose main idea is to encode the data to be transmitted to form \"water droplets\", while destination can recover the original file with high probability by receiving enough water droplets, regardless of the specific \"water droplets\". Relative algorithms include LT [40], Raptor [38], and RaptorQ coding [41]. Raptor evolved from the LT code, and RaptorQ is an improved version of Raptor. The Raptor encoder generates \(n_{1}\)-encoding packets from \(k\) original packets (\(k<n_{1}\)), and then sends them to the destination.  
Decoding failure probability [38] with \(n_{2}\) received encoding packets of the RaptorQ is:  
\[P^{\prime}_{f}=\begin{cases}\begin{array}{cc}1,&n_{2}<k\\ 0.01\times 0.01^{n_{2}-k},&n_{2}\geq k\end{array}\end{cases} \tag{1}\]  
Thus, the decoder can recover all the source files with a very high probability by collecting an arbitrary set of encoding packets that are only slightly more than the original packets number \(k\). It should be noted that other coding techniques such as Reed Solomon codes, network coding, etc., can also be used in the proposed transport mechanism.
## 3 Technology
### Current Research Challenges  
It is worth noting that the parallel development of compact pulsed laser sources [101] and timing electronics [39] is going to allow the design of cheap, compact and wearable systems. However, the technique is still far from a widespread adoption at all levels (research, medicine, market, etc.) due to some barriers which should be addressed in the years to come. Here we discuss, in agreement with the scope of this paper, those mostly related to the TG detector. The discussion of other limitations (e.g., limited power of the laser pulse), more related to the other components of a TD-DO systems, can be found elsewhere [39; 61; 102].  
Figure 9: Schematic concept of TG optical waveform acquisition. (**a**) Optical waveform (red line) and background noise (black line) as detected by a free-running detector; (**b**) same waveform detected by a TG SPAD: different slices of the signal are acquired at different delays, increasing the signal on the detector through a variable optical attenuator to increase the photon detection rate at delays where the signal is weaker; (**c**) after normalization for the attenuation introduced, slices are combined to reconstruct the signal over a wide dynamic range. In this way the signal is extracted from background noise at late times, where a standard free-running system could not detect photons in the same acquisition time.
# Effect of Demand Side Management on the Operation of PV-Integrated Distribution Systems
## 3 Results and Discussion
### Case VII: With CVR and DLC, without DG  
The combination of CVR and DLC was tested based on the two methods mentioned above. Figure 16 illustrates the variation of power and Figure 17 demonstrates the voltage profile; these figures include the base case and the aforementioned methods. The results obtained for this case regarding the active and reactive power are summarized in Tables 8 and 9. The advantages of this scenario are the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bus/Load (\#)** & \begin{tabular}{c} **Active Power** \\ **without DLC (kW)** \\ \end{tabular} & \begin{tabular}{c} **Active Power with** \\ **DLC (kW)** \\ \end{tabular} & \
\begin{tabular}{c} **Active Power** \\ **Reduction (kW)** \\ \end{tabular} &
\begin{tabular}{c} **Reduction** \\ **Percentage (\%)** \\ \end{tabular} \\ \hline B10 & 1575.830 & 1371.041 & 204.789 & 12.996 \\ B12 & 3046.189 & 2512.851 & 533.339 & 17.508 \\ B14 & 1675.009 & 1362.938 & 312.  
reduced system losses, reduced consumed power and the increased voltage control capability; thus, any voltage variation caused by DLC can be rectified by the CVR algorithm. Obviously, the dominant result was established by the implementation of the first method, where DLC and CVR are running through the entire 24 h. DLC forms the major impact on power reduction compared to CVR. As the power decreases, the voltage decreases, hence CVR does not take part in curtailing power at some points. Thus, the 24-h scheme has much better results.  
Figure 16: Case VII: Power vs. time with/without CVR and DLC.  
Figure 17: Case VII: Voltage profile with/without CVR and DLC.  
Tables 10 and 11 show the results for this case. By comparing these tables with the tables for previous case, one can see that the effect the DG has on the CVR and DLC is a slightly negative one. The DG decreased the saved active and reactive powers. However, the active and reactive powers consumed by the loads in the presence of the DG are slightly less. Thus, the Overall effect of the DG is non-positive but it is not that influential. The pros and cons of this case are similar to those of Case VII alongside more technical, environmental and economic benefits due to the integration of the DG. From the irradiance perspective, the comparison between a summer day of good irradiance and a cold day of zero irradiance (in worst case) can be related to Cases VIII and VII, respectively. As indicated above, the PV integration causes minor negative effects on DSM techniques; if Case VII were presumed to be the zero irradiance of Case VIII in certain weather conditions, the variation in irradiance would have very small effect in terms of voltage profile and saved energy.
