esa-sceva/satcom-chunk-collection huggingface dataset:
# Arbitrary Sampling Fourier Transform and Its Applications in Magnetic Field Forward Modeling
## 3 Sampling Rules
### Forward Transform Sampling Rules  
The forward transform sampling rules are related to the distribution of the integrand function in the spatial domain. If the function in the spatial domain changes rapidly, the subdivision needs to be encrypted. If the change in the spatial function is relatively gentle, it can be appropriately sparse. Set the space domain sampling interval as \(\Delta x_{j}\), the sampling element number as \(N\), and the number of sampling points as \(2N+1\). Set the first sampling point coordinate and the space domain sampling coordinate as \(x_{j}\)  
\[\left\{\begin{array}{l}x_{2j-1}=x_{0}+j\Delta x_{j}\\ x_{2j}=x_{0}+(j+1)\Delta x_{j}\\ x_{2j+1}=x_{0}+2j\Delta x_{j}\end{array}\right.\;\;j=1,2,\cdots,N. \tag{15}\]  
If the sampling interval is uniform, \(\Delta x_{j}\) is the fixed value; if the sampling interval is non-uniform, \(\Delta x_{j}\) will have different values in different intervals. Set the minimum sampling interval as \(\Delta x_{min}\) and the maximum sampling interval as \(\Delta x_{max}\).
## 2 Intelligent Compaction System
### System Hardware  
In order to have a precise location, a GPS controller with a high-precision DGPS antenna was installed. The data are transmitted to a Web-GIS-based compaction management system according to the National Marine Electronics Association (NMEA) 0183 standards. These standards define electrical signal characteristics, data types, and formats, allowing the software to display, record, and interpret GPS receiver information [27]. Among the various formats of the NMEA 0183 standards, the GGA format was adopted to provide information on latitude, longitude, and Universal Time Coordinated (UTC) time on the GIS map (Table 1). The RADIAN accelerometer is attached to the roller drum. It has a data logger that records the data and a data transmitter that transmits the data in 0.01 s for Fast Fourier Transform (FFT) Analysis. The entire system hardware used in this research is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Field** & **Example** & **Details** \\ \hline c & GGA & Global Positioning System Fix Data \\ UTC & 123,519 & hhmmss.ssss \\ Latitude & 4807,038 & ddmm.mmmm \\ N/S indicator & N & N = North, S = South \\ Longitude & 01131.324 & dddmm.mmmm \\ E/W indicator & E & E = East, W = West \\ Position Fix & 1 & 0 = Invalid, 1 = Valid SPS, 2 = Valid DGPS, 3= Valid PPS \\ Satellite Used & 08 & Satellite being used (0–12) \\ HDOP & 0.9 & Horizontal dilution of precision \\ Altitude & 545,4 & Altitude in meters according to WGS-84 ellipsoid \\ Altitude Unit & M & M = Meters \\ Geoid Separation & 46.9 & Geoid separation in meters according to WGS-84 ellipsoid \\ Separation Units & M & M = Meters \\ DGPS Age & (empty field) & Age of DGPS data in seconds \\ DCPS Station ID & (empty field) & - \\ Checksum & * 42 & - \\ Terminator & (CR)/(LF) & - \\ \hline \end{tabular} Note. * Checksum data always begins with asterisk symbol (*)  
\end{table}
Table 1: GGA data details and format.  
Figure 2: System hardware of the IC roller.
## 4 Industry Perception Study
### Feedback about Ability of DePaaS to Address SDP Challenges
#### 4.3.3 Solving SDP Problems Related to Feature Selection  
The sample pool was briefed about SDP problems related to feature selection and was asked whether DePaaS could help solve those problems.  
Many respondents (45%) indicated that DePaaS could help feature selection. However, this belief was not found to be statistically significant (\(\chi^{2}\) = 1.64, CV = 3.84, \(\alpha\) = 0.05, \(p\) = 0.2008). The front-end application could suggest optimal combination of features that could yield higher prediction accuracy. Such a suggestion could also be made based on the historical data of SDP model performance.
