esa-sceva/satcom-chunk-collection huggingface dataset:
# Construction and Evaluation of a Control Mechanism for Fuzzy Fractional-Order PID
## 2 Materials and Methods  
### Pneumatic System  
Pneumatic pressure is a critical variable in a variety of industries, including power plants, chemical reaction control, pneumatic position servo systems, well drilling, heating, ventilation, air conditioning systems, and automobiles. Due to the compressibility of air, load fluctuations, and external disturbances, the dynamic characteristics of pneumatic pressure plants are highly nonlinear. In addition, industrial pneumatic pressure plants are frequently interconnected and operate at various pressure levels. As a result of the uncertainties and non-linearity, precise control of a pressure plant is difficult. As a result, for trouble-free operation of the pneumatic system in industries, an effective control strategy is required. Because of their advantages, traditional PI and PID controllers have been widely employed in industrial applications in the past.  
Current FO control research trends point to the use of a fuzzy with FO control strategies to increase control performance. When it comes to building sophisticated industrial control systems, the rule-based fuzzy set theory gives you additional options. Linguistic notations are used in fuzzy set theory to concisely convey observations and construct a control framework. Without knowing the complete mathematical description of the system, the construction of a fuzzy logic controller (FLC) is becoming easier, even for more complex and nonlinear industrial processes. FLC is also used in conjunction with the FO controller to fine-tune parametric gains and ensure optimal performance in the face of nonlinearities, load disturbances, and changes in plant parameters. A fuzzy-based FOPID control method is developed for faster reaction and better control performance, taking into account all of these factors. A pneumatic pressure system model is built and the system performances are evaluated under load disturbances and changes in set-point circumstances to demonstrate the performance of the proposed control technique.
## 3 Results and Discussion
### TEM Characterization  
Catalysts Rh25 Ce25 and Rh250 Ce25 were selected for TEM microscopy characterization. Figure 8 shows, as an example, a representative micrograph of each catalyst and the particle size distribution of rhodium for Rh25 Ce25.  
In the catalyst prepared by conventional ramp calcination (Rh25 Ce25), RhOx particles smaller than 3 nm are distinguished. In this case, the number of RhOx particles which are clearly distinguished from  
Figure 8: TEM pictures of catalysts (**a**) Rh25 Ce25; (**b**) Rh250 Ce25 and (**c**) particle size distribution of rhodium for Rh25 Ce25.  
the ceria support is enough to make a rhodium particle size distribution. This distribution shows that most rhodium oxide nanoparticles present a size of 0.5-1 nm.  
In the catalyst prepared by flash calcination of rhodium nitrate (Rh250 Ce25; Figure 8b), RhOx particles have been hardly observed, assuming that most of them are much smaller than 1 nm, and evidencing a better dispersion of rhodium on this catalyst.  
As a summary, smaller RhOx particles are supported on ceria by flash calcinations of impregnated rhodium nitrate in comparison to conventional ramp calcinations. Catalysts prepared by this procedure are more active than the counterpart prepared in ramp, since the RhOx-ceria interaction is improved.
## 5 Discussions and Contributions
### Comparison with Other Personalization Approaches  
In this study, we introduce the Cross-Lingual Optimization Framework (CLOF), which dynamically adjusts gradient aggregation weights to enhance translation for low-resource languages. To better understand its significance, it is essential to compare CLOF with other personalization methods, such as meta-learning and domain adaptation, which aim to improve model generalization across tasks and domains. Meta-learning, for example, enables a model to learn how to adapt to new tasks quickly, but it often requires additional meta-learners and task-specific adaptation mechanisms, which can be computationally expensive and impractical in low-resource settings where data are limited.  
In contrast, domain adaptation focuses on fine-tuning models for specific domains using data from related domains. While effective in certain scenarios, domain adaptation relies heavily on sufficient data from the target domain. In low-resource languages, where data are often sparse or noisy, domain adaptation can struggle to perform well, requiring significant preprocessing and fine-tuning for each new domain, limiting its scalability in multilingual contexts.  
