esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Databases
### Contactless-Based Palmprint Databases
#### 6.2.2 CASIA-MS (2007)  
The CASIA Multi-Spectral (CASIA-MS) corpus [34] constitutes a substantial contactless palmprint compilation encompassing 7200 images gleaned from 100 distinct cases. Data acquisition utilized a customized multi-illumination imaging apparatus to capture six distinct samples from each palm across two independent sessions. This process was conducted under varying electromagnetic exposures, including 460 nm, 630 nm, 700 nm, 850 nm, 940 nm, and white light channels. Notably, these palmprint acquisitions occurred without any peg limitation, and deliberate measures were taken to introduce diverse variations intentionally. This purposeful introduction of variability amplifies the diversity of samples within a class and emulates practical usage scenarios, thereby rendering the CASIA-MS database highly valuable for advanced research in palmprint recognition.  
Figure 7: Illustrative instances from the aforementioned contact-based palmprint datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Database** & **Year** & **Developer** & **\#fings** & **\#fings** & **\#fings/ID** & **Res.** & **Norm.** & **Var.** & **Acqui. Dev.** \\ \hline PolyU [6] & 2003 & The Hong Kong & 7782 & 386 & \(\approx\)20 & 384 \(\times\) 284 & No & No & Scanner \\  & & & & & & & & & \\ \hline PolyU-UAS [28] & 2009 & The Hong Kong & 6000 \(\times\) 4 (red, green, blue, and NIR) & 500 \(\times\) 4 & 12 & \(700\times\) 500 & Yes & No & Scanner \\ \hline \multirow{3}{*}{IIITDM [29]} & \multirow{3}{*}{2015} & Indian Institute of & & & & & & & \\  & Information Technology, & Design and & & & & & & & \\  & Manufacturing, Jabulpur & & & & & & & & \\ \hline \multirow{3}{*}{BTU\_PalmV1 [30]} & \multirow{3}{*}{2019} & Institute of Information & & & & & & & \\  & Science, Beijing Jiaotong & 2431 & 174 & From 8 to 10 & \(1792\times 1200\) & No & Large & 2 CCD cameras \\ \cline{1-1}  & & & & & & & & & \\ \hline PV\_790 [31] & 2020 & University of Macau & 5180 & 518 & 10 & N/A & No & No & Scanner \\ \hline COEP [32] & N/A & College of Engineering, & & & & & & & \\ \cline{1-1}  & & & & & & & & & \\ \end{tabular}
\end{table}
Table 1: Comparative summary of the contact-based palmprint databases (#fings: number of images, #ID: number of identities, #fings/ID: number of images per identity, Res: resolution, Norm: normalization, Var: variation, Acqui.Dev: acquisition device).
# A Modified Compact Flexible Vivaldi Antenna Array Design for Microwave Breast Cancer Detection
## 2 Materials and Methods
### Breast Model  
A large-scale study of microwave dielectric properties of normal, benign, and malignant breast tissue was reported in [8; 9; 10]. It has been found that normal breast tissue spans a very large range of dielectric properties dependent on the adipose content of the sample. Low water content (high adipose) exhibits a low dielectric constant, and high water content glandular or fibroconnective tissue exhibits a high dielectric constant. In this paper, a half-spherical breast phantom was designed with electrical properties that match the human breast. The phantom radius is 45 mm, and it consists of three layers. The skin layer width is 2.5 mm with a dielectric constant and conductivity of 36 and 4 S/m, respectively. The width of the fat (tissue) layer is 42.5 mm with a dielectric constant and conductivity of 9 and 0.4 S/m, respectively. A spherical tumor of 2.5 mm and 5 mm radius is inserted inside the phantom in different places. The tumor has a relatively high dielectric constant of 55 and conductivity of 4 S/m. The proposed phantom presented in our work has similar dielectric properties and thicknesses to phantoms found in [3; 14; 20]. The proposed model is developed in HFSS, as shown in Figure 18.  
Figure 17: Radiation Pattern at 4.3 GHz: (**a**) E-Plane; (**b**) H-Plane.  
Figure 18: Proposed Breast Phantom.
