esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Mineralogy
The peaks of quartz found between 1200 cm\({}^{-1}\) and 900 cm\({}^{-1}\) (1115.98 cm\({}^{-1}\), 1006.31 cm\({}^{-1}\), 904 cm\({}^{-1}\)) for all the samples because of the stretching vibration of Si-O confirms the presence of this mineral and corroborates with the results from XRD analysis [51]. However, the absorption at 3364.33 cm\({}^{-1}\) and 740 cm\({}^{-1}\) indicates the presence of illite [52]. These characteristics confirmed the origin of the sediments of the studied samples [50]. According to [52], water molecules are revealed by the wide band at 1632 cm\({}^{-1}\) due to the H-O-H vibrations of the adsorbent. In this work, the band at 1632.81 cm\({}^{-1}\) or 1612 cm\({}^{-1}\) in all samples is attributed to the absorption of the water molecule (OH), i.e., the presence of hydrated phyllosilicates. This progressive decreasing of the band is related to the removal of octahedral cations, causing the loss of water. The presence of kaolinite, quartz and illite in the material gives it a better capacity in the production of ceramics and terracotta due to their refractory properties at a liquidus temperature of about 1800 \({}^{\circ}\)C [4; 53]. Accordingly, the mineralogical compositions are suitable for ceramics products. \begin{table}
\begin{tabular}{|c c c c c c c c c c c|} \hline \hline
**Localities** & \multicolumn{3}{c}{**Yansoki**} & \multicolumn{3}{c}{**Japoma**} & \multicolumn{3}{c}{**Ndogpassi**} \\ \hline
**Samples** & **Y1C1** & **Y1C2** & **Y1C3** & **J1C1** & **J1C2** & **J1C3** & **N1C1** & **N1C2** & **N1C3** & **N1C4** \\ \hline Kaolinite & 50.5 & 52.3 & 49.5 & 50.4 & 48.3 & 51.4 & 54.7 & 69.2 & 50.1 & 49.8 \\ \hline Quartz & 40.8 & 39.2 & 41.2 & 35.6 & 39.4 & 38.6 & 26.4 & 20.5 & 31.4 & 33.4 \\ \hline Illite & 3.9 & 3.1 & 4.7 & 2.4 & 8.1 & 3.4 & 8.4 & 3.4 & 9.7 & 10.2 \\ \hline Hematite & 1.4 & 3.5 & - & 2.9 & - & 2.7 & 2.5 & - & 3.8 & 2.6 \\ \hline Goethite & - & 1.9 & 2.4 & 3.5 & 3.2 & 1.4 & 1.7 & 3.6 & 3.3 & 2.3 \\ \hline Feldspar & 2.5 & 1.8 & - & 1.6 & - & - & 3.4 & 1.2 & - & 0.5 \\ \hline Muscovite & 1.9 & 1.4 & 2.2 & 1.8 & - & 1.2 & 2.7 & 1 & - & - \\ \hline Gibbsite & - & 0.6 & - & 1.8 & 1 & 1.3 & 1.8 & 1.1 & 1.7 & 1.2 \\ \hline Qu= quartz & Ka= kaolinite & d=3.34 Qu - II & & & & & & & \\ II= illite & Mu= muscovite & & & & & & & & \\ He= hematite & Fl= feldspath & & & & & & & \\ Go= goethite & Gi= gibbsite & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Semi-quantitative estimation of the mineralogical composition (%) of clay materials of the eastern part of the Douala sub-basin based on peak intensities of XRD patterns. Figure 4: XRD patterns of raw clay materials of the eastern part of the Douala sub-basin. Figure 5: XRD patterns of the clay fraction: (**a**) Y1C1; (**b**) Y1C3; (**c**) J1C2; (**d**) J1C3; (**e**) N1C2 and (**f**) N1C4. N: normal sample, CH: heated at 500 \({}^{\circ}\)C, EG: treated with ethylene glycol. Figure 6: IR curves of the samples of the eastern part of the Douala sub-basin.
## 4 Data Smoothing Algorithm
### Hardware Scheme of Inertial Track Geometry State Detector  
As shown in Figure 5, the inertial track geometry state detector system was composed of six parts including an inertial measurement unit, odometer, satellite positioning system, gauge measuring instrument, data computer, and vehicle body. The inertial measurement unit (IMU) was rigidly connected to the car body and was used to measure the motion parameters of the railway track geometry state detector in the course of orbit operation. The IMU is the core component of the integrated navigation system. The odometer was fixed to the vehicle body motion wheel to measure the speed information of the vehicle body. The satellite positioning system used a single antenna scheme, which was fixed to the vehicle body to provide high-accuracy vehicle position and speed information. The high-accuracy linear variable differential transformer distance measurements were used to determine the track gauge values. A data computer was used for the acquisition, storage, and processing of the sensor data.  
