esa-sceva/satcom-chunk-collection huggingface dataset:
# Secret Image Sharing Revisited: Forbidden Type, Support Type, and Their Two Approaches
## 2 Review of Previous Work  
[PERSON] and [PERSON] [4] proposed a \((t,n)\) secret image sharing scheme in 2002. Their work has had many citations. In the scheme, the input image is partitioned and distributed into \(n\) shares by using a polynomial. The size of every share is only \(1/t\) the size of the original secret. Any \(t\) shares can reconstruct the image, whereas shares fewer than \(t\) cannot. The \((t,n)\) secret image sharing scheme of [PERSON] and [PERSON] [4] is as follows:  
Polynomial-based \((t,n)\) secret image sharing  
Step 1. Input a secret image. Assume it has \(m\) pixels. Permute all the pixels according to a key to obtain a noisy-looking image \(Q\), which still has \(m\) pixels.  
Step 2. Divide \(Q\) into \((m/t)\) nonoverlapping segments so that every segment has \(t\) pixels.  
Step 3. For each \(t\)-pixel segment \(j=1,\,2,\,\ldots,\,m/t\), use the gray values of its \(t\) pixels as the \(t\) coefficients \(\{a_{0},\,\ldots,\,a_{t-1}\}\) in the segment-dependent polynomial:  
\[p(x)=(a_{0}+a_{1}x+a_{2}x^{2}+\,\ldots\,+a_{t-1}x^{\,t-1})\ mod\ Z, \tag{1}\]  
Step 4. Then, for each segment, the share \(S_{i}\) receives a value \(p(i)\), true for each \(i=1,\,\ldots,\,n\). As the segments are processed sequentially, the data size of each of the \(n\) shares also grows. Finally, when all \(m/t\) segments are processed, there are \(n\) shares. For each \(i=1,\,\ldots,\,n\), the share \(S_{i}\) receives one value from each of the \(m/t\) segments of image \(Q\); thus, each share \(S_{i}\) has \(m/t\) values. (Therefore, each share is \(t\) times smaller than the \(m\)-pixel secret image.)  
In these steps, the value of \(Z\) in Equation (1) can be set to 256, if all arithmetic operations in Equation (1) are in terms of the arithmetic operations in the Galois Field GF(256). For readers who are not familiar with the Galois Field, simply set \(Z\) to be a prime number near 256. For example, a study used \(Z=251\)[4], with auxiliary preprocessing that splits one pixel into two pixels for each pixel whose gray value is greater than 250.  
The steps to recover the secret image are also executed segment by segment. For each segment, the \(t\) coefficients \(\{a_{0},\,\ldots,\,a_{t-1}\}\) in the segment-dependent polynomial (1) is solved using Lagrangian interpolation polynomial method. We omit a discussion of this method because it can be learned through Internet resources or from any textbook on Numerical Analysis, such as [20].
# Matched-Filter Thermography
###### Abstract  
Conventional infrared thermography techniques, including pulsed and lock-in thermography, have shown great potential for non-destructive evaluation of broad spectrum of materials, spanning from metals to polymers to biological tissues. However, performance of these techniques is often limited due to the diffuse nature of thermal wave fields, resulting in an inherent compromise between inspection depth and depth resolution. Recently, matched-filter thermography has been introduced as a means for overcoming this classic limitation to enable depth-resolved subsurface thermal imaging and improving axial/depth resolution. This paper reviews the basic principles and experimental results of matched-filter thermography: first, mathematical and signal processing concepts related to matched-fileting and pulse compression are discussed. Next, theoretical modeling of thermal-wave responses to matched-filter thermography using two categories of pulse compression techniques (linear frequency modulation and binary phase coding) are reviewed. Key experimental results from literature demonstrating the maintenance of axial resolution while inspecting deep into opaque and turbid media are also presented and discussed. Finally, the concept of thermal coherence tomography for deconvolution of thermal responses of axially superposed sources and creation of depth-selective images in a diffusion-wave field is reviewed.  
thermography; matched filtering; thermal-wave radar; thermal coherence tomography; photothermal coherence tomography; thermal waves; photothermal radiometry +
Footnote †: journal: Applied Physics Letters
## 3 Service Delivery in 5G-Enabled D2D Communications Network
### D2D Communications Process  
The D2D communications process consists of device discovery, link step up and data transmission stages similar to that defined for ProSe in [7] to satisfy a service request and establish a communication session. The communication of the UE in cellular coverage and initiation of direct communication between two UEs are controlled by the base station, whereas the complete direct communication and out of coverage are controlled by the D2D devices. The requesting UE can send a broadcast message to discover any UE in proximity or a specific content and the UE with the content can send a broadcast about its data. If a UE receives a request message, it sends a response message via link setup stage. The data transmission can be established with or without the help of gNB. The full security process is presented in Section 5.
