esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Measuring Principle  
As shown in Figure 1, the measuring device includes a polyhedral prism, two auto-collimators, and three laser displacement sensors. The polyhedral prism is fixed with the target rotary axis to be measured and has a plane reflector in the center of the top surface. The polyhedral prism synchronously rotates with the target rotary axis.  
The laser displacement sensors are placed in the \(X\), \(Y\), and \(Z\) directions of the target rotary axis to measure the radial motion error of the rotary axis along the \(X\) and \(Y\) directions and the axial motion error along the \(Z\) direction. The autocollimators are placed in the \(X\) and \(Y\) directions to measure the angular positioning error and the tilt motion error around the \(Y\) axis and \(X\) axis of the target rotary axis.  
The specific measurement principle is this: The autocollimator beam placed in the X-axis direction is reflected along the X-axis incident polyhedral prism working surface, and then enters the autocollimator to obtain the angular positioning error around the \(Z\)-axis and the tilt motion error around the \(Y\)-axis. The autocollimator beam placed in the \(Y\)-axis direction is reflected along the working surface of the polyhedral prism, and then enters the autocollimator to obtain the tilt motion error of the target rotary axis around the \(X\)-axis. The radial motion error of the target axis along the \(X\) axis can be obtained by the laser displacement sensor which is placed in the direction of the \(X\) axis after the ray of the laser displacement sensor is reflected along the working surface of the \(X\)-axis incident polyhedral prism. Similarly, the radial motion error along the \(Y\) axis and the axial motion error along the \(Z\) axis can be measured by the other two laser displacement sensors. The whole measuring device can simultaneously measure all six DOF geometric motion errors of a rotary axis.
## 5 Performance Analysis
### Impact of Hop-Based Caching  
The desired contents for the end-users may be situated in the one-hop, two-hop, or multi-hop distances from the end-users or the content requesters. If several hops are required to reach the content providers, how it may affect the network is very important because forwarding an interest packets towards one more hop means the consumption of network bandwidth, increase in network traffic, the potential creation of congestion, and the potential increase of the packet loss ratio. Therefore, serving the interest packet from nearby content providers may save network bandwidth, reduce the network traffic in a certain region, reduce the network congestion, and reduce the packet loss  
Figure 10: Comparison among proposed and state-of-the-art caching mechanisms.  
Figure 9: Comparison among proposed and state-of-the-art caching mechanisms.  
ratio. All these parameters are directly related to the content access time. Therefore, we simulated a network topology where the same content was available at one-hop to six-hop distances from the content consumers. The content consumer was in the last row of the matrix and the node number was thirty-two. Figure 11 shows the impact of hop-based caching where a content consumer was sending the interest packet for the same contents where the content providers were in one-hop to six-hop distances from the content consumers. Although the simulation result shows that the content access time is reducing, at the same time the other parameters are also getting the benefits if the content access time is low. If the required contents can be served from one-hop or two-hop distances, then the bandwidth will be saved above two-hop distances from the end-users. Similarly, the network traffic will reduce, and network congestion will be affected above the two-hop distance.
# A Review of Artificial Intelligence Research in Peer-Reviewed Communication Journals
## 2 Literature Review  
### The Evolution of Artificial Intelligence as a Research Area in the Communication Field  
AI is marked by seminal milestones that have shaped its progression over centuries. The concept of AI finds its roots in ancient tales and folklore, often portraying narratives of the creation of artificial beings [12]. However, the contemporary era of AI witnessed a notable surge in the mid-20 th century, epitomized by the 1956 Dartmouth workshop--a watershed moment acknowledged as the inception of AI as a scholastic discipline [13]. The introduction of AI has led to an important turning point across a range of scholarly disciplines including the communication field.  
The previous literature provided a panoramic view of the intersection between AI and the communication field, traversing various dimensions and implications [14; 15; 16; 17; 18]. Studies on the intersection between AI and the communication field begin with an exploration of attention and information consumption in the context of algorithms and decision support systems, revealing concerns about individual autonomy and ethical considerations. The European perspective emphasized the need for transparency and a human-centric approach to AI development [14].  
Research also has examined human-machine communication (HMC), emphasizing the evolution of AI in roles historically exclusive to humans [19]. In addition, research in the communication field advocates for an expanded understanding of the implications of increasingly lifelike AI technologies on society. AI-mediated communication (AI-MC) emerges as a disruptive force within computer-mediated communication (CMC) [15]. The profound impact of AI on message content and the blurring of sender-receiver agency lines challenge traditional communication paradigms. The authors navigated dimensions like language impact, interpersonal dynamics, self-presentation, and trust, underscoring AI-MC's ethical, cultural, and policy implications.  
