esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The scattering of electromagnetic waves by an ensemble of dielectric spheres has a long history of research beginning with [PERSON] who presented a rigorous theory for scattering by a single dielectric sphere [1]. The overwhelming majority of papers since the pioneering papers by [PERSON] and his coauthors [2; 3; 4] considered the periodical two- and three-dimensional arrays [5; 6; 7; 8]. Surprisingly, less interest has been payed to scattering by a linear array of dielectric nanoparticles mostly restricted to aggregates of a finite number of spheres [9; 10; 11]. Guiding of electromagnetic waves by a linear array of dielectric spheres below the diffraction limit attracted more attention. There were two types of consideration: finite arrays [12; 13; 14; 15; 16] and infinite arrays which were studied by means of the coupled-dipole approximation [17; 18; 19; 20; 21; 22; 23]. Only in 2013 a full-wave analysis of waves on linear arrays of dielectric spheres below the light cone was provided by [PERSON], [PERSON], and [PERSON] [24].  
It has been widely believed that only those modes whose eigenfrequencies lie below the light cone, are confined and the rest of the eigenmodes have finite life times. However recently confined electromagnetic modes were shown to exist in various periodical arrays of:  
1. long cylindrical rods [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43]
2. photonic crystal slabs [44; 45; 46; 47; 48; 49; 50; 51] and
3. two-dimensional arrays of spheres [40; 52].  
Surprisingly, less attention is paid to the one-dimensional array of dielectric nanoparticles. Similar one may expect light trapping in the one-dimensional array of nanoparticles with the bound frequencies above the light cone. The array of dielectric spheres or discs is interesting due to the rotational symmetry giving rise to that the orbital angular momentum (OAM) of light is preserved. Therefore one can expect the the BSCs with non-zero OAM. The angular momentum is composed of the spin angular momentum (SAM) and OAM describing the polarization and the phase structure distribution of EM fields, respectively [53; 54; 55]. In this review we summarize our resent results on BSCs in arrays of dielectric spheres [56] including some findings on transport properties of dielectric arrays [57] and BSCs with OAM [58; 59]. For a thorough review of BSCs the reader is addressed to [60].
# Improved Short-Term Clock Prediction Method for Real-Time Positioning
###### Abstract  
The application of real-time precise point positioning (PPP) requires real-time precise orbit and clock products that should be predicted within a short time to compensate for the communication delay or data gap. Unlike orbit correction, clock correction is difficult to model and predict. The widely used linear model hardly fits long periodic trends with a small data set and exhibits significant accuracy degradation in real-time prediction when a large data set is used. This study proposes a new prediction model for maintaining short-term satellite clocks to meet the high-precision requirements of real-time clocks and provide clock extrapolation without interrupting the real-time data stream. Fast Fourier transform (FFT) is used to analyze the linear prediction residuals of real-time clocks. The periodic terms obtained through FFT are adopted in the sliding window prediction to achieve a significant improvement in short-term prediction accuracy. This study also analyzes and compares the accuracy of short-term forecasts (less than 3 h) by using different length observations. Experimental results obtained from International GNSS Service (IGS) final products and our own real-time clocks show that the 3-h prediction accuracy is better than 0.85 ns. The new model can replace IGS ultra-rapid products in the application of real-time PPP. It is also found that there is a positive correlation between the prediction accuracy and the short-term stability of on-board clocks. Compared with the accuracy of the traditional linear model, the accuracy of the static PPP using the new model of the 2-h prediction clock in N, E, and U directions is improved by about 50%. Furthermore, the static PPP accuracy of 2-h clock products is better than 0.1 m. When an interruption occurs in the real-time model, the accuracy of the kinematic PPP solution using 1-h clock prediction product is better than 0.2 m, without significant accuracy degradation. This model is of practical significance because it solves the problems of interruption and delay in data broadcast in real-time clock estimation and can meet the requirements of real-time PPP.  
