esa-sceva/satcom-chunk-collection huggingface dataset:
# New Space Engineering Design: Characterization of Key Drivers
## 3 Results and Discussion
### General Discussion of New Space Design Drivers and Enablers
#### 3.1.2 Category B: Emerging Manufacturing and Integration Practices
By drawing inspiration from sectors such as automotive and cloud computing, the combination of standardization and modularity can significantly reduce manufacturing costs and shorten lead times while enabling system scalability in both the number of system units and system capability, from the early design phases [46]. In terms of production scalability, the limited production volumes and unique solutions of space projects have usually not justified scalable manufacturing approaches. Mass production was considered mainly incompatible with the traditional space industry. However, in contexts where scaling becomes relevant, such as satellite constellations, the adoption of intelligent, sensor-based, and software-driven manufacturing methods is increasingly being considered, including those associated with smart manufacturing of Industry 4.0, where artificial intelligence plays an enabling role in process optimization and automation [28]. Another notable technology of Industry 4.0 is additive manufacturing (AM), also known as 3D printing. AM enables the production of complex geometries in different materials (i.e., polymers, metals, ceramics, etc.) at very competitive costs. This results in mass savings through both a reduction in the number of required parts to perform a function, which positively impacts reliability and promotes standardization, and lower component weight compared to parts machined from solid blocks due to the lower material densities and topology optimization. Therefore, AM is fundamental to rapid prototyping, accelerating the time-to-market by enabling quick fabrication, reducing the assembly time, and facilitating iterative low-cost testing of prototypes to validate alternative designs before eventual mass production, thereby minimizing risks [47]. This advantage is especially critical in space projects, where technology maturity needs progressive advancement through the development and testing of scalable breadboards and functional models. Nevertheless, being a relatively young technology, certain drawbacks exist, including generally lower thermo-mechanical properties compared to traditionally manufactured parts, limited process scalability for mass production due to limitations in speed and consistency, and the absence of regulatory frameworks [48]. Furthermore, AM technology can support a circular economy by minimizing material waste compared to conventional fabrication methods while promoting the principles of recycling, reuse, remanufacturing, and repair [27; 49].
# Registration of Dental Tomographic Volume Data and Scan Surface Data Using Dynamic Segmentation
## 3 Results & Discussion
### Discussion  
#### 3.2.1 Evaluation of MICP Registration Results  
Some patients who take both CBCT volume data and dental scanning surface data may have uneven teeth geometry as in our data set. Even with this uneven and poor teeth condition, the proposed algorithm showed fine results, even for the case with artifacts without needing any extra processing. The convergence error of MICP is similar to that of ICP.  
As the proposed algorithm selects reliable points, it is robust to artifact cases. In the proposed procedure, registration and segmentation work complementarily; the better registration result causes the dynamic segmented points to increase, and the increased dynamic segmented points cause a better registration result. For artifact cases, the ratio of the increasing number of segmented points is lower.  
The D value is computed in order to compare the result with the conventional registration result. As mentioned earlier, there is no ground truth for the registration of volume data and surface data. In other words, D = 0 does not exactly correspond to a perfect result. However, it is taken as the ground truth on the basis that the conventional registration is currently used in all dental applications by experts in this field. Maximum tolerance in the registration of volume data and surface data for dental applications ranges from 1.0 mm to 2.0 mm. In most cases, the distance values D between the conventional registration result and the result of the proposed method were less than 2.0 mm. This means that the proposed MICP registration result has fine registration accuracy and can be used for conventional applications without any problem. Also, from the early iterations of MICP, the D values decreased in all test cases. This proves that the surface is moving in the right direction.  
Figure 10: Average point distance of the modified iterative closest point (MICP) result. (a) set no.1; (b) set no.2; (c) set no.3; (d) set no.4.  
While the established registration framework takes more than 20 min to register the volume data and the surface data, the proposed MICP registration takes less than 10 min for all cases because the segmentation process became automatic.
# Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles
###### Abstract
* The feature extraction methods used to detect the roads are the spatial values of pixels, the RGB value of pixels, entropy, HSV color space, texton features, the local distance distribution and local binary pattern (LBP). These different kinds of beneficial features are used to classify the images for extreme lighting conditions as well as in the shadow regions. * Further, a two-level fusion of classifiers is used during the classification. At the first level, the LeNet-5, LSTM and ResNet are used to extract the feature maps from the feature vectors. Here, the TLFC uses multiple classifiers for feature map extraction, because the road scene images are acquired from a different perspective and angle. Then, the cross fold is used in the TLFC to create a different input image frame for each classifier. Additionally, this input frame (i.e., vehicles) is introduced to the deep learning classifiers, where it is randomly selected based on the vehicle's preference. * Subsequently, the SVM is used in the second level for the precise classification of the road into drivable and non-drivable areas based on the fused feature maps. Hence, the combination of the cross fold and TLFC is used to improve the classification accuracy. The overall organization of this paper is as follows: Section 2 provides the existing works conducted on road detection. Section 3 provides the problem statement acquired from the related work, along with the solution provided by the TLFC-RD method. A clear explanation of the TLFC-RD method is provided in Section 4. Section 5 provides the performance and comparative analysis of the TLFC-RD method. Finally, the conclusion is drawn in Section 6.
# Dynamic Energy Efficient Control of Induction Machines Using Anticipative Flux Templates
## 6 Simulations
### Influence of Iron Losses  
In Section 2.4, we discussed why iron losses are neglected in this article. To illustrate that this simplification does not significantly affect the optimization procedure for dynamic operation given in Section 3, a simulation study is presented. In this study, the optimal flux trajectory is applied to a motor model including iron losses and to a model not including iron losses. The resulting power losses according to (12) are compared in Figure 13.  
The losses are calculated for a 0.4 s speed ramp from 500 rpm to 1500 rpm starting at \(t=0.2\) s and the flux trajectory corresponding to the 370 W motor in Figure 11. As the iron losses are flux dependent, the losses will also increase due to this particular flux linkage trajectory. It can be observed that the iron losses are higher at high speeds, thus resulting in high losses at the end of ramp period. However, in the interval \(t\in[0.05s\quad 0.2s]\), that is within the anticipation time \(\Delta t_{\rm a}\), the difference between the simulated losses without considering the iron losses and the simulated losses with consideration of the iron losses is almost constant. Thus, it can be concluded that the effect of the anticipative flux rise on the iron losses is not significant. In principle, iron losses could be included in the optimization procedure and template generation. However, this would increase the complexity significantly for two reasons. First, the template will then also depend on the speed. Second, the template adaptation algorithm Section 4.2 must consider the speed as well. Thus, the main benefit of this method, its simplicity, is no longer provided.
###### Abstract
However, further investigation is required to assess whether the use of a wearable MSD in telerehabilitation can increase patients' engagement in the in-home exercise program, thereby improving knee function after TKA. Focusing on the potential benefits of MSD in the post-TKA telerehabilitation scenario, this study replicated the previous experience of using the MSD for treating adhesive capsulitis to verify the feasibility of an MSD-based treatment model in assisting patients with home-based exercises after TKA. The application of the MSD in this study, which contained two wearable IMU-based sensors to trace the knee angular motion and was integrated with interactive mobile apps designed for patients and physiotherapists to follow and monitor in-home exercise progress, was anticipated to improve patient compliance with post-TKA rehabilitation exercises. We hypothesized that the use of a wearable MSD can help patients perform an in-home exercise program correctly and increase their motivation for daily exercise training, thereby improving functional outcomes after TKA. The remainder of this paper is organized as follows. Section 2 presents the materials and methods, including the design of the MSD and the study protocols for (1) the reliability of knee mobility measurements using the MSD and (2) the feasibility of using the MSD for the post-TKA in-home rehabilitation. Section 3 provides our preliminary results and the participants' user experience of the MSD design. Section 4 discusses the effect and clinical value of the MSD and the limitations of this study. Lastly, conclusions are presented in Section 5.
