esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Section
Isolated EXAFS spectra were then Fourier-transformed (FT) over a \(k\) range of 3.3-15.3 A\({}^{-1}\) with an adaptive Hanning window and back FT over a non-phase-corrected radial distance (R) range of 1.6-2.7 A to isolate the first nearest neighbor (NN) shell. Structural \begin{table}
\begin{tabular}{c c c c c c} \hline
**Energy** & **Range** & \(\mathbf{S_{e}}\) & **SiO\({}_{2}\)** & \(\mathbf{S_{e}}\) **Co** & \(\mathbf{S_{n}}\) **SiO\({}_{2}\)** & \(\mathbf{S_{n}}\) **Co** \\
**(MeV)** & **(\(\mu\)m)** & **(keV/nm)** & **(keV/nm)** & **(keV/nm)** & **(keV/nm)** \\ \hline
9 & 2 & 2.6 & 5.2 & 1.4 & 3.9 \\
27 & 6.3 & 4.8 & 9.4 & 0.7 & 2 \\
54 & 9.9 & 9 & 19.4 & 0.4 & 1.2 \\
89 & 13 & 12.7 & 29.8 & 0.3 & 0.9 \\
110 & 14.6 & 14.2 & 34.5 & 0.2 & 0.7 \\
185 & 19.2 & 17.6 & 45.1 & 0.2 & 0.5 \\ \hline \end{tabular}
\end{table} 
Table 1: Parameters for Au ions in SiO\({}_{2}\) and Co calculated by TRIM [33]. parameters were determined with ARTEMIS [36] utilizing the IFEFFIT [37] code with theoretical scattering amplitudes and phase shifts calculated ab initio with the FEFF8 code [38]. The amplitude reduction factor (\(S_{0}^{2}\)) was determined for the HCP metal standard (0.80 \(\pm\) 0.02) and then fixed for the subsequent fitting of all NP spectra. The energy shift parameter (\(E_{0}\)) was fitted for the standards and then fixed for the NP samples. The coordination number (\(CN\)) was set to 12 for the FCC and HCP standards and fitted for the NP samples. The bondlength (\(BL\)), DebyeWaller factor (\(\sigma^{2}\)), and third cumulant (\(C_{3}\)) were also fitted. All data sets were fitted with multiple \(k\)-weights of 1-4 to reduce inter-parameter correlation. For the measurement-temperature-dependent EXAFS analysis, the evolution of the first NN structural parameters was determined by analysing each data set over the 15-300 K temperature range individually with the \(CN\) and \(E_{0}\) fixed to the values determined from the lowest-temperature data (15 K). The \(BL\), \(C_{3}\) and \(\sigma^{2}\) were allowed to vary freely and the temperature dependence of \(\sigma^{2}\) and \(C_{3}\) were fit with a correlated Einstein model (Equations (1) and (2)) to determine the static and temperature dependent contributions to the total \(\sigma^{2}\) and \(C_{3}\). Transmission SAXS measurements were performed at beamline 15 ID-D of the Advanced Photon Source, Argonne, IL, using 11.27 keV X-rays (1.1 A wavelength). The scattering intensity _I(Q)_ was collected for 5 s at a camera length of 1,871 mm. A scattering spectrum from an unimplanted SiO\({}_{2}\) layer was subtracted from all NP spectra. The maximum entropy method (MEM) [39,40] was used to determine the NP size distribution.
## 2 Study Area and Data Sources  
### Study Area  
Zhaotong City is located in the hinterland of the Wumeng Mountains in the north-eastern Yunnan province, at the junction of Yunnan, Sichuan, and Guizhou provinces, and has a complex topography with undulating terrain [24], and it has many local climate types and significant vertical variation, with an average annual temperature of 11.6 \({}^{\circ}\)C, an average annual minimum temperature of 7.3 \({}^{\circ}\)C, an average annual maximum temperature of 18.3 \({}^{\circ}\)C, and an annual frost-free period of about 220 days. Additionally, the sum of daily temperatures greater than or equal to 10 \({}^{\circ}\)C during the year is 3, 217 \({}^{\circ}\)C, the average annual sunshine hours is 1902 h, and the average annual rainfall is 738.2 mm. It is a great national base for apple cultivation in the south.  
The study area (Figure 1) is located next to Ganhe Reservoir, southeastern Zhaoyang District, Zhaotong City (103\({}^{\circ}\)43\({}^{\prime}\)55\({}^{\prime\prime}\) F-103\({}^{\circ}\)45\({}^{\prime}\)17\({}^{\prime\prime}\) E, 27\({}^{\circ}\)16\({}^{\prime}\)50\({}^{\prime\prime}\) N-27\({}^{\circ}\)17\({}^{\prime}\)41\({}^{\prime\prime}\) N), which is one of the best apple growing areas in Zhaoyang District. The study area had numerous crop species and was large and highly representative; therefore, it was chosen as the study area. The study area was divided into five categories, according to land use characteristics: apple orchards, croplands, artificial surfaces, water bodies, and other land. Most of the apple orchards in the study area were in full bloom, with apple trees starting to bud from March to April and the stable growth period from June to August, with high vegetation cover in the orchards and fruit ripening in September to October.  
The image size of the study area was 251 rows and 157 columns, with a total area of 3.94 km\({}^{2}\). According to the visual interpretation, the proportions of apple orchards, croplands, artificial surfaces, water bodies, and other lands were 9.77%, 13.56%, 8.46%, 62.11%, and 6.1%, respectively. The presence of thin clouds in the October Sentinel-2 images and the complex topography of the study area made it challenging to distinguish apple orchards using RGB Bands alone.  
Figure 1: Sentinel-2 images of the study area.
###### Abstract  
Metal-matrix-impregnated diamond composites are used for fabricating many kinds of diamond tools. In the efforts to satisfy the increasing engineering requirements, researchers have brought more attention to find novel methods of enhancing the performance of impregnated diamond composites. In this study, deep cryogenic treatment was applied to Fe-Co-Cu-based diamond composites to improve their performance. Relative density, hardness, bending strength, and grinding ratio of matrix and diamond composite samples were measured by a series of tests. Meanwhile, the fracture morphologies of all samples after the bending strength test were observed and analyzed by scanning electron microscope. The results showed that the hardness and bending strength of matrix increased slightly after deep cryogenic treatment. The grinding ratio of impregnated diamond composites exhibited a great increase by 32.9% as a result of deep cryogenic treatment. The strengthening mechanism was analyzed in detail. The Fe-Co-Cu-based impregnated composites subjected to deep cryogenic treatment for 1 h exhibited the best overall performance.  
deep cryogenic treatment; impregnated diamond composites; metal matrix; wear resistance
# Iterative MLFMA-MADBT Technique for Analysis of Antenna Mounted on Large Platforms
## 3 Formulation
### Iterative MLFMA-MADBT Method  
Although the total far field can be obtained by summing up the contributions from the MLFMA region and the BT region, the MLFMA region may be disturbed by the induced field from the BT region and the whole electromagnetic (EM) system cannot reach stable status. Figure 6 shows the couple intersection between the MLFMA region and BT region.  
The near field \(\mathbf{E}_{near}^{i}\) from the MLFMA region acts as the incident field of the initial beams launched towards the BT region; the near field \(\mathbf{E}_{near}^{PO}\) from the BT region can disturb the field of the MLFMA region. Thus, an iterative process should be introduced to handle the coupling between the two regions.
# Numerical Assessment of Side-Wind Effects on a Bus in Urban Conditions
## 3 Results
### Obtained Force Values  
In the simulations, \(F_{X}\) forces were obtained from different surfaces of the bus and analysed. It can be noted that, in general, the median value of \(F_{X}\) was also the most common; the peaking values only occurred on the frontal edge of the surface, where the corner vortices appeared. Along the length of the bus, this kind of \(F_{X}\) deviation decreases.  
The presented results are the sums of the \(F_{X}\) forces. When a high magnitude of the force was present, its effect could decrease due to the vortices. To analyse the force distribution on the bus (see Figure 6), the surface of the bus was divided into seven sections--left, right, frontal, bottom, top, wheels, and rear surfaces.  
The \(F_{X}\) force distribution shows that the rear surface and the wake region behind it generate most of the drag. It is also notable (Figure 6a) that, at 10 km/h on the frontal and wheel surfaces, the sums of the forces are relatively the same, while at higher speeds, the ratio of the front force decreases (Figure 6b,c). The lowest forces were obtained on the sides, which are magnified for the 20 km/h case in Figure 7.  