## 4 Findings Results
### Trajectory and Trends in ML and DL Applications
#### 4.2.2 Thematic Evolution  
From the thematic evolution analysis, we gather significant insights that demonstrate how dynamic this area of research is (Figure 12). Notably, artificial intelligence (AI) shifted its focus from risk assessment to supply chain management from 2014 to 2024, marking a significant turning point. This transformation indicates a strong association with risk assessment (inclusion index score of 0.50) while also showing a moderate connection to inventory control and optimization. This suggests an increased emphasis on reducing risks and improving operational efficiency.  
Deep learning has maintained continuity, particularly in its application to supply chain management and commerce, despite having a high stability index. This indicates that while it may not be the most prominent theme in the research landscape, it remains consistent and integral within the domain. Supply chain management continues to be a key topic across all time periods, increasingly interacting with emerging technologies, such as deep learning and learning algorithms. This ongoing integration demonstrates strong thematic growth, with supply chain management maintaining its original focus on predictive methods and machine learning while also incorporating a variety of emerging technologies and approaches.  
The steady growth trend, initially present from 2014 to 2023, began to be associated with deeper learning by 2024. Although this relationship is still developing, it suggests a growing integration of sustainable practices with advanced AI technologies. A nearest-neighbor search and production control highlight the thematic dynamics of marketing and supply chain processes in later temporal periods. Although these correlations are not very stable, they indicate areas that may require further investigation and stabilization.  
Overall, this study shows that artificial intelligence, deep learning, and supply chain management are interconnected. New applications and methodologies, particularly those related to risk assessment and optimization, are emerging, driving the future of supply chain management toward more efficient, streamlined, and sustainable practices. This continuous improvement underscores the importance of flexible predictive models and innovative approaches to addressing today's supply chain challenges.  
Figure 12: Thematic evolution of AI applications in supply chain management.
# Automated Evaluation of C-ITS Message Content for Enhanced Compliance and Reliability
## 3 Results
### Scenario 4  
In this scenario, the third-party unit was configured to broadcast a DENM with the following setup:  
* causeCode set to \"stationaryVehicle\"
* relevanceTrafficDirection set to \"upstreamTraffic\"
* informationQuality set to 4 The messages were broadcast from the third-party OBU, and the C-ITS SIM logged them for analysis in order to further determine if the received messages adhered to the predefined parameters.  
Table 4 presents the results of the analysis using the Karlose tool. The numbers are derived in the same manner as those in Table 3.  
Let us discuss the parameters with warnings or errors:  
* altitudeValue, altitudeConfidence: The third-party OBU cannot adequately set the z-axis (altitude) of the event. For example, if an event occurs under a bridge, not having the altitude value could affect drivers on the bridge. Although this results in a warning, the value in the OBU is set to \"unavailable\", indicating a limitation in the OBU's ability to provide complete information.
* speedValue, speedConfidence: In DENM, the Event Speed part of the situation container is relevant for moving use-cases, such as a slow-moving vehicle. The third-party OBU omitted this information entirely. It could either include this part with a speed set to 0 or omit it. The omission leads to warnings in terms of Karlos.
* headingConfidence: The OBU set this parameter to \"unavailable\", which [PERSON] flagged as a warning. While this is not a critical issue, it suggests that the OBU was unable to provide a confidence level for the heading.
* headingValue: The third-party OBU transmitted a headingValue of 4800, which is outside the acceptable range. According to ETSI standards, headingValue should be between 0 and 3600. This discrepancy is considered a critical error as it can lead to  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Container** & **Total** & **OK** & **Warning** & **Error** \\ \hline Header & 3 & 3 & 0 & 0 \\ Management & 10 & 8 & 2 & 0 \\ Situation & 3 & 3 & 0 & 0 \\ Location & 5 & 1 & 3 & 1 \\ Alacarte & 1 & 1 & 0 & 0 \\ \hline
**Total** & 22 & 16 & 5 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: DENM analysis summary for Scenario 4 (by container), showing counts of parameters categorized as OK, Warning, or Error. Parameters are classified based on their occurrence in messages: always OK, containing at least one warning, or containing at least one error.  
interoperability issues. The manufacturer should be contacted to correct this issue to ensure that the OBU meets the validation criteria and functions correctly within the system.  
Overall, while some warnings indicate minor issues, the critical error with heading Value requires immediate attention to ensure proper functionality and compliance with standards.
