esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Simulation of Rockfall
### Back Analysis of Rebound Parameters  
As mentioned above, a typical rockfall in Zhaxilin Village (Figure 6a) was selected as the back analysis area for rebound parameters. The elevation range from the foot to the top of the slope is 3140-5180 m, and the slope direction is 170\({}^{\circ}\). It is an ancient rockfall site, confirmed by the morphology and deposit conditions of rocks around the slope. The rear edge of the slope is extremely steep with sparse shrubs grown around the foot of the relief (as shown in Figure 6b). The Yarlung Tsangpo River passes the front of the slope from west to east. Along the bank of the river, there are many conglomerate blocks deposited with different sizes, in which the maximum volume is up to \(7\times 5\times 5\) m\({}^{3}\) (as shown in Figure 6c). The source area of this rockfall is mainly located at the exposed conglomerate belt in back analysis area, as shown in Figure 6b,d.  
According to the lithology, roughness, and the vegetation coverage, the slope materials from the relief to the other side of the Yarlung Tsangpo River are divided into four categories (as shown in Figure 7). Subareas 1-2, respectively, correspond to the brittle rock zone, soft rock zone, quaternary alluvium zone, and the Yarlung Tsangpo River. For the whole study area, the brittle rock zone contains conglomerate and granite, while the soft rock zone contains phyll  
Figure 6: Picture of rockfall in Zhaxilin village (**a**). Full view of the rockfall (**b**). The deposition around the rear edge of the slope; (**c**). Deposited rocks along the river bank (**d**). A brief geological profile of the slope (profile 3-3’ in Figure 2).  
the accessibility limitation of the back analysis area, we use conglomerate and phyllite to obtain the parameters of the brittle rock area and the soft rock area, respectively. The selected size of falling block is based on the statistical analysis result of the size of deposited blocks around the slope. The mean size is taken as \(4\times 3\times 3\) m\({}^{3}\), and the size variation range is \(\pm 20\%\). The shape is set as cuboid, and the density of rock mass is set as 2800 kg/m\({}^{3}\) considering the experimental data of conglomerate and granite rocks. In Figure 7, there are 553 grids in the source area. The calculation is set for 1000 times, which means \(5.53\times 10^{5}\) blocks are thrown down. Using Rockyfor3D numerical simulation software, based on the distribution of deposited blocks on the slope surface, the parameters for 3D rockfall simulation were back analyzed. It was calculated that the deposited numbers of blocks in the three regions A, B, and C in Figure 7 account for 34%, 61%, and 5% of the total amount of thrown blocks, respectively, which was in line with the distribution characteristics of deposited blocks observed on site. It is considered that the selected parameters were reasonable. The parameters of slope materials obtained by back analysis are shown in Table 1.
## 4 Observation of LRB Failure Mode through Dynamic Test
### Investigation of Damaged LRB Response through Dynamic Tests  
The simulation of the ultimate dynamic test through BDBE analysis using the finite element model is discussed in the previous paragraph. In this paper, the observation results of the failure mode in the dynamic test using a 3-axis shaking table were examined to determine the actual dynamic response characteristics before and after the failure in the ultimate load condition by BDBE. For reference, the shaking table used in the test is a vibration table with a capacity of 30 tons for 3-axis dynamic test. The detailed specifications are listed in Table 5.  
To observe the dynamic characteristics and extreme behavior of the LRB isolating a small rigid structure against a BDBE, shaking table tests were performed [15,16]. The overall schematic of the test is shown in Figure 9. As explained in the paper for the simulation and verification analysis, four LRB mockup bearings are set at the bottom corners to support a 4-ton dummy mass expressing a small lumped-mass-type superstructure.  
The shaking table tests were performed using two types of approaches with different purposes. Test-I was used to observe the response characteristics of the LRB with partial damage during a BDBE. In contrast, Test-II was used to investigate the maximum seismic response, behavioral stability, and potential influence on the surrounding equipment due to excessive displacement when the entire LRB fails [17, 18, 19]. In Test I, the input earthquake was scaled down to limit the maximum displacement to less than the design earthquake at the operating floor level. The acceleration-time history in Figure 2 was applied as an input earthquake for the dynamic test. As shown in Table 6, the ZP  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ZPA [g] of FRS (BDBE)** & **Rubber Layer [MPa]** & **Steel Layer [MPa]** \\ \hline
1.7 (0.4) & 3.9 & 437.4 \\
2.2 (0.5) & 4.8 & 546.7 \\
2.6 (0.6 g) & 5.8 & 656.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Analysis results of max. equivalent stress on the rubber and steel layers.  
