esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulations and Results
### Retrieval of DSD Parameters  
As the ratio of \(a_{V}(40)/a_{H}(40)\) holds the key to retrieving the slope parameter \(\Lambda\), we examine the retrieved ratio for different slope parameters. Because the retrieval of specific attenuations is relatively more accurate for areas with higher values, we will focus on area \(V\) in Figure 2(d) and evaluate the performance of retrieving \(\Lambda_{2}\). In total 15 simulation groups are designed, in each of which parameter \(\Lambda_{2}\) takes values from 2.8 mm\({}^{-1}\) to 4.2 mm\({}^{-1}\) in 0.1 mm\({}^{-1}\) increments. In each of the simulation groups, 100 independent simulations are carried out. Figure 4(a) shows the retrieved \(\hat{a}_{V}(40)/\hat{a}_{H}(40)\) for area \(V\), in which the blue circles mark the average retrieved ratio across 100 simulations and the error bars show the standard deviation. It is found that the relationship between true \(\Lambda_{2}\) and the mean of the retrieved \(\hat{a}_{V}(40)/\hat{a}_{H}(40)\) is almost linear. Using a linear least-squares regression fit of the blue circles, this linear relationship is generated and shown by the black line and the equation in the figure.  
Our following simulations utilize the linear relationship above to infer \(\Lambda_{2}\) from the retrieved \(\hat{a}_{V}(40)/\hat{a}_{H}(40)\). In the new 141 groups of 100 independent simulations, the true parameter \(\Lambda_{2}\) takes values from 2.8 mm\({}^{-1}\) to 4.2 mm\({}^{-1}\) in 0.01 mm\({}^{-1}\) increments, and the retrieved \(\Lambda_{2}\) is calculated from the retrieved \(\hat{a}_{V}(40)/\hat{a}_{H}(40)\) using the linear equation in Figure 4(a). Figure 4(a). Figure 4(b) shows the retrieval outcome, in which the blue line is the average retrieved \(\Lambda_{2}\) and the black line is a reference line with a gradient of 1. The red bars show the standard deviation across 100 simulations when the true \(\Lambda_{2}\) is at some selected values.  
Furthermore, the intercept parameter \(N_{0}\) for each of the 16 voxels in area \(V\) of Figure 2(d) is calculated from the retrieved \(a_{H}(40)\) and \(\Lambda_{2}\), and then compared with its true value. In Figure 4(c), the retrieved \(N_{0}\) is compared with its true value for voxel A in Figure 3(a) in 15 different \(\Lambda_{2}\) (true \(\Lambda_{2}\) being from 2.8 mm\({}^{-1}\) to 4.2 mm\({}^{-1}\) in 0.1 mm\({}^{-1}\) increments) with each one containing 20 independent simulations. The black line is also a reference line with a gradient of 1. The same was carried out for voxel B in Figure 3(a) where the true \(N_{0}\) is less than that for voxel A, and the results are shown in Figure 4(d). It can be concluded from the two figures that the retrieved \(N_{0}\) has a close agreement with the true \(N_{0}\) for the 15 different \(\Lambda_{2}\). More simulations confirm that the retrieval outcomes are very similar for all of the 16 voxels in area \(V\). As the retrieved \(\hat{a}_{H}(40)\) is quite accurate, the level of error in retrieved \(N_{0}\) is mainly due to the fluctuation in retrieved \(\Lambda_{2}\), which can be seen in Figure 4(b).
## 2 Materials and Methods
### Synthesis and Characterization of the Catalyst  
Cobalt ferrite was synthesized by alkaline co-precipitation using carboxymethylcellulose to prevent nanoparticle agglomeration and facilitate pore formation [11]. The desired amounts of iron(III) chloride and cobalt(II) chloride were dissolved in a 1 wt% solution of sodium carboxymethylcellulose, heated to 80-90 \({}^{\circ}\)C, and stirred for 30 min. Then, 5 M NaOH solution was added dropwise to a pH of 14, and the resulting suspension was further stirred for 30 min. The formed CoFe\({}_{2}\)O\({}_{4}\) precipitate was separated using a neodymium magnet, thoroughly washed, and dried. The obtained powder material was pressed into pellets of 10 mm \(\times\) 3 mm and sintered at 1150 \({}^{\circ}\)C for 6 h. The sintered pellets were then crushed into irregularly shaped grains and sieved. The fraction with a size from 0.8 to 1 mm was used.  
