esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Potential Waste Reduction by Using Different Structural Systems
In a similar vein, 12.93%, 10.87%, and 64.09% of reductions for the ready-mixed concrete, rebars, and forms, respectively occurred through the application of a flat plate slab system with 180 mm of thickness. Likewise, a reduction of 6.62% and 4.43% of the ready-mixed concrete and rebars occurred from the flat plate slab with 210 mm of thickness. For the flat plate slab with 210 mm of thickness, the amount of waste generated from the ready-mixed concrete slightly increased by 0.26% compared to the solid slab. The slight increase of waste generation in the ready-mixed concrete from the flat plate slab seemed to be due to the added amount of ready-mixed concrete in the drop panels to prevent punching shear. In this study, the void slab system could reduce the construction waste of plywood formwork by decreasing the amount of cast-in-place concrete used because the steel decking is applied to mould the concrete as well as fix the void formers. Based on the results of the case study, the most effective approach to mitigate the construction waste generation from the different slab systems would be the void slab system.
# Experimental Study on Strength of Polypropylene Fiber Reinforced Cemented Silt Soil
## 2 Sample Preparation and Test Scheme of Unconfined Compressive Strength
### Analysis of Test Scheme  
The orthogonal experiment design is a design method used to study multiple levels and factors. The representative points are selected by orthogonal experiment and the better scheme is obtained by fewer experiments. Therefore, this paper intends to conduct an orthogonal test of three factors and four levels to determine the optimum dosage of cement dosage (A), fiber content (B), and fiber length (C). As shown in Table 3, the orthogonal table of L16 (3\({}^{4}\)) was used in this experiment. The four levels of cement dosage (A) were 9%, 12%, 15%, and 18%; the four levels of fiber content (B) were 0%, 0.1%, 0.2%, and 0.4%; and the four levels of fiber length (C) were 3, 6, 9, and 12 mm. Based on these parameters, a total of 16 groups of different ratio tests were conducted. The ratio of solidified soil in each group is detailed in Table 4. Here, it was necessary to point out that for fiber-reinforced soil, the strength of the interface between fiber and matrix had an important impact on the strength enhancement effect of fiber. However, the main work of this paper is to systematically analyze the influence of test type, cement content, fiber content, and fiber length on the strength of fiber-reinforced cement soil, and at the same time, to deduce its constitutive relationship according to the deformation form of fiber reinforced cement soil. Therefore, no relevant research has been carried out on the interface strength between a single fiber and soil, but this influence factor should be fully considered in the follow-up work.  
In this paper, the UCS test of the sample cured for 28 days was carried out. The influence of three factors and four levels on the strength of the sample was obtained by calculating the range, and the optimal scheme was determined. UU test was used to study the stress-strain characteristics and shear strength characteristics of fiber cement soil under confining pressure. The test ratio was cement dosage 15%, fiber length 9 mm, fiber content 0%, 0.1%, 0.2%, 0.4%. The UCS test was carried out after 28 days of curing by using samples with a cement dosage of 15%, fiber length of 9 mm, and fiber content of 0%, 0.1%, 0.2%, and 0.4%. Photographs were taken to record the failure pattern of fiber cement soil, and some samples were taken for drying treatment and SEM test.  
Figure 4: Strain-controlled triaxial apparatus.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Level**} & \multicolumn{3}{c}{**Factor**} \\ \cline{2-4}  & **A Cement Dosage/\%** & **B Fiber Content/\%** & **C Fiber Length/mm** \\ \hline
1 & 9 & 0 & 3 \\
2 & 12 & 0.1 & 6 \\
3 & 15 & 0.2 & 9 \\
4 & 18 & 0.4 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Factors and levels of orthogonal test.
# Imitating Human Go Players via Vision Transformer
## 4 Experiments and Results
### Performance Evaluation for Imitating Human Moves
#### 4.2.3 Inference Time Analysis on Different Devices  
In this section, we will discuss the model's inference time during the inference phase.  
When learning to play Go, users may not always have access to high-performance computers. Many players might rely on lightweight devices to predict the next move. To evaluate the model's feasibility across different systems, we tested it on three devices: the GTX 1650, the RTX 3090, and the RTX 4090.  
The results are illustrated in Figure 11, using a test dataset containing 1,376,321 positions. The total inference times were 280.23 s for the RTX 4090, 354.90 s for the RTX 3090, and 1523.62 s for the GTX 1650. These times correspond to an average inference time per position of 0.00021 s, 0.00026 s, and 0.0012 s, respectively.  
These findings indicate that the model is computationally efficient and can be utilized effectively on lightweight devices equipped with a GPU.  
