esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Critical Analysis
### Detection
proposed a profile component decomposition model with holistically nested edge detection (PCDM-HED), a framework for 3D road crack detection. It can effectively enhance the crack edge features and improve the accuracy of crack recognition in complex environments. Evaluated on LS-3D, LS-3D, LS-3D, LCMS, ESAR, and 2D-I datasets, it achieves excellent results. PCDM-HED shows great promise for improving the robustness and generalization of road crack detection systems, especially in environments with limited marker data. [PERSON] et al. [78]. extended the original YOLOv5 network, called YOLOv5-CBoT. The model borrowed the bottleneck transformer and C2f module from YOLOv8, which can effectively improve the accuracy and efficiency of crack identification. The model effectively improves the acquisition of global information and further enriches the gradient information. The model is tested on the RDD2020 dataset and compared with eight other models,including Faster R-CNN, Cascade R-CNN, CenterNet, etc. The experimental results show that YOLOv5-CBoT achieves competitive results with fewer parameters. This makes it a viable solution for efficient and cost-effective road maintenance. [PERSON] et al. [79]. proposed an efficient road crack detection method that can perform object detection and semantic segmentation simultaneously. The method is based on the YOLIV4 model and uses an attentional feature pyramid network (AFPN) to improve the feature extraction capability. A denoising autoencoder (DAE) network is integrated to improve the accuracy of crack segmentation. Tested on the DeepCrack dataset, the performance is similar to that of seven models, including Faster R-CNN, DeepLabv3+, U-Net, etc., while significantly reducing the computational requirements. It has great potential for future practical applications. [PERSON] et al. [80]. [PERSON] et al. integrated deep learning models into a UAV for road crack detection. The authors tested the YOLOv2-tiny, Darknet-19-based YOLOv2, ResNet50-based YOLOv2, YOLOv3, and YOLOv4-tiny models to see how well they could find cracks quickly and accurately. The ResNet50-based YOLOv2 and YOLOv4-tiny emerged as the top performers, with accuracies of 94.54% and 91.74% and processing speeds of 71.71 fps and 108.93 fps, respectively. The system demonstrates the ability to detect road cracks under challenging conditions. [PERSON] et al. [81]. proposed a deep learning model that combines the ResNet-18 model, the whale optimization algorithm (WOA), and the random forest (RF). The model can effectively optimize the feature set and improve the classification performance. The training accuracy on the self-constructed dataset reaches 97.16%. This approach not only improves the efficiency and accuracy of road crack detection but also provides a scalable solution that can be applied to a wider range of road maintenance applications.
# Spitzer Resurrector Mission: Advantages for Space Weather Research and Operations
## 3 Mission Design  
### Orbit, Trajectory Past Earth-Sun L4, and Station-Keeping near Earth-Sun L3  
After a launch as a ride-along on an Evolved Expendable Launch Vehicle (EELV) Secondary Payload Adapter (ESPA) ring to lunar transfer orbit, the spacecraft uses electric propulsion to achieve the necessary \(\Delta v\). The SRM trajectory, shown in Figure 2, comprises one year of thrusting to descend to 0.875 AU, coasting for roughly a year and a half, and then another year of thrusting to raise its orbit to 1.023 AU \(\times\) 1.003 AU. The total transit time from the Lunar Transfer Orbit (LTO) to the final destination is approximately three and a half years.  
This course takes Resurrector in front of the west side of the Sun, which is relevant for space exploration due to the magnetic connection of eruptions on this hemisphere to the Earth-Moon system. Station keeping near the Earth-Sun L3 point allows continuous beaconing to characterize solar flow and perform real-time CME detection using an Earth-based ground aperture. In addition, simultaneous helioimaging is performed to augment the radio beacon data.  
The far-side location near L3 also allows Resurrector to rendezvous with and restore operations to the Spitzer Space Telescope, placed in safe mode in 2020. The spacecraft allows a two-for-one mission in space weather and spacecraft servicing/recovery.  