The X-ray powder diffraction (XRD) pattern of the cobalt ferrite catalyst was recorded at room temperature using a Shimadzu XRD-7000 X-ray diffractometer (Shimadzu Corporation, Kyoto, Japan) with monochromatic CuK\(\alpha\) radiation (\(\lambda\) = 1.5418 A). The powder samples were scanned from 5\({}^{\circ}\) to 65\({}^{\circ}\) with a step angle of 0.02\({}^{\circ}\) and 2 s per step. XRD analysis was performed using the full-profile Rietveld refinement method implemented in the FullProf software package (ver. January 2018). Peak deconvolution was carried out employing the pseudo-Voigt function.  
X-ray fluorescence (XRF) measurements were performed using an EXERT 3L XRF spectrometer (INAM, Kyiv, Ukraine) with helium flushing. SEM images were recorded using a REMMA-102_02 scanning electron microscope (JCS SELMI, Sumy, Ukraine) operated at an accelerating voltage of 20 kV. An energy-dispersive spectrum (EDS) was recorded at the location where the SEM image was taken.  
Raman spectra were recorded using a Bruker Raman Microscope (Billerica, MA, USA) with a 1 mW laser emitting light at a wavelength of 785 nm, a magnification of \(\times\)20, an aperture of 50 \(\upmu\)m, and 800 scans with a resolution of 1.5 cm\({}^{-1}\).
## 3 Material & Methods
### L4 Evaluation Vehicle  
To demonstrate the TARA methodology, we analysed an ACS of automation L4, annotated throughout this paper as _L4 Evaluation Vehicle (L4V)_. The selected vehicle was used for testing highly automated driving and on-demand services for public transport on a pilot site in Geneva (Switzerland). The vehicle has a capacity of 15 passengers and drives at an average speed of 18 km/h within a predefined region of 38 hectares. Thanks to its several sensors, which include cameras, GPS, RADAR, LiDAR, and odometers, the vehicle is capable of autonomously building a picture of its surroundings, recognising obstacles, and bypassing them [31]. However, due to legal obligations, a safety operator remains required to intervene if needed, which makes it an L4 instead of an L5 vehicle.
## 3 Materials and Methods
### Chatbot Usability
#### 3.4.1 Outcome Measures  
We evaluated the effectiveness of the chatbot program using measurements of vaccination information, motivation, self-efficacy, and behavioral intention as variables. Based on the IMB research model, we measured if there was a difference in the child's vaccination information, motivation, self-efficacy (behavioral skills), and behavioral intention scores between the experimental and control groups.  
In order to evaluation of the effectiveness of the chatbot and measure the outcomes, every 3 weeks, the measurements of vaccination information, motivation, self-efficacy, and behavioral intention were taken and scored.  
The questionnaires were provided via the chatbot. First, to measure \"Vaccination Information\", we verified the content validity of the 12 questions. Five experts were asked to provide their views to the measurement tools. These experts included a pediatrician, a researcher at the Vaccination Management Division of the KCDC, a professor at a nursing college, and two doctors at a nursing college. Twelve questions with a CVI index of 80% or more were selected, with a slight revision. In total, 12 yes or no questions regarding vaccination were formulated. Correct answers to each question scored one point. A higher correct answer rate indicated a higher level of child vaccination information.  
Second, to measure \"Vaccination Motivation\", six questions were revised from the pertussis vaccination motivation measurement tool developed by [PERSON] (2008). The reliability of the original tool was [PERSON]'s alpha = 0.82. The supplemented tool by experts was the same.  
Third, to measure \"Self-Efficacy\", five questions were modified from [PERSON] and [PERSON] (2017) with reference to the tools developed by [PERSON] (2012). The original tool's [PERSON]'s alpha was 0.91 and that of the supplemented tool in this is 0.85.  
Fourth, to measure \"Behavioral Intention\", based on [PERSON] et al. (2010) and [PERSON] (2011), seven questions were used to revise and supplement the measurement for the intention of vaccination, with [PERSON]'s alpha = 0.9.  
Conveying vaccination information via a chatbot has not been previously applied in the vaccination field. Therefore, the chatbot can be a new method to facilitate vaccination information communication. Thus, it is expected that the groups using the chatbot will have better access to information than those who do not, and that obtaining information through the chatbot will be useful.