CLOF offers a lightweight and modular alternative. Unlike meta-learning and domain adaptation, CLOF does not require additional meta-learners or complex task-specific fine-tuning. Instead, it adjusts gradient aggregation weights during training to prioritize the low-resource language while still benefiting from high-resource data. This simplicity makes CLOF especially well suited for low-resource environments where computational resources are limited and the data are imbalanced. By providing a scalable solution for multilingual machine translation, CLOF ensures that models can be adapted more efficiently without requiring complex adjustments or large amounts of task-specific data.
# Numerical Integration Schemes Based on Composition of Adjoint Multistep Methods
## 2 Preliminary
### Composition of the Methods  
Let us consider a single step of numerical integration method with step-size \(h\) as discrete map \(F_{h}:R^{N+1}\to R^{N+1}\):  
\[\hat{y}_{n}=F_{h}(\hat{y}_{n-1}),\]  
where \(\hat{y}_{n}=(y_{n},x_{n})\). Then a composition of the methods is a composition of corresponding discrete maps \(F_{h_{1}}\) and \(\Psi_{h_{2}}\):  
\[\Psi_{h_{1}+h_{2}}=\Phi_{h_{2}}\circ F_{h_{1}}, \tag{4}\]  
The adjoint of a discrete map \(F_{h}\) is a discrete map \(F_{h}^{*}\) for which the next equation is true:  
\[F_{-h}^{*}\circ F_{h}=F_{-h}\circ F_{h}^{*}=I,\]  
where \(I(x)=x\) is an identity map. The numerical integration method is called symmetric if the corresponding discrete map is self-adjoint. Adjoint for simple numerical integration methods can be often found by the next rule of thumb:  
1. take step \(-h\)
2. shift indices such that the term \(y_{n}\) became the oldest
3. express the term \(y_{n}\)  
The second step can be skipped and performed only for uniform representation. For example, the following is a process of finding the adjoint to [PERSON]'s method (2):  
\[-h\text{:}\quad y_{n}=y_{n+1}-hf_{n+1}\] \[\text{shift:}\quad y_{n-1}=y_{n}-hf_{n}\quad.\] express: \[\quad y_{n}=y_{n-1}+hf_{n}\]  
The result is a well-known implicit [PERSON] method:  
\[y_{n}=y_{n-1}+hf_{n}. \tag{5}\]  
The direct and reverse compositions of (2) and (5) with stepsize \(\frac{h}{2}\) give a rise to midpoint rule:  
\[y_{n}=y_{n-1}+hf_{n-0.5},\]and trapezoidal rule:  
\[y_{n}=y_{n-1}+\frac{1}{2}h(f_{n-1}+f_{n}). \tag{6}\]  
Both methods are symmetric, and the latter case is an example of a symmetric multistep method obtained by the composition of two adjoint methods one of which is the linear 0-step method and another is the linear 1-step method. Therefore, the question arises: can one obtain new efficient numerical schemes by the composition of linear multistep methods with \(k\geq 2\)? Moreover, can the composition of adjoints be a stable basis for this operation?
# Fake It Till You Make It: Guidelines for Effective Synthetic Data Generation
## 4 Results and Discussion
### Question 4: Is Propensity Score a Good Indicator for Prediction Accuracy?  
Although various synthetic data generators have been developed in literature, empirical evidence of their utility has not been fully explored. Various measures for synthetic data utility exist in the literature, some are considered global measures (focusing on the features of the entire dataset) [41], others are analysis specific measures (to assess the utility in replicating a specific analysis done on real data) [30], and some are process related measures (these are measure related to the data generation technique used itself such as bias and stability [33]). The existence of one measure that accurately predicts synthetic data utility is beneficial on multiple fronts. It would allow data holders and scientists to concentrate on this one measure when assessing synthetic data utility. It would also enable data holders to include such measure in the data generation process as an optimization parameter.  
Propensity score, has been cited as the best measure for the global utility of synthetic data, as such, we examine empirically whether propensity can be a strong predictor of classification accuracy across the different synthesizers.  
Table 2 reports on the propensity scores as well as the difference in accuracy between real and synthetic datasets across all four machine learning algorithms. Propensity is taken for Path 2 (data synthesized from raw original data), and accuracy is taken for Case 3 (default settings) as tested on the real data.  