The trajectory necessitates long-duration continuous burns of one or more ion thrusters. A multi-thruster version would allow differential thrusting to simplify the propulsion  
Figure 1: This snapshot of an Earth-directed CME on 16 February 2010 was captured by the imagers on STEREO-A from the Lagrangian L4 region. The SRM Imager (FoV is marked) can acquire images \(\sim\)10\(\times\) higher contrast and 4\(\times\) higher spatial resolution than HI-2 thanks to its vantage point and larger aperture while leveraging the designs matured in Parker Solar Probe and Solar Orbiter missions.  
system by eliminating the need for a gimbal and tankage while simultaneously using a compact, scalable thrust array. Substantial \(\Delta\varpi\) is possible using such an array, up to 6 km/s depending on the overall mass of the spacecraft.
## 1 Introduction
established a prediction model of pipe pile bearing capacity using machine learning methods and verified the feasibility of this approach [19]. [PERSON] et al. proposed a new method for the static test modeling of pipe piles. The contact between the pile ad soil was well-treated and the accuracy of the modeling was verified [20; 21]. [PERSON] et al. investigated the pile lateral resistance of open-ended PHC piles. They showed that the pile lateral resistance is gradually transmitted along the depth, and its magnitude is closely related to the soil properties. The pile lateral resistance at the same depth decreases with an increase in the number of cycles [22; 23; 24]. [PERSON] et al. found that the flexural stiffness of reinforced PHC piles was greater than that of conventional PHC piles, and the moment-bearing capacity of reinforced piles was significantly higher than that of conventional piles by comparing the flexural performance of reinforced PHC piles with that of conventional PHC piles [25; 26; 27; 28]. [PERSON] et al. studied the bearing capacity of pipe piles at the end, and the results showed that the bearing capacity of pipe piles is close to the limit value, which is less than or at most equal to the end-bearing capacity of an embedded strip foundation of width equal to the pipe wall thickness. Scholars have also studied the long-term performance of PHC piles [29; 30]. [PERSON] et al. studied the bidirectional diffusion of chloride ions in coastal pipe piles and analyzed how the chloride ion diffusion effect was affected by parameters such as bidirectional erosion of chloride ions, mortar, and concrete delamination and temperature, and found that the difference in wall temperature correction coefficients was significant for temperature differences between the inner and outer walls of the pipe piles greater than 15 \({}^{\circ}\)C, which should be corrected according to reality. The effects of bidirectional chloride ion erosion and centrifugal delamination on the pipe pile can be improved by adjusting the mortar layer thickness [31]. Despite the obvious advantages of the current PHC method for construction in the inner digging method [32; 33], its monopile vertical load-bearing mechanism is different from other pile types. There is less research on the load-bearing mechanism of pipe piles constructed in the inner digging method [34; 35], so it is necessary to conduct a comparative study on its monopile vertical compressive load-bearing capacity and load transfer law. Therefore, this paper compares and analyzes the vertical load-bearing characteristics of hammering method pipe piles and medium-drilling method pipe piles and discusses the force transmission mechanism of PHC piles in detail, taking a mega-bridge reconstruction and expansion project in Anhui Province as an example. The effect of different wall thicknesses of PHC piles on the bearing characteristics is discussed by establishing finite difference models and comparing them with field tests. The research results can provide a reference for further theoretical research and engineering applications.
# A Bivariate Volterra Series Model for the Design of Power Amplifier Digital Predistorters
## 2 A Bivariate Volterra Series Perspective
### Model Order Reduction  
Not all the regressors of the bivariate model are significant and a first step to extract the coefficients is the identification of the active regressors set. Starting with the whole stock of regressors, the procedure follows a doubly orthogonal matching pursuit (DOMP) algorithm to search the most significant regressors [19; 20]. The active regressors set is delimited following a Bayesian information criterion (BIC) to provide a reduced-order model. The BIC rule stops the search by adding to the normalized mean squared error (NMSE) a penalty term to avoid overfitting [19]  
\[\text{BIC}(n_{a})=\text{NMSE}(n_{a})+\frac{n_{a}}{N_{s}}10\log(2N_{s}), \tag{11}\]  
where \(n_{a}\) is the number of active regressors and \(N_{s}\) is the number of signal samples used in the estimation. In that form, the optimum set of active regressors is identified, and the result is a pruned model. If the bivariate regressors are excluded in the initial stock, the search identifies a univariate Volterra model with limited precision. Then, it is possible to compare the capabilities of the two optimized models, the conventional and the bivariate Volterra models.  
