esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Datasets
### In Situ Measurements from Long-Term Monitoring
#### 2.2.2 Active-Layer Thickness  
ALT ranges between 40 cm in peat and up to 120 cm on sandy, poorly vegetated surfaces across the central Yamal site [30; 31; 32; 33]. There are extremes observed on high-center sandy polygons, which can be 1-1.5 m high and up to 10 m in diameter, with active layer exceeding 200 cm. While the spatial distribution of ALT depends on lithology and surface covers, temporal fluctuations are controlled by ground temperature, summer air temperature and summer precipitation [21].  
In 1993 a CALM site was established at Vaskiny Dachy, placed on the top and slope of a highly dissected plain, affected by landslides, with sandy to clayey soils. Three additional sites were established within areas with more or less homogeneous vegetation [34]. The site Vaskiny Dachi-1 (VD-1) has clay soils. Soils at Vaskiny Dachi-2 (VD-2) are a mix of sand and clay, and at Vaskiny Dachi-3 (VD-3) sandy [34].  
ALT is measured by a metal probe according to CALM protocol. This involves a late season mechanical probing, in our case late August, when ALT is near its end-of-season maximum. A one-centimeter-diameter graduated steel rod is inserted into the soil to the depth of resistance to determine the depth of thaw [35]. ALT is measured at a spacing of 10 m within the 100 \(\times\) 100 m grid at the CALM site, resulting in 121 measuring points (see Figure 3). The VD sites feature five transects, respectively. At VD-1 and VD-2 these transects form grids of 50 \(\times\) 50 m. Transects are 12.5 m apart and ALT is measured every 5 m, resulting in 55 measurement points per site. The transects at VD-3 are arranged to areas of homogeneous vegetation [34]. The site of VD-3 features higher ALT values, most likely because of the present sandy soils, which yield a greater conductivity and water permeability (higher convective heat exchange).  
The CALM grid site is comparably heterogeneous. It features patches of dry cryptogam crust, grasses and mosses, low and high shrubs as well as some wet sedge spots. Cryptogam crust is encountered at the concave hilltop, while high shrubs are mostly located at the foot of the slope and on landslide bodies. A detailed survey of near surface moisture and vegetation description is provided in [PERSON] et al. [36]. ALT is locally higher at this location due to high salinity of clayey deposits which contain no ice under negative temperature and do not resist to probing.  
An increase in active-layer thickness since the start of the records has been already observed for Yamal [21]. Approximately 10% of grid nodes can exceed the maximum measurable depth of 170 cm. The average ALT for the CALM grid can be therefore not exactly determined and is expected to be underestimated. It is at least 103 cm.
## 2 Signal Transmission System  
Generally, the signal transmission approaches based on the human body medium can be divided into two types, which include electrostatic coupling type and galvanic coupling type [6, 10]. Compared with the latter type, the former has the characteristic of less signal attenuation, which is very important for decreasing power consumption. Therefore, in our investigation the electrostatic coupling type was chosen as the approach for signal transmission based on the human body medium.
## 6 Related Work
Recently, researchers have focused on extracting code features and detecting clones using machine learning [18; 20; 23; 24; 33; 34; 35]. CCLearner [33] uses a token analysis technique as well as a deep learning technique, in which tokens are used to generate inputs for a deep learning model, which then determines whether the given inputs are clones. DeepSim [20] encodes both control flows and data flows into a semantic matrix and uses a deep learning method to detect clones based on the semantic matrix. [PERSON] et al. [34] is an AST-based approach and used a bilateral tree-based convolutional neural network for automated program classification. RtvNN [23], CDLH [24], and [PERSON] et al. [35] are AST-based approaches that transform AST into feature vectors to use deep learning models. Whereas RtvNN uses recursive neural networks to detect both textual and functional similarity, CDLH and [PERSON] et al. apply LSTM on ASTs to detect functional clones. [PERSON] et al. [18] use both AST and CFG to extract syntactic and semantic information of code. They use a DNN classifier model by using fused syntactic and semantic feature vectors as input. CCLearner, RtvNN, and CDLH perform tolerably well in experiments using BigCloneBench but perform particularly poorly at detecting T3- and T4-type clones. DeepSim performs well at detecting all types of clones, but its preprocessing procedure is unwieldy. The experiments of [PERSON] et al. are about clone detection between the two different programming languages. [PERSON] et al. [36] use both AST and PDG to detect semantic information of code, and they compare T3- and T4-type clone detection performance of machine learning algorithms such as Rotation Forest, Random Forest, and Xgboost. They conclude that Xgboost is an excellent classification algorithm for the detection of T3- and T4-type clones.
