esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy and Utility of Private Synthetic Data for Medical Data Analyses
## 3 Data Privatization Techniques  
As a prerequisite for our experiments, the definitions of several PETs will be recalled in this section. At first, more traditional techniques such as \(k\)-anonymity and \(l\)-diversity are described. Differential privacy is also presented, as it is used for comparison with synthetic private data generation, which is also outlined in this section. Following the \(k\)-anonymity and \(l\)-diversity data model, personal data can be categorized into at least three types [3; 15]:  
* Identifier: A data point that can be used to uniquely identify a person, e.g., a name or a personal ID.
* Quasi-Identifier: A data point that must be combined with other quasi-identifiers to allow inferences, e.g., zip code, age, gender.
* Sensitive attribute: A data point that holds some sensitive information about a person, e.g., a concrete illness.  
Please keep in mind that these types are not mutually exclusive. A concrete illness can be sensitive information and a quasi-identifier at the same time. Potentially, this could be a concern with genetic data analyses in the near future.
# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
## 4 Results
### Crop Classification Results
#### 4.1.3 Crop Classification Results Based on Sentinel-1 PolSAR Imagery (S1 Model)  
In this scenario, we use only the Sentinel-1 PolSAR features to train the SVM classifier. As expected, the model performs worse without the information of the optical sensor, but interestingly OA is quite decent, with a value of 82.83%. This result shows that polarimetric data are informative enough to adequately classify crops. The classification results of the each crop category are presented in Figure 6 (and Table S6). For some crop types in particular, i.e., maize, barley, sunflower, soft wheat, and rapeseed, the performance is very good. However, per-class accuracies for the all-year crop types (i.e., cheery trees, vineyards, and shrub grass), as well as the broad beans, are significantly low. On the one hand, this classification report demonstrates the rich information content of PolSAR data and showcases its great potential in crop mapping. On the other hand, these results reinforce the argument that the dual-polarized PoSAR data might have the ability to only partially describe scattering characteristics, providing models of limited accuracy.
## 2 Results and Discussion
### Results
#### 2.1.4 Male and Female Differences in RNAP II Processivity in the _Imp_ Gene  
Our data suggest a link between male-specific alternative splicing of _Imp_ pre-mRNA and higher accumulation of H3K79 me2. H3K79 me2 modification is tightly associated with active transcription [32, 33, 34]. Moreover, several observations have suggested a close relationship between RNAP II dynamics and alternative splicing [35, 36]. Based on these observations, we analyzed whether RNAP II processivity in _Imp_ differs between males and females. To this end, we utilized an observation previously identified by others that transcription by a slower mutant RNAP II results in an increase in the ratio between promoter-proximal and promoter-distal pre-mRNA [15, 36]. qRT-PCR of _Imp_ pre-mRNA with primer sets located at each end of _Imp_ (Figure 5A) showed that the  
Figure 4: Effect of _DOT1L_ knockdown on alternative splicing of _Bm-Sxl_ and distribution of H3K79 me2 around the alternatively spliced exon in _Bm-Sxl_. (**A**) Schematic representation of _Bm-Sxl_ showing the distribution of qPCR amplicons used in the analysis; (**B**) The ratio of _Bm-Sxl-L_ to _Bm-Sxl-S_ was analyzed by qRT-PCR. SD; \(n\) = 5 individuals. **_p_ \(<\) 0.01, Student’s _t_-test; (**C**) Western blotting analysis of H3K79 me2 protein extracted from negative control or _DOT1L_ siRNA-injected embryos using anti-H3K79 me2 and anti-H3 antibodies (lower left panel). Quantification of H3K79 me2 protein levels, as detected by Western blotting analysis (upper left panel). The intensity of each band was measured using Bioimage Analyser LAS1000. H3K79 me2 protein level was normalized to the H3 protein level. Values represent the means \(\pm\) SE of six bands from one representative of two independent experiments. **_p_ \(<\) 0.01, Student’s _t_-test. The same analysis was performed on H3K36 me3 protein extracted from negative control or _SETD2_ siRNA-injected embryos using anti-H3K79 me2 and anti-H3 antibodies (upper and lower right panels); (**D**) Mapping of H3K79 me2 (black) and H3K36 me3 (white) around _Bm-Sxl_ exon 2 in larval tissues by ChIP-qPCR. Values represent the means \(\pm\) SE of six qPCR values from one representative of five independent experiments.  