## 3 Results
### Analysis of GPP Estimation Using Multisource SIF Satellite Products
#### 3.2.3 Analysis of the Impact Factor Responsiveness  
From Figure 5, it can be seen that the responsiveness of RTSIF, CSIF, SIF_OCO2_005, and GOSIF satellite products to the main influencing factors (photosynthetically active radiation (PAR), soil temperature (Tsoil), air temperature (Tair)) of GPP varies in different ecosystems in arid regions. In the desert vegetation area (Figure 5a), RTSIF exhibited the highest responsiveness to Tsoil, PAR, and Tair, followed by CSIF, and GOSIF showed the weakest response. In the pasture grassland area (Figure 5b), RTSIF showed the highest responsiveness to PAR, followed by SIF_OCO2_005, while GOSIF exhibited the weakest response. For Tsoil and Tair, the highest responsiveness is observed for SIF_OCO2_005, followed by RTSIF, with GOSIF showing the weakest response. In the cultivated and farmland area (Figure 5c), the highest responsiveness to PAR and Tair was observed for RTSIF, followed by SIF_OCO2_005, with GOSIF exhibiting the weakest response. For Tsoil, the highest responsiveness was observed for SIF_OCO2_005, followed by RTSIF, with GOSIF exhibiting the weakest response.  
Figure 4: The spatial distribution characteristics of annual mean values of multisource SIF satellite products.  
Figure 5: Responsiveness of multisource SIF satellite products to major influencing factors of GPP (** indicates significance at the 0.5 level). (**a**) Kelameili Station, desert vegetation area. (**b**) Ulastai Station, pasture and grassland area. (**c**) Ulan Usu Station, cultivate land and farmland area.  
These results indicate that the responsiveness of the four satellite products in arid regions to the main influencing factors varies greatly among different ecosystems. The overall responsiveness to Tsoil is ranked as follows: SIF_OCO2_005 > RTSIF > CSIF > GOSIF. While the overall responsiveness to Tair and PAR is ranked as follows: RTSIF > SIF_OCO2_005 > CSIF > GOSIF. RTSIF shows an overall significance greater than 0.5 with influencing factors in different ecosystems, indicating that RTSIF satellite products have the highest overall responsiveness.
## 3 Distributed Mission Scheduling Model
### ATSA for Dynamic Scheduling
#### 3.4.1 Impact of Emergency on Initial Solution  
New Mission Arrival  
Newly arrived missions are the most frequent cases of emergency conditions. When emergency happens, such as natural disaster monitoring, public health incidents, or when new targets with high priority appear, new targets are added to observation requests. It is unpractical to put newly arrived missions into a new target set and reschedule them as regular circumstances because the scheduling plan with regards to new targets might conflict with the initial plan in the same execution period. Therefore, new targets should be considered as a whole with old ones; in other words, new genes representing newly arrived targets will be numbered subsequently and inserted at the bottom of the initial solution; the solution structure update is shown in Figure 3.  
Change of Scheduling Plan  
Sometimes, when some targets cannot be observed as required due to weather condition or satellite resources malfunction, these targets should be rescheduled without disturbing the undergoing plan of satellites that are properly functioning. In other words, most targets can be normally executed as initially planned, while those failed missions should update their solution state (replace the window ID with 0) and reschedule, as shown in Figure 4.