# Quantum Approximation for Wireless Scheduling
## 4 Quantum Approximate Optimization for Scheduling (QAOS)
### Apply to QAOA Sequence  
The application of the designed Hamiltonian to the QAOA sequence starts to occur when the design of Hamiltonians, i.e., \(H_{P}\) and \(H_{M}\), are completed. First, the parameterized state \(\left|\gamma,\beta\right\rangle\) can be generated by applying \(H_{P}\) and \(H_{M}\), defined in Equations (21), (27), (28) and (29), to (11). Here, the initial state \(\left|s\right\rangle\) is set to the equivalent superposition state using the Hadamard gates. The expectation value of \(H_{P}\) can be measured on the generated parameterized state \(\left|\gamma,\beta\right\rangle\). The parameters \(\gamma\) and \(\beta\) are iteratively updated in a classical optimization loop. When the QAOA sequence terminates, the optimal parameters \(\gamma_{opt}\) and \(\beta_{opt}\) are obtained. Thus, the scheduling solution can be obtained by the measurement of the expectation value of \(H_{P}\) on the optimal state \(\left|\gamma_{opt},\beta_{opt}\right\rangle\) as follows:  
\[\left\langle F\right\rangle=\left\langle\gamma_{opt},\beta_{opt}\big{|}H_{P} \big{|}\gamma_{opt},\beta_{opt}\right\rangle, \tag{30}\]  
where \(\left\langle F\right\rangle\) is the expectation value of the objective function Equation (14) over the returned solution samples.
# Digital Systems in Smart City and Infrastructure: Digital as a Service
## 6 Digital Transmission
### Bluetooth  
Bluetooth [66] enables the direct connectivity between devices at very short distances. It was developed as the wireless alternative to the RS-232 data cables and connectors. A Bluetooth network is a Personal Area Network (PAN), or piconet, configured as single master and slaves (Table 4). Bluetooth Low Energy (BLE) is used to provide a more accurate location than GPS which can be used to better manage the interaction with users within the physical space while transmitting positional information without a LAN or WLAN network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Frequency** & **Data Rate** & **Power** & **Range** \\ \hline Class 1 & 2.45 GHz & 768 kbps & 100 mW & 100 m \\ BLE & 2.45 GHz & 1 Mbps & 10 mW & 30 m \\ Class 2 & 2.45 GHz & 3 Mbps & 2.5 mW & 10 m \\ Class 3 & 2.45 GHz & 24 Mbps & 1 mW & 1 m \\ Class 4 & 2.45 GHz & 24 Mbps & 0.5 mW & 0.5 m \\ \hline \hline \end{tabular}
\end{table}
Table 4: Bluetooth types.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Code** & **Standard** & **Data Rate** \\ \hline Digital Subscriber Line & DSL & G.933.1 & 52 Mbit/s \\ Integrated Services Digital Networks & ISDN & EG 201 730 & 2.048 Mbit/s \\ European Trunk Carrier & E4 (CMI) & G.751 & 140 Mbit/s \\ Optical Carrier & OC-768/STS-768 & G.803 & 39.81 Gbit/s \\ Asynchronous Transfer Mode & ATM & ANSI and ITU & 10 Gbps \\ Frame Relay & FR & ANSI and ITU & 45 Mbps \\ Multiprotocol Label Switching & MPLS & RFC 3031 & 1 Gbps \\ \hline \hline \end{tabular}
\end{table}
Table 2: Wide Area Network (WAN) data access.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protocol** & **Frequency** & **Bandwidth** & **Data Rate** & **Coverage** \\ \hline
802.11a & 5 GHz & 20 MHz & 54 Mbps & 120 m \\
802.11b & 2.4 GHz & 22 MHz & 11 Mbps & 140 m \\
802.11g & 2.4 GHz & 20 MHz & 54 Mbps & 140 m \\
802.11n & 2.4/5 GHz & 20/40 MHz & 285/600 Mbps & 250 m \\
802.11 ac & 5 GHz & 20/40/80/160 MHz & 347/800 Mbps & 100 m \\
802.11 ax & 2.4/5 GHz & 20/40/80/160 MHz & 10.5 Gbps & 100 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Wireless Area Network (WLAN) protocols.
