esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
High-pressure die casting (HPDC) technology is indeed a popular method used in the production of castings, particularly for smaller, lighter, and more intricate products with precise dimensional requirements. This process is commonly employed for manufacturing components with complex shapes and thin walls. HPDC emphasizes achieving a high-quality surface finish, typically smooth in appearance. Most castings produced using this technique are made from non-ferrous metal alloys, such as aluminum, zinc, and magnesium [1; 2].  
The electrotechnical, gas, aviation, and automotive industries have stimulated an abrupt development of progressive foundry technologies. Vehicles consist of a few parts produced from steel, cast iron, and non-ferrous metals. Approximately 15-20% of automotive parts are castings produced by diverse casting methods. The castings represent mainly key components of the energetic system and important construction elements. Die casting technology of non-ferrous metals and their alloys under high pressure ranks among the newest technologies in the foundry industry. Over a relatively short period, the technology has won recognition in the case of mass and bulk production of castings. The increase of casting share in modern structures requires knowledge of the foundry processes [3; 4; 5].  
Indeed, high-pressure die casting technology is known for its ability to produce precise castings with minimal material waste. It is a highly efficient method that allows for the transformation of raw materials into a final product with excellent dimensional accuracy and surface finish. It is a means of casting production in the case that the molten metal is injected into a permanent mold at a high speed of 10-100 m\(\cdot\)s\({}^{-1}\) such that the metal reaches the gating system under the influence of high pressure (the melt is being cast by the pressure of up to 500 MPa). Throughout solidification, the metal remains under pressure, i.e., the process is referred to as multiplication. High filling speed and high pressure allow the casting of thin-walled and rather fragmented castings which in many cases do not require further machining other than the removal of sprues and fins [6; 7].  
The properties and quality of castings produced using die casting technology are influenced by various factors throughout the entire process. These factors play a crucial role in achieving the desired final shapes, dimensions, and surface quality. By carefully considering and optimizing these factors, manufacturers can achieve high-quality castings with complex shapes, accurate dimensions, and excellent surface finish. Close attention to mold design, gating and venting systems, cooling, alloy selection, mold quality, machine parameters, and metallurgical factors is crucial in ensuring successful die casting production [8; 9; 10]. A complex of all the aforementioned factors and parameters determines a precondition to produce high-quality castings. Technological parameters significantly influencing mechanical properties involve casting pressure (holding pressure) and thermal conditions of the die casting process [11; 12].
## 6 Conclusions  
In this paper, a GPS/INS Tightly Coupled Assisted PLL (TCAPLL) architecture with a monitoring system was proposed to evaluate the advantages of integrating low-quality MEMS inertial sensors with a GPS receiver. The monitoring system is responsible for checking the quality of measurements coming from the MEMS inertial sensors and the GPS tracking loops in order to ensure robust tracking and accurate navigation. Design and implementation of the TCAPLL were presented and assessment under different scenarios was conducted. It was shown that low-cost sensors, when properly initialized and their errors are well modeled, can bring enhancements to the GPS receiver especially in challenging environments. Also it was shown that they can provide reliable assistance to the PLL to avoid re-acquisition, compensate for receiver dynamics, and hence reduce the loop bandwidths and improve sensitivity. Moreover, it was revealed that low-cost MEMS inertial sensors in the TCAPLL have some limitations, especially for increasing the PLL coherent integration time. Furthermore, they  
Figure 31: PLI and prompt correlator outputs of SV 22 at the exit of the tunnel in Trajectory 1.  
Figure 32: PLI output of SV 19 during high dynamics in Trajectory 1 when \(B_{n}=2\) Hz for TCAPLL and 7 Hz for GPS alone.  
are not well suited when long GPS outages occur, but can provide a navigation solution for short outages depending on the quality of the sensor and the navigation accuracy requirements. Finally, the TCAPLL was evaluated in a field vehicle test with low-quality MEMS inertial sensors. The measurements obtained were in accordance with the simulation results. The TCAPLL outperformed the MTi-G Xsens receiver using the same type of inertial sensors, and in the absence of GPS outages, it provided comparable results to the Javad Alpha GPS receiver in autonomous mode. Finally, it can be concluded that with the continuation of the price decrease and performance increase of MEMS inertial sensors, it is most likely that future mass market GPS receivers will have integrated MEMS-based INS to provide a more reliable, accurate and robust navigation solution.  
