esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Femtosecond Laser Direct Writing of Gradient Index Flat Lens
### Phase Variation with Laser Energy and Writing Speed  
GRIN Fresnel optics are more easily described by their phase variation rather than by their refractive index changes because these Fresnel optics are defined by a phase variation \(\Delta\varphi=2\pi\) rad for a first-order Fresnel lens. To write a GRIN Fresnel plate, we needed to know the precise phase variation implemented by the fs-laser as a function of the writing speed and the laser pulse energy. Therefore, several laser-written disks were made at varying energies and speeds within the type I regime. The phase variations of these disks were then measured using Quantitative Phase Microscopy (QPM) [42], and the results are reported in Figure 7 as a function of laser pulse energy. This technique consists of using the transport of intensity equation (TIE), which relies on intensity variation and phase variation. Using an optical microscope, a set of images was taken: one in-focus and two very slightly (a few microns) positively/negatively defocused [43]. Phase images given by QPM of the three laser-written simple disks are shown in Figure 7.  
We notice that a strong phase variation appeared at very low energy in our material, illustrating the technique's capability to write a significant phase variation even at 1 mm/s scanning speed. Higher writing speeds were investigated (up to 25 mm/s, as shown in Figure 6) with success, but a dedicated setup (higher acceleration stages or beam steering mirrors) would be more appropriate for accurately writing such complex 3D shapes at high speed. Therefore, we mostly used a 1 mm/s writing speed in our investigations.  
The results presented in Figure 7 confirm a very high photosensitivity of GeS\({}_{2}\)-based chalcogenide glasses to fs-laser writing. Indeed, phase changes up to 38 rad were implemented. A typical laser track length of around 100 \(\upmu\)m corresponds to a refractive index change \(\Delta\)n of about \(5.5\times 10^{-2}\) with respect to pristine glass. Preliminary annealing  
Figure 7: Phase variation implemented by fs-laser direct writing at a writing speed of 1 mm/s. Inset: quantitative phase map examples measured in 75(GeS\({}_{2}\)) \(-\) 15(In\({}_{2}\)S\({}_{3}\)) \(-\) 10(CsCl) using QPM technique for the different regimes (Type I, transition regime and spatial broadening regime). _Parameters: 1030 nm, 800 fs, 100 kHz, 0.6 NA, 1 mm/s, linear polarization. QPM wavelength: 550 nm_.  
tests revealed that a thermal treatment of 3 h at 300 \({}^{\circ}\)C (typically around 0.8 Tg for our 75(GeS2) \(-\)15(In2S3) \(-\)10(CsCl) sample) led to a 30% erasure only, which was mostly attributed to stress relaxation and related photo-elastic changes. In these conditions, we can expect a lifetime of at least 10 years at 50 \({}^{\circ}\)C with no significant decay (below 1% erasure) of the refractive profile, provided the optical component has been previously stabilized to erase the unstable part of the refractive index changes [37].
# Brake Torque Sensor Calibration Device
## 7 Discussion  
As can be seen from Figures 10-16, the original calibration method worked with relatively large differences between the measured values. The smallest differences were measured in the case of a required torque value of 4 Nm. The difference between the maximum and minimum deviation from the desired value was 0.076 Nm. On the contrary, the values at the required torque values of 2 and 9 Nm represented the largest differences, where the maximum and minimum torque values were 0.212 and 0.225 Nm. The distribution of the measured values in relation to the desired value also played a significant role. While in calibration values at 2, 3, 7, and 9 Nm, the measured values oscillated around the required torque values; with calibration at 1 Nm, almost all measured values were above the required value, and in the case of calibration at 4 and 5 Nm, all measured values were below the required value of the torque. The variance values of the measured torque values reached a maximum in the case of calibration at 9 Nm (0.00527 Nm) and a minimum in the case of calibration at 4 Nm (0.00059 Nm). On average, the variance of the original calibration method was 0.00275 Nm.  
The new calibration method proved to be much more accurate. The differences between the maximum and minimum deviations of the measured values from the desired value were only from 0.004 to 0.007 Nm, with the highest differences found with calibration at torque 1 (0.006 Nm) and 2 Nm (0.007 Nm) and the smallest difference of 0.004 Nm in the remaining measurement moments. The variance values for the new method were very small; they ranged from 1.16 \(\times\) 10\({}^{-6}\) when calibrated to 7 Nm and to 3.56 \(\times\) 10\({}^{-6}\) when calibrated to 1 Nm. It can be seen from this that the measured values were very close to the required values and showed a high measurement stability.  
The proposed new torque sensor calibration method has positively contributed to the accuracy of torque sensor calibration, which will significantly increase the reliability and accuracy of measuring the residual torque of automobile brakes. Due to the method's simplicity, it can also be used when calibrating other torque sensors used for various purposes, of course when checking the stability of the calibration wheel strip for the expected load.  
Figure 17: Scheme of the original calibration device.
## 5 Experiments
### Evaluation  
Following previous work, we used Exact Match and Partial Match precision(P), recall(R) and F1-measure(F1) as evaluation metrics in our experiments. In particular, we used Exact Match in dataset COER and Partial Match in dataset SpanSAOKE. Since there are some nested cases in the dataset SpanSAOKE, the predicate may be rewritten during the manual labeling process. However, the extracted predicate by our mode is the original fragment in the sentence. Therefore, Partial Match was used in the dataset SpanSAOKE evaluation.  
For Exact Match, the starting and ending positions of each element of the extracted triplet must be exactly the same. For Partial Match, the criterion for judging whether two triples \(T=(s,p,o)\) and \(T\prime=(s^{\prime},p^{\prime},o^{\prime})\) match is to satisfy: (1) \(g(s,s^{\prime}),g(p,p^{\prime}),g(o,o^{\prime})\geq\delta\) or (2) \(g(Cat(T),Cat(T^{\prime}))\geq\delta\), where \(g(*,*)\) is the gestalt pattern-matching function [36], \(Cat(*)\) concatenates triple components as a whole string and the threshold \(\delta=0.85\).
# Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model
## 4 The Proposed System
### Analytics Tier Structure  
Data analytics is the core tier of the system because it enables the data to be analyzed and used for further predictions [48]. This tier uses real-time analytics to perform analysis of any events right after their occurrence. This process requires an efficient structure to monitor many events to perform an efficient analysis [49, 50]. In this context, the big data must be partitioned to evaluate the model. Data partitioning is a critical process that makes the data more powerful by dividing them into smaller pieces. This data are then used for various purposes to improve data functionality, such as improvements in prediction accuracy [51]. Our study suggests using the Knime analytics platform in the analytics tier to make the system more efficient. Knime can perform real-time analysis of high-volume data and predict power consumption through its many plugged-in machine learning and artificial intelligence algorithms, depending on specific purposes. One of the features is to forecast future energy consumption and conditions. Figure 3 shows the top view of the process flow in the PIAS analytics tier.
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 5 Simulations, Results, and Discussions
### Factors for HSN Bandwidth  
#### 5.5.1 Simulation  
This section primarily analyzes the main factors influencing the bandwidth of the hybrid network. The determining factors for both the theoretical maximum bandwidth and the actual operational bandwidth from practical perspectives are examined. The first simulation is designed to study the effect of the number of wireless links, where the number of nodes in each access network is six. The small cycle period is set to 1 ms, 20 ms, 30 ms, and 40 ms, respectively. Other parameter settings are consistent with those described in Section 5.2. The second simulation aims to observe the impact of the cycle period and buffer size. Most parameters are kept the same as in the first simulation, except for the number of wireless links, which is set to 24, and the range of buffer sizes that have been modified.
## 1 Introduction: Pathologies of the Orbit and the Aim of This Study
### Methods and Objective of This Study  
The search approach complied with PRISMA criteria. We conducted a systematic literature review utilizing the PubMed, Scopus, and Web of Science databases, encompassing the timeframe from January 2015 to April 2025. The subsequent keywords utilized were as follows: ('artificial intelligence' OR'machine learning' OR 'deep learning') AND ('orbit' OR 'orbital pathology' OR 'orbital tumor' OR 'orbital fracture' OR 'orbital inflammation'). We incorporated authentic publications in English that reported on AI applications in orbital diseases from January 2015 to April 2025. The exclusion criteria included review papers, non-orbital applications, and studies that did not include diagnostic performance data. Supplementary filters encompassed studies involving human participants, diagnostic imaging, and the English language. Two reviewers separately performed the study selection, while a third addressed any differences.  
This study aims to systematically evaluate the clinical applications of AI in the orbital setting by analyzing the type of models used, the input data employed, and the diagnostic or predictive efficacy obtained, with a focus on the validation methods and clinical outcomes reported.
## 4 Construction of Prediction Model  
The construction process of the prediction model is shown in Figure 1. First, 80% and 20% of the UBC data were selected based on statistical consistency as training and test sets, respectively. Second, using the optimal hyperparameters configuration, the prediction model was fitted based on the training set using the trial-and-error method. We adjusted the hyperparameters so as to maximize the likelihood of the training data. We initialized the hyperparameters to random values (in a reasonable range) and then used an iterative method to search for optimal values of the hyperparameters. Numerous trials were carried out to find optimal values of primary kernel parameters--omega (\(\omega\)) and sigma (\(\sigma\))--and the values were 1 in the GPR model. Fourth, the test set was adopted to judge the performance of the proposed GPR model according to the four standard statistical measures: \(R^{2}\), \(RSR\), \(NSE\), and \(MBE\). Higher values of \(R^{2}\) and \(NSE\), and lesser values of \(RSR\), indicate a better estimation accuracy of the proposed model. Last, the developed model was compared with the commonly used bearing capacity theories by comparing the comprehensive performance if the prediction performance of this model was acceptable, then it could be adopted for deployment and made sensitivity analysis. The entire calculation process was performed in Waikato Environment for Knowledge Analysis (WEKA) software [48]. In this study, for the GPR model, the Pearson VII function-based universal kernel [49] was used.
## 2 System Model
### Favorable Propagation  
Favorable propagation conditions are defined as mutual orthogonality between the channel vectors of different terminals, which is a key feature of massive MIMO. The asymptotically favorable propagation condition can be defined as follows [16]:  
\[\frac{g_{k}^{H}g_{j}}{\sqrt{\mathbb{E}[\|g_{k}\|^{2}|r]\mathbb{E}[\|g_{j}\|^{2 }|r]}}\to 0,\text{as }M\rightarrow\infty,k\
eq j. \tag{8}\]  
Table 2 shows that, unlike the channel hardening phenomenon, the favorable propagation conditions of the cell-free massive MIMO system have not diminished. In regard to centralized massive MIMO, the channel vectors between terminals become increasingly orthogonal as the number of antennas increases.
## 2 Materials and Methods
### Meteorological Data  
The monthly temperature and precipitation data with a resolution of 1 km from 2001 to 2015 in China are from the National Earth System Science Data Center. Data can be obtained from the National Earth System Science Data Center and the National Science and Technology Infrastructure Center ([[http://www.geodata.cn](http://www.geodata.cn)]([http://www.geodata.cn](http://www.geodata.cn)), accessed on 25 November 2022).  
The Chinese terrestrial soil moisture dataset is monthly data with a resolution of 0.05\({}^{\circ}\) from the National Tibetan Plateau Data Center. The time series we used is 2001-2015. This data product was made by [PERSON] from three passive microwave remote-sensing products [35]. The soil moisture dataset is a product with good accuracy that was verified and can be used for hydrological and drought monitoring. The dataset can cover the 10-20 cm surface soil layer.  
Sun-Induced Chlorophyll Fluorescence (SIF), Photosynthetically Active Radiation (PAR), and Leaf Area Index (LAI) Products  
The SIF data we used are from Li [36]. The product is based on OCO-2 SIF, MODIS, and re-analysis of meteorological data, using a machine learning algorithm to obtain the GOSIF products with a global continuous 8 days, 0.05 degree, and long time series (2000-2018). We used the data from 2001 to 2015.  
The practical spectral component of plant photosynthesis in solar radiation is photosynthetically active radiation (PAR). The 2001-2015 PAR data were obtained from the Global Land Surface Satellite (GLASS) product ([[http://www.glass.umd.edu/Download.html](http://www.glass.umd.edu/Download.html)]([http://www.glass.umd.edu/Download.html](http://www.glass.umd.edu/Download.html)), accessed on 25 November 2022). The PAR data are 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) with a time resolution of 1 day, respectively.  