# Effects of Kerr Nonlinearity in Physical Unlonable Functions
## 4 Conclusions  
We have investigated how Kerr nonlinearity affects the sensitivity of a speckle to the cloning of the involved multiple-scattering optical token. Our results suggest that for focusing nonlinearity, the correlation between the speckles of a reference token and its clones can be smaller than the ones for zero or defocusing nonlinearity. Hence, PUFs that rely on optical tokens with focusing nonlinearity are expected to be more robust against cloning in the sense that they can distinguish easier between a reference token and its clones, with high probability.  
This advantage is becoming more pronounced for optical tokens with a spectral correlation bandwidth equal or comparable to the bandwidth of the pulse as well as for the increasing thickness of the token, and/or the increasing peak intensity of the pulse. The spectral correlation bandwidth of a token decreases quadratically with its thickness \(L\)[19; 20; 21; 22], whereas for a given pulse duration, the peak intensity should be below the damage threshold for the medium. For our purposes, we had to vary the Kerr nonlinearity \(n_{2}\) in our simulations while keeping all other parameters fixed, which would not have been possible if the present work had been restricted to a specific material. However, for the focusing nonlinearity, where we have the most interesting findings, the main parameters pertain to fused silica [24; 27; 28]. This is a well-studied material in the literature, while it has been also discussed in the framework of PUFs [5]. The present findings are expected also to be present for other highly scattering media typically used in connection with PUFs, such as TiO\({}_{2}\) and ZnO [1; 4; 19; 20; 21; 22].  
To the best of our knowledge, the present work is the first to investigate the effects of Kerr nonlinearity in optical PUFs. The present results may serve as a benchmark for any future work on this topic. The identification of appropriate materials and systems for the validation of the present findings in the framework of more accurate theoretical models and experiments is an open question that deserves the attention of the community in the field.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 2 Theoretical Background of TD-NIRS
### Diffusion Approximation and Diffusion Equation (DE)  
The TE of the \(P_{1}\) approximation is further simplified to the time-domain diffusion equation (TD-DE) by adding conditions of (i) strong scattering meaning \(\mu_{s}{{}^{\prime}}\gg\mu_{a}\) or \(D\mu_{a}\ll 1\), (ii) slow temporal changes in the fluence rate and the light source leading to,  
\[\frac{3D(\textbf{r})}{c}\bigg{|}\frac{\partial^{2}}{\partial t^{2}}\phi( \textbf{r},t)\bigg{|}\ll\bigg{|}\frac{\partial}{\partial t}\phi(\textbf{r},t )\bigg{|},\frac{3D(\textbf{r})}{c}\bigg{|}\frac{\partial}{\partial t}q_{0}( \textbf{r},t)\bigg{|}\ll|q_{0}(\textbf{r},t)|, \tag{6}\]  
and (iii) the source emission is isotropic meaning \(\textbf{q}_{1}(\textbf{r},t)=0\). Then, Equation (5) reduces to the TD-DE,  
\[\frac{1}{c}\frac{\partial}{\partial t}\phi(\textbf{r},t)+\mu_{a}(\textbf{r}) \phi(\textbf{r},t)-\
abla\cdot[D(\textbf{r})\
abla\phi(\textbf{r},t)]=q_{0}( \textbf{r},t), \tag{7}\]  
Equation (7) is a parabolic type of partial differential equation describing diffusion phenomena, and the net flux of the fluence rate is given by [PERSON]'s law for diffusion phenomena, \(\textbf{F}(\textbf{r},t)=-D(\textbf{r})\
abla\phi(\textbf{r},t)\).  
The regime map of Figure 2 shows the valid region of the DE where the demarcating curve is drawn as \(t=10/(\mu_{s}{{}^{\prime}}c)\approx 10(3D/c)\) under the condition of \(\mu_{s}{{}^{\prime}}\gg\mu_{a}\). Here, \(3D/c\) is the characteristic time of interaction. For a case of \(\mu_{s}{{}^{\prime}}=1.0\) mm\({}^{-1}\) typical for biological tissue, the DE fails for light propagation within times shorter than 0.05 ns (50 ps), and the RTE is required in this period of times.  
The net fluxes of the fluence rate, \(\textbf{F}(\textbf{r},t)\), can be measured by time-resolved (TR) detectors and the measured fluxes such as TR reflectance and TR transmittance are often expressed as the time-of-flight distribution (TOF-distribution) in this article.  
Figure 2: Regime map for the DE and the RTE.
## 3 Results and Discussion
### Optimization Results  
The trained ANN model demonstrated excellent prediction capability for output responses, as shown in Figure 13. The model achieved high accuracy with R-squared values of 0.992, 0.988, and 0.971 for effective strain, maximum pressing load, and strain inhomogeneity, respectively. The corresponding low MSE values of 0.090, 0.018, and 0.010 further confirm the model's reliability. As seen in Figure 13a-c, the comparison between predicted and actual values shows a strong correlation, indicating that the ANN successfully captured the nonlinear relationships between input parameters and output responses. This high predictive accuracy validates the ANN model's capability to effectively map the complex relationships in the Vo-CAP process.  
Later on, by using the best-performing networks obtained through the trained ANN model, the NSGA-II optimization process resulted in 35 Pareto-optimal solutions as shown in Figure 14. Subsequently, the TOPSIS method was applied, assigning equal weights to each objective, to identify the most balanced solution among these Pareto-optimal results. As shown in the figure, the identified optimal solution corresponds to an effective strain of 16.49, a maximum pressing load of 15.69 kN, and a coefficient of strain inhomogeneity of 0.375. This optimal point, highlighted in red in the figure, represents the best compromise between maximizing effective strain while simultaneously minimizing both the strain inhomogeneity and maximum pressing load.  
Figure 13: The comparison between prediction values and targets of (**a**) effective strain, (**b**) max. pressing load, and (**c**) strain inhomogeneity.  
The optimal design parameters were determined at a twist angle of 166.33\({}^{\circ}\), T\({}_{\rm L}\) of 10.33 mm, and TC\({}_{\rm L}\) of 34.2 mm. To validate the optimization results, a new FEM analysis was performed using these optimal parameters. The simulation results showed excellent agreement with the predicted optimization values: a maximum pressing load of 15.65 kN (compared to the predicted 15.69 kN) as seen in Figure 11, an effective strain of 16.60 (compared to the predicted 16.49), and a strain inhomogeneity of 0.378 (compared to the predicted 0.375). This close correlation between optimization predictions and FEM results validates the accuracy of our optimization approach. The reason for the T\({}_{\rm L}\) value being close to the lower bound is that, as shown in Table 3, increasing the T\({}_{\rm L}\) value leads to a decrease in effective strain values while simultaneously increasing the pressing load. Strain inhomogeneity is only partially affected by changes in the T\({}_{\rm L}\) value. Following this successful validation, the optimized die was manufactured, with its geometrical details shown in Figure 15.
## 2 Materials and Methods
### Performance Tests for Frictional Properties  
To evaluate the frictional characteristics of HFST samples, a number of experiments were conducted. In order to ascertain how the surface texture and friction characteristics of HFST slabs changed during fast polishing, these performance tests were essential. These tests comprised the CTM and the DFT, which were utilized in conjunction with the Three-Wheel Polishing Device (TWPD) of the National Center for Asphalt Technology (NCAT) (Auburn, AL, USA). While the TWPD simulated vehicle traffic polishing to evaluate how the surface texture evolved over time, the DFT evaluated the HFST surface's frictional qualities, giving information regarding the friction coefficient at various speeds. The CTM assessed the macrotexture of the HFST surface, providing information on how the surface MPD changed under accelerated polishing conditions. All experiments were carried out in controlled laboratory conditions to guarantee statistical validity and reliable measurements. Randomized replicate DFT and CTM measurements were executed on the surface of each slab. This methodology reduced localized texture or wear impacts and offered enough replication for evaluating friction and texture. Figure 4 illustrates the performance testing equipment employed in this research [28; 29; 30].