Figure 9: Schematics of 3D shaking table test.  
applied to Test I have an acceleration range of 0.3-1.0 g, which were amplified through the building structure model from the ground level OBE and SSE. The input earthquake ZPA in Test-II is greatly amplified up to approximately 1.3 g. OPT 1 was used for Test-I, whereas OPT2 was used for Test-II.
## 2 Materials and Methods
### Addressing Technical Difficulties in Managing Large-Scale Input Datasets within ISs  
In most cases where large-scale IS systems are developed to arrange georeferenced data, the data to be organized within a system (before the input in the system) typically display various notable features:  
* Serious _structural complexity_ and complicated interconnections among their components [40].
* Substantial _diversity in their configuration_, encompassing various definitions, contents, forms, and formats [41].  
* Highly increased _thematic diversity_ due to numerous of additional actors and heterogeneous procedures [42].
* Most importantly, a notable degree of _polymorphism_, indicating variability in how data are defined and their content across different versions. These versions are tailored to meet specific purposes for which the data are used [43].  
As a consequence of these identified traits, most of these systems do not easily carry out domain adaptation (thematic shift) to different missions/operations. These systems are not easily manageable, and they become exceedingly challenging to be _maintained_, _updated_, and _upgraded_ and they reach a state of _unmanageability_. Consequently, this leads to their rejection as domain-agnostic solutions. The geographical nature of the data further exacerbates these challenges by significantly amplifying the data's complexity [44].
## 4 Results and Discussion
### Interactions After the Seismic Event
Between 73 and 96 h, the interactions between the probes and the channels were different before and after the seismic event. Prior to event no. 2, the correlations were negative between the signals from channel 2 and probe 3. After the event, this correlation was reversed. The signal from probe 3 before and after event no. 4 was positively correlated with the signal from channel 2. The signal from channel 4 correlated with the probe signals before and after events no. 2, 3, and 4. Prior to the event, this correlation was observed for signals from probes 2, 3, 5, and 6, and after the event, only with the signal from probe 4. The correlation before the event was negative, negative, and positive. However, after the event, it was always positive. In the time range From 97 to 120 h after the event, interactions were observed for the signal on channels 2 and 4. Prior to the event, the signal from channel 2 correlated positively with the signal from probe 4. After the event, a similar correlation was observed with the signals from probes 2, 4, 5, and 6. At the same time, before events 1 and 4, the signal from channel 4 was positively correlated with the signals from probes 2, 3, and 5. After events no. 2 and 3, it was negatively correlated with the signals from probes 2, 3, and 5. After events no. 2 and 4, the signal from channel 4 was positively correlated with the signals from probes 4 and 5. Between 121 and 144 h, correlations were observed only for the signal on channel 1, both before and after all four events. Except for event no. 2, all events showed positive correlations. Prior to the event, the signal from channel 3 was positively correlated with the signal from probe 4, and after the event, with signals from probes 2 and 5. After event no. 3, the correlation was inversely proportional (negative). Between 145 and 168 h, the correlation of the signal from channel 2 with the signals from probes 2, 5, and 6 was positive after events no. 1, 3, and 4, and negative after event no. 2. The signal from channel 2 was also negatively correlated with the signal from probe 3 after events no. 1 and 2 (negatively), as well as 4 (positively).
