esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### TiO\({}_{2}\) Thickness Effect  
Figure 5 shows the J-V curves of C106 and Y123 devices with an increased TiO\({}_{2}\) film thickness (12 \(\upmu\)m) in comparison to devices reported in Table 1 (7 \(\upmu\)m). The J8* electrolyte has been used as it is more performant than HSE. With a thicker TiO\({}_{2}\) layer (12 \(\upmu\)m), the PCE of C106 and Y123 devices have similar results, unlike a thinner layer (7 \(\upmu\)m), as reported in Table 1, where C106 is significantly more performant. As discussed previously, C106 has a greater absorbance in comparison to Y123 (Figure 2b) and saturates the light absorption at 7 \(\upmu\)m thickness, as observed in IPCE spectra (Figure 4a). Comparing data of Table 1 (7 \(\upmu\)m) and Table 2 (12 \(\upmu\)m), considering the same measurement condition (black background) and electrolyte, J8*, C106 dye shows an almost unchanged PCE (5.6% with 7 \(\upmu\)m and 5.9% with 12 \(\upmu\)m). Indeed, Y123, at the same precedent condition, shows a PCE boost, respectively, to the TiO\({}_{2}\) increment from 4.9% to 6.3%, mainly due to the Jsc (from 9.8 to 12.5 mA/cm\({}^{2}\)). This phenomenon is due to the C106 self-absorption processes which occur with increasing thickness [45]. Differently, Y123 has a lower absorbance, and it is not influenced by this effect at 12 \(\upmu\)m. Figure 5 shows the J-V curves relative to the measurements made on the same cells subjected to irradiation without a mask and with a white background, which represent the working conditions in an outdoor environment where one obviously tries to maximize the collection of sunlight. One of the main advantages of the Gratzel cells (DSSCs), compared to the traditional silicon-based solar cells, is found in their ability to be passed through by parts of solar radiation, therefore it is possible to use the \"albedo effect\": the diffused and reflected light by the ground they are laying. This is obviously not possible for silicon solar cells or, in general, for opaque ones. While C106 and Y123 devices measured with a mask in a black background obtained similar results in terms of PCE (5.9 for Y123 and 6.34 for C106), the same devices measured without a mask and on a white background show significant differences in terms of PCE (12.78 for Y123 and 10.28 for C106), mainly due to the density current (26.68 mA/cm\({}^{2}\) for Y123 and 22.60 mA/cm\({}^{2}\) for C106) (Table 2 and Figure 5). Since the Y123 dye absorbs less radiation than C106 (see Figure 2b, more residual light is impinging on the white background and can be reflected to re-cross the solar cell: a greater albedo effect can explain both the higher photocurrent and efficiency found for the Y123 DSSC compared to the C106 one.  
Figure 4: IPCE spectra of C106 and Y123 device. Cell were measured in a black background: (**a**) C106 device with J8* and HSE electrolytes, front and rear side; (**b**) Y123 device with J8* and HSE electrolytes, front and rear side.
## 5 HABC-DCNN Design  
In recent years, DCNNs have been extensively applied in various fields as one of the most potentially effective DL techniques. The layering capability of DCNNs is a significant advantage that contributes to their success. Figure 4 reveals that the main structure of a DCNN is composed of four types of layers: convolution layers, pooling layers, activation functions, and fully connected neural networks. The role of the convolution layer is to identify characteristics in the input data by employing a convolution filter. This involves taking a set of weights and multiplying them with the input data. The outcome  
Figure 3: An example of the migration model, movement towards the global best, and neighborhood search of the proposed HABC.  
of this convolution process is then passed through a Rectified Linear Unit (ReLu) as a non-linear activation function. The purpose of the pooling layer is to gradually decrease the dimensions of the input data while preserving only the essential information. Following numerous iterations of convolution and pooling processes, the DCNN culminates in a fully connected neural network [17].  
The deep architecture used in our experiment consists of four 1D convolutional layers. The number of filters in each layer increases sequentially (32, 64, 128, and 256) and each layer uses a kernel size of 5. To reduce the dimensionality of the feature maps, max-pooling layers with pool sizes of 2 are applied after each convolutional layer. To prevent overfitting, each convolutional layer is followed by a dropout layer with a rate of 0.25. The output of the fourth convolutional layer is flattened and passed through two fully connected layers with 512 and 256 units, respectively. Each of these layers is also followed by a dropout layer with a rate of 0.5.  
This paper's significant contribution is training the DCNN using the HABC algorithm, which has been mentioned multiple times. In this approach, we treat the weights and biases of the DCNN as optimization parameters. The HABC algorithm optimally updates the vector of weights and biases to meet the problem conditions, replacing the traditional BP algorithm. Figure 5 illustrates the structure of a bee in the proposed HABC algorithm. Each bee is a vector that contains the weights and biases of the DCNN. The cost function is defined by Equation (14).  
\[\textit{Mean Square Error}\left(MSE\right)=\frac{1}{k}\sum_{i=1}^{k}(O_{i}-D_{ i})^{2} \tag{14}\]  
where \(k\) = the total number of samples, \(O_{i}\) = system output, and \(D_{i}\) = desire.  
Figure 4: Different layers of a standard DCNN.
## 4 Results  
### Compositional Characteristics of Natural Gas  
As shown in analysis of gas samples from the northern and southern parts of the Dixi area and the Dongdaohaizi Depression (Table 1), the methane (C\({}_{1}\)) content in the northern Dixi area ranges from 89.05% to 90.46%, and that of ethane (C\({}_{2}\)) ranges from 2.15% to 4.29%, and propane (C\({}_{3}\)) from 0.13% to 0.75%. In the southern Dixi area, the C\({}_{1}\) content varies between 80.76% and 92.00%, and that of C\({}_{2}\) ranges from 2.20% to 9.20%. For the Dongdaohaizi Depression, the C\({}_{1}\) content ranges from 82.20% to 93.35%, with that of C\({}_{2}\) between 5.15% and 11.35%. Overall, the C\({}_{1}\)/(C\({}_{1-5}\)) ratios fall within the range of 0.88 to 0.98, the nitrogen (N\({}_{2}\)) content ranges from 1.37% to 11.58%, and the carbon dioxide (CO\({}_{2}\)) content remains low, ranging from 0% to 0.41%. Across various stratigraphic intervals, the natural gas is dominated by alkane hydrocarbons, characterized by high dryness coefficients and low N\({}_{2}\) and CO\({}_{2}\) contents--features indicative of gases derived from mature source rocks.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Area**} & \multirow{2}{*}{**Well**} & \multirow{2}{*}{**Depth (m)**} & \multirow{2}{*}{**Formation**} & \multicolumn{6}{c}{**Composition of Natural Gas**} \\ \cline{4-10}  & & & & **C\({}_{1}\)\%** & **C\({}_{2}\)\%** & **C\({}_{3}\)\%** & **C\({}_{1}\)/(C\({}_{1-5}\))** & **N\({}_{2}\)\%** & **CO\%** \\ \hline \multirow{4}{*}{Northern} & Dx18 & 3998.5 & T\({}_{1}\)S & 90.46 & 2.15 & 0.24 & 0.98 & 7.90 & 0.41 \\  & Dx18 & 3906 & T\({}_{1}\)S & 89.13 & 3.20 & 0.26 & 0.97 & 8.21 & 0.14 \\  & Dx10 & 2192 & T\({}_{1}\)S & 89.05 & 4.29 & 0.23 & 0.95 & 6.22 & 0.18 \\ \hline \multirow{4}{*}{Southern} & Dx9 & 2242–2234 & K\({}_{1}\)tg & 89.87 & 5.68 & 0.24 & 0.94 & 4.28 & 0.19 \\  & Dx9 & 2109.8 & K\({}_{1}\)tg & 89.85 & 6.05 & 0.25 & 0.94 & 4.10 & 0.00 \\  & Dx12 & 1707 & K\({}_{1}\)tg & 90.43 & 3.78 & 0.26 & 0.96 & 5.56 & 0.07 \\  & Dx12 & 2205 & K\({}_{1}\)tg & 84.25 & 7.36 & 0.14 & 0.92 & 5.00 & 0.21 \\  & Dx12 & 2287 & K\({}_{1}\)tg & 89.40 & 5.45 & 0.13 & 0.94 & 4.47 & 0.35 \\  & Dx180 & K\({}_{1}\)tg & 80.76 & 9.20 & 0.14 & 0.90 & 8.11 & 0.03 \\  & Dx205 & 2293.5 & K\({}_{1}\)tg & 84.04 & 8.75 & 0.26 & 0.91 & 6.52 & 0.04 \\  & D210 & 1815.5 & K\({}_{1}\)tg & 88.40 & 5.22 & 0.25 & 0.94 & 6.19 & 0.06 \\  & Dx15 & 2657 & J\({}_{2}\)t & 92.00 & 3.90 & 0.42 & 0.96 & 3.10 & / \\  & Dx15 & 2190 & K\({}_{1}\)tg & 85.05 & 2.80 & 0.24 & 0.97 & 11.41 & 0.34 \\  & Dx15 & 2451 & J\({}_{2}\)t & 85.13 & 6.67 & 0.24 & 0.93 & 7.02 & 0.40 \\  & Dx15 & 2657 & J\({}_{2}\)t & 90.56 & 3.86 & 0.42 & 0.96 & 4.63 & 0.18 \\  & Dx15 & 2144 & K\({}_{1}\)tg & 85.11 & 2.2 & 0.43 & 0.97 & 11.58 & 0.27 \\ \hline \end{tabular}
\end{table}
Table 1: Composition of natural gas testing results in the Dixi area, Junggar Basin.
## 2 Materials and Methods
### EMG Data Processing  
The raw sEMG data were recorded using Delsys EMGworks Acquisition software (Common Mode Rejection Ratio: CMRR > 80 dB at 60 Hz; gain of 300; band pass filtered 20-450 Hz) and were sampled at 2000 Hz. Signal analysis was conducted using Delsys EMGworks Analysis software. Three seconds after the start of the maximum isometric contraction measurements of key pinch and ECR, an intermediate 3-s interval was selected for analysis. In the scissors opening/closing task, the middle 80 s during the 100-s task was analyzed.  
Figure 2: The developed mechanism of the scissors with a power-support device, and the protocol of the experiment. **(A)** Model diagram of the power-support mechanism of the scissors with power-support device. **(B)** Surface electromyogram (sEMG) data acquisition method. The myoelectric sensor was placed on the extensor carpi radialis (ECR) and adductor pollicis (ADP) muscles of the right forearm, and the myoelectric potential obtained from the sensor was analyzed. The recording computer and sensor were connected to Wi-Fi, and a metronome sound was outputted to maintain opening and closing timing. **(C)** The experimental procedure. A 180-s break was provided after performing the maximum isometric contraction task, conditions were set using a random table for the presence or absence of assistance, and the subjects asked to perform scissors opening and closing exercises.  
The digital signal of the EMG amplitude before analysis was expressed as EMG root mean square (RMS) values with a window size of 1 s. To detect the maximum RMS amplitude in the MVC, we used a window size of 1 s, moving in 100 ms increments across the MVC. In the scissors opening and closing task, the RMS value was determined and normalized using the maximum RMS detected in each muscle MVC, representing the relative %MVE. The event was set for the entire period and analyzed with a 1-s epoch.  
An APDF was used to evaluate the muscle activity over the total window, with muscle activity identified in the APDF as the 10 th (P0.1), 50 th (P0.5), and 90 th (P0.9) percentiles of the entire recording period for the normalized RMS processed signal, and summarized for each level as the mean [22]. The percentile was represented as an indicator of static muscle activity (P0.1, low level amplitude over 90% exercise time), central muscle activity (P0.5), and peak levels of muscle activity (P0.9, high levels amplitude).
# Fronthaul Design for Wireless Networks
## 3 Fronthaul Design Methodology
### Link Design Algorithm
#### 3.1.2 Link Costs Analysis  
When planning a link, the costs associated with the project will always go beyond the costs of the equipment itself; it is important to mention that when referring to the equipment of a certain technology (MRT, FSO, or FO), it includes all the necessary items for installing and operating the respective link. In particular, two different types of costs have to be considered in the scope of these projects:  
* CAPEX: These include the fixed costs related to the network infrastructure, such as equipment and respective deployment, spare parts, and project studies. With respect to wireless technologies, one has to consider emitter and receiver costs, as well as the costs of cables, stands, and in the case of FSO systems, an auto-tracker that allows the receptor to align with the received signal (in order to reduce the impact of atmospheric turbulence). Moreover, as stated in Section 2, the costs associated with CAPEX for MRT and FSO technologies do not depend on the link distance (if no repeaters are considered), unlike FO links. Accordingly, CAPEX related to FO systems can be divided into a fixed term, which accounts for Optical Line Terminations (OLTs), Optical Network Units (ONUs), and other miscellaneous electronics, plus a variable term that corresponds to the costs of the fiber itself and the costs of deploying it, which varies with the length of the link, \(d\).
* OPEX: These do not contribute to the infrastructure itself, since they include operational expenses, such as maintenance costs, energy consumption, government taxes, and repayments. Accordingly, any economical analysis regarding OPEX is usually performed taking into account the lifetime of the communication link--e.g., a period of 10 years, as adopted in this work.  
After gathering the values for both CAPEX and OPEX lifetime (for a given lifetime) regarding the use of a certain equipment, the total costs of the link project are given by the summation of these values, i.e.,  
\[\text{Total Costs}=\text{CAPEX}+\text{OPEX}_{\text{lifetime}}. \tag{25}\]  
Based on what was previously mentioned and recalling the information provided in Section 2, the total costs depend on the link length in the case of MRT and FO systems, whereas the total costs can be regarded as a fixed value (i.e., independent of the link length) for FSO systems; thus, Expression (25) can be rewritten with respect to the different technologies as:  
\[\text{Total Costs}^{\text{(MRT)}} = \text{F.Costs}^{\text{(MRT)}}+\text{V.Costs}^{\text{(MRT)}}\times \sqrt{d}, \tag{26}\] \[\text{Total Costs}^{\text{(FSO)}} = \text{F.Costs}^{\text{(FSO)}},\] (27) \[\text{Total Costs}^{\text{(FO)}} = \text{F.Costs}^{\text{(FO)}}+\text{V.Costs}^{\text{(FO)}}\times d, \tag{28}\]  
where F.Costs and V.Costs stand for fixed costs and variable costs, respectively, with respect to the associated technology (MRT, FSO, or FO); please note that these costs (i.e., F.Costs(MRT), F.Costs(FSO), F.Costs(FSO), V.Costs(MRT), and V.Costs(FSO)) also represent user-specified inputs of the link design algorithm.
## 3 Results and Discussion
### Electrical Properties  
All synthesized types of sulfur copolymers have exceptionally good insulating properties. The specific contact resistance ranges from 10\({}^{15}\)-10\({}^{16}\)\(\Omega\cdot\)cm (Figure 9). Thereby, they provide similarly good properties as previously known insulating materials, such as conventional hydrocarbons like PMMA with 10\({}^{15}\)\(\Omega\cdot\)cm or PE, PP and PB with 10\({}^{16}\)\(\Omega\cdot\)cm [31; 32]. The specific contact resistance of sulfur copolymers is one order of magnitude higher than that of elemental sulfur with 10\({}^{15}\)\(\Omega\cdot\)cm. Furthermore, sulfur copolymer materials have a much higher resistivity than the brittle elemental sulfur that does not have any ductility. Thus, sulfur copolymers would be a good alternative for insulating materials with high amounts of inexpensive elemental sulfur. Only materials such as PTFE have a higher specific contact resistance of 10\({}^{17}\)\(\Omega\cdot\)cm [33].  
