esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Interactions between Parallel Grid-Forming Inverters in a Microgrid
## 4 Stability Analysis
### Mode Shape Analysis  
The nodal admittance matrix in (30) is further subjected to mode shape analysis as described in Section 4.2. The analysis helps to identify the contribution of each node to the resonance mode.  
In Figure 9a-d, five nodal voltage mode shapes regarding \(\lambda_{1,2}\), \(\lambda_{3,4}\), \(\lambda_{5,6}\), and \(\lambda_{7,8}\) are presented.  
For \(\lambda_{1,2}\), nodes 2, 3, and 4 have larger nodal mode shapes than nodes 1 and 5. \(\lambda_{3,4}\) have larger mode shapes at nodes 1 and 5. The nodal mode shapes in these two modes have similar phases for two inverters. They can be described as coherent modes where the two converters act as a whole.  
For \(\lambda_{5,6}\), nodes 1 and 5 have opposite phases. This means that this mode is due to inverter interactions. This observation matches the results in Figure 8b, where \(\lambda_{5,6}\) values are moving when changing the virtual impedance values.  
\(\lambda_{7,8}\) is also an interaction mode where the two inverters are out of phase. Node 2 and 4 have more influence, while nodes 1, 3, and 5 have less influence. This observation confirms the eigenvalue analysis, which shows that the virtual impedance loop has a slight effect on the unstable mode \(\lambda_{7,8}\).  
For comparative studies, the nodal participation factor approach previously introduced in [29] is used. The results are shown in Table 2, and the participation factor is computed via (28). Based on the results, \(\lambda_{1,2}\) is related to nodes 2, 3, and 4, \(\lambda_{3,4}\) and \(\lambda_{5,6}\) are related to nodes 1 and 5, and \(\lambda_{7,8}\) is related to nodes 2 and 4. The observed results match the mode shape analysis in Figure 9. However, the participation factor cannot pinpoint which mode is coherent or interactive, whereas the nodal mode shape has this appealing ability.  
Figure 8: Eigenvalues of the paralleled VSC system. (**a**) The effect of different distribution line impedances. Increasing the line impedance improves the system’s stability. (**b**) The effect of virtual impedance. There is no effect on the unstable modes. (**c**) The effect of voltage feed-forward control. The system becomes stable with voltage feed-forward.
## 5 Framework Application  
Multiple case studies were conducted using EBIOS version three in critical infrastructure [54] and IoT [55] environments. In this section, we apply the proposed framework to the scope of the national telecommunications sector. The context consists of a national gateway that provides Internet connectivity for multiple Internet service providers, operated by a private entity, providing services for other private and public entities, and supervised by a national regulatory authority. To conduct the complete study, multiple meetings and interviews were held with the experts responsible for the gateway's security, including the stakeholders responsible for maintaining and operating the gateway. As described by the staff during the interviews, the country's telecommunications are an essential part of its critical infrastructure. Communications with the worldwide Internet ensure critical functionalities. Any disruption that might occur has nationwide consequences.
# Investigation of Debonding Effect in Internal Replacement Pipe System Under Lateral Loading
## 3 Surface Preparation and Its Impact on Debonding
### Behaviour of IRP System in 12.7 Mm (0.5 inch) Discontinuous Host Pipe
#### 3.1.4 Maximum Stress and Catastrophic Failure  
Figure 25 illustrates the distribution of equivalent stress in the IRP layer of a 12.7 mm (0.5 inch) discontinuous IRP system using the representative adhesive shear strength of 20 MPa (2.9 ksi) under a maximum load of 71.2 kN (16 kip). It can be observed that the maximum stress is located at the inverted midspan of the IRP layer at a maximum load of 71.2 kN (16 kip). For small discontinuities in the host pipe and if the load keeps increasing, catastrophic failure may potentially occur because the concentrated stress at the discontinuity edge goes beyond the strength of the IRP. This phenomenon is consistent with the experimental findings from previous studies [15]; therefore, a low level of adhesive strength between the IRP and the host pipe is preferable to facilitate debonding at the discontinuity edge. This relieves stress concentration and, consequently, enables the repair system to withstand higher loads.  