Figure 6: \(F_{X}\) forces on the bus at (a) 10 km/h; (b) 15 km/h; (c) 20 km/h.  
Figure 7: (a) \(F_{X}\) forces on the side of the bus at 20 km/h box plot; (b) force–time connection.  
The mean values are close to each other; however, the deviations are slightly higher on the right side. To examine it more closely, the \(F_{X}\) and time connection is also plotted in Figure 7b. The two forces are altering symmetrically with a wavy trend. It could also be noted that on the right side, the increased deviation is due to the \(F_{X}\) jump at the 30 th second. The regression of the \(F_{X}\) fluctuations, showed an R\({}^{2}\) = 0.43 when it was examined for the sine function. Thus, further analyses were not made. Since consistent stochastic deviations were present, after the discussed values, the standard deviations were also added.
## 4 Results and Discussion
### Results of Single Parameter Evaluation
#### 4.1.2 Vertical Net  
Net recharge refers to the amount of water per unit area that infiltrates the surface and reaches the aquifer. Contaminants can enter the aquifer vertically through this recharge water [38]. In the DRASTIC model, net recharge represents the total amount of water applied to the surface and infiltrated into the aquifer. In Tangshan City, atmospheric precipitation serves as the primary source of regional recharge, and therefore, the vertical net recharge can be estimated using precipitation infiltration [39]. Generally, the equation  
Figure 3: Mapping of DRASTIC parameters ((**a**) Depth of groundwater; (**b**) Vertical net recharge; (**c**) Aquifer thickness; (**d**) Soil media; (**e**) Topography; (**f**) Impact of vadose zone; (**g**) Hydraulic conductivity).  
\(R=P\times\alpha\) is used, where \(R\) denotes the vertical net recharge, \(P\) represents the precipitation infiltration coefficient, and \(\alpha\) represents the annual precipitation.  
The annual precipitation data for the region were obtained from the 2020 precipitation records of four gauging stations. In 2020, Tangshan City experienced rainfall ranging from 480-720 mm, with higher precipitation observed in the northern mountainous area compared to the southern plain area. Notably, Luannan County in the southern plain area received relatively higher precipitation. In the study area, the values of \(\alpha\) range from 0.15-0.2 in the plain area, 0.09 in the igneous and metamorphic rock area, and 0.08 in the carbonate rock area. The empirical values of the precipitation infiltration coefficient were adjusted based on field seepage test results (30 groups) to ensure accuracy. The resulting vertical net recharge is illustrated in Figure 3b.
# A Brief Review of Multipath TCP for Vehicular Networks
## 6 Multipath Routing in Vehicular Networks
### Stochastic Models  
In wireless networks, multiple paths are influenced by each other due to mutual interferences or path coupling. In [7], [PERSON] et al. proved that node-disjoint paths are more efficient than single paths when path-coupling and source-destination distance are chosen properly. To reduce the route coupling, [PERSON] et al. [131] presented that the performance of multipath routing improves when using directional antenna on wireless environments. In [132], [PERSON] et al. proposed a routing protocol called multipath reliable range node selection distance vector (MRRNSDV). MRRNSDV considers that the data should be transferred through the routes as soon as possible. Therefore, MRRNSDV comes up with a concept, called reliable node, which is a node neither too close nor too far from sender nodes. By selecting a closer node, it will increase the number of hops. In contrast, if selecting a farther node, it will make the route unreliable. In [133], map-based multipath routing protocol (MBMPR) was proposed. MBMPR utilize a GPS, digital map, and sensors to provide supplementary information to vehicles. With the additional information, it discovers the best path and an alternate path by the Dijkstra algorithm. VANETs have the characteristic of intermittent connectivity. Named data networking (NDN) is considered to be able to solve such problems. A multi-hop routing approach was proposed for NDN in [134] where the authors show their approach can achieve faster data delivery with less network resources.
## 1 Introduction
These attributes make them well-suited for the creation of high-performance SAs, such as the PSM. Compared to the inherent properties of PS materials, transforming them into PSM photonic crystals can modulate their absorption characteristics, altering their nonlinear properties and offering a potential approach for the fabrication of organic and inorganic material photonic devices. An assessment of the nonlinear properties of PSM was performed using nonlinear test equipment, leading to the generation of a Q-switched pulse laser with a pulse width of around 2 us and a repetition rate of 40 kHz at a wavelength of 1562 nm, by incorporating PSM-SA into a homemade ring fiber laser system. These findings not only unveil a novel avenue for exploring photonic crystals, but also extend their scope of application within nonlinear optical devices, thereby significantly advancing many fields such as radar, communication, medical treatment, and scientific research [29].