Keywords:real-time precise point positioning; real-time clock estimation; short-term prediction +
Footnote †: journal: Sensors
# A Blockchain Solution for the Internet of Vehicles with Better Filtering and Adaptive Capabilities
## 5 IoV System Model Based on AE-PBFT Algorithm  
### System Model  
As shown in Figure 7, the IoV system applicable to the proposed AE-PBFT algorithm is a multi-layered model, including layers for Certificate Authority (CA) authentication, edge interaction, and data storage.  
(1) CA authentication layer. The IoV system using consortium chain technology has the advantage that the identities of all members are verified to be authentic and reliable, which greatly reduces the cost of achieving trust. The CA authentication layer includes the CA center and the cloud server. The CA center records and verifies the identities of each entity, such as issuing identity certificates and issuing, managing, and destroying keys for RSUs and on-board units (OBUs). The cloud server stores and manages the identity data of each entity and verifies the legitimacy and timeliness of audit records. The CA center places entities that engage in malicious behavior on the list of dishonest persons and broadcasts them to the IoV system through 5G NR-V2X communication technology, prohibiting them from participating in subsequent operations;(2) Edge Interaction Layer. This layer includes an OBU and RSU. Vehicles equipped with various sensors and unit modules act as mobile nodes and need to send messages to the surroundings and receive them in real time. Interaction methods include vehicle-to-vehicle (V2V) and vehicle-to-roadside (V2R) units. V2V establishes a trusted collaboration channel between vehicles, while V2R establishes relevant service request and response channels;  
(3) Data Storage Layer. The collected trust values and rating information will be uploaded to the distributed blockchain database to ensure permanent and secure data storage. In contrast, traditional centralized database storage methods are more susceptible to attacks, which can result in data loss or tampering.
## 3 Results  
### Mechanical Behavior of Retrofitted BCJs  
As mentioned, both specimens were retrofitted with C-FRP ropes due to insufficient conventional reinforcement. In Figure 6, the mechanical behavior of the reference and the examined BCJs is illustrated. The horizontal axis represents the imposed displacement, while the vertical axis represents the corresponding load. Both specimens displayed the capability to preserve their structural integrity up to the eighth loading step, representing a story drift of 5%. Contrarily, unretrofitted specimens, published by [PERSON] et al., with identical geometrical properties and reinforcing details, subject to the same testing protocol, experienced a brittle failure on the seventh loading step, resulting in a 4% story drift [5]. Specim 1's mechanical response peaks at around \(-70\) kN during the first negative cycle of the fourth, fifth, and sixth steps and gradually degrades in the following cycles, reaching \(-34\) kN in the first negative cycle of the 8 th step. Although the specimen was reinforced symmetrically, there is a noticeable disparity of 5 kN in their positive and negative responses. This difference is likely attributable to the pattern of the cracks formed in the joint panel, which is evident in the unretrofitted specimen also. Furthermore, there is a progressive reduction in the bearing capacity of Specim 1 as the repeated deformation cycles continue beyond the sixth loading step, indicating that significant damage occurred at these steps.  
Additionally, as seen in Figure 6b, the mechanical response peaks of Specim 2 reach 83 kN during the first positive cycle of the sixth step and gradually degrade in the following cycles, reaching 40 kN in the first negative cycle of the eighth step. Similarly, as with Specim 1, there is a clear difference between their positive and negative responses and a progressive reduction in its bearing capacity as the repeated deformation cycles continue beyond the sixth loading step. The retrofitting technique has notably improved  
Figure 6: Comparative experimental mechanical behavior of BCJs with the unretrofitted specimen under cycle loading. (**a**) Specim 1 and (**b**) Specim 2.  
the specimens' ductility and overall post-peak behavior while exhibiting a slight increase in its load-bearing capacity.  
