esa-sceva/satcom-chunk-collection huggingface dataset:
# An Economical and Precise Cooling Model and Its Application in a Single-Cylinder Diesel Engine
## 1 Introduction
In this paper, we propose an economical and precise model for an electric cooling system; the model is validated and applied to an electronically controlled single-cylinder engine called the DK32 [22] (Figure 1). The engine is equipped with an electronic unit pump and 4-valve in the valve train, among other components. The key parameters of the engine are as follows: bore and stroke = 113 mm\(\times\)140 mm, rated power = 23.5 kW @ 2600 rpm, max torque = 100 N\(\cdot\)m @ 1500 rpm, minimum brake-specific fuel consumption (BSFC) is 199 g/(kW\(\cdot\)h). Most importantly, the engine meets the China-IV emission standards [23], which set limits on particulate matter (PM) \(<\) 0.6 g/(kW\(\cdot\)h), CO \(<\) 5.5 g/(kW\(\cdot\)h) and CH + NOx \(<\) 7.5 g/(kW\(\cdot\)h). In this study, we demonstrate that the proposed economical and precise model of the electric cooling system without a thermostat is capable of precisely regulating the coolant temperature; this is an important aspect of ensuring good engine performance. The lumped-parameter model [24] is a commonly used model for cooling systems and is based on several assumptions that are too imprecise for a high-level engine. In general, there are several interactions between the cooling systems and the engine performances. Figure 1: The DK32 engine designed by Tsinghua University. Firstly, the cooling system consumes engine power, which is the direct effect. Secondly, the cooling system influences the temperature field of the engine body and further lubrication and friction, combustion, engine heat rejection, and charge density and temperature, which are the indirect effects. Many studies have been conducted on these separate issues but the universal relationship between the coolant temperature and the engine performance has yet to be determined; thus, in this study, we treat the engine as a 'black box'. We measure the coolant temperature and heat rejection in an engine bench system when the performance of engine is satisfactory then tune the cooling system according to the measurement of coolant temperature and heat rejection to obtain the expected engine performances. It is well known that an engine is a multi-variable complex system; other variables also need to be tuned according to the relevant data. The remainder of this paper is organized as follows. Section 2 describes the proposed mathematical model of the cooling system based on the experimental data of heat rejection and target temperature of the water jacket. Section 3 describes the model solutions to determine the speed of the fan and pump at different speeds and loads under the constraints of minimal total power consumption, and Section 4 examines the problem of overcooling and discusses the benefits of an electric cooling system. The final section concludes the paper.
In this paper, we present a novel methodology for rectifying curved text, targeted for OCR and computer vision applications. Utilizing generalized additive models (GAMs), our proposed method accurately estimates text curvature and converts it into a straight format, thereby significantly enhancing text recognition accuracy. The process includes image binarization techniques, morphological operations, curve estimation, and the application of the Bresenham line drawing algorithm [18]. The proposed methodology can be integrated into a broader document image processing pipeline, working synergistically with other advanced techniques such as text line segmentation [19; 20; 21], scene text detection [11; 12; 21], and scene text recognition [5; 8; 11; 12; 13]. We demonstrate that by incorporating our method into such pipeline frameworks, the overall accuracy and efficiency of the entire document processing workflow can be enhanced.  
Many existing methods struggle with highly curved or distorted text images due to geometric constraints, perspective, and curvature distortions, often failing or encountering difficulties in transforming the text accurately. This work aims to address the challenges of text recognition, particularly with diverse text types in many arbitrary-shaped scene texts such as multi-oriented, perspective, horizontal and curved text. This is attributed to the use of a specialized family of mathematical models that can generalize effectively to multi-oriented, perspective, and curved text without the need for feature engineering, feature extraction, or training. It serves as a plug-and-play solution.  