The correlation between these values (propensity and accuracy difference) is depicted in Figure 11 As expected, the correlation is always negative. As for strength, it is of moderate degree for SP-p, of low degree for DS and SP-np and weak for SDV [47].  
Table 3, reports on the overall propensity scores, accuracy difference and relative accuracy per synthesizer. The values represent the average across the 15 datasets and the four machine learning algorithms. The table is ordered by relative accuracy and AD. The overall results suggest an agreement between propensity and accuracy for all synthesizers except SDV (SDV shows better accuracy than SP-p, although it has slightly lower propensity).  
When taken together, the results suggest that propensity score may be a reliable forecaster for prediction accuracy for SP-p and DS, and less so for SP-np, but may not be a good accuracy predictor for synthetic data generated from SDV.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**SP-np**} & \multicolumn{2}{c}{**SP-p**} & \multicolumn{2}{c}{**SDV**} & \multicolumn{2}{c}{**DS**} \\ \hline  & **AD** & **Prop** & **AD** & **Prop** & **AD** & **Prop** & **AD** & **Prop** \\ \hline \(D_{1}\) & 1.031553 & 0.074972 & 3.4375 & 0.155349 & 2.038835 & 0.155198 & 0.039442 & 0.099199 \\ \(D_{2}\) & 0
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 3 Overview of Big Data
### Characterization of Big Data
#### 3.1.1 Volume  
_Volume_ is certainly the one best described by the term _Big_ in \"Big Data\". Volume refers to the amount of too-large information to be acquired, stored, processed, analyzed, and distributed by standard tools. This data volume is constantly growing. With the exponential growth in the number of electronic smart devices, the world is steadily becoming more connected. Moreover, connected objects will continue to grow over the coming years. For instance, according to computer company IBM, 2.5 exabytes, i.e., 2.5 billion gigabytes, of data were generated every day in 2012. This amount of data will reach 500 zettabytes by the end of 2019 [1]. Furthermore, some analyses report that, in the last two years, about ninety percent of these data has been generated. In most cases, most of the generated data come from the digital world. In 2013, IBM reported that about 12 terrabytes of tweets were created each day, and 5.2 billion daily Google searches in 2017. Table 1 highlights the volume of data generated by the digital sector. Volume is certainly the main criterion that comes to mind when we talk about Big Data. However, summarizing Big Data volume is too simplistic. There are other criteria, such as velocity and variety, to characterize Big Data.
# Power Grid Reliability Evaluation Considering Wind Farm Cyber Security and Ramping Events
## 5 Power System Reliability Assessment  
In this section, the model of wind power and wind power ramping is proposed. The output and states of WTs are further contained in a bi-level OPF model, which is used to quantify the consequence of a worse-case attack. Then, the flows of a system reliability assessment, considering both a worst-case attack and an IPA, are established.
# Low-Cost, Distributed Environmental Monitors for Factory Worker Health
## 2 Materials and Methods  
The objective of the monitor design was to support a sensor network of 40 units operating in a large manufacturing plant, with each sensor consisting of parts costing a few hundred dollars. The final design includes sensors for airborne particles, carbon monoxide, oxidizing gases, temperature, humidity, and noise integrated within a 20 cm \(\times\) 10 cm \(\times\) 7.5 cm sealed plastic case. These integrated devices are powered with a 5 V USB power adaptor and communicate wirelessly over the factory's existing wireless infrastructure using HTTP calls through the OpenWRT operating system. The devices are programmed to check the server and network status periodically and are able to recover from a network or power failure. Sensor values recorded by the monitors are stored in a central database. This report focuses on the design of the monitors and the distributed network.