# An Improved Shuffled Frog Leaping Algorithm for Electrical Resistivity Tomography Inversion
###### Abstract  
In order to improve the inversion accuracy of electrical resistivity tomography (ERT) and overcome the limitations of traditional linear methods, this paper proposes an improved shuffled frog leaping algorithm (SFLA). First, an equilibrium grouping strategy is designed to balance the contribution weight of each subgroup to the global optimal solution, suppressing the local optimum traps caused by the dominance of high-quality groups. Second, an adaptive movement operator is constructed to dynamically regulate the step size of the search, enhancing the guiding effect of the optimal solution. In synthetic data tests of three typical electrical models, including a high-resistivity anomaly with 5% random noise, a normal fault, and a reverse fault, the improved algorithm shows an approximately 2.3 times higher accuracy in boundary identification of the anomaly body compared to the least squares (LS) method and standard SFLA. Additionally, the root mean square error is reduced by 57%. In the engineering validation at the Baota Mountain mining area in Jurong, the improved SFLA inversion clearly reveals the undulating bedrock morphology. At a measuring point 55 m along the profile, the bedrock depth is 14.05 m (ZK3 verification value 12.0 m, error 17%), and at 96 m, the depth is 6.9 m (ZK2 verification value 6.7 m, error 3.0%). The characteristic of deeper bedrock to the south and shallower to the north is highly consistent with the terrain and drilling data (RMSE = 1.053). This algorithm provides reliable technical support for precise detection of complex geological structures using ERT.  
electrical resistivity tomography; Shuffled Frog Leaping Algorithm; balanced grouping; adaptive moving step size; Baota Mountain mining area +
Footnote †: journal: Applied sciences  
0000-0001-8827-330][PERSON]  
1 School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China; [EMAIL_ADDRESS] (R.H.); [EMAIL_ADDRESS] (M.D.); [EMAIL_ADDRESS] (J.N.); [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (O.X.); [EMAIL_ADDRESS] (S.Z.)
# Efficient Video Frame Interpolation Using Generative Adversarial Networks
## 5 Conclusions  
We have presented a lightweight GAN for frame interpolation. The proposed method neither involves complex networks and nor requires auxiliary data. A suitable loss function is designed, and the ablation study on the loss functions indicates that our choice can increase PSNR and keep more details for the generated frames. The experimental results substantiate a favorable visual quality given by the proposed method as compared to the state-of-the-art approaches, both objectively and subjectively, with a more stable performance. Among the investigated methods, the proposed method without refinement achieves a high average SSIM value of 0.835 with the shortest training time and encoding time. The pre-trained framework in our experiment takes approximately 60 ms to derive a frame from the UCF101 dataset videos with our experiment environment. The PSNR versus time diagram substantiates the advantage of the proposed method. We also investigate the use of a GAN-based refinement process, which is found to improve the estimation of color and motion.  
Conceptualization, Q.N.T. and S.-H.Y.; Methodology, Q.N.T.; Software, Q.N.T.; Validation, Q.N.T., and S.-H.Y.; Formal Analysis, Q.N.T.; Investigation, Q.N.T. and S.-H.Y.; Resources, S.-H.Y.; Data Curation, Q.N.T.; Writing--Original Draft Preparation, Q.N.T.; Writing--Review & Editing, S.-H.Y.; Visualization, Q.N.T.; Supervision, S.-H.Y.; Project Administration, S.-H.Y.; Funding Acquisition, S.-H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported in part by the Ministry of Science and Technology of Taiwan (R.O.C.) under Grant MOST 108-2221-E-027-065-MY2.  
The authors declare no conflict of interest.
## 1 Introduction
Second, although some recent studies have explored frequency-dependent structural responses [42; 43; 44], most methods remain limited to separate frequency band computations without true fusion. Existing fusion strategies typically apply fixed energy-based weighting, which fails to fully exploit the complementary information across frequency bands. As a result, these approaches struggle to simultaneously preserve large-scale fault continuity and resolve fine-scale fracture details. To overcome these limitations, we propose an Optimally Oriented Coherence Attribute (OOCA) method that integrates geological guidance with multi-frequency structural analysis. The method constructs a set of structure-oriented paired complex model traces based on local dip estimation, enabling directional coherence evaluation across dip-orthogonal trace pairs. The direction with the lowest coherence--indicating maximum discontinuity--is automatically selected as the optimal direction. A distance-weighted scheme is further introduced to enhance local structural sensitivity, and a Gabor-based multi-frequency fusion strategy is employed to jointly enhance the expression of both large-scale fault frameworks and subtle discontinuities. This approach offers a more adaptive, geologically consistent, and computationally efficient solution for fault and fracture detection in carbonate reservoirs.
## 2 Data Processing and Track Information Extraction of Floating Vehicle
### Definition of Basic Parameters of Floating Car Method  
(1) The coverage of the target path to the road  
Section path is defined as an acyclic directed path \(\pi=(k_{s},\,k^{\prime},\,\dots\,,\,k^{\prime\prime},\,k_{e})\), and \(o_{s}\) is defined as the distance from the starting point of the road section \(k_{s}\) to the starting point of the route; \(o_{e}\) is the distance from the start of the section \(k_{e}\) to the end of the route. For a road section \(k\), if the length covered by the path \(\pi\) on the road section \(k\) is \(a_{k}\), then:  
\[a_{k}=\left\{\begin{array}{ll}o_{e}-o_{s}&k=k_{s}{=}k_{e}\\ l_{k}&k=k_{s}\
eq k_{e}\\ o_{e}&k=k_{e}\
eq k_{s}\\ l_{k}&k\in\pi\backslash\{k_{s},k_{e}\}\\ 0&\textit{otherwise}\end{array}\right. \tag{1}\]  
According to Formula (1) [29], the coverage degree \(\alpha_{k}\) of the route to the road section \(k\) can be calculated by comparing the covered length \(a_{k}\) with the total length \(l_{k}\) of this road section, and the calculation formula is \(\alpha_{k}=a_{k}/l_{k}\).  
(2) The coverage of the floating car track road section  
It is assumed that for the road section \(k\) where the \(i\)-th observation point is located, the length covered by the floating car track is expressed as \(r_{ik}\). Then:  
\[r_{ik}=\left\{\begin{array}{ll}o_{i+1}-o_{i}&k=k_{i}=k_{i+1}\\ l_{k}-o_{i}&k=k_{i+1}\
eq k_{i}\\ o_{i+1}&k=k_{i+1}\
eq k_{i}\\ l_{k}&k\in p_{i}\backslash\{k_{s},k_{e}\}\\ 0&\textit{otherwise}\end{array}\right. \tag{2}\]  
According to Formula (2) [29], the coverage degree \(\rho_{ik}\) of the floating car track to the road section \(k\) can be expressed as \(\rho_{ik}=r_{ik}/l_{k}\).  
(3) The coverage of the floating car track on a single road section to the target path  
For the road section \(k\), the coincidence ratio of the trajectory of the floating car and the target path is expressed as \(\beta_{ik}=\min\,\{\alpha_{k},\,\rho_{ik}\}\).  
As can be seen from Figure 2, the object of this study contains three lines: the trajectory of the floating car, the road network path composed of road network nodes and the target path to be calculated. In practice it is almost impossible for these three lines to coincide with each other.
