esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
[36] proposed a DDPG-based scheme to minimize service costs and task execution delays by generating optimal computation offloading policies. Additionally, [PERSON] et al. [37] introduced an attention-based MAPPO algorithm to optimize UAV computation offloading policies, minimizing weighted energy consumption while maintaining service fairness. To address resource allocation and energy efficiency in UAV-assisted networks, [PERSON] et al. [38] developed a deep reinforcement learning framework to enhance spectral efficiency, network capacity, and resource distribution in beyond 5G networks, employing a deep energy-efficient resource allocation (EERA) method for dynamic and energy-conscious resource management. [PERSON] et al. [39] proposed a block successive upper-bound minimization (BSUM) strategy to optimize resource allocation within a two-stage UAV-assisted edge computing system. [PERSON] et al. [40] introduced a communication-enabled multi-agent decentralized double-deep Q-network (CMAD-DDQN) approach to simultaneously improve energy efficiency and network coverage. These methodologies collectively address critical challenges in UAV-assisted networks, leveraging innovative optimization techniques to achieve enhanced performance. From the above literature review, it can be found that current algorithmic approaches for MEC optimization exhibit methodological deficiencies, particularly in reinforcement learning (RL)-based methods applied to continuous action spaces. The pervasive issue of Q-value overestimation stems from inherent flaws in conventional Q-learning and DQN architectures, where the maximization bias in Bellman updates coupled with function approximation errors leads to systematic overestimation of the action values. This phenomenon critically undermines the stability and convergence properties of algorithms designed for fine-grained control tasks such as dynamic task offloading and resource allocation. In response to the above deficiencies, recent breakthroughs in RL theory have established dual-critic architectures (e.g., TD3/MATD3) and delayed update mechanisms as theoretically-grounded solutions to the Q-value overestimation problem. The twin Q-network paradigm fundamentally mitigates overestimation bias through independent value function training with minimum operator-based updates, while the policy delay mechanism prevents premature convergence by decoupling policy updates from value function optimization. These innovations are particularly salient for MEC systems requiring multi-agent coordination, as demonstrated by distributed task offloading in UAV swarms or collaborative resource allocation among edge servers. The MATD3 framework extends these advantages through its decentralized critic architecture and gradient delay mechannism, enabling stable optimization of continuous control variables in high-dimensional action spaces.
# An Improved Fuzzy Vector Signature with Reusability
## 5 Performance Analysis
### Storage or Transmission Costs
For \(n=512\), 1024, and 2048 cases, Table 2 shows that the \(P_{ID}\) size of the FVS schemes are overwhelmingly smaller than that of the fuzzy extractors. Compared to Reference [20], our FVS scheme obtains a slightly shorter size of SP with the same parametrization. Regarding the signature size, the \(\sigma_{ID}\) in our scheme consists of \(n+2\) elements in \(\mathbb{G}_{1}\) plus 2 elements in \(\mathbb{Z}_{p}\) that are transmitted to the server. When taking the Type-3 paring and \(n=512\) again, the amount of \(\sigma_{ID}\) transmission is about \((512+4)\times 256/2^{13}=16\) KB. \(\delta=1/2\) is the the probability that verification fails, and we expect the user to run step [A2] of the authentication protocols twice, during which the transmission cost of \(\sigma_{ID}\) becomes about 32 KB.
## 1 Introduction
The merging of the droplets is induced by the capillary pressure, along with pressure differences across the interface, which causes droplets to expand. The liquid bridge can induce a vertical upward force near the top of the ridge. The merged droplet exhibits an upward motion, which initiates oscillations. The retraction of the merged droplet begins, and the contact area between the droplet and the substrate surface progressively diminishes. The merged droplet ascends into the air while oscillating horizontally in both directions. The rebound direction of combined multiple droplets still predominantly remains perpendicular to the plane [12]. When droplets re-approach the surface, they can be removed again due to collisions with other droplets, but horizontal movement can still be restricted [19; 20]. The process of droplets re-approaching the surface is very similar to the process of colliding with the surface at low collision speeds. Structures such as point-like defects [21; 22] and tapered post arrays [23] have had a significant impact on droplet collision dynamics. It has been reported that adjusting surface wettability during droplet collision can increase the horizontal displacement distance by several tens of times the droplet size [24; 25]. However, most studies have implemented changes in the micro/nanostructures of flat surfaces to achieve asymmetric wettability, without considering the influence of wettability on droplet behavior on macro-structures. Furthermore, the durability of micro/nanostructures and surface coatings on macro-structures to maintain superhydrophobicity under practical conditions (such as extreme temperatures) imposes limitations on anti-icing performance. This would lead to asymmetric wettability phenomena on macro-structures and potentially cause defects in vertical jumping performance. Therefore, it is essential to investigate the influence of macro-structure wettability on the horizontal movement of droplets. This study analyzed numerically the influence of ridge's wettability distribution on droplet coalescence jumping performance using the volume of fluid (VOF) method [26]. As shown in Figure 1, spontaneous merging of two droplets is induced, and due to the presence of superhydrophobic ridges, the droplets completely detach from the surface and recoil. The dynamics of droplet recoil were investigated based on the initial contact angle ratio (\(\beta=\theta_{R}/\theta_{L}\)) and aspect ratio (\(AR\)) of the ridges. Changes in these two factors were examined with respect to the height of the jumping, the horizontal offset distance, and behavioral characteristics, based on the average speed of jumping droplets along each axis. The role of ridge's wettability distribution and geometric aspect ratio in efficient droplet transportation was analyzed. This study can provide strategies for controlling the direction of droplet jumping and increasing the coalescence frequently between adjacent droplets in related applications.
# Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks +
## 4 Methodology
### Dataset  
Our study used the LoRaWAN dataset published by [PERSON] et al. in [46], gathered in an area of 52 km\({}^{2}\) in Antwerp in Belgium, and is publicly available. Data were collected by attaching LoRa modules to postal service vehicles, whereby a total of 130,343 data points were gathered from these nodes sending a message every minute to 72 LoRaWAN gateways deployed by a private company called Proximus for three months. A map showing a random sample of data points in Antwerp evenly distributed in the city streets is shown in Figure 2. For every data point or message, the authors recorded X and Y position coordinates of each LoRa node with its RSSI values received by 72 different gateways. If any of the gateways did not receive a particular message, a RSSI value of \(-200\) was recorded. The distribution of RSSI values is presented in Figure 3 and varies between \(-122\) dBm and \(-79\) dBm.
# FFT-Based Scan-Matching for SLAM Applications with Low-Cost Laser Range Finders
## 1 Introduction
There are many image registration approaches that can match two positions in a long distance without any good initial guess for searching. However, scan images are quite different from normal images, since they have sparse points collected by lidar. As a result, normal feature-based image registration methods, such as Scale-Invariant Feature Transform (SIFT) [40], Speeded-Up Robust Features (SURF) [41], ORiented Brief (ORB) [42], etc., may not be suitable. We need to find the shift of the image by considering all the data in the image. By changing the image to the frequency domain, the FFT transform is suitable for this step. The idea in this paper is mainly derived from FFT-based image registration [43; 44; 45; 46], because the scan image is simple and lies in one plane. In our previous works [47; 48], we proposed the FFT-based scan-matching algorithm and its improvement FFT-ICP. In this paper, we detailed previous work about data pretreatment. To address the low detection range problem of low-cost LRF sensors, we added a pre-alignment module based on missing data features to determine the rough rotation angle before FFT processing under certain conditions. We also propose a new solution for FFT-ICP scan-matching with low-cost LRF sensors. The rest of the paper is organized as follows: In Section 2, we propose robot modeling and data pretreatment, as well as missing data feature extracting and matching; In Section 3, the FFT-based scan-matching algorithm is introduced; In Section 4, the complete solution of the improved FFT-ICP scan-matching is proposed; In Section 5, by using a cleaning robot with only a low-cost LRF sensor in different complex indoor environments, experiments are completed with the proposed method and comparative methods. Section 6 outlines our conclusions.
# A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking
## 5 Analysis of Image Encryption and Decryption
### Security Attacks  
#### 5.2.1 Differential Attacks  
Common indicators for the analysis of these types of attacks are employed by calculating the changing pixel rate (\(NPCR\)) between the original input image and the encrypted image and the unified averaged changed intensity (\(UACI\)). The calculations for these values are performed in the following way. Let \(\Phi^{1}\) and \(\Phi^{2}\) represent two encrypted images obtained before and after changing one pixel of the input image. Let the bipolar array be denoted \(G(\ell_{1},\ell_{2})\) and defined by  
\[G(\ell_{1},\ell_{2})=\begin{cases}0,if\ \[PERSON]^{1}(\ell_{1},\ell_{2})=\[PERSON]^{2}( \ell_{1},\ell_{2})\\ 1,if\ \[PERSON]^{1}(\ell_{1},\ell_{2})\
eq\[PERSON]^{2}(\ell_{1},\ell_{2})\end{cases} \tag{16}\]  
where \(\[PERSON]^{1}(\ell_{1},\ell_{2})\) and \(\[PERSON]^{2}(\ell_{1},\ell_{2})\) are the value of pixels at \(grid(\ell_{1},\ell_{2})\) for \(\[PERSON]^{1}\) and \(\[PERSON]^{2}\). Then  
\[NPCR=\sum_{\ell_{1},\ell_{2}}\frac{D(\ell_{1},\ell_{2})}{K}\times 100\%, \tag{17}\]  
where \(K\) is the pixel number.  
