esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Influence of GCPs  
The influence of GCPs in the generation of absolute DSMs was studied by analyzing dozens of 25 m resolution DSMs generated for both Mausanne les Alpilles and Salon de Provence using different GCPs number and configuration. Their accuracy was validated both locally using Independent Check Points (ICPs) and on the whole study areas using the reference DSMs/DTMs supplied by the Principal Investigators. With respect to ICPs, for Mausanne les Alpilles the best results were achieved using five GCPs (four in the corners and one in the centre), obtaining a mean value of residuals (\(\upmu\)) of -0.0 m, a standard deviation (\(\upsigma\)) of 1.7 m and a RMSE of 1.7 m. For Salon de Provence, the best results were achieved using nine GCPs regularly distributed obtained a mean value of residuals of 0.5 m, a standard deviation of 1.4 m and a RMSE of 1.2 m. We should note, however, that similar results were achieved using fewer GCPs: using four GCPs we obtained a RMSE of 1.3 m, while using six GCPs we found a RMSE of 1.2 m. Consequently, for the test field studies we can affirm that the sensor orientation can be carried on with at least least GCPs. This outcome has been also confirmed by other studies handling the same dataset or other datasets [15-17,43,48-50].  
NATO standards [38] suggest the use for evaluation control of data more than twice as accurate as the expected final DSMs. Nevertheless, the accuracy assessment based on a reduced number of spot elevations (ICPs) is biased and not realistic and tends to overestimate the real accuracy [27]. For that reason, we also compared the Cartosat-1 DSMs with reference DSMs/DTMs on the whole test fields to obtain more reliable results. For Mausanne les Alpilles, passing from only one GCP to five GCPs the improvement in the computation of the DSM's accuracy was +34% in terms of RMSE. No further significant enhancement was observed using more than nine GCPs (data referred to the high-resolution reference DSM). For Salon de Provence, passing from only one GCP to four GCPs the improvement in the computation of the DSM was +12% in terms of RMSE, while passing from four GCPs to nine GCPs the improvement was only +6% in terms of RMSE. Again, no further significant enhancement was observed using more GCPs (data referred to MNT DBTOPO(r) DTM).  
The reason for the accuracy difference obtained using only one against four (or more) GCPs is due to the use of RPCs for image orientation. Data processed without any GCPs mainly show linear systematic errors and few GCPs can be used to improve the positioning accuracy by fitting the RFM calculated coordinates to the coordinates of the additional GCPs; with one GCP is possible only to correct for shifts while using more GCPs an additional transformation in the image space can be applied (e.g., 2-D affine transformation) [51].
# Improved Calculation of Load and Resistance Factors Based on Third-Moment Method
## 2 Computation Procedure of Load and Resistance Factors
### Mori Method for the Computation of Load and Resistance Factors  
When \(R\) and \(Q_{i}\) are independent and the lognormal distribution is used to describe all random variables, the performance function is expressed as:  
\[g(X)=\ln R-\ln Q_{i} \tag{26}\]  
The function of reliability index can be interpreted as:  
\[\beta_{2M}=\frac{\lambda_{R}-\lambda_{Q}}{\sqrt{{\xi_{R}}^{2}+{\xi_{Q}}^{2}}} \tag{27}\]  
where \(\lambda_{R}\) and \(\lambda_{Q}\) are the mean of \(\ln R\) and \(\ln Q\), respectively; \(\zeta_{R}\) and \(\zeta_{Q}\) are the variance of \(\ln R\) and \(\ln Q\), respectively. Using the coefficient of variation (\(V\)), \(\lambda\) and \(\xi\) can be calculated from the following formula:  
\[\zeta^{2}=\ln(1+V^{2}) \tag{28}\]  
\[\lambda=\ln\frac{\mu}{\sqrt{1+V^{2}}} \tag{29}\]  
Substituting Equation (27) into Equation (4), the load and resistance factors can be expressed as:  
\[\lambda_{R}-\alpha_{R}\zeta_{R}\beta_{T}>\sum(\lambda_{Qi}+\alpha_{Qi}\zeta_{Qi }\beta_{T}) \tag{30}\]  
Substituting Equations (28) into (30), the load and resistance factors can be expressed as:  
\[\gamma_{i}=\frac{1}{\sqrt{1+V_{Qi}^{2}}}\exp(\alpha_{Qi}\zeta_{Qi}\beta_{T}) \frac{\mu_{Qi}}{Q_{ni}} \tag{31}\]  
\[\phi=\frac{1}{\sqrt{1+V_{R}^{2}}}\exp(-\alpha_{R}\zeta_{R}\beta_{T})\frac{\mu _{R}}{R_{n}} \tag{32}\]  
\(\alpha_{Qi}\) and \(\alpha_{R}\) are determined by the iteration computation, as shown by the calculation steps below. \(\alpha_{Qi}\) and \(\alpha_{R}\) are the relative weight coefficient of the corresponding loads and resistance, which are expressed as:  
\[\alpha_{Qi}=\alpha_{Qi}{{}^{*}}\cdot u \tag{33}\]  
\[\alpha_{R}=\alpha_{R}{{}^{*}}\cdot u \tag{34}\]  
\(\alpha_{R}{{}^{*}}\alpha_{Qi}{{}^{*}}\), and \(u\) are determined by the following functions:  
\[\alpha_{Qi}{{}^{*}}=\frac{c_{i}\xi_{Qi}}{\sqrt{\xi_{R}{{}^{2}}+\sum\limits_{j }{(c_{j}\cdot\xi_{Qi})}^{2}}} \tag{35}\]  
\[\beta_{2M}=\frac{\lambda_{R}-\lambda_{Q}}{\sqrt{{\xi_{R}}^{2}+{\xi_{Q}}^{2}}} \tag{27}\]0  
\[\beta_{2M}=\frac{\lambda_{R}-\lambda_{Q}}{\sqrt{{\xi_{R}}^{2}+{\xi_{Q}}^{2}}} \tag{27}\]1  
where \(\Phi\) denotes the cumulative distribution function of the standard normal variable. \(c_{i}\) is the ratio of the average of the sum of \(Q_{i}\) to the average of \(Q_{i}\), which is expressed as:  
\[\beta_{2M}=\frac{\lambda_{R}-\lambda_{Q}}{\sqrt{{\xi_{R}}^{2}+{\xi_{Q}}^{2}}} \tag{27}\]2  
The steps for determining the load and resistance factors using the new method are shown below:  
1. Calculate \(\mu_{RT}\) using Equation (42). 2. Calculate \(\sigma_{Z}\), \(\alpha_{3Z}\), and \(\beta_{2T}\) using Equations (5a,b), (12) and (40), respectively. Then, calculate \(\alpha_{R}\) and \(\alpha_{Qi}\) with Equation (8). 3. Determine the load and resistance factors with Equation (18a,b). In the proposed method, there is no need for step c and d in the existing 3M method, that is, iterative calculation is unnecessary. Besides, there is no mathematical limitation in Equation (39).
# Fast Reroute Mechanism for Satellite Networks Based on Segment Routing and Dual Timers Switching
## 2 Related Work
### Node Behavior Design  
Within the SR backup path, the source node is responsible for converting base route packets into SR backup path packets and pushing the SR backup path labels. Intermediate nodes forward the packets by examining the labels according to the SR mechanisms. Upon reaching the backup path endpoint, the node converts the SR backup path packets back into base route packets and forwards them using the onboard underlay routing configuration.
## 3 Behavior of Test Specimens
### Specimen SPE3
#### 3.1.2 Prefabricated RACSWs  
(1) Specimen SPE4  
Specimen SPE4 was in the elastic stage with no observable behavior when the horizontal load was less than 120 kN. An initial crack appeared at the lower right corner of the wall when the load was 140 kN. Multiple fine oblique cracks appeared in the wall center and extended when the load was 220 kN. The specimen yielded locally, then the load was applied by controlling the displacement.  
In the displacement control stage, multiple short cracks with a width of approximately 1 mm appeared along the diagonal direction of the wall when the displacement was 1.5 \(\delta_{y}\). The concrete at the lower right corner of the wall began to fall off, and the number of horizontal cracks gradually increased (Figure 7a). Cracks in the horizontal direction formed at the bottom of the wall when the displacement was 2.5 \(\delta_{y}\) (Figure 7b). The upper ear plate had a relative slip of approximately 10 mm with the T-shape connector (Figure 7c) accompanied by a friction sound among steel plates. Bending deformation occurred on the wall when the displacement was 4.5 \(\delta_{y}\). Then, the corner concrete fell off, exposing the steel bars. The top flange of the steel beam exhibited a brittle fracture when the displacement was 5.0 \(\delta_{y}\) (Figure 7d), and the column base buckled locally. Finally, the horizontal load was reduced by more than 15%, and the specimen lost its carrying capacity.  
(2) Specimen SPE5  
Specimen SPE5 was in the elastic stage with no observable behavior when the horizontal load was less than 60 kN. Multiple fine oblique cracks appeared along the diagonal direction of the wall when the load was 80 kN. The number of fine cracks gradually increased and continuously expanded when the load was 120 kN. The specimen yielded locally, then the load was applied by controlling the displacement.  
In the displacement control stage, intersecting cracks formed along the diagonal direction of the wall when the displacement was 2.5 \(\delta_{y}\), and the principal cracks were connected (Figure 8a). The concrete at the lower right corner of the wall was crushed and fell off when the displacement was 3.5 \(\delta_{y}\), exposing the steel bars. Multiple horizontal cracks with a width of 2-3 mm appeared at the bottom of the embedded T-shape connector and extended from right to left (Figure 8b). The lower left flange of the steel beam noticeably bulged upward when the displacement was 4.0 \(\delta_{y}\). The upper ear plate had a relative slip of approximately 10 mm with the T-shape connector, accompanied by a friction sound. A large area of concrete fell off when the displacement was 6.0 \(\delta_{y}\). Consequently, the steel bars were exposed, and cracks in the horizontal direction formed at the bottom of the wall (Figure 8c). The end plate warped (Figure 8d), and the column base buckled. Finally, the horizontal load decreased by more than 15%, and the specimen lost its carrying capacity.  
Figure 7: Local failure of SPE4.
# Considering the Life-Cycle Cost of Distributed Energy-Storage Planning in Distribution Grids
## 3 ESS Installation Site and Capacity Optimization
### The First-Stage Optimization
\tag{9}\]  
Accumulating the charging/discharging energy of ESS through the output power \(P_{ESS,i}(t)\) from time 0 to time \(t\) which is obtained by GA, the energy fluctuation \(E_{ESS,i}(t)\) at time t of node i relative to the initial state of ESS at every installation nodes can be obtained [18]. For charging  
\[E_{ESS,i}(t)=E_{ESS,i}(t-1)+P_{ESS,i}(t)\Delta t\eta_{C}\eta_{PCS} \tag{10}\]  
For discharging  
\[E_{ESS,i}(t)=E_{ESS,i}(t-1)+\frac{P_{ESS,i}(t)\Delta t}{\eta_{D}\eta_{PCS}} \tag{11}\]  