The academic inquiry extends its focus to communication, journalism, and advertising, examining the rapid and transformative evolution of AI's significance in these industries [20; 21]. For example, [PERSON] and [PERSON] [20] underscore AI's remarkable ability to navigate extensive datasets, identify intricate patterns, and generate precise predictions, solidifying its indispensable role within these sectors. The initial phases of AI application in these fields primarily involved rudimentary data analysis and automated reporting. However, the pivotal juncture in AI's impact coincided with the ascendancy of the internet and the advent of social media, propelling transformative shifts in communication practices [22].
###### Abstract  
The increasing importance of utilizing new technologies, such as GNSS (Global navigation satellite system) devices, total stations, drones, scanners, etc., in forestry has become evident with the development of these tools. Combined with precise GNSS devices, UAVs represent an efficient tool that facilitates field measurements and reduces time spent in the field while also providing highly accurate data. To accurately determine the application of spatial data for calculating the longitudinal slope of forest/logging roads in a research project, measurements based on UAVs were conducted and compared with GNSS data. GNSS data were considered the reference values for the studied area, assuming that satellite positions and internet signals were good. An accurate longitudinal slope was necessary to define fuel consumption on forest/logging roads and slopes throughout the project and to verify the calculation method that could be applied for such or similar terrain conditions and overgrowth. The main goal of this research phase was not to study fuel consumption but to obtain an accurate longitudinal profile of forest/skid roads on rocky terrain that is not overgrown or poorly grown. The study put forth two hypotheses, one of which was ultimately confirmed, while the other was refuted. The results show no significant differences when comparing the absolute elevation of the points in the profiles for defining the longitudinal slope of the road using the program for designing forest/skid roads. While applying such a method of data collection to create the main projects for the (re)construction of forest/skid roads, there are significant deviations in earthwork masses. There was a difference of over 22.64% in one of the types of earthwork excavation on the designed forest road, so the method implemented in the research does not give sufficiently accurate results.  
longitudinal road slope; terrain-measuring method; data analysis; GNSS data; UAV data +
Footnote †: journal: _forests_  
_Article_
# Characterization of a Novel Emaravirus Affecting Ash Species (_Fraximus_ spp.) in Europe
## 3 Results
### Establishment of a Virus Specific RT-PCR Detection  
We tested six segment-specific primer sets (Table 1) targeting the RNA1-5 to detect the putative novel emaravirus ASaV by RT-PCR. The suitability and specificity of the detection method was evaluated using total RNA extracted from 11 symptomatic and four asymptomatic _F. excelsior_ leaves and four additional leaf samples infected by the putative and established emaraviruses, i.e., CORaV-infected oak, MaMaV-infected map, AsMaV-infected aspen, and FMV-infected Figure ASaV genome segments RNA1-RNA5 were detectable by all six developed primer pairs in _F. excelsior_ leaf material showing chloride ringsspots and curling or shoestring symptoms (Figure 4, track 4, 5, 8). However, the RNA1-specific primer set S50-1081F/S294-160R also amplified a fragment of the expected size 289 bp from the MaMaV-infected map (_A. pseudoplatanus_; Figure 4, track 2) and from the FMV-infected fig (_Ficus carica_) (Figure 4, track 6) samples in RT-PCR. Additionally, the primer pair targeting RNA3 generated a weak amplification product of the expected size (342 bp) from the MaMaV-infected sample. The other primer sets were found to have a high specificity for ASaV detection by RT-PCR, with segment-specific primer sets amplifying the expected 425 bp fragment of the RNA1, 243 bp of the RNA2, 498 bp of the RNA4 and 401 bp of the RNA6 in infected ash only. Direct Sanger-sequencing of generated PCR products from both directions confirmed the respective virus species to be present in the investigated samples (data not shown).  
Figure 3: Phylogenetic placement of ash shoestring-associated virus (ASaV) based on RdRp (**a**) and N (**b**) amino acid sequences from _F. excelsior_ (E55270) and _F. ornus_ (HTS data) and corresponding sequences of selected putative and classified emaraviruses: actinidia chlorotic ringspot-associated virus (AcRxeV), blackberry leaf mottle-associated virus (BLMaV), European mountain ash ringspot-associated virus (EMAReV), fig mosaic virus (FMV), High Plains wheat mosaic virus (HPWMoV), pea-associated emaravirus (PaEV), pigeompea sterility mosaic virus 1 and 2 (PPSMV-1 and PPSMV-2), pistacia virus B (PiVB), raspberry leaf blotch virus (RLBV), rose rosette virus (RRV), actinidia virus 2 (AcEV-2), aspen mosaic-associated virus (AsMaV), camellia japonica-associated virus 1 (CajV-1), perilla mosaic virus (PerMV). Branches report numbers that represent statistical support based on bootstrap analysis (1000 replicates). Tomato spotted wilt virus (TSWV, _Orthotospovirus_) was used as an outgroup species. Subgroups of emaraviruses are indicated by dashes with the letters A, B, C, and D, respectively.
