esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Reusability of the Sorbent
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Microextraction** \\ **Method** \\ \end{tabular} & \begin{tabular}{c} **Detection** \\ **Technique** \\ \end{tabular} & \begin{tabular}{c} **Sorbent or** \\ **Extractant** \\ \end{tabular} & \begin{tabular}{c} **Derivatization** \\ **Agent** \\ \end{tabular} & \begin{tabular}{c} **Sample** \\ **[ng mL\({}^{-1}\)]** \\ \end{tabular} & \begin{tabular}{c} **SMD** \\ **[\%]** \\ \end{tabular} & **Refs.** \\ \hline SPME & GC-FID & PDMS/DVB & PFBHA & \begin{tabular}{c} hair gel particle \\ board indoor air \\ \end{tabular} & - * & - * & [15; 16] \\ \hline SPME & ID-MS & PDMS/DVB & PFPH & Cosmetic products & 3.9 & 3.8-7.8 & [23] \\ \hline USAEME & micro-SP & CH\({}_{2}\)Cl\({}_{2}\) & Nash reagent & Cosmetic products & 0.02 & 5.9 & [17] \\ \hline IL-DLLME & HPLC-UV & [C\({}_{8}\)MIM][PF\({}_{6}\)] & DNPH & room air, tap & 0.01 & 1.3–6.8 & [8] \\ \hline LDM-DLLME & HPLC-UV & BA-MEOH & DNPH & \begin{tabular}{c} tap water, \\ juice, vaccine \\ \end{tabular} & 0.05 & 3.0–8.0 & [7] \\ \hline RP-DLLME & HPLC-UV/Vis & H\({}_{2}\)O & Nash reagent & Cosmetic products & 0.7 & 1.9–9.2 & [9] \\ \hline DES-VA-LLME & HPLC-DAD & \
\begin{tabular}{c} TAC:CP \\ (1:1) \\ \end{tabular} & DNPH & indoor air & 0.2 ** & 1.1–3.5 & [10] \\ \hline AA-DLLME-SPO & SF & thymol & Nash reagent & milk & 15 & \textless{}9.0 & [18] \\ \hline DES-TF-SPME & HPLC-UV/Vis & DES & Nash reagent &
\begin{tabular}{c} lake water \\ river water \\ \end{tabular} & 0.15 & 3.3–4.8 & this work \\ \hline \hline \end{tabular}  
* not given; ** ug mL\({}^{-1}\)**; SPME—solid phase microextraction; GC-FID—gas chromatography with flame ionization detector; MS—mass spectrometry; PDMS—poly(dimethylsiloxane); DVB—divinylbenzene; PFBHA—o– (2,3,4,5,6-pentafluorophenyl)hydroxylamine hydrochloride; PEPH—pentafluorophenylhydrazine; RP—reversed phase; USAEME—ultrasound-assisted emulsification microextraction; micro-SP–micro-spectrophotometry; IL—ionic liquid; DLLME—dispersive liquid-liquid microextraction; DCM—low density miniaturized; DES—deep eutectic solvent; VA—vortex assisted; LLME—liquid-liquid microextraction; TAC—trotylmethylammonium chloride; CP—4-cyanophenol; DNPH—2,4-dinitrophenylhydrazine; AA—air assisted; BA—butyl acetate; SF—spectrofluorimetry; HPLC-UV/Vis—high performance liquid chromatography with ultraviolet/visible detection.  
\end{table} 
Table 5: Overview of the analytical performance of different microextraction methods for preconcentration of formaldehyde and comparison with the proposed method.
# Applications for Microwave Kinetic Induction Detectors in Advanced Instrumentation
## 5 MKIDs Applications in Material Science with Synchrotrons
[134; 147] are developing TKIDs for X-ray spectroscopy in the keV range with the potential future application of measuring the mass of neutrinos. These TKIDs are made of Ti/TiN multilayers, with an absorber suspended on an Si\({}_{3}\)N\({}_{4}\) membrane. Three different films were produced, with Ti/TiN thicknesses of 10/7 nm (total thickness of 100 nm), 10/10 nm (total thickness of 102 nm), and 10/12 nm (total thickness of 110 nm), giving critical temperatures of 0.6 K, 0.8 K, and 1.2 K respectively and kinetic inductances of 30 pH/\(\square\), 20 pH/\(\square\), and 12 pH/\(\square\). Each chip produced contained four resonators, each comprising of two interdigitated capacitors and an inducting line which acts as the absorber. These resonators have been tested successfully, with quality factor Q\({}_{\mathrm{c}}\) = 17,000 and Qi = 82,000, at a resonant frequency of 5.598 GHz. Signal pixel optimization is being carried out, after which the best performing resonator design will be implemented in a larger array.
