esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Studies
### Traditional Methods for Variance Adjustment in VarDA Systems  
#### 2.1.1 Empirical Variance Rescaling  
[PERSON] (1999) introduced a fixed rescaling factor (0.81) to **B** of the European Centre for Medium-Range Weather Forecasts (ECMWF) assimilation system, mitigating variance overestimation inherent in the National Meteorological Center (NMC) method [29]. Although this adjustment improved medium-latitude forecasts, its globally uniform application failed to capture spatial and temporal variations, particularly in tropical regions where weaker geostrophic balance necessitates localized corrections.  
To address the variance underestimation of Ensemble of Data Assimilation (EDA)-based **B**, [PERSON] et al. (2012) applied a global factor of approximately 2 in ECMWF's 4D-Var system [23]. Despite improving deterministic analysis quality, the lack of flow dependency limited its ability to account for regional and seasonal variations in background error characteristics. Similarly, [PERSON] et al. (2016) examined the role of observation error covariance (**R**) adjustments in IASI data assimilation, demonstrating that when inter-channel error correlations were ignored, an inflation factor ranging between 2.5 and 3 improved assimilation performances, while explicitly modeling these correlations reduced the optimal factor to 1.75 [24]. These findings underscore that variance tuning in variational assimilation must consider the interdependence between background and observation error specifications in order to achieve optimal performance.  
[PERSON] et al. (2024) scaled the background error standard deviations in **B** by a factor of 1/3 to match the 6 h assimilation cycle in JEDI-MPAS 3D-Var, where **B** was statistically derived from 366 forecast differences between 48 h and 24 h lead times using NCEP GFS data [25]. They also halved the diagnosed horizontal correlation lengths for the stream function and unbalanced velocity potential, correcting discrepancies between training-derived correlations and the Background Error on Unstructured Mesh Package (BUMP) assumptions. These adjustments increased velocity variance by a factor of 4, aligning better with training data and improving assimilation performance. Their results underscore the need for adaptive variance rescaling in enhancing the DA system's accuracy.
# Electric Field Distribution Induced by TMS: Differences Due to Anatomical Variation
###### Abstract  
Transcranial magnetic stimulation (TMS) is a well-established technique for the diagnosis and treatment of neuropsychiatric diseases. The numerical calculation of the induced electric field (EF) distribution in the brain increases the efficacy of stimulation and improves clinical outcomes. However, unique anatomical features, which distinguish each subject, suggest that personalized models should be preferentially used. The objective of the present study was to assess how anatomy affects the EF distribution and to determine to what extent personalized models are useful for clinical studies. The head models of nineteen healthy volunteers were automatically segmented. Two versions of each head model, a homogeneous and a five-tissue anatomical, were stimulated by the model of a Hesed coil (H-coil), employing magnetic quasi-static simulations. The H-coil was placed at two standard stimulating positions per model, over the frontal and central areas. The results show small, but indisputable, variations in the EFs for the homogeneous and anatomical models. The interquartile ranges in the anatomical versions were higher compared to the homogeneous ones, indicating that individual anatomical features may affect the prediction of stimulation volumes. It is concluded that personalized models provide complementary information and should be preferably employed in the context of diagnostic and therapeutic TMS studies.  