# Image-Text Matching Model Based on CLIP Bimodal Encoding
## 3 Joint Encoding Model for Image-Text Matching  
This paper proposes an image-text matching model that aggregates two encoders. Specifically, the model aims to match images with descriptive text or retrieve relevant images based on text queries, addressing key challenges in cross-modal retrieval tasks. The proposed approach involves utilizing the Vision Transformer (ViT) model as the image encoder to capture spatial features effectively and derive a robust image representation vector. For text encoding, we employ Bidirectional Encoder Representations from Transformers (Bert), which leverages bidirectional context to generate a comprehensive text representation vector. The similarity between the two modalities is then measured using cosine  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Model** & **Key Techniques** & **Advantages** & **Limitations** \\ \hline [PERSON] et al. [5] & Shared Vector Space Model & Cosine similarity, global feature mapping & Simple, effective overall similarity & Misses fine-grained image–text details \\ \hline [PERSON] et al. [6] & SCAN Model & Attention-based fine-grained matching & Captures detailed region–word alignment & Ignores inter-modal relationships \\ \hline [PERSON] et al. [9] & CLIP & Contrastive learning, global + fine-grained mapping & Strong generalization and efficiency & Limited handling of subtle, nuanced details \\ \hline [PERSON] et al. [12] & Cross-Modal Attention & Multi-head inter- and intra-modal attention & Enhances semantic alignment and matching & High computational cost \\ \hline [PERSON] et al. [14] & ALADIN & Fine-grained alignment, KNN search & Fast, efficient cross-modal retrieval & Requires accurate feature alignment \\ \hline Our Method & CLIP-Based & ViT + Bert encoders, LiT-tuning, cosine decay & Superior convergence, robust performance, captures both global and fine-grained semantics & Potential computational complexity increase \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of Classic Image–Text Matching Methods.  
similarity, enabling efficient and accurate image-text alignment. This integration of the ViT and Bert, based on the principles of Contrastive Language-Image Pretraining (CLIP), enhances the model's ability to align and retrieve cross-modal information effectively. The overall framework is shown in Figure 1.  
This paper is based on the principles of CLIP, where individual discrimination proxy tasks are employed in the model as the model's supervisory signal. Individual discrimination involves treating the image and text of the current sample in the batch as a positive sample pair, and the other images and texts in the current batch as negative sample pairs for this positive sample pair. During training, the distance between positive sample pairs is minimized, while the distance between negative sample pairs is maximized. Then, the model uses a contrastive learning loss function to calculate its loss value, which guides the training direction of the model.
# Prediction of Soil Compaction Parameters Using Machine Learning Models
## 2 Materials and Methods
### Model Performance Evaluation Metrics  
Prediction models in machine learning are generally evaluated with evaluation metrics such as Root Mean Square Error (RMSE) and coefficient of determination (\(R^{2}\)). In the extremely perfect model, RMSE is equal to 0. When RMSE is smaller, the model is more accurate, and, on the contrary, it is worse. The range of \(R^{2}\) is from 0 to 1, and the closer its value is to 1, the better the model fits the data. The mathematical expressions are as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{14}\]  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum\limits_{i=1}^ {n}(y_{i}-\overline{y})^{2}} \tag{15}\]  
where \(n\) is the number of samples, \(y_{i}\) is the true value, \(\hat{y}_{i}\) is the predicted value, and \(\overline{y}\) is the mean value.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 5 Sensors Driving Underwater SLAM
### Vision-Based SLAM
#### 5.1.2 Stereo Vision Approaches  
Stereo camera configurations provide direct depth estimation through epipolar geometry, offering more robust 3D reconstruction than monocular systems. The work of Lu et al. [53] showcases this advantage through their ORB-SLAM3-VIP implementation, which achieves precise navigation by fusing stereo depth with IMU data. Stereo systems typically maintain better trajectory estimation at greater depths compared to monocular alternatives. The trade-off comes in computational complexity and hardware requirements--stereo processing demands approximately twice the computational resources of monocular systems while also requiring careful calibration to maintain accuracy in varying water conditions.
## 2 Mathematical Model
### Hydrogen-Powered Hybrid UAV System Model
#### 2.1.2 Lithium Battery  
The lithium battery operates in a battery pack configuration, and its total power output is expressed as follows:  
\[P_{\text{E}}^{\text{B}}=P_{\text{E}}^{\text{b}}\times n_{1}\times n_{2} \tag{4}\]  
where \(P_{\text{E}}^{\text{B}}\) is the total power output of the battery pack, \(P_{\text{E}}^{\text{b}}\) is the power of a single battery cell, \(n_{1}\) and \(n_{2}\) represent the number of rows and columns of the battery cells, respectively.  