Figure 24: Photos of IRP specimen \({}^{\prime}\)before\({}^{\prime}\) and \({}^{\prime}\)after\({}^{\prime}\) mechanical aging tests: (**a**) internal and (**b**) cut apart joint. Courtesy images from Cornell University [27].  
Figure 26 provides a visual representation of the distribution of equivalent stress along the invert line of the IRP under a maximum load of 71.2 kN (16 kip) for various bonding strength scenarios between the host pipe and the IRP. The results show that the maximum equivalent stress is 41.781 MPa (6.060 ksi) for 5 MPa (0.7 ksi) shear strength, 44.793 MPa (6.497 ksi) for 20 MPa (2.9 ksi) shear strength, and 47.166 MPa (6.841 ksi) for the highest shear strength of 40 MPa (5.8 ksi). This indicates that there will be no potential material failure in any of the cases, as the maximum stresses are significantly lower than the strength of the ALTRA10, which is 79.420 MPa (11.519 ksi). Furthermore, it can be observed that under the maximum load, the lower the adhesive shear strength, the lower the maximum stress yield in the IRP. It can be inferred, therefore, that debonding occurs in the scenarios of 5 MPa (0.7 ksi) and 20 MPa (2.9 ksi), which helps alleviate stress over a more extended free length of the IRP.  
Figure 25: Equivalent stress results of the IRP layer of a 12.7 mm (0.5 inch) discontinuous IRP system employing the adhesive shear strength of 20 MPa (2.9 ksi) at a fatigue load of 71.2 kN (16 kip) (magnified 13 times).  
Figure 26: Equivalent stress distribution in the longitudinal direction of the bottom line of the 12.7 mm (0.5 inch) discontinuous IRP system utilizing the debonding mechanism under a maximum load of 71.2 kN (16 kip).
## 3 Results
### Experimental Setup
There is a statistical significance between the results of the AlexNet and VGG, EffNetv2, ConvNeXt-T, ConvNeXt-T with AdamW, RF-ResNet50 hybrid; ResNet50 and VGG, EffNetv2, ConvNeXt-T with AdamW, RF-ResNet50 hybrid; VGG and ConvNeXt-T with AdamW, RF-ResNet50 hybrid; and ConvNeXt-T with AdamW, RF-ResNet50 hybrid; ConvNeXt-T and ConvNeXt-T with AdamW, RF-ResNet50 hybrid; ConvNeXt-T with AdamW and RF-ResNet50 hybrid models. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{\(p\) **\textless{} 0.05**} \\ \hline
**Model** & **AlexNet** & **ResNet50** & **VGG** & **EffNetv2** & **MaxViT** & **ConvNeXt-T** & **ConvNeXt-T** & **RF-ResNet50** \\ \hline AlexNet & **0.002 \(\uparrow\)** & **0.041 \(\leftarrow\)** & **0.012 \(\leftarrow\)** & 0.67 & 0.059 & **0.001 \(\uparrow\)** & 0.542 \\ \hline ResNet50 & & **0.0004 \(\leftarrow\)** & 0.154 & 0.193 & **0.001 \(\leftarrow\)** & 0.354 & 0.292 \\ \hline VGG & & & **0.0001 \(\uparrow\)** & 0.311 & 0.877 & **0.001 \(\uparrow\)** & 0.892 \\ \hline EffNetv2 & & & & **0.005 \(\uparrow\)** & **0.0004 \(\leftarrow\)** & 0.288 & 0.075 \\ \hline MaxViT & & & & & **0.016 \(\leftarrow\)** & 0.175 & 0.393 \\ \hline ConvNeXt-T & & & & & & **0.006 \(\uparrow\)** & 0.877 \\ \hline ConvNeXt-T & & & & & & & 0.151 \\ AdamW & & & & & & & & 0.151 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Wilcoxon test results for DL models. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{\(p\) **\textless{} 0.05**} \\ \hline
**Model** & **AlexNet** & **ResNet50** & **VGG** & **EffNetv2** & **MaxViT** & **ConvNeXt-T** & **ConvNeXt-T** & **RF-ResNet50** \\ \hline AlexNet & 0.715 & **0.013 \(\leftarrow\)** & **0.039 \(\leftarrow\)** & 0.423 & **0.0003 \(\leftarrow\)** & 0.028 \(\uparrow\)** & **2.51 \(\times\) 10\({}^{-29}\)**\(\uparrow\)** \\ \hline ResNet50 & & & **0.0016 \(\leftarrow\)** & **0.016 \(\leftarrow\)** & 0.178 & **9.76 \(\times\) 10\({}^{-6}\)**\(\leftarrow\)** & **0.039 \(\uparrow\)** & **1.01 \(\times\) 10\({}^{-30}\)**\(\leftarrow\)** \\ \hline VGG & & & 0.586 & 0.069 & 0.257 & **9.76 \(\times\) 10\({}^{-6}\)**\(\uparrow\)** & 7.9 \(\times\) 10\({}^{-24}\)**\(\uparrow\)** \\ \hline EffNetv2 & & & & 0.216 & 0.107 & **0.0001 \(\uparrow\)** & **1.80 \(\times\) 10\({}^{-25}\)**\(\uparrow\) \\ \hline MaxVit & & & & & **0.002 \(\leftarrow\)** & 0.003 \(\uparrow\)** & 2.92 \(\times\) 10\({}^{-28}\)**\(\uparrow\) \\ \hline ConvNeXt-T & & & & & **4.14 \(\times\) 10\({}^{-8}\)**\(\uparrow\)** & **1.52 \(\times\) 10\({}^{-19}\)**\(\uparrow\)** \\ \hline ConvNeXt-T & & & & & & **4.47 \(\times\) 10\({}^{-34}\)**\(\uparrow\) \\ AdamW & & & & & & & \\ \hline RF-ResNet50 & & & & & & & \\ \hline Hybrid & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: [PERSON]’s test results for DL models.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Input Features** & **r** & **_p_ Value** & **Mean** & **Std** \\ \hline gender & \(-0.106\) & \(0.12\) & \(0.64651\) & \(0.47917\) \\ \hline ssc\_p & \(-0.17\) & \(0.01\) & \(67.3034\) & \(10.8272\) \\ \hline ssc\_b & \(-0.05\) & \(0.45\) & \(0.46047\) & \(0.499598\) \\ \hline hsc\_p & \(-0.24\) & \(0.00\) & \(66.3332\) & \(10.8975\) \\ \hline hsc\_b & \(0.002\) & \(0.97\) & \(0.6093\) & \(0.48905\) \\ \hline hsc\_s & \(0.17\) & \(0.01\) & \(1.37209\) & \(0.58098\) \\ \hline degree\_p & \(-0.21\) & DOT00 & \(66.3702\) & \(7.35874\) \\ \hline degree\_t & \(0.08\) & \(0.21\) & \(0.6\) & \(0.89024\) \\ \hline worker & \(-0.19\) & \(0.00\) & \(0.34419\) & \(0.47621\) \\ \hline etest\_p & \(-0.23\) & \(0.00\) & \(72.1006\) & \(13.276\) \\ \hline mba\_p & \(-0.1\) & \(0.12\) & \(0.44186\) & \(5.83339\) \\ \hline status & \(-0.25\) & \(0.00\) & \(0.68837\) & \(0.46424\) \\ \hline ssc\_p\_catg & \(0.15\) & \(0.02\) & \(0.50698\) & \(0.84759\) \\ \hline hsc\_p\_catg & \(0.16\) & \(0.01\) & \(0.45116\) & \(0.80081\) \\ \hline mba\_p\_catg & \(0.1\) & \(0.14\) & \(0.37674\) & \(0.4857\) \\ \hline degree\_p\_catg & \(0.24\) & \(0.00\) & \(0.3814\) & \(0.78158\) \\ \hline etest\_p\_catg & \(-0.011\) & \(0.86\) & \(0.641860\) & \(0.80716\) \\ \hline Salary & \(-0.14\) & \(0.07\) & \(288\),\(655\),\(405\) & \(93\),\(457\),\(45\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Spearman correlation between student specialization and other input variables. anxiety and confusion and could lead to significantly better study program completion and increase graduation rates in the future. Having early awareness of the estimated number of incoming freshmen per study specialization program could also be of high value to the college administration. With this great insight, they would be able to allocate required resources per specialization field and better prepare schedules.