Figure 10: Black to play. All positions marked with an X are considered equivalent according to the rules.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Symmetries** & **Ext. Top-1 Acc.** & **Ext. Top-5 Acc.** \\ \hline
12-layer CNN & 1 & 0.4943 & 0.8274 \\
12-layer CNN & 8 & 0.4962 & 0.8272 \\ \hline Policy network & 1 & 0.4930 & 0.8144 \\ Policy network & 8 & 0.5091 & 0.8237 \\ \hline Vit/L = 4 & 1 & 0.4533 & 0.7704 \\ Vit/L = 4 & 8 & 0.4751 & 0.7993 \\ Vit/L = 8 & 1 & 0.4892 & 0.8063 \\ Vit/L = 8 & 8 & 0.5106 & 0.8278 \\ Vit/L = 12 & 1 & 0.5013 & 0.8259 \\ Vit/L = 12 & 8 & **0.5187** & **0.8349** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The extended top-k accuracy of the ViT and CNNs is compared.
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 4 Building Blocks of the CPS Communication Hub
### Attachment of Physical CPS Resources  
The attachment of physical resources to the CPS hub can be either direct or indirect, as clarified in Section 3.4. Let us have a look at some physical resources of the traffic accident case; Figure 11. In the experimental case, the direct attachment of the Polar smart watch was applied using a smart phone as a gateway. The embedded CPS hub connector enabled the communication with the virtual spaces of Polar in the CPS hub; green oval in Figure 11. While, for example, the Tracker dog collar applied the indirect method for communication with the CPS hub. The reason for this was the specialized optimized communication between the collars and the Tracker router and service cloud. A specialized gateway with the CPS hub connector was applied to register the presence of collars to the CPS hub. In this way, the presence of all the online collars in the Tracker service cloud could be registered in the CPS hub. However, the scalability of the experimental CPS hub services was found to be a challenge in this case. The indirect attachment methodology was also applied with the Bittium medical Electroencephalography (EEG) sensor, IMEC CO\({}_{2}\) sensor, and Valopaa street illuminator in the traffic accident case.
# Experimental Research into the Ultrasonic P-Wave Velocity of Coal Slime Based Backfill Material
## 6 Parametric Analysis Model of Ultrasonic P-Wave Velocity
### Parametric Predictive Model  
Various influencing factors and parametric curves of materials in Figure 13 were fitted. In this way, the relationships of the mass concentration of coal slime, content of high-water-content material, cement content, and content of fly ash with the ultrasonic p-wave velocity were acquired (Table 12).  
The data show that the ultrasonic p-wave velocity has a quadratic polynomial relationship with factor A, while it follows an exponential relationship with factors B, C, and D. The fitted curves of the four factors were plotted, as illustrated in Figure 20.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factor** & **Index** & **Fit Relation** & **General Formula** &
\begin{tabular}{c} **Degree of** \\ **Fit R\({}^{2}\)** \\ \end{tabular} \\ \hline Mass concentration of & \multirow{2}{*}{Wave velocity} & \(v_{1}=\) & \(v_{2}=\) & \(v_{1}={aP_{A}}^{2}+bP_{A}+c\) & 0.99 \\  & & \(v_{2}=5.67317-1.12815e^{-\frac{(p_{2}-0.0405)^{2}}{2.040509^{2}}}\) & \(v_{2}=y_{0}+Ae^{-\frac{(p_{2}-x_{0})^{2}}{2\omega^{2}}}\) & 0.99 \\  & & \(v_{3}=4.85317+0.02483e^{(p_{0}-0.04)/0.02091}\) & \(v_{2}=y_{0}+A_{1}e^{(R-x_{0})/t_{1}}\) & 0.99 \\ Fly ash content(P\({}_{\text{D}}\)) & Wave velocity & \(v_{4}=6.31636-3.0221e^{-P_{0}/0.06643}\) & \(v_{2}=y_{0}+A_{1}e^{-P_{0}/t_{1}}\) & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Fitting relationship.  
Figure 20: For the wave velocity fitting curves of different factors. (**a**) The effects of coal slime; (**b**) The effects of high-water-content material; (**c**) The effects of cement; (**d**) The effects of fly ash.
