esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Underwater Biological Detection Based on Improved YOLOXs
## 1 Introduction
This approach achieved average accuracies of 81.4% and 88.6% in fish detection and classification tasks, respectively, effectively handling the complexity and diversity of underwater environments. In 2021, [PERSON] et al. proposed a method combining the YOLOv3 network with a color recovery-based multi-scale retinal enhancement algorithm (MSRCR) [18]. This approach addressed color shifts, image noise, and blurriness in underwater images through image enhancement before using the YOLOv3 network for underwater target detection. The experimental results demonstrated a 10% improvement in average precision compared to the original YOLOv3 while maintaining the detection speed. In 2022, [PERSON] et al. introduced an underwater target detection algorithm based on FAttention-YOLOv5 [19]. This algorithm incorporated the FAttention attention mechanism, which adaptively adjusts attention in different regions by learning the weights of feature maps. This enhances the accuracy and robustness of target detection in underwater environments. Due to the complex and variable nature of the underwater environment, collected underwater images often suffer from issues such as blurriness, color distortion, and low contrast [20; 21; 22; 23; 24]. These conditions can significantly impede underwater target detection tasks. Additionally, in practical applications, devices equipped with intelligent target detection algorithms are commonly used for underwater operations. However, underwater mobile devices typically have limited memory space, necessitating lightweight improvements to the target detection model to meet practical requirements. To address these challenges, this paper proposes a new model called YOLOXs-GCE. The main contributions of this paper are as follows:  
1. In the backbone network, standard convolutions are replaced with Ghost convolutions, and Bottleneck1 is replaced with GhostBottleneck. This helps reduce the model's parameter count and computational load, meeting the real-time requirements of underwater operations. In the feature fusion network, the Contextual Transformer module is introduced into CSPBottleneck_2, replacing the 3 \(\times\) 3 standard convolution. This utilizes contextual information among input keys to guide self-attention learning, contributing to improved detection accuracy. 3. Focal_EIoU Loss (Focal and Efficient Intersection over Union) is employed instead of IoU (Intersection over Union) Loss to enhance the precision of the predicted bounding boxes and accelerate model convergence. These improvements aim to make the model more adaptable to the poor image quality that is characteristic of underwater environments and, through a lightweight design, cater to the practical application needs of underwater mobile devices.
## 1 Introduction
This inhomogeneity will depend not only on size of the local field disturbance as reflected by the combination of particle size and magnetization, or magnetic moment, but also on the number of particles per unit volume and their spatial distribution. For example, increasing the volume fraction of MNP will expectedly enhance transverse relaxation due to increased local gradients, however, these gradients may saturate even though the value of the field itself continues to increase with the particle volume fraction. Theoretical models manage to predict transverse relaxation rates (\(R_{2}=1/T_{2}\) and \(R_{2}*=1/T_{2}*\)) for both spin-echo and gradient-echo experiments using physical parameters describing both MNP and the system such as the particle size and magnetization, the volume fraction occupied by the MNP, and the diffusion coefficient of water molecules diffusing around the MNP, in addition to pulse sequence parameters. Two limiting models known as the motional averaged (MAR) [24; 25; 26] and static dephasing (SDR) [23; 27] Regimes describe transverse relaxation and agree with both experimental and computational studies. These models were developed under the conditions that the MNP volume fraction is small (\(\sim\)10\({}^{-5}\)). This work examines transverse relaxation behavior when the MNP volume fraction becomes large and compares MC simulation predictions with the existing models. The disagreement between them is examined as a function of particle volume fraction, particle size, and echo-time under both limiting motional regimes. Large concentration of MNP can ensue due to clustering or compartmentalization inside cells [28; 29; 30; 31]. The ability to track and quantify these agents depends on the accuracy of measuring relaxation times. However, large errors in measuring \(T_{2}\) and \(T_{2}*\) have been reported and the linearity of these parameters with the MNP concentration is compromised. This work uses MC simulation, proven to be highly accurate in predicting relaxation times under low MNP concentrations, to study the effect of high concentration on the already available theoretical models mentioned above and explained in more details in the Methods section below. We prefer this approach over the experimental approach in this preliminary work because it offers high accuracy and control, and avoids problems of clustering and particles size spread usually associated with synthesis techniques. These problems intensify when high temperatures are used or when particles enter biological tissues.