The overall weight of the railway track inspection instrument with a split design structure was 37 kg, and an operator is easily able to assemble and disassemble the instrument. The instrument is driven by the operator on foot, and the design speed is less than 8 km/h. Using a multisensor time synchronization mechanism, the data for the inertial measurement unit, satellite positioning system, odometer, and gauge measuring  
Figure 5: The composition of the inertial track geometry state detector system.  
instruments are collected and joined. Then, the track geometry parameters are calculated using post-processing software. The main components and technical indicators of the inertial track geometry state detector are shown in Table 1.
# Carbon Emission Analysis of RC Core Wall-Steel Frame Structures
## 2 Materials and Methods
### Inventory Analysis  
In the general standard titled \"Standard for building carbon emission calculation\" implemented in China (GB/T51366-2019) [49], the emission factor method is employed to measure carbon emissions. The carbon emission factors (CEFs), which are also called the carbon emission coefficient (CEC), represent the amount of carbon emissions generated per unit of consumption. Research on CEFs of buildings has made significant progress, aiming to simplify the application of the emission factor method and enhance the accuracy and efficiency of carbon emission calculations [50; 51].  
Here, two critical stages that significantly affect life-cycle carbon emissions are mainly considered: the materialization stage and the waste recycling stage. The materialization stage is the primary source of carbon emissions in the building life cycle, including building material production, transportation, and construction stages. Low-carbon construction and design approaches can directly influence material costs, thereby significantly impacting carbon emissions during the materialization stage. The CEF method is utilized in this work for quantitative analysis of carbon emissions through BIM models. The CEF database from GB/T51366-2019 [49] is mainly utilized.  
Additionally, for inventory analysis, Autodesk Revit 2018 software is employed to create the BIM model, which includes graphic information, materials data, and building components. The BIM model is then imported into Chenxi Quantity Calculation 2021 software [52] to obtain the material list and quantities for the building. By combining this with the CEF database, the carbon emissions of the building life cycle can be calculated. The analysis method will be detailed in the next section.
# Distributed Inference Models and Algorithms for Heterogeneous Edge Systems Using Deep Learning
## 2 Related Work
### DNN Model Modification  
Various model compression techniques have been proposed to facilitate DNN inference directly on edge devices [5; 6; 7; 8; 18; 19]. For example, weight-pruning methods [5; 6] are used to eliminate redundant and insignificant parameters from trained DNN models, thereby reducing the computational complexity and model size. Knowledge distillation techniques [7; 8] are utilized to extract knowledge from large teacher models and distill it into smaller student models, thereby reducing the resource requirements for inference. The study in [18] employs knowledge distillation to deploy DNN models on resource-constrained UAVs for edge intelligence. By utilizing a full model trained in the cloud, it generates lightweight models with high accuracy. To enable the deployment of federated learning on edge devices, ref. [19] adopts a random pruning method to reduce communication overhead and model size, thereby improving the efficiency of federated learning on edge devices. However, these methods may lead to a decline in model performance, incurring losses in accuracy, precision, or other evaluation metrics. Additionally, some compression techniques introduce irregular sparsity, which can hinder effective hardware acceleration and reduce processing speed. As the primary focus in the aforementioned studies was training new lightweight networks to reduce resource demands, only homogeneous edge devices, which have limited adaptability, were considered. When different types of edge devices are introduced, these models must be retrained to fit the new device configurations; thus, they are unsuitable for heterogeneous edge-computing systems.
## 3 Results and Discussion
### Feasibility of Applying Circular Economy Strategies to Different Building Types
#### 3.3.3 Procurement Stage  
Procurement had the third greatest impact based on the interview responses with a score of 19 with no high ratings but a majority of medium scores. Participants A1, A2, C2, C3, and D1 emphasized that the procurement phase heavily relies on market availability and pre-established design specifications. If CE principles are not prioritized early, procurement has a limited ability to drive circularity. CN2, who is a procurement manager, stated that \"_The availability of circular materials often comes down to whether local  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Lifecycle Stage**} & \multicolumn{8}{c}{**Participant Responses**} & \multicolumn{2}{c}{**Total**} & \multirow{2}{*}{**Rank**} \\ \cline{2-2} \cline{4-11}  & **A1** & & & & & & & & & & & & \\ \hline Initiation and Planning & 2 & 2 & 2 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 30 & 1 \\ \hline Design & 1 & 1 & 1 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 30 & 1 \\ \hline Procurement & 5 & 4 & 3 & 4 & 4 & 3 & 3 & 3 & 3 & 3 & 19 & 3 \\ \hline End of life & 3 & 3 & 6 & 5 & 6 & 4 & 4 & 4 & 5 & 4 & 16 & 4 \\ \hline Construction & 6 & 6 & 4 & 3 & 3 & 6 & 5 & 5 & 4 & 5 & 14 & 5 \\ \hline Operation and Maintenance & 4 & 5 & 5 & 6 & 5 & 5 & 6 & 6 & 6 & 11 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ranking of the impact of building’s project lifecycle on the adoption of CE.  
suppliers are around. Without a local supply chain, it can be really challenging to source materials that meet CE standards. On top of that, cost pressures at this stage can lead to tough choices, where cheaper, less circular options get picked over more than CE-aligned materials\". D1 highlighted that a well-developed circular supply chain is essential for the availability of materials and products, which is critical for delivering CE projects. They commented that, \"_Without a well-developed circular supply chain, designers are limited in their ability to create sustainable solutions, as the availability of CE-compliant materials and products, directly shapes the feasibility of their designs\"_. CT2 highlighted that the procurement phase is crucial for engaging suppliers who understand and support CE principles and further emphasized that decisions about suppliers and availability of circular material specifications during this stage can have a significant impact on the building's overall sustainability.
## 2 Cross-Domain Collaborative Task Allocation Model
### Mathematical Model of Control Allocation
#### 2.4.1 The Advantages of Radar Detection Capability  
The detection capability of the air relay control agent mainly depended on the airborne detection radar, which was affected by sea clutter, hydrometeorology, the target characteristics, and other factors. The signal-to-noise ratio (SNR) of the airborne radar was as follows (Equation (7)):  
\[SNR=\frac{P_{r}}{C+P_{n}}=\frac{P_{\text{t}}C^{2}\lambda^{2}\sigma}{(4\pi)^{3 }L(C+P_{n})R^{4}}=\frac{P_{\text{t}}C^{2}\lambda^{2}}{(4\pi)^{3}l}\frac{\sigma }{(C+P_{n})R^{4}} \tag{7}\]  
where \(P_{\text{t}}\) is the peak transmission power of the radar; \(G\) is the antenna main lobe gain; \(\lambda\) is the wavelength; \(\sigma\) is the target RCS; \(l\) is the radar environmental factor; \(C\) is the energy intensity of the sea clutter; \(P_{n}\) is the radar noise power; and \(R\) is the target slant range.  
The aerial relay control agent was always maneuvering at a high speed and the relative position between the target and the target agent changed constantly, resulting in a changing angle of the view of the target relative to the airborne detection radar. Therefore, the Swerling type I was used to characterize the RCS fluctuation in the target, and the detection probability of the Swerling I target [42] was used to characterize the radar detection ability advantage. The detection probability of the Swerling I target was used to characterize the radar detection ability advantage as Equations (8) and (9).  
\[Q_{1}=\frac{\sum\limits_{i=1}^{b}\sum\limits_{j=1}^{d}q_{ij}}{d} \tag{8}\]  
where \(q_{ij}\) is the detection probability of the target agent \(T_{j}\) by the aerial relay control agent \(A_{k}\) controlling the load agent \(M_{i}\).  
\[q_{ij}=\left\{\begin{array}{ll}e^{\frac{-V_{T}}{1+SNR}},n_{p}=1\\ 1-\Gamma_{1}(V_{T},n_{p}-1)+(1+\frac{1}{n_{p}SNR})^{n_{p}-1}\times\Gamma_{1}( \frac{V_{T}}{1+\frac{n_{p}SNR}{n_{p}SNR}},n_{p}-1)\times e^{\frac{-V_{T}}{1+n_{ p}SNR}},n_{p}>1\end{array}\right. \tag{9}\]  
where \(n_{p}\) is the number of accumulated radar pulses; \(SNR\) is the signal-to-noise ratio of the airborne radar; \(V_{T}\) is the detection threshold; \(\Gamma_{1}\) is an incomplete function \(\gamma\).