# Detecting the Speed Change Intention from EEG Signals:
## 3 Results
### BMI Offline  
#### 3.1.1 Feature Selection Methodology  
As specified in the previous section, 540 configurations were tested for Both, Increase and Decrease classifier models. As it was mentioned in Section 2.5.3, those that met the indicated criteria were used to analyze the most relevant features to differentiate the searched potential: a feature is chosen if it is present for at least three subjects (value between brackets in Table 2) and its average accuracy is over the median (63%). Using these criteria, six values were chosen. The chosen features and bands configurations are highlighted in bold in Table 2: \(HJ_{A}\) in band 8-14 Hz and in band 23-30 Hz, \(HJ_{M}\) in band 8-14 Hz, and \(HJ_{C}\) in band 8-14 Hz, 15-22 Hz and 23-30 Hz. Pburg, Shannon and Weibull were the features with the lower accuracies. Temporal features were much more effective than frequency features.  
Averaging the band values in Table 2 for each feature, the band with the highest percentage and the one that appeared the most was 8-14 Hz with a ratio of 2.16 and a percentage of 64.24% being the second band 23-30 Hz with 53.31% and a ratio of 1.8.  
For all the results that passed the acceptance criteria, a statistical analysis was carried out to analyze differences between the model types: Both, Increments and Decrements, and if there were differences between subjects:  
* Model type in terms of average accuracy: Since data of each methodology did not follow a normal distribution, as assessed by Shapiro-Wilk test, Kruskal-Wallis test was used. It showed that the performance was different based on the methodology (\(p\)-value \(<\) 0.05). Post-hoc analysis revealed statistically relevant differences between both increments and decrements (\(p\)-value \(<\) 0.05).
* Subject difference in terms of performance: Shapiro-Wilk test was applied to assess the normality assumption of ANOVA. Data of every subject did not show a normal distribution (\(p\) < 0.05), so Kruskal-Wallis test was employed. Results from this test showed no significant differences among subjects (\(p\)-value \(>\) 0.05).
## 6 Survey Findings and Future Directions
### Future Directions
Furthermore, the societal impact of summarisation technologies should be considered, particularly in preventing misuse and ensuring accessibility for all users. Future research should focus on refining models to address these ethical challenges while advancing summarisation techniques. Limitations  
While this study provides a comprehensive analysis of text summarisation research, it has some limitations. The analysis relies solely on publications indexed in the WoS database, which may exclude relevant studies from other sources. Only English-language publications were included, potentially missing valuable contributions in other languages. Insights from this study may not generalize to all fields or domains beyond text summarisation. Addressing these limitations in future work could enhance the robustness of the findings.
## 1 Introduction
There have been previous reviews of deep learning for construction applications, from which we stand out. [PERSON] et al. [25] and [PERSON] et al. [26] have provided extensive surveys on deep learning with point clouds, with the latter focusing specifically on semantic segmentation. However, these surveys primarily concentrate on 3D scene understanding with a general approach. Further, they do not address the developments in the recently dominant Transformer network architectures. Other related review studies, such as those conducted by [PERSON] and [PERSON] [27], [PERSON] et al. [28], and [PERSON] and [PERSON] [29], have explored machine learning applications in the construction industry. While they touch on point-cloud-based object classification, they focus on imaging methods for construction site monitoring and lack an in-depth and unbiased comparison of various deep learning methods specifically tailored for point cloud segmentation. Therefore, this paper presents an unbiased assessment and detailed comparison of the most popular deep learning methods developed in recent years, emphasizing their applicability to point cloud segmentation in the construction industry. For the first time, we address a critical challenge in this domain--the scarcity of industry-specific datasets for training deep learning models. To fill this gap, we have compiled an extensive table of relevant training and validation datasets, which, to our knowledge, has not been previously published to this extent. By offering novel insights and original perspectives in comparison to existing review studies, our research reinforces its significance and contribution to the broader field of deep-learning-based approaches for the construction industry. Our goal with this article is to support future research in the fields of applied civil engineering and computer vision by establishing a solid foundation for further exploration. By outlining the most recent methodologies and resources available for training and validating new algorithms, we hope to unify benchmarking efforts and encourage wider participation within the research community. The structure of this paper is as follows: Section 2 describes the methodology adopted in this study. Section 3 addresses the state of the art in industry-specific datasets for the construction domain. Section 4 discusses the issue of unbalanced datasets during training and presents potential solutions. Section 5 introduces and compares the most popular developments in deep-learning-based point cloud segmentation in recent years. Section 6 examines the findings of this survey, and finally, Section 7 concludes the paper.
## 2 Simulation Model Development
### Tractor Power Transmission System  
In this study, we used a mechanical front wheel drive (MFWD) tractor (S07, TYM Co., Ltd., Gongju, Korea) which is widely used in Korea; its major specifications are shown in Table 1. The dimensions (Length \(\times\) Width \(\times\) Height) of the tractor are \(4225\times 2140\times 2830\) mm. The tractor's empty vehicle weight is 3985 kg, and the ratios of the front and rear axles are 42.4% and 57.6%, respectively. The tractor is equipped with a 78 kW engine (D34P, Doosan Infracore Co., Ltd., Incheon, Korea). The rated torque of the tractor is 324 Nm at the rated rotational speed of 2300 rpm and its maximum torque is 430 Nm at 1400 rpm, as shown in Figure 1. The type of tractor transmission is a power shuttle, and the number of gear stages are Forward 32 and Reverse 32. The transmission consists of two power shifts (High and Low), four driving shifts (1, 2, 3, and 4) of synchronesh type, and four range shifts (C, L, M, and H) of constant mesh type.  
The tractor power transmission system was constructed as shown in Figure 2. The output shaft of the engine is engaged/disengaged with the drive shaft through a clutch. The drive shaft is first connected to the forward/reverse shift gear and, then, sequentially connected to the high/low shift gear, driving shift gear, range shift gear, and spiral bevel gear. In the spiral bevel gear, the power input from the range shift shaft is diverted to the left and right rear wheels.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Specifications** \\ \hline Dimension & \(4225\times 2140\times 2830\) mm \\ Weight (ratio of front and rear axle) & 3985 (42.4\% and 57.6\%) \\ Model & D34P \\ Engine & Rated power & 78 kW at 2300 rpm \\  & Rated torque & 324 Nm at 2300 rpm \\  & Maximum torque & 430 Nm at 1400 rpm \\  & Type & Power Shuttle \\  & Number of gear stages & 32 Forward/32 Reverse \\ Powertrain & Power shift & 2 (High and Low) \\  & Driving shift & 4 (1, 2, 3, and 4) \\  & Range shift & 4 (C, L, M, and H) \\  & Type & Bias ply \\ Tire & Size & 13.6–24 8 PR and 18.4–34 10 PR \\ (Front and rear axle) & Pressure & 216 and 157 kPa \\  & Static loaded radius & 0.545 and 0.740 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of agricultural tractor used in this study.  
Figure 1: Engine performance map of the tractor used in this study.
## 7 Limitations and Conclusions  
### Limitations  
The proposed PER-iSAC algorithm demonstrates promising performance for image segmentation in the IoV, but several limitations remain. First, while the framework is scalable in principle due to its deep reinforcement learning foundation and use of prioritized experience replay, system responsiveness may degrade in large-scale scenarios with high vehicle density and task heterogeneity. Future work will explore distributed or federated learning strategies to enhance scalability. Second, although the iSAC framework is structurally general-purpose and applicable to other vision-based tasks (e.g., object detection, pedestrian tracking), its performance on non-image or multi-modal workloads has not been validated. Third, the current model does not support task prioritization or preemption, limiting its applicability in mixed-criticality environments. Fourth, it does not explicitly model cross-domain edge handovers caused by vehicular mobility, which could impact task offloading performance in dynamic real-world settings. Fifth, cost considerations such as CAPEX and OPEX are not evaluated; while edge offloading reduces per-vehicle hardware costs, future work should include quantitative cost-performance analysis for large-scale deployment feasibility. Lastly, although our evaluation compares PER-iSAC with PPO and Standard SAC, it does not include more recent or advanced approaches such as hybrid offloading methods, federated learning, multi-agent DRL, or LLM-based task  
Figure 7: Performance metrics.  
schedulers. We recognize this as a limitation and plan to incorporate such baselines in future studies to further validate and benchmark our method.