The Leaf Area Index (LAI) is a comprehensive indicator of vegetation utilization of light energy and canopy structure, and it is directly and closely related to the final yield. The LAI is a structural parameter of the ecosystem, which reflects the change in the plant leaf number, the canopy structure, the life vitality of the plant community, and its environmental impact. The LAI data used in this study were obtained from the MOD15A2H (V6) with a spatial resolution of 0.05\({}^{\circ}\) and the 8-day data product released by the National Aeronautics and Space Administration (NASA), which was pre-processed with a radiation and atmospheric correction. We used the maximum value composite (MVC) to generate month-scale LAI data. The data period we used is 2001-2015.  
Figure 2: Average (**a**) precipitation; (**b**) temperature during 2000–2015 in Southwest China.
## 5 Computational Cost Reduction Methods
### Clustering Techniques  
The computational cost for determining a position is directly proportional to the number of RPs. Therefore, a coarse positioning is introduced in the online phase to confine the positioning process within a small area. The clustering process is achieved in an offline phase where the RPs of the AOI are classified into small groups which are named clusters. Each cluster has a representative which is calledan exemplar to represent RPs subset of cluster in an online phase. In this article, K-means and fuzzy C-means are discussed in detail.
# Security Establishment in ADS-B by Format-Preserving Encryption and Blockchain Schemes
## 3 Methodology and Solutions
### FFX Scheme Implementation  
Figure 2 illustrates the structure of the airborne position message. The downlink format (DF), capability (CA), ICAO address, payload, and parity identity (PI) are the five fields that make up the 112 bits (14 bytes) [26]. The DF field, which has a constant value of 17, describes the message format (10001 in binary). These bits do not undergo encryption, allowing position reports to be recognized in every message. The other fields may be secured. To make it simpler to provide byte-aligned input, the downlink format and capability fields remain unencrypted, since they are constant in airborne mode [12]. Fifty-two (52) bits are equally distributed across the A and B parts of the message and can be used as a Feistel-based encryption function. However, encrypting the ICAO address field is also proposed as a possible solution to conceal information by disconnecting the link between the ICAO address field and other fields [24].  
Overall, the following test scenarios are going to be explored in this research. Therefore, the following numbers of bytes are encrypted for each test case.  
1. 14-Byte (whole message encryption)
2. 13-Byte (excluding DF and CA fields)
3. 8-Byte (ICAO and PI fields)
4. 3-Byte (ICAO field)  
FFX encryption and decryption functions must be added in both the ADS-B In and the ADS-B Out systems. Figure 3 illustrates SADS-B Out subfunction blocks. Positioning and  
Figure 2: ADS-B message structure and split to use as FFX input [26].  
velocity information are received from the navigation system via the interface function. The message then encodes and assembles. In this stage, the message is encrypted by using the FFX algorithm and a symmetric key. The preamble message then attaches to the encrypted message. In the last stage, the message is scheduled based on the message transmission rate to be broadcast by the transmitter block.  
Similarly, Figure 4 illustrates SADS-B In subfunction blocks. ADS-B Out signals are the input of the message receiver function. After demodulation, preamble pulse detection and enhanced bit detection blocks detect preamble pulse and data bits, respectively. In this phase, the detected burst of data is decrypted by using the FFX algorithm and the symmetric key. It should be noted that the data input of the FFX algorithm is one of the above test scenarios for 14, 13, and 8 bytes. The 112 bits of data can thus be verified as CRC error, and in case of error, the corresponding correction is applied. The message is ultimately decoded and displayed.  
Figure 4: SADS-B In’s subfunction block diagram.  
Figure 3: SADS-B Out subfunction block diagram.
## 5 Implementation and Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Feature/Capability** & **SQL Search** & **ML-Based** & **RALLRee**[38] & **Ours** \\ \hline Query type & Keyword only & Keyword/structured & Text, prompts & Natural language, semantic \\ Semantic understanding & ✘ & Limited & ✔ & ✔ \\ Vector embeddings & ✘ & Sometimes & ✔ & ✔ \\ Context-aware recommendation & ✘ & Limited & ✔ & ✔ \\ Privacy mechanisms & ✘ & ✘ & ✔ (ROT13) \\ Industrial testing & ✔ & Sometimes & ✔ & ✔ \\ Unstructured/noisy data support & ✘ & Partial & ✔ & ✔ \\ Hybrid dense/sparse retrieval & ✘ & ✘ & ✔ & ✔ \\ Evaluation metrics & Precision/Recall & Accuracy, F1 & Custom & Relevance, Faithfulness, Recall/Precision \\ Unique features & Simple, interpretable & Pattern learning & LLM-based reasoning & Semantic, privacy, context-aware \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of key features across four inventory search and recommendation systems.