## 3 Results and Discussion
### Adsorption Experiments
#### 3.6.1 Influence of pH on Adsorption  
The pH values of 10 mg/L Cr(VI) solutions in ten beakers were adjusted to 3, 4, 5, 6, 7, 8, 9 and 10, respectively, with 0.1 M HNO\({}_{3}\) and NaOH solution. The ceramsite fired at 650 \({}^{\circ}\)C was added into Cr(VI) solutions with different pH according to the ratio of solid to liquid of 1:40, and the total Cr concentration was measured after reaction according to the method of 2.2.5. The removal qualities of Cr by ceramsite at different pH are shown in Figure 3.  
Figure 1: XRD images of ceramsite at different temperatures (from the bottom to the top, the temperature goes up).  
It can be seen from Figure 3 that when pH < 7, the removal quantity of Cr(VI) is higher. In this case, the surface is positively charged, and the electrostatic interaction with Cr(VI) is favorable for adsorption. After the point zero charge, the adsorption rate drops sharply. Because the surface of the ceramsite is negatively charged, which produces electrostatic repulsion to Cr(VI). Therefore, the subsequent adsorption experiments were carried out at pH = 5.
# Parafoveal and Perifoveal Accommodation Response to Defocus Changes Induced by a Tunable Lens
## 4 Discussion  
For all six subjects, a reduction in the parafoveal accommodation response at large eccentricity was noted, which agrees with earlier studies [8]. At 7\({}^{\circ}\) and beyond, there was no longer a clear correspondence between defocus stimuli and the ocular response. This is smaller than in some studies [9], plausibly due to the reduction in the number of accommodative cues when using a TL. All subjects showed similar peripheral accommodative tendencies, and therefore no more subjects were recruited in this study. A related study [12] found that at 7\({}^{\circ}\) retinal eccentricity (14\({}^{\circ}\) diameter), an accommodative response was present when subjects looked onto a black-and-white textured target made with a circle containing a Maltese cross target at the center while experiencing sinusoidal induced defocus (\(\pm\)1.5 D) caused by a TL at frequencies of 0.1, 0.2, 0.5 and 1 Hz. The presence of the central Maltese cross target can possibly be the reason why an accommodative response was still present. This differs from the methodology employed in the present study, where concentric annular targets were displayed at increasing retinal eccentricities. Accommodation is impacted by spatial frequencies, and not necessarily at the highest spatial frequencies, as shown in a study with digital blur of natural images [28]. The spatial frequency content of the annular target on a dark background is narrow when compared to that of an extended Maltese cross.  
In our study, the TL induced random defocus instead of a well-known periodic defocus change, thereby reducing possible accommodative cues. Small fluctuations were noticed throughout accommodation, and it is plausible that these may provide a cue to the accommodative response [29]. Blur adaptation can improve visual acuity by approximately 0.2 logMAR within approximately an hour [30; 31], and a related acuity reduction occurs after removing a prescribed correction [32]. This adaptation may partially be of optical origin, as photoreceptors dynamically adapt to the direction of incident light [22; 23; 33]. When it comes to the contrast of the stimuli, previous studies found that it does not have any influence on the accommodative response while reading on an electronic micro-display [34] or for near vision [35], and it has no significant impact on the accommodative lag [36]. Yet, other studies show that pupil size and higher-order aberrations may have some influence in the accommodation stimulus response [37], while illumination intensity impacts the amplitude of accommodation [38].  
Another key parameter is the retinal ganglion cell density that also drops off with increasing eccentricity [39]. The parafoveal results are similar between emmetropic and myopic subjects. Thus, refractive error may matter less [40] than monocular, which possibly limits the outer-segment leakage of light [41; 42].
## 3 Model Description
### Heat Generation and Boundary Conditions  
As explained in the introduction, the FSW is a thermo-mechanical process. During FSW, the plastic deformation and friction between the tool and workpiece are two sources of heat [23; 41]. The heat generation due to plastic deformation, \(Q_{pl}\), can be calculated using Equation (13):  
\[Q_{pl}=\eta\sigma_{f\mathrm{g}}\varepsilon^{\mathrm{c}}_{pl} \tag{13}\]  
where \(\eta\) is the amount of plastic deformation converted into thermal energy (taken as 90%), \(\varepsilon_{pl}\) is the plastic strain rate, and \(\sigma_{f\mathrm{g}}\) is the flow stress of the workpiece material.  
The heat generated from friction can be calculated as follows:  
\[Q_{f}=\tau\gamma \tag{14}\]  
where \(\tau\) is the frictional shear stress and \(\gamma\) is the slip rate.  
