esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Bifacial Study on Two Different Electrolytes and Dyes
#### 3.2.1 The Electrolyte  
All devices show better performances with electrolyte J8* than electrolyte HSE. J8*'s solvent is indeed composed by 85% (v:v) of acetonitrile, while HSE's solvent is methoxy propionitrile (3 MPN), as explained in Section 2. Acetonitrile is less viscous and more volatile than 3 MPN and this leads to a faster dye regeneration with the beneficial effect of Jsc increment in a device with J8* electrolyte. Moreover, J8* electrolyte also increases results due to less charge recombination: a faster dye regeneration due to a faster electrolyte diffusion means that the probability of dye's recombination for a back-electron-transfer phenomenon [40] is lower.  
In [PERSON] et al. [41], a C106-based device with an iodine-iodide commercial electrolyte, high-performance electrolyte (HPE), similar to HSE, was developed. The main difference between HPE and HSE is the solvent: HPE employs acetonitrile, while HSE contains MPN. Despite the use of a more performant electrolyte, for reasons discussed above, the Pt-based/C106 device in [PERSON] et al. shows similar PCE (4.9%) to our work (4.8%) in similar condition (black background and thickness around 7-8 \(\upmu\)m). This is due to the fact that [PERSON] et al. fabricated the DSSC device without the use of a blocking layer, as we made in this work. Considering our Pt-based C106 device, with J8* electrolyte, therefore, a similar solvent to HPE, we obtained 5.6% of PCE, a higher result due to the use of blocking layer in our device as discussed before. Concerning the Y123 dye, in [PERSON] et al. a similar procedure to ours was used to fabricate the cells. Indeed, the Y123-based device results in better performances: PCE in [PERSON] et al. is 9% while in this work is 4.92% in similar conditions (black background and thickness). In this case, the difference lies in the use of a cobalt-based electrolyte, more performant than an iodide/triiodide one. In a recent work that employs Y123 as a dye [30], with similar material and conditions to the ones of this work, a PCE of 5.3% was obtained. The slight difference between 4.92% (this work) and 5.3% [30] on a black background concerns the use of a scattering layer in the last work. Indeed, when our cell is measured in a white background (albedo effect) that works as a scattering layer, our result (5.6%) is in accordance with that in literature.
## 4 Numerical Simulations of Underwater Rock-Plug Blasting  
### Simulation Model  
The numerical model shown in Figure 13 was established in accordance with the characteristics of Fluent software and the size of the experimental set-up. Figure 13a shows the Fluent model, in which the dimensions of the rock plug and the downstream water tank are the same as those in the experiment, while the height of the upstream water tank is 4 mm higher. The red boundary face in the Fluent model represents a pressure outlet for which the gauge pressure is the atmospheric pressure and the operating density is \(1.25\) kg/m\({}^{3}\), while the other boundaries are non-slip surfaces. The fluid region of the model was divided using elements with a side length of 20 mm. The block particles were modeled with a side length of 10 mm and composed of eight basic spherical particles. To comply with the filling in of the area of the rock-plug sample with the block particles in the experiment, the user-defined function (UDF) of EDEM was adopted to compile the particle factory program and enable the neat accumulation of particles in the numerical simulation model of the rock plug. As shown in Figure 13d, a total of 740 block particles were used for filling in the model. To simulate the constraint of the screw-type connectors used to connect the upstream and downstream water tanks on the outermost ring of the block particles in the experiment, a ring structure was established in the EDEM model (Figure 13e). Multi-phase flows were set in the Fluent model and water and air materials were activated. In accordance with the charge position in the field experiment, a high-pressure area was set at a position 40 mm from the upstream end face of the rock-plug model.
## 3 Experiments and Performance Assessment  
### Test Images  
The proposed _LSI_ shadow detection approach is developed over a DELL personal computer under the 64-bit Windows7 operation system equipped with a 3.2 GHz CPU and 4 GB RAM. In order to verify the shadow detection performance of the proposed _LSI_ algorithm, comparative experiments are carried out with many test images from WorldView-3 of Tripoli, Libya and Rio de Janeiro, Brazil, and WorldView-2 of Washington DC, USA captured at a different time (called WV3-Tripoli, WV3-Rio and WV2-WDC respectively), which is discussed in next section (Section 4: Discussion). In this section, both qualitative and quantitative assessments are especially provided in the following to evaluate the shadow detection performance of the proposed _LSI_ method and several standard shadow detection algorithms (i.e., MC3 [18], NSVDI [25], LSRI [24], SDI [26] and SRI [20]) with two WorldView-3 test images of Tripoli, Libya [37], as shown in Figure 4a,b. Additionally, reference images of shadow regions are also provided with the corresponding panchromatic versions of test images in Figure 4a,b with a spatial resolution of 0.31 m, as shown in Figure 5a,b. Particularly, the test image Tripoli-1 in Figure 4a is a \(400\times 300\) pixel image that covers typical ground objects, such as shadow, various scale urban buildings, asphalt roads, bare land and grass. The test image Tripoli-2 in Figure 4b is a \(260\times 195\) pixel image mainly consisting of shadow, buildings, asphalt roads, grass, playgrounds and parks.  