## 3 Beamforming Optimization with AN
### Beamforming Optimization with AN in the MISO SWIPT CR-SDMA Network  
In this subsection, we describe the baseline scheme based on SDMA for the MISO SWIPT CR system. In SDMA, the messages from other users are considered interference, and no SIC procedure exists at the receiver. Therefore, the SINR at the \(l\)-th user is as follows:  
\[\text{SINR}_{\text{SDMA},l}=\frac{\theta_{l}\big{|}\mathbf{g}_{l}^{H}\mathbf{v }_{l}\big{|}^{2}}{\theta_{l}\left(\sum\limits_{t^{\prime}=1,t^{\prime}\
eq l} ^{L}\big{|}\mathbf{g}_{l}^{H}\mathbf{v}_{l^{\prime}}\big{|}^{2}+\sum\limits_{m =1}^{M}\big{|}\mathbf{g}_{l}^{H}\mathbf{w}_{m}\big{|}^{2}+\big{|}\mathbf{g}_{l }^{H}\mathbf{f}\big{|}^{2}+\sigma_{l}^{2}\right)+\sigma_{cn,l}^{2}},\,\forall l. \tag{14}\]  
Although the energy harvested by the SUs and the EH users, as well as the power interfering with the PUs, are expressed in the same way as NOMA, and are defined in (7)-(9), respectively. Then, the minimization of the transmit power for the SUs and EH users under the constraints of minimum SINR, minimum EH required, and maximum interference with the PUs can be expressed as follows:  
\[\min\limits_{\{\mathbf{v}_{l},\mathbf{w}_{m},\theta_{l}\},\mathbf{f }} \sum\limits_{l=1}^{L}\left\|\mathbf{v}_{l}\right\|^{2}+\sum\limits_{m=1}^{M} \left\|\mathbf{w}_{m}\right\|^{2}\] (15a) s.t. \[\frac{\theta_{l}\big{|}\mathbf{g}_{l}^{H}\mathbf{v}_{l}\big{|}^{2}} {\theta_{l}\left(\sum\limits_{t^{\prime}=1,t^{\prime}\
eq l}^{L}\big{|} \mathbf{g}_{l}^{H}\mathbf{v}_{l^{\prime}}\big{|}^{2}+\sum\limits_{m=1}^{M} \big{|}\mathbf{g}_{l}^{H}\mathbf{w}_{m}\big{|}^{2}+\big{|}\mathbf{g}_{l}^{H} \mathbf{f}\big{|}^{2}+\sigma_{l}^{2}\right)+\sigma_{cn,l}^{2}}\geq\gamma_{l},\,\forall l\] (15b) (11c),..., (11g)  
Problem (15) is non-convex and can be solved by using the SDR technique described in Section 3.1. Then, problem (15) can be reformulated as follows:  
\[\min\limits_{\{\mathbf{v}_{l},\mathbf{W}_{m},\theta_{l}\},\mathbf{ F}} \sum\limits_{l=1}^{L}\text{Tr}\left(\mathbf{V}_{l}\right)+\sum\limits_{m=1}^{M} \text{Tr}\left(\mathbf{W}_{m}\right)\] (16a) s.t. \[-\text{Tr}\left(\mathbf{G}_{l}\mathbf{V}_{l}\right)\leq 0,\,\forall l\] (16b) (12c),..., (12i)  
After removing the rank-one constraints, the problem is denoted as (16)-SDR, which is convex and can be solved with the CVX toolbox of MATLAB. Then, the Gaussian randomization technique can be used if the precoder matrices, \(\left\{\mathbf{V}_{l}^{*},\mathbf{W}_{m}^{*}\right\},\mathbf{F}\), do not satisfy the rank-one condition.