The frictional shear stress can be calculated according to the classical coulomb's law from Equation (15):  
\[\tau=\mu P \tag{15}\]  
where \(\mu\) is the coefficient of friction and \(P\) is the contact pressure. For FSW, the coefficient of friction is a function of contact pressure \(P\), slip rate (\(\gamma\)), and temperature. However, accurate values for the friction coefficient during FSW cannot be determined because of the process complexity [42]. Subsequently, a constant coefficient of friction values is used. [PERSON] et al. [43] took the friction coefficient of 0.3 in their simulation work of FSW of low alloy steel, while [PERSON] et al. [44] used a coefficient of friction equal to 0.4 to simulate the FSW of 304 austenitic stainless steel. Therefore, a constant coefficient of friction of 0.35 is considered in this investigation.  
In this study, the heat loss due to conductance between the workpiece bottom surface and the backing plate is taken into consideration as the main source of heat loss, while the heat loss from other surfaces to the surrounding environment is neglected. A constant convection coefficient at the workpiece bottom surface of 3000 W/m\({}^{2}\cdot\)K is assumed instead of the backing plate. [PERSON] et al. [24] and [PERSON] et al. [43] assumed a convection coefficient of 2000 W/m\({}^{2}\cdot\)K at the bottom surface, and [PERSON] et al. [45] defined it as 4000 W/M\({}^{2}\cdot\)K. In order to prohibit the material flow out of the Eulerian domain, a velocity boundary condition is applied as shown schematically in Figure 4. As in the experimental work, the simulation work consists of three stages: the plunge, dwell, and traverse stages. In addition, the position control method is applied in the tool to achieve a tool shoulder plunge depth of 0.3 mm. After reaching the predefined plunge depth, the tool dwells for 3 s to heat the tool and workpiece before the traverse stage.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Density** & **Yield Strength** & **Thermal Conductivity** & **Specific Heat** & **Young’s Modulus** \\ (**kg/m\({}^{3}\))** & **(MPa)** & **(W/m\(\cdot\)K)** & **(J/kg\(\cdot\)K)** & **(MPa)** \\ \hline
19,700 & \(1100\) & 35 & 156 & 430,000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Physical and mechanical properties of W–25%Re tool material.
## 3 Results and Discussion
### Pore Shape: Sphericity Factor and Shape Factor  
Two parameters can be used to study pore shape, namely, sphericity factor and shape factor. The sphericity factor is defined as the ratio between the pore volume and the product of its area times its length, according to the following equation (Equation (7)):  
\[spf=\frac{V}{A_{p}\cdot d_{p}} \tag{7}\]  
where \(V\) is the pore volume, \(A_{p}\) is the pore surface area and \(d_{p}\) is the pore length. The sphericity factor can take values between 0 and 1/6.  
On the other hand, the shape factor is defined as the ratio between the volume of the pore and the volume of the circumscribed sphere, according to the following equation (Equation (8)):  
\[sf=\frac{V}{\frac{1}{6}\cdot\pi\cdot d_{p}}^{3} \tag{8}\]  
where \(V\) is the pore volume and \(d_{p}\) is the pore length. The shape factor can take values between zero and one.  
Both the sphericity factor and the shape factor are parameters that indicate the degree of roundness of the pores, where larger values correspond to pores closer to a sphere and smaller values to pores that are less round.  
The histograms of sphericity factor and shape factor distribution of the specimens in this work are shown below (Figures 23 and 24).  
Figure 22: Histograms of pore-volume distribution (in length).  
The results show some very interesting data. First, it can be observed that the pores are not spherical at all. The most common sphericity factor is in the range 0.135-0.14 and the most usual shape factor is between 0.55 and 0.6. This is an important fact because almost all cracking mechanisms in concrete start with the birth of a crack from the pores. More elliptical pores are more likely to initiate a crack.  
In this case, Figures 23 and 24 represent all pores. Based on the information obtained from the CT-scan, it is possible to plot histograms of the sphericity and shape factors for a certain range of sizes (e.g., for pores belonging to a given range of lengths or volumes). This can allow us to identify which pores may be more prone to crack initiation and propagation. In addition, the influence of components and additives on pore shape can also be analyzed using these parameters.  
Figure 23: Histogram of the sphericity factor.  
Figure 24: Histogram of the shape factor.
# A Source Code Similarity Based on Siamese Neural Network
## 2 Related Work
### Source Code Representation  
Source code representation plays a key role for code similarity. In this section, we review some recent and representative work about code similarity tasks. There are two common code representation methods: statistical-based and vector-based approach. Statistical-based methods introduce some probabilistic models to represent source codes. TF-IDF is a popular statistical representation method usually used in Information Retrieval applications. The representation of text consists of a term-document matrix which usually describes the word frequencies in the document. The TF-IDF model assigns relatively low weights to very frequent words and high weights to rare words. For a source code snippet \(c\), it is represented by N reversed words, \(c=\left\{v_{1,}v_{2},\ldots,v_{N}\right\}\), where the weight of \(v_{i}\) is \(f_{i}\), \(f_{i}\in F=\left\{f_{1},f_{2},\ldots,f_{N}\right\}\), F is a TF-IDF set. Word embedding is a vector-based representation that produces a low-dimension continuous distributed vector for word. The word2 vec model proposed by [PERSON] et al. is the most common word embedding method, which constructs a three-tier shallow neural network to predict the distributed vector of each word [29]. It is also introduced into the representation of source code. [PERSON] et al. trained a low-dimensional vector for code snippets extracted from API documents to measure the similarity of documents. This model were used to software search and fault locating tasks based on the trained vector space distance [30; 31]. [PERSON] et al. extracted a large number of code corpus from the stack overflow and other programming Q&A communities, and processed the synonym problems based on the word vector. It provided the basis for code conversion and other work [32]. [PERSON] et al. parsed the abstract syntax tree for the source code, and mapped it to its corresponding real-valued vector representations, so that similar AST node have similar vectors [33]. [PERSON] extracted abstract syntax trees from codes, and provided a tree-based CNN to learn word vectors of codes which was used in code classification and searching [34]. [PERSON] proposed a new DNN network to extract lexical and grammatical features of code in each layer [24]. [PERSON] proposed an auto-encoder deep learning framework applied in code clone detection,which learn lexical and syntactic feature of code [35]. [PERSON] used a deep belief network to learn semantic features from token vectors extracted from ASTs to perform defect prediction [36].  
Most of the aforementioned works are developed for a specific task, not purely code representation. Unlike such approaches, our method uses deep learning only for training word embedding to learn more complex semantic and syntactic structure features underlying source code. Then we model a code snippet as a weighted series of word vectors and the weights were learned from their TF-IDF values.
# FedCO: Communication-Efficient Federated Learning via Clustering Optimization +
## 3 Preliminaries and Definitions
### FL State-of-the-Art Algorithms
#### 3.3.1 FedAvg  
FedAvg, proposed by [PERSON] et al. in [PERSON] et al. (2016) can be viewed as a communication-light implementation of the standard centralized SGD, wherein the local updates are aggregated in the server after \(E\) local steps, where \(E\geq 1\).
## Appendix A MVSS Beamforming Methodology  
### Signal Time Delay Calculation  
The time delay \(\tau_{i}\) of each receiver can be calculated by Equation (A1) in a 2D imaging problem where \(c\) is the background velocity. By adding delayed signals together, the amplitude at the target point can reveal whether the waves were reflected from the target point. As shown in Figure A1, the waves produced by the shot source \((x_{s},z_{s})\) arrive at the scatter point \(\left(x_{f},z_{f}\right)\) and are reflected; then, the reflected wave signals are received by a receiver \((x_{r},z_{r})\). Here, the time difference among the signals at different receivers is caused by the position differences among the receivers and sources. The delay time of each shot-receiver pair includes two parts: \(\tau_{n}\), the time from the source point \((x_{s},z_{s})\) to the target point \(\left(x_{f},z_{f}\right)\), and \(\tau_{m}\), the time from the target point \(\left(x_{f},z_{f}\right)\) to the receiver \((x_{r},z_{r})\):  
\[\tau_{i}=\tau_{s}+\tau_{r}=\frac{\sqrt{\left(x_{s}-x_{f}\right)^{2}+\left(z_{s }-z_{f}\right)^{2}}}{c}+\frac{\sqrt{\left(x_{r}-x_{f}\right)^{2}+\left(z_{r}- z_{f}\right)^{2}}}{c}\] (A1)