where \(E_{ESS,i}(t-1)\) is the charging/discharging energy at time \(t-1\) of node \(i\) (\(E_{ESS,i}(0)=0\)). \(\eta_{C}\) and \(\eta_{D}\) are the charging efficiency and discharging efficiency, respectively. The charging and discharging efficiencies are estimated as the root of the round-trip efficiency. According to the energy fluctuations in the whole sampling cycle (which was one day, in this study), the difference between the maximum and minimum energy can be calculated. Then considering the limitations of the SOC, the minimization of capacity that must meet the ESS output can be obtained [15]. \[E_{0,i}=\frac{\max\{E_{ESS,i}(t)\}-\min\{E_{ESS,i}(t)\}}{SOC_{\max}-SOC_{\min}} \tag{12}\]  
where \(E_{0,i}\) is the minimum capacity that must meet the ESSs' output. \(SOC_{max}\) and \(SOC_{min}\) are the minimum and maximum SOC limits of ESS. Ideally, the value of \(SOC_{max}\) is 1 and \(SOC_{min}\) is 0. To avoid the impact of over-charging/discharging during the battery lifetime, the short-cut process of \(SOC_{max}\) and \(SOC_{min}\) should be in the range of 0-1. \(\max\{E_{ESS,i}(t)\}\) and \(\min\{E_{ESS,i}(t)\}\)are the maximum and minimum energy fluctuation of the ESS in the whole sampling cycle, and \(\max\{E_{ESS,i}(t)\}\) \(-\min\{E_{ESS,i}(t)\}\) is the maximum range of the energy fluctuation. The minimum installation capacity of the ESSs can be calculated by the charging and discharging power of each preferred node in the optimized period with Equations (10)-(12). In this study, if the minimum installation capacity \(E_{0,i}\) of a preferred node was less than 10% of average capacity of each pre-selected nodes, the node was abandoned. The algorithm continued to optimize the capacity of the remaining preferred nodes, until the nodes and the capacities on the nodes met the requirement. Then the first-stage optimization was completed and the results were sent to the second stage.
## 4 Experimental Evaluation
### Scenario 2: Distance between STA and AP
#### 4.3.3 Results of Wireless Network Layer in Scenario 2  
We now examine the results of the wireless network layer. Figure 12 shows the RSSI of TCP and UDP communications on both sides. Compared with the RSSI results of Scenario 1 (see Figure 6), the RSSI at 10 m is already significantly degraded, i.e., approximately 10 dBm. After that, there is a relatively small difference in the RSSIs, but RSSI values suddenly dropped at 30 m. In addition, by analyzing the log file in detail, we found that the log file records two types of errors. One error is that the _iw_ result included inaccurate values of 0 in RSSI. The other is that all information about the wireless interface, including Tx bitrates, was not logged. Since this type of erroneous information is output when the wireless connection is unstable, it would be helpful as one of the decision indicators.  
Table 5 summarizes the occurrence of invalid and missing values. The table indicates the average number of RSSI values of 0 and no information. The invalid RSSI began to emerge at 30 m. On the other hand, no information began to occur at 65 m. The results show that assessing the trend between throughput and RSSI is difficult.  
Table 6 shows the percentage of each Tx bitrate observed by the _iw_ on the STA. Note that the values in bold in the table indicate the mode of the Tx bitrate at each distance. For instance, the mode of Tx bitrate at 20 m is 1925.0 Mb/s, and the Tx bitrate for the mode degrades as the distance increases. Even if the RSSI is not significantly reduced,  
Figure 8: Throughputs of TCP and UDP communications (Scenario 2).  
Figure 9: Jitter of UDP communication on receiver side (Scenario 2).  