The authors are grateful for financial support from the Swiss National Science Foundation ([[http://www.snf.ch/](http://www.snf.ch/)]([http://www.snf.ch/](http://www.snf.ch/))) which supports this work under grant 200020_134766/1.
## 2 Materials and Methods
### Manual Segmentation of Hematomas on Head CTs as the Ground Truth  
The ground truth hematoma lesion masks on admission and follow-up head CT scans were based on manual segmentation by trained research associates at Yale (using 3D-Slicer [22]) and Charite (using ITK-SNAP [16; 23]). Hematoma lesions were segmented on axial head CT slices applying 40 to 200 Hounsfield Unit (HU) intensity thresholds to facilitate the distinction of hemorrhages from brain parenchyma, osseous structures, and cerebrospinal fluid. To assess the reliability of segmentations, a subset of scans from both centers underwent additional segmentations to determine intra- and inter-rater reliability using intra-class correlation (ICC).
## 4 Parametric Investigation
### B/R\({}_{c}\)  
The influence of the ratio \(B/R_{c}\) (i.e., the buttress width over the radius of the reference circle) on the seismic capacity factor \(\lambda^{\star}\) was studied. For both circular and basket-handle buttressed arches, the ratio of \(H/h\) was set to 3.0. The width of buttress \(B\) up to 1.0 m with a constant step of 0.2 m was studied. Like in Section 4.1, a large friction coefficient \(\mu\) was set to avoid sliding. The variations of \(\lambda^{\star}\) against \(B/R_{c}\) are shown in Figure 18. Pre-examinations on the minimum \(B/R_{c}\) to guarantee the arches are statically stable (i.e., they can stand under gravity) were conducted, and the values are tabulated in Table 5. Any buttress with a smaller \(B\) will lead to the collapse of the structure due to the horizontal thrust of the arch induced by gravity. Thus, all the curves in Figure 18 start from the minimum \(B/R_{c}\) that the arch structures are statically stable under gravity.  
For both circular and basket-handle buttressed arches, \(\lambda^{\star}\) increases monotonically with an increase in \(B/R_{c}\). Since \(R_{c}\) is constant, the wider the width \(B\), the larger the \(\lambda^{\star}\). For buttressed circular arches, the angle of embrace \(\beta\) has nearly no influence on \(\lambda^{\star}\), as the three curves are quite close to each other. For buttressed basket-handle arches, the same applies except the \(\lambda^{\star}\) with \(\beta=180^{\circ}\) and \(B/R_{c}=1.0\), as a much higher seismic capacity is obtained. The reason is that with the combination of \(\beta=180^{\circ}\) and \(B/R_{c}=1.0\), the horizontal thrust of the arch is too small to push the buttress down, and the failure of the structure is dominated by hinge rotations of arch voussoirs only, resulting in a much higher \(\lambda^{\star}\).  
Figure 17: Variation of \(\lambda^{\star}\) against \(H/h\): (**a**) circular arch; (**b**) basket-handle arch.
# Low Delay Inter-Packet Coding in Vehicular Networks
## 5 Conclusions  
In summary, we have shown that significant gains come through our proposed erasure codes. From the figures of the previous section, it is clear that there is a significant difference in the probability of successful delivery, between codes and uncoded transmissions. Our new algorithm has low delay and low complexity, and is based on Wyner-Ash convolutional codes. We conducted an erasure performance analysis for both these Wyner-Ash convolutional codes, and also for corresponding ones based on Reed-Solomon convolutional codes. Thus, our main recommendation for V2R communications is to apply erasure codes for boosting packet reception rates.  
Our future work towards the support of C-ITS safety testing is planned in three main directions.  
First is the experimental proof-of-concept validation of the developed inter-packet coding scheme, to be performed at AstaZero.  
Secondly, we are planning to propose such inter-packet coding in the ongoing ISO 22133-1 standardization of messages formats and communication protocols for automotive testing facilities.  