distal/proximal (D/P) pre-mRNA ratio was more than twofold higher in males than in females (Figure 5B). In contrast, no significant difference in the D/P ratio was observed in the control gene, _B. mori elongation factor-1 (EF-1)_ (Figure 5C). These data suggest that a link between male-specific alternative splicing of _Imp_ pre-mRNA and higher RNAP II processivity at this locus. As shown in Figure 5B, \(DOTIL\) knockdown reduced the D/P ratio in the male to a level similar to that in the normal female. Taken together with the ChIP-qPCR data indicated in Figure 3C, this result supports the possibility that higher distribution of H3K79 me2 across _Imp_ results in increased RNAP II processivity at this locus.
# Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data
## 6 Impact of the Butterworth Filter on Classification Performance
### Real-Time Processing and Embedded System Implementation  
The Random Forest model processes sensor data in real time, allowing for rapid classification of road events such as \"speed bumps\", \"sudden braking\", and \"potholes\". The computational time of the model per feature vector is minimal, enabling the system to make predictions rapidly. We balance the accuracy and processing speed of the model by adjusting the number of trees (\(N_{T}\)) and the depth of the tree, optimizing the performance without compromising real-time requirements. The optimized Random Forest model developed in this study was designed with the potential for deployment in embedded systems, particularly for real-time event detection tasks. Several optimization techniqueswere applied to balance model accuracy with the processing and memory requirements typical of systems with limited resources.
* Model Pruning: The tree depth was reduced to decrease memory usage and computational load, making the model more suitable for platforms with limited processing power.
* Feature Selection: Only the most important features were used, reducing the input dimensionality and improving the processing speed without significantly affecting the model's performance.
* Quantization and Optimization: Techniques such as weight quantization were applied to reduce memory and computational demand, improving the feasibility of deploying the model on embedded devices constrained by resources.  
These optimizations contribute to reduced latency and improved efficiency, making the model suitable for real-time applications such as vehicle event detection systems. In conclusion, the optimized Random Forest model provides a good balance of performance, speed, and efficiency, allowing for deployment in embedded systems without the need for specialized hardware.
## 3 Proposed Work  
This section details the methods employed in the design and implementation of the ML models used in this research. To identify the most accurate and efficient model for predicting the battery RUL, we will be using Python 3.13.3 for implementation and Google Collab as the development environment. The goal is to enhance battery management while supporting sustainable energy solutions by reducing reliance on resource-intensive physical cycling and degradation tests. This research adopts a comprehensive framework that integrates data-driven methodologies and advanced computational tools to develop machine learning models for accurately forecasting the RUL of Li-ion batteries. Beyond algorithm design and validating predictive algorithms, the study extends to building a web-based application for real-time RUL prediction, addressing gaps in predictive modeling, dataset utilization, and user accessibility. The research is guided by two main objectives which is developing reliable machine learning models to forecast RUL using advanced algorithms and degradation metrics and deploying a user-friendly web platform to make RUL prediction accessible to non-experts across various industries. The methodology integrates descriptive, diagnostic, and predictive analytics to derive actionable insights from the dataset. Descriptive analytics involves exploring degradation trends in battery features such as discharge time and voltage decrements through statistical summaries, visualizations, and correlation analyses. Diagnostic analytics focuses on identifying relationships between operational parameters and battery degradation by engineering features, identifying key predictors, and performing exploratory data analysis. Predictive analytics utilizes machine learning models like Random Forest with Extra Trees Regressor, Long Short-Term Memory (LSTM) networks, and Linear Regression to forecast RUL with high accuracy. To ensure seamless implementation, computational tools are integrated throughout the workflow. Python libraries like Pandas, NumPy, and Scikit-learn are used for data preprocessing tasks like cleaning, normalization, and feature engineering. Machine learning frameworks like TensorFlow 2.16.1, Keras 2.14.0, and XGBoost 1.0 facilitate model development and validation. For deployment, the Django and Streamlit framework enables creating an interactive web application, allowing users to input battery parameters and receive real-time RUL predictions in an operational environment. Insights derived from descriptive and diagnostic analytics inform model design, feature selection, and algorithm analysis. This ensures that the predictive models are aligned with real-world battery behavior and degradation patterns. By combining these methodologies, the research effectively bridges theoretical insights with practical applications, setting the foundation for scalable and user-friendly RUL prediction systems.