The current of a battery cell is as follows:  
\[I_{\text{b}}=\frac{V_{\text{oc}}-\sqrt{V_{\text{oc}}^{2}-4R_{\text{b}}P_{ \text{E}}^{\text{b}}}}{2R_{\text{b}}} \tag{5}\]  
where the open-circuit voltage \(V_{\text{OC}}\) and the internal resistance \(R_{\text{b}}\) of the battery cell can both be considered constant values.  
The dynamic characteristic of the battery cell, SOC can be expressed as follows:  
\[\text{SOC}_{t+1}=\text{SOC}_{t}-\frac{I_{\text{b},t}}{Q_{\text{B}}}\Delta T \tag{6}\]  
where \(t\) (\(0\leq t\leq T\)) is the scheduling time, \(T\) is the scheduling period, \(Q_{B}\) is the battery cell capacity, and \(I_{\text{b},k}\) is the current, with charging being negative and discharging being positive.  
The energy currently stored in the lithium battery is related to the energy stored at the previous time step and the energy charging and discharging between the two-time steps. The upper and lower limits of the stored electrical energy are determined by the device's capacity, and the charging and discharging power also has upper and lower constraints. Due to the continuity of device operation, the initial storage state of the device at each moment is equal to the final storage state after the previous cruise. When typical cruise data are input, it can be assumed that the storage state at the beginning and end of each cruise is continuous. The energy storage device model can be represented by Equation (7).  
\[E_{BT,t}=\begin{cases}E_{BT,t-1}(1-\sigma_{BT})+\left(E_{BT,t-1}^{in}\eta_{BT} ^{in}-E_{BT,t-1}^{out}/\eta_{BT}^{out}\right)\times\Delta t&t\
eq 1\\ E_{BT,N}(1-\sigma_{BT})+\left(E_{BT,N}^{in}\eta_{BT}^{in}-E_{BT,N}^{out}/\eta_ {BT}^{out}\right)\times\Delta t&t=1\\ SOC^{\text{min}}_{BT}\leq E_{BT,t}\leq SOC^{\text{max}}_{BT}\\ 0\leq E_{BT,t}^{in},E_{BT,t}^{out}\leq Pe_{BT}\end{cases} \tag{7}\]  
where \(E_{BT,t}\) is the energy storage state of the lithium battery, in kWh; \(\sigma_{BT}\) is the self-loss coefficient of the lithium battery; \(E_{BT,t}^{in}\) and \(E_{BT,t}^{out}\) are the charging and discharging power of the lithium battery, in kW, respectively; \(\eta_{BT}^{in}\) and \(\eta_{BT}^{out}\) are the charging and discharging efficiencies of the lithium battery, respectively; \(SOC_{BT}^{\min}\) and \(SOC_{BT}^{\max}\) are the lower and upper limits of the battery's storage state, in kWh, respectively; \(Pe_{BT}\) is the maximum electrical power of the lithium battery; and \(\Delta t\) is the time interval.
# Influence of Perforated Soils on Installation of New Piles
## 2 Removal by Pulling Out Existing Piles
### Soil Condition after Removal of the Existing Piles (Pulling-Out Holes)  
When pulling out existing piles using the pulling-out method, it is essential to treat the pulling-out holes. If the pulling-out holes are not treated properly, they will create a negative soil impact, resulting in various problems. In order to treat the pulling-out holes, therefore, a backfilling treatment with a filler is performed [5]. The fillers used in the backfill treatment include sand, fluidized soil, cement milk, or equivalent [10; 11; 12; 13; 14].  
The backfill treatment with soil, such as sand, is advantageous in that the treatment is easy and inexpensive. However, because the rolling compaction works involved with this backfill treatment are difficult, uniform filling is also difficult, and stable strength cannot be secured. In addition, there are cases where settlement has occurred in the backfill treatment area approximately one month after the treatment due to backfilling with loose soil, such as sand, and where settlement and depression have occurred because of heavy rain.  
Figure 1: Examples of existing pile buried in an unhealthy state.  
Fluidized soil is a filler obtained by mixing soil, water, and a cement-based solidifying material [10; 11; 12; 13; 14]. It is considered a recycled material when construction sludge is reused as the soil. As fluidized soil has high fluidity and self-hardening properties due to the cement-based solidification material, it can be filled into minute spaces and has the characteristic of exhibiting strength, even in spaces where compaction work is impossible. In addition, because fluidized soil has an impermeable property, it has the advantage of exhibiting stable strength against the infiltration of groundwater. However, fluidized soil must be brought in from an external facility; thus, its use is limited to sites which are located within a reasonable proximity. Moreover, it is difficult to cast this soil in accordance with the construction situation.  