Finally, we intend to address tighter requirements on decoding delays. The studied coding scheme introduces some delays which are acceptable for non-critical (quasi real-time) VANET scenarios such as maps updates, but these delays are not acceptable for hard real-time data traffic (e.g., the tracking of objects under test). Therefore, designing of appropriate coding schemes which meet the reliability requirements for hard real-time C-ITS applications will be a subject of our future work.  
Conceptualization E.F. and A.V.; methodology and formal analysis, B.K. and I.B.; software, N.L.; investigation, B.K., I.B., M.R. N.L. and A.V.; writing--original draft preparation, B.K. and I.B.; writing--review and editing, M.R.; supervision, A.V.  
The research leading to the results reported in this work has received funding from the Knowledge Foundation in the framework of AstaMoCA \"Model-based Communication Architecture for the AstaZero Automotive Safety\" project (2017-2019), CSOI Action CA15127 (\"Resilient communication services protecting end-user applications from disaster-based failures--RECODIS\") supported by COST (European Cooperation in Science and Technology), Swedish Foundation for Strategic Research (SSF) in the framework of Strategic Mobility Program (2019-2020), from Eesti Teadusagentuur under the grant PRG49 and from the ELLIIT Strategic Research Network. This support is gratefully acknowledged.  
The authors declare no conflict of interest.
## 2 Review of Related Works and Analysis of Unsolved Problems
### Requirements for System Development
Using the AU device, the service provider realizes services based on the capabilities of the OBU. An AU can be a device dedicated to specific security programs or a regular device, such as a smartphone or tablet, that provides Internet services. The distinction between an OBU and an AU is often logical [41; 42]. The component-based architecture of the service delivery subsystem, and connection with other components and packages is presented, in Figure 2.  
An RSU is the facility of WAVE devices that are installed near the road or in other specially designated areas, e.g., at intersections or parking lots. This device is equipped with short-range wireless technologies, such as IEEE 802.11p, or others that connect it to the network infrastructure. According to previous research [41; 42], the main functions and procedures associated with RSU are as follows:  
* Help to extend the distance of the ad-hoc network by forwarding information to other OBUs or RSUs;
* Help to run safety programs, such as crash warnings and road surface information, using the V2I mode and acting as a source of information;
* Provide OBUs for Internet access. Areas of automotive communication:* The INV domain consists of an OBU and one or more AUs. OBUs and AUs can be realized as a single device. In the automotive field, contextual information is collected from installed sensors and stored in a database [41; 42]. * The V2V domain is the area of an ad-hoc type network that consists of cars equipped with an OBU. The cars communicate with each other via the OBUs, thus forming a MANET that provides communication in a fully distributed decentralized manner. Cars can communicate directly with each other, if direct wireless communication between them is possible, by forming one-jump car-to-car communication (V2V). If direct communication is not possible, the data is transferred to other cars as intermediaries until the addressee is reached. These forms of multi-hop relationships are described in [21]. The infrastructure of the domain is related to all of these devices and, in particular, to the RSUs that connect to the infrastructure of the networks or the Internet by providing OBU access to that network. OBU devices communicate with various nodes by providing non-security services using other mobile technologies (GPRS, GSM, 4G, HSDPA, UMTS, and WiMAX) [41; 42].
# Cooperative Power-Domain NOMA Systems: An Overview
## 3 Types of NOMA
### Power Domain NOMA
#### 3.2.1 Cooperative Relaying Communication
Under three different situations, the numerical results of this method were evaluated utilising parameters such as OP. The powers of BS and relay were fixed in the initial condition. The relay's and BS's power were optimised in the second condition to decrease the OP. Users' fairness was considered, and the relay's and BS' power were tuned to maximise each user's individual rate. Figure 8: Cooperative communication to enhance the networks spectral efficiency. _NOMA-relaying broadcast channel (RBC):_ The analysis of cooperative SISO-NOMA relaying was investigated using a two-user model [95]. A compress-and-forward (CF) protocol was used by users close to the BS and functioned as an FD relay to facilitate relay to farther users in this approach. With noisy network coding (NNC) and CF relaying, its performance was evaluated by the attainable rate region of RBC. A dirty paper coding (DPC) was applied to the BS to improve its performance with respect to the acquired attainable rate region using CF and NNC. When compared to traditional NOMA, it produced superior outcomes. Types of cooperative communication in the current NOMA schemes are highlighted in Figure 9. Also, comparison of cooperative NOMA communication methods is provided in Table 3.
# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
## 4 Discussion
### Classifier Generalization Verification  
In this study, Village 1 and Village 2 were used for training, while Village 3 served as the validation set. The performance of classifiers across binary, ternary, and quintuple tasks (XGBoost, RF, DT, LR-GD) was systematically compared. To enhance the comparability and practical applicability of model validation, PCA feature extraction was applied consistently across classifiers. After reducing the number of features from 47 to 36, the model's recognition accuracy increased by an average of 5.2% for valence and 6.4% for arousal, with other performance metrics also improving. These findings demonstrate that the PCA algorithm effectively reduces data redundancy and noise, thereby enhancing model classification capabilities. However, obtaining a sufficient number of meaningful features remains a challenge, one that requires further academic consensus through extensive experimentation. Furthermore, the integration of SMOTE and Bayesian optimization techniques was employed to compare the performance of integrated versus single classifiers. The results showed that, for valence, the highest binary classification accuracy was 84%, ternary classification accuracy was 77.2%, and quintuple classification accuracy was 64%. For arousal, the highest accuracy was 77.9% for binary classification, 74.3% for ternary classification, and 59.6% for quintuple classification. Notably, the integrated classifiers exhibited superior performance in binary and ternary classification, with higher performance metrics, while the quintuple classification accuracy was notably lower--64% for both valence and arousal--compared to 79% achieved by [PERSON] and [PERSON] [80]. This discrepancy is attributed to the varied emotional responses elicited by image-based experiments versus real-world scenarios [81], an issue that will be further investigated through virtual scene construction or field experiments in the future. Furthermore, the proposed model underwent external validation to address the limitations associated with deriving both training and validation data from the same image set. When new data were introduced into the model, a decline in performance was observed, with average decreases of 4.25% in valence classification accuracy and 3.31% in arousal. These findings underscore the necessity of incorporating external validation, as relying solely on a unified dataset is insufficient for robust classification research. Additionally, it was observed that the average accuracy for valence exceeded that of arousal by 7.4% across binary, ternary, and quintuple classifications, supporting the weak V-shaped relationship and individual differences identified by [PERSON] et al. [82] in their analysis of average relationships.
## 3 Results and Discussion  
### Measurement Setup  
To measure the S-parameters of the proposed antenna, the Anritsu S820E Antenna Analyzer is used. Figure 8 shows the fabricated antenna prototype. The far-field characteristics of the antenna are measured through a commercial far-field measurement system in a shielded RF anechoic chamber. The horn antenna (SGH-series) having a standard gain of 24 dBi is utilized as a transmitter, while the proposed antenna was measured as a receiving antenna. Power amplifiers were used to provide stable power reception. The antenna is rotated in 360\({}^{\circ}\) to measure the far-field results.
## 1 Introduction
As shown in Figure 2, a dual-branch encoding-decoding learning framework is designed based on a multi-attention mechanism. This dual-branch learning architecture can process under-sampled images of different modalities simultaneously. In this architecture, we introduce an innovative triple attention block that can accurately capture key features at multiple levels and identify important features shared across different modalities. In addition, to  
Figure 1: Illustration of different MRI modalities and the reconstruction comparison results on fastMRI brain datasets. (**a**) Fully-sampled T1 WI and T2 WI. (**b**) Zero-filled T1 WI and T2 WI with 1D random sampling at 8\(\times\) accelerate factors. (**c**) Reconstruction results of DuDoRNet [14]. (**d**) Reconstruction results of STUN [15]. (**e**) Reconstruction results of MHCFIN (Ours). Green boxes highlight detailed structures. effectively preserve important features and reduce information loss, we upgrade the traditional skip connection to a residual learning mechanism. In the decoder, we designed a multi-modal feature interaction module (MMFIM) to merge multi-scale features using a dual attention mechanism, capturing complementary information between modalities. Finally, the convolution-based single-modal feature fusion module (SMFFM) integrates and refines multi-scale features, further enhancing feature representation and establishing long-range dependencies between contexts. The proposed method demonstrates superior performance across various experimental settings, as illustrated in Figure 1c,d,e. Compared to other methods [14; 15], it effectively restores details and exhibits better performance. The main contributions are as follows:  
* In this study, we introduce an innovative multi-modal reconstruction approach, termed MHCFIN, that facilitates high-fidelity image reconstruction by harnessing the latent, learnable complementary information inherent across modalities. * We propose a dual-branch encoder-decoder learning structure based on a multi-attention mechanism, which can accurately capture key features at different levels and identify important shared features across modalities, while dynamically optimizing the learning process and using residual learning to reduce information loss. * We integrate dual cross-attention at the decoder to achieve complementary knowledge transfer across modalities and integrate multi-scale features through a single-modal feature fusion module within the same modality to establish contextual dependencies. * Experimental results show that the proposed method outperforms the current state-of-the-art reconstruction methods in multi-modal image reconstruction.