## 3 Algorithm Description
### Reception phase  
During the backoff phase of the frequency control process, a reader may receive requests from other readers. If a REQUEST1 frame is received, the reader evaluates whether it is affected by the request based on a predefined threshold probability (Pr < Threshold). Additionally, the reader checks for any interference from readers listed in the READER_IN_CHAIN list.  
If no interference is detected, the reader transitions from Carrier Sense Multiple Access (CSMA) to Frequency Time Division Multiple Access (FTDMA). It then adds its identification to the READER_IN_CHAIN list and listens for the ADD_TO_CHAIN message to utilize the affected frequency. However, if the reader is unaffected by the request or if interference is present, it retains the current READER_IN_CHAIN list in its table.  
In the event of receiving a REQUEST2 message, the reader responds with a RESPONSE frame. If a NEW_CHAIN message is received, indicating the need for a new communication chain, the reader switches from CSMA to FTDMA. Additionally, it selects a new frequency and adjusts its operation accordingly. If no NEW_CHAIN message is received, the reader returns to the IDLE state.  
Figure 5: Possible cases for the frequency distribution process.  
These actions ensure that readers in the frequency control process respond appropriately to requests and interference, facilitating efficient frequency assignment and allowing for adaptability to changing network conditions.
## 3 Test Results  
### Failure Modes  
Figure 4 shows the test specimens at the end of push-out testing. The overall behavior and failure mode varied with the type of steel anchor.  
In the case of the S-group (H-S, UI-S, U075-S, and U0100-S using stud anchors; Figure 4a-d), the test specimens failed by fracture of the studs at the bottom of the studs. Crushing and cracking of concrete occurred first around the root of studs. The bottom and sides of the concrete slabs also showed crushing and minor longitudinal cracking. Under further loading, splitting of concrete occurred on the exterior surfaces of the concrete slabs (but the splitting was limited) and the load increment was gradually decreased. The peak load was reached by fracture of the studs, and then the load was suddenly dropped and concrete slabs were separated (about 1-2 mm) from the steel beam.  
In the case of the Z-group (H-Z130, H-Z230, UI-Z130, UO75-Z230, and UO100-Z230 using Z-channel anchors; Figure 4e-i), the test specimens failed by splitting of the concrete. The splitting of concrete was initiated at the locations of the steel anchors and propagated to the outside. Compared with the S-group, the peak load was reached earlier, and the splitting on the exterior surfaces of concrete slabs and the crushing of concrete around the interface between the steel beam and concrete slabs were more prominent. However, the degradation in load after the peak was less drastic. The test specimens of C-group (H-C130, H-C230, UI-C130, and UO100-C230 using C-channel anchors: Figure 4j-m) were also failed by splitting of concrete as in Z-group. In Z- and C-groups, the separation of concrete slabs from the steel beam was about 6-15 mm after the test.  
The failure mode was not clearly distinguished by the geometry of steel beams, but in the case of using the U-channel with outward bent lips (UO) for the steel beam, the longitudinal slip between the steel beam and concrete slabs was most prominent.
## 4 Results and Discussion  
### Constant Current Discharging  
In order to verify the accuracy of the models under the simple operating conditions, constant current discharging tests were performed. First of all, the tested battery was fully charged according to the standard charging method and then the battery was left in the open-circuit condition to rest for 4 h to achieve electrochemical and heat equilibrium. Subsequently, the battery was discharged at a constant current 1C (2.35 A) to its discharging cut-off voltage 2.7 V. For the simulation models, first the initial SOC was set to 1 and the magnitude of the input current to 2.35 A, and then simulation models were run. Simulation results of the first-order RC model are shown in Figure 11, where the red dotted line represents the simulation output voltage and the solid blue line represents the actual voltage value. Similarly, the simulation results of the second-order RC model are shown in Figure 12, where the red dotted line represents the simulation output voltage, and the solid blue line represents the actual voltage value. It can be seen from the figures that the simulated voltage could basically follow the battery terminal voltage changes in the constant current discharging process. However, at the beginning and end stages of discharging, the error was relatively larger; this is at the beginning stage and end stage of discharging, lithium-ion batteries are deepening gradually in the polarization process, and the electrochemical reaction taking place in the battery tends to be unbalanced; the two factors above can lead to the change of parameters in a relatively large range, resulting in a relatively large increase in output error of the models. For better comparison, the output error of the first-order RC model and second-order RC model is analyzed as shown in Figure 13, in which it can be seen that the second-order RC model has a smaller output error than the first-order RC model in general.  