# LoRaWAN Networking in Mobile Scenarios Using a WiFi Mesh of UAV Gateways
## 1 Introduction
This mesh of flying LoRaWAN gateways will receive the data from the GNs and relay it through WiFi to the command post, where the LoRaWAN Network Server resides. Since the WiFi technology presents a higher data rate, it allows the aggregation of traffic originated from a high number of GNs. On the other hand, its shorter range is somewhat compensated by the fact that communication between UAVs takes place in LoS. Since GNs are expected to move, the UAV mesh must adapt its position and topology according to the GN movement patterns. In LoRaUAV, this is addressed by a topology control algorithm based on Virtual Spring Forces (VSFs). In order to solve the problem of disconnections due to disruption caused by continuous movement of GNs and transient UAV topology adaptation, a Connection Recovery and Maintenance (CRM) extension is proposed. A Movement Prediction (MP) extension is also proposed  
Figure 1: High level view of LoRaUAVsystem architecture. in order to make it easier to recover isolated GNs based on a combination of movement prediction and VSFs. LoRaUAV has been successfully implemented in ns-3 and its performance has been evaluated and compared with an existing VSF proposal by [PERSON] et al. [4]. Packet Reception Ratio (PRR) and end-to-end delay were used as the main performance metrics. From the results, it can be concluded that the CRM and MP mechanisms implemented in LoRaUAV effectively help improving the PRR, with the only disadvantage of a higher delay affecting a small percentage of packets, which is caused by buffer delays and disconnections. The main contributions of this paper are the following. * Proposal and comparative performance evaluation of a system providing LoRaWAN coverage in wild area firefighting operations by means of a WiFi mesh of UAV gateways. * Novel CRM and MP extensions to VSFs topology control, which are shown to significantly improve the performance of this kind of mechanism. * Novel mobility model of wild area firefighting scenarios considered in the performance evaluation. The rest of this paper is organized as follows. Section 2 presents a review of the related works. Section 3 presents LoRaUAV and its main algorithms and explains the main design choices. Section 4 presents the developed simulation model. Section 5 presents the simulation results to evaluate all the algorithms. Finally, conclusions are reported in Section 6.
## 4 Field Test Results and Discussion  
### Environmental Factors Measurement Results  
In consideration of the application in a special environment like a greenhouse, some field tests was carried out to prove the ability and feasibility of this system. These tests were carried out in the Town of Shelin in Jilin Province, China. In field deployment, three nodes were fabricated and deployed in the greenhouse. The curve number in the software platform was able to monitor the information from different nodes. Through more than a one-week test, the stable and accurate performance with reasonable reproducibility indicated that the implemented system is applicable in a greenhouse environment. A practical installment photo is shown in Figure 11(a), where the terminal monitoring and control software platform based on LabVIEW is embedded in an ARM A8 hardware platform in a white box. This sensor node is connected with the embedded terminal directly by a power line communication (PLC) because of a short distance. The interface is connected by the aviation plug. In other nodes, the data transmission line was replaced by a wireless transmission mode in WSNs to supply a fixable installment in the complicated environment of a greenhouse. In addition, wireless communication avoids the difficult wiring and corrosion to the lines in a PLC under complex circumstance in a greenhouse. However, in order to satisfy the driving power, the CO\({}_{2}\) fertilization control facilities are controlled and driven by a PLC. Two-day measurement results, as an example, are shown in Figure 11(b),\(c\), including the CO\({}_{2}\) concentration, luminance, temperature, and humidity. The repeated monitoring results were compliant with the photosynthesis law, which implies no detection errors during the two-day field test. From the measurement results, with plenty of light, the CO\({}_{2}\) concentration rapidly decreased to 242 ppm, nearly 13:45, because of the strong plant photosynthesis. The lack of CO\({}_{2}\) concentration suppressed the plant growth and decreased the crop yield. With the sunset, the luminance decreased to zero dynamically and the CO\({}_{2}\) concentration began to accumulate because of the plant respiration. In addition, because the data was measured in April, the temperature is relatively high, up to 38 \({}^{\circ}\)C. In sum, through measurement results without human impact, the CO\({}_{2}\) concentration was under a lacking state in the long-term. Therefore, a reasonable CO\({}_{2}\) fertilization is a desired requirement to increase final production.
## 4 Experiments and Results
### Sample Size  
For the analysis, we defined \(\text{CWA}_{L}\) as the lower bound for selecting students. In other words, the final dataset used in our experiments included students who achieved a CWA equal to or greater than \(\text{CWA}_{L}\). Figure 7 shows how the total number of students and the class distribution (certified vs. not certified) changed with \(\text{CWA}_{L}\). With a \(\text{CWA}_{L}\) threshold of 5%, 2437 students did not achieve certification (\(y=1\)), and 347 did (\(y=0\)), resulting in an imbalanced dataset. Using that threshold would have included students with limited interaction on Moodle, which could have led to noisy data. In contrast, when we increased \(\text{CWA}_{L}\) to 75%, the dataset contained students with substantial interactions but fewer samples: 148 students who did not achieve certification (\(y=1\)) and 347 who did (\(y=0\)), maintaining imbalance and potentially filtering out important early-stage dropout patterns crucial for identifying at-risk students.  
Figure 6: Distribution of the mean multimedia views for \(\text{CWA}_{U}\) thresholds of 20% and 60%.  