# WTA: A Static Taint Analysis Framework for PHP Webshell
## 3 Motivation  
The lack of the ability to perform interprocedural analysis and detect unknown webshells is the main challenge in the field of webshell detection. We present two examples to illustrate the significance of interprocedural analysis and the difficulty of unknown webshell detection. **Interprocedural analysis**[27]. There are two types of webshells that require interprocedural analysis. The first is **include-type webshells**, and the second is **user-defined function-type webshells**. Include-type webshells refer to an attacker who puts the body of the webshell into a text file, image file, or any file in other formats. For example, the attacker puts the body of the webshell into hello.txt, while the webshell file (attack.php) has one \"include\" statement used to include the file hello.txt. Therefore, if the webshell detection tool only scans the file attack.php, without an in-depth analysis of the contents of the file hello.txt contained in attack.php, the webshell can bypass the detection. Listing 2 shows two include-type webshell examples called include-webshell-1 and include-webshell-2. Listing 3 shows the file hello.txt included in an include-type webshell. In fact, **include-webshell-1** and **include-webshell-2** have the same function: they all include the webshell body hello.txt. However, detecting the two files using the popular tool D-Shield [15] obtains different results. The detection results indicate that **include-webshell-1** is a webshell of Level 3, and the reason is \"suspicious include\". D-Shield considers **include-webshell-2** not to be a webshell. From this experiment, we can see that the tool D-Shield simply uses **include 'filename'** as a matching pattern and considers the file to be a webshell once the match is found, while include-webshell-2 bypasses detection by replacing spaces with parentheses. This also indicates that the tool does not detect the contents of the include file and cannot detect the include-type webshells, which will be further explained in Experiment 1 of Section 6. ```
<?php include'hello.txt';?> (a)include-webshell-1 <?php include('hello.txt');?> (b)include-webshell-2
```  
Listing 3: hello.txt. User-defined function-type webshells refer to the way that attackers bypass the scanning of known dangerous functions by creating user-defined functions and executing system commands in the user-defined functions. Listing 4 shows a user-defined function webshell. Two user-defined functions, **dynamic** and **newassert**, are included in the sample to obtain the taint source and to call the dangerous function assert(). The sample is tested by D-Shield [15], and the test results show that the sample is a webshell of Danger Level 1 (a webshell of Danger Level 1 can be considered as a normal file).
## 3 Influence Metrics
### Network Density  
As mentioned earlier, opportunistic networks are a type of low-density networks that traditionally focus on self-organized and ad hoc mobile networks. These networks often experience frequent disruptions, delays, and intermittent connectivity, leading to a lack of end-to-end connections within the environment. In such scenarios, wireless devices can temporarily store information and forward it to other devices that are more likely to be within communication range of the intended destination when an opportunity for connection arises.  
The density of an opportunistic network is determined by the ratio of edges present in a graph to the maximum number of edges the graph can contain. This ratio provides a conceptual idea of the network's connectivity in terms of link density. Specifically, network density is defined as the ratio of the number of connections to the maximum possible connections.  
A network is considered dense when the number of links is close to the maximum possible, where every pair of devices is connected by a single link. Conversely, a network with few links is considered sparse. This concept provides an understanding of the level of connectivity and density within the network [35; 36]. Therefore, to determine the maximum number of connections in the network, we can derive it as follows:  
\[MaxConn=\frac{|V|(|V|-1)}{2} \tag{24}\]Let us now introduce the formula for calculating network density. The network density is calculated by dividing the total number of connections existing in the network \(G(V,E)\) by the maximum possible number of connections that could potentially exist within the network. Let us examine the formula in detail:  
\[Density=\frac{\frac{|E|}{|V|(|V|-1)}}{2}=\frac{2|E|}{|V|(|V|-1)} \tag{25}\]  
In our study, Figure 6 displays the likelihood of the presence of complete pathways, which is linked to the density of connections within the Reality Mining datasets. This likelihood, denoted as \(P(EE)\), can be defined as the ratio of the number of established end-to-end routes to the total number of possible connections.  
\[P(EE)=\left(\sum_{s=1}^{|V|-1}\sum_{d=1}^{|V|-1}\sum_{i=1}^{|V|-1}M_{T}^{i}(s, d)\right)\left(\frac{|V|(|V|-1)}{2}\right)^{-1} \tag{26}\]  
Figure 6 illustrates the network structure of the used dataset, which is characterized as a sparsely connected network with a consistent connection density among devices that does not exceed 8.5% throughout its dynamic nature. The majority of density values are below 1%. As a result, the upper limit for the probability of end-to-end connections remains below 55%, with a significant concentration below 0.5%. Therefore, it is imperative to consider that the utilized datasets define an opportunistic network with a very low density of connections.
## 4 Discussion
### Degradation of the Organics  
The Fe-TiO2 additions in the BP and BPM systems increased the degradability activity as shown with an increase in the COD removal (Figure 3). Evidently, the anaerobic hydrolysis and methanogenesis were thermodynamically favoured when Fe-TiO2 was magnetically seeded with electron donors (Fe, OH, H, and Ti species) [5,20]. This electroreceptive reaction expressed in Equations (3) and (4), demonstrated the release of the Fe3+ species in the bioreactors facilitated the sulphur-reducing microbes [20,25].  
\[\mathrm{Fe}+2\mathrm{H}_{2}\mathrm{O}\rightarrow\mathrm{Fe}\left(\mathrm{OH} \right)_{2}+\mathrm{H}_{2} \tag{3}\]  
\[4\mathrm{Fe}+\mathrm{SO}_{2}+4\mathrm{H}_{2}\mathrm{O}\rightarrow 3\mathrm{Fe}\left(\mathrm{OH} \right)_{2}+\mathrm{Fe}+2\mathrm{OH}^{-} \tag{4}\]  
According to [PERSON] et al. [26], the aggregation of trace metals is responsible for the acidification or alkalinity surge in the reactor system. The microbial activity in the system indicates metal imbalance scorn the nutritional benefits of most metals [25,26, 27]. This shows that bioavailability of metal species for microbial uptake in the reactors (AD alone, BM, and BPM) may vary based on chemical and biological circumstances. Thus, improving the reactor requires metals as macro or micronutrients for microbial metabolic enzymatic activities and growth. The nutritional balance aided substrate breakdown and biogas production [6,15,22].  
Figure 5: Bioreactor maximum cumulative biogas yield (mL) and methane composition (% CH4) after 30 days of incubation.
## 4 Applications of CDs in Cancer Theranosis
### Cancer Therapy via Drug Delivery, PTT, PDT, PTT+PDT, siRNA Delivery, SDT, and Gas Therapy
#### 4.2.4 Synergistic PDT+PTT
As shown in Figure 13h,i, while the PDT group showed some cancer reduction, it was not sufficient for complete elimination of the cancer, whereas the synergistic PDT+PTT group achieved total cancer inhibition without recurrence at the 14 th day after treatment, indicating superior efficacy. The control groups, HBCDs, and laser irradiation alone exhibited rapid cancer growth, showing no therapeutic effect. Importantly, the body weight of the mice remained the same throughout the treatments, confirming the low biotoxicity of HBCDs in vivo. Figure 13: (**a**) Cancer volume growth curves over time of the four 4T1 cancer-bearing mice groups after treatments, (**b**) photographs of mice on the 0 th, 3 rd, and 13 th days after treatments and (**c**) excised cancers on the 13 th day after treatments [39]. (**d**) Photographs of excised cancers from four 4T1 cancer-bearing mice groups on the 16 th day after treatments and (**e**) cancer volume growth curves over time (**\({}^{\text{st}}\)**_p_ \(<\) 0.01) (-: no irradiation and +: irradiation) [177]. (**f**) Cancer volume changes in HeLa cancer-bearing mice over time and (**g**) photographs of excised cancers on the 16 th day after treatments (L: laser) [179]. (**h**) Photographs of 4T1 cancer-bearing nude mice on the 0 th and 14 th days after treatments and (**i**) cancer volume growth curves over time [176]. The relative cancer volume is the cancer volume with respect to initial cancer volume before therapeutic treatment. _p_-values were calculated by Student’s _t_-test.
# Algorithmic Advances for 1.5-Dimensional Two-Stage Cutting Stock Problem
## 4 Solution Approach
### Row-and-Column Generation Algorithm
#### 4.2.1 First-Stage Column-Generating PSP  
The aim of the first-stage Column Generation Pricing Subproblem (1S-CG-PSP) is to find a negatively priced column \(\mathbf{a}_{1}\) for the first stage. For each distinct first-stage length \(L_{1}\), the following optimization problem is solved:  
\[\text{maximize}\quad\sum_{i=1:t_{i1}=L_{1}}^{m_{1}}u_{1i}^{\star}a_{1i}- \gamma_{1}L_{1} \tag{17}\]  
subject to  
\[\gamma_{1}=w_{0}-\sum_{i=1:t_{i1}=L_{1}}^{m_{1}}w_{1i}a_{1i} \tag{18}\] \[\gamma_{1}\geq t_{min,1}\] (19) \[a_{1i}\geq 0\text{, integer}\qquad\qquad\qquad\qquad\qquad \qquad\qquad\qquad\qquad\forall i=1,\ldots,m_{1}:\ell_{1i}=L_{1} \tag{20}\]  
Here, \(a_{1i}\) is the decision variable representing the quantity of the \(i\)-th order of the first stage with length \(\ell_{1i}=L_{1}\) and width \(w_{1i}\) included in the cutting pattern. \(u_{1i}^{\star}\) is the optimal dual variable obtained from the current RSRMP. \(w_{0}\) is the width of the input roll at the first stage, \(t_{min,1}\) is the minimum allowable trim loss and \(\gamma_{1}\) represents the actual trim loss in the cutting pattern. Equation (17) tries to maximize the reduced cost of a potential new first-stage cutting pattern, where the cost associated is calculated as the product of the trim loss and the pattern length.  