Furthermore, Figure 7a,b illustrate the cracking patterns formed at the end of the loading sequence in the joint panels of Specimen 1 and Specimen 2, respectively. The application of C-FRP ropes restrains the width of the cracks and enhances the seismic capacity of both specimens. Despite the lower longitudinal reinforcement of Specimen 1's beam, it exhibited greater severe damage than Specimen 2. However, both specimens demonstrate significantly improved seismic performance compared to unreinforced BCJs regarding ductility and the severity of BCJs' panel cracking.
# Application of Machine Learning Methods for Pallet Loading Problem
## 3 Methodology
### Number of the Boxes Loaded onto the Pallet  
The process to ascertain the number of identical boxes to be placed on a typical pallet was divided into two phases. The dimensions of a typical box are coded as a tuple \((l,w,h,\text{ and }m)\), where \(l\) is the length of the box, \(w\) is the width of the box, \(h\) is the height of the box, and \(m\) is the weight of the box. Similarly, the dimensions of the pallet are defined as a tuple \((L,W,H_{max},\text{ and }M_{max})\), where \(L\) is the pallet length, \(W\) is the pallet width, \(H_{max}\) is the maximum allowable pallet load height, and \(M_{max}\) is the maximum allowable weight on the pallet, in such a way that \((L\geq l,W\geq w)\). In the first phase, the total number of boxes loaded on the horizontal layer is identified. These boxes should be placed completely onto the pallet without any overlap or overhang. If the value of (pallet area)/(box area) is less than 101, many studies [25; 28; 31; 32; 33] have shown that various heuristics, such as one-block, three-block, five-block, hollow-block, and G5-heuristics, can determine the maximum number of boxes to be arranged in each horizontal layer. In the second phase, the total number of horizontal layers per pallet is calculated on the basis of \(H_{max}\). Both these phases are elaborated as follows.
# An Investigation into the Influence of Sample Height on the Consolidation Behaviour of Dredged Silt
## 3 Test Results and Analysis
### Coefficient of Consolidation  
The coefficient of consolidation was ascertained using an automatic air pressure consolidometer, with results graphically represented by the time square root curve. This coefficient, for samples of varying heights under different consolidation pressures, was derived using the \"time square root method\", as depicted in Figure 9.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sample Height (cm)** & **Structural Yield Stress \(\sigma_{\text{c}}\) (kPa)** & **Compression Index \(C_{\text{c}}\)** \\ \hline
2 & 50.1 & 0.5335 \\
3 & 46.8 & 0.5276 \\
4 & 52.5 & 0.5206 \\ \hline \end{tabular}
\end{table}
Table 4: Structural yield stress and compression index of samples with different heights.  
Figure 8: \(\ln(1+e)\)-l\(gp\) curve of a soil sample of 4 cm height.  
Figure 7: \(\ln(1+e)\)-l\(gp\) curve of a soil sample of 3 cm height.  
For a consistent consolidation pressure, an increase in sample height--which corresponds to an extended drainage distance--results in a gradual decrease in the consolidation coefficient of the soil sample. Concurrently, the consolidation rate of the soil sample also diminishes. At a lower consolidation pressure (\(p=12.5\) kPa), the consolidation coefficient for a soil sample with a height of 2 cm was approximately double that of a 4 cm sample. However, the trend in the consolidation coefficient in relation to consolidation pressure remained uniform across varying sample heights. Specifically, when the consolidation pressure was below the structural yield stress \(\sigma_{\text{c}}\) (50 kPa), the consolidation coefficient rose with an increase in the consolidation pressure, peaking at the structural yield stress. Conversely, when the consolidation pressure surpassed the structural yield stress \(\sigma_{\text{c}}\), the consolidation coefficient diminished with increasing pressure until it stabilised. When the consolidation pressure was beneath the structural yield stress, the soil sample maintained a relatively expansive void ratio. This resulted in enhanced permeability, elevating the consolidation coefficient. However, when the consolidation pressure exceeded the structural yield stress, the soil underwent swift compression deformation, leading to a sharp decline in the void ratio. As a consequence, the soil particles became more densely packed, compromising the interconnectivity between them. This shift influenced the soil's permeability, causing a rapid decrease in permeability and, in turn, reducing the soil sample's consolidation coefficient.  
However, one key observation was that the consolidation coefficient reduced as the soil sample height increased. Relying solely on the consolidation coefficient acquired from conventional soil samples (of 2 cm height) to forecast the consolidation rate and settlement process of dredged silt foundations would be an oversimplification. It underscores the need for an in-depth exploration into the impact of size effects on the consolidation process.
# The Non-Linear Impact of Industry 4.0 on Carbon Emissions in China's Logistics Sector
###### Abstract  
This study investigates the impact of Industry 4.0 on carbon emissions within China's logistics sector, focusing on its potential to support global carbon reduction goals. Utilizing panel data from 30 Chinese provinces between 2011 and 2019, this research employed an Industry 4.0 index alongside IPCC guidelines to estimate logistics emissions. Through fixed effects and dynamic panel models, this study indicated an inverted U-shaped relationship among the development of Industry 4.0 and carbon emission management. Industries move to emitting more pollution to provide their required needs to all but once technologies advance, drastic decreases in combustion and emissions take place as the industries move more towards conservation and the integration of information continues to improve. This study investigates the non-linear impact of Industry 4.0 on carbon emissions in China's logistics sector. Utilizing panel data from 30 Chinese provinces between 2011 and 2019, the research employs a dynamic panel model to quantify the relationship between Industry 4.0 adoption and carbon emissions. The findings reveal an inverted U-shaped relationship, with emissions initially increasing by 10% at lower levels of technology adoption before decreasing by 15% as the Industry 4.0 index reached higher levels. This study provides empirical evidence supporting the strategic deployment of Industry 4.0 technologies to achieve sustainable logistics growth. Key policy implications and future research directions are discussed.  
Al; carbon emissions; logistics sector; China; non-linear relationship; dynamic panel models; regional disparities; environmental sustainability; technological adoption +
Footnote †: journal: Applied sciences
## 1 Introduction
Surface electromyography (sEMG) can be used in sports as it allows for the study of neuromuscular patterns organized by the central nervous system during dynamic activities [23; 24; 25]. Electromyographic measurements are of great importance to test protocols and improving equipment for better sports performance. Evaluation of the electrical activity of a muscle is fundamental to assess the movement performance during different activities, as it can be used to guide the adequate and economic use of muscles, improve activity and prevent the risk of injury [26]. The study of neuromuscular patterns through EMG provides important information that can be used in sports to guide performance, injury prevention, management of muscle conditioning, skill improvement, and motor control [23]. In the present study, the most important muscles of the elbow and shoulder were assessed through surface electromyography (sEMG), namely the biceps (Bicp), the lateral head of the triceps (TLat), and the long head of the triceps (TLong). Biceps is the muscle responsible for flexing the elbow and supinating the forearm. It also acts as an accessory flexor of the shoulder as its long head originates from the scapula. The long head prevents superior movement of the humerus on the glenoid cavity, which is an important role during the movement of lifting the upper limb. The triceps muscle is responsible for elbow extension, and at the shoulder, through its long head, it also participates in abduction and extension. More precisely, TLat is normally recruited for movements that require occasional spikes in strength, while TLong is used when sustained force is required, or when synergistic control of the shoulder and elbow is required [27]. Both muscles, biceps and triceps, work together during the badminton smash to adjust the speed of the elbow motion and maintain balance so that the shuttlecock gains the intended velocity [28]. To the authors' knowledge, there have been no studies in the performance of badminton players' upper limb muscles, despite the commonality of upper limb badminton injuries [16]. This study, therefore, evaluates muscular activity in a group of volunteer badminton players with the goal of assessing the impact of the rackets' stiffness on the muscular activity and activation time during the smash movement, using two types of racket of different stiffness. The findings of this study should help coaches, sports professionals and athletes choose the best racket according to the athlete's level, and to help prevent the occurrence of injuries.