\[UACI=\sum_{\ell_{1},\ell_{2}}\frac{|\Phi^{1}(\ell_{1},\ell_{2}),\Phi^{2}(\ell _{1},\ell_{2})|}{M\cdot K}\times 100\%, \tag{18}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Image**} & \multirow{2}{*}{**Watermarking Level**} & \multicolumn{2}{c}{**Entropy**} \\ \cline{3-3}  & & **Encrypted Image** \\ \hline Peppers & Level 1—Lena & 7.997024 \\
256 \(\times\) 256 & Level 2—Fingerprint & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Entropy analysis of the peppers image with level 2 decomposition.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Image**} & \multirow{2}{*}{**Watermarking Level**} & \multicolumn{2}{c}{**Entropy**} \\ \cline{3-3}  & & **Encrypted Image** \\ \hline Peppers & Level 2—Lena & 7.997292 \\
256 \(\times\) 256 & Level 3—Fingerprint & \\ \hline Peppers & Level 3—Lena & 7.997724 \\
256 \(\times\) 256 & Level 3—Fingerprint & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Entropy Analysis of peppers image with 3-Level decomposition.  
where \(M\) is the largest pixel value based on the encrypted image. For a highly secure image the maximum _NPCR_ is 100%, the closer the _NPCR_ is to 100 the higher the security level of the encrypted images. Numerical values of the calculated _NPCR_ and _UACI_ for encrypted images of different input images are listed in Table 8. The resistance of the algorithm towards differential attacks is evident from the _NPCR_ values close to 100.
# Intelligent Transportation System Technologies, Challenges and Security
## 2 Intelligent Transportation Systems
### Intelligent Transportation Communication Technologies
#### 2.2.6 Cooperative Intelligent Transportation Systems  
Cooperative Intelligent Transportation Systems (C-ITS) are used in systems that are important in terms of ITS applications and in terms of efficiency, safety, and environmental friendliness in transportation. It is also one of the innovative technologies aimed at improving these systems, allowing tools and applications to interact directly with each other. According to C-ITS, communication devices placed inside the vehicle and on the roadside will provide vehicle-to-vehicle and vehicle-to-infrastructure communication, informing drivers about all events occurring on the road and ensuring safe and comfortable travel [27]. The interoperability of C-ITS communication systems and all transportation systems is shown in Figure 7.
# Evaluation of Ultrasonic Bonding Strength with Optoacoustic Methods
## 4 Results and Discussions
### Evaluation of Bonding Strength with Frequency Response  
Figure 11 illustrates the frequency response of the bonded specimen. The horizontal axis is the driving frequency. The vertical axis is an amplitude spectrum that means the displacement of specimen. The five peaks are seen at 163, 606, 1918, 3487, and 6033 Hz in the frequency response. We interpret that these peaks are the resonant frequencies. Figure 12 shows the bonding strength plotted against the resonant frequency. The red circle plots show the interface fractures. Blue triangle plots show the base material fractures. In the interface fracture, the bonding strength exponentially increased with the resonant frequency. This tendency appeared at each of the resonant frequencies shown in Figure 11. It is noted that the bonding strength of the specimen exhibits a correlation with the resonant frequency better than the vibration time. The approach in this study is similar to the \"tapping mode\" in atomic force microscopy (AFM), which utilizes the forced vibration of a cantilever. In AFM, the potential energy of the object alters the resonance behavior of the cantilever. In the present case, the resonant frequency of the vibrating joint (the cantilever) changes depending on the interfacial strength between the two sheets. The results indicate usefulness of this method in the evaluation of the bonding strength in a non-destructive manner in the case of interface fractures. On the other hand, in the case of the base material fracture, the bonding strength is deviated from the relation observed in the interfacial fracture. Figure 13 shows bonding parts of specimens. Indentations on the bonding part became bigger due to penetration of the knurled edges on the ultrasonic horn tip. With the development of the micro-bonds at the bonding interface, a relative motion between the bