esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries  
### Searchable Encryption  
Searchable encryption is divided into symmetric searchable encryption (SSE) and public key encryption with keyword search (PEKS). In this paper, we focus on PEKS.  
PEKS, proposed by [PERSON] et al., is an asymmetric cryptosystem that enables the keyword-based retrieval of encrypted data. It involves the generation of a public key and a private key by the data receiver, the encryption of a keyword by the data owner, the generation of a trapdoor by the data receiver, and the testing of the keyword by the cloud server.  
PEKS consists of four algorithms: \(keyGen\), \(Enc\), \(Trapdoor\), and \(Test\).  
(1) \((pk,sk)\leftarrow\)\(KeyGen(\lambda)\): \(KeyGen\) is used by the data receiver to generate public and private keys \((pk,sk)\) with a security parameter \(\lambda\).  
(2) \(C_{w}\leftarrow\)\(Enc(pk,w)\): \(Enc\) is used by the data sender to encrypt a keyword and generate a ciphertext \(C_{w}\) for the keyword.  
(3) \(T_{w}\leftarrow\)\(Trapdoor(sk,w)\): \(Trapdoor\) is used by the data receiver to generate a trapdoor \(T_{w}\) for a given keyword \(w\).  
(4) \(b\leftarrow\)\(Test(pk,C_{w},T_{w^{\prime}})\): \(Test\) is used by the cloud server to test whether the trapdoor \(T_{w^{\prime}}\) for a given keyword \(w^{\prime}\) corresponds to the same keyword as the keyword ciphertext \(C_{w}\) in the index, i.e., whether \(w\) is equal to \(w^{\prime}\).
## 3 Relative Motion Dynamics of Two Satellites in a Near-Circular Orbit
Equation (12) closes the feedback loop. In it, \(y\) is the output of the linear controller in the feedback, \(\varphi(\cdot)\) is a nonlinear function describing the limitation of the control action, which is assumed to be the saturation function, \(\varphi(y)=\mathrm{sat}_{u_{\mathrm{max}}}(y)\). Since for each satellite the aerodynamic drag force is negative (that is, it acts against the direction of motion along the longitudinal axis and lies in the interval \([-u_{\mathrm{max}},0]\)), then in order to provide the desired differential control \(-u_{\mathrm{max}}\leq u_{ij}\leq u_{\mathrm{max}}\), the actual steering (braking) action should only be applied to the first (along X-axis) satellite of the pair, see [21]. Matrices of dynamics model (11) have the form:  
\[A = \begin{bmatrix}0&1&0&0\\ 0&0&0&-2\omega\\ 0&0&0&1\\ 0&2\omega&3\omega^{2}&0\\ \end{bmatrix} ,\;\;B= \begin{bmatrix}0\\ 1\\ 0\\ \end{bmatrix} \] \[K = \begin{bmatrix}k_{1}&k_{2}&k_{3}&k_{4}\\ \end{bmatrix} , \tag{13}\]  
Coefficients \(k_{i}\), \(i=1,\ldots,4\) are chosen at the stage of the control law synthesis.
# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
## 6 Discussion  
In practical environmental applications, when performing search and detection tasks in some open and flat dangerous areas, our algorithm can enable the UAV to act as a mobile acquisition sensor to collect environmental information of the area to be detected and the location information of the target to be searched, and to transmit the data back to the central base station with minimum time cost. Our multi-UAV cooperative search algorithm can efficiently complete the rapid collection of information in the detection area. After evaluation and testing, among the five paths proposed in this paper, the \"square wave signal\" curve path shows obvious superiority in the cruise route of the UAV. Compared with the MUCS-GD algorithm, the MUCS-BSAE algorithm shows satisfactory effects and performance. The IMUCS-BSAE algorithm is a further optimization of the MUCS-BSAE algorithm in rare cases. Our algorithm takes the UAV energy consumption limit and work efficiency into the performance evaluation range, which provides a basis for the practical application of the algorithm. Compared with existing related research Table 1, our algorithm supports multi-UAV cooperative search under the constraint of UAV energy consumption, and, because we use the \"square wave signal\" curve path as the flight path of the UAV, the scalability of the algorithm and the full coverage of the target area has strong robustness.  