To select the value of \(\text{CWA}_{L}\) and obtain the final dataset, we evaluated the performance of the random forest classifier in predicting whether a student would achieve the certificate of participation at four different values of \(\text{CWA}_{L}\): 5%, 25%, 55%, and 75%. We used a \(\text{CWA}_{U}\) threshold of 20% for all sample sizes to determine each student's Feature Aggregation Time Point (FATP). Figure 8 shows the confusion matrix of the model for each value of \(\text{CWA}_{L}\). We observed a bias toward the positive class at lower thresholds, while higher thresholds favored the negative class. A threshold of 55% offered the best trade-off between the true positive and true negative rates. Consequently, we selected the 55% \(\text{CWA}_{L}\) threshold, which corresponded to a dataset with 663 samples: 316 students who did not achieve a participation certificate (\(y=1\)) and 347 who did (\(y=0\)).
## 4 Stability Analysis of Single-Step Slope with Different Slope Ratios Under Rainfall Conditions
### Stability Changes of Different Slope Ratios Under Short-Term Heavy Rainfall  
To investigate the stability of slopes with different slope ratios under rainfall conditions for slope heights ranging from 5 to 30 m, Figure 7 presents the safety factor variation curves over time for slopes with slope ratios from 1:1.5 to 1:2.25 and heights ranging from 5 to 30 m, under identical rainfall conditions.  
As shown in Figure 7, after 10 h of heavy rainfall, the safety factors for slopes with heights of 15 m, 20 m, and 25 m and a slope ratio of 1:2.0 are 1.187, 1.194, and 1.088, respectively. Therefore, when the step height is \(\geq\)25 m, a slope ratio of 1:2.0 no longer meets the stability requirements. However, for slopes with heights of 25 m and 30 m, a slope ratio of 1:2.25 yields safety factors of 1.301 and 1.300, both of which satisfy the stability criteria. Initially, as rainfall infiltrates the slope, the overall stability of the slope declines sharply. For slopes with a height of \(\leq\)15 m, the safety factor decreases rapidly at first, then stabilizes. For slopes taller than 15 m, the safety factor curve declines slowly for the first 4-5 h. However, as the rainfall infiltration continues, the safety factor curve begins to drop sharply, eventually stabilizing.
## 4 Experimental Results and Analysis
### Analysis of Different Tampering Test Results
#### 4.2.4 Image Deletion Attack  
An image deletion attack is used to remove part of an image; this operation can be approximately reconstructed after image recovery. The tamper detection and recovery results are shown in Figure 10 and partially enlarged details are presented in Figure 11. Table 5 lists the PSNR and SSIM values of the different methods. As Figure 10e-l shows, all four methods achieve high tamper location and image recovery performances. Figure 11 shows some of the differences. Figure 11a shows some black noise. Compared with Figure 11b, Figure 11c presents a clearer visual effect and Figure 11d has the smoothest edges among all the results. With the threshold adjustment process, several unsuccessfully restored pixels are recovered. Meanwhile, the filtering operation, effectively smooths the jagged edges without confusing their characteristics. However, as shown by comparison values listed in Table 5, the objective quality of the proposed method does not outperform the best result, obtained by [7]. This can be explained by the drawback of the median filter. As can be seen in the enlarged images, several details exist in single or small pixel regions that can easily be lost if they are not in the middle. However, this problem can be solved by threshold adjustment to a large extent. In general, the proposed method achieves a better visual effect and a fairly good objective quality compared with the other methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indexes** & **[PERSON] et al. [8]** & **[PERSON] et al. [9]** & **[PERSON] et al. [7]** & **Proposed** \\ \hline PSNR (dB) & 24.76 & 47.17 & 47.79 & 47.97 \\ SSIM & 0.8016 & 0.8390 & 0.9925 & 0.9930 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Objective comparison of the recovery performances for a splicing attack on the Car2 image.  
Figure 9: Tamper detection and recovery results of a splicing attack on the Car2 image: (**a**) original image; (**b**) watermarked image; (**c**) tamper reference; (**d**) tampered image; (**e**) tamper detection result of [8]; (**f**) tamper detection result of [9]; (**g**) tamper detection result of [7]; (**h**) tamper detection result of the proposed method; (**i**) recovery result of [8]; (**j**) recovery result of [9]; (**k**) recovery result of [7]; (**l**) recovery result of the proposed method.