Figure 4: Test images from WorldView-3 images of Tripoli, Libya. (**a**) Tripoli-1. (**b**) Tripoli-2.  
Specific details can be further discussed through qualitative visual comparison in the subjective evaluation way. Moreover, the shadow detection performance of a certain shadow detection algorithm is also quantified with shadow detection accuracy measurements by employing the objective evaluation method. Qualitative and quantitative evaluation are both carried out over the shadow detection results by the proposed _LSI_ approach and five other comparative methods (i.e., MC3 [18], NSVDI [25], LSRI [24], SDI [26] and SRI [20].) in the following comparative experiments.
## 2 STRC Shear Wall Failure Modes and Small Sample Database Establishment  
### Failure Modes of STRC Shear Walls  
The failure modes of STRC shear walls are similar to those of RC shear walls, mainly including flexural failure, flexural-shear failure, shear failure, and sliding failure [25]. Flexural failure is characterized by flexural deformation, where horizontal flexural cracks first appear in the tensile zone of the wall under the action of horizontal cyclic loads. These cracks gradually extend to the compressed zone of concrete, resulting in a reduction in the height of the compressed zone. Finally, the tensile steel bars yield, accompanied by the crushing of the compressed zone edge concrete or the buckling of the steel tube, the protective layer peeling off, and the wall producing numerous intersecting diagonal cracks [8], exhibiting ductile failure, as shown in Figure 1a. Flexural-shear failure is subjected to both flexural and shear forces, where horizontal flexural cracks first appear in the lower part of the wall and gradually extend obliquely towards the compressed zone. The oblique cracks continue to develop until they reach the compressed zone at the bottom of the wall, and the slope gradually increases to become vertical. Finally, the compressed concrete in the shear zone of the wall undergoes splitting failure under the action of axial compression [26], as shown in Figure 1b. Shear failure is characterized by shear deformation, where diagonal or intersecting diagonal cracks appear in the wall. With the increase in load, the bottom concrete of the wall is crushed, or the steel tube buckles, exhibiting obvious brittle failure characteristics [27], as shown in Figure 1c. Sliding failure is caused by the construction joint set in the wall, resulting in the discontinuity of wall stiffness. Under shear force, the wall slides as a whole, and cracks mostly occur at the construction joint, exhibiting obvious brittle failure characteristics [26; 27], as shown in Figure 1d.  
The failure mode of STRC shear walls is closely related to the wall type. According to ASCE/SEI 41-17 [28], RC shear walls are classified into slender walls (\(\lambda\geq 3\)), moderate walls (\(1.5\leq\lambda\leq 3\)), and squat walls (\(\lambda\leq 1.5\)) based on the aspect ratio. Slender walls are more prone to flexural failure, while squat walls, especially those with \(\lambda<1\), have a lower occurrence rate of flexural failure and tend to exhibit unpredictable shear failure. Moderate walls are often controlled by both flexural and shear deformation and commonly experience flexural-shear failure.
## 5 Security Requirements
### Access-Level Security Requirements  
**Authentication:** This property enables a smart device to verify the identity of its communicating peer. Hence, a smart device ensures that devices or users with valid credentials get access to its services and resources. For instance, a Coordinator device authenticates a network administrator before allowing her to perform administrative tasks, such as remote reprogramming of a smart device, in an IoT network. Similarly, a user has to be authenticated to a smart device before the device performs actions according to a command issued by the users.  
**Access Control**: The access control methods ensure that users and devices with valid credentials only get access to authorized services. This security property protects smart devices and services from unauthorized access. For instance, a patient's wearable medical sensors can be configured such that a physician can issue read and write commands to them. However, a nurse may only be authorized to issue read commands to the medical sensors.  
**Accounting**: Accounting ensures that the activities of the entities of an IoT system can be used for auditing purposes. Security analysts use activity logs to investigate forensic incidents. An analyst can track the communications that took place between things and users, things and things, or things and clouds in the past as evidence to resolve disputes between users and IoT service providers or to find facts in cyber-criminal cases. As such, accounting services should have the ability to store logs and allow auditors to obtain logs for investigating cyber incidents.