Figure 10: The terminal voltage calculation submodule of the second-order RC model for Simulink simulation.
# Solutions to Increase PV Hosting Capacity and Provision of Services from Flexible Energy Resources
## 2 Simulation Model and Study Cases  
### Simulated Study System  
The initial choices made for this paper were based on conclusions from previous research in [39]. Paper [39] studied new multi-objective ANM scheme by simulating local smart grid pilot Sundom Smart Grid (SSG) with different future potential DER scenarios. Currently there are two DG units connected to SSG [39]. One 3.6 MW full-power-converter based wind turbine (WT) connected to MV network with own MV feeder J08 (Figure 1) and another LV network connected inverter based PV unit (33 kW) at MV/LV substation TR4318 (not included in Figure 1).  
However, in the study cases of this paper also future scenarios with higher amount of PV units as well as with BESSs connected in LV network of SSG (Figure 1) are evaluated. As shown in Figure 1, in the studied future scenario there are two 450 kW PV units (PV 1 and PV 2) at MV feeder J07 and three 300 kW PV units (PV 3-5) at MV feeder J06. Respectively, three 200 kW BESS units (BESSs 1-3)  
Figure 1: One-line diagram of the studied SSG in future scenario with more PV and BESS units in LV network, as well as with control functions applied in WT, PVs, and BESSs.  
were added to MV feeder J07 in different locations (Figure 1). Figure 1 also shows what type of control functions are applied in the studies of this paper for WT, PVs, and BESSs. Differences to the previous studies in [39] can be also seen in Table 1.
## 3 Design
### Contrastive Training Framework
#### 3.2.1 Block Augmentation Model
1 Function Augmentation (block \(B\), flag isPositive, num \(N\))
2\(S_{ret}\leftarrow\{\ \}\) ;
3 whileTruedo
4 ifisPositivethen
5\(BlockSource\leftarrow\) GetBlockSource(\(f\));
6 else
7\(BlockSource\leftarrow\) DiffBlockSource(\(\circ\));
8\(compiler\leftarrow\) SelectCompiler(\(\circ\));
9\(opt\leftarrow\) SelectOpt(\(\circ\));
10\(arch\leftarrow\) SelectArch(\(\circ\));
11\(B^{\prime}\leftarrow\) ConstructBlock(\(BlockSource\), \(arch\), \(opt\), \(compiler\)) ;
12 ifnotisPositiveandEmulation(\(B\))==Emulation(\(B^{\prime}\))then
13 continue ;
14 ifisPositiveandGetMetaData(\(B\))==GetMetaData(\(B^{\prime}\))then
15 continue ;
16\(S_{ret}\gets S_{ret}\cup\{B^{\prime}\}\) ;
17 iflen(\(S_{ret}\)) >= \(N\)then
18 break ;
19 return\(S_{ret}\) ;
20
21\(isPositive\leftarrow\) True ;
22\(S_{pos}\leftarrow\) Augmentation(\(B\), isPositive, \(N_{pos}\)) ;
23\(isPositive\leftarrow False\) ;
24\(S_{neg}\leftarrow\) Augmentation(\(B\), isPositive, \(N_{neg}\)) ;
25
26 Output\(S_{pos}\), \(S_{neg}\);
```  
**Algorithm 1**Block Augmentation Algorithm  
To generate positive and negative samples for a given basic block \(B\), we employ the _Augmentation_ function. The parameters of _Augmentation_ determine the quantity of positive and negative samples generated. When generating positive samples, our objective is to produce basic blocks that share the same semantics as basic block \(B\), while exhibiting distinct instruction sequences. To this end, we maintain the source code lines without alteration (as shown in Line 5 of Algorithm 1), and introduce variations in the compiler, optimization techniques, and architecture employed, ensuring that the augmented basic block differs from the original basic block \(B\). Consequently, the augmented basic blocks possess identical semantics to basic block \(B\), but display different instructions. These augmented basic blocks pose challenges for the representation generation model, contributing significantly to the loss calculations during model training and resulting in accelerated convergence. When generating negative samples, we randomly pick basic blocks with distinct source code lines and I/O emulation results in comparison to the anchor basic block \(B\). Subsequently, \(N_{pos}+N_{neg}+1\) contrastive samples are constructed for model training.