## 1 Introduction  
The cyclic rise and fall of the reservoir water level will cause huge changes in the slope's internal pore water pressure, and the shear strength of slope rock will also be deteriorated, which will easily cause slope damage and instability. Once the slope is affected by the change in reservoir water level, the deformation occurs by lightly pressing the hydraulic gate, affecting the normal operation of the dam. When the slope destabilization collapses, it seriously threates the safety of the reservoir and the residents in the reservoir area.  
The effect of water on the mechanical behavior of rocks has received extensive attention in the past decades, and several studies [1, 2, 3, 4, 5, 6, 7] verified that increased water content in rocks significantly reduces their strength. [PERSON] et al. [8] compared the geomechanical properties of carboniferous claystones immersed for 3 h and 6 h. The predicted correlation of different compositions and bulk densities in the rock masses on the geomechanical properties of the rock masses after immersion was determined. [PERSON] et al. [9] conducted compression and tensile tests on sandstones with different water contents and studied the water distribution in the rock using nuclear magnetic resonance technique to obtain the hydrogenic deterioration characteristics of sandstone. [PERSON] et al. [10], using the finite difference method, reproduced the formation and evolution of the gushing water channel in high water pressure mining with the advancement of the working face, and obtained the volumetric strain of the rock fracture surface by the interaction of the stress field and the seepage field. It is evident from the above studies that water content can largely affect rock properties. It is also an important factor that threatens the safety of many hydropower projects, such as high slope construction and operational safety.  
Many studies [11; 12; 13; 14; 15; 16] have analyzed and studied the changes in rocky slope stability under hydraulic action. [PERSON] et al. [17] found that sudden fluctuations in reservoir water levels are critical to slope stability. Meanwhile, [PERSON] et al. [18] studied the stability of slopes during reservoir impounding and obtained the conclusion that stability increases first and then decreases. It can be seen that the slope stability of the reservoir area affected by the water level is a great concern.  
In this paper, the slope of the inlet of the flood discharge structure of the Lianghekou Hydropower Project is taken as the research object, and the slope deformation characteristics are analyzed with GNSS monitoring data; the GA-LSSVM-BP back analysis method is used to study the changes of the rock mechanical parameters of the slope at different water impoundment stages. On this basis, a numerical calculation model is established to study the changes of high slope deformation and of the safety factor under practically different reservoir water level conditions.
## 2 Structure and Design
### Esiw  
ESIW lines [2] are a modification of SIW lines [4], where the dielectric material is replaced by air, improving certain aspects of device performance, such as reducing losses while maintaining integration capabilities. A basic ESIW consists of three layers: a central layer with inner cladding where the wave travels and two covers that contain the field within the waveguide. ESIW lines operate in mono-mode status determined by the width of the waveguide \(a\), which in this case is \(a=22.86\) mm, corresponding to the WR-90 standard waveguide, covering its frequency band (8-12 GHz).  
To measure the prototype, a planar-to-ESIW transition is needed. In this case, the microstrip-to-ESIW transition [23] was selected due to its high performance, making it well suited for this application. This transition affects the measurement and cannot be avoided because coaxial access is required for the Vector Network Analyser (VNA). In an integrated system within an ESIW, transitions would not be necessary, and the antenna would perform significantly better. The transition is modelled and simulated to meet the needs of the patch; since the patch does not require any special characteristics in terms of bandwidth or impedance matching, the initial simulation was sufficient, and no optimisation was needed for the transitions.  
This feeding ESIW line is composed of three layers: two covers (top and bottom) made of FR-4 substrate and a central layer made of Rogers 4003C substrate, with respective heights of 0.8 mm (_hcover_) and 0.813 mm (_hro_).  