The superior performance of models with \(n\)-even terms, i.e., bivariate Volterra regressors, has been already advanced for MP models [10; 11]. It is interesting to note here that the ET-PA fits the schematic of Figure 2 due to the presence of a mechanism to generate the envelope for the modulated supply voltage. A significant accuracy improvement has been reported in an experimental work when the model incorporates the GMP bivariate Volterra terms [21]. In this work, we also demonstrate the performance improvement attained with the bivariate Volterra model for two cases, a class AB and a class J PA.
## 3 Results and Discussion  
### Component Analysis  
The average particle sizes of the raw materials of kaolin, feldspar, and quartz were measured as 6.1, 122.5, and 6.8 \(\upmu\)m, respectively. Figure 2a shows the XRD patterns of the raw materials and specimens fired at different temperatures from 1200 \({}^{\circ}\)C to 1350 \({}^{\circ}\)C. The major phases of kaolin, feldspar, and quartz can be observed clearly in the XRD patterns of the precursor materials [28]. During the firing process, kaolin transforms to metakaolin at around 980 \({}^{\circ}\)C and the metakaolin transforms to mullite at around 1100 \({}^{\circ}\)C [29; 25]. Meanwhile, feldspar starts to melt at temperatures above 1100 \({}^{\circ}\)C [22]. The feldspar disappeared in all of the fired ceramic composites with fibers. Mullite and quartz were the major crystalline phases observed in the final composites fired at different temperatures from 1200 \({}^{\circ}\)C to 1350 \({}^{\circ}\)C [30]. The conclusions from the XRD characterization in the present study is consistent with that from the thermogravimetric analysis (TGA) data in a previous report [31].  
Figure 2: (a) X-ray diffraction (XRD) patterns of the raw materials and the obtained specimens fired at different temperatures, (b) Raman spectra of the carbon fibers sintering at different temperatures and (c) carbonization in and outside the ceramic.  
Raman scattering spectroscopy was used to investigate the carbonization degree of PAN during the one step firing process. Figure 2b,c show the Raman scattering spectra of the carbon fibers from different composites. Two peaks at 1350 cm\({}^{-1}\) and 1590 cm\({}^{-1}\), attributed to the D peak and G peak of carbon materials, were observed for all fibers fired at the four different temperatures, which demonstrates the successful carbonization of precursor PAN fibers. The D peak at 1350 cm\({}^{-1}\) can be attributed to the disordered and defect carbonaceous structure. The G peak at 1590 cm\({}^{-1}\) represents the ordered graphitic structure [32; 33]. R value as the intensity ratio of D peak (I\({}_{\text{D}}\)) and G peak (I\({}_{\text{G}}\)) is used to analyze the graphitization degree of carbon fiber. As the temperature increased from 1200 \({}^{\circ}\)C to 1350 \({}^{\circ}\)C, the R value increased from 0.94 to 1.15 (Figure 2b), which is typical of the onset of a carbonized structure [34]. R values of CF-1200 (extracted from Composite-1200) and PAN-1200 (PAN fiber directly carbonized without ceramic precursor, tension, and compression) were 0.94 and 1.10, respectively (Figure 3c). This implies that the ceramic precursor environment and the compression facilitate the formation of highly ordered carbonaceous structure.
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 9 Future Directions and Development Trends
### Fairness  
Bias and fairness, if not adequately addressed, pose serious societal implications in the form of biased language generation and its impact on some segments of society. Basis can creep into LLMs from several sources discussed below. The first source of bias, _dataset bias_, stems from the datasets that were used to train the LLMs. If the datasets contain biases related to race, gender, religion, or socioeconomic status, the models inherit and amplify them.  