## 2 Algorithm Theory and Method
### Pretreatment and Feature Extraction
#### 2.1.1 Wavelet Denoising  
If a signal \(f(t)\) becomes \(s(t)\) after being polluted by noise, then the basic noise model can be expressed as:  
\[s(t)=f(t)+\sigma\times n(t) \tag{1}\]  
where \(n(t)\) is noise and \(\sigma\) is the noise intensity. Wavelet denoising tries to attenuate the noise component \(n(t)\) so as to achieve the purpose of noise reduction.  
In general, the wavelet denoising process of a one-dimensional signal can be divided into three steps as follows:  
1. Wavelet decomposition of the signal: select the wavelet basis function, determine the appropriate number of wavelet decomposition layers, and then perform \(n\)-layer wavelet decomposition of the original signal \(s(t)\).
2. Threshold quantization of the wavelet decomposition coefficients: for the wavelet coefficients from the first layer to the Nth layer, the appropriate threshold is selected for threshold quantization.
3. Signal reconstruction by inverse wavelet transform.  
The result of wavelet denoising depends on the following two points:  
1. The denoised signal should have the same smoothness as the original signal, namely, the correlation \(\rho\) between the two columns of data should be high;
2. The smaller the root mean square error (\(RMSE\)) and the larger the signal to noise ratio (\(SNR\)) between the processed signal and the original signal are, the better the attained effect is.  
The calculation equations of \(SNR\), \(RMSE\), and \(\rho\) for the evaluation indexes of noise reduction are as follows:  
\[SNR=10\lg[\sum_{t=1}^{N}f(t)/\sum_{t=1}^{N}(f(t)-n(t))\,] \tag{2}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{t=1}^{N}[f(t)-n(t)]} \tag{3}\]  
\[\rho=\frac{N\sum_{t=1}^{N}f(t)n(t)-\sum_{t=1}^{N}f(t)\times\sum_{t=1}^{N}n(t)} {\sqrt{N(\sum_{t=1}^{N}f^{2}(t))-(\sum_{t=1}^{N}f(t))^{2}}\times\sqrt{N(\sum_{t =1}^{N}n^{2}(t))-(\sum_{t=1}^{N}n(t))^{2}}} \tag{4}\]  
According to analysis of the steps of wavelet denoising, the types of wavelet bases, the number of decomposition layers, and the threshold quantization method all impact the final denoising effect. This paper also analyzes these three aspects and selects the most suitable wavelet denoising method.
# Optimization of the Battery Pack Heat Dissipation Structure of a Battery-Type Loader
## 1 Introduction
[PERSON] et al. [19] discussed the effects of longitudinal and transverse spacing on the cooling performance of cylindrical battery packs and, based on the analysis results, showed that the average temperature rise in individual cells decreased with increasing longitudinal spacing, while increased transverse spacing led to an increase in cell temperature rise. Research by [PERSON] et al. [20] shows that the hierarchical porous carbon extracted from MS husk can be used in symmetric/asymmetric capacitors, which has positive implications for the development of new carbon materials. [PERSON] et al. [21; 22] have summarized the cathode and anode materials and provided a reasonable cell and material design. They present a novel design, preliminary development, and results for an inexpensive reusable, liquid-cooled, modular, hexagonal battery module that may be suitable for some mobile and stationary applications that have high charge and/or discharge rate requirements. Finally, [PERSON] et al. 's research in \"Next Generation Battery Management System\" shows that not only the cooling structure, but also the battery connection structure influences the charge and thermal imbalances of the battery pack [23]. Although researchers have analyzed heat dissipation in battery packs, there are still many factors to be further studied in the heat dissipation structure, and few studies have focused on the optimization of heat dissipation structure parameter combinations. In this study, we took a 3 m\({}^{3}\) battery-type loader power battery pack as the research object and investigated the influence of various structural parameters, including the battery pack outlet location, outlet size, bulkhead width, and inverted flow plate angle, on the heat dissipation performance of the battery pack. However, there existed differences in the battery's temperature performance under different structural parameters; therefore, the optimal combination of structural parameters was determined using an RBF neural network and the PSO algorithm. Based on the thermal performance of the battery pack under each structural parameter, the optimal combination of structural parameters was obtained, and the thermal effect of the optimized battery pack was improved significantly,demonstrating that the optimization method proposed in this paper is feasible and can be used as a reference for research related to the heat dissipation structure of battery packs.
# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 3 Proposed Positioning Framework  
### Unmanned Aerial Vehicle  
The LIDAR system (Horizon, Livox, Shenzhen, Guangdong, China) and camera (RealSense, Intel, Santa Clara, CA, USA) were suspended on the UAV to obtain the LIDAR point clouds and an image of the earth's surface. The LIDAR and camera were installed close to one another and pointing in the same direction to maximize the degree of overlap in terms of the field of view (FOV). The camera, which has two lenses, is a stereo camera, and only one lens was used in combination with the LIDAR. Its main purpose was to conduct comparative experiments with several stereo camera-based systems. The GPS on the UAV in the proposed system was not involved in positioning, although it synchronized the initialization time of the LIDAR-camera systems based on GPS-supplied time pulses.
# Fabric Defect Detection Method Using SA-Pix2 pix Network and Transfer Learning
## 2 Materials and Methods
### Experimental Environment and Dataset  
The experimental data were acquired using a CCD camera, and a dataset was generated by shifting a cropping box of size 256 \(\times\) 256 on the captured images. To augment the dataset, the cropping box had a stride of less than 256. We collected five different types of fabric defect patterns, as depicted in Figure 5. Samples 1 and 2 are of cotton and linen fabric, while samples 3, 4, and 5 are made of cotton silk fabric. All five samples are woven fabrics, and the defect types primarily include oil stains, abrasions, foreign objects, tears, and missing warp threads. Both the test and training set images are obtained from different positions on the same fabric roll. Within the training set, each sample category was obtained through cropping and contains a small number of positive samples. According to statistics, the approximate ratio of positive to negative samples is 1:4.  
Figure 4: Flow of defect detection process. (**a**) Original input image; (**b**) reconstruction map; (**c**) defect map; (**d**) denoising map using FT algorithm; (**e**) defect location map using the binary method.  
A significant number of artificial defect images were created individually on the flawless images of five distinct patterns, as illustrated in Figure 6. In the transfer learning experiments, only 200 genuine defect images per pattern class were employed as the target domain dataset, with 1000 artificial defect images used as the source domain dataset.  
The computer system configuration is detailed in Table 1. In the experiments, the Adam optimizer [47] was utilized using the parameters \(\beta 1=0.5\) and \(\beta 2=0.999\). During training, the initial learning rate was set to 0.0002, which linearly decayed to 0 over the last 200 epochs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System** & **Memory** & **GPU** & **CPU** & **Deep Learning Framework** \\ \hline Windows 10 & 64 G & NVIDIA & Intel Core i7-8700K & Pytorch, CUDA10.1, \\  & & GTX-1080 Ti & @ 3.70 GHz & CUDNN7.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computer system configuration.  
Figure 5: The real data set of defects used in the experiment. (**a**) Sample 1; (**b**) sample 2; (**c**) sample 3; (**d**) sample 4; (**e**) sample 5.  
Figure 6: Artificial dataset of defects used in the experiment. (**a**) Sample 1; (**b**) sample 2; (**c**) sample 3; (**d**) sample 4; (**e**) sample 5.
# Correlation Plenoptic Imaging: An Overview
## 6 Conclusions  
We have reviewed the basic principles and most relevant implementations of correlation plenoptic imaging, while focusing both on the improvement with respect to standard plenoptic imaging and the comparison among different CPI strategies. Unlike standard plenoptic imaging, CPI has no constraints on image resolution, which stays diffraction limited as in standard imaging systems. At the same  
Figure 14: In (**a**), we plot the resolution of standard ghost imaging (SI), classical plenoptic imaging (PI) with \(3\times 3\) pixels for each microlens, and CPI with entangled photons, as a function of the longitudinal displacement \(z_{b}-z_{bF}\) of the sample from the focused object plane. In (**b**), we simulate the images of triple slit, placed in the points A (top row) and B (bottom row) in the case of SI, PI and CPI. Reproduced with the permission from [76], copyright 2018 Society of Photo Optical Instrumentation Engineers.  
time, CPI enables increasing the DOF well beyond the typical values of standard imaging, as reported in Figures 7 and 14. The results are unchanged for reflective and transmissive samples. CPI is also expected to work with different kinds of sources, of either photons or particles [77], provided they are characterized by correlation in both position and momentum [61; 78].  
CPI is promising for both microscopy and 3D imaging, due to its ability to decouple transverse and axial resolutions, to achieve high DOF, and to acquire multiperspective images with a single device. In the future, we will employ both hardware (fast CMOS, smart sensors [79]) and software (compressed-sensing and sparse measurement techniques [80]) solutions to optimize the acquisition time and regain the single-shot advantage of conventional plenoptic imaging. Moreover, we will investigate the potential of the intrinsically coherent nature of CPI, to achieve superresolution and perform tasks, like wavefront sensing, in which quantum state estimation techniques [81; 82], based on the reconstruction of the coherence function of light, have already proved to be effective.
# Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles
## 3 Proposed Method
### Clustering Algorithm for Applying the Path-based Clustering Model  
In this section, we will discuss the PCDP's clustering process and the message format required.