Poorly mixed cement milk is a filler that contains water, cement, and bentonite [3]. As cement milk has high fluidity and self-hardening properties, it can be filled into minute spaces and exhibits strength. In addition, quality control, such as strength, can be easily performed by controlling the water/cement ratio, and the kneading operation can be performed in an all-purpose plant facility. In general, however, poorly mixed cement milk may have uneven strength in the depth direction due to the separation of the water and the cement materials. Furthermore, it is possible that the strength may decrease to below the predetermined value due to the infiltration of groundwater, or that solidification failure may occur.
# In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning
## 2 Development of BMS
### Sensor Measurement  
Accurate measurement of current and temperature is essential for the safe and efficient operation of electric propulsion systems [31]. This section explores methodologies for current, voltage, and temperature measurement in battery applications. The enhancementswere also highlighted to improve measurement accuracy and accommodate real-time scenarios. Through these efforts, we aim to ensure precise and reliable monitoring of critical parameters crucial for battery system performance and safety.
## 4 Authentication in UAV Networks
To construct a secure communication channel, authentication and encryption are essential security features [63]. Cryptography is frequently employed in authentication systems. Typical authentication systems utilize cryptography during the basic steps of verification and certification [92; 93]. In resource-constrained contexts, joint authentication is prone to man in the middle attacks. However, encryption prevents the denial of service attacks [94]. The authentication paradigm of UAVs is shown in Figure 12.
## 5 Conclusions  
To determine the water content in the Huola Mountain Tunnel site and ensure construction safety, we conducted HTEM and GAFEM electromagnetic surveys in the Huola Mountain Tunnel site where ground personnel experience difficult access and the tunnel is deeply buried. In this HTEM and GAFEM geophysical exploration, 15 survey lines were surveyed, and we conducted apparent resistivity imaging for each line. The apparent resistivity results of each profile obtained by the fusion of two kinds of electromagnetic detection data were consistent, proving the reliability of the results. We determined that the overall distribution of apparent resistance was basically consistent with the site's landform, hydrogeology, and tectonic geology. In accordance with relevant geological data, topographic conditions in the survey area, and aerial image data, we speculated that the surrounding rock in some deep sections of the tunnel is complete. However, there are two low-resistance abnormal areas at the entrance and exit of the tunnel, which are consistent with the positions of two known faults. It is inferred that the shallow surrounding rock in the tunnel site was seriously weathered and rock fissures had developed, especially in low-lying areas, where the rocks have been eroded by the drainage of the mountaintop for a long time. When construction reaches the area with large water content, roof fall, block fall, and water or mud gushing may occur. Making geological predictions and implementing drainage and other relevant measures in advance, such as tunnel seismic prediction, ground penetrating radar, and bore-tunneling electrical monitoring, are recommended to ensure construction safety.  
Conceptualization, T.Z. and Y.W.; data curation, H.Z., S.W. and G.L.; formal analysis, C.Z. and D.L.; funding acquisition, Y.W.: Investigation, H.Z., S.W. and F.Z.; methodology, H.Z., S.W. and C.J.; resources, G.L. and F.Z.; validation, H.L.: writing--original draft preparation, T.Z., C.Z., D.L. and C.J.: writing--review and editing, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 42004059, and National Key R&D Program of China, grant number 2017 YFC0601802.  
Figure 10: Apparent resistivity imaging result of the parallel pilot tunnel, which is located 35 m southeast of the tunnel.
# COSMIC-2 RO Profile Ending at PBL Top with Strong Vertical Gradient of Refractivity
## 3 Methodology  
### Refractivity and Bending Angle Calculation  
In order to use the ERA5 reanalysis to analyze the atmospheric refractivity distribution near the lowest height of the COSMIC-2 RO profiles that did not reach the surface, the  
Figure 1: (a) Vertical distributions of the monthly COSMIC-2 RO data count in the SEP area in 2020, and (b) data counts at the 0.2 (blue), 3 (green) and 8 km (red) altitudes.  