# Active Equalization of Lithium-Ion Battery Based on Reconfigurable Topology
## 6 Conclusions  
In this paper, a double-layer equalization method combining the reconfigurable topology with the converter active equalization method is proposed. The equalization strategies of the inner and outer layers are discussed in detail, and the feasibility of the proposed method is validated by simulation. The main concluding remarks can be made below:  
1. The inner layer proposed in this paper realizes balance among cells in the same group through the reconfigurable topology. Compared with the conventional active equalization method, the proposed method uses fewer components, has lower cost, higher efficiency, and simpler control complexity. Moreover, the equalization process goes on equally well regardless of the positions the cells needed to be balanced in the group.
2. The method of connecting the outputs of converters in series is used in the outer layer. The proposed outer layer can realize the balance among battery groups and keep the output voltage and power of the system stable at the same time. It solves the problem that using the reconfigurable topology alone cannot stabilize or adjust the output voltage.
3. The efficiency of the proposed equalization method was calculated and compared with the conventional converter active equalization method. The results showed that the proposed method can improve the balancing efficiency, and the higher the unbalance of the battery system is, the more significant the improvement is. The proposed method is suitable for scenarios like retired battery energy storage systems which have high power, large capacity, and high requirements for balancing efficiency and robustness.  
Conceptualization, Y.L.; methodology, P.Y.; validation, Y.L., J.C. and P.Y. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Key Research and Development Program of China (2021 YFB160020) and the Key Research and Development Projects of Shaanxi Province, China (2021 KW-13).  
Not applicable.  
The authors declare no conflict of interest.
# An Interpretable XGBoost Framework for Predicting Oxide Glass Density
## 3 Results
### Error Analysis (XGBoost)  
Histograms of the percentage error for the XGBoost model for the tested feature sets are presented in Figure 5.  
A clear trend is observed across the subplots in Figure 5: as the features become more physically descriptive, the error distribution becomes narrower and more centered around zero, indicating higher model accuracy and reliability. The model using the basic Compositional set (Figure 5a) exhibits the widest error distribution, with the largest mean percentage error of approximately 4.4%. For this model, the median error is 1.8%, but a significant portion of predictions have larger deviations, with the 75 th percentile of error reaching 5.2%. The inclusion of attributes describing the structural role of atoms (Atomic type) leads to a notable improvement, reducing the mean error to 3.5%. The most accurate predictions are achieved with the physically informed Meredig and Magpie feature sets. These models yield the lowest mean percentage errors, at 2.3% and 2.2%, respectively. For these advanced descriptors, the error distribution is highly concentrated around zero, with a median error below 1% and 75% of predictions falling below a 2.4% error margin.  
Figure 4: Predicted vs. experimental glass densities for MLP model for feature sets: (**a**) Compositional; (**b**) Atomic types; (**c**) Meredig, (**d**) Magpie.  
Detailed error statistics are summarized in Table S6. A linear trend line was fitted to a plot of percentage error versus experimental density for each feature set. The resulting slope and intercept parameters, also given in the table, allow for an estimation of the average prediction error at a specific glass density. In all cases, the positive slope of the regression line indicates that the model tends to make larger prediction errors for higher-density glasses. This may be due to a lower prevalence of such samples in the training set or to more complex physical relationships governing this group of materials.