# Adversarial Optimization-Based Knowledge Transfer
## 2 Related Works
### Output-Distribution-Based Knowledge Transfer Using Adversarial Networks  
Based on the general GAN-based architecture, an adversarial network-based knowledge transfer approach adopted by the KTF was first introduced in [30]. The relaxed output probability of neural networks described in [27] was used as the pre-trained knowledge in their method. GAN-based previous work in [30] considered couple of different optimization procedures for knowledge transfer method such as solving a discriminator's maximization problem and solving a minimization problem for a target DNN model. To update the discriminator and target DNN for adversarial training in the KTF. First, the discriminator's goal is required to distinguish whether a relaxed output distribution is provided by a pre-trained DNN or target DNN. In contrast, the target DNN, which plays the same role as the generator of the original GAN-based structure [32], is adversarially trained, similar to the relaxed output probability of the pre-trained DNN. Therefore, the knowledge transmitted from the discriminator leads the target DNN to provide the probability result of the output layer, similar to the pre-trained DNN. In addition, according to [27], the cross-entropy loss for the supervised training approach is considered in the KTF. Experimentally, their method proved that the adversarial network-based approach could effectively transfer output-distribution-based knowledge from the pre-trained DNN to the target DNN, compared to the \(l^{2}\)-norm-based knowledge transfer [27] without using adversarial training loss. However, the optimized target DNN using [30] is usually inferior to the pre-trained original DNN, especially when considering wider and deeper neural networks as a source DNN.
# Bolt Loosening Detection Using Key-Point Detection Enhanced by Synthetic Datasets
## Materials and Methods
### Calculation of Bolt Loosening Angle
#### 2.4.2 Bolt Loosening Angle Calculation  
By matching the center coordinates of the two bolts in the image, a reference line was obtained. This reference line represents the position relationship between neighboring bolts, which is fixed and does not change with the camera shooting angle. For each bolt corner point, the angle between the vector with the center pointing to the corner point and the reference line was calculated. The angle calculation formula is shown in Equation (2), which gives the angular state of the bolt to the reference line in the current state, as shown in Figure 13.  
\[\left\{\begin{array}{c}ang_{0}=tan^{-1}\Big{(}\frac{cy_{2}-cy_{1}}{cx_{2}- cx_{1}}\Big{)}\\ ang_{i}=tan^{-1}\Big{(}\frac{y_{i}-cy_{1}}{x_{i}-cx_{1}}\Big{)}\\ \alpha_{i}=ang_{i}-ang_{0}\end{array}\right. \tag{2}\]  
where \(\alpha_{i}\) is the angle between the vector with the center pointing to the corner point and the reference line, \((cx_{1},cy_{1})\) is the coordinate of the center point of the aim bolt, \((cx_{2},cy_{2})\) is  
Figure 12: The result of key-point marking and recognition box generation.  
the coordinate of the center point of the neighbor bolt, and (\(x_{i}\), \(y_{i}\)) is the coordinate of each corner point.  
In this way, it is only necessary to obtain an image of a tightened bolt by undertaking the same process. Then, simply by comparing the current state of the bolt with the tightened state of the bolt at each corner point, you can accurately judge whether the bolt has loosened and calculate the angle of the bolt loosening, as shown in Figure 14. The bolt loosening angle calculation formula is shown in Equation (3).  
\[\left\{\begin{array}{c}\theta_{i}=\alpha_{i1}-\alpha_{0i}\\ \theta=\frac{\alpha_{i}}{6}\end{array}\right. \tag{3}\]  
where \(\theta_{i}\) is the rotation angle of each corner point and \(\theta\) is the average of the rotation angle of all corner points and is used as the bolt loosening angle value.
## 4 Results
### Comparative Analysis of PSInSAR and Validation Heights  
While the PSInSAR technique has proven invaluable in urban mapping and vertical growth analysis, its effectiveness is significantly influenced by the accuracy of the placement of PSC. In ideal scenarios, these PS points should be located at the highest architectural elements of buildings, such as rooftops. However, our analysis identifies a prevalent issue where many PS points are not positioned on rooftops but are instead found on lower structural elements like balconies and facades. This misplacement can lead to systematic underestimations of actual building heights, as PS points on lower building parts fail to capture the structure's total vertical extent. Additionally, the noise within the PSInSAR data presents another substantial challenge and causes an overestimation of height.  