The goal of our research is to develop an efficient collaborative search algorithm for multiple UAVs. Through evaluation and testing, the function and performance of our proposed IMUCS-BSAE algorithm are consistent with the research objectives and achieve the expected results. However, in the extreme case that the working area is too large, the endurance of the UAV cannot support the UAV to fly to the farthest area of the working area, resulting in the situation that the UAV cannot completely cover the whole working area.  
Figure 17: Comparison of enhancement effects of the improved MUCS-BSAE algorithm for different working area sizes under the “square wave signal” curve path.  
area. In this paper, the applicability of the algorithm is limited by the fact that only one starting base station is set in this research scenario and the UAV must return to the central base station after performing the task. In future research, a UAV CPP with multiple starting locations will be considered to make the algorithm more applicable.
## 1 Introduction
Moreover, two future electric distribution system structures are analyzed: the urban distribution network with connected microgrids (DNMG) and active managed distribution network (AMDN). The impact of DG technology used, energy source locations, and their power capacity on the power supply reliability have been analyzed. The reliability models of electrical power devices, conventional and renewable energy sources, as well as information and communication technology (ICT) components have also been proposed. The main contribution of this paper is investigating the analyzed subject in a more thorough way, that is: giving detailed data on considered distribution networks structures; on reliability parameters of distribution network components; on DG source types, as well as their power capacities and locations, for which the reliability calculations have been made, taking into account distribution system automation (automatic stand-by switching on (ASS) and automatic power restoration (APR)); presenting the results (seven commonly known reliability indices) achieved from the carried out computations and discussing the results (indices). The impact of DG type on these reliability indices has been investigated. It is worth noting, the reliability assessment of smart grid, i.e., electric power network coupled with the communication network, has also been done. We would like to highlight that this paper relates to MV distribution grids, for which reliability indices are the worst among all electric power distribution networks, as it is reported e.g., in [47; 48; 49]. Therefore, the importance of this paper on practical applications can be seen. This paper evaluates reliability of four electric distribution system structures (two present ones and two future ones) and presents the reliability indices obtained for these structures. In our opinion, more detailed modeling and consideration of system automation of the distribution grids with DG units coupled with the communication systems allows for the design and application of such MV network structures for which the best reliability indices can be obtained.
# Layer-Wise Query Selection to Eliminate Redundant Queries in DETR
## 5 Limitations and Future Work  
This study has several limitations. First, although SQ-DETR ranks queries purely by their class-probability scores, it currently ignores complementary cues such as bounding box scale, the spatial distribution of proposals, and inter-object geometric relations; incorporating these factors could yield a more reliable importance metric. While SQ-DETR does not use an explicit relation module, Transformer self-attention inherently captures pair-wise dependencies, and our query selection strategy keeps high-confidence representatives, making complete loss of an object's relational cues unlikely in practice.  
Second, the _drop ratio_ (i.e., the number of queries discarded at each decoder layer) is treated as a fixed hyper-parameter. An adaptive scheduler--driven by image complexity, per-layer uncertainty, or reinforcement learning--may further improve the accuracy-efficiency trade-off. Third, our evaluation is confined to the COCO benchmark; we have not yet tested on widely used alternatives such as LVIS or CrowdHuman. Additional experiments on datasets with denser crowds or domain-specific imagery (e.g., CrowdHuman and aerial or medical datasets) are required to verify the generality of SQ-DETR.  