## 4 Technological Challenges and Integration Issues  
Building a digital twin of the European electricity grid is an enormous technological undertaking, rife with challenges in system integration, data management, modeling,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Identified Barriers** & **Critical Remarks** & **Proposed Solutions** \\ \hline \multirow{2}{*}{Data Governance and Privacy} & GDPR constraints and national fragmentation & Current regulations are insufficiently harmonized & Support CEEDS, privacy-by-design, and data-sharing frameworks \\ \hline \multirow{2}{*}{Interoperability} & Lack of mandatory standards & Voluntary adoption is too slow & Enforce standards via implementing Acts \\ \hline \multirow{2}{*}{Legacy Regulation} & No mandate for digital twin platforms & Outdated roles and cost allocation rules & Reform grid codes and define governance \\ \hline \multirow{2}{*}{Incentives Misalignment} & CAPEX bias in regulation & Discourages digital investment & Promote TOTEX and innovation allowances \\ \hline \multirow{2}{*}{Legal Mandates} & No requirement for participation & Risk of fragmented progress & Consider EU-level mandates for participation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of regulatory barriers, critical issues, and proposed solutions for the EU-wide grid digital twin.  
and cybersecurity [20]. The electric power system is often cited as one of the most complex machines ever built, and creating its real-time digital replica at EU scale pushes the state-of-the-art in several domains [3]. Here we analyze the key technological hurdles: interoperability and data integration, real-time simulation and computational demands, model accuracy and validation, cross-sector integration, and cybersecurity and reliability of the digital twin [5].
## 2 Methods
### Structure Characterizations  
Field emission scanning electron microscopy (FESEM, Quanta 200 FEG, FEI Company, Hillsboro, OR, USA) was used to observe the domain and surface morphology of the h-BN studied in this work. The quality of graphene was characterized by Raman spectroscopy (RX210 Analyser, Renishaw, Wotton-under-Edge, UK) using the 514 nm line of an Ar ion laser as an excitation source. X-ray photoelectron spectroscopy (XPS, K-Alpha spectrophotometer, Thermo Fisher, Waltham, MA, USA) was conducted with an AXIS Ultra DLD model with a monochromatic Al K\(\alpha\) line at 1486.69 eV. The surface topography of h-BN on copper foils after a film-induced frustrated etching (FIFE) test was investigated with an atomic force microscope (AFM, XE-200 System, PSIA, Suwon, Korea) in tapping mode. The contact angle was measured using a water contact measurement (PHX300, Surface Electro Optics, Suwon, Korea). The water vapor transmission rate (WVTR) was evaluated by a commercial AQUATRAN model 3 WVTR analyzer (MOCON, Minneapolis, MN, USA) at room temperature under 1 atm.
## 4 Traffic Model  
To verify the effectiveness of this scheme, a reliable simulation is essential. As a condition of simulations, the traffic model must be realistic and reasonable. By the analysis of the requirements, it can be known that NTSN is a supplementary technology for terrestrial networks. NTSN mainly provides two services, namely the access services and bearer services. The former service is responsible for the Internet access of remote areas, oceans and mountains. The latter service provides a device-to-device (D2D) dedicated connection for special applications. The access services require ground-satellite-link (GSL), and bearer services require both the GSL and inter-satellite-link (ISL).  
The traffic models of two services are related to population distributing, time period and the size of the constellation. In order to match the actual application, two independent traffic models are defined for two different services. There are three parameters defined in the traffic model, namely: the number of data flows (\(NF\)), the allocation of data flows (\(AF\)) and traffic density (\(TD\)). NF determines the number of flows generated in a contribution cycle, \(AF\) determines the position of flows and the \(TD\) determines the number of packets generated in a flow. They are described as:  
\[NF=Poson[NS\cdot D_{t}(t)\cdot\alpha] \tag{19}\]  
\[AF=RS[NTE,NF|D_{p}(lat)] \tag{20}\]  
\[TD=Poson[l] \tag{21}\]  
\[Poson[k]=\frac{\lambda^{k}}{k!}e^{-\lambda} \tag{22}\]  
where \(Poson[\cdot]\) is a Poisson distribution function; \(\lambda\) is the expectation and variance of distribution. \(RS[A,B]\) is a random selection function. The return value of this function is a node table. The number of nodes in this table is B and they are randomly selected from network A. \(D_{t}(t)\) and \(D_{p}(lat)\) are time and position distribution correction factors. \(\alpha\) is an adjustment factor and \(l\) is the mean of the traffic density. \(NS\) is the total number of satellites. \(NTE\) is the network topology of the NTSN constellation.  
During the simulation, the traffic flow is periodically generated according to the above traffic model. Two independent traffic models are introduced for two different services. Both routing algorithm and simulation statistics are based on the above models.