Figure 1: Depicted ESIW prototype. Yellow represents the copper material and dark blue the dielectric substrate.  
Figure 2: Flow chart of the designing process.
## 3 Results
### Steady-State Results of the Optimized Design  
The steady-state simulation of the 3D LFMSR model was performed in two steps. The core region is 4.1 m in height and has a radius of 3.0 m. The first step is a steady-state case with only thermal hydraulics active. A second steady-state simulation is launched with neutronics and energy equations active and starting from the results of the first step. The spatial velocity and pressure distribution for the 3D reactor system is shown in Figure 5a,b. All steady-state results shown were gathered after the neutronics and energy solvers converged.  
The pressure profile is higher at the bottom because of the salt static pressure and the pump momentum head. The spatial-temperature distribution is shown in Figure 6a along with the density profile in Figure 6b. Linear dependence of density on temperature can be  
Figure 5: Steady-state velocity (**a**) and pressure (**b**).  
observed. As expected, the temperature is higher near the core outlet and the upper wall, and lower at the core inlet.  
The precursor distributions for delayed neutron groups (families) 1 and 8 are shown in Figure 7. These results are available because GeN-Foam solves the diffusion equation for the entire primary loop domain. Since the Group 1 precursors have the longest half-life (55.6 s), they do not decay immediately and are convected by the fuel salt from the core into the heat exchanger, then into the pump, and then back into the core again where they accumulate near the outlet in the upper region.  
Figure 6: Steady-state temperature (**a**) and density (**b**).
# Design and Optimization of a Neuro-Fuzzy System for the Control of an Electromechanical Plant
## 2 Electromechanical Plant  
Figure 2 displays the diagram of the electromechanical plant considered. This is composed of a DC motor, a gearbox, and a wheel (with moment of inertia) coupled to the gearbox. The analysis of this plant is presented in [31], corresponding to the example A-3-9 pages 95-97. The motor plays an important role in this plant since it is a fundamental part of the dynamic model, as it provides the required torque to move the inertia. The parameters of the DC motor are:  
* \(K_{t}\): Torque constant.
* \(K_{c}\): Electromagnetic feedback constant.
* \(R_{a}\): Shield resistance.
* \(L_{a}\): Shield inductance.
* \(J_{r}\): Motor moment of inertia.
* \(B_{r}\): Motor friction constant.  
It is noteworthy that the diagram in Figure 2 is given as an example since in a real case the gearbox has more gears. The motor torque is proportional to the current of the shield for the modeling of the system as indicated in the following equation:  
\[T_{M}=K_{t}i_{a}(t) \tag{1}\]  
The counter-electromotive force is proportional to the angular speed of the motor:  
\[e(t)=K_{c}\omega_{r}(t) \tag{2}\]  
Figure 1: Methodology employed.  
The equation that governs the electrical part is:  
\[u(t)-e(t)=L_{a}\frac{di_{a}(t)}{dt}+R_{a}i_{a}(t) \tag{3}\]  
The torques in the motor shaft are considered to incorporate the mechanical part into the model. The relation between the generated torque and the load torque is obtained by performing a summation of torques on the motor shaft as shown below:  
\[T_{M}(t)-N\,T_{L}(t)=J_{e}\frac{d\omega_{r}(t)}{dt}+B_{e}\omega_{r}(t) \tag{4}\]  
where:  
\[B_{e}=B_{r}+B_{m}N^{2};\qquad J_{e}=J_{r}+J_{m}N^{2};\qquad N=\frac{n_{1}}{n_{ 2}} \tag{5}\]  
In these equations \(J_{m}\), \(B_{m}\) are the inertia and friction of the mechanical part and \(N\) the transformation ratio of the gearbox such that \(\theta(t)=N\theta_{r}(t)\). In addition, Equation (6) is employed to complete the model.  