Keywords:TMS; personalized treatment planning; EF modeling; anatomy; deep TMS +
Footnote †: journal: Applied
# Extended Reality (XR) Engines for Developing Gamified Apps and Serious Games: A Scoping Review
## 4 Discussion
### RQ3. What Serious Game Attributes Are Supported in XR Game Engines?
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{1}{c}{**Rules**} & \multicolumn{1}{c}{**Goals and Choices**} & \multicolumn{1}{c}{**Task/Challenges**} & \multicolumn{1}{c}{**Collaboration**} & \multicolumn{1}{c}{**Feedback/**} \\  & & & **and Competition** & **Assessment** \\ \hline  & & & CC1—Role-play & FA1—Game hints \\  & & TC1—NPC-based task & CC2—Community & FA2—NPCs \\ RL1—Scoring & GC1—Game journal & description & collaboration & FA3—Game levels \\ RL2—Moving & GC2—Missions & TC2—Progress bars & CC3—Epic meaning & FA4—Gaining/losing \\ RL3—Timers & GC3—Objective cards & TC3—Multiple choice & CC4—Bonuses & lives \\ RL4—Levels & GC4—Storytelling & TC4—Major tasks & CC5—Contest & FA5—Progress bars \\ RL5—Progress bars & GC5—Nested & TC5—Branch tasks & CC6—Scoring & FA6—Dashboards \\ RL6—Game & dialogues & TC6—Puzzles & CC7—Isernes & FA7—Lives/virtual \\ instructions, including & GC6—Puzzles & TC7—Research points & CC8—Coins & currencies to be used \\ victory conditions & GC7—NPCs/avatars & TC8—Study & CC9—Inventories & for buying game items \\  & & TC9—Requirements. & CC10—Leaderboards & from an online \\  & & & CC11—Commul & inventory \\  & & & & FA8—Progress trees \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Abbreviations of game attributes for every game category. to show users either how far along they are in a process or their life level; and game instructions, which can include victory conditions--instructions that help players fulfill the conditions required to gain a victory. The game attribute support obtained by game rules is very similar among all of the XR engines; however, Vuforia--the proprietary XR game engine--offers less game attribute support, and A-Frame--the JavaScript-based XR engine--offers fewer game attributes. The \"Goals and Choices\" category indicates that games should be goal-directed, competitive, and designed within a framework of rules, choices, and feedback to enable teachers and students to monitor progress toward the goal. Goals are achieved through specific choices that need to be made by the player, and these in-game choices refer to the number of decisions a player can make before and during gameplay. The game attributes for the Goals and Choices category are game journals, missions, objective cards, storytelling, nested dialogues, puzzles, and NPCs/avatars. For this category, Apple ARKit is the proprietary XR engine with the least support, followed by Verge 3D. The game attributes with less support in proprietary XR engines are storytelling and the nested dialogues. In the JavaScript-based XR Engines, React XR offers less support for game attributes, and the game attribute with the least support is storytelling.
# Delamination Detection in Bimetallic Composite Using Laser Ultrasonic Bulk Waves
###### Abstract  
In this study, a method based on laser ultrasonic bulk waves is used to detect delamination in a bimetallic composite. For this purpose, several artificial delamination defects were created in a copper-aluminum sample using wire-electrode cutting. The research includes numerical simulation and experimental analysis. The propagation process of laser ultrasonic in Cu/Al bimetallic composite, the interaction between bulk waves and composite interface, and the effect of delamination defects on the ultrasound field were studied by numerical simulation. Suitable parameters and features were determined by numerical simulation, which provided a basis for the parameter selection of experimental research. The reflected shear waves from the composite interface can act as a sensitive feature to detect the delamination in Cu/Al bimetallic composites. The distance between the detection point and the excitation point was set to 2 mm to take into account the detection resolution and efficiency. The experimental results were in good agreement with the simulation results, and the C-scan image can intuitively show the location and size of delamination defects. The detection method based on laser ultrasonic bulk waves can effectively detect the delamination in Cu/Al bimetallic composite, which is suitable for the on-line detection of the rolling composite process.  
bimetallic composite; delamination; nondestructive testing; laser ultrasonic; bulk wave sciences Article  
D 2021  
11 636[[https://doi.org/10.3390/app11020636](https://doi.org/10.3390/app11020636)]([https://doi.org/10.3390/app11020636](https://doi.org/10.3390/app11020636))
# Load Balancing Strategies for Slice-Based Parallel Versions of JEM Video Encoder
## 2 Description of the New Characteristics of the Joint Exploration Test Model (JEM)
### Spatial Prediction
Figure 5: MPMs: neighbors of the current PU in JEM. the first and last angular modes, corresponding to intra-modes 2 and 34 in HEVC) or a three-tap filter (for modes between intra-modes 3-6 and 30-33 in HEVC), as shown in the example in Figure 6.  