The rest of the paper is structured as follows: Section 2 presents the dataset and methodologies, which include an elaborated explanation of the Bresenham line algorithm [18] and application of generalized additive models for curve estimation. Section 3 describes the proposed methodology in detail, explaining the different steps of image pre-processing, control point extraction, and the final process of aligning text. The results and implementation details are presented in Section 4, along with the performance evaluation concerning real-world and synthetic curved text samples. Finally, Section 5 discusses the findings, evaluates the impact of the proposed method on OCR accuracy, and outlines future research directions.
## 2 Related Work
### Ensemble Methods  
Ensemble methods improve the overall performance of models by combining the predictions of multiple base learners. These methods have been widely applied and extensively studied in the field of natural gas demand forecasting [14]. The primary ensemble methods include boosting, bagging, and stacking.  
Boosting combines multiple weak learners to form a strong learner by continuously adjusting the weights of the samples, ensuring that subsequent weak learners focus more on the samples misclassified by previous weak learners [15; 16]. [PERSON] et al. [17] applied the boosting method (AdaBoost) to enhance the accuracy of short-term natural gas consumption forecasting, demonstrating outstanding performance, particularly with complex short-term consumption data. Bagging, in contrast, is an ensemble method that trains multiple base learners using several resampled training sets [18; 19]. [PERSON] et al. [20] implemented bagging with random forest to reduce the variance of the natural gas demand forecasting model and improve its accuracy, outperforming individual models.  
Stacking has demonstrated superior integration capabilities and predictive performance in various fields, such as electricity load forecasting [21][PERSON] However, an extensive literature search reveals that no research has yet applied the stacking ensemble to natural gas demand forecasting.  
In natural gas forecasting, the integration strategies of learners in ensemble learning mainly include simple averaging, weighted averaging, and voting methods (such as majority voting and weighted voting) [22; 23]. [PERSON] et al. [24] [PERSON] combination methods to [PERSON] the predictions of base models, including time series models, regression models, and neural networks, to enhance overall natural gas consumption forecasting performance, consistently outperforming individual models.  
It is generally believed that bagging primarily improves performance by integrating predictive models obtained from different perspectives. Boosting, in contrast, focuses on integrating a series of models by identifying and correcting failed predictions to achieve better prediction accuracy. Stacking, a third approach, derives the final result through secondary learning of the base learner's outputs, making it particularly effective for problems where nonlinearity is challenging to express.
## 2 Vehicle State Estimation  
The accurate and reliable state estimation is essential for the high-precision tracking control of autonomous driving. The traditional method is implemented by designing the estimator based on tire force estimation with the vehicle dynamics model. Considering that the related vital parameters are difficult to update, the tire model's precision is unreliable. With the advantages of onboard sensing and measurement equipment of autonomous vehicles, the vehicle state estimator is carried out by fusing the information of IMU and GPS with the vehicle dynamics model.
# The Tractor-Cart System Controller with Fuzzy Logic Rules
This study aims to address the automatic control of a towed cart by designing a fuzzy logic based adaptive control algorithm and controller to steer the lateral and longitudinal position of tractor and its towed cart in combination. The objective is to minimize viable position errors of the whole system while driving between plant rows. An analysis of previously reported research showed that a fuzzy logic-based control method applied in different control problems has been found to be an appropriate tool because of its simplicity and adaptability to varying conditions and control parameters. These features of the fuzzy logic-based controller approach motivated this study to apply the fuzzy logic controller principles in designing an adaptive controller for a tractor-cart steering system. Mathematical formulations of tractor and a cart motion are developed based on a kinematic bicycle model. A novel fuzzy logic based adaptive control algorithm is developed to steer the motion of the cart ensuring the motion of the tractor-cart system in accordance with the desired trajectory. To the best knowledge of the authors, the idea of using an adaptive fuzzy logic-based control algorithm for cart motion control of the tractor-cart system was never studied before. The cart's position is controlled with additional steering torque actuated by a pair of hydraulic cylinders with respect to the difference between its current position and reference pattern. The proposed hitch hydraulic design along with its mathematical model formulation and numerical simulations have never been undertaken before. The novel adaptive fuzzy logic based steering control algorithm with the designed hydraulic drive is validated via numerical simulations in the MATLAB/Simulink (MathWorks, Natick, MA, USA, 2019) environment. The advantages and disadvantages of proposed method are highlighted. This paper is organized in the following order: (1) Section 2 describes the derived equations of motion of the tractor-cart system and the designed active hydraulic hitch. (2) Section 3 explains the proposed control algorithm. (3) Section 4 presents the proposed fuzzy logic rules. (4) Section 5 describes the hydraulic cylinder design. (5) Section 6 presents simulation results of the developed controller in a MATLAB/Simulink environment, and (6) Section 7 highlights discussions of the study results.