# Integrated GNSS/IMU-Gyrocompass with Rotating IMU. Development and Test Results
## 3 Materials
### Description of Prototype Model  
To confirm the compass functionality and to estimate the expected accuracy we used a prototype model of the described integrated GNSS/IMU gyrocompass with separately installed antenna module and IMU. The prototype appearance is presented in Figure 4. The system prototype model includes* Compact GNSS compass with two GLONASS/GPS satellite receivers with a common clock and antenna baseline of about 0.18 m forcedly rotated by the electric drive by the harmonic law with the angle amplitude of 180\({}^{\circ}\) (position 1 in Figure 4);
* IMU on tactical grade FOGs (with drift level of 30\({}^{\circ}\)/h) and accelerometers (bias instability of 1 mg), forcedly rotated by electric drive by harmonic law with the angle amplitude of 180\({}^{\circ}\) (position 2 in Figure 4).  
Antennas in the prototype model were spaced by 0.18 m (Figure 3b). Doubling the antenna baseline possible in the created structure can further improve the attitude accuracy accordingly. Data sampling rate for gyros, accelerometers, and angle transducers of antenna module and FOG-based IMU was 100 Hz, and for GNSS receivers, 5 Hz. During the experiment it was not needed to rotate the GNSS compass and IMU by the same algorithm. Various control laws for the drives could be used. However, GNSS compass and IMU data were synchronized at least accurate to 200 ms.
# Mobility-Multihoming Duality
## 2 Combined Mobility and Multihoming
### Host Multihoming  
_Host multihoming_ enables the use of multiple connectivity available to the host simultaneously and continuously, for example, through multiple network paths or multiple PoAs. This has several applications, such as failover, resilience to Denial-of-Service (DoS) traffic attacks, and load sharing. Specifically for mobile devices, host multihoming may improve throughput using load sharing, as mobile connectivity might be constrained.  
Today, host multihoming is not well-supported with IPv4, and with IPv6, the support is present but limited. Similar to host mobility, it might need a middlebox approach, an application-specific mechanism, and/or special support from a network provider, with the constraints listed above.
## 2 Materials and Methods
### Image Acquisition and Processing  
The RGB images were acquired in three plots at different dates and with different equipment. Plots A and C were imaged with a multi-rotor UAV, the DJI Phantom 3 (DJI, Shenzhen, China) with an RGB digital camera--PowerShot S100 (zoom lens 5.2 mm; 12.1 Megapixel CMOS sensor; 4000 \(\times\) 3000 resolution). Plot B was imaged with a fixed-wing UAV, senseFly's eBee (senseFly SA, Lausanne, Switzerland) integrated with the senseFly Duet T camera, which has an RGB sensorSensefly S.O.D.A. (zoom lens 35 mm; 5472 \(\times\) 3648 resolution) and a thermal sensor that was not considered in this study. Table 1 summarizes information related to image acquisition, including equipment and flight configuration.  
Pix4 Dmapper Pro software (Pix4D SA, Lausanne, Switzerland) was used for the photogrammetric processing of the images acquired by the UAVs, using sequentially the following techniques: alignment optimization, construction of dense mesh of points, classification of ground points, elaboration of DEM-based data (such as the Digital Surface Model (DSM) and Digital Terrain Model (DTM)) and ortho-mosaics. In addition to the products generated by the software, the Canopy Height Model (CHM) of each plot was also computed. The CHM is the result of the subtraction of the DSM by the DTM, generating an elevation model that considers only the heights above the ground and eliminates the terrain slope. The canopy height information is important to define the separation between two  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Plot** & **Date** & **UAV** & **Fly Height** & **GSD** & **Overlap (\%)** \\  & & & **(m)** & **(cm)** & **(Front/Side)** \\ \hline A & 5 December 2019 & DJI Phantom 3 & 50 & 2.2 & 80/70 \\ B & 27 September 2021 & SenseFly’s eBee & 76 & 7.1 & 80/75 \\ C & 20 January 2020 & DJI Phantom 3 & 50 & 2.5 & 80/80 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the imaging flights of each studied plot.  
Figure 1: Location map of the study areas and details of the adopted plots.  
dense canopies. At Figure 2, one observes that from the RGB image at nadir, it is difficult to differentiate between the canopies even by visual interpretation. However, the altitude information of the canopies suggests the separation from the formation of the saddle point between the top points.
