esa-sceva/satcom-chunk-collection huggingface dataset:
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 7 Challenges in D2D Communication
### Challenges in Interference Management
#### 7.2.2 D2D in mmWave Communication  
One of the features that are considered crucial in 5G cellular networks that recently gained notable attention is the mmWave band communication [87]. It is fully anticipated that for future 5G cellular networks, mmWave mesh networks will replace the traditional structure that used copper or fiber, in order to offer mesh-like connectivity and rapid deployment [88]. An mmWave communication has the capacity to offer exceptionally high data rates, which lead to significant network capacitybecause mmWave communication runs on a larger frequency band. However, it can lead to interference management challenges due to some important propagation characteristics in the mmWave band communication that are inconsistent with the microwave band. Different interference conditions are initiated inside each cell in D2D-enabled mmWave cellular networks. The majority of previous studies focused on enhancing resource sharing algorithms to maintain the interference. However, the requirement for interference management schemes that consider the direction in mmWave 5G cellular networks is crucial in order to allow multiple D2D communication.
## 4 Simulation and Model Experiment Validation
### Experimental Results for SOC Balancing Control Loop  
For the test of SOC balancing control loop, the SOC value of three battery modules were changed suddenly from SOC\({}_{1}\) = SOC\({}_{2}\) = SOC\({}_{3}\) = 70% under balanced conditions to SOC\({}_{1}\) = 75%, SOC\({}_{2}\) = 70%, and SOC\({}_{3}\) = 65%. The experimental results are shown in Figure 10.  
It can be observed from Figure 10 that once there was a change in SOC values, V\({}_{01}\) increased while V\({}_{03}\) decreased. This is because the value of SOC\({}_{1}\) was larger than the average SOC value of three battery modules (i.e., (75% + 70% + 65%)/3 = 70%), therefore resulted in a larger value of the battery module output voltage V\({}_{01}\) for faster discharge. The variation of V\({}_{03}\) for battery module 3 followed the same behavior but in the opposite direction. The value of SOC\({}_{2}\) was equal to the average SOC value, and therefore, its corresponding battery module output voltage remained constant. Figure 10 also shows that the simulation results and hardware experimental results are in good agreement under the variation of SOC values test condition.
# Spatiotemporal Image Fusion in Remote Sensing
## 2 Fusion Methods to Increase Spatiotemporal Resolution of Satellite Images
### Learning-Based Spatiotemporal Image Fusion Methods  
Learning-based methods use machine learning to predict finer temporal resolution images from coarse spatial resolution images [56]. Compared to reconstruction-based and unmixing-based methods which allows spatiotemporal fusion of images with unified spectral values, learning-based methods allows fusion between images with different spectral values. One of the first studies dedicated to using sparse representation method into data fusion is presented by [PERSON] et al. [7]. Sparse representation methods learn the differences between fine spatial resolution images and high temporal coverage images [59] by making use of a dictionary created from the image patches generated from the two image types. Similar to reconstruction-based methods, important research efforts were dedicated to developing a learning-based method that considers the phenology of vegetation and other disturbances caused by land cover changes that might occur before the prediction date. In this context, [PERSON] et al. [7] developed an image fusion method which accounts for both vegetation phenology and land cover changes occurring over the observation period of the fused images. The original implementation of the method relies on fine-coarse resolution image pairs before and after prediction date, and one coarse resolution image at prediction date. Later, the authors extended their method to consider one fine-coarse resolution image-pairs instead of prior and posterior pairs of images. The new spatiotemporal image fusion method is called SP-One [49].  
[PERSON] et al. [41] proposed a hierarchical spatiotemporal adaptive fusion method capable of predicting temporal changes such as seasonal phenology and land-cover changes using only one image pair (one prior or posterior image pair). The authors compared the results obtained by this method with those obtained by the spatiotemporal image fusion methods proposed by [12; 32; 49] and concluded that their method performed better especially in capturing land cover changes in the predicted fine resolution reflectance images. [PERSON] et al. [60] proposed a spatiotemporal image fusion method which relies on learning the mapping between MODIS images (or between overlapping or non-overlapping patches from the two images obtained by \(k\)-means classifier) and is applied to an earlier acquired Landsat image to predict an image at a later time. The method can be easily adapted to new multisource images. However, it performed worse than other spatiotemporal image fusion methods when applied in a heterogeneous landscape, where the pixels from the coarser satellite images are not pure. Therefore, a new category of spatiotemporal image fusion methods can be used to address this challenge, namely those based on spectral unmixing models [61].