In JEM, the results of the intra-prediction planar mode are also improved by including a position-dependent intra-prediction component (PDPC) method that processes a specific combination of the unfiltered boundary reference samples with the filtered ones, thus improving the perceived quality of the predicted block when the planar mode is used. This process uses different weights and filter sizes (three-tap, five-tap, seven-tap) based on the block size. To reduce some of the redundancy that remains after the prediction process between the luma and chroma components, JEM uses cross-component linear model (CCLM) prediction. In this process, the chroma samples are predicted based on the reconstructed down-sampled luma samples of the same CU, using a linear model for square blocks. For non-square blocks, additional down-sampling is needed to match the shorter boundary. There are two CCLM modes: single- and multiple-model CCLM modes (MMLM). In the single-model CCLM mode, JEM employs only one linear model to predict the chroma samples, while in MMLM, there can be two models. In MMLM, the models are built based on two groups of boundary samples that serve as a training set for deriving the linear models [15]. A summary of the main differences between HEVC and JEM related to spatial prediction is shown in Table 2.
## 1 Introduction
A method for estimating the current flowing through nonlinear power inductors in DC/DC converters was proposed in [31] and applied to Boost converters. The proposed method uses a polynomial third-order inductor model to estimate the current distribution at which the inductor reaches saturation, providing a complete evaluation of the current, including distribution, peak value, root mean square value, and spectrum. In [32], a dedicated measurement rig was developed to reflect the behavior of inductance during saturation and temperature rise. And it is pointed out that the saturation current and loss of the inductor decrease and increase respectively with the increase in temperature. To achieve a high power factor while maintaining a low intermediate capacitor voltage and ensuring high efficiency with wide input voltages, by operating the input inductor at DCM and the output inductor at CRM, a DCM-CRM Cuk PFC converter utilizing variable inductor control is proposed in this paper, wherein the input inductance value is precisely regulated by DC magnetic flux bias. According to [33; 34; 35], when the inductor in a Boost converter operates in DCM, the converter can inherently realize PFC without the need for additional control circuitry. Furthermore, studies in [36; 37] indicate that a Buck converter operating in CRM exhibits higher efficiency compared to its operation in DCM. Given that the input stage of a Cuk converter is analogous to a Boost converter and its output stage mirrors a Buck converter, the Cuk converter inherently benefits from these advantageous characteristics. Therefore, this paper configures the input inductor to operate in DCM and the output inductor in CRM. A detailed analysis of the converter topological principle, control strategy, and operational characteristics is provided in this paper. To validate the probability of the proposed approach, a 108 W experimental prototype was built and tested. The test results confirmed the accuracy of the theoretical analysis and demonstrated the effectiveness of the proposed control method. The experimental results demonstrate that the proposed converter not only reduces the intermediate capacitor voltage but also enhances the PF and improves efficiency with wide input voltages. This paper is organized into 5 sections. In Section 2, the characteristics and operating principles of the conventional DCM-CRM Cuk PFC converter are presented. In Section 3, the proposed DCM-CRM Cuk PFC converter with VI control is introduced, including a detailed explanation of its operational principles and analysis of key features. A comprehensive comparison of experimental results of the conventional and proposed Cuk PFC converters is also provided in Section 4, and the conclusions are summarized in Section 5.
# Simulation of Partial Discharge Induced EM Waves Using FDTD Method--A Parametric Study +
## 2 PD Measurement Using the UHF Method  
Partial discharge measurements were carried out to study the frequency characteristics of EM signals emitted by the discharge. For this purpose, two types of PD were generated: surface discharge on XLPE, Teflon and acrylic samples, and cavity discharge on XLPE samples of different thicknesses. The former was created under uniform and non-uniform field electrode configurations. The PD measurements were carried out using the IEC 60270 method, the 40 MHz HFCT and the UHF method simultaneously. Figure 1 shows the experimental set-up, with the test cells used for the uniform and non-uniform electrode gaps showing material samples in the insert. The UHF signals were captured using a Schwarzbeck 1/4\(\lambda\) double ridge aperture antenna with a frequency band between 0.8 GHz and 5 GHz, and a 120 MHz-900 MHz monopole antenna to capture the lower UHF range signals. This section summarises the frequency characteristics of the signals measured with the aperture antenna. The details of the test results for the other methods and their analyses will be reported separately. Figure 2a shows examples of measured time domain UHF signals generated by a surface discharge when a 9.2 kV AC voltage was applied across the non-uniform gap with a 6-mm thick Teflon sample in between. The UF sensor was vertically polarized (i.e., such that the E-field is parallel to the double ridge of the antenna) and located at two different horizontal distances and one vertical distance from the test object.  