## 4 Invariant Multi-Mode Vectors Pushover Analysis
### Upper-Bound (UBPA) Pushover Analysis  
Engineers often use simplified nonlinear static analytical procedures, also known as pushover analyses, to assess the seismic demands of tall buildings, as these methods are simpler than nonlinear response history analysis. However, this conventional approach has limitations in accurately predicting the inelastic seismic demands of high-rise buildings. To address this, a study [19] introduced an improved simplified pushover analysis method, which incorporates higher-mode effects into the analysis. This method consists of three key components: (1) response spectrum-based higher-mode displacement contribution ratios; (2) an alternative formula for determining the lateral load pattern; and (3) an upperbound modal combination rule for establishing the target roof displacement. This proposed method was validated using five buildings of varying heights and compared with three other types of analysis [19]. Results show that the proposed method is more accurate in estimating important response attributes of tall buildings, including roof displacements, story drift ratios, and plastic hinge rotations.  
The principal steps of the proposed pushover analysis procedure are as follows:  
Build the selection and elastic properties. Select a building and determine its elastic structural properties, including natural frequencies and mode shapes. Normalize mode shape \(\Phi_{n}\) such that the roof degree of freedom \(\Phi_{m}=1\) for all modes.  
Calculate the upper-bound contribution of the second mode. Use the elastic response spectrum of the selected earthquake to determine the upper-bound of the contribution of the second mode \((q_{2}/q_{1})UB\), as given by:  
\[\begin{pmatrix}q_{2}\\ q_{1}\end{pmatrix}_{UB}=\left|\frac{\Gamma_{2}D_{2}}{\Gamma_{1}D_{1}}\right|, \tag{29}\]  
Determine the lateral load distribution vector. Compute the distribution vector of the lateral loads over the height of the building using:  
\[f_{s}=F_{1}+F_{2}=\omega_{1}^{2}m\varphi_{1}+\omega_{2}^{2}m\varphi_{2}\left( \frac{q_{2}}{q_{1}}\right)_{UB}, \tag{30}\]  
Compute the target roof displacement \(Ur\). Determine the target roof displacement \(Ur\) as per the expression:  
\[u_{r,UB}=u_{r,TLP}\left[1+\left(\frac{q_{2}}{q_{1}}\right)_{UB}\right], \tag{31}\]  
Perform a pushover analysis. Conduct a pushover analysis using the lateral load calculated in Step 3 until the target displacement computed in Step 4 is reached (single-run analysis).  
Extract the seismic responses. Determine the desired seismic responses (e.g., story drifts, element internal forces, etc.) at the step corresponding to the target displacement obtained from the single run in the previous step.
# Ocean Currents Reconstruction from a Combination of
###### Abstract  
Measuring the ocean surface currents at high spatio-temporal resolutions is crucial for scientific and socio-economic applications. Since the early 1990s, the synoptic and global-scale monitoring of the ocean surface currents has been provided by constellations of radar altimeters. By construction, altimeter constellations provide only the geostrophic component of the marine surface currents. In addition, given the effective spatial-temporal resolution of the altimeter-derived products (\(\mathcal{O}\) (100 km) and \(\mathcal{O}\) (10 days), respectively), only the largest ocean mesoscale features can be resolved. In order to enhance the altimeter system capabilities, we propose a synergistic use of high resolution sea surface Chlorophyll observations (Chl) and altimeter-derived currents' estimates. The study is focused on the Mediterranean Sea, where the most energetic signals are found at spatio-temporal scales up to 10 km and a few days. The proposed method allows for inferring the marine surface currents from the evolution of the Chl field, relying on altimeter-derived currents as a first-guess estimate. The feasibility of this approach is tested through an Observing System Simulation Experiment, starting from biogeochemical model outputs distributed by the European Opernicus Marine Service. Statistical analyses based on the 2017 daily data showed that our approach can improve the altimeter-derived currents accuracy up to 50%, also enhancing their effective spatial resolution up to 30 km. Moreover, the retrieved currents exhibit larger temporal variability than the altimeter estimates over annual to weekly timescales. Our method is mainly limited to areas/time periods where/when Chl gradients are larger and are modulated by the marine currents' advection. Its application is thus more efficient when the surface Chl evolution is not dominated by the biological activity, mostly occurring in the mid-February to mid-March time window in the Mediterranean Sea. Preliminary tests on the method applicability to satellite-derived data are also presented and discussed.  
ocean currents; altimetry; earth observations synergy; ocean colour +
Footnote †: journal: Remote Sensing  
0000-0001-9389-2289-2899
# A Topology Optimization Method for Reducing Communication Overhead in the Kalman Consensus Filter
## 2 System Model and Problem Formulation  
In this section, the system model is introduced, and the problem to be resolved is formulated.