## 3 Numerical Analysis Model of the NCS
### Material Parameters  
#### 3.3.1 Concrete  
The concrete plastic-damage (CDP) model is considered to be effective in simulating the nonlinear behavior of concrete material, which systematically incorporates the plastic deformation and damage evolution processes of concrete. Through the introduction of damage variables, the CDP model captures the cumulative damage experienced by concrete structures during loading, enabling accurate predictions of structural responses under substantial deformation and high-stress conditions. The diagram of the CDP model is depicted in Figure 5, and its basic expressions are as follows:  
\[\sigma_{\rm t}=(1-d_{\rm t})E_{0}\Big{(}\epsilon_{\rm t}-\widetilde{\epsilon}_{ \rm t}^{\rm Pl}\Big{)} \tag{1}\]  
\[\sigma_{\rm c}=(1-d_{\rm c})E_{0}\Big{(}\epsilon_{\rm c}-\widetilde{\epsilon}_ {\rm c}^{\rm Pl}\Big{)} \tag{2}\]  
where \(\sigma_{\rm t}\) and \(\sigma_{\rm c}\) represent the tensile and compressive stress, respectively; \(E_{0}\) represents the initial modulus of elasticity. \(\epsilon_{\rm t}\) and \(\epsilon_{\rm c}\) represent the tensile and compressive strain, respectively; \(\widetilde{\epsilon}_{\rm t}^{\rm pl}\) and \(\widetilde{\epsilon}_{\rm c}^{\rm pl}\) represent the equivalent tensile and compressive plastic strains, respectively.  
The \(d_{\rm t}\) and \(d_{\rm c}\) in Equations (1) and (2) are parameters that express the tensile and compressive damage of concrete and can be determined according to the following equations:  
\[d_{\rm t}=1-\frac{\sigma_{\rm t}/E_{0}}{\widetilde{\epsilon}_{\rm t}^{\rm Pl}( 1/b_{\rm t}-1)+\sigma_{\rm t}/E_{0}} \tag{3}\]  
\[d_{\rm c}=1-\frac{\sigma_{\rm c}/E_{0}}{\widetilde{\epsilon}_{\rm c}^{\rm Pl}( 1/b_{\rm c}-1)+\sigma_{\rm c}/E_{0}} \tag{4}\]  
where \(b_{\rm t}\) and \(b_{\rm c}\) represent the calculated variables, which can be determined in accordance with the relevant literature [25].  
To fully characterize the CDP model, it is necessary to provide the uniaxial stress-strain relationships of concrete and the corresponding plasticity parameters. In this paper, the constitutive relationships for concrete in uniaxial tension and compression used in the literature [24] are adopted. The plasticity parameters required for the CDP model consist of the dilation angle (\(\Psi\)), shape parameter (\(K_{\rm c}\)), flow potential eccentricity (\(\lambda\)), the ratio of biaxial to uniaxial compressive strength (\(f_{\rm b0}/f_{\rm c}\)), and the viscosity parameter (\(\mu\)). These  
Figure 5: Diagram of the CDP model.  
parameters are used to characterize the shape of the yield surface and the flow rules of the concrete. Table 1 shows the plasticity parameters selected for this paper.
## 4 Experiments and Discussions
### Comparison with Other SOTA Models  
To validate the enhanced YOLOv5-S algorithm, this study assessed its performance by conducting experiments on a custom dataset. The proposed method was compared with NanoDet-Plus, YOLOv5 series, and YOLOv7 under identical experimental conditions. The performance measures utilized in this study comprised parameters, FLOPs, mAP, latency, and model size. The specific results are showcased in Table 3.  