# A Multilevel Switched Capacitor Inverter with Reduced Components and Self-Balance
## 3 Power Losses Analysis
### Ripple Losses  
The ripple losses can be produced by the voltage fluctuation of the capacitor, and the voltage ripple of capacitors \(C_{1}\), \(C_{2}\), and \(C_{3}\) can be obtained as  
\[\Delta V_{\rm C1}=\Delta V_{\rm C2}=\frac{\Delta Q_{1}}{C_{1}}, \tag{21}\]  
\[\Delta V_{\rm C3}=\frac{\Delta Q_{3}}{C_{3}}, \tag{22}\]  
\begin{table}
\begin{tabular}{c c c} \hline \(j\) & \(V_{\rm o,\,\,\,j}\) & \(R_{\rm eq,\,\,j}\) \\ \hline
0 & 0 & \(R_{\rm s}+R_{\rm d}\) \\
1 & \(\pm V_{\rm dc}\) & 2 \(R_{\rm s}+2\)\(R_{\rm d}\) \\
2 & \(\pm 2\)\(V_{\rm dc}\) & 3 \(R_{\rm s}+3\)\(R_{\rm d}/2+ESR_{\rm C}/2\) \\
3 & \(\pm 3\)\(V_{\rm dc}\) & 4 \(R_{\rm s}+R_{\rm d}+2 ESR_{\rm C}\) \\
4 & \(\pm 4\)\(V_{\rm dc}\) & 3 \(R_{\rm s}+R_{\rm d}+2 ESR_{\rm C}\) \\
5 & \(\pm 5\)\(V_{\rm dc}\) & 4 \(R_{\rm s}+R_{\rm d}/2+3 ESR_{\rm C}/2\) \\
6 & \(\pm 6\)\(V_{\rm dc}\) & 5 \(R_{\rm s}+3 ESR_{\rm C}\) \\ \hline \end{tabular}
\end{table}
Table 3: Parameters of the equivalent circuits.  
where the \(\Delta V_{\rm C1}\), \(\Delta V_{\rm C2}\), and \(\Delta V_{\rm C3}\) are the voltage ripples of capacitors \(C_{\rm 1}\), \(C_{\rm 2}\), and \(C_{\rm 3}\), respectively. According to the calculation method in [28], the ripple losses of the capacitors are expressed as  
\[P_{\rm rip}=\frac{f_{\rm o}}{2}\sum_{k=1}^{3}C_{k}\Delta V_{\rm Ck}^{2}. \tag{23}\]  
Overall, the power losses of the proposed 13-level topology are expressed as  
\[P_{\rm loss}=P_{\rm sw}+P_{\rm con}+P_{\rm rip}. \tag{24}\]  
Finally, the theoretical efficiency is indicated by \(\eta\), which is defined by  
\[\eta=\frac{P_{\rm o}}{P_{\rm sw}+P_{\rm con}+P_{\rm rip}+P_{\rm o}}, \tag{25}\]  
where \(P_{\rm o}\) is the output power of the inverter.
## 3 Proposed Method
### Cluster-Head Rotation
A larger amount of energy is consumed if at a given cycle the sensor node detects an important event and reports it to the CH node. Lower energy consumption is encountered when the event is not detected, and the sensor node suppresses data transmission. Therefore, in the proposed approach energy \(EM\) is estimated by taking into account the probability of event occurrence for i-th sensor node \(p_{i}\):  
\[EM=EMH\cdot p_{i}+EML\cdot(1-p_{i}), \tag{3}\]  
where \(EMH\) denotes energy consumed by the sensor node (cluster member) during a cycle when an event is detected and data transmission is performed, \(EML\) is energy consumption of sensor node during a cycle when no event is detected and transmission is suppressed. The event occurrence probability \(p_{i}\) is evaluated based on historical data. The fractions of time when particular sensor nodes take the CH role (\(t_{i}\)) must be carefully selected to maximize the network lifetime. According to the proposed method, the time fractions \(t_{i}\) are determined by solving the following system of linear equations to ensure the same expected lifetime for all sensor nodes:  
\[\left\{ \begin{array}{c}\frac{EI_{i}}{e_{i}}=\frac{EI_{i+1}}{e_{i+1}},i=1,...,n -1\\ \sum_{i=1}^{n}t_{i}=1\end{array} ,\right. \tag{4}\]  
where \(n\) is the number of sensor nodes in a WSN cluster, and the remaining symbols are defined above. The equation system (4) can be solved by CH node or base station (sink node). It should be noted that various algorithms for solving the system of linear equations are available in the literature, e.g., [29; 30; 31]. After calculating time fraction \(t_{i}\), the length of time when sensor node \(i\) has to take the CH role is determined as \(TCH_{i}=T\cdot t_{i}\), where \(T\) denotes time of one round, i.e., a predetermined time during which each sensor node in the cluster acts as CH.