In future work, we therefore plan to (i) enrich the query-scoring function with multi-scale positional features and cross-query geometric relations; (ii) devise an uncertainty-guided or reinforcement learning strategy that dynamically selects the drop ratio or absolute query budget in accordance with run time;and (iii) conduct large-scale cross-dataset studies, including LVIS and CrowdHuman, to validate robustness and real-world applicability.  
Conceptualization, C.-Y.C.; methodology, S.-J.H.; software, S.-J.H.; formal analysis, S.-J.H.; writing--original draft preparation, C.-Y.C. and S.-J.H.; writing--review and editing, C.-Y.C., S.-J.H. and S.-W.L.; data analysis, S.-J.H.; supervision, S.-W.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Challengeable Future Defense Technology Research and Development Program through the Agency For Defense Development (ADD) funded by the Defense Acquisition Program Administration (DAPA) in 2025 (No.915073201) and also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2025-00520064).  
Not applicable.  
The datasets used in this study, COCO and CrowdHuman, are publicly available.  
The authors acknowledge the use of ChatGPT (OpenAI, GPT-4o) for English editing support during the preparation of this manuscript.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Synthesis and Analysis of Planar SALT Trajectories  
To design a SALT trajectory, the four-body dynamics should be considered. However, when the spacecraft just departs the Earth or approaches the Moon, the Sun's gravity can be neglected and the Earth-Moon CR3 BP describes the dynamics accurately enough. Therefore, it seems reasonable to divide any SALT trajectory into three legs: the departing and arriving legs inside the Earth-Moon region of prevalence (thus, designed in the Earth-Moon CR3 BP) and the exterior leg outside the RoP connecting the two interior legs. It is this leg that should reflect the effect of the Sun's gravity pull.  
The most comprehensive analysis of the SALT trajectory design problem can be done in the planar case where useful geometrical instruments are available, but before proceeding to this analysis, let us prove one general result known as _the quadrant rule._
## 4 Results and Analysis
### Relationship between the Apparent Resistivity and Water Content  
Figure 7 shows the relationship between the apparent resistivity \(\rho\) and water content \(\theta\) in the rainfall-affected slope. The relationship between these parameters can be described by Equation (12) [37].  
\[\rho=A_{1}e^{(-\theta/t_{1})}+A_{2}e^{(-\theta/t_{2})}+\rho_{0} \tag{12}\]  
where, \(A_{1}\), \(A_{2}\), \(t_{1}\), \(t_{2}\) and \(\rho_{0}\) are constants.  
Figure 7: Relationship between apparent resistivity and water content.  
The water content and saturation of the rainfall-affected slope increased with rainfall seepage. With increasing water content and saturation of the rock and soil mass, the charge mobility increased, and the apparent resistivity of the slope gradually decreased. Therefore, the change in the water content in the survey line area at the different stages can be described by the change in the apparent resistivity.  
From Figure 7, it can be seen that the slope resistivity undergoes a process of a rapid decrease, a slow decrease, and then stabilization. At the beginning of rainfall, water in the slope transitioned from adsorbed water to capillary water with increasing water content, and the charge mobility increased. The conduction mode of the current changed from double electric layer conduction on the particle surface to double electric layer conduction and water-soil series joint conduction [38]. With increasing conductive path, the apparent resistivity of the slope rapidly decreased. As the water content continued to increase and the dump slope approaches saturation, the connectivity of the pore water in the rock and soil was enhanced. A current gradually came to be conducted by the pore water, and the rate of decrease in resistivity slows. After reaching the saturation state, the form of water changed to gravity water, and the current was mainly conducted through pore water. The influence of the increase in the water content on the resistivity was gradually weakened, and the resistivity tended to stabilize [39].
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 2 Methods
### Supervised Classifications
#### 2.5.2 Artificial Neural Network (ANN)  
ANN was designed based on the nervous systems of animals [36]. It can be used to estimate the complicated unknown functions based on a large number of inputs. ANNs are often used for supervised classification for their adaptive nature. They can often obtain good performance when the training samples are sufficient. In this paper, the feed-forward neural network (FFNN) with three layers is considered. In the FFNN, each neuron (or call \"unit\") contains a transfer function. The neuron of the hidden and output units performs the nonlinear sigmoid function, while the input units have an identity transfer function. Then, layers are connected to each other by a system of weights, which multiplicatively scale the values traversing the links. The weights and bias of these links in the network is firstly randomly initiated and then fine-tuned through the backpropagation process.
###### Abstract  
While the technique of auralization has been in use for quite some time in architectural acoustics, the application to environmental noise has been discovered only recently. With road traffic noise being the dominant noise source in most countries, particular interest lies in the synthesis of realistic pass-by sounds. This article describes an auralizator for pass-bys of accelerating passenger cars. The key element is a synthesizer that simulates the acoustical emission of different vehicles, driving on different surfaces, under different operating conditions. Audio signals for the emitted tire noise, as well as the propulsion noise are generated using spectral modeling synthesis, which gives complete control of the signal characteristics. The sound of propulsion is synthesized as a function of instantaneous engine speed, engine load and emission angle, whereas the sound of tires is created in dependence of vehicle speed and emission angle. The sound propagation is simulated by applying a series of time-variant digital filters. To obtain the corresponding steering parameters of the synthesizer, controlled experiments were carried out. The tire noise parameters were determined from coast-by measurements of passenger cars with idling engines. To obtain the propulsion noise parameters, measurements at different engine speeds, engine loads and emission angles were performed using a chassis dynamometer. The article shows how, from the measured data, the synthesizer parameters are calculated using audio signal processing.  
auralization; road traffic noise; passenger cars; sound synthesis 2016 6 5 [Applied Sciences]  
Auralization of Accelerating Passenger Cars Using Spectral Modeling Synthesis][PERSON] [  
][PERSON] and [PERSON]
# SS3 DNet-AF: A Single-Stage, Single-View 3D Reconstruction Network with Attention-Based Fusion
## 2 Related Work
### Learning-Based Methods  
With the advent of deep learning, there have been alternative methods aiming to estimate the missing depth cues directly from 2D images and reconstruct the 3D structures. Early methods, such as dual-stream neural networks [9], utilized coarse and refined streams to estimate depth maps. However, these methods commonly produced blurry depth maps as they used L2 loss during training. To improve performance, multi-view depth map fusion techniques were developed, projecting depth maps into unified 3D spaces [10]. Although effective, these methods added computational overhead by requiring intermediate depth map and multiple loss functions. Recent advancements focus on reconstructing 3D structures directly from single 2D images, addressing challenges like missing depth cues and the intrinsic complexity of geometric data [11; 12; 13; 14; 15; 16]. Various approaches treat 3D reconstruction as a classification problem, employing diverse datasets to infer 3D shapes and structures. For example, some approaches assemble composite models by retrieving and deforming shape components [11; 17]. However, extracting accurate 3D shapes directly from images is difficult due to wide variations in object geometries and structures.  
Alternatively, some techniques adopt a modular approach, where segmentation masks, depth cues, and normal maps are treated as intermediate representations and then combined to reconstruct the object's geometry through geometric transformations (e.g., 3D back-projection) [1]. End-to-end architectures have further simplified the process by integrating intermediate stages [18; 19], often using autoencoder-decoder frameworks to generate volumetric grids. Moreover, some methods incorporated shape priors to improve reconstruction accuracy [20; 21]. The transition to end-to-end methods has reduced reliance on intermediate estimates, thereby minimizing accuracy loss. However, challenges persist, including memory limitations that restrict grid resolution to 10243. Recently, methods utilizing learnable 3D representations, such as voxels and point clouds, have achieved higher accuracy and enabled a broader range of applications. These advancements are explored in detail in the following subsections.