If the optimal value is strictly positive, a new first-stage cutting pattern is generated, defined as \(\mathbf{a}_{1}^{\tau\star}=\left(a_{11}\quad\ldots\quad a_{1m_{1}}\right)^{T}\). The generated column \(\mathbf{a}_{1}^{\star}\) is then added to the coefficient matrix \(A\), as follows. By adding the new column, the cost vector \(\mathbf{c}_{1}^{T}=\left(c_{11}\quad\ldots\quad c_{1n_{1}}\quad\gamma_{1}L_{1 }\right)^{T}\) and the \(\mathbf{x}_{1}\) are also expanded with a new decision variable for the new pattern.  
\[A=\left(\begin{array}{cc}A_{11}&\mathbf{a}_{1}^{\star}&A_{12}\\ O&\mathbf{0}&A_{22}\end{array}\right) \tag{21}\]
# OCT Capillary Depth Measurement in Copper Micro Welding Using Green Lasers
## 3 Results
### Capillary Depth Measurement Using OCT
#### 3.2.1 OCT Positioning
Figure 4: Schematic illustration of the optical coherence tomography (OCT) beam position relative to the capillary and the laser beam origin, **(a)** OCT beam aligned with laser beam, insufficient depth measurement; **(b)** OCT beam shifted to the deepest point of the capillary, true capillary depth measurement; **(c)** OCT beam reflected on the capillary surface, depth measurement too deep. In heat conduction welding, only minor depth could be measured; therefore, the signal intensity was used as the main criterion for the determination. The offset values were determined in the range of \(10\,\mathrm{\SIUnitSymbolMicro m}\) to \(30\,\mathrm{\SIUnitSymbolMicro m}\), depending on the process parameters. For deep penetration welding, both the measured depth and the signal intensity were taken into consideration. The determined offset values were the range of \(85\,\mathrm{\SIUnitSymbolMicro m}\) to \(190\,\mathrm{\SIUnitSymbolMicro m}\), depending on the process parameters. For a low laser power and a low welding speed, only small offsets values need to be used, while, for high laser power and high welding speeds, the shift increases. In conclusion, it could be shown that the deepest point of the capillary is trailing the laser beam; therefore, the measurement beam needs to be shifted behind the laser beam. By scanning the OCT beam along the welding direction during the welding process, the optimal offset of the measurement beam relative to the origin of the laser beam was determined. In experiments to determine the capillary depth, the optimal offset values were used to adjust the position of the OCT beam relative to the laser beam origin to ensure the measurement of the deepest point of the capillary. Figure 5: (**a**) Schematic illustration of the OCT beam movement during the welding process for determining the optimal lag distance of the OCT beam; (**b**) exemplary measurement result for determining the optimal lag distance. Figure 6: Optimal lag distances of the OCT beam relative to the the laser beam origin, depending on the process parameters.
# Design of W-Band GaN-on-Silicon Power Amplifier Using Low Impedance Lines
## 4 Conclusions  
In this work, the W-band three-stage power amplifier IC was presented using the commercially available GaN-on-Si HEMT foundry. The low impedance lines were utilized in the impedance matching networks to reduce the microstrip losses and thus improve the output power and efficiency. The load of each stage was matched to the optimum impedance to produce enough output power to drive the next stage. In the measurement, the GaN-on-Si power amplifier IC showed the high output power of 28.9 dBm with high power density (1.94 W/mm) and PAE (13.3%) at W-band, which are comparable to the GaN-on-SiC counterpart. This work demonstrates that the GaN-on-Si HEMT technologies can be potentially applied to low-cost solutions for W-band high-power commercial applications.  
There is still a need to improve the performance of GaN-on-Si power amplifiers, especially output power per chip. For this purpose, further research will be carried out to combine the larger number of unit transistors using the efficient on-chip power combiners. The thermal design is also required to reduce the performance degradation by the heat generated by the transistors. In addition, the research should be conducted to develop the high-quality epitaxial growth of the GaN channel on the silicon, allowing for similar performance of the GaN-on-SiC transistor.  
The authors declare no conflict of interest.