# Dynamic Mechanical Properties of Heat-Treated Shale under Different Temperatures
## 6 Dynamic Damage Constitutive Model  
### Definition of Damage Variable  
[PERSON] and [PERSON] [40] started with the random distribution of flaws within the rock material and used the rock's microelement strength to follow the Weibull distribution to build the statistical damage constitutive equation of the rock fracture process. A granite thermal-mechanical coupling damage model was developed by [PERSON] et al. [41] using the Drucker-Prager criterion and the Weibull distribution. [PERSON] et al. [42] developed a statistical damage model using the modified Mohr-Coulomb criterion as the failure condition for the microelement strength. An enhanced statistical damage constitutive model of rock thermal treatment was put out by [PERSON] et al. [43] and is based on the longitudinal velocity correction of the thermal damage variable.  
As a typical non-homogeneous material, shale contains a large number of randomly distributed microcracks, pores, and other defects. Assuming that shale consists of many microbodies, whose dimensions are large enough in a spatial sense to contain many microdefects but small enough in a mechanical sense to be considered as particles, the microbodies have the following properties: (1) The shale is isotropic, i.e., having an isotropic-damaged body. (2) The shale conforms to [PERSON]'s law before failure but has no bearing capacity after failure. (3) The strength of each microbody follows the Weibull distribution. The expression of its probability density function is as follows [44]:  
\[P(F)=\frac{m}{F_{0}}\bigg{(}\frac{F}{F_{0}}\bigg{)}^{m-1}\exp\bigg{[}-\bigg{(} \frac{F}{F_{0}}\bigg{)}^{m}\bigg{]} \tag{2}\]  
where \(F\) is the distribution variable of micro-body strength, and \(F_{0}\) and \(m\) are Weibull distribution parameters, reflecting the mechanical properties of shale materials.  
When shale is subjected to impact loading, microelements are gradually damaged, and a statistical damage variable, \(D\) can be introduced to represent this process. Under a certain load, \(D\) can be expressed as:  
\[D=\frac{Nf}{N} \tag{3}\]  
where \(N\) is the total number of microelements, and \(N_{f}\) is the number of destroyed microelements. When loading to a specific load level, \(F\):  
\[N_{f}=\int_{0}^{F}NP(x)dx \tag{4}\]  
By substituting Equations (2) and (4) into Equation (3), the expression of the damage variable, \(D\) can be obtained as:  
\[D=1-\exp\bigg{[}\bigg{(}-\frac{F}{F_{0}}\bigg{)}^{m}\bigg{]} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Temperature** & **60 °C** & **90 °C** & **120 °C** & **150 °C** \\ \hline Peak stress & 151 MPa & 109 MPa & 146 MPa & 115 MPa \\ Ultimate strain & 0.0073 & 0.0068 & 0.0061 & 0.0056 \\ \hline \end{tabular}
\end{table}
Table 4: Values of each group at 0.3 MPa.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Temperature** & **60 °C** & **90 °C** & **120 °C** & **150 °C** \\ \hline Peak stress & 112 MPa & 121 MPa & 123 MPa & 118 MPa \\ Ultimate strain & 0.0038 & 0.0033 & 0.0042 & 0.0075 \\ \hline \end{tabular}
\end{table}
Table 3: Values of each group at 0.2 MPa.
###### Abstract  
Due to the global demand for dimension stones, fast and reliable evaluation tools are essential for assessing the quality of dimension stones. For this reason, this study aims to develop comprehensive tools for estimating the abrasion resistance of various dimension stones from Turkey. Non-destructive rock properties, including dry density (\(\rho_{\text{d}}\)), water absorption by weight (\(\text{w}_{\text{a}}\)), and pulse wave velocity (\(\text{V}_{\text{p}}\)), were determined to build a comprehensive database for soft computing analyses. Three predictive models were established using multivariate adaptive regression spline (MARS), MSP, and artificial neural networks (ANN) methodologies. The performance of the models was assessed through scatter plots and statistical indicators, showing that the ANN-based model outperforms those based on MSP and MARS. The applicability of the models was further validated with independent data from the existing literature, confirming that all models are suitable for estimating varying Bohme abrasion values (BAVs). A MATLAB-based software tool, called Bohme abrasion calculator (v1.00), was also developed, allowing users to estimate BAV values by inputting adopted non-destructive rock properties. This tool is available upon request, supporting the dimension stone industry and fostering future research in this field.  