Underrepresentation or misrepresentation of certain groups in the training data can lead to _representation bias_ and biased language generation. The LLM developers should have checks and balances to ensure that all perspectives are adequately represented in the datasets. Otherwise, the model will produce inaccurate or skewed output for underrepresented groups. If the training data contain stereotypes, models amplify stereotyping and perpetuate prejudices. Fairness across demographics is a complex challenge but essential for advancing LLMs.  
_Contextual bias_ stems from the context in which the language models are used. This poses severe and negative implications in applications such as recommender systems, employee hiring and promotions, clustering, and sentiment analysis. The model evaluation metrics and benchmarks used in traditional machine learning are inadequate to capture bias in LLMs. Comprehensive evaluation methods are needed to consider various aspects of bias in LLMs. A multifaceted approach is required to address bias and fairness issues in LLMs. Approaches to data curation, model development, evaluation strategies, and ethical issues need to be reexamined for their suitability for LLMs. Mitigating biases in the datasets using debiasing approaches such as modifying loss functions, altering training data distributions, and adversarial training requires LLM-contextualized research.
# A Bibliometric Overview of Twitter-Related Studies Indexed in Web of Science
## 4 Results and Discussion
### Performance Analysis
#### 4.1.1 Annual Scientific Production  
The annual scientific production (Figure 2) consists of four parts, productions by year, relative growth rate (RGR), doubling time (DT) and average citation rate (ACR). As we retrieved our bibliographic data in April 2020, the total number of scientific publications of 2020 is not complete, hence, we did not include the data of 2020 in this analysis. RGR represents the increase in thecumulative number of publications per unit of time (year), while DT refers to the required time for publications to become double the existing amount [40; 41], and the ACR represents the normalized number of citations per document. It should be mentioned that in this section, only bibliographic data with year information can be calculated, in our retrieved dataset, there are 297 documents have no such information, so the total number of calculated documents in this section is 18,474 (with publications of the year 2020 excluded).  
In general, the production of academic research kept increasing year by year, however, the number of Twitter-related publication of 2019 is less than 2018. The RGR and DT demonstrated that although the quantity of related research keeps growing, their growth rate and speed have been largely turned down in recent years. As for ACR, due to the very limited number of publications in the first three years, the ACR index in those years is considered meaningless, in general, the ACR presents a negative  
Figure 2: Annual Scientific Production, RGR = (ln c2 - ln c1) / (t2 - t1), ln = natural logarithm, cl = cumulative number of publications in period one, c2 = cumulative number of publications in period two. DT = ((t2 - t1)* ln 2) / (ln c2 - ln c1). ACR = citations / documents / years_since_publication. (**a**) Publications by year, (**b**) relative growth rate, (**c**) doubling time, (**d**) average citation rate.  
growth trending, it is understandable, because older articles tend to be more cited than new published articles [42].
## 2 Design Methodology
Optimized dimensions of inset slots in combination with a partial ground plane give an excellent wideband response. Figure 1a,b depict a simulated 3D view; the partial ground plane of the proposed MPA and Figure 2a shows the dimensions of substrate, patch, and feedline of the proposed X-band MPA and Figure 2b depicts the fabricated prototype view \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Full Form of** & **Dimensions** & \multirow{2}{*}{**Parameters**} & **Full Form of** & **Dimensions** \\  & **Parameters** & **(mm)** & & **Parameters** & **(mm)** \\ \hline \multirow{2}{*}{\(L_{S}\)} & Length of & \multirow{2}{*}{23.8} & \multirow{2}{*}{\(L_{f}\)} & Length of & \multirow{2}{*}{10} \\  & Substrate & & & & Feed Line \\ \hline \multirow{2}{*}{\(L_{G}\)} & Length of & \multirow{2}{*}{28.2} & \multirow{2}{*}{\(W_{f}\)} & Width of & \multirow{2}{*}{3} \\  & Ground & & & & Feed Line \\ \hline \multirow{2}{*}{\(W_{G}\)} & Width of & \multirow{2}{*}{9.6} & \multirow{2}{*}{\(L_{i}\)} & Length of & \multirow{2}{*}{5} \\  & Ground & & & & \\ \hline \multirow{2}{*}{\(L_{P}\)} & Length of & \multirow{2}{*}{13.8} & \multirow{2}{*}{\(W_{i}\)} & Width of & \multirow{2}{*}{3.6} \\  & Patch & & & & \\ \hline \multirow{2}{*}{\(W_{P}\)} & Width of & \multirow{2}{*}{19} & \multirow{2}{*}{\(M_{tP}\)} & Thickness of & \multirow{2}{*}{0.035} \\  & Patch & & & & patch \\ \hline \multirow{2}{*}{\(h\)} & Thickness of & \multirow{2}{*}{1.6} & \multirow{2}{*}{\(M_{t}\)} & Thickness of & \multirow{2}{*}{0.035} \\  & substrate & & & Ground & \\ \hline \multirow{2}{*}{\(W_{S}\)} & Width of & \multirow{2}{*}{28.2} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} \\  & Substrate & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Antenna design parameters. of the proposed wideband MPA that has the capability to satisfy X-band applications requirement.