Figure 1: Experimental set-up for partial discharge (PD) measurement.  
Figure 2: _Cont._  
The frequency spectrum of the noise level (Figure 2b) and that of the measured signals shown in Figure 2c confirm the frequency range of the EM signals emitted by the surface discharge. Peaks detected at 0.9 GHz, 1.85 GHz and 2.5 GHz are superimposed GSM and long-term evolution (LTE) signals. The results show that the UHF detection can be improved by placing the sensor closer to the discharge source in a horizontal direction. Signals produced by a cavity discharge were measured by using a specially made XLPE block samples with a needle-plane electrode system and were also found to be within the UHF band.
# Evaluation on Radiometric Capability of Chinese Optical Satellite Sensors
## 2 Materials and Methods
### Data
The calibration uncertainties of the MODIS TOA reflectance products are \(\pm\)2%, whereas a 5% uncertainty requirement is specified for the reflective solar bands at sensor spectral radiance calibration [22]. The MODIS instrument onboard the Earth Observing System's (EOS) Terra and Aqua platforms have a state-of-the-art onboard calibration system with an absolute accuracy better than 2% [23], which has calibrated the solar reflective bands to reflectance units. Therefore, MODIS as a well-calibrated instrument is very practical for radiometric cross-calibrating those sensors without a good onboard calibration system by using targets of known reflectance [24; 25]. Furthermore, as one of the most stable sensors, MODIS has been widely used for radiometric capability evaluation of other sensors [6; 11; 25]. In this paper, the MODIS level 1b data including MOD02 (Level-1B Calibrated Geolocation Data Set) and MOD03 (Geolocation Data Set is chosen as the reference data to evaluate the radiometric capability of the three sensors. The primary characteristics of the CCDs, MERSIs, VIRRs and MODIS are listed in Table 1.
# Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model
## 3 Materials and Methods
### Ensemble Classifier  
In this section, we present an ensemble classifier to enhance the performance of the skin lesion classification. Classification is performed via the final fully connected (fc) layer. Our ABC ensemble model includes a total of five classifiers, and each model employs different normalization methods and activation functions. ViT-B/16 adopts GELU, EfficientNet-B0 uses Swish, MobileNetV2 applies ReLU6, and both ResNet50 and DenseNet121 utilize ReLU functions. The inconsistent use of different activation functions and normalization methods across models can degrade performance due to varying scales in each model's predictions. Therefore, instead of using a different final fc layer for each model, we use a classifier, applying the same activation function and normalization across all models. Batch normalization is applied to the batch input, using the mean and variance as shown in Equation (9), while layer normalization applies to the layer input as described in Equation (10).  
\[\hat{x}_{B}=\frac{x_{B}-E(x_{B})}{\sqrt{Var(x_{B})}}, \tag{9}\]  
where \(\hat{x}_{B}\) is the normalized value of the batch \(x_{B}\). \(x_{B}\) represents a batch of input values from a dataset. \(E(x_{B})\) is mean of the batch \(x_{B}\). \(Var(x_{B})\) is the variance of the batch \(x_{B}\).  
\[\hat{x}_{L}=\frac{x_{L}-E(x_{L})}{\sqrt{Var(x_{L})}}, \tag{10}\]  
where \(\hat{x}_{L}\) is the normalized value of the layer's input \(x_{L}\). \(x_{L}\) represents the input values to a specific layer in a neural network. \(E(x_{L})\) is the mean of the input values \(x_{L}\). \(Var(x_{L})\) is the variance of the input values \(x_{L}\).  