dimension stone; abrasion resistance; bohme abrasion value; predictive model; soft computing Article
## 1 Introduction
[11] combined CFD and machine learning to design an intelligent HVAC system capable of controlling the air temperature and velocity in different areas to create personalised thermal comfort areas for multiple users. [PERSON] [12] created a 3D model to analyse a palace in Italy for a future rewithishment project. In [13], the authors proposed plant rewithishment in a palace in Italy using CFD tools to simulate the energy performance of their solution. [PERSON] et al. [14] used CFD to optimise the ventilation strategy and air distribution in a palace in Turin, Italy. [PERSON] et al. [15] analysed the efficacy of a HRV system coupled with a low-temperature radiator in satisfying IEQ indices inside a retrofitted dormitory room through CFD analysis, concluding that, while higher ventilation rates could significantly decrease the age of the air, this would cause local discomfort in some parts of the room. Later, in [16], the authors proposed a CFD model to investigate the effects of a combined MVHR-fan-coil system in heating mode on IAQ and thermal comfort parameters inside a retrofitted room. The CFD method is abundantly applied to the evaluation of thermal comfort in different built environments. Nonetheless, there are few numerical studies in the literature addressing discomfort in historical buildings triggered by their particular thermophysical characteristics. In this context, this study proposes CFD analysis performed in multi-physics computational fluid dynamics software StarCCM+ (Siemens; Academic Power On Demand [17]) to evaluate the thermal comfort levels under summer-cooling and winter-heating conditions in an office inside a historical building of the Alma Mater Studiorum, University of Bologna, Italy. The building, designed by rationalist architect [PERSON], was inaugurated in 1935. It is characterised by the presence of huge single-glazed glass windows that represent a critical component of the structure from a thermal point of view, due to their low thermal resistance and their low efficiency in filtering the sunlight. The methodology adopted for the study here presented is validated through a comparison with previous experimental measurement performed by [18]. In this previous study, [PERSON] et al. [18] performed thermal-comfort analysis of the same office room in winter-heating conditions, concluding that the internal layout of working desks should be rearranged. In particular, enough space between the working desks and the windowsshould be given in order to satisfy local comfort. The obtained results in the present study allow for evaluating thermal comfort at different work stations in both winter and summer conditions, and for identifying discomfort zones that are related to the characteristics of the building and HVAC system. The focus of the analysis was to combine the global \(PMV\) index with local discomfort indices to obtain a more detailed evaluation of the level of comfort within the studied office.
# EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
## 4 Proposed Method
### Phase III: EEG Feature Extraction  
Three different feature extraction techniques are used in this work. These techniques include time domain features, entropy features, and frequency domain features as extracted in [8].
# Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling
## 5 Results
### BBR over NewReno and Excessive Bottleneck Buffer Size  
Figure 9a shows four examples of throughput over time generated in the user-space framework, while Figure 9b depicts results from the MP-DCCP testbed. Figure 9c,d show contour plots of a large set of results; the tunnel server-side endpoint location is specified as the tunnel RTT, as a percent of the end-to-end RTT; a high percent means the proxy is near the server. In the figures, the prefixes \(A\) and \(B\) denote the asymmetry in ms and buffer size in packets. RTT values obtained in the kernel-space testbed are labeled * while the RTT values set in the user-space emulation are labeled **.  
Figure 9: Tunneling BBR over NewReno or CCID2. (**a**) Downloads with BBR over NewReno or CCID2. Blue and red are for paths 1 and 2, respectively; (**b**) Downloads with BBR over CCID2, the RTT is _long_. Blue and red are for paths 1 and 2, respectively; (**c**) Contour plot of a large set of results; (**d**) Contour plots for different scenarios.  
This section examines an issue that becomes particularly evident when tunneling BBR over NewReno. Rather counter-intuitively, the challenge here is that the RTT has to be long enough so that BBR at the server retains more in-flight traffic than tunnel-NewReno can sustain over the primary path. It is important to highlight that this presents an opposite problem to the one described in section IV-B.  
**A short RTT inhibits aggregation**. In Figure 8(c), we see a nearly vertical red-blue divide where the end-to-end RTT is around 40 ms. Figure 9 plots the throughput for different results, while Figure 8(a) provides further real-world testbed results obtained using the Linux kernel implementation of MP-DCCP. Matching these results with the corresponding points in Figure 8(d) demonstrates that these examples are consistent with the results in Figure 8(c).  
**Excessive bottleneck buffer size inhibits aggregation**. The vertical red-blue divide in Figure 8(c) is reproduced in all six plots in Figure 8(d), albeit not at the same RTT. The key pattern that can be observed is the shifting of the vertical line to the right as the bottleneck buffer size increases. It is well known that NewReno tends to fill bottleneck buffers, leading to the buffer size determining the tunnel RTT. As a result of this, a larger bottleneck buffer necessitates a longer end-to-end RTT when the average number of packets kept in-flight end to end by BBR exceeds the number kept in-flight by NewReno over the primary path tunnel.