## 5 Proposed Method
### Convolutional Neural Network Architecture  
Our convolutional neural network (CNN) processes input matrices of features transformed into Gram matrices using a lambda function, \(\Lambda_{\beta}\), based on pair relevancy. These matrices, treated as multichannel images \(G\), are passed through the CNN architecture depicted in Figure 7. The network begins with convolutional layers, each containing \(n_{1}\) filters of size \(k_{1}\times k_{1}\), paired with \(2\times 2\) average pooling to retain critical features while reducing spatial dimensions. We selected average pooling for its ability to compute the average value within specific regions, offering a compact representation while preserving overall feature characteristics [38]. This approach effectively maintains the essential traits of the input data. Each convolutional layer sequence employs LeakyReLU activation functions and zero padding to preserve dimensionality. This is followed by batch normalization to standardize inputs to subsequent layers. This configuration is mirrored in additional layers to enhance pattern recognition capabilities. The CNN outputs are passed to a dense layer sequence, beginning with a flattening operation that transforms the multi-dimensional array into a one-dimensional vector suitable for deep learning. The dense layer, containing \(n_{3}\) nodes, incorporates a dropout mechanism to mitigate overfitting and improve generalization. The final layer comprises a single neuron with a sigmoid activation function for binary classification. The loss function combines binary cross-entropy with physics-based rules to address class imbalance and employs class weights to ensure balanced representation during training. This architecture improved overload prediction accuracy, reducing the required data window to 15 min compared to the 30-min windows used in our previous work, with a forecasting horizon of 10 min.
## 1 Introduction
Addressing this problem, [18; 19] presented a novel method to weight these sometimes conflicting objectives by prioritizing collision avoidance and adding slack variables to hard constraints. If necessary, they even allow the violation of the stabilization criteria temporarily to avoid collisions. Nevertheless, most of these methods neglect the roll dynamics of AGVs and the effects of road topography, which are not always feasible [20]. For example, it is important to account for the roll dynamics when handling an AGV with a high center of gravity (CoG) or driving on a road with a large path curvature and road banks. Nowadays, many studies accounting for the roll dynamics have been published [21; 22]. Many vehicle roll stability metrics, such as the static stability factor (SSF) [23], stability moment (SM) [24], load transfer ratio (LTR) [25], time-to-rollover (TTR) [26], zero-moment-point (ZMP) [27], etc., have also been discussed. While these metrics do provide important results under certain conditions, they are limited by their inherent assumptions. Static metrics such as the SSF are easy to calculate, but do not capture vehicle behavior during dynamic maneuvers. Meanwhile, dynamic metrics such as the LTR and SM rely on information that is difficult to obtain [28]. Metrics using state prediction models, such as the TTR, do not account for the road topography, which significantly affects a vehicle's rollover propensity. Of all the metrics mentioned above, ZMP is preferable due to its ability to explicitly account for the effects of road topography in its derivation [29]. Moreover, the ZMP can be expressed as a linear combination of the vehicle states, allowing it to be included as an output of any vehicle model. In this work, we present a novel MPC scheme for stabilization control of high-speed autonomous ground vehicles considering the effects of road topography. Accounting for the effect of path curvature and the road bank, a single-track vehicle model with roll dynamics is derived. To enable collision avoidance in the long-term without compromising the prediction accuracy in the near-term, variable time steps are employed for model discretization. Then, the handling of the stability collision avoidance-related constraints is expressed as the sideslip envelope, limits on ZMP and lateral safety corridor. Accordingly, an MPC problem with feedback corrections is formulated and solved to ensure optimal vehicle operation within these safe driving constraints. Simulations carried out in the MATLAB/CarSim environment validated the effectiveness and real-time ability of the proposed scheme.