## 1 Introduction
In addition to NFIB cost maintenance, the upstream Interest processing procedure allows the immediate neighbors of the nominated node to take part in communication and play a pivotal role in minimizing the redundant broadcasts when the designated node does not have information about the potential next hop or fail to further forward the Interest packets. Furthermore, the CIDF-WSN's unique Data forwarding procedure reduces the incoming Data packet loss due to unpredictable channel variations and upholds the QoS of the network. Finally, the cache tables, such as Temp Cache Table (TCT) and Interest Cache Table (ICT), developed by the CIDF-WSN play an indispensable role and decrease the overall Interest and Data packet losses. The main contributions of our proposed work are summarized as follows:  
1. **Layered Architecture:** CIDF-WSN puts forward a layer-based architecture for Interest and Data packet forwarding for resource-constrained Named Data Internet of Things (IoT). 2. **NFIB for single radio WSN nodes:** We extend the existing NDN architecture and propose a new table named NFIB which enable the node to select the optimal next forwarder in Interest forwarding. 3. **Interest packet processing:** CIDF-WSN develop an efficient Interest processing mechanism to avoid Interest packets loss and to update the cost associated with the NFIB entries. 4. **Data packet recovery:** CIDF-WSN presents a robust Data packet recovery mechanism to avoid Data packet loss when the designated lower layer node in the breadcrumb path fails to forward a packet. 5. **Interest Cache Table (ICT) and Temp Cache Table (TCT):** we devise an Interest Cache Table (ICT) and Temp Cache Table (TCT) to avoid transmission(s) losses. Through the extensive NDNSim [17] based simulation study, we demonstrated that CIDF-WSN outperforms recently developed schemes for NDN-based WSNs, by reducing the total energy consumption, data retrieval delays, and communication overhead. The rest of this paper is organized as follows: Section 2 outlines challenges in addressed communication architecture, an overview of NDN and its benefits for WSNs are provided in Section 3. Related work is discussed in Section 4. A detailed description of the CIDF-WSN is provided in Section 5. Section 6 is devoted to a performance evaluation study and finally, Section 7 concludes the paper.
# An Adaptive Data Rate Algorithm for Power-Constrained End Devices in Long Range Networks
###### Abstract  
LoRa (long range) is a communication technology that employs chirp spread spectrum modulation. Among various low-power wide area network (LPWAN) technologies, LoRa offers unique advantages, including low power consumption, long transmission distance, strong anti-interference capability, and high network capacity. Addressing the issue of power-constrained end devices in IoT application scenarios, this paper proposes an adaptive data rate (ADR) algorithm for LoRa networks designed for power-constrained end devices (EDs). The algorithm evaluates the uplink communication link state between the EDs and the gateway (GW) by using a combined weighting method to comprehensively assess the signal-to-noise ratio (SNR), received signal strength indication (RSSI), and packet reception rate (PRR), and calculates a list of transmission power and data rates that ensure stable and reliable communication between the EDs and the GW. By using ED power consumption models, network throughput models, and ED latency models to evaluate network performance, the Zebra optimization algorithm is employed to find the optimal data rate for each ED under power-constrained conditions while maximizing network performance. Test results show that, in a single ED scenario, the average PRR achieved by the proposed ADR algorithm for power-constrained EDs in LoRa networks is 14% higher than that of the standard LoRaWAN ADR algorithm. In a multi-ED link scenario (50 end devices), the proposed method reduces the average power consumption of EDs by 10% compared to LoRaWAN ADR, achieves a network throughput of 6683 bps, and an average latency of 2.10 s, demonstrating superior performance overall. The proposed method shows unique advantages in LoRa networks with power-constrained EDs and a large number of EDs, as it not only reduces the average power consumption of the EDs but also optimizes network throughput and average latency.  
LoRa; ADR; power-constrained; Zebra optimization algorithm; network throughput  
1
# Reliability and Efficiency of Metamodel for Numerical Back Analysis of Tunnel Excavation
## 2 Methods  
This study utilised numerical analyses, ANNs, and GSA. Figure 1 illustrates the flow of this study. First, we generated a database by performing numerical analyses on tunnel excavations. The numerical analyses were performed by changing the values of the five ground parameters 10 times to generate a total of 100,000 datasets. An optimal metamodel was then determined through grid search by changing the number of nodes and activation functions using this database for ANNs.  