The specific contact resistance of pS-DVB increases with increasing cross-linker DVB (Figure 9a). Thus, a higher DVB content increases the insulating properties. The pS-DIB does not show any correlation with added DIB content and lies in the same order of magnitude (Figure 9a). In this connection, the terpolymer pS-DVB-DIB elucidates the correlation of the increasing specific contact resistance with an increasing DVB content (Figure 9b). In contrast to this, the terpolymer pS-DVB-STY (Figure 9b) refutes the insight that there is an influence on the insulating properties. Certainly, its value is similar and also the influence of the styrene (STY) could be higher than that of DVB.  
The resistance of insulating sulfur copolymers against high voltage or dielectric strength is very low. The value of dielectric strength of all test specimens is about 9 kV\(\cdot\)mm\({}^{-1}\), similar to the value of glass (10 kV\(\cdot\)mm\({}^{-1}\)). Conventional polymers (hydrocarbons) have a dielectric strength of one to two orders of magnitude higher than sulfur copolymers [31]. This test is very sensitive towards air inclusions and inhomogeneity. Whether these low values are due to the material itself or caused  
Figure 8: Glass transition of terpolymer based on pS-DVB with STY and DIB. The monomer content amounts to 20 wt %.  
by the imperfections of the test specimens could not be resolved definitively. It indicates that the presence of elemental sulfur in the copolymer strongly affects its stability and the reproducibility of the results obtained by different analytical methods. If these negative side effects could be eliminated, the sulfur copolymers with STY and DVB contents in particular would have the potential to be well insulating materials.
# Inspection and Structural Rehabilitation of an Existing Masonry Arch Railway Bridge
## 3 Seismic Vulnerability Analysis and Results
### Result of Pushover Analysis  
According to current codes [10; 11], verification of the nonlinear static analysis is satisfied if the following relation (1) is valid for each performed analysis:  
\[U_{max}\geq d_{max} \tag{1}\]  
where U\({}_{\max}\) is the displacement capacity of the structure and d\({}_{\max}\) is the required displacement of the actual MDOF system. This displacement is obtained by multiplying the required displacement of the equivalent system d\({}_{\max}^{*}\) by the modal participation factor \(\Gamma\) (d\({}_{\max}=\Gamma\)d\({}_{\max}^{*}\)). The required displacement of the equivalent system is a function of the initial proper period associated with the elastic range T\({}^{*}\) and it is evaluated using Equation (2) if T\({}^{*}\geq\) T\({}_{\rm c}\) or Equation (3) if T\({}^{*}\leq\) T\({}_{\rm c}\):  
\[d_{max}^{*}=d_{e,max}^{*}=S_{De}(T^{*}) \tag{2}\]  
\[d_{max}^{*}=\frac{S_{De}(T^{*})}{q^{*}}\bigg{[}1+(q^{*}-1)\frac{T_{\rm c}}{T^{ *}}\bigg{]}\geq S_{De}(T^{*}) \tag{3}\]  
\[q^{*}=\frac{S_{e}(T^{*})}{\frac{F_{\rm c}^{*}}{m^{*}}} \tag{4}\]  
According to the Italian Standard [11], the behavior factor q* must be less than 3 for life-saving limit state (SLV); otherwise, the assessment result must be considered negative.  
The obtained values of U\({}_{\max}\) for the four MDOF capacity curves are the following:  
* MDOF Capacity Curve Push. long. mode: U\({}_{\max}\) = 7.6 mm;
* MDOF Capacity Curve Push. long. masses: U\({}_{\max}\) = 10.4 mm;
* MDOF Capacity Curve Push. trans. mode: U\({}_{\max}\) = 7.1 mm;
* MDOF Capacity Curve Push. trans. masses: U\({}_{\max}\) = 7.2 mm.  
To evaluate d\({}_{\max}^{*}\), the q* values were calculated:  
* equivalent bilinear Push. long. mode: q* = 2.2 \(<\) 3;
* equivalent bilinear Push. long. masses: q* = 1.53 \(<\) 3;
* equivalent bilinear Push. trans. mode: q* = 3.88 \(>\) 3;
* equivalent bilinear Push. trans. masses: q* = 1.25 \(<\) 3.  
Because in the case of Push. trans. mode q* = 3.88 \(>\) 3, the verification in terms of displacement is \"not satisfied\".
# Context-Aware Wireless Sensor Networks for Smart Building Energy Management System
## 4 Proposed EMS for SEEB
### WSN Network Packet Delivery Evaluation
#### 4.5.2 Gateway Test  
As presented in Figure 6, the Raspberry Pi gateway performs the best in both full mesh and cluster tree topologies. It reaches a 100% successful packet arrival rate at around 5 s in inter-packet interval time for full-mesh. The packet loss starts at 6 s inter-packet interval time in the cluster tree because there are fewer alternative routes, and the network is congested at the gateway. Arduino Uno gateway does not perform well in short inter-packet interval time (less than 9 s). It achieves 20% and 30% successful packet delivery rate in cluster tree and full-mesh topologies. The packet delivery rate increases as the inter-packet interval time increase, reaching 100% between 10 and 15 s. The original XBee gateways performed well in both network topologies with performance little lower but comparable to RaspberryPI.
## 3 Results
### Washability (Wet Scrub Resistance)
The combined performance of binders and synthetic polymers such as polyvinyl acetate (PVAc) shows that the binder's performance in paint film formation is very satisfactory. This is because monomer grafting can increase the adhesion and polarity of NRL, and PVAc shows high suitability and effectiveness in improving the mechanical properties of natural polymer-based materials, especially in terms of resistance to scrubbing and wet abrasion [18; 32; 53]. It can be seen in Figure 4 that the combination of NRL grafted with 20% styrene with PVAc at a ratio of 70:30 (NRL\(-\)g\(-\)20%St/PVAc) showed a significant decrease in binder performance at 10% binder concentration (Sample NgSt20/PVAc-10). The decrease in performance in the NgSt20/PVAc-10 sample may suggest a competitive radical binding reaction [30; 47]. The styrene monomer radical site is very active in binding to the PVAc radical site, which reduces the radical site and decreases the adhesion to substrate binding. Further studies are needed to investigate the effect of mixing NRL grafted with styrene and PVAc on its polarity and hydrophilicity. NRL grafted with 30% MMA combined with PVAc (NRL\(-\)g\(-\)30% MMA/PVAc) succeeded in increasing the paint binder resistance to scrubbing and wet sloughing much better than the combination of NRL\(-\)g\(-\)20%St/PVAc. As seen in Figure 4, the performance of the paint binder increased as the PVAc content increased, until it reached an optimal performance of 10% binder concentration, namely, the sample NgMMA30/PVAc-10 (23 cycles). Based on this, it can be concluded that PVAc plays an important role in increasing the resistance to wet scrub and abrasion while grafting MMA monomers in the NRL polymer chain improves adhesive properties, forms a film layer that is harder, more elastic, and has higher solvent resistance [8; 10; 53; 54]. When comparing the performance of the combined NRL binder with styrene monomer or MMA and PVAc, the combined NRL with grafted MMA and PVAc monomer had significantly better performance. MMA monomer can polymerize with NRL faster than styrene and is more stable and non-solvent. MMA polymerized NRL is resistant to wet friction and abrasion [55]. The combination of NRL modification with monomer grafting and synthetic polymer blending has proven to be successful in covering the weaknesses and even increasing the performance of NRL-based binders as adhesives or binders. With the success of the modification of NRL as a paint binder, it is necessary to pay attention to the cost of producing paint when using a modified NRL-based binder. It is very important to use NRL as a paint binder. Figure 4: Washability Combination of NRL grafting Styrene or MMA and PVAc.