# Integrating Geometric Dimensioning and Tolerancing with Additive Manufacturing: A Perspective
## 8 GD&T in AM Assembly Parts  
As widely recognized, GD&T plays a crucial role in ensuring the dimensional and geometric accuracy of assemblies by allowing the control of both dimensional and geometric tolerances by leveraging ISO 2692-2001 [37] and ASME Y14.5 - 2018 (R2024) [38] rules listed in Table 1.  
The aforementioned rules are clearly applicable to 2D drawings, ensuring the compliance of the manufactured parts to be assembled with their designed functional scope. Since AM parts are purely model-driven manufacturing processes [39], the role of 2D drawings in designing AM parts is of little value. Therefore, GD&T annotation practices should move toward 3D annotations. This is a big challenge in the GD&T field, especially due to complex geometries, e.g., for Topologically Optimized parts. For these parts, geometric and dimensional annotations may not be able to capture either the complexity of the manufactured surfaces or the surface of mating features. To partially solve this issue, GD&T practices are moving from purely symbolic definition to semantic CAx-integrated annotation, the latter being this intended for computer-based use.
# Key Issues on Integrating 5G into Industrial Systems
### The Necessity of 5G Integration with Industry  
In industry, most executives and experts believe that 5G networks can provide solutions to industrial applications in terms of Ultra-Reliable Low Latency Communication (URLLC) and massive Machine Type Connection (mMTC) [17; 18]. The advancement of the 5G standard, particularly 3 GPP Release 18, has significantly contributed to the enhancement of cross-sector applications [19]. Indeed, 5G networks can provide more comprehensive bandwidth connectivity for I4.0 environments, which entail extremely fast and tremendous data transmission [20].  
In the feature, 5G can meet the requirements of the industrial Internet in terms of connectivity diversity, performance differentiation, communication diversity, high-speed data acquisition, remote control, stable and reliable data transmission, business continuity, and other conditions in industrial scenarios, thus helping deliver digitalized, networked, and intelligent industrial Internet. In a word, the profound combination of 5G mobile communication technology and industrial Internet can promote the rapid development of the latter.  
Despite its broad application prospects in industries, 5G still faces numerous challenges in its industrial application. Technically, 5G has not yet fully met the stringent requirements of industrial field communication; in terms of cost, there is still a significant gap to traditional industrial communication methods and in terms of system integration, the introduction of 5G imposes new requirements and challenges for the existing stable industrial models [21; 22; 23]. By exploring key technologies for the integration between 5G and industrial systems, this study aims to solve the current problems and promote further application of 5G in the industrial field.  
This paper is organized as follows: Section 2 analyzes industrial system communication networks; Section 3 discusses the industrial applications that require wired or wireless communication; Section 4 uses a typical field bus communication protocol, i.e., Modbus, to illustrate how 5G could match the real-time support by field buses; Section 5 details the key findings of this study with regard to how 5G would be applied in industrial settings; and finally, Section 6 concludes this study.
# GDPR and Large Language Models: Technical and Legal Obstacles
## 1 Introduction
### Data Protection by Design and by Default  
Article 25 of the GDPR requires that data controllers implement data protection measures from the very beginning of designing data processing systems, ensuring that by default only the personal data necessary for each specific purpose is processed [17]. This principle calls for the proactive integration of privacy measures throughout the development lifecycle.  