Figure 2: Monthly-mean liquid water path (LWP) in August 2020 from the ERA5 reanalysis (color shading). Also indicated are the Southeast Pacific (SEP) and the South-Central Pacific (SCP) areas, which are defined as (115\({}^{\circ}\)W–70\({}^{\circ}\)W, 40\({}^{\circ}\)S–0\({}^{\circ}\)) and (170\({}^{\circ}\)W–130\({}^{\circ}\)W, 40\({}^{\circ}\)S–0\({}^{\circ}\)), respectively.  
refractivity should be calculated from ERA5 reanalysis. We use the following equation of refractivity provided by [PERSON] [PERSON] and [PERSON] (2018):  
\[N=N_{d}+N_{w}=k_{1}\frac{P_{d}}{T}+k_{2}\frac{e}{T}+k_{3}\frac{e}{T^{2}} \tag{1}\]  
where \(k_{1}=77.6890,\ k_{2}=71.2952,\ k_{3}=375463\), and \(e\) represents the water vapor pressure and can be calculated by:  
\[e\approx\frac{qP}{0.622+0.378q} \tag{2}\]  
Based on Equations (1) and (2), the ERA5 refractivity can be calculated from the ERA5 temperature (\(T\)) and specific humidity (\(q\)) at the 37 pressure levels. In the following, the first term on the right-hand-side of Equation (1) will be called the dry refractivity, and the second two terms on the right-hand-side of Equation (1) is called the wet refractivity. The vertical distributions of the dry and wet refractivity in the atmosphere will be shown and analyzed below.  
Once the refractivity is calculated from the ERA5 reanalysis, the bending angle (\(\alpha\)) can be calculated by the Abel transformation (1979):  
\[\alpha(a)=-2a\int_{a}^{\infty}\frac{1}{\sqrt{\mu^{2}-a^{2}}}d\ln n \tag{3}\]  
where \(a\) represents the impact parameter, \(n=1+10^{-6}N\) is the index of refractivity, \(r\) is the geocentric radial distance, and \(\mu=nr\). The refractivity and bending angle calculated from the ERA5 reanalysis will be used for a comparison with the COSMIC-2 RO profiles.
# Vehicular Localization Enhancement via Consensus
## 4 Experiment
With the adoption of the practical implementation, each iteration of the update includes a portion of the compensation. When the update is halted by the convergence of the estimates, all portions of the compensation is conveyed to all vehicles in the network. If the practical implementation of the proposed strategy is applied under the identical system settings, the estimates therefore converge to the ones shown in Figure 2(c) instead of the ones in Figure 2(b). For the same test case, the reduction of the localization error at each step is shown in Figure 4 to compare both implementations of the compensation. Figure 3(a) shows the strategy where the  
Figure 3: Changes of estimates during the execution of the proposed strategy. consensus-based updates and the compensation are separately applied, while Figure 4b shows the strategy under the practical implementation. For clear representation, only a subset of 20 vehicles' localization errors are presented; the vehicles are sorted by the amount of initial localization error and then every third vehicles are selected. Thus, the localization errors of 7 out of 20 vehicles are represented by the dashed lines. In addition, the average localization error of all 20 vehicles is shown together by the solid red lines. At the initial step, the amounts of the localization errors highly differ from each other. For instance, the anchor vehicle represented by the blue dashed line has no initial localization error while that of another vehicle is higher than 50 m. Such randomness rapidly diminishes in the following steps. First, in Figure 4a, as the consensus-based updates are executed from step 1 to step 4, the magnitudes of the vehicles' localization errors are gradually homogenized. In particular, from step 2 to 4, little changes in the localization errors are observed as measurement noises are averaged out on the consensus basis. A yellow vertical line at step 4 indicates that the halt condition (11) for the consensus-based updates is met at the fourth update. At step 5, the compensation reduces the remaining localization errors effectively, leaving the estimates much more accurate than before. On the contrary, Figure 4b makes no distinction between the consensus-based updates and the compensation. While most features regarding the reduction of errors are retained, it is noted that the localization errors achieved in Figure 4a are also achieved in Figure 4b, indicating that the practical implementation of the compensation step does not degrade the localization performance. The performances in more diverse scenarios are discussed in the following section.
# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 5 Method
### Classifiers
* Logistic regression (LR): Mean accuracy = 71.6%, CI = [70.6%, 72.6%]
* Gaussian Naive Bayes (Gaussian-NB): Mean accuracy = 70.8%, CI = [69.4%, 72.2%]  
The results indicate that random forest, decision trees, and K-Nearest Neighbors achieved the highest performance, with random forest slightly outperforming the others. In contrast, logistic regression and Gaussian Naive Bayes had the lowest accuracy, with relatively narrow confidence intervals, suggesting more consistent but lower predictive power.