Near-infrared (NIR), mid-infrared (MIR), and fluorescence spectroscopy are commonly used to evaluate the agricultural product quality and determine the geographical origin [12; 13; 14; 15; 16]. These techniques are simple to employ for data collection and can avoid the physical and chemical pre-treatment of the sample [3]. Several studies have been performed for identification of the geographical origin of beans and walnuts using NIR spectroscopy [7; 9]. Meanwhile, MIR and fluorescence spectroscopy exploits the inherent properties of the product components. They have thus been used to distinguish the geographical origins of Emmental cheeses and green coffee beans [17; 18]. However, in cases of hybrid agricultural products, spectroscopy cannot determine which parts have different geographical origins or the extent of hybridization because this technique analyzes all spaces of the samples. Hyperspectral reflectance and fluorescence imaging is a relatively new technique for measuring the quality and safety of agricultural and other food products and investigating their geographical origins [19; 20]. This image processing technique uses the specific fluorescence characteristics of the sample to enable acquisition of both spectral and spatial information. It thus provides certain advantages over conventional spectroscopy techniques in detecting the geographical origins of these products [21]. Furthermore, by employing the spectral information of each pixel, it is possible to discriminate other sample properties and to measure them, such as the sample components. Accordingly, this method can be applied to determine the geographical origin of agricultural and other food products [1]. For example, recent research has shown that hyperspectral imaging (HSI) combined with fluorescence imaging is useful for detecting the geographical origin of honey. Moreover, the model accuracy is higher than that of NIR spectroscopy [22]. Given the above background, the objective of this study was to develop a method for geographical origin discrimination between South Korean and Chinese rice using an HSI technique and multivariate analysis. Specifically, PLS-DA and PLSR were performed on fluorescence spectral data sets to develop white-rice discriminating models based on the two different geographical origins. Furthermore, the optimal pixel size and spectral pre-processing of the HSI were determined to improve the discrimination accuracy.
## 2 Results and Discussion
### Effect of Breed on Dry Cured Ham Aroma  
Once the differences in volatile composition between the locations of the ham slices were established, the next step in the study was to select those compounds that were able to distinguish samples by breed type (Iberian _vs._ non Iberian ham). Table 2 shows the mean concentrations of the volatile compounds quantified in Iberian and non-Iberian hams. These values corresponded to the average concentration for the studied muscles and subcutaneous fat. The Brown-Forsythe test [43] was applied to select those compounds that distinguished these two types of hams (\(p<0.05\)). The selected volatile compounds were those with the codes 5-6, 13, 17-18, 21, 24-27, 29-31, 34, 36-38, 40-42 (Table 2). However, not all of them effectively contribute to dry cured ham aroma. In order to explain the differences in aroma of Iberian and non-Iberian dry cured hams, it is needed to take odor thresholds into consideration. Considering the odor activity value or OAV--as the ratio between the mean concentration of the volatiles and their odor thresholds, it was determined that 25 out of 46 volatiles (coded as 3, 6, 12, 15, 17, 21-28, 30-38, 40-42) could contribute to dry-cured aroma. Sixteen of these volatiles were aldehydes or alcohols. The highest concentrations of these compounds were mostly found in SF and SM muscle. The analyses by GC-O pointed out the association of fruity, woody, spicy and green fatty sensory descriptors with these volatiles in both kinds of hams, although their concentration was usually higher in Iberian hams. On the other hand, the relative importance of these volatile compounds also differed between the different locations. For example, in terms of OAV, 3-methyl-butanol is the major contributor to aroma in the three muscles, while in SF 2-heptanol had the highest OAV value. After 3-methyl-butanol, 2-heptanol was the second most relevant volatile compound according to their contribution to aroma in the three muscles, and in SF hexanal was the second most relevant volatile.