# Urban Rural Differences in Breast Cancer in New Zealand
## 3 Results
The hazard ratios for mortality using the Cox proportional hazards model (Table 4) showed that rural NZ European women have similar breast cancer-specific mortality and all-cause mortality compared to urban NZ European women after adjustment for age, cancer stage, tumour size, grade, hormonal status (ERPR), year of diagnosis, mode of detection and comorbidity. However, survival was poorer for rural Maori women compared with urban Maori women for both cancer-specific and all-cause survival. The unadjusted hazard ratio for breast cancer-specific mortality and the all-cause mortality for rural Maori compared with urban Maori was 1.31 (95% CI 0.97-1.76) and 1.33 (95% CI 1.05-1.68), respectively. The hazard ratio increased to 1.47 (95% CI 1.00-2.16) and 1.43 (95% CI 1.08-1.91), respectively, after adjustment for other factors. The proportionality assumption was tested for each of the 6 models shown in Table 4, with no significant departure from proportionality shown in all models except that of all rural versus all urban women for total mortality, where there
# A Digital Twin System for Adaptive Aligning of Large Cylindrical Components
## 3 Digital Twin Multi-Dimensional Modeling for the DT Aligning System
### Functional Modeling for the DT Aligning System  
Based on the analysis of the full aligning process of large cylindrical components, the basic process units of the aligning process include visual measurement, large component posture adjustment, aligning trajectory planning, aligning process execution, and aligning process monitoring. Based on IEC61499 function block (FB) technology [22; 23; 24; 25], these process units can be designed and packaged into corresponding FBs for adaptive planning and execution control of the aligning process. Thus, pose measurement FB (PM-FB), pose estimation FB (PE-FB), pose adjustment trajectory planning FB (PA-FB), aligning process execution FB (DP-FB), and process monitoring FB (PN-FB) are designed in this research.  
* PM-FB is used to measure key measurement points of the aligning system, establish coordinate systems for aligning platforms and large components, e.g., the global coordinate system, the workpiece coordinate system for large components, which provides a unified coordinate reference for the aligning process.
* PE-FB is to evaluate the optimal pose of large components and generate the optimal pose adjustment parameters in terms of considering the engineering constraint conditions of the as-built state of large components.
* PA-FB is utilized to generate and simulate the pose adjustment trajectory of the large component and transmit the verified adjustment trajectory to DP-FB to enable the control system to generate motion commands to drive the physical execution system to complete the aligning task of large components.
* PN-FB is mainly used to monitor and control the aligning process of large components, including the real-time monitoring of the pose change and aligning stress, and to predict and compensate for the aligning trajectory error based on the measured pose data, to ensure the quality of large component aligning.  
Note that PM-FB, PE-FB, and PA-FB are mainly used to plan the aligning process of the large component; on the other hand, DP-FB and PN-FB are mainly designed for aligning process execution and control, and these FBs are basic function blocks, which are high-level process templates that encapsulate process knowledge and algorithms, and they can be combined into composite functional blocks (CFBs) under a service-oriented event-driven mechanism (as shown in Figure 7); thus, a CFB for the aligning process of the large component is designed, i.e., DPLC-CFB, as shown in Figure 8, which can achieve adaptive aligning process planning and execution control of the large component.
Using the Oryx detector, tomography beamlines can acquire 1500 projections of 9 s each (with each consisting of 2048 \(\times\) 2448 pixels) at a data rate exceeding 1 GB/s [4]. Using these techniques, it is possible to study time-dependent phenomena for several weeks, accumulating an enormous amount of data. According to the statistics from the National Synchrotron Light Source-II (NSLS-II) [5; 6], solely in 2021, over 1 PB of raw data was generated, and the future data volumes are expected to further increase. Furthermore, it is expected that the High Energy Photon Source (HEPS) under development in China will generate, with its 14 beamlines, 24 PB of raw experimental data per month during the initial phase [7]. Therefore, due to the vast amount of data generated during these experiments, novel capabilities in terms of on-site real-time data analysis, processing, and interpretation at the beamlines are a crucial and urgent need for the synchrotron radiation users. Not addressing this issues may then result in a large portion of the users' data not being effectively analyzed, obscuring any potential scientific discovery hidden within these data [8].