In analyzing the results of Table 3, our method performed well at the same level as the YOLOv5 algorithm and reduced the model size, params and calculated complexity while successfully maintaining the model precision. This fully demonstrates the efficacy of the enhanced strategy in object inspection and recognition tasks. Since we have taken measures such as a lighter backbone network, depthwise separable convolutions, limiting the maximum number of channels, and introducing a small target detection layer, our model exhibits a substantial decrease in size, parameter count, and computational complexity in comparison with YOLOv7. At the same time, the precision is also significantly improved. Further analysis of YOLOv7's mAP @5 and mAP @5:95 scores did not reach the best level and reached the highest in terms of parameter number and calculation amount, which may be attributed to overfitting caused by the excessive size of the model. Compared with NanoDet-Plus, the improved YOLOv5-S is comparable in terms of parameter quantity. Although its computational complexity and detection speed are slightly inferior, our model is much larger than NanoDet-Plus in mAP, which shows the superiority of our model in handing small target detection tasks.
## 3 Analysis of Traffic Characteristics  
The data for the normal and contraflow lanes collected from each observation point consisted of 5-min aggregated data of the traffic flow and speed. To understand the characteristics of mass evacuation traffic, the data for saturated and unsaturated traffic conditions collected in the present study were analyzed with respect to the road capacity, traffic flow, and relationship between the traffic density and flow speed.
## 3 Test Bench and Experimental Results  
### Test Bench  
Two images of the test bench are presented in Figure 4a,b. The spoke-type rotor PMSM is mechanically connected to a direct-current machine (DCM), by an intermediate toothed belt. Both electrical machines should operate as motor and generator alternatively (i.e., if PMSM is motoring, the DCM is generating and vice versa). To achieve this operating mode, DCM is electrically connected to a Bidirectional DC Power Supply, model PSB 9000 3U manufactured by Elektro-Automatic, a German company with the headquarters located in Viersen, North Rhine-Westphalia, and able to feed or consume power to and from DCM.  
The main elements used for experimental tests are presented in Figure 5, highlighting the power flow, the control, and measurement connections between the various components. A dedicated, commercially available, SEVCON power converter was used to power the PMSM, fed from a 48V LiFePO4 battery. The power converter is controlled from a dSPACE board that emulates the signals from throttle and brake pedals considering that the PMSM speed should follow the reference speed. The control of the throttle/brake pedals' signals is made with PI controllers to reduce the error between the reference and measured speed. Several current, voltage, torque, and speed sensors are used to measure the important parameters during tests. Their signals are recovered using the dSPACE board, and some are presented in the following figures. The power converter is connected to a computer, from where readings of power, speed, and estimated torque can be made. Before or between experimental tests, using DVT customer software, parametrizations of the power converter were made. The DCM acts as a generator when the PMSM is in motor mode and as a motor when the PMSM is in generator mode. Thus, a bidirectional DC power supply was used and controlled from the same 1103 dSPACE board. This power source/load will absorb the electrical energy generated by DCM when PMSM is in motor mode and will feed the DCM when the PMSM is in generator (braking) mode. Between the DCM shaft and transmission belt, a torque/speed sensor was used to measure the mechanical power between the two machines. On the other DCM shaft end, an incremental encoder is used to measure the DCM speed. In addition, a sin/cos encoder is mounted on the PMSM shaft, sending rotor position information to the SEVCON converter.  
Figure 4: _Cont._
## Materials and Methods
### Preparation of Spacers  
The spacers used in this study were prepared in a manner that could be easily integrated with the current manufacturing process in a spacer factory. Through a field investigation of the spacer manufacturers and construction sites in Guangzhou, China, it was observed that commercial cementitious spacers are produced in a relatively short time (i.e., the average time for the mixing and curing of a spacer among the investigated factories was 5 days) compared with the time needed for the preparation of the standard concrete specimen (i.e., 28 days). This is because the economic cost of manufacturing spacers (e.g., materials, equipment, labour) would increase as the manufacturing time increases, leading to decreased benefits. Moreover, it has been noted that the water curing method is adopted at the early stage of the cement-hardening process in most manufacturing processes, which is expected to improve the hydration of the cement and ensure the strength development and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Mix ID**} & \multirow{2}{*}{**W/B**} & **Total Binder** & **GGBS** & **Fly Ash** & **Superplasticiser** & **Sand Ratio** \\  & & **(kg/m\({}^{3}\))** & **Dosage (\%)** & **Dosage (\%)** & **(\%)** & **(\%)** \\ \hline C * & 0.35 & 430 & 50 & 15 & 1.5 & 40 \\ SS + & 0.36 & 500 & 33 & 27 & 1.2 & 50 \\ MS & 0.36 & 625 & 33 & 27 & 1.2 & 100 \\ \hline \hline \end{tabular}  
* C5–20 aggregate was used for the chloride-resistant HPC(C). + G5-10 coarse aggregate was used for the small-size coarse aggregate concrete spacer (SS).  