## 3 Frequency Response Calculation of the CLD Plate Considering Two Kinds of Damping
### Derivation of the Frequency Response
In practice, it is not necessary to consider all the \(n\) order modes and the used order \(m\) only needs to be larger than that of the highest order of the structure in the frequency range of interest, that is, modal truncation is used in the actual analysis. Because of the frequency dependence of viscoelastic materials, the above solution only obtained the response corresponding to a certain frequency point. In order to obtain the responses in the whole frequency range, the iterative calculation needs to be carried out, referring to Figure 3.
## 2 Materials and Methods
### Feature Selection  
To achieve the optimal performance of SVM and LR models, backward elimination based on [PERSON]'s score (F-score) was used for the feature selection. F-score was calculated using the following equation:  
\[\text{F(i)}=\ \frac{\left(\overline{x_{i}}^{(+)}-\overline{x_{i}}\right)^{2}+ \left(\overline{x_{i}}^{(-)}-\overline{x_{i}}\right)^{2}}{\frac{1}{n_{+}-1} \sum_{k=1}^{n+}\left(x_{k,j}{}^{(+)}-\overline{x_{i}}^{(+)}\right)^{2}+\frac{1 }{n_{-}-1}\sum_{k=1}^{n-}\left(x_{k,j}{}^{(-)}-\overline{x_{i}}^{(-)}\right)^{ 2}}. \tag{1}\]  
In this equation, \(\overline{x_{i}}\), \(\overline{x_{i}}^{(+)}\), and \(\overline{x_{i}}^{(-)}\) are the average QSM values of the \(i\)th feature of the whole, PD (positive), and HC (negative) datasets, respectively, and \(x_{k,i}{}^{(+)}\) and \(x_{k,j}{}^{(-)}\) are the \(k\)th QSM value of the \(i\)th feature for the positive and negative subjects, respectively. \(n_{+}\) and \(n_{-}\) represent the numbers of positive and negative subjects, respectively.  
F-score measures the deviation between the two groups of real numbers [23]: a larger F-score represents a larger deviation. We included values from the ROI with the highest F-score to the ROI with the lowest F-score to apply backward elimination.  
For a subclassification model, PD cases were divided into two groups based on the median NMSS score in study patients (15.5 \(\pm\)11.5); when median NMSS was set as the threshold, the two groups were defined as high NMSS score (mean \(\pm\) SD, 26.6 \(\pm\) 10.0) and low NMSS score (mean \(\pm\) SD, 9.4 \(\pm\) 3.8). The F-scores of ROIs for these groups were compared.
# Optimization of Machining Parameters for Corner Accuracy Improvement for WEDM Processing
###### Abstract  
Corner accuracy occurring in wire electrical discharge machining (WEDM) is influenced by machining parameters such as wire vibration, wire lag, and excessive discharge, etc. This study proposed an optimization method which can improve the corner accuracy of the WEDM process. The parameters of pulse-on time (ON), pulse-off time (OFF), open circuit voltage (OV), servo voltage (SV), wire tension (WT), and flushing pressure (WA) were selected to investigate the influences of the major parameters on the machining accuracy in this study. Three different corner angles of 30\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\) were chosen for the verification experiments. The response surface methodology (RSM) was used to analyze and investigate the effect of each parameter on the corner error. After integrating the response surface value and algorithm, an optimization system with a friendly human-machine interface, which has a procedure guiding function, was developed with use of C# language. The system can predict the corner error and also recommend optimal machining parameters for smaller corner error and faster machining speed based on the original machining parameters. Finally, cutting experiments were conducted to verify the proposed system, and the results showed that the proposed method can effectively improve the corner accuracy by 39%, 20%, and 33%.  
WEDM; corner accuracy; response surface methodology; optimization Article
## 2 Experimental Setup
### Room Topology  
The room in which the measurements took place is a multi-purpose lecture hall of size \(17.7\,\mathrm{m}\times 8.7\,\mathrm{m}\) with a \(3.9\,\mathrm{m}\) ceiling (see Figure 1). The ceiling is located \(0.4\,\mathrm{m}\) below the concrete floor from the second storey of the three storey building. This multi-purpose lecture hall is equipped with easily removable furniture, a feature which is suitable for conducting experimental VR studies due to its open and obstacle-free space.  