# Analysis of the Efficiency of Slope Evacuation for Dense Crowds in Urban Street-Type Public Spaces
## 2 Related Works
### Simulation Study of Pedestrian Flow on Slopes  
At present, a small number of scholars are concerned with the simulation study of pedestrian flow on slopes. For example, [PERSON] analyzed the personnel evacuation test in different slope scenarios from the perspective of dynamics, and obtained the empirical formulas of the relationship between walking speed, step length, and slope, which provide a basis for the fire evacuation design, evacuation modeling, and the accuracy of the grid division of complex and large-scale buildings [18]. [PERSON] established a potential function field cellular automata model of counter flow in a sloped channel and realized the simulation of the pedestrian flow dynamics and the phenomenon of falling down [19]. [PERSON] improved the social force model to realize the simulation of pedestrian falls, injuries, and impatience in the scenario of opposing pedestrian flows on slopes and conducted a single-file pedestrian flow experiment with varying slope angles to investigate the effects of slope angle on stepping behaviors [20; 21].  
The studies mentioned above on pedestrian flow on slopes have mainly concentrated on the movement of pedestrians in low-density scenarios. They overlook the complex and realistic situations that occur during high-density events, such as festivals or emergencies. Therefore, there is an urgent need for more comprehensive models to address the various evacuation challenges associated with slopes in the real world, especially in high-density pedestrian street spaces, and our study achieves this by considering multiple factors simultaneously.
## 2 Background and Related Work
### English-Chinese Machine Translation
#### 2.2.1 Designing New Learning Models  
[PERSON] et al. address the problem of how to define and accurately measure human parity in translation and describe Microsoft's machine translation system [30]. They see that the translation quality of the latest neural machine translation system is at human parity. To address the issue of duplicate or missing translations, [PERSON] et al. proposed neural machine translation improvements based on a novel beam search evaluation function [31]. They show that the proposed methods can effectively improve the English to Chinese translation quality.  
SMT often performs better than NMT in translation adequacy and word coverage. Thus, it is a promising direction to combine the advantages of NMT and SMT. [PERSON] et al. propose a deep neural network-based system combination framework leveraging both minimum Bayes-risk decoding and multi-source NMT, which take as input the N-best outputs of NMT and SMT systems and produce the final translation [32]. This approach has been shown to significantly outperform the conventional system combination methods.  
[PERSON] et al. propose to enhance encoding components with different levels of composition [33]. This model takes (1) the original word embedding for raw encoding with no composition and (2) a particular design of external memory in a neural turing machine (NTM) for more complex compositions. An empirical study on Chinese-English translation shows that their model can improve by 6.52 BLEU points. [PERSON] et al. describe the Sogou neural machine translation systems for the WMT 2017 Chinese-English news translation tasks [34]. Their translation systems are built based on a multi-layer encoder-decoder architecture with attention mechanism. The best translation is obtained with ensemble and reranking techniques. Their translation system achieved the highest BLEU among all 20 submitted systems.  
Tencent neural machine translation systems were designed for the WMT 2020 news translation tasks [35]. Their systems are built on deep Transformer and several data augmentation methods. They propose a boosted in-domain finetuning method to improve single models. They achieved a BLEU score of 36.8 on the Chinese-English task. In 2021, Tencent introduced a system based on the Transformer with several novel and effective variants. Their constrained systems achieve very good BLEU scores.
# Firmware Updates over the Air via LoRa: Unicast and Broadcast Combination for Boosting Update Speed
## 3 Proposed Update Methods
### Only Broadcast Method  
There is an improvement that can be applied to the previous method, which modifies stage (c). It mainly consists of sending the pending chunks in broadcast frames (see an example in Figure 3): the gateway sends a _SEND ME THE BITMAP_ frame to the node, which answers with a _BITMAP_ frame specifying the missing chunks: in this example, chunks #1 (0x01) and #542 (0x021D) are missing. Then, the gateway sends each of them once, in broadcast frames. In the example, the former is received correctly, but the latter is not. Then, the gateway sends again the _SEND ME THE BITMAP_ frame. The node answers with the updated bitmap, in this case, only including fragment #542 as _missing_. The gateway sends it again and then requests the bitmap. In the example, the node answers with an _ALL CHUNKS RECEIVED_ frame.  
The advantage of this method is that, as the gateway has sent chunk #1 (once) and #542 (twice) in broadcast frames, any other nodes which are missing them will also be able to store them in the corresponding position of the flash memory.  
As before, the gateway follows an order, and it will not ask node \(j\) about the chunks it is missing until node \(j-1\) has received all the chunks. Therefore, the final nodes are more likely to have received the complete firmware by the time the gateway queries them.  
Figure 3: Detail of stage (c) of the _only broadcast_ method.