the throughput will also be reduced as the Tx bitrate degrades. Therefore, the Tx bitrate is a more appropriate indicator of wireless link quality than RSSI, and it is an easy-to-understand metric for non-specialists.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Distance (m)** & **10** & **20** & **30** & **40** & **50** & **60** & **65** & **70** & **75** & **80** & **85** & **90** & **95** & **100** \\ \hline TCP & Invalid & 0 & 0 & 0 & 1.4 & 0 & 0 & 1.6 & 1 & 0 & 0 & 0 & 0.1 & 4.9 & 1.8 \\ sender & Missing & 0 & 0 & 0 & 0 & 0 & 0 & 1.2 & 0 & 0 & 0 & 0 & 0 & 34.9 & 40.5 \\ \hline TCP & Invalid & 0 & 0.2 & 0.7 & 0 & 0 & 1.8 & 0 & 0 & 0.1 & 0.2 & 0.2 & 5.4 & 2.8 \\ receiver & Missing & 0 & 0 & 0 & 0 & 0 & 1.2 & 0 & 0 & 0 & 0 & 34.5 & 40.4 \\ \hline UDP & Invalid & 0 & 0 & 0 & 0 & 0.2 & 14 & 0.3 & 0.6 & 0 & 0 & 2.9 & 6.8 & 0.1 \\ sender & Missing & 0 & 0 & 0 & 0 & 0 & 3 & 0 & 0 & 0 & 12 & 1 & 589 & 968 \\ \hline UDP & Invalid & 0 & 0 & 0 & 0 & 0.2 & 0.4 & 7.8 & 0.4 & 1 & 0.1 & 0 & 7.9 & 4.1 & 0.8 \\ receiver & Missing & 0 & 0 & 0 & 0 & 0 & 1.1 & 0 & 0 & 0 & 1.3 & 0.6 & 57.4 & 96.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Invalid RSSI values observed with the _iw_ tool (Scenario 2).  
Figure 10: RTTs of TCP and UDP communications on both sides (Scenario 2).
# Rice Sesquierpene Plays Important Roles in Antixenosis against Brown Planthopper in Rice
## 3 Materials and Methods  
### Plant Materials and BPH Treatment  
Based on their resistance to BPH, 20 rice varieties/lines were chosen for this study. Some rice varieties contain known BPH resistance genes. Mudge (_BPH_1) and ASD7 (_BPH_2) [22], RH (_BPH_3) from Sri Lanka [23], PTB33 (_BPH3_, _BPH3_2) [24], ARC10550 (_BPH_5), and Pokkali (_BPH_9) from India [25; 26] were provided from the International Rice Research Institute. For KD-nitrogen-lines (IL) inherited _BPH3_ gene from RH, UBN3078-101-342-4-143; IL143, UBN3078-101-342-4-162; IL162, UBN3078-101-342-4-283; IL283, UBN3078-101-342-6-302; IL302 and UBN3078-432-6-308; and IL308 [27] were selected. PL2 is the elite Thai BPH-resistant rice variety. The moderately susceptible elite Thai rice variety was PK3. The elite BPH-susceptible Thai rice varieties were PK2, HCS, RBR, JHN, and KD. Nipponbare and TN were the susceptible check from Japan and China, respectively.  
Eighteen rice varieties/lines including ARC10550, ASD7, HCS, IL143, IL162, IL302, IL308, JHN, Mudge, Nipponbare, PK2, PK3, PL2, Pokkali, PTB33, RBR, RH, and TN1 were used for the expression analysis of the _OsSTPS2_ gene. Five rice varieties/lines, including ASD7, IL308, JHN, Mudge, and Pokkali, were utilized for the antixenosis BPH resistance mechanism study. Finally, three rice varieties/lines, including IL283, KD, and RH, were used for plant volatile compound analysis.  
The UBN-BPH population was used for BPH infestation in this study. The insects were grown on 7-10-day-old seedlings of TN1 in a temperature control room. Second- to third-instar nymphs were used for BPH treatment of the rice plants. The rice plants were sown by row in a seedbox, with 30 seeds each row, separated by 5 cm between rows. The seedboxes of 2-week-old seedlings were transferred to a cage for BPH feeding treatment 1 day before the test to allow the plant to settle into the experimental environment. After that, 5 to 10 nymphs per plant were released into the cage, and BPH feeding was conducted for 48 h.  
Figure 5: BPH-feeding-induced emission of the major sesquiterpenes. Changes of \(\beta\)-ionone (**A**), \(\beta\)-ionone epoxide (**B**), trans or E-\(\beta\)-farresene (**C**), and linalool (**D**) emission after BPH feeding for 0–10 days in RH (red line), IL283 (green line), and KD (blue line). D = Days after brown planthopper (BPH) infestation.  