A floor plan of the multi-purpose lecture hall is shown in Figure 2. The walls consist of different materials including wooden walls, glass windows with blinds, wooden doors with small windows, a whiteboard, and a multi-screen arrangement. Further, a few items of furniture and information and communication technology (ICT) equipment were in the room during the measurement campaign. The distance between the transmitter \(\mathrm{Tx}_{\mathrm{v}}\) and the receiver \(\mathrm{Rx}_{\mathrm{v}}\) (receiving HMD on the full-swivel chair) was chosen as \(d\in\{2.5,5,7.5,10,12.5,15\}\,\mathrm{m}\). The location of the receiver \(\mathrm{Rx}_{\mathrm{v}}\) remained fixed while the transmitter \(\mathrm{Tx}_{\mathrm{v}}\) was moved to the different locations within the multi-purpose lecture hall at the specified distances. It is noted that the distance \(d=15\,\mathrm{m}\) extends the transmitter placement from the wide open room into the narrow corridor-like appendix seen on the right-hand side of the floor plan. Two SteamVR base stations (infrared (IR) tracking) were used for room-scale tracking of the locations of the HMD and controller locations. The computer table in the vicinity of the receiving HMD on the full-swivel chair holds the equipment for recording the video stream traces.  
To allow for examining the impact of the rotational orientation of the HMD on the video freeze characteristics in relation to the related changing propagation environment, the multi-purpose lecture hall is partitioned into quadrants Q1, Q2, Q3, and Q4. The origin of the associated coordinate system is anchored at the center of the receiver Rx,v with the anticlockwise azimuthal angle \(\phi\) defining the range of the quadrants, i.e., Q1: \(\phi\in[0^{\circ},90^{\circ})\), Q2: \(\phi\in[90^{\circ},180^{\circ})\), Q3: \(\phi\in[180^{\circ},270^{\circ})\), and Q4: \(\phi\in[270^{\circ},360^{\circ})\).
# Image-Text Matching Model Based on CLIP Bimodal Encoding
## 3 Joint Encoding Model for Image-Text Matching
### Similarity Calculation  
In image-text matching, there are various methods for calculating similarity. Word Mover's Distance (WMD) measures the semantic similarity between two texts, considering the semantic relationships between words. It represents each text as the weighted sum of word embeddings and then calculates the distance between them. Hamming distance represents the text as binary vectors, and then calculates the number of differing bits at corresponding positions in the binary vectors. When calculating the Hamming distance, the length of the strings must be equal, otherwise, it would be meaningless. The calculation formula is as follows:  
\[d_{H}(s_{1},s_{2})=\sum_{i=1}^{n}\delta(s_{1_{i}},s_{2_{i}}) \tag{7}\]  
where \(\delta(s_{1_{i}},s_{2_{i}})\) is an indicator function that takes the value 1 if the strings are not equal and 0 if they are equal.  
The Euclidean distance calculates the straight-line distance between two points, which measures the difference in size between vectors. The calculation formula is as follows:  
\[distance(p,q)=\sqrt{\left(x_{1}-x_{2}\right)^{2}+\left(y_{1}-y_{2}\right)^{2}} \tag{8}\]  
In this model, cosine similarity is used to measure the similarity between vectors. For vector \(A\) and vector \(B\), the cosine similarity value is calculated by the following formula:  
\[cosin\_sim(A,B)=\frac{A\times B}{\|A\|\|B\|} \tag{9}\]  
The magnitude of the cosine similarity is related to the angle between the two vectors, with a smaller angle indicating a higher similarity between the vectors. In the image-text matching model, we learn a vector space where we map the vector representations of two modal data. Then, we calculate the similarity value between the vectors.  
To enhance the performance of our model in learning these vector representations, we employ the LiT-tuning paradigm (Language-Image Pre-training Tuning). This paradigm fine-tunes the pre-trained Vision Transformer (ViT) and Bert encoders, which were initially trained on large-scale image and text datasets. LiT-tuning allows the model to transfer learned knowledge effectively, optimizing the encoders for the image-text matching task. By leveraging LiT-tuning, the model can achieve better alignment between visual and textual features, leading to more accurate similarity measurements.
Design Methodology and Robustness Analysis of a 13-15 GHz Three-Stage Low-Noise Amplifier in pHEMT GaAs Technology +
Footnote †: Corresponding: [EMAIL_ADDRESS] (F.A.); [EMAIL_ADDRESS] (P.E.L.)  
