esa-sceva/satcom-chunk-collection huggingface dataset:
# A Connectivity-Based Clustering Scheme for Intelligent Vehicles
## 3 Connectivity Models for Vehicular Communications
### Vehicle Connectivity Models  
Two vehicles are said to be connected on the highway if they are within each other's transmission range. A stable and strongly connected network is required to guarantee the quality-of-service (QoS) of real-time data [22]. In [11], the connectivity of vehicles depends on the generalized speed factor (GSF), which reflects the density of vehicles on a particular road segment with unit h/km or s/m and the impact of the relative velocity on the inter-vehicle spacing. Specifically, the definition of GSF is based on the normal distribution of relative speed and the exponential distribution of inter-vehicle spacing [11]. The definition of GSF is given below:  
\[\text{GSF}=\int_{v_{\text{min}}}^{v_{\text{max}}}\frac{f_{v}^{*}(v)}{v}dv \tag{2}\]  
where  
\[f_{v}^{*}(v)=\frac{f_{v}(v)}{\int_{v_{\text{min}}}^{v_{\text{max}}}\frac{1}{ \mu}e^{-\frac{v}{\mu}}ds} \tag{3}\]  
where \(v_{\text{min}}\) and \(v_{\text{max}}\) denote the minimum and maximum speed of vehicles, respectively. Additionally, \(s\) represents the inter-vehicle spacing. The speed \(v\) and inter-vehicle spaces  
Figure 1: An overview of the suggested connectivity-based clustering scheme.  
have an indirect proportional relationship with each other. According to the definition of GSF, the probability of the connectivity of \(N\) vehicles at time \(t\) can be obtained as follows:  
\[P_{c}(N)_{t}=\prod_{i=1}^{N-1}\left(1-e^{-\rho\text{-GSF-}R}\right)=\left(1-e^{ -\rho\text{-GSF-}R}\right)^{N-1} \tag{4}\]  
where \(\rho\) and \(R\) denote the vehicle density and V2V transmission range, respectively. Equation (4) indicates that vehicle speed, vehicle density, and V2V transmission range affect the vehicle's connectivity in a free-flow highway.
## 7 Results
### Global Classification Accuracy
In Tables 3 and 4, we give more details about the full-spectrum detection accuracy from the datasets collected at Location A and Location B. The classifier showed notable performances at Location A, being able to determine the correct source of interference 98.2% of the time when the channel was free and 98.9% when the RF emissions from the IEEE 802.11 network were overlapping the sensed channel. The average detection accuracy appeared lower at Location B, down to 77.9% for IEEE 802.11 interference. This is because the channel allocation of the resident IEEE 802.11 networks present at Location B was more challenging, including multiple overlapping networks with weaker signals and thus complicating the task of correctly classifying the interference on some WSN channels. This fact will be further analyzed in Section 7.2, where we provide in-channel detection accuracy analysis.
# Free Space Optics Communication for Ultra-High-Speed Train Running in Evacuated Tube
## 4 Results and Discussion
### Discussion of Some Practical Limitations  
In this section, we discuss some practical challenges faced by the proposed ETT approach along with potential methods of overcoming them.  
In this paper, we considered the diameter of the Gaussian beam limiting signal strength. Additional concerns such as alignment and focus could also be significant factors. However, given that the ETT is running by using the magnetic field, the impact of vibration should be less severe than in traditional trains. Nevertheless, to the best of the authors' knowledge, most relevant publications assume ideal conditions and no research discussing in detail the vibration issue in ETT has been found. Thus, this is a topic worthy of further investigation. In [1], the authors suggested the use of a Powell lens to expand the beam, which allows dealing with the difficulty of detection in the case of vibration. With this approach, the energy would be concentrated over a line, as opposed to a central focus in the case of a Gaussian beam profile, where the energy is reduced as we move from the center. This solution can be applied to our scenario, although in our case the presence of a moving barrier to align the beam (instead of having the transmitter at the side of the track or above the train as in the literature), could also benefit from other techniques. For example, a potential solution could be the use of a feedback control system with servo motor in order to maintain alignment. Given the high achievable data rates with FSO, the amount of data needed for feedback would be negligible and would not disrupt user data communications.  