Next, the numbers of values of the ground parameters were reduced from 10 to 8, 6, 4, and 3. These values allowed creation of datasets of sizes 32,768 (8\({}^{5}\)), 7776 (6\({}^{5}\)), 1024 (4\({}^{5}\)), and 243 (3\({}^{5}\)). The optimal models for these four groups of datasets were generated like the optimal model for the group with 100,000 datasets. Each optimal model was subsequently tested with 25% of the datasets left for testing when training the ANNs. In addition, the reliability of each metamodel was verified by testing new numerical analysis datasets resulting from the ground parameter values that were not used previously. Finally, the GSA was performed on the 100,000 datasets to confirm the priorities of the five ground parameters for vertical displacement of the tunnel crown.
# A Novel Deep Learning Model to Predict Ultimate Strength of Ship Plates under Compression
## 2 Methodology
### Grid Convergence Study and Initial Deflection Application  
In the FEM analysis, the size of the grid significantly affects the analysis results [9]. Additionally, because initial deflections due to welding and thermal processing are detrimental to plate structures, initial deflection must be provided for accurate analysis results [10]. A modal analysis was performed and applied as the curved plate had a shape different from the initial deflection shape of the plate due to the influence of the curvature. The analysis was applied in the first mode shape in the analysis results because it was the most deformable with minimum strain energy. Figure 3 shows the shape of the initial deflection according to the curvature.  
In addition to the effect of the initial deflection of the curved plate, the size of the grid also affects the FEM result. The denser the grid size, the closer the FEM value is to the results using the actual experiment. The result from the grid convergence shows that the denser the lattice, the lower is the stress. If the number of grids is insufficient, the result overestimates the ultimate strength of the plate, leading to inaccurate analysis results. In contrast, if the number of grids is excessively large, each node point generated by the grid increases dramatically, thereby increasing the analysis time and decreasing the efficiency [11]. The model of this study was created by fixing 20 square grids in the width direction, similar to the grid convergence survey of a previous study by [PERSON] [12]. Figure 4  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Yield Stress (MPa)** & **Thickness (mm)** \\ \hline
235 & 7, 10, 13.5, 16.5, 20, 24, 28.5, 32, 36.5, 42 \\
315 & 8.5, 12, 15.5, 18.5, 22, 26, 30, 34, 38.5, 44.5 \\
355 & 9, 12, 17, 19, 23, 26.5, 30.5, 34.5, 42, 50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scenario of curved-plate.  
Figure 3: Initial deflection of curved plate.  
Figure 2: Buckling under uniaxial compression.  
shows the results of the ultimate strength analysis according to the number of elements along the width of the curved plate. According to the analysis model used in this study, the curved plate with 20 elements was selected for analysis. The details of the plates used in the analysis are provided in Figure 4. NoE is the number of elements along the width. For different curved plates subjected to identical conditions, these values were set to 6, 8, 10, 12, 14, 20, and 30 to investigate the results of each ultimate strength analysis. Figure 4 shows that the value converges at 20 NoE. Thus, the required grids were created using this value.
## 2 Materials and Methods
### Radiation Protection
#### 2.6.2 Minimum Source-to-Skin Distance (SSD)  
Radiation doses in interventional radiology (IR) can exceed the onset for deterministic effects. Therefore, regulations limit the minimum source-to-skin distance (SSD) that is to be achieved during an IR procedure to 45 cm. This requirement was tested by placing plastic spacers on the surface of the X-ray tube collimator assembly [14].
## 2 Preliminaries
### Memory-Based CF  
Based on the similarity between users or items, the memory-based CF technique tries to predict the most appropriate recommendations to users. There are two forms of memory-based CF: (i) user-oriented (or user-user) CF [23] and (ii) item-oriented (or item-item) CF [24]. Of the two approaches, the latter is more favored in practice due to its superior accuracy and better scalability [5]. An item-item CF system (ii-CF) recommends to a specific user those items which are the most relevant to the items rated or purchased by her.  
At the core of these systems is a similarity measure which indicates the analogy \(s_{ij}\) between two items. After computing the similarity degree between items using popular similarity measures such as Cosine similarity function (Cos) or Pearson Correlation Coefficients (PCC), the \(k\) most similar items of item \(i\) rated by user \(u\) can be identified. This set of \(k\) items is called k-nearest neighbors (kNN) which is denoted by \(S^{k}(i,u)\). The most simple formula to estimate \(\hat{r}_{ui}\) is a weighted average of the ratings of similar items \(i\in S^{k}(i,u)\) (named kNNBasic model):  
\[\hat{r}_{ui}^{kNNBasic}=\frac{\sum_{j\in S^{k}(i,u)}s_{ij}r_{uj}}{\sum_{j\in S ^{k}(i,u)}s_{ij}} \tag{1}\]  
A modification of Equation (1) adjusts the final prediction using baseline estimate, hence the name kNNBaseline model [25], as follows:  
\[\hat{r}_{ui}^{kNNBaseline}=b_{ui}+\frac{\sum_{j\in S^{k}(i,u)}s_{ij}\big{(}r_ {uj}-b_{uj}\big{)}}{\sum_{j\in S^{k}(i,u)}s_{ij}} \tag{2}\]  
Besides Cos and PCC, advanced similarity measures, such as PCCBaseline [25] or cubedPCC [26], also effectively improve the performance of kNN models.