\[\omega_{r}=\frac{d\theta_{r}(t)}{dt} \tag{6}\]
# Design Criteria for High-Gradient Radio-Frequency Linacs
## 5 Design Considerations for High-Gradient Accelerating Structures
### Surface Magnetic Fields and Pulsed Surface Heating  
When a metal surface is exposed to a pulse of high rf magnetic fields, a small surface layer heats up and expands, thus creating mechanical stress near the surface [112]. When this stress reaches a value comparable with the yield stress of the metal, microscopic damage can be observed, which usually starts at grain boundaries [54]. Copper reaches this stress at about 50 \({}^{\circ}\)C of peak pulse surface heating. A simple formula, which was derived using linear heat propagation models, calculates the peak pulse surface heating for copper [53, 112]:  
\[\Delta T[^{\circ}\mathrm{C}]=127|H_{||}[\mathrm{MA/m}]|^{2}\sqrt{f_{rf}[ \mathrm{GHz}]t_{p}[\mu\mathrm{s}]}. \tag{1}\]  
where \(H_{||}\) is the surface rf magnetic fields, \(f_{rf}\) is the rf frequency, and \(t_{p}\) is the pulse length. I note a \(\sqrt{t_{p}}\) pulse-length dependence of the peak temperature. The typical pulse length of the X-band accelerating structures is 100...1000 ns.  
When designing accelerating structures, we want the rf magnetic fields to be low enough that the peak surface heating during the rf pulse stays below 50 \({}^{\circ}\)C to avoid any damage to the copper surface.
## 4 Evaluation
### Experimental Setup
#### 4.1.2 Baseline Techniques  
To evaluate the effect of TPL detection, the proposed method BBDetector is evaluated and compared with ModX algorithm [11] and B2 SFinder algorithm [7]. ModX is a state-of-the-art method for binary-to-binary TPL detection. It extracts syntactic features (strings literal, constant numbers) and semantic features (function call graph, functions accessing common data) to make the TPL detection. Due to ModX is not open source for the time being, we only compare it with its results presented in the paper. B2 SFinder is a state-of-the-art method for binary-to-source TPL detection. It extracts seven kinds of code features (String literal, Switch/Case, If/else, Export function name, String Array, Integer Array, Enum Array) to make a separate analysis and detection. In process of binary-to-binary TPL detection, only the first four types are applicable. In our experiment, we consider three kinds of features (String, Switch/Case, If/else) due to the binaries being stripped of function names.
# Drum Water Level Control Based on Improved ADRC
## 1 Introduction
For example, ADRC has been used in robot control [17; 18; 19], manipulator control [20; 21], aircraft control [22; 23], power system control [24; 25], etc. [PERSON] [26] proposed that the objective function should be composed of the variation of error and control variables, and the weighting factor of the objective function should be selected so as to turn the tuning problem of controller parameters into an optimization problem. [PERSON] [27; 28] estimated the control gain with the feed water flow formula, estimated the system state with the linear extended state observer, and took the measure of antiactuator saturation. These works have better antijamming ability, robustness, and control quality than traditional PID cascade control. They show the potential advantages and performance of ADRC in solving the classical industrial problem of drum water level control. At present, LADRC has a simple structure, relatively mature parameter tuning rules, and more convenient application than nonlinear ADRC. In this paper, based on the structure of LADRC and the advantages of LADRC, an improved linear active disturbance rejection controller (ILADRC) for drum water level is designed by adding the same linear extended state observer (LESO) and introducing the estimated error value of total disturbance [29]. The extended state observer (LESO2) and the original extended state observer (LESO1) adopted uniform parameters. Based on the bandwidth method of LADRC and the practical experience of tuning parameters, the tuning parameters method of ILADRC is summarized. This method is simple for adjusting parameters. The anti-interference performance and robustness of traditional LADRC and ILADRC for drum water level are simulated and analyzed under external disturbance and changing model parameters. The results show that compared with LADRC, ILADRC of drum water level control system has good dynamic characteristics, strong anti-interference ability, and strong robustness. In addition, its parameters are easy to adjust. Therefore, it has certain application value in engineering. The paper is organized as follows. Section 2 shows the design of the proposed ILADRC controller for the drum water level system. It includes the LADRC design and the ILADRC design. The performance comparison between ILADRC and LADRC is discussed in Section 3. It includes the simulation and analysis of the antidisturbance performance under the action of external disturbance and the mismatch of model parameters. Finally, conclusions are drawn in Section 4.