## 3 Antenna Design and Analysis  
This section presents the details about the dimension calculation of the DR and the parametric study of the design along with the effects of the cylindrical air-gap and metal strips on the mutual coupling between the ports.
## 3 Architectural Layers and Digital Twin Integration for Headlamp Systems
### Role of DT in Virtual Validation  
Building upon the abstraction model introduced in the previous section, this part outlines the practical validation capabilities enabled by DT integration across the L0-L4 levels. The digital twin enables early-stage software verification, real-time interaction testing, and predictive analysis in both simulated and physical environments. In head-lamp system development, these capabilities facilitate a shift from hardware-dependent validation toward proactive, model-based strategies aligned with AUTOSAR-compliant ECU architectures.  
In practical terms, the proposed abstraction levels allow engineers to progressively develop and reuse models, test scenarios, configuration data, and validation results throughout the V-Cycle. For example, consider the case of an automatic headlamp function that activates an LED string. At the conceptual level (L0), the requirement is defined as a basic lighting behavior specification, described through logical flowcharts or functional diagrams without implementation. At the SiL stage (L1), the logic is implemented and tested in a virtual environment using simulated light sensor input. At L2 (HiL), the same logic is integrated with real hardware components--such as the ECU and lighting control unit--allowing engineers to verify switching behavior under real-time conditions and actual CAN messages. During L3 (test drive), the system is validated in a real vehicle under dynamic conditions, confirming performance under real-world scenarios. Finally, in L4 (DT), data from physical tests is reused to refine the digital representation of the system, enabling engineers to simulate similar lighting events without requiring physical testing setups. This structured flow ensures continuity across development levels and supports traceability, reusability, and increasing system realism.  
Figure 4: Architecture of digital twin in concept, SiL, and HiL—a schematic showing how the DT wraps around the test ecosystem (software + systems).
# Evolution and Testing of NB-IoT Solutions
## 4 NB-IoT Evolution
### NB-IoT in Release 16  
As commented before, NB-IoT specifications for Release 16 [23] are focused on network operation and efficiency improvements:  
* Mobile-terminated (MT) early data transmission (EDT) for user data.
* UE-group wake-up signals (WUS). The aim of UE grouping for WUS is reducing the false alarm probability.
* Transmission in preconfigured resources
* Scheduling enhancement: scheduling multiple DL/UL transport blocks with or without DCI (Downlink Control Information) for SC-PTM (Single-Cell Point-To- Multipoint) and unicast to support separate/share SC-TMCH (single cell multicast traffic channel) transmission.
* Network management tool enhancement: Including SON (Self-Organizing Networks) support for ANR (Automatic Neighbour Relation), random access performance, and RLF (Radio Link Failure) report.
* Inter-RAT cell selection: NB-IoT network may indicate frequency identifiers of neighboring eMTC/LTE/GERAN carriers to assist inter-RAT selection. eMTC/LTE network may indicate NB-IoT assistance information for inter-RAT cell selection.