Another issue worthy of further investigation is the use of shorter wavelengths to reduce beam divergence, which would also increase the received power and the data rate as well. However, this should be carried out while taking into account the characteristics of the laser transmitter and receiver, and especially safety issues: In fact, the wavelength should be suitable with the sender angle to make the laser safe to use in any area, and to ensure there is no effect on the human eyes, according to the International Electrotechnical Commission (IEC) guidelines [31].  
Finally, we comment on the issue of the curvature of the earth and maintaining line of sight, which affects the number of base stations. The curvature problem could be overcome by installing a system of mirrors for example; however, these should be carefully aligned and maintained to relay the signal properly, and would pose an additional maintenance overhead. Moreover, they would be installed on the side of the track and cannot be perfectly aligned with the receiver which would not lead to significant distance extension, as is the case when using the moving barrier of Figure 1, unless a mirror will also be carried by a moving barrier.
## 1 Introduction
implemented two FFNN models for each of the magnetic and electric fields. Their models' accuracy was reported using RMSE and R-squared as indices. For the electric model, RMSE and R-squared were 0.6172 and 0.9121, while for the magnetic field, they were 0.3602 and 0.9471, respectively. However, since these papers used analytical models with some simplifications, the final model might not be accurate enough for real-world study cases [36]. While there have been efforts to estimate or measure EMF near the OHTLs, a significant gap exists in the literature regarding the development of a fast, precise, and experimental-based EMF estimation approach, as opposed to relying solely on conventional analytical models. Recent research has predominantly focused on utilizing FFNN for EMF estimation, resulting in suboptimal accuracy. Consequently, there is a pressing need for a more advanced model capable of effectively handling highly non-linear data. One of the best AI models is the CFNN, renowned for its ability to provide accurate predictions for complex and non-linear problems. The CFNN's sophistication lies in its capacity to update layer parameters based on the outputs from preceding layers, enabling the model to derive more optimal weight and bias factors, ultimately yielding higher accuracy results. This paper aims to propose the CFNN models for estimating the electric and magnetic fields of OHTLs. These models have been assessed through sensitivity analysis on the effective parameters, ensuring that they are trained with the best setup to reach the most accurate and stable results. In the following, first, the models will be introduced and explained in detail. Then, the sensitivity analysis process will be discussed. In the Section 3, the results of each step of sensitivity analysis will be presented and the performance of both FFNN and CFNN models will be discussed. Finally, a brief conclusion will be made in the Section 4.
# Sparse Low-Rank Based Signal Analysis Method for Bearing Fault Feature Extraction
## 5 Experimental Verification  
In this section, two experimental signals collected from a locomotive bearing test rig are used to further verify the validity of the FISLRA method. The test rig mainly consists of a driving wheel, hydraulic motor and locomotive wheel as presented in Figure 11. The outer race of the bearing is driven while the inner race is fixed. Therefore, the envelope spectrum of collected vibration signal is expected to contain sidebands when an outer race defect occurs. During the experiment, the experimental signal is collected at the sampling frequency of 76,800 Hz and the time length of the data is 1 s. Furthermore, the speed signal can be acquired by a tachometer while collecting the vibration signal. Parameters of tested bearing in experimental verification are listed in Table 2. In case 1, the outer race defective REB as shown in Figure 12a is mounted on the test rig. In case 2, the REB with an inner race defect as shown in Figure 12b is mounted on the test rig. Therefore, in the measured experimental signals, there should be repetitive transients produced by the defect in the measured test data.
## 5 Finite Element Analysis
### Variation in Wall Thickness Uniformity  
As shown in Figure 14a,b, with the increase in drawing speed and friction coefficient, the wall thickness uniformity of the tube after drawing initially decreases and then increases, regardless of the outer die taper angle. When the friction coefficient is 0.05, the drawing speed is 0.6667 m/s and the outer die taper angle is 15\({}^{\circ}\), the wall thickness uniformity reaches a minimum value of 0.014%. When the drawing speed is 0.3333 m/s and the taper angle is 13\({}^{\circ}\), the wall thickness uniformity reaches a maximum value of 0.114%. When the drawing speed is 1.3333 m/s, the friction coefficient increases from 0.0125 to 0.025, and the wall thickness uniformity decreases from 0.22% to 0.015%. As the friction coefficient further increases from 0.025 to 0.08, the wall thickness uniformity slightly increases to 0.11%. At low drawing speeds and low friction coefficients, the wall thickness uniformity is more sensitive to changes in drawing speed and friction coefficient. The maximum and minimum values of wall thickness uniformity are observed at low drawing speeds and low friction coefficients.  