## 2 Components and Methods
### Finite Element Analysis  
As shown in Figure 6, types A, B, C and D models are all analyzed by finite element method. In type A, the annular CFRP rod body and metal washers are modeled with frictional contact at 0.1. Mesh sizes are 3 mm for washers and 1 mm for CFRP. A fixed support and 147 kN tensile load are applied to the upper and lower washers, respectively. Then, in type B, the assembly model consolidates metal components into one entity, with frictional contact at 0.1. Mesh sizes are 3 mm for metal (Y-shaped bolts) and 1 mm for CFRP. A fixed support and 147 kN force are applied to the upper and lower Y-shaped bolts, respectively, parallel to the rod's length.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Type A** & **Type B** & **Type C** & **Type D** \\ \hline L1 (mm) & 140 & 140 & 105 & 105 \\ L2 (mm) & 260 & 260 & 240 & 240 \\ LSUM (mm) & / & / & 490 & 465 \\ LR (mm) & 100 & 20 & / & / \\ Cross-section area & 90 mm\({}^{2}\) & 90 mm\({}^{2}\) & 90 mm\({}^{2}\) & 75.40 mm\({}^{2}\) \\ Cold screen opening area & 6000 mm\({}^{2}\) & 1050 mm\({}^{2}\) & 804 mm\({}^{2}\) & 804 mm\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison table of dimensions of CFRP rod bodies for four types of rod groups: A, B, C and D (corresponding to the dimensions marked in Figure #).  
Figure 6: Simulation model, boundary condition setting and load application method of four tie rods: A, B, C and D.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material Name** & **T300-CFRP** & **T1100 GS-CFRP** \\ \hline Fiber code & T300 & T1100 GS \\ Resin name & \(\mathrm{HexPl^{\circ}}\) 3631 & Gurit PRIMEIII 20 \\ Fiber content & \% & 65 & 60 \\ Fiber angle & & 0\({}^{\circ}\) & 0\({}^{\circ}\) \\ Tensile yield strength & MPa & 2290 & 3997 \\ Tensile modulus of & \(\mathrm{GPa}\) & 143 & 192 \\ elasticity & & 0.23 & 0.22 \\ Poisson’s ratio & & 4.09 & 6.25 \\ Thermal conductivity & W/m:K & 4.09 & 6.25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Two types of carbon fiber resin composite materials available for selection and their performance parameters.  
Additionally, in type C, the rod has a simpler structure, with threaded outer cylindrical surfaces on the metal sleeves at both ends, which are fixed to the CFRP rod body using high-strength adhesive. The contact method between the components is set to bonded contact. The mesh size for the metal sleeves at both ends is set to 3 mm, while the mesh size for the CFRP rod body is set to 1 mm. Fixed support is applied to the outer cylindrical surface of the upper metal sleeve, and a force vector is applied to the outer cylindrical surface of the lower metal sleeve, with the direction parallel to the length of the rod, F = 147 kN. Finally, for type D, the rod features a simplified design with metal sleeves at both ends, threaded and bonded to the CFRP rod body with high-strength adhesive. The contact is set to bonded. Mesh sizes are 3 mm for the metal sleeves and 1 mm for the CFRP. A fixed support and a 147 kN force are applied to the upper and lower metal sleeves, respectively, aligned with the rod's length.
## 5 Simulation Results and Analysis
### Simulation Setting  
For a CoMP-enabled system, the macrocell UEs are served solely by the MBS while small cell UEs are served by both MBS and their respective SBS. On the other hand, for a Non-CoMP system, each UE is served only by its respective BS. Unless specified otherwise, the network considered in this work typically consists of an MBS, three SBSs and five UEs per cell. Algorithm 6 shows the simplified simulation steps of EE evaluation. The model was simulated for non-peak hours to observe the energy wastage caused by the BSs serving in under-loaded cells. The performance gain used in the evaluation is based on the average of the difference between the compared techniques, which is the reason a single value is used instead of a series of values (i.e., The explanation of Figure 5 shows that EE performance of random BS-Sw technique is 25% worse than the binomial BS-Sw method. The number of SBSs vary from 2 to 8. So, the EE performance gain is measured at each x-axis point and averaged as 25% instead of choosing just one point.). Table 4 tabulates the simulation settings and parameters used in this work.