## 2 Materials and Methods
### Anatomical Measurement of PTFJ  
Patients were asked to lie in a supine position of relaxed lower limbs with fully extended knees. The lower limb of the patients was scanned by a CT machine (SOMATOM Spirit, Siemens, Germany) from approximately 20 cm superior to the knee joint line to ankle joint with a slice distance of 0.600 mm and a field of view (FOV) of 500 mm. Exterior cortical bone edges were segmented (threshold value: 400 HU-1610 HU) [16] and constructed using Mimics 21.0 (Materialise, Leuven, Belgium), and 3D models of the fibula and tibia were created. The PTFJ model included both the intact fibula and tibia. The morphology of the PTFJ was evaluated by shape type, PTFJ surface area, inclination angle of PTFJ, relative articular height, and declination angle of the PTFJ. According to the shape of the fibular articular facet, shape types were classified into three types: plane type, trochoid type, and double trochoid type [17]. The fibular contact surface with the tibia was regarded as the articular surface area [17]. The angle between the PTFJ surface and XY plane was calculated as the PTFJ inclination angle. In order to avoid individual differences caused by patient bone size [8], \(h\) was used to represent relative articular height, which was the ration of \(H\) (fibula articular surface height to tibia) divided by \(T\) (medial-lateral width of the tibial plateau) (shown in Equation (1)). The articular declination angle (\(\beta\)) was calculated as the angle between \(O_{T}O_{F}{}^{\prime}\) and the \(X\) axis (Equation (2)). A larger value of \(\beta\) indicated a more posterior positioning of the PTFJ (shown in Equation (2)) (Figure 1). Details of the measurement methods used are described in a previous study by the authors [12].  
\[h=\frac{H}{T} \tag{1}\]  
\[\beta=arctan\bigg{(}\frac{Y}{X}\bigg{)} \tag{2}\]  
Figure 1: Measurement of the declination angle \(\beta\). (\(O_{T}\) is the center of tibial plateau and was used as the origin of the tibial coordinate system. The Z axis was determined using a line connecting the center of the tibial plateau to the center of the distal tibial articular surface, with the positive direction running from proximal to distal. The Y axis ran from posterior to anterior, and the X axis ran from medial to lateral. \(O_{F}\) was designated as the centroid of the articular surface on the fibular side. T was the largest medial-lateral width of the tibial plateau. The articular declination angle \(\beta\) was the angle between \(O_{T}O_{F}{}^{\prime}\) and the X axis. \(O_{F}{}^{\prime}\) was the projection of \(O_{F}\) in the XY plane.)
## 3 Numerical Modeling of the Filling Phase
### Rheological Models
#### 3.2.2 Second-Order  
The Second-Order model, developed by [PERSON], is aimed at enhancing the precision of viscosity estimations at low shear rates [24]. This model is mathematically formulated as follows [26]:  
\[\ln(\eta)=A+B\ln(|\dot{\gamma}|)+CT+D\ln(|\dot{\gamma}|)^{2}+E\ln(|\dot{\gamma }|)T+FT^{2} \tag{11}\]  
The equation presented herein encompasses material specific parameters \(A\), \(B\), \(C\), \(D\), \(E\), and \(F\), in addition to viscosity \(\eta\), shear rate \(\dot{\gamma}\), and temperature \(T\).
