esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Platform for Real-Life Testing of Communication Protocols
### Testbed Architecture
#### 3.2.1 Testbed Net IO  
One of the most critical components of the testbed architecture is the Net IO blocks. These blocks connect the application being tested to the network stack of the environment,  
Figure 8: The architecture of the testbed for GNU radio applications evaluated through simulations.  
Figure 9: The architecture of the developed testing framework for GNU radio applications evaluated in real channel conditions.  
allowing test traffic to be fed into the GNU radio application through the environment's network interfaces. The test traffic is injected into the application through the environment's TUN/TAP interfaces, as explained in Section 3.1. In the current implementation, the GNU radio built-in TUN/TAP blocks are used to read/write data from/to the interfaces. These blocks pass the protocol data units (PDUs) through the messaging-passing application program interface (API). Since the GNU radio application evaluated in this research works with tagged streams, it is essential to convert the input data flow into tagged streams. This conversion is achieved by the PDU to TS block operation.  
When testing communication protocols like the PHY layer of the GNU radio transmission system, data are transferred from the PDUs to the data frames. However, this requires a size matching operation because the PHY layer frame size can be smaller than the upper layer PDU size and can change during transmission, making it impossible to directly use the MTU parameter to control the upper-layer PDU size. To deal with this issue, a PDU reconstruction/defragmentation block is added to the Net IO block on the receiving path (the flow defragmentation block in Figure 8). This block reconstructs the upper-layer PDUs before passing them further to other blocks. It is worth mentioning that fragmentation occurs when the PDUs are loaded into the PHY frames. The issue arises when the upper layer passes a PDU that does not fit into a single PHY frame. As our environment supports both TUN and TAP interfaces, the defragmentation operations are implemented for both layer 3 and layer 2 PDUs. The defragmentation mechanism discussed here assumes that the upper-layer protocol data units (PDUs) are synchronized with the physical (PHY)-layer frame. The mechanism tries to identify the beginning of the upper-layer PDU to start buffering the PHY-layer frames until the PDU length is reached or a new PDU is detected. The detection of the beginning of the PDU depends on the upper-layer protocol, which, in this case, is the TCP/IP stack-based networking.  
In the case of the TUN interface setup (layer 3), the algorithm identifies the beginning of the PDU by using the IP header checksum. On the other hand, in the case of the TAP interface setup (layer 2), the algorithm uses the destination MAC address in the Ethernet header, which is known at the receiver, to identify the start of the PDU.
# Research on an Index System for the Earthquake Disaster Resistance Capability of Highway Tunnels
## 2 Analysis of Seismic Damage Scenes in Highway Tunnels
### Analysis of the Influencing Factors of Earthquake Disasters in Portal Sections of Highway Tunnels
#### 3.3.1.  
Lining Structure Cracking, lining Structure Dislocating and Lining Structure Collapsing  
Lining structure cracking, lining structure dislocation and lining structure collapse are caused by the action of seismic force on the tunnel body section, which causes damage  
Figure 5: Fault tree of seismic damage scenes of the highway tunnel portal section.  
to the tunnel lining structure. There is a progressive relationship between them, which is accompanied by the general situation. Through a case investigation [26], during the Wenchuan Earthquake, a large number of transverse cracks occurred in the right line lining of Zipingpu Tunnel, among which, the right arch to the sidewall was the most severe, with a crack width of 5-10 mm, accompanied by a small amount of lining structure dislocation and lining structure collapse. Lining longitudinal cracks with a length of approximately 5 m and a width of approximately 2 mm appear on both sidewalls of Youyi Tunnel, and lining network cracks are formed in the local area; Ma'anshi Tunnel experiences dense diagonal lining cracking, and the cracking width is 0.5-3 mm. The lining structure of Baiyunding Tunnel has continuous cracks accompanied by staggered lining structure dislocating cracks. The reasons for the damage of the above tunnels are roughly the same and include the nonuniform surrounding rock, such as the junction zone of the two surrounding rocks. At the interface, the displacement difference is generated due to the different vibration characteristics of the surrounding rock on both sides, which, in turn, affects the local deformation of the tunnel and causes damage to the tunnel lining. In the construction process of Longxi Tunnel, the collapse accident was caused by excessive ground stress and too-loose surrounding rock, and monitoring revealed that there were holes behind the lining, which caused the overall collapse of the lining when the earthquake occurred. Due to the weak surrounding rock, the seismic damage to Jiujiaya Tunnel is serious, and the lining structure has collapsed.  