# An Information Entropy Based Event Boundary Detection Algorithm in Wireless Sensor Networks
## 5 Simulation Results
### Simulation Results
of the mobile node increases, the value of detection rate decreases. As can be seen from Figure 13, the updating interval greatly affects the event boundary detection rate. The relationship between updating interval and boundary detection rate is inversely proportional. Figure 13: _Cont._  
Figure 14 presents the computer simulation diagram of marine event boundary detection using 25 buoy nodes that integrate multiple marine sensors. Figure 15 shows the visualized simulation result of boundary detection obtained by the EEBD algorithm. As can be seen from Figure 15, the EBBD algorithm achieves accurate event boundary detection at low buoy node densities. Figure 13: The boundary detection rates of EEBD with different densities of marine mobile nodes. (**a**) 50%. (**b**) 60%. (**c**) 70%. (**d**) 80%.
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## 2 Review of [PERSON] et al.'s Scheme
### Login and Authentication Phase
#### 2.2.1. CASE-1: Login and Authentication Phase without Password Updating  
**Step 1:**: The client inserts the memory device and inputs \(ID_{i}\) and \(PW_{i}\). Then, \(V_{i}=h(ID_{i}||PW_{i}||N_{c})\) is computed. Next, a random integer \(r_{c}\) and current timestamp \(T_{1}\) are generated, and \(R_{c}=r_{c}\cdot P\), \(F_{i}=h(V_{i}||A_{i}||C_{i}||T_{1})\), \(k_{1}=r_{c}K_{s}\), \(H_{i}=E_{k_{1}}(realm||F_{i}||A_{i}||C_{i}||T_{1})\) is computed. Lastly, the REQUEST message \(\{H_{i},R_{c}\}\) is sent to the server.
**Step 2:**: When the server receives the REQUEST message, it obtains the data \(\{realm||F_{i}||A_{i}||C_{i}||T_{1}\}\) by decrypting \(H_{i}\) with \(k_{2}=k_{s}R_{c}\). Then, it verifies the validity of \(T_{1}\). If \(T_{1}\) is valid, \(B_{i}^{\prime}=h(A_{i}||k_{s})\) is computed and \(V_{i}^{\prime}\) is obtained by decrypting \(C_{i}\). Next, it computes \(F_{i}^{\prime}=h(V_{i}^{\prime}||A_{i}||C_{i}||T_{1})\) and checks whether \(F_{i}^{\prime}=F_{i}\) holds or not. If it holds, then the server executes Step 3; otherwise, the authentication process is stopped.
**Step 3:**: The server generates a random integer \(r_{s}\), timestamp \(T_{2}\), and computes \(R_{s}=r_{s}\cdot P_{j}I_{i}=h(V_{i}^{\prime}\oplus F_{i}^{\prime}\oplus r_{s} R_{c}\oplus T_{2})\), session key \(SK=h(V_{i}^{\prime}\oplus r_{s}R_{c}\oplus T_{2})\), and \(L_{i}=E_{k_{2}}(R_{s}||I_{i}||T_{2}||realm)\). Finally, the server sends the ACCEPT message \(\{L_{i}\}\) to the client.