## 2 Materials and Methods
### Area Analysis and Comparison  
The results of the four methods here described were compared with the true wound areas, which have been determined by an expert user through the manual tracing of the edges of the cell-free region. As this operation is highly time consuming, only half of the images and three experiments were considered for the ground truth definition (about 40% of the total images available).  
Prior to the comparison, all the results were normalized with respect to the initial extension of the corresponding cell-free region. This operation eliminates the wound area dependence on its initial extension and, thus, the effect on the results of the variability of the scratch area at time 0.  
Besides comparing the average wound areas obtained with each method and presented as the mean and standard error, the percentage difference with respect to the ground truth was computed (Equation (3)).  
\[\Delta GroundTruth(m)=\frac{WoundArea(m)-WoundArea(GT)}{WoundArea(GT)} \tag{3}\]  
This equation, where \(m\) represents one of the wound healing analysis strategies and \(GT\), the ground truth, was solved for all the considered methods and every image for which the true wound area was available.  
Furthermore, the average analysis time, recorded with a stopwatch, was studied. This aspect is often disregarded, but might influence the quality and the reliability of the results, especially when the assay includes a significant number of conditions and time points.  
Finally, the differential invasiveness of the two populations was evaluated. This analysis consists of computing, for every tested condition, the angular coefficient of the linear function describing the wound area decrease over time and then dividing the value obtained for the treated condition by the one measured for the control. As the number of considered time points and the total experiment duration can vary significantly, depending on the experimental conditions, we have analyzed the effect of these factors on the result, by computing the differential invasiveness using all the possible combinations of 2, 3, 4, 5 and 6 time points and studying the resulting distribution.
# Policy-Based Smart Contracts Management for IoT Privacy Preservation
###### Abstract  
This paper addresses the challenge of preserving user privacy within the Internet of Things (IoT) ecosystem using blockchain technology. Several approaches consider using blockchain and encryption to enhance the privacy of IoT applications and constrained IoT devices. However, existing blockchain platforms such as Ethereum and Hyperledger Fabric already use encryption to store data blocks and secure communication. Therefore, introducing an additional cryptographic layer on top of these platforms could potentially increase processing overhead and reduce response time. In this work, we investigate the integration of IoT and blockchain for privacy preservation. More specifically, we propose a new model that leverages the properties of private blockchain and smart contracts to ensure user data privacy when shared with others. We define policy-based algorithms and notations to assist users in managing smart contracts responsible for registering and controlling their IoT devices. We also specify multiple smart contracts designed to enhance privacy by creating a private channel for communication between the user and the blockchain network.  
privacy; policies; IoT; blockchain; smart contract Article  
Policy-Based Smart Contracts Management for IoT Privacy Preservation  
1  
[PERSON], [PERSON], [PERSON], [PERSON], N. Policy-Based Smart Contracts Management for IoT Privacy Preservation  
1  
[PERSON], [PERSON], [PERSON], M.  
privacy concerns are crucial factors in building trust in IoT systems across a variety of areas [3;4]. Unconscious use, a lack of defined standards, and a lack of device upgrades are some examples of vulnerabilities that might lead to new cybersecurity threats, allow malicious programs to access sensitive data in IoT systems, and expose users to various hazards to their privacy [5]. Privacy issues are often associated with the production of sensitive data that are, therefore, vulnerable to leaks and potential assaults. Users should be able to manage their exposure because the data transferred and handled by the devices enabling such applications typically include personal information. Therefore, they should be equipped with robust and efficient solutions to preserve the privacy of their identities and control the exposure and usage of their data. For instance, a smart home raises several privacy concerns since personal data may be leaked if the security controls are not well established. Smart home IoT cameras may be visible and accessible through the use of internet device-scanning tools such as Censys [6] due to permission misconfigurations. Several approaches to IoT privacy have been proposed [7;8], and almost all of them focus on the privacy of the data rather than user privacy and exposure. The privacy of the data should take into account the state of the data, whether they are stored or processed by the devices and systems supporting the application [9]. This means that the preservation should consider both the edge layer and cloud layer where data are exchanged, stored, and processed.