Figure 13 provides a visual comparison between PSInSAR-derived height estimates and ground-truth validation heights for 20 randomly selected sample grids within the surveyed area, depicted through a connected scatter plot. This plot method effectively highlights the discrepancies between the two datasets, with each point connected by lines that visually encode the nature of each discrepancy: red lines indicate instances where PSInSAR measurements are higher than validation data, suggesting potential noise or data errors, whereas blue lines indicate underestimations by PSInSAR relative to validation data, often due to misplacement of PS points on non-rooftop structures. However, the number of such instances is a minority compared to the whole sample. Such discrepancies are particularly pronounced where PS points are not atop buildings, highlighting the need for precise placement of these scatter points to ensure accurate height estimation. On the other side, most data points exhibit a reasonable correlation with surveyed heights, demonstrating PSInSAR's utility in capturing urban structural heights.  
Figure 13: Comparative analysis of PSInSAR and validation heights of 20 sample grids.
## 2 Results and Discussion
### Alphanumeric and Graphical Output  
Standard alphanumeric output for a NT having three layers of six carbon atoms each (written as (C6)3) generated by MCAD, compatible with other software packages for property calculations (i.e. HyperChem), is given below (for simplicity, hydrogen atoms are not shown).  
forcefield mm+ mol 1 atom 80 - C ** - 0 0.60370505 -1.0456481 5.11 2 72 s 87 s atom 121 - C ** - 0 0.60370517 1.045648 0.73 3 114 s 115 s 85 s atom 99 - C ** - 0 -0.60370517 -1.045648 0.0 2 92 s 107 s atom 109 - C ** - 0 0.6037053 1.045648 5.11 2 101 s 72 s atom 108 - C ** - 0 -0.6037051 1.045648 2.92 3 100 s 101 s 115 s atom 107 - C ** - 0 -1.2074103 1.7564588E-15 0.73 3 99 s 100 s 114 s atom 94 - C ** - 0 -1.2074103 1.2763726E-7 5.11 2 87 s 101 s atom 93 - C ** - 0 -0.6037052 -1.045648 2.92 3 86 s 87 s 100 s atom 92 - C ** - 0 0.60370517 -1.045648 0.73 3 85 s 86 s 99 s atom 101 - C ** - 0 -0.60370505 1.0456481 4.38 3 94 s 108 s 109 s atom 100 - C ** - 0 -1.2074103 6.381863E-8 2.19 3 93 s 107 s 108 s atom 79 - C ** - 0 1.2074103 -6.381863E-8 2.92 3 72 s 86 s 115 s atom 72 - C ** - 0 1.2074103 -1.2763726E-7 4.38 3 79 s 80 s 109 s atom 115 - C ** - 0 0.6037052 1.045648 2.19 3 108 s 121 s 79 s atom 114 - C ** - 0 -0.60370517 1.045648 0.0 2 107 s 121 s atom 87 - C ** - 0 -0.6037053 -1.045648 4.38 3 80 s 93 s 94 s atom 86 - C ** - 0 0.6037051 -1.045648 2.19 3 79 s 92 s 93 s atom 85 - C ** - 0 1.2074103 -2.9573022E-16 0.0 2 92 s 121 s endmol 1  
Figure 8: Finishing processes used for creating a nanotube.  
This output example is written as a \".hin\" format which will be open with HyperChem program; it can be saved as a \".ent\" format as a protein data bank (PDB) compatible file.  
Other graphical outputs of MCAD are represented in Figure 9, which shows different NT configurations: (a) (10,10) armchair NT (perspective view); (b) (4,2) chiral NT (front view); (c) (6,0) zigzag (C4N2)10 NT modified with nitrogen atoms (in blue) (perspective view); (d) (4,2) chiral NT (side view; same as in (b))
## 5 Optimal Power Allocation Design and Energy Efficiency Analysis
### Energy Efficiency Analysis  
According to [28], the EE can be given as follows:  
\[\varsigma=\frac{R_{T}}{P_{T}} \tag{28}\]  
Then, with the found \(P_{th}\), the analysis of EE after OPA can be presented as follows.  