The normalized features are then passed through the ELU activation function. The selection of ELU as the activation function is based on its ability to combine characteristics of GELU, Swish, and ReLU6, which all have non-linear and linear properties. ELU operates non-linearly for negative values and linearly for positive values, as expressed in Equation (11).  
\[ELU(x)=\begin{cases}x&if\ x\geq 0\\ \alpha(e^{x}-1)&if\ x<0\end{cases} \tag{11}\]  
\(ELU(x)\) stands for the Exponential Linear Unit, which is a type of activation function used in neural networks. \(x\) is the input value to the activation function. \(\alpha\) is a parameter that controls the saturation level for negative inputs, typically set to a constant value.  
After deriving the predictions from each model through the ensemble classifier, we conducted the ensemble using a weighted soft voting method. The weights were assigned as follows: [2.0, 2.0, 1.0, 1.0, 1.0] in the order of ViT-B/16, DenseNet121, EfficientNet-B0, MobileNetV2, and ResNet50. Among the five blocks in our ABC ensemble model, a higher weight of 2.0 was applied to the general blocks, which captures the overall characteristics of the data. In contrast, the remaining three blocks, which consider the ABCD rule, were assigned a relatively lower weight of 1.0.
# Quantum Compressive Sensing: Mathematical Machinery, Quantum Algorithms, and Quantum Circuitry
###### Abstract  
Compressive sensing is a sensing protocol that facilitates the reconstruction of large signals from relatively few measurements by exploiting known structures of signals of interest, typically manifested as signal sparsity. Compressive sensing's vast repertoire of applications in areas such as communications and image reconstruction stems from the traditional approach of utilizing non-linear optimization to exploit the sparsity assumption by selecting the lowest-weight (i.e., maximum sparsity) signal consistent with all acquired measurements. Recent efforts in the literature consider instead a data-driven approach, training tensor networks to learn the structure of signals of interest. The trained tensor network is updated to \"project\" its state onto one consistent with the measurements taken, and is then sampled site by site to \"guess\" the original signal. In this paper, we take advantage of this computing protocol by formulating an alternative \"quantum\" protocol, in which the state of the tensor network is a quantum state over a set of entangled qubits. Accordingly, we present the associated algorithms and quantum circuits required to implement the training, projection, and sampling steps on a quantum computer. We supplement our theoretical results by simulating the proposed circuits with a small, qualitative model of LIDAR imaging of earth forests. Our results indicate that a quantum, data-driven approach to compressive sensing may have significant promise as quantum technology continues to make new leaps.  
quantum compressive sensing; quantum algorithms; born machines; compressive sensing; quantum circuits; LIDAR; tensor network compressed sensing +
Footnote †: journal: Applied
# A New Methodology for Type Synthesis of Planar Linkages for Exoskeletons up to Five Angular Outputs
## 5 Generation of Multiple Angular Outputs
### Generation of Three Angular Outputs Using Parallel Connection  
A mechanism that has three angular output motions can be generated from an initial six-bar mechanism that has two angular outputs as illustrated below.  
1. The existing angular output links are represented at the right of the graph (links 4 and 5) as shown in Figure 5a.  
2. Adding the RRR dyad starts from link-5 (including two additional links: 7, 8).  
Figure 5: Generation of three angular outputs using parallel connection. (**a**) Initial linkage having two outputs; (**b**) new output ‘link 7’ is added; (**c**) available topologies of three outputs with parallel connection; (**d**) corresponding linkage diagrams for topologies P2 and P4.  
Link-7 is located directly above link-5 as a third angular output link as shown in Figure 5b.
4. The distributive link-8 is located at the left of link-7.
5. Joints between links (5, 7 and 8) are represented by solid edges (j, k) as shown in Figure 5b.
6. The last revolute joint, \(l\), has five alternatives to connect distributive link-8 with other links in the original linkage graph, excluding the connections that produce rigid sub-chains. These alternatives generate five available eight-bar topological graphs that can achieve three angular output motions as shown in Figure 5c.
7. Linkage diagrams can be constructed from their corresponding topological graphs as illustrated in Section 4.4. Some of these linkage diagrams are shown in Figure 5d.  
Therefore, each of the six-bar linkages having two angular outputs, shown in Figure 3, can generate five available eight-bar linkages with three angular outputs. Therefore, there are a total of 15 available eight-bar linkages.