# Analytical Selection of Leakage Inductance for Single-Phase Dual Active Bridge Converters
## 4 Design of High-Frequency Transformer
### Selection of the Core Material  
The selection of the core material is a vital aspect in designing a transformer as it directly influences factors such as cost, losses, and size. The core materials [19] with high flux density, high Curie temperatures, and low losses are most preferred. These characteristics ensure efficient performance and reliability of the transformer. Popular core materials for high-frequency transformers in DAB converters include ferrite, nanocrystalline, and amorphous metal. Ferrite offers a good balance of cost and performance at lower frequencies, while nanocrystalline provides superior performance at higher frequencies but comes at a higher cost. Amorphous metal exhibits the best high-frequency performance but is the most expensive option. Additionally, the core's ability to handle power is assessed by its area product (\(A_{p}\)). For a given power (\(P_{metal}\)), the area product is calculated from [20] as part of the design process to ensure optimal performance and efficiency of the transformer using the following equation:  
\[P_{rated}=K_{f}K_{u}JfB_{m}A_{p} \tag{31}\]  
where the parameters \(J\), \(K_{f}\), and \(K_{u}\) denote the current density, waveform coefficient, and utilization factor, respectively. To meet the specific requirements of the transformer, a ferrite core 9478116002 with a cross-sectional area (\(A_{c}\)) of 284.289 mm\({}^{2}\)[21] is selected.
## 4 Conclusions  
In summary, a novel TLM-VM-PML method is proposed for the analysis of the tunnel-soil dynamic interaction. The basic theory of the TLM, VM, and PML method was derived. A laboratory test was performed under the impact excitation to obtain the frequency response functions in the tunnel-soil system. Based on the TLM-VM-PML method, a model was established to numerically calculate the frequency response functions. The experimental and calculated results were compared. The main conclusions are as follows:  
1. The vibration response decreases gradually with the increase in distance. The maximum vibration acceleration at different locations occurs at different times, reflecting the time difference in vibration propagation in the soil layer under the impact excitation.
2. The calculated and experimental ground vibrations are in good agreement in most frequency bands at 5, 30, and 80 m. These two vibration responses have slightly different magnitudes at 20, 25, and 30 m. The experimental results are significantly larger than the calculated ones in the frequency band between 40 and 80 Hz.
3. Considering the differences in soil parameters between the test site and the numerical model, as well as the differences caused by the simplification of the tunnel structures, the above results prove that the proposed method can be used for the analysis of the tunnel-foundation dynamic interaction.  
Conceptualization, Y.C. and M.M.; Methodology, M.Z., Y.C. and M.M.; Software, M.Z.; Validation, M.Z. and X.W.; Formal analysis, Y.W.; Investigation, Y.W.; Writing--original draft, Y.W.; Writing--review & editing, Z.L. and M.M.; Supervision, Y.C. and M.M.; Funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Beijing Natural Science Foundation (Grant Number: L221023) and National Natural Science Foundation of China (No. 51978043).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflict of interest.  
Figure 18: RMSE at different locations.