In the case of LLMs, integrating data protection by design poses significant challenges. The intricate and complex architectures of these models make it difficult to seamlessly embed data protection measures. Moreover, LLMs often require continuous updates with new data, complicating the consistent maintenance of data protection standards. There is also a delicate balance between enhancing data protection and maintaining model performance, as robust privacy measures may sometimes adversely impact functionality [22; 39; 54].  
To mitigate these issues, organizations are adopting a range of strategies. Techniques such as differential privacy, federated learning, and homomorphic encryption are being utilized to safeguard personal data during both training and inference phases. Additionally, designing models with modular components that can incorporate privacy measures independently, and integrating automated compliance tools into the development lifecycle, are promising approaches to ensure that data protection is woven into the fabric of the model from the outset.  
Potential Solutions  
To effectively implement data protection by design in LLMs, organizations can adopt the following approaches:* **Privacy-Preserving Techniques**: Utilizing methods such as differential privacy, federated learning, and homomorphic encryption to protect personal data during training and inference [54; 55].
* **Modular Design**: Designing models with modular components that can incorporate data protection measures independently [56].
* **Automated Compliance Tools**: Integrating tools that automatically enforce data protection policies throughout the model development and deployment lifecycle [41; 42].  
These measures facilitate the integration of data protection principles into the core design of LLMs, ensuring compliance with GDPR from the ground up [17].  
Figure 2 illustrates a comprehensive Privacy-Preserving LLM Development Pipeline with integrated GDPR compliance measures. The diagram visually maps out the end-to-end process--from data collection and privacy assessment to data processing, model training, evaluation, compliance documentation, deployment, and continuous monitoring. By embedding privacy-preserving techniques and automated compliance checks at each stage, the figure reinforces the modular design and integrated approach needed to meet GDPR requirements in LLM development.
## 5 Applications
### H\({}_{2}\) Storage  
Metal-decorated GR, like Ca-decorated GR, has shown promising potential for hydrogen storage. Studies have found that Ca binds strongly to GR due to the presence of the px-py\(\gamma\)/\(\pi\)* states, and each Ca can bind up to six H\({}_{2}\) molecules (Figure 21a,b), resulting in a maximum hydrogen storage capacity of 9.6 wt% with an average hydrogen binding energy of \(\sim\)0.2 eV, which is suitable for practical applications. Moreover, Ca-decorated GRs have been found to be thermodynamically stable. In comparison with G, Ca-decorated G has a lower hydrogen storage capacity of 8.4 wt%, while on the other hand, Li-decorated GR shows higher values w.r.t G [155]. Li-decorated GR is also a promising system for H\({}_{2}\) storage, as Li has the smallest atomic mass among different metals, and it has a high binding energy with H\({}_{2}\) (Figure 21c,d). In fact, the maximum storage capacity reported for Li-decorated GR is 18.6 wt%, as shown by Guo et al. [156].
# Visual Tracking Control of Cable-Driven Hyper-Redundant Snake-Like Manipulator
## 3 Kinematics Modeling and Control Methodology
### Forward Kinematics Analysis
#### 3.2.2 Mapping Relation between Cable and Joint  
As shown in Figure 5, \(P_{i,j}\) represents the mounted point of \(j\)-th\((j=1,2,3)\) cable on the rear endplate in the Frame \(F_{i}\) and the counterclockwise angle \(\varphi_{i,j}\) can be derived as Equations (12) and (13) where \(M\) represents the total number of the holes in an endplate.  