**Step 4:**: On receiving the ACCEPT message, the client obtains \(\{R_{s}||I_{i}||T_{2}||realm\}\) by decrypting \(L_{i}\) with \(k_{1}\). Then, the client verifies the validity of \(T_{2}\), and if it is valid, \(J_{i}^{\prime}=h(V_{i}\oplus F_{i}\oplus r_{c}R_{s}\oplus T_{2})\) is computed and checks whether \(J_{i}^{\prime}=J_{i}\) holds or not. If it holds, then the session key \(SK=h(V_{i}\oplus r_{c}R_{s}\oplus T_{2})\) is computed; otherwise, the authentication process is stopped.
## 3 Results
### Surface Gloss (GU)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Control**} & \multicolumn{2}{c}{**Mechanical Polishing**} & & \\ \cline{2-6}  & **Mean \(\pm\) SD** & \begin{tabular}{c} **Median** \\ **(Min–Max)** \\ \end{tabular} & **Mean \(\pm\) SD** & \begin{tabular}{c} **Median** \\ **(Min–Max)** \\ \end{tabular} & **Test Statistic** & \(p\) \\ \hline CT & 64.37 \(\pm\) 6.81 & \begin{tabular}{c} 64.82 \\ (54.2–75.33) \\ \end{tabular} & 104.33 \(\pm\) 3.03 \({}^{\text{c}}\) & \begin{tabular}{c} 103.77 \\ (99.97–111.6) \\ \end{tabular} & t = \(-\)18.555 & **\textless{0.001}** \\ \hline LS Block & 62.79 \(\pm\) 6.94 & \begin{tabular}{c} 60.15 \\ (55.43–76.47) \\ \end{tabular} & 102.34 \(\pm\) 4.27 \({}^{\text{bc}}\) & \begin{tabular}{c} 102.6 \\ (94.1–108.17) \\ \end{tabular} & t = \(-\)12.003 & **\textless{0.001}** \\ \hline EC & 62.37 \(\pm\) 5.9 & \begin{tabular}{c} 61.03 \\ (54.37–75.9) \\ \end{tabular} & 95.76 \(\pm\) 5.66 \({}^{\text{b}}\) & \begin{tabular}{c} 95.37 \\ (85.4–103.47) \\ \end{tabular} & t = \(-\)13.666 & **\textless{0.001}** \\ \hline KAT & 69.29 \(\pm\) 8.37 & \
\begin{tabular}{c} 68.25 \\ (56.93–84.73) \\ \end{tabular} & 161.7 \(\pm\) 15.72 \({}^{\text{a}}\) &
\begin{tabular}{c} 167.25 \\ (129.37–179.93) \\ \end{tabular} & t = \(-\)16.035 & **\textless{0.001}** \\ \hline Test Statistic & F = 2.023 & F = 48.902 & & \\ \hline \(p\) & 0.128 & \textless{0.001} & & \\ \hline \hline \end{tabular}  
* t: Paired two-sample \(t\) test; F: one-way analysis of variance; mean \(\pm\) SD (standard deviation); median (minimum–maximum);*: there is no difference between ceramics with the same letter in each column.  
\end{table} 
Table 5: Comparison of the gloss values after mechanical polishing for each ceramic. the grinding process, significantly increased after repolishing. No significant difference was found between the glazing and repolishing groups for any of the ceramic materials. The mean \(\pm\) SD and median (minimum-maximum) values of the gloss values obtained after glazing and mechanical polishing of the materials and the differences between the groups are presented in Table 7. A statistically significant difference was found between the gloss values obtained in the KAT ceramics after glazing (102 GU) and mechanical polishing (167.25 GU) (\(p<0.001\)). For CT, LS Block, and EC, no significant difference was found between the groups (\(p>0.050\)). Two-way ANOVA demonstrated that the effect of ceramic type, surface treatment, and ceramic- surface treatment interaction was statistically significant on the gloss variable (\(p<0.001\)).
## 2 Materials and Methods
### Antioxidant Activity (DPPH Free Radical Method)  
The DPPH free radical method was employed to assess the antioxidant capability of the acquired FPEs [31]. These studies were conducted using the Thermo Scientific GENESYS 50 instrument at the wavelength \(\lambda\) = 517 nm. Trolox (Tx) was used as a reference substance.  