###### Abstract
This period was characterized by a continuous increase in both potential and corrosion rates. In the second stage (15 h), substrate stabilization was allowed, with stable corrosion potential and corrosion rate values, and low Fe2+ concentrations, because of its precipitation. During the third stage, they added Ca2+, observing that, for higher concentrations, this caused the potential to decrease and the corrosion rate to increase. [PERSON] et al. [16] studied the role of Ca2+ ions on the CO2 corrosion of N80 carbon steel in a stagnant medium with bubbling in CO2 + N2 at 150 \({}^{\circ}\)C. To this end, they performed measurements using solutions ranging from 0 to 10,000 ppm Ca2+ (from CaCl2) in a medium with the required amount of NaCl, for a fixed Cl\({}^{-}\) concentration of 22,009 ppm. They found that the general corrosion rate increased to 0.0180 mm/y when [Ca2+] = 0 ppm and up to 0.3589 ppm when [Ca2+] = 2000 ppm, decreasing to 0.3070 mm/y when [Ca2+] = 10,000 ppm. Without the presence of Ca2+, the formed FeCO3 corrosion film exhibited a relatively good corrosion resistance, confirmed by the lowest corrosion rate. In view of the above mentioned results, not many works assess the CO2 corrosion behavior in the presence of calcium ions. They that do incorporate this cation from CaCl2, preferentially; although, some authors used CaCO3 CaCO3. In addition, most of the times, they worked under stagnant conditions. Ca2+ ions can be added from different salts by adding chlorides, carbonates, and sulfates, whose presence may alter the metal surface attack. It is worth noting that corrosion processes and the precipitation of corrosion products and/or encrustations are significantly affected by concentration gradients and, thus, by mass transfer between the bulk solution and steel surface [17]. The aim of this study is to investigate the corrosion rate of carbon steel subjected to the attack of electrolytic media saturated with CO2 at a working temperature of 65 \({}^{\circ}\)C under dynamic conditions. The objective is to assess the effect of the Ca2+ ion on the corrosion rate and its dependence on the associated anion (Cl\({}^{-}\) or CO3\({}^{2-}\)) to that cation. Measurements are also compared to values obtained when Na+ is incorporated into the medium. All of this is performed to evaluate whether the addition of Ca2+ affects the corrosion rate and to what extent this effect is attributed to the cation. Linear polarization-resistance measurements are conducted using a potentiostat/galvanostat coupled with a rotor-stator system in a ring configuration with AISI 1040 Steel, rotating at 600 RPM. Solutions are prepared with different salts added, such as NaCl, CaCO3, Na2 CO3, and CaCl2, which are saturated with CO2.
# Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
## 3 System Model
### Problem Formulation  
A MEC system must provide a reasonable service level in terms of the task service delay. Specifically, we aim to restrain the maximum task service delay of the tasks accommodated by a MEC system in each time slot below a given threshold. Therefore, our sleep control problem related to a MECS involves determining an optimal sleep control vector \(\vec{\beta}^{*}(t)=\{\beta_{1}^{*}(t),\dots,\beta_{|N|}^{*}(t)\}\) while maintaining \(\max\{s_{l_{0}}(t),s_{tr}(t)\}\) below a predetermined threshold value \(s_{th}\). Thus, the MECS sleep control problem can be expressed as follows:  
\[\vec{\beta}^{*}(t)=\arg\min_{\vec{\beta}(t)}\sum_{i\in N}\beta_{i }(t)e_{i}^{p}(t)+(1-\beta_{i}(t))e_{i}^{tr}(t)\] \[s.t.\quad\quad\max\{s_{l_{0}}(t),s_{tr}(t)\}\leq s_{th},\quad \forall i\in N,\forall t. \tag{6}\]  
The optimization problem in Equation (6) is a binary integer programming problem, which is one of [PERSON]'s NP-complete problems. Therefore, in this study, we adopt a bio-inspired approach and devise a lightweight heuristic algorithm that can resolve this problem in a distributed manner.