As shown in the above cases, there are two causes of lining structure cracking: lining structure dislocation and lining structure collapse. One is that the relaxation of the surrounding rock has a poor constraint on the tunnel structure. During an earthquake, the effect of the seismic inertial force increases, and the surrounding rock passes through different lithologies. Different displacements in earthquakes cause large fault movements, which are considered engineering geological factors. The tensile and shear strengths of concrete are very low. When the tensile force, shear force and bending moment generated by ground vibration act on the lining during an earthquake, the local tensile stress and shear stress of the lining increase and cause damage, which are tunnel structural factors.
## 4 Experiments
### Representation Disentanglement  
We perform representation visualization analysis to validate the effectiveness of representation learning in our model. The experiments are conducted on the ETTh1 dataset with an input sequence length of 96 and a prediction length of 24. The learned seasonal and trend representations are visualized using the T-SNE technique. As shown in Figure 4, compared with Autoformer [2] and the Fedformer [3], the clustering effects of both seasonal and trend representations are more obvious, and there exists a clear boundary between seasonal representation and trend representation learned by DESTformer. We argue that such representations learned by the proposed multi-scale attention mechanisms in both trend and seasonal domains lead to improved performance in the forecasting tasks.  
Figure 3: Efficiency analysis of the two special attention mechanisms proposed in this article with the DESTformer under the same experimental setup as Autoformer.
## 3 Battery Mathematical Models
### Battery Degradation Model  
Accurate prediction of lithium-ion batteries' lifetime in any application (residential buildings, factories, stand-alone RES energy stations, cars) is of great importance so as to secure their stability and safety [16; 17]. As it is analyzed in [18; 19; 20] the lifetime expectancy of a lithium-ion battery can be affected by several factors such as: (a) environmental temperature (\(T\)), (b) charging/discharging rate (\(C_{ntle}\)), (c) depth of discharge (\(DOD\)), (d) number of full charge/discharge cycles and (e) time intervals  
Figure 4: Data fitting curves: (**a**) For I = 15 A; (**b**) For I = 20 A.  
Figure 5: Data fitting curves: (**a**) For I = 25 A; (**b**) For I = 30 A.  
between full charge/discharge cycles. In this work, the experimental validated battery ageing model from [21] is utilized. This model comprises the aforementioned ageing factors including the taper voltage of the battery.  
As it is thoroughly analyzed in [21,22] the battery ageing model can be stated as an Arrhenius equation. Therefore, the capacity loss is increasing exponentially through time (number of cycles).  
\[Q_{loss}\ =\ Ae^{\frac{-E_{a}}{RT}}n^{z} \tag{11}\]  
Equation (11) depends on the temperature and number of cycles. The validation of the model requires the identification of the following battery ageing parameters (Table 1): (a) pre-exponential factor \(A\), (b) the activation energy \(E_{a}\) and (c) the cycle's exponent \(z\). Those parameters are going to be identified by charging/discharging rate, depth of discharge and taper voltage [21].
###### Abstract  
To investigate the influence of external prestressing with carbon fiber-reinforced polymer (CFRP) tendons on the flexural and shear performance of reinforced concrete beams, a total of six simply supported beams with a length of 3.5 m and two two-span continuous beams with a length of 7 m were studied. The influences of the prestressing level of CFRP tendons, the pattern of straight-line and flexural reinforcement of externally prestressed CFRP tendons, and the existence of initial cracks in the beams on the flexural bearing capacity of the reinforced beams were explored through modeling tests. The calculating equation for anticipating the ultimate flexural bearing capacity of concrete beams reinforced with externally prestressed CFRP tendons was modified based on the test results. The findings indicate that the flexural bearing capacity of the test beams was significantly enhanced through both flexural and straight-line reinforcement. Specifically, the ultimate flexural bearing capacity of the simply supported beams exhibited improvements of approximately 160% and 170%, respectively, whereas the ultimate flexural bearing capacity of the continuous beams was enhanced by approximately 113% through the reinforcement of flexural arrangement. Additionally, the shear resistance capacity of the concrete beams was notably improved through the flexural arrangement reinforcement, and the shear resistance capacity of the beams exhibited a substantial increase as the prestressing level of the CFRP tendons increased. The flexural moments in the vicinity of the support point of the reinforced beam exhibited an increase ranging from 41.7% to 112.5% when the stirrup strains started to escalate rapidly. Additionally, the rigidity of the test beams showed a close correlation with both the prestressing level and the existence of initial cracks.  
simply supported and continuous beams; simultaneous flexural and shear strengthening; flexural arrangement of CFRP tendons; experimental results +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Preliminary Study to Evaluate the Amount of SSA to Incorporate in the Asphalt Mixtures
#### 3.3.1 Particle Size Adjustment of Asphalt Mixtures for Different SSA Incorporation Rates  
The grading curves of two asphalt mixtures were studied in this preliminary phase based on the relevant specifications [26]: one for the surface course, AC 14 surf 35/50 (BB), and the other for binder/base courses, AC 20 bin/base 35/50 (MB). Different amounts of SSA and NA fractions were used, bearing in mind the objective of producing AC 14 mixtures with 50% and 80% SSA and AC 20 mixtures with 50% and 95% SSA incorporation rates. The maximum SSA incorporation rates (80% and 95%) resulted from the grading envelope restrictions of the specifications, stricter for surface courses. Figure 6 shows the particle size adjustments obtained for both mixtures (AC 14 and AC 20), resulting from using the compositions shown in Table 4. The values indicated in Table 4 were determined based on the aggregates' size, thus considering volumetric proportions. The equivalent mass values used during production were calculated based on the aggregates' density.
## 1 Introduction
We have conducted extensive simulation to validate the proposed scheme, and a field experiment with a wireless sensor network that implements this proposed scheme is also conducted. Both simulation and experimental results demonstrated superior performance. The main contributions of this paper can be summarized as follows:  
* We demonstrated the feasibility of incorporating compressive sensing as an error correction measure to facilitate communication over lossy wireless links. * We propose a new cyber-physical measurement process model in applying compressive sensing where the packet loss in a wireless link is modeled as a random sampling process. * We propose a novel wireless link performance metric, called the information acquisition rate, to measure the actual information content that is transferred. We show that this metric better reflects the actual performance of data transmission than the conventional data-oriented criteria, such as the packet reception rate. * We established important relations between packet lengths and the mutual coherence, which is critical to the success of compressive sensing reconstruction. The rest of this paper is organized as follows. Sections 2 and 3 review the related work and compressive sensing (CS) theory, respectively. We present the proposed sparse signal transmission framework in Section 4. Packet length control for sparse signal transmission is shown in Section 5. Section 6 shows comparison simulations between the conventional ARQ interpolation technique and the CS-based method, as well as the packet length effect on sparse signal transmission. Experimental verification is presented in Section 7. Finally, Section 8 concludes this paper. The following notation is used throughout the paper. Vectors and matrices are denoted by boldface lowercase and boldface uppercase letters, respectively. The identity matrix is denoted by \(\mathbf{I}\). For any matrix \(\mathbf{A}\), \(\mathrm{A}^{\prime}\) or \(\mathrm{A}^{\mathbf{T}}\) refers to the matrix transpose; \(\mathrm{Tr}(\mathbf{A})\) is the trace. The \(l_{p}\) norm of a vector \(\mathbf{x}\), for \(1\leq p\leq\infty\), is denoted \(||\mathbf{x}||_{p}=(\sum_{i=1}^{N}|x|)^{1/p}\), while \(||\mathbf{x}||_{0}\) denotes the number of nonzero elements in \(\mathbf{x}\). The main notation in this paper is listed in Table 1.
## 4 Results
### Performance Comparison with Previous Work  
All previous work on routing in WBANs assumed unique application and system assumptions as the setting for algorithm design and evaluation. The proposed protocol is designed for physiological monitoring applications. Algorithm design therefore targeted energy balancing as a means to reduce maintenance by achieving infrequent recharging of batteries while maintaining reliability and wearability. As a consequence, performance analysis focused on network lifetime, energy per bit and connectivity parameters rather than latency.  
Figure 13: Network connectivity outage ratio as a function of normalized maximum transmission power. Note the threshold effect, where beyond a normalized power level the network is almost always connected.  
There is no previous work on global routing in WBANs, and previous work on routing in WBAN did not consider these settings. Some even considered other system assumptions, e.g., use of dedicated relays. It follows that there is no meaningful way of comparing performance. It is however possible to position the proposed global routing protocol within state-of-the-art by summarizing performance results of relevant methods to date. Table 2 presents such a summary. It includes the work reported in [3; 4; 5; 6] briefly described in the Introduction Section. These past works were selected since they considered energy consumption and network lifetime rather than latency. It is clear from the summary that the proposed protocol excels at its original design goal of increasing network lifetime without using extra hardware such as dedicated relay nodes.
## 6 Real Jamming Results Analysis  
In this section, the performance of the GNSS receiver is assessed based on its ability to provide a reliable positioning solution and track GNSS satellites under real jamming conditions. The reference solution was logged using the Orolia GSG-8 GNSS simulator. The jamming signal was inserted from minute 14 to minute 16 on the logged data for both the low and high jamming conditions.
## 4 Results and Discussion
### Consensus on Challenges
#### 4.1.2 Device Discomfort  
Most of the collected papers reported the use of AR through the implementation of software in a headset device such as \"Trimble XR10\" and also reported a challenge related to the weight of the device on the head of the user [19; 33; 34; 36; 37]. The weight is considered to be a considerable constraint of the device supporting the application, especially when using the device over a long period of time. Twelve papers reported the same attribute related to the device being uncomfortable which showed a consensus among them. Even though AR hardware and software are being consistently improved, the size of the hardware installed on the head is a challenge considering the incorporated hardhat needed to address safety and PPE requirements. Therefore, this challenge is related to the type of device in use.  
Figure 3: Number of attributes (i.e., benefits and challenges) showing consensus/divergence.
## 3 Results and Discussion
### Morphology and Contact Angle Analysis of the LDHs  
Figure 3A illustrates the SEM images for the four LDHs. S-NiMgAl-Mn and S-NiMgAl-Co show better effect of grafting for the stearate. In particular, the surface of S-NiMgAl-Mn was uniformly coated by the stearate. Therefore, S-NiMgAl-Mn can be expected to show better compatibility with the EVA matrix. However, the unmodified particles of many petal-like structures for S-NiMgAl-Zn can be seen clearly. This result indicates that S-NiMgAl-Zn has the least grafted amount of stearate. This result is consistent with that of the IR analysis. In addition, TEM images show flake particle morphology and crystal sizes of nanometre thickness (as shown in Figure 3B).  
The contact angles were measured to investigate the influences of metal cations (Mn\({}^{2+}\), Co\({}^{2+}\), Cu\({}^{2+}\) and Zn\({}^{2+}\)) on the hydrophobicity of LDHs (as shown in Figure 4). The contact angle values for S-NiMgAl-Mn, S-NiMgAl-Co, S-NiMgAl-Cu and S-NiMgAl-Zn are 92\({}^{\circ}\), 83\({}^{\circ}\), 68\({}^{\circ}\) and 88\({}^{\circ}\), respectively. S-NiMgAl-Mn has the largest contact angle. One possible reason is that S-NiMgAl-Mn has better grafting effect of the stearate than the other three LDHs. However, note that although S-NiMgAl-Zn has the least stearate, the hydrophobicity of S-NiMgAl-Zn is greater than those of S-NiMgAl-Co and S-NiMgAl-Cu. This indicates that the inherent nature of different transition metal ions has an important role in the hydrophobicity of LDHs.  
Figure 2: **(A)** FTIR (Fourier transform infrared spectroscopy) spectra of the synthesized LDHs. **(a)** S-NiMgAl–Mn; **(b)** S-NiMgAl–Co; **(c)** S-NiMgAl–Cu and **(d)** S-NiMgAl–Zn; **(B)** Schematic illustration of stearate surface modified LDHs.
# Numerical Simulation-Based Study of Controlled Particle Deposition Technology for Wafer Surfaces
## 2 Materials and Methods
### Experimental Materials and Methods
Polystyrene latex (PSL) particles with diameters ranging from 70 to 200 nm were used. To evaluate deposition results, the distribution and size of particles on the wafer surface must be determined. A calibrated SSIS can fulfill some of these requirements by comparing light scattering signals and analyzing scanning results based on calibration data. However, its accuracy depends on the precision of the light scattering model. In contrast, scanning electron microscopy (SEM) employs a high-energy electron beam to scan the sample surface, utilizing electron interactions with sample atoms to generate secondary and backscattered electron signals. These signals are captured by a detector and converted into images and data, enabling precise particle size measurement for traceability.
# Smart Nanocomposites for Nanosecond Signal Control: The Nano4 waves Approach
## 3 Results and Discussion
### Electro-Magnetic Characterization of Nano-Inclusions
#### 3.2.1 Electrical Characterization  
As first illustration, Figure 10 shows the influence of oxidation on the dielectric constant \(\varepsilon\), and conductivity \(\sigma\), of graphenic materials. As expected, the oxidized nature of graphene oxide (GO) confirmed by XPS induces a lower conductivity than for GNPs. In turn, the dielectric constant of the latter is higher due to its conductivity, according to the Maxwell Sillars effect described in [13] and model-predicted in ref. [14; 21].  
The influence of the presence of MNPs on \(\varepsilon\) and \(\sigma\) was also investigated. Figure 11 compares electrical parameters \(\varepsilon_{r}\) and \(\sigma\) measured for a same 30% weight ratio of nickel (Ni) and magnetite (Fe\({}_{3}\)O\({}_{4}\)) MNPs on GO, respectively. An important difference in conductivity is observed between nickel and magnetite, in accordance with the oxidized hence insulating nature of the latter. As explained before, this difference reflects on the composite's dielectric constant, \(\varepsilon_{r}\).  
Figure 10: (a) Comparison of electrical parameters \(\varepsilon_{r}\) and \(\sigma\) for GNPs and GO; (b) XPS analysis confirming the higher oxygen content in GO, responsible for lower \(\sigma\).  
Figure 9: TEM images of Fe\({}_{3}\)O\({}_{4}\) deposited onto oxidized (a) or pristine (b) MWCNTs using the solvothermal method [16].
## 8 Results
### Results Hypothesis 2 (Answer Search Task)  
Hypothesis 2: RAAVE is better at allowing users to search for information in different parts of a video compared to baseline. By better, we mean the following:  
* Users were able to answer the question more accurately with RAAVE compared to Baseline.
* Users were able to search the answers efficiently with RAAVE compared to Baseline.  
Table 8, shows the results of answer search task. Table 8 shows the overall performance of users using both systems.  
Overall results in Table 8 show that in terms of answering correctly users did better using the baseline systems 9.5 correct answers compared to 9.16 using RAAVE. However, for the two videos containing visual aid (ED+TP) users were able to perform better using RAAVE i.e., on average, the user performed better with RAAVE for difficult videos whereas their performance was better using the baseline system for easier videos. (detailed in Section 7.4 for discussion about easy and difficult videos).  
In terms of efficiency, users seem to perform better using the RAAVE system, as the normalized duration per correct question is lower for RAAVE compared to Baseline. The duration per question is calculated by measuring the normalized duration (Section 7.6.3) spent by the user performing the task, divided by correct answers (lower is better).  
It can be seen in Table 8 that while on average user answer more correctly using the baseline system (9.50 using baseline vs. 9.17 using RAAVE) in terms of efficiency user spent less time per correct answer using RAAVE compared to the baseline (10.04 using RAAVE vs. 10.75 using Baseline (lower is better)). The Student T-Test p-value of the result is 0.27 which means the results are not statistically significant.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Row\#** & **Videos** & \multicolumn{2}{c}{**RAAVE**} & \multicolumn{2}{c}{**Baseline**} \\ \hline  & & **Correct** & **Per Question** & **Correct** & **Per Question** \\ \hline
1 & Overall Avg. (avg. corr. / avg. dur) & 9.16 & 9.57 & 9.50 & 10.23 \\ \hline
2 & Average all users & 9.17 & 10.04 & 9.50 & 10.75 \\ \hline
3 & Median all users & 10 & 8.80 & 10 & 10.60 \\ \hline
4 & CF+PC (Avg.) (no slides) & 10 & 8.12 & 11.16 & 8.46 \\ \hline
5 & ED+TP (Avg.) (with slides) & 8.33 & 11.21 & 7.83 & 12.75 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Answer search task results (overall performance).  
Figure 6: Blue (darker) bars represent RAAVE while green (lighter) bars represent the baseline system. Simple T-Test to check if the scale means of the two systems differ significantly.