[PERSON]  
1  
[PERSON]  
2 Department of Electronic Engineering, University of Roma Tor Vergata, 00133 Rome, Italy;  
1  
[PERSON]  
3 Correspondence: [EMAIL_ADDRESS] (F.A.); [EMAIL_ADDRESS] (P.E.L.)  
[PERSON]  
3 Correspondence: [EMAIL_ADDRESS] (F.A.); [EMAIL_ADDRESS] (P.E.L.)  
[PERSON]  
1  
2  
2  
3  
4  
5  
6  
7  
8  
9
10  
11  
12  
13  
14  
15  
16  
17  
18  
19  
2025  
1  
1  
1  
1  
1  
1  
1  
1  
1  
1  
1  
1  
1  
1  
1  
1  
1  
1  
1  
1  
1  
1  
1  
some applications, a two-stage LNA is insufficient to achieve the required amplification gain [10]. Consequently, a third stage is often introduced to increase the gain provided by the preceding stages [11]. While adding more stages to the LNA can enhance performance metrics such as gain and noise Figure under certain conditions, it also introduces several drawbacks, including increased power dissipation [12], higher cost [13], greater design complexity [14], and larger chip area [15]. The deterministic design approach proposed in this work enables a more precise and time-efficient optimization, facilitating a better trade-off among key design parameters.  
In our previous work [9], we introduced a straightforward synthesis methodology for a three-stage LNA, building upon the two-stage deterministic technique described in [16]. In that study [9], only the small-signal parameters were characterized. This article presents a comprehensive design methodology for each stage, including a load-pull simulation of the third amplifier stage. The nonlinear performance of the LNA, as well as its robustness under input overdrive conditions for both continuous-wave (CW) and pulsed signals, is also examined.  
The LNA MMIC test vehicle, developed using the proposed design approach, was fabricated on 150 mm GaAs wafers employing the WIN Semiconductors PIIH1-10 high-integration E-mode pHEMT process with an integrated monolithic PIN diode.  
This paper is organized as follows. Section 2 presents the related content in the open literature and highlights the new contributions presented in this article. Section 3 demonstrates the detailed design methodology and the related design figures of merit. The pHEMT MMIC LNA, test bench setups with EM simulations, and measurements are presented in the fourth Section 4. The robustness and reliability of the LNA are discussed in Section 5. Furthermore, Section 6 presents the state-of-the-art LNAs from the open literature along with a figure of merit to distinguish between them. Lastly, a discussion of the work is presented in Section 7.
## 5 Methodology
### Feature Fusion  
In this study, a feature-level fusion strategy was adopted to combine image-based features and laser reflectance data for fish freshness classification. Prior to fusion, modality-specific normalization techniques were applied to ensure scale alignment and avoid distributional bias.  
Laser reflectance features were normalized using z-score normalization, which centers the data by subtracting the mean and scales them via the standard deviation. This approach is effective in handling sensor-based data with varying dynamic ranges.  
Image features extracted via pretrained CNN models (ResNet50, EfficientNet, ConvNeXt) were normalized using min-max normalization, which rescales feature values into the [0, 1] range, preserving the relative distances and enhancing the compatibility with the classifier input range.  
Following normalization, the two feature vectors were concatenated into a single unified representation, which enabled the joint use of both modalities in subsequent classification tasks. This approach, classified as early fusion, makes use of the complementary strengths of each data type. While the laser data captures information about the physical surface structure, the image data provide valuable visual appearance cues. Together, they contribute to a more comprehensive understanding for the classification process.
## 3 Data and Methods
### Image Classification  
Given the challenges posed by the region's spectral similarity between vegetation classes, spatial fragmentation, and limited availability of ground truth data, the U-net architecture was selected for its robustness in learning complex spatial and spectral patterns with limited training samples. The entire study area is categorized into six classes (Table 2). The classification process integrates multi-source satellite imagery, including  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Training Dataset** & **Validation Dataset** \\ \hline Amazon forest & 3,735,681 & 1,265,871 \\ Cerradao & 3,418,998 & 516,018 \\ Savanna & 2,831,667 & 957,963 \\ Agriculture & 6,575,880 & 1,490,778 \\ Water & 274,896 & 72,138 \\ Other & 857,598 & 120,912 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Total number of pixels per land cover class.  
Figure 4: Location of vegetation survey plots and training and validation areas. (**a**) Location of vegetation survey plots; (**b**) Location of training and validation samples. The background imagery comes from the World Imagery of Esri ArcGIS Pro (v3.2.0) software. Source: Esri, DigitalGlobe, GeoEye, i-cubed, USDA FSA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community.  
high-resolution PlanetScope data, multispectral Sentinel-2, and SAR-based Sentinel-1, to leverage complementary information across sensors. In order to examine the classification performance of different datasets, this study additionally prepared several training sets using various data combinations. Each training set was trained and evaluated for accuracy independently. The complete list of data combinations is provided in Table 3. The following section details the architecture, training process, and implementation of the U-net model used in this study.