# Spectral Information Divergence-Driven Diffusion Networks for Hyperspectral Target Detection
## 2 Related Work
### Diffusion Models  
A generative model generates samples resembling the training data by learning its probability distribution. The model is robust because it continuously learns the characteristics of the data distribution. In general, the generative models can be divided into naive Bayes, hidden Markov models, GANs, VAEs and diffusion models. Diffusion models have advantages over GANs and VAEs, including training stability, strong interpretability, and the ability to generate high-quality results. Therefore, they have become the mainstream approach for generative models. Denoising diffusion probabilistic models (DDPMs) are the most frequently utilized models [49]. The core concept involves generating data through forward and reverse data denoising processes. Both processes are modeled as parametric Markov chains. The forward process, which lacks learnable parameters, converts samples into standard Gaussian noise through iterative noise addition. The reverse process contains learnable parameters, and the image is denoised and gradually restored during the process, usually using a neural network approach. The model is generally a unet-based model. DDPMs have strong feature generation capabilities and are currently widely used in HSI denoising [50], HSI restoration [51], HSI classification [52], anomaly detection [53] and other tasks, but are rarely involved in the field of HTD. In HTD applications, DDPMs enhance spectral differences between the target and background, facilitating their separation.
## 2 Samples and Experimental Methods
### Geochemical and Mineral Investigation  
The Rapid CS Cube analyzer (Bremen, Germany) was used for the TOC test. All forty samples were crushed into 250 mesh and a select 1 g for pretreatment. First, 30% dilute hydrochloric acid was used to remove the inorganic components in the samples, and we then used deionized water to centrifuge five times and dried at 60 \({}^{\circ}\)C for 48 h. After that, the pre-treated samples were put into the combustion chamber for complete combustion at a high temperature of 950 \({}^{\circ}\)C. The TOC content of the sample can be calculated by the amount of CO\({}_{2}\) gas produced.  
The mineral components were tested by D8 Discover (Bremen, Germany) and the Automatic Mineral Identification and Characterization System (AmicSCAN, Bremen, Germany). The powder sample of 200 mesh was irradiated with Cu Ka of 100 mA at 40 kV. The relative mineral percentages were estimated by the semi-quantified area of the major mineral component peaks with correction for the Lorentz polarization. And AmicSCAN  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample ID** & **Burial Depth** & \multirow{2}{*}{**TOC (wt.\%)**} & \multicolumn{2}{c}{**Sample ID**} & \multirow{2}{*}{**Burial Depth**} & \multirow{2}{*}{**TOC (wt.\%)**} \\ \cline{1-1}
**\#Well** & & & & **\#Well** & & **(m)** \\ \hline MS-1(XK-1) & 135.2 & 4.21 & MS-11(XK-3) & 589.1 & 0.27 \\ MS-2(XK-2) & 341.8 & 3.88 & MS-12(XK-3) & 617.3 & 1.17 \\ MS-3(XK-3) & 644.1 & 3.72 & MS-13(XK-3) & 627.1 & 1.29 \\ MS-4(XK-3) & 653.4 & 5.14 & MS-14(XK-3) & 635.9 & 1.34 \\ MS-5(XK-2) & 374.5 & 4.19 & MS-15 & outcrop & 1.04 \\ MS-6(XK-1) & 118.3 & 4.06 & MS-16 & outcrop & 0.23 \\ MS-7(XK-1) & 64.1 & 0.58 & MS-17 & outcrop & 1.27 \\ MS-8(XK-1) & 92.2 & 1.49 & MS-18 & outcrop & 0.35 \\ MS-9(XK-2) & 332.1 & 0.88 & MS-19 & outcrop & 1.03 \\ MS-10(XK-2) & 347.5 & 1.14 & MS-20 & outcrop & 1.12 \\ \multicolumn{5}{c}{Average TOC of marine shale: 1.92 wt.\%} \\ CS-1(JK-1) & 111.5 & 7.68 & CS-11(IK-2) & 44.5 & 3.14 \\ CS-2(K-1) & 124.9 & 5.87 & CS-12(K-3) & 53.8 & 4.36 \\ CS-3(K-2) & 64.5 & 7.58 & CS-13(K-3) & 62.4 & 3.36 \\ CS-4(JK-3) & 105.6 & 6.94 & CS-14(K-3) & 63.6 & 6.21 \\ CS-5(JK-2) & 71.6 & 4.98 & CS-15 & outcrop & 1.42 \\ CS-6(K-3) & 112.9 & 5.63 & CS-16 & outcrop & 2.34 \\ CS-7(JK-1) & 54.6 & 3.14 & CS-17 & outcrop & 0.87 \\ CS-8(JK-1) & 61.1 & 3.99 & CS-18 & outcrop & 1.36 \\ CS-9(K-1) & 125.8 & 8.91 & CS-19 & outcrop & 0.98 \\ CS-10(JK-2) & 20.3 & 4.35 & CS-20 & outcrop & 3.38 \\ \multicolumn{5}{c}{Average TOC of continental shale: 4.32 wt.\%} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample information of well, burial depth, and TOC content.  