# Machine Learning Techniques for Non-Terrestrial Networks
## 5 Machine Learning General Aspects
### Data Collection  
As stated in the previous sections, supervised learning techniques require a lot of data to train a model. In many cases, the amount of data is directly proportional to the resulting accuracy. Although having a large training dataset is recommended, it is also necessary that the data are balanced, and the whole dataset is representative of the environment under study. In communication networks, there are many data sources, and most of them can be used to train a ML model. Starting from the physical layer, baseband signals or channel state information can be obtained. Moving to the medium access control (MAC)/link layer, several dimensions may be observed, such as the frame error rate (FER), throughput over time, and also random access load and latencies in various scenarios. Additionally, in the network layer, much information can be gathered. As an example, UE battery levels, mobility patterns, outage rates, global positioning system (GPS) location, traffic load and statistics are a few. Eventually, in the application layer, more structured data can be collected. Content demands, service subscription information, traffic statistics, user behavior patterns, user preferences and metrics from the quality of service (QoS) process.
# Impulse Signal Detection for Bearing Fault Diagnosis via Residual-Variational Mode Decomposition
## 3 Simulation and Case Study  
### Simulation Analysis  
The simulation signal includes the harmonic component (\(y_{1}\)), the periodic impulse (\(y_{2}\)), the aperiodic impulse (\(y_{3}\)), and the Gaussian noise (\(y\)). The simulation signal can be written as:  
\[y(t)=y_{1}(t)+y_{2}(t)+y_{3}(t)+n(t) \tag{17}\]  
\[y_{1}(t)=0.15\times\sin(2\pi\times 200t) \tag{18}\]  
\[y_{2}(t)=2\times e^{0.1\times 2\pi\times 2000t}\sin(2\pi\times 2000t\times\sqrt{1 -0.1^{2}}) \tag{19}\]  
\[y_{3}(t)=3\times e^{0.1\times 2\pi\times 1200t}\sin(2\pi\times 1200t\times\sqrt{1 -0.1^{2}}) \tag{20}\]  
In the simulation signal, the frequency of the impulse signal is set as \(f_{i}=40\) Hz. The sampling frequency is 20 kHz, and the length of the simulation signal is 10 k. The density  
Figure 2: The flowchart of RVMD.  
of Gaussian noise is 0.4. The simulation signal is shown in Figure 3, and its SES is shown in Figure 4. As Figure 4 shows, it is difficult to find the feature frequency due to the existence of interferences from Gaussian noise and aperiodic impulses.  
The proposed method was applied to analyze this signal, and the results are shown in Figures 5 and 6. The time-domain waveform (TDW) shown in Figure 5b includes some periodic impulses, consistent with the results shown by NEK. The SES of the sub-mode shown in Figure 5b is presented in Figure 6. The fundamental frequency and its harmonics are clearly shown in it. Thus, the proposed method can effectively extract the periodic impulse features and it has a strong resistance against the interferences from the aperiodic impulses.  
Figure 4: SES of the simulation signal.  
Figure 5: Results by RVMD for the simulation signal: (**a**) IMF1 and (**b**) IMF2.  
Figure 3: Simulation signal: (**a**) harmonic component \(y_{1}\), (**b**) periodic impulse component \(y_{2}\), (**c**) interface impulse component \(y_{3}\), and (**d**) composite signal \(y\).