## 3 GAs for Multi-Objective Routing Optimization  
To address the multi-objective routing optimization problem in the context of EnFVs, GAs have emerged as a powerful optimization technique. GAs are inspired by the process of natural selection and evolution, and they have been widely applied to various optimization problems, including routing optimization. The main advantage of using GAs is their ability to efficiently search and explore the solution space, considering multiple conflicting objectives simultaneously [10].  
Multi-objective optimization (MOO) is a mathematical framework used to solve complex decision-making problems where multiple conflicting objectives need to be simultaneously optimized. In MOO, the goal is to find a set of solutions that represent a trade-off between these conflicting objectives, rather than a single optimal solution. Mathematically, a multi-objective optimization problem can be defined as follows:  
Let us consider an optimization problem with m objectives, represented as \(f_{1}(x),f_{2}(x),\)\(\ldots,f_{m}(x)\), where x is a vector of decision variables.  
_Minimize (or maximize) the objectives:_ For minimization, the general form of a multi-objective optimization problem can be expressed as:  
Minimize:  
\[f_{1}(x),f_{2}(x),\ldots,f_{m}(x) \tag{1}\]Subject to constraints:  
\[g_{i}(x)\leq 0,i=1,2,\ldots,p \tag{2}\] \[h_{i}(x)=0,j=1,2,\ldots,q, \tag{3}\]  
Here, the objective functions \(f_{i}(x)\) represent the quantities to be minimized, and the constraints \(g_{i}(x)\) and \(h_{i}(x)\) represent inequalities and equalities, respectively. These constraints define the feasible region for decision variables \(x\).  
For maximization, you would replace \"Minimize\" with \"Maximize\" in the problem statement. The key challenge in MOO is finding a set of solutions that represent trade-offs among the conflicting objectives. The GA has the potential to choose the best dominant solution out of solution space. The foundation of a GA lies in its key components, including the representation and encoding of routes, the fitness evaluation and objective functions, the selection, crossover, and mutation operators, as well as the concept of Pareto dominance and non-dominated sorting [14], explained in following subsections.
## 2 Background
### Autonomous Updates  
The key feature of an autonomic system is the ability to self-manage. Other, and not entirely unrelated, features are: interacting with users at the policy level, hardware and software self-protection, and interacting with legacy systems. An autonomic system is aware of its environment, and can anticipate and react to environmental changes [20], and these are all features that are essential for applying over-the-air software and configuration updates in large and widely-distributed wireless sensor networks.  
Extending the model presented in [2], we identify the following seven software components for autonomous software/configuration updates, as shown in Figure 1:  
* On the management-host: 1 Self-Management* Preparation
* Insertion
* Monitoring
* Distributed across the network:
* Dissemination
* On the nodes:
* Activation
* Fault Detection and Recovery  
Figure 1 contains the following data components:  
* Update--the generated update.
* Policy--specification of the user update policy.
* Update*--the prepared update along the necessary parameters to control dissemination, activation, fault detection, fault recovery, and feedback.
* Feedback--the status of the various activities, returned to the management host.
* Status--the status of the entire update, returned to the user management application.  
Some of the key aspects, when compared to a _manual_ update, are:  
* Support for policy-driven operation: in addition to the data comprising the update, this requires specification of the policies specifying how the update is to be performed, and especially how error conditions are to be handled.  
Figure 1: Autonomous Update Components.  
Self-Management: the management host must incorporate the required policies both in disseminating and activating the update, and also in responding to feedback (again, error handling is the essential component here).
* Planning: the self-management component needs to do planning (for example, using simulated updates) in order to determine whether it is possible to perform an update on the current network according to the specified policies, and also to determine the optimum parameters for doing so (primarily to minimize power consumption).
* Dissemination: the dissemination needs to operate according to the various parameters provided, and to also report feedback.
* Activation: the activation needs to operate according to the various parameters provided, and to also report feedback.
* Autonomous Fault Detection and Recovery: whereas data faults can be detected at the management node or network level, software update faults must be detected at the node level (to enable recovery when communication has been lost). The faults must be automatically detected, and recovery action must be self-initiated (possibly following communication with neighbours).
* Feedback: all the update components need to provide feedback to both allow control of the current update, and to allow planning for future updates.