When the drawing speed and friction coefficient remain constant, the wall thickness uniformity is always highest when the outer die taper angle is 13\({}^{\circ}\) and lowest when the  
Figure 14: Wall thickness uniformity change rule. (**a**) Drawing speed; (**b**) friction coefficient.  
Figure 13: Wall thickness variation. (**a**) Drawing speed; (**b**) friction coefficient.  
wall thickness and wall thickness uniformity at different drawing speeds are presented in Table 8.  
The uniformity of wall thickness measured at each point in Table 6 for different times is about 0.106%, and the maximum deviation of wall thickness measured is between 0.001 mm and 0.003 mm. Compared with the uniformity of wall thickness in Table 6, it can be seen that the measurement error will have a slight influence on the uniformity of copper pipe wall thickness, and the calculated uniformity of wall thickness is within a reasonable range.  
In actual production, the drawing speed is known to be 0.6 m/s. When the drawing speed is increased, both the wall thickness and wall thickness uniformity decrease. In the finite element model, when the drawing speed exceeds 0.6 m/s, both wall thickness and wall thickness uniformity show a decreasing trend. In the actual production process, measurement errors can lead to inevitable discrepancies in the measured values. As the drawing speed increases beyond 0.6 m/s, both the finite element model and actual measurements show a decreasing trend in wall thickness and wall thickness uniformity.
## 2 Structures, Synthesis, and Magnetic Properties of Fe\({}_{3}\)O\({}_{4}\) Nanoparticles
### Fe\({}_{3}\)O\({}_{4}\) Spherical Nanoparticles (SNPs)
They found that the overall size of particles exhibited an extremely wide range (82 to 1118 nm) when the volume percentage of added water increased (from 5.5% to 20.5%). The crystallite size of NPs changes with an increasing water volume ratio and follows an inverse U-shaped curve, with the peak located at a 14.5% water volume ratio. In another study, [PERSON] et al. used a solvent mixture composed of a 1/3 ratio of EG/DEG to obtain 100 nm Fe\({}_{3}\)O\({}_{4}\) NPs and only an EG solvent with a higher concentration of iron precursors to prepare magnetite NPs with sizes of 440 and 720 nm [43]. These NPs were prepared in a pressure vessel at 188 \({}^{\circ}\)C under continuous vigorous agitation. Based on these results, which are summarized in Table 2, the solvothermal reduction of the FeCl\({}_{3}\) precursor and the controlled hydrolysis of iron cations are useful methods to prepare Fe\({}_{3}\)O\({}_{4}\) in a wide range of sizes, from a few tenths to a few hundred nm. These Fe\({}_{3}\)O\({}_{4}\) NPs have tunable degrees of crystallinity and exhibit different magnetic properties, ranging from superparamagnetic to ferromagnetic. With featured properties and size characteristics, these spherical Fe\({}_{3}\)O\({}_{4}\) nanoparticles can be used in multiple applications, such as sensing, biomedical applications, environmental remedies, and catalysis. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Diameter** **(nm)** & **Precursor and Reagents** & **Solvent** & **Size Control Factor** & **Grain Size (nm)** & **M\({}_{8}\) (emu/g)** & **H\({}_{\text{C}}\) (Oe)** & **[Ref.]** \\ \hline
200, 400, 800 & FeCl\({}_{3}\)-6H\({}_{2}\)O, NaAc, PEG & Ethylene glycol (EG) & Rxn time & UN & 81.9 & UN & [48] \\ \hline
31, 53, 71, 93, 141, 174 & FeCl\({}_{3}\), NaOH, PAA & Diethylene glycol (DEG) & NaOH stock solution & \(\approx\)10 & UN, 30.9, UN, 56.7, UN, 63.5 & SPM & [53] \\ \hline
120, 190, 560 & Fe\({}_{3}\)O\({}_{4}\) NPs functionalized with OA and DTAB in chloroform. PVP in EG. & Concentration of & & & & [67] \\ \hline
280 & FeCl\({}_{3}\).6H\({}_{2}\)O & EG & - & 5.9, 6.9, 8.3, 13.5 & 36.2, 38.7, 46.5, 67.2 & SPM & \\ \hline
6, 60, 120, 170 & NaAc & EG/DEG & Solvent composition & 10 & UN & SPM & [46] \\ \hline
20, 90, 165, 300. & FeCl\({}_{3}\)-6H\({}_{2}\)O, NaAc, PVP & EG/DEG & Solvent composition & 10–20 & 62.1, 62.1, 62.8, 63.9 & 8, _20, 28, 16_ & [47] \\ \hline
82, 139, 188, 544, 728, 1116 & FeCl\({}_{3}\)-6H\({}_{2}\)O, NaAc, PAA, H\({}_{2}\)O & EG & H\({}_{2}\)O & 15.4, 20.7, 23.9, 18.6, 17.7, 17.6 & 56, 71, 73, 79, 80, 80.27 & _115, 141, 149, 139, 136, 127_ & [70] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Syntheses and magnetic properties of SNPs with sizes larger than 25 nm.
## 2 Localization System Architecture
### Mobile Tag Architecture  
The mobile tag subject to the positioning shares its architecture with the anchor node with the exception of the ethernet connection, the SBA and its associated switch. The power supply is provided by two 1.5 V AA batteries. In this work, the SoC is connected with a planar CP patch antenna, as shown in Figure 5a. The radiation pattern is provided in Figure 5b. The radiation pattern of the target node is characterized by a main radiation lobe directed along the antenna axis and exhibits a sufficiently regular behavior around it, [20]. This shape resembles that of a typical Planar Inverted F-Antenna (PIFA), as implemented in generic mobile terminals, [24]. In fact, PIFA antennas cannot be considered to have \"pure\" radiation patterns, as these devices always show an upward oriented non-isotropic pattern when considering a typical handset use case. In addition, the PIFA is not suitable for CP which is instead preferable for this application to face the severe multipath effects that can be present in many realistic scenarios. More elaborated mobile tag antenna designs can be taken into account, [25], nevertheless they require a complex feeding network and in many circumstances result not compatible with the applicative scenario. For instance, this constraint is undesirable for a compact and low cost device such as the proposed tag node and have therefore not been used in this work.
## 4 Simulation and Experiment Results  
Simulations and experiments are done to verify the effectiveness of the proposed method. In this section, ESO-based method only for speed loop is named single-ESO method, ESO-based method for speed loop and \(q\)-axis is named double-ESO method. The parameters of PMSM are as follows: \(R_{s}\) is 4 \(\Omega\), \(L_{d}\) and \(L_{q}\) are 0.0006 H, \(J\) is 0.002 Kg\(\cdot\)m\({}^{2}\), \(B_{m}\) is 0.00054, \(n_{p}\) is 4.
# Compatibility Analysis Between RedCap Non-Public Networks and 5G NR in TDD FR1 and FR2 Bands
## 1 Introduction  
The need to deploy massive machine-type communication (mMTC) and massive Internet of Things (mIoT) services in 5G NR networks has driven the development of RedCap technology. Standardized in Release 17 of the 3 GPP Partnership Project, RedCap aims to enhance connectivity for IoT devices and enable seamless machine-to-machine communication [1, 2, 3, 4]. RedCap is strategically positioned as a technology that refines the capabilities of the 5G NR (New Radio) air interface established in Releases 15 and 16 of 3 GPP. Its primary objective is to streamline the complexity of 5G NR subscriber devices by leveraging a simplified NR Light air interface. This interface aligns with the standard requirements of Internet networks, bridging the gap between NB-IoT (4G) and URLLC (ultra-reliable low-latency communication).  
This article delves into the distinctive features of RedCap technology, highlighting disparities between RedCap-equipped devices and conventional 5G NR solutions. Additionally, it explores the potential prospects for the technology's evolution and application in Internet networks.
# Reliability-Enhanced Camera Lens Module Classification Using Semi-Supervised Regression Method
## 2 Related Work
### Semi-Supervised Learning
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Type** & **SSR Method** & **Algorithm** & **Real-World Applications** & **Publications** \\ \hline \multirow{8}{*}{Non-parametric} & \multirow{4}{*}{Semi-supervised co-regression} & COREG & Real world dataset (UCI, StatLib), Sunglasses lens & [14; 15] \\  & & CoRCReg & N/A & [16] \\  & & SAFER & Real world dataset (NTU) & [17] \\ \cline{3-5}  & & \multicolumn{4}{c}{Semiconductor manufacturing,} \\  & & \multicolumn{4}{c}{Soft-sensing process,} \\  & & \multicolumn{4}{c}{Benchmark (Boston, Abalone, etc.),} \\  & & SS-SVR & Hyperspectral satellite images, & [18; 19; 20; 21] \\  & & MERIS, & & \\  & Semi-supervised kernel regression & & Corn, & \\  & & \multicolumn{4}{c}{Real world dataset (Delve, TSDL, StatLib)} \\ \cline{3-5}  & & \multicolumn{4}{c}{Real world dataset (Boston housing, California housing, etc.),} \\  & & \multicolumn{4}{c}{RIRS, Colon cancer, Leukemia,} \\  & & Real world dataset (Boston, kin32H) & \\ \cline{3-5}  & & POKR & Real world dataset & [26] \\ \cline{3-5}  & & \multicolumn{4}{c}{GLR} & Geographical photo collections, \\  & & Delve Census, Orange juice & [27; 28] \\ \cline{3-5}  & \multicolumn{4}{c}{SSR via graph regularization} & Hessian Regularization & Real world dataset & [29] \\  & & Parallel Field Regularization & Temperature, Moving hands & [30] \\  & & Spectral Regression & MNIST, Spoken letter & [31] \\ \cline{2-5}  & \multicolumn{4}{c}{Local linear SSR} & L1 SR & Real world dataset & [32] \\ \cline{2-5}  & \multicolumn{4}{c}{Semi-supervised Gaussian process regression} & SSMR & N/A & [33] \\ \hline Parametric & Semi-supervised linear regression & SSRM & German & [16] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Previous studies regarding SSR and its real world applications.
# All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar Decoder
## 5 Experimental Results
### Optimal Integer Quantization Level  
To determine the optimal quantization level, we implement the integer-based min-sum algorithm in the SC decoder, considering quantization levels ranging from 3 to 6 bits. First, the performance of polar codes with a length of 256 bits and a rate of 0.5 is analyzed, as shown in Figure 4(a). At an FER of \(10^{-5}\), the integer-quantized MS-SC decoder with 4-bit quantization exhibits a 1.25 dB performance degradation compared to the floating-point MS-SC decoder. However, increasing the quantization level to 5 bits results in a significant improvement, showing a 2.5 dB enhancement over the 3-bit decoder. Notably, both the 5-bit and 6-bit integer-quantized decoders deliver a performance closely matching that of the high-precision floating-point MS-SC decoder, indicating that higher quantization levels effectively mitigate the loss in accuracy caused by integer approximation.  
The performance analysis extends to larger code lengths, with Figure 4(b) illustrating the decoding performance of polar codes with a length of 1024 bits and a code rate of 0.67. As the code length increases, the performance improvement depends on the quantization level. For the polar codes with 1024 bits, the 5-bit integer-quantized MS-SC decoder closely matches the performance of the high-precision decoder. This trend continues with even larger code lengths, as shown in Figure 4(c) for polar codes with a length of 2048 bits and a rate of 0.5, and Figure 4(d) for polar codes with a length of 4096 bits and a rate of 0.83. In both cases, the integer-based MS-SC decoding algorithm with 5-bit quantization achieves an optimal balance, delivering performance nearly indistinguishable from the infinite-precision decoder. As the code length increases, the impact of quantization on performance becomes more apparent, and the results clearly demonstrate how different quantization levels influence the accuracy of the decoding process across these extended code lengths. Collectively, these results demonstrate that the 5-bit quantized MS-SC decoding algorithm provides a robust and efficient solution, delivering a performance close to that of the high-precision method.  
Figure 4: Cont.