The antioxidant activity of the FPEs was measured as follows: 2850 \(\upmu\)L ethanolic solution of the DPPH radical (the concentration of 0.3 mmol/L) of absorbance about 1.000 \(\pm\) 0.020 (at \(\lambda\) = 517 nm) was placed in the tube, and 150 \(\upmu\)L of appropriate extract was added. Blank samples without FPE were prepared in the same way, using distilled water in an amount of 150 \(\upmu\)L. The tubes were enveloped in aluminum foil, sealed with a stopper, and then incubated for 10 min at ambient temperature. Three independent experiments were carried out, with each sample analysed being diluted 50-fold before the assay was performed. The antioxidant activity was expressed in mmol Tx/L FPE, based on the obtained calibration curve \(y=-1.0321\)x + 1.1342, R\({}^{2}\) = 0.997.  
The antioxidant activity (\(AA\)) of fermented plant extract was determined using the following Formula (2):  
\[AA=-0.019\ (A_{t.s.}-A_{b.s.})+1.1342 \tag{2}\]  
where  
\(AA\)--antioxidant activity by DPPH method [mmol Tx/L],  
\(A_{t.s.}\)--absorbance of the tested sample [-],  
\(A_{b.s.}\)--absorbance of the blank sample [-].
## 4 Climatology (2002-2022) of Longitudinal Structures in the Lower Thermosphere
### SABER Temperature WN3 Structure  
The WN3 structure in the lower thermosphere, besides DE2, can also be generated by DW4, SE1, T0, and SPW3 (based on \(\mid\)s + \(k\mid\) = 3).  
Figure 10a shows the LT variability of the climatologically mean (2002-2022) SABER WN3 structure in July at different latitudes; the plots are arranged in the same way as those in Figure 11a. Again, the climatologically mean WN3 structure denotes the superposition of the climatologically means DE2 + DW4 + SE1 + T0 + SPW3. The main features of the WN3 structure in the lower thermosphere (altitude of 110 km) and in July can be summarized as: (i) the WN3 structures at latitudes of \(\pm\)10\({}^{\circ}\) and 50\({}^{\circ}\) S is stronger than the climatological mean of the DE2 at altitude of 110 km (8.7 K, Figure 1); (ii) at all latitudes the WN3 structures reveal one main enhancement during the daytime and a secondary one in the late evening or early morning hours; (iii) the daytime maxima fluctuate between 11 LT and 13 LT with the latitude in the NH, while in the SH the fluctuation range is larger, between \(\sim\)8 LT and 13 LT; (iv) at 50\({}^{\circ}\) N, the evening WN3 disappears, while at 50\({}^{\circ}\) S it is very weak; and (v) this structure does not show clear hemispheric asymmetry in July.  
Considering the largest WN3 structures at all latitudes and months, it worth noting that: at latitudes of 0-10\({}^{\circ}\) N and at \(\pm\)(40-50\({}^{\circ}\)), the strongest WN3 is observed in July, while at (10-20\({}^{\circ}\)) S it is in June, at (20-30\({}^{\circ}\)) N it is in December, and at 30\({}^{\circ}\) S it is in February. This is the reason Figure 10b shows the latitudinal dependence of the tidal and SPW3 contribution to the WN3 structure in the months when it has large amplitudes these are July (upper left plot), June (upper right plot), December (bottom left plot), and February (bottom right plot). These results also confirm the already known features that the DE2 has the largest contribution to the WN3 followed by the SE1; the latter contributes mostly in July and June at latitudes of \(\pm\)50\({}^{\circ}\) and at \(\sim\)10\({}^{\circ}\) S, i.e., exactly when and where the SE1 has the largest climatologically mean amplitudes at an altitude of 110 km. The third contributor is the SPW3, while the T0 and DW4 are the weakest ones.