For control conditions, the same procedures were applied, except no BPHs were released into the cage for testing.
## 3 Channel Proximity Discovery
### IP Multicast  
In creating a multicast forwarding tree rooted at the sender (dynamically changed according to the locations of the receivers in the network), receiving hosts join and leave a group from a LAN using the IGMP protocol [11]. Multicast routing protocol (Protocol Independent Multicast-Sparse Mode (PIM-SM) [12] is by far the most commonly used) grafts and prunes the branches based on the availability of the receivers in the IGMP domain.  
Each multicast router maintains a table of multicast addresses (with the corresponding interfaces), called Tree Information Base (TIB)/Multicast Routing Information Base (MRIB), to appropriately replicate data packets in the forwarding plane. Forwarding is based on a matching of the destination multicast address in the IP header with an entry in the MRIB. The philosophy of the use of multicast is that the network traffic does not increase in the uplink as the number of participants' increases in the downward network.  
As illustrated in Figure 2, user A has joined with the multicast group 232.0.1.1 (using IGMP) which delivers content of channel-1. Then PIM-SM establishes a forwarding path from source to the destination A keeping the forwarding states at the routers X and Y. Multicast data packets are forwarded based on the destination address 232.0.1.1 in the IP header and a matching in the MRIB. When user B joins with this group, the PIM-SM join message reaches to router X and it adds the corresponding interface \(\mathrm{I_{X2}}\) to the already existing entry for destination 232.0.1.1 in MRIB. When a packet reaches to the router X, it replicates and sends two separate packets to A and B respectively through the interfaces \(\mathrm{I_{X1}}\) and \(\mathrm{I_{X2}}\). However, the second join by user B does not incur extra traffic to the network between the source and the router X. Similarly user C has joined with the channel-2 (232.0.1.2) via the multicast routers Z and Y.
## 7 Validation of Numerical Model  
The numerical models were first used to evaluate the applied impact energy required to achieve crack initiation and failure of the tested specimens, and their results were subsequently compared to that experimentally acquired in a recent study by [PERSON] et al. [6]. Figure 26a,b illustrate the experimental and numerical values of the impact energy sustained until crack initiation and final fracture of the ECC specimens, respectively. It can be observed that there was a reasonable agreement between the numerical predictions and experimental results. The numerically estimated results using the CDP and DP models were 18.7% and 39.2% lower than the corresponding experimental data, respectively. For instance, per the CDP model, the sustained impact energy until first crack initiation and final fracture for the ECC2-0, ECC2-0.5, ECC2-1, and ECC2-1.5 specimens were lower than the experimental values by 15.5%, 16.2%, 18.7%, and 17.1% and 16.3%, 17.4%, 16.7%, and 18.2%, respectively. Likewise, the sustained impact energy until first crack initiation and final fracture of the ECC2-0, ECC2-0.5, ECC2-1, and ECC2-1.5 using the DP model was lower than the experimental data by about 30.2%, 36.1%, 32.5%, and 35.5% and 32.3%, 36.5%, 34.5%, and 39.2%, respectively. The CDP model outperformed the DP model in evaluating the fracture energy of ECC specimens under impact loading, which supports earlier discussion. Differences between the experimental and numerical results can be ascribed to the inability of the simulation to model how the fibers were pulled out along with approximations related to the definition of materials parameters.  
The various hybrid composite HECC-SMAF specimens achieved superior impact resistance to that of the control PVA-ECC specimens. This is ascribed to the higher energy dissipation capability and pull-out resistance of SMA fibers compared to that of PVA fibers, requiring more energy before fracture of the specimens. The ECC2-1 specimen exhibited superior impact resistance compared to that of all other ECC specimens, which was observed both experimentally and numerically. Higher fiber dosage beyond 2% PVA and 1% SMA by volume fraction led to tensile strength and impacted resistance degradation due to fiber clustering and increased porosity, as evidenced elsewhere by [PERSON] et al. [6].