cooperated with the ZEISS Crossbeam 540 can automatically match the mineral composition in the database through X-ray diffraction, while the imaging accuracy can reach 1\(\upmu\)m. During the test, forty samples were divided into eight groups, and each group was analyzed by AmicSCAN (results within 0.1% absolute accuracy) and compared with XRD data (results within 5 wt.% absolute accuracy).
## 4 Results and Analysis
### Fog Attenuation Based on Mie Theory  
In order to investigate the attenuation of advection fog and radiation fog under various visibilities, detailed simulation parameters are shown in Table 2. Figure 5 illustrates the advection fog attenuation and radiation fog attenuation of terahertz waves. The fog attenuation exponentially increases as the frequency increases and the visibility decreases. Specifically, the attenuation exhibits a slow increase at 0.1-0.7 THz and a rapid increase at 0.7-1 THz. The advection fog attenuation at 1 THz is 8488.33 dB, 1999.12 dB, 255.05 dB, and 26.44 dB corresponds to visibility values of 30 m, 50 m, 100 m, and 200 m, respectively. Overall, advection fog causes significantly higher attenuation of terahertz waves compared with radiation fog, and the rate of increase in advection fog attenuation is more pronounced than that of radiation fog attenuation.  
The calculation of fog attenuation through Mie theory involves the extinction efficiency factor and particle distribution. While the results are precise, this model is most appropriate for large-sized particles to characterize the absorption and scattering effects. In comparison, Rayleigh approximation is used to analyze the absorption and scattering effects of small-sized particles in the low-terahertz band. Thus, there is a significant difference between fog attenuation calculated based on these two models. Moreover, radiation fog is characterized by a low water content and relatively small droplet particles. Hence, Mie theory may not be suitable for calculating terahertz wave attenuation in radiation fog. According to the numerical simulation results, in the case of advection fog, relatively accurate results can be obtained by using [PERSON] approximation theory at 0.1-0.8 THz and Mie theory at 0.8-1 THz. In the case of radiation fog, it is recommended to employ Rayleigh approximation theory.  
Figure 4: Fog attenuation of THz waves under diverse visibility and comparison with [18].
## 2 Methods
### Trajectory Clustering
#### 2.2.2 Incremental Clustering  
In this paper, the incremental DBSCAN [44] algorithm was used for trajectory clusters. The incremental DBSCAN is an efficient algorithm based on DBSCAN for mining data with density-based connectively [45]. The key idea of DBSCAN is that for each object of a cluster, if the neighborhood of a given radius (Eps) contains at least a minimum number of objects (MinPts), a density region is formed. These density regions can be connected by density-reachable objects. Owing to the density-based nature of DBSCAN, the insertion or deletion of an object affects the current clustering only in the neighborhood of this object. Thus, in an insert-or-delete operation, the incremental DBSCAN algorithm can get seed objects of affected objects, that is, objects that may potentially change cluster membership after the update. There are four possible cases when inserting a new object \(p\): \(p\) is noise, a new cluster is created, \(p\) and some possible noise objects are absorbed into the cluster, or several clusters and the object \(p\) are merged into one cluster. There are three possible cases when deleting an old object \(p\): \(p\) is deleted directly, some objects may become noise,  
Figure 5: \(A\), \(B\) and \(C\) are three trajectories, they are composed by several line segments and sample points. The green arrows represent the direction of the line segments, the green dots represent the sample points. Under the LCSS measure, \(A\) and \(B\) are similar, while \(B\) and \(C\) are dissimilar.  
or the deletion of \(p\) may cause a potential split. The incremental DBSCAN algorithm can dynamically maintain the clustering state and yields the same result as the DBSCAN. To distinguish the newly generated trajectory from the expired trajectory, a fixed time window width (TWW) was set to slide along the time axis. A small square represents a trajectory (Figure 6). On the whole-time axis, the trajectory is divided into the expired trajectory, the removal trajectory, the processing trajectory, and the inserted trajectory, according to the position of the time window. The expired trajectory represents data that has been removed from the clustering algorithm. The removal trajectory represents data that has expired but has not yet been removed. The processing trajectory represents data that has been clustered. The inserted data represent data that have not yet been clustered.