\[p_{i,j}=[R\times\cos\varphi_{i,j},0,R\times\sin\varphi_{i,j}]^{T} \tag{12}\]  
\[\varphi_{i,j}=i\times\frac{360^{\circ}}{M}+(j-1)\times 120^{\circ} \tag{13}\]  
The through hole (instead of the mounted point if this cable is used for driving subsequent tubular unit) is denoted as \(p^{{}^{\prime}}_{i,j}\) in the Frame \(F^{{}^{\prime}}_{i}\) and the Euclidean distance from \(p_{i-1,j}\) to \(p^{{}^{\prime}}_{i,j}\) denoted as \(l_{i,j}\) which represents the cable's length between the \(i\)-th tubular unit and the \((i+1)\)-th one in the condition of considering the cable is always straight through  
Figure 5: The cable’s mounted point on the rear endplate.  
the tube and ignoring its deformation. The total lengths of cables in k-th tubular units can be derived as Equation (15).  
\[l_{i,j}=\left\|\dot{l}_{i-1}T\times p_{i,j}-p_{i,j}^{{}^{\prime}}\right\| \tag{14}\]  
\[L_{m,j}=\sum_{i}^{m}(l_{i,j}+H)\qquad m=1,2\ldots N \tag{15}\]  
\(L_{m,j}\) is formulated as a function \(f(\varphi_{i,j},\alpha_{i},\beta_{i})\) once all the cables' lengths are given, each joint angle \([\alpha_{i},\beta_{i}]\) will be derived uniquely and definitely with respect to Equations (10)-(15).
## 6 Local Rainfall Rates and Experimental Verification  
As seen in the global rain-rate map of Figure 17, the Central and Eastern European countries fall mainly into the rain-rate zones of 25 mm/h, 32 mm/h, and 42 mm/h, called E, H, and K zones in the ITU recommendations. In the planning phase, the rainfall rate is often approximated with one single value for medium and small countries, i.e., 42 mm/h for the entire territory of Hungary, following ITU-R P.837-1 [37].  
Figure 16: Recommended hop lengths as a function of rainfall intensity. (**a**) Hops with vertical polarization, 1 GHz channel and \(\circ\) 12 cm dishes. (**b**) Hops with vertical polarization, 1 GHz channel and \(\circ\) 39 cm dishes. (**c**) Hops with vertical polarization, 1 GHz channel and \(\circ\) 69 cm dishes. (**d**) Comparison of vertical and horizontal links at the different modulation modes. (**e**) Comparison of hops with different dish sizes. Modulations are quadrature phase-shift keying (QPSK) and 128-QAM. (**f**) Example of monitoring the local and remote received signal level (RSL) and modulation modes.  
It is worth mentioning that advanced link planning tools provide the possibility to define rain-rate polygons inside a country when more accurate data are available. Even for medium- or relatively small-size European countries, the rainfall rate may change significantly inside the country. Switzerland is shown in Figure 18a as an example, where the \(R_{0.01\%}\) rain rates vary from 26 mm/h to 46 mm/h in the different rain zones. Research activities are ongoing in several countries to determine accurate local rainfall rates, specific for the region. Fixed \(\mu\)/mmW links are monitored together with meteorological data continuously. There are also remarkable long-term rain-rate measurement activities in the Czech Republic and in Hungary [12; 44; 52; 53]. Rainfall and RSL results are stored with high accuracy and time resolution. The statistical evaluation of rain attenuation is based on the long-term data collected experimentally. Figure 18b shows significant rainfall intensity differences calculated for Hungary [53]. Long-term trial and operator network experience were also published for the 58 GHz and 73 GHz links [44; 45; 52; 53].  
Figure 17: Global map of rain-rate zones, reproduced with permission from RACOM [51].  
Figure 18: \(R_{0.01\%}\) in mm/h, the rainfall intensities exceeded 0.01% of the time. (**a**) Rain-rate contour lines in Switzerland. (**b**) Rain-rate regions in Hungary as calculated in Reference [53]. The figure is reproduced with permission from [PERSON].  
A very accurate meteorological report is available, e.g., for Norway [54]. Rainfall intensities exceeded for 0.5%, 0.1%, 0.01%, and 0.001% of the time were measured over several decades. Such an accurate report is very helpful in the availability design of \(\mu\)/mmW links (Figure 19).