\end{table}
Table 1: Mix proportions of concrete and spacers.  
low-porosity of the inner structure of the spacer. Additionally, after water curing for a certain amount of time, the cementitious spacers were usually exposed to the air to complete the drying process, which reduces the curing cost and also prepares the spacers in a convenient state for trade and transport. Taking the aforementioned manufacturing process into account, the spacers in the present study are prepared through the following steps:  
1. The mixed materials of the chloride-resistant small-size coarse aggregate concrete spacers (SS) and chloride-resistant mortar spacers (MS) were cast into a 40 mm \(\times\) 40 mm \(\times\) 160 mm rectangular steel mould and were unmoulded after 24 h;
2. The spacers were first cured in water at 20 \({}^{\circ}\)C for 3 days and then were exposed to the air at room temperature for 1 day;
3. The spacers were cut to the size of 40 mm \(\times\) 40 mm \(\times\) 80 mm for the subsequent experiments.  
It is worth pointing out that the considered curing scheme for spacers is plausible and efficient for mass production in a factory and the optimal curing scheme is currently under investigation by the authors of this study. Other factors (e.g., dehydration due to the time lag between the manufacturing and application of spacers) that may affect the quality of the spacers were beyond the scope of this study.
# Experimental Study of the Air Demand of a Spillway Tunnel with Multiple Air Vents
## 2 Experimental Setup
### Test Program and Procedure  
The following parameters were systematically varied to investigate their effects on the air demand, as shown in Table 2: (1) To study the effects of hydraulic factors, water velocity (\(U_{w}\)) was increased from 6 m/s to 12 m/s in steps of 1 m/s, and the flow depth (\(h_{w}\)) was varied from 4 cm to 10 cm in steps of 2 cm. (2) The air vent areas (\(a_{1}\), \(a_{2}\), and \(a_{3}\)) were increased from 28 cm\({}^{2}\) to 140 cm\({}^{2}\) in steps of 28 cm\({}^{2}\) with three air vents. (3) To study the effects of the air vent location, position (\(X_{2}\)) of air vent #2 was increased from 1.0 m to 3.0 m in steps of 0.5 m. (4) To study the effects of the number of air vents, the number was varied; for example, keeping only air vent #1, keeping air vents #1 and #2, and keeping air vents #1 and #3. Hydraulic jumps and pressurized flow in the downstream tunnel were not investigated in this study. (5) This paper proposes an interfacial shear stress formulation in Section 4.2 with model tests; that is, the water velocity (\(U_{w}\)) was increased from 6 m/s to 12 m/s in steps of 2 m/s and the hydraulic roughness (\(\Delta\)) was varied from 12 \(\upmu\)m to 240 \(\upmu\)m. The five cases exhibited the following dimensionless parameter ranges: Reynolds number (Re) = 1.93 \(\times\) 10\({}^{5}\)-7.54 \(\times\) 10\({}^{5}\), Weber number (We) = 1.61 \(\times\) 10\({}^{4}\)-1.25 \(\times\) 10\({}^{5}\), and Froude number (Fr) = 6.06-19.16.  
Figure 1: Schematic of spillway tunnel physical model at SKLH: (**a**) Panoramic view. (**b**) Front view. (**c**) Top view.
# DNA Barcoding as a Plant Identification Method
## 4 Chloroplast DNA Barcodes
### psbK-psbl (Intergenic Spacer)  
The _psbK_ and _psbl_ loci encode two low molecular weight polypeptides, K and I, of the photosystem II [52]. The non-coding _psbK-psbl_ intergenic spacer is conserved and can be easily amplified with PCR, sequenced, and aligned [53; 54]. It also demonstrates high discriminatory power but low sequence quality and universality [35]. Despite its discriminatory power, the CBOL Plant Working Group propose its use as a supplementary locus due to the inconsistency in obtaining bidirectional unambiguous sequences [30]. Nevertheless, it constitutes a suitable marker for bryophyte, lycophbyte, and monilophyte identification [47].