1. When \(P_{T}>P_{th}\), the total transmit power can be expressed as \(P_{T}^{s}=P_{1}^{s}+P_{2}^{s}\). Combining (8), (9), (23) and (28), and \(P_{T}^{s}\), the EE can be expressed as  
\[\varsigma^{s}=\frac{R_{T}^{s*}}{P_{T}^{s}}=\frac{R_{1}^{s*}+R_{2}^{s*}}{\frac {Q_{1}a_{2}-Q_{2}a_{1}+2Q_{1}Q_{2}a_{1}a_{2}}{2Q_{2}a_{1}a_{2}}+\frac{Q_{2}a_{ 1}-Q_{1}a_{2}+2Q_{1}Q_{2}a_{1}a_{2}}{2Q_{1}a_{1}a_{2}}} \tag{29}\]  
where \(\begin{cases}R_{1}^{s*}=\frac{1}{2}\log_{2}\Big{(}1+\frac{(1-p)\rho\eta(Q_{1}a _{2}-Q_{2}a_{1}+2Q_{1}Q_{2}a_{1}a_{2})|f_{1}|^{2}|g_{1}|^{2}}{[(1-p)\rho\eta P _{1}[f_{1}]^{2}|l_{2}]^{2}+(1-\rho)P_{1}[l_{1}]^{2}+N_{1}]2Q_{2}a_{1}a_{2}}\\ R_{2}^{s*}=\frac{1}{2}\log_{2}\Big{(}1+\frac{(1-\rho)\rho\eta(Q_{2}a_{1}-Q_{1 }a_{2}+2Q_{1}Q_{2}a_{1}a_{2})|f_{1}|^{2}|g_{1}|^{2}}{[(1-\rho)\eta P_{1}[g_{ 1}]^{2}|l_{2}]^{2}+(1-\rho)P_{1}[g_{1}]^{2}|l_{2}]^{2}+N_{2}]2Q_{1}a_{1}a_{2}} \end{cases}\).  
2. When \(P_{T}\leq P_{th}\), the total transmit power can be expressed as \(P_{T}^{d}=P_{1}^{d}+P_{2}^{d}\). Combining (11), (27) and (28), and \(P_{T}^{d}\), the EE can be expressed as  
\[\varsigma^{d}=\frac{R_{T}^{d*}}{P_{T}^{d}}=\frac{R_{1}^{d*}+R_{2}^{d*}}{\frac {Q_{1}n-Q_{2}m+Q_{1}Q_{2}mm}{2Q_{2}mm}+\frac{Q_{2}m-Q_{1}n+Q_{1}Q_{2}mm}{2Q_{ 1}mm}} \tag{30}\]  
where \(R_{1}^{d*}=\log_{2}\bigg{(}1+\frac{(Q_{2}m-Q_{1}n+Q_{1}Q_{2}mm)h^{2}}{(P_{P}l _{1}^{2}+\sigma^{2})2Q_{1}mm}\bigg{)}\) and \(R_{2}^{d*}=\log_{2}\bigg{(}1+\frac{(Q_{1}n-Q_{2}m+Q_{1}Q_{2}mm)h^{2}}{(P_{P}l _{2}^{2}+\sigma^{2})2Q_{2}mm}\bigg{)}\).  
With the aforementioned analyses and designs based on the SWIPT-enabled CRN model, the implementation method of the proposed solution can be given as follows.  
Firstly, we select the energy-constrained best relay node as the best relay selection process of the BMR transmission in Section 3.1 to achieve a better BRT transmission.  
Secondly, based on the better BRT transmission, we use the TTPT determining algorithm to find the TTPT. Based on the TTPT, we can achieve the adaptive transmission as the adaptive transmission strategy in Section 4.1.  
Thirdly, based on the two cases of the adaptive transmission strategy, according to the OPA method in Section 5.1, we optimize each node's power under the constraint of the Pus interference threshold. Then, we let the nodes transmit signals with the optimized power.  
At the same time, it should be noted that the implementation conditions of the physical devices are out of our research scope, and they can be checked in other related literature.
## 4 Data and Methods
Several ancillary data sets were used to contextualize and interpret the interferometric results in a geographic information system (GIS) environment. These included open access data from the Geological Survey Ireland (GSI) [54], on bedrock geology, superficial Quaternary sediments, karst features, fault traces, geotechnical boreholes and land use. CORINE 2012 land-cover data provided by the Environmental Protection Agency (EPA) [55] were used to produce the vegetation land-cover maps of both AOIs. We used infrastructure data provided by Transport Infrastructure Ireland (TII) [56] with a buffer distance of 10 m to define PSs corresponding to the motorway and railway networks in AOI-1. The buffer size was chosen to take into account uncertainties in correspondence of PSs with those linear features, while also excluding as much as possible PSs belonging to adjacent buildings and other reflectors (cf. [57]). For all localities of detected motion, 0.25 m resolution aerial imagery acquired by BlueSky International in May 2017 were used to provide visual context to the obtained PSs. Finally, field surveys were carried out in June and July of 2018 at representative sites of InSAR-defined ground movements. The surveys consisted of visual inspection of the areas to document the nature of the surface features that provide the backscattering signal, to record any damage to buildings or infrastructure (e.g., cracks and fissures) and to check for signs of ground deformation (e.g., uneven surfaces, bulging or depressions of road pavement, etc. ).