## 5 Deformation and Fracture Analysis  
Monitoring the deformation of 45\({}^{\circ}\) sandstone during the loading process was conducted using a CCD camera, and the deformation field on the surface of the sandstone was later calculated using software. The results can be seen in Figure 15. The deformation is primarily concentrated in the final loading stage, hence six monitoring points from the last loading curve were selected for analysis.  
From Figure 15, it can be observed that, at point A, there are small deformations at the four directions of the tips of the pre-existing crack. As the load increases to point B, the deformation increases, with the deformation primarily concentrated at the tips of the pre-existing crack. At point C, the deformation further increases, and wing cracks are generated at the tips of the pre-existing crack.  
When approaching the peak load at points D, E, and F, significant deformations become apparent. The deformation develops from the tips of the pre-existing crack toward the microcracks that have initiated. The upper tip of the pre-existing crack deforms downward, while the lower tip deforms upward. Only two macroscopic microcracks develop, and there is also a trend for the lower tip of the pre-existing crack to extend downward. Macroscopic  
Figure 14: Characterization of the temporal evolution of sandstone rupture.  
Figure 15: Sandstone deformation cloud map.  
cracks initiate multiple directional deformations at different locations and exhibit gradual spreading, branching, and deflection toward the ends [52].  
Under prolonged cyclic loading, rocks may exhibit stress relaxation and creep phenomena, i.e., a decrease in stress with time at a constant strain or an increase in strain with time at a constant stress. Microcracks within the rock may expand and join, leading to damage accumulation and thus affecting the deformation response.  
Figure 16 presents the failure surface of 45\({}^{\circ}\) sandstone. The red areas in the figure indicate regions where shear failure has occurred, while the remaining areas are primarily characterized by tensile failure. The shear regions are concentrated in the middle of the sandstone, and the tensile failure regions are mainly at the ends of the sandstone. The research also found that the area of the shear region is relatively smaller compared to the tensile region, further indicating that the failure of sandstone is mainly dominated by tensile failure with a greater number of tensile cracks generated internally. Additionally, it was observed that a distinct shear fracture surface appears at the moment the sandstone reaches its peak load, with shear cracks primarily forming at the instant of failure.
## 2 Related Work
### Head Model Reconstruction and Filtering  
Fields such as computer graphics or geometric modeling are quite advanced regarding representation and mesh processing. Some of the wide variety of modeling techniques that represent data structures of meshes focus on faces (Face Set and Shared Vertices) [27], and others focus on edges (Winged-Edge and Half-Edge) [28].  
Face-based representations are considered the simplest kind of representation and are implemented in the most common file formats, such as OFF, OBJ and STL. However, they do not provide any connectivity information for triangles; thus, edge representationspresent themselves as a more complete strategy for storing a mesh geometry, such as the position of the vertices, the incident faces on an edge and the vertices that make up a face.  
As for the filtering, it represents a phase where the mesh points that render the mesh irregular and defective are smoothed. Since 3D camera-acquired models are invariably noisy, the process of mesh smoothing is of great importance. There are several techniques in the literature used to obtain results for denoising [21]. Some of these techniques are based on the use of filters, such as the Gaussian filter and the Savitzky-Golay filter, among others. However, these filters are generic, which for faces that have intrinsic characteristics for the model itself may lead to distortions such as the flattening of the nose or deformation of the mouth and eyes.
# Mobility Support 5G Architecture with Real-Time Routing for Sustainable Smart Cities
## 2 Related Work
It makes the network attackers pursue fake targets and accordingly maintains the stable structure of the observing field. In [51], the authors proposed a deep-reinforcement-learning-based quality-of-service (QoS)-aware secure routing protocol (DQSP). It ensures the QoS along with the extraction of knowledge from traffic history. In addition, it optimizes the routing policy and improves the data delivery performance against other solutions. The authors in [52] proposed a telemedicine system based on MEC and artificial intelligence for remote health monitoring and automatic disease diagnosis. The concept of mobile edge computing (MEC) among users and cloud systems reduces the problem of 5G scenarios in terms of latency and processing. Different computing technologies are also utilized in the proposed solution to significantly improve the efficacy of the patient treatment by decreasing the computing cost using an intelligent paradigm. IoT technology has gained prominent attention for the development of sustainable smart cities with the support of a 5G network. Table 1 describes the research findings along with limitations based on the discussed work.
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 3 Properties of Video Watermarking
### Imperceptibility  
Imperceptibility, also called invisibility, requires that the watermark information embedded in the video cannot be perceived by the human eye. In other words, the embedding of watermark information cannot significantly affect the visual quality of the video. Although many watermarking algorithms are visible now, their application is limited to specific occasions. Invisible watermarking algorithms occupy the mainstream position. How to find embedding positions in the video, which can not only minimize the influence on the visual quality, but also have strong robustness, is one of the key contents in the research of robust video watermarking algorithms.  
Mean peak signal-to-noise ratio (MPSNR) and mean structural similarity index (MSSIM) [19] are commonly used to quantitatively evaluate the imperceptibility of video watermarking algorithms. If the number of watermarked frames is \(K\) and the size of video frames is \(M\times N\), the definition of MPSNR \(I_{\text{MPSNR}}\) is given as:  
\[I_{\text{MPSNR}}=\frac{1}{K}\sum_{k=1}^{K}PSNR_{k}, \tag{5}\]  
\[\text{PSNR}_{k}=10\log_{10}\frac{255^{2}}{\text{MSE}_{k}}(\text{dB}), \tag{6}\]  
\[\text{MSE}_{k}=\frac{1}{M\times N}\sum_{m=1}^{M}\sum_{n=1}^{N}{[f_{k}(m,n)-f_{ kw}(m,n)]}^{2}, \tag{7}\]  
where \(f_{k}\) is the \(k\)-th original video frame; \(f_{kw}\) is the \(k\)-th watermarked frame; and MSE is the mean square error between \(f_{k}\) and \(f_{kw}\).  
Sometimes, MPSNR cannot be well associated with subjective evaluation results, so MSSIM is introduced to evaluate video quality. The definition of MSSIM \(I_{\text{MSSIM}}\) is shown as:  
\[I_{\text{MSSIM}}=\frac{1}{K}\sum_{k=1}^{K}SSIM(f_{k},f_{kw}), \tag{8}\]  
\[\text{SSIM}(f_{k},f_{kw})=\frac{(2\mu_{f_{k}}\mu_{f_{kw}}+C_{1})(2\sigma_{f_{ k}/f_{kw}}+C_{2})}{(\mu_{f_{k}}^{2}+\mu_{f_{kw}}^{2}+C_{1})(\sigma_{f_{k}}^{2}+ \sigma_{f_{kw}}^{2}+C_{2})}, \tag{9}\]  
where \(\mu_{f_{k}}\) and \(\mu_{f_{kw}}\) represent the mean values of the original frame and the watermarked frame, respectively; \(\sigma_{f_{k}}\) and \(\sigma_{f_{kw}}\) are the variances of the original frame and the watermarked frame, respectively; \(\sigma_{f_{k}/f_{kw}}\) denotes the covariance of the original frame and the watermarked frame; and \(C_{1}\) and \(C_{2}\) are two constants to maintain stability.
# Calibration and Image Reconstruction in a Spot Scanning Detection System for Surface Defects
## 3 System Calibration and Image Reconstruction Methods
### System Adjustment and Image Reconstruction Method
Find the positions at which the elements in \(Wt(\mathbf{u})\) have the value of zero and fill the elements at these positions in \(Ig(\mathbf{u})\) (no-value pixels) using the average of 8-neighborhood. The output \(Ig(\mathbf{u})\) is the ultimate reconstructed image. This method can not only solve the problems of non-integral coordinates and no-value pixels, but also suppress the signal noise through the weighted average strategy. For any detection system based on spot scanning, this method can effectively improve the quality of the reconstructed image.
## 3 Simulation Framework
### Modules
#### 3.2.1 Hybrid User Terminal (HUT)
Figure 4 illustrates the file parsing, data splitting into packets and data transmission process on the HUT side. In the demonstrated example, the duration of the timeslot is set to T, and the entire simulation contains R timeslots. The Data Receiver submodule receives Data Messages from the T/R module, extracts all relevant information and updates the associated channel data structure. An update of the channel data structure triggers the Logger submodule that records all Data Receiver activities into a log file. This log file is later analyzed to extract the relevant information.