## 5 Application Deployment Strategies
### Medical Services  
FL-enabled systems primarily focus on constructing deep models, privacy-preserving mechanisms, distributed learning architecture, and data partitioning. In healthcare systems, an advanced confidential system requires extensive studies because of possible cross-device/silo collaboration. The institutional clients or local medical devices collect highly sensitive data, where a privacy-enhanced FL framework needs to be deployed. FL in medical services requires complementary functions for resource-aware, incentive-aware, and personalized FL [92]. Furthermore, add-on functions for autonomous and accurate decision-making solutions should be extended with DRL [94]. Applications of DRL in IoT networks have become an active topic for research and deployment in the new era and beyond. In medical services, DRL greatly provides the applicability of prioritizing the ultra-low latency service requirements, enabling remote applications, handling heavy congested network states, and contributing intelligent core control. In [93], a converged approach between FL and DRL was proposed for evaluating the weights of client contributions that upgraded the deployment efficiency to a better level, particularly for smart healthcare applications. The distributed edge FL requires ensuring the enhanced confidentiality of practical healthcare systems and edge aggregation processes.
## 1 Introduction
Therefore, the scaffolds have a suitable surface that is ideal for cell attachment, cell growth, and cell differentiation [8]. Thus, tissue engineering does not focus on the complete replacement of defective tissue, but rather on the regeneration of damaged tissue [10]. The scaffolds can either be implanted directly into the defect or the desired patient-specific cells are harvested by biopsy and cultured on the scaffold before being used [16]. The difficulty in creating such scaffolds is to provide the cells with an extracellular matrix in which they can proliferate and carry out their normal metabolic activity. Only in this way can new tissue be established in the scaffolds [17]. In addition, the mechanical properties of the scaffold should be similar to those of the recipient tissue so that the stress-shielding factor is as low as possible [18]. These biological and mechanical properties can be significantly influenced by means of a suitable scaffold geometry. The pore size plays the most important role [19]. The minimum pore size is about 100 \(\upmu\)m due to cell size and transport and migration requirements [20]. Numerous studies have shown that the pore size should be between 100 \(\upmu\)m and 300 \(\upmu\)m to allow good cell-penetration, -migration and -growth properties [19]. Regarding the mechanical properties, there is still no clear consensus in the scientific community. Our aim was to investigate the influence of collagen-I coating on 3D printed PCL scaffolds on the growth of three different cell types: MG-63; MLO-Y4 and MSC. The geometries of the scaffolds were kept constant with strand widths of 200 \(\upmu\)m and strand spacing of 250 \(\upmu\)m. Because PCL scaffolds are very difficult to populate with cells and because the final constructs are to be used for bone regeneration, our scaffolds have been coated with collagen-I (a major component of bone).
# The Elasto-Plastic Contact Models of Cylinder-Based and Sphere-Based Fractal Rough Surfaces
## 1 Introduction
The fractal contact model has been extended to different fields, such as resistance spot welding, adhesive wear, contact stiffness, etc., and has been perfected by more and more researchers [23; 24; 25; 26; 27]. The elasto-plastic contact models of cylinder-based and sphere-based fractal rough surfaces are presented in this paper. In the two models, the critical contact areas of a single asperity in the fractal rough surface are scale-dependent. With an increase in the contact load and contact area, the elastic, elasto-plastic, and full plastic deformation occurs in this order. The truncated asperity size distribution functions of different frequency indexes in different contact zones are deduced. The relations between the total real contact area and the total contact load for cylinder-based and sphere-based fractal rough surfaces are obtained. The analytic solutions of contact models for cylinder-based and sphere-based fractal Rough Surface Characterization are derived. The aim of this research is to construct the elasto-plastic contact models for cylinder-based and sphere-based fractal rough surfaces, determine the scale-dependent critical contact areas of single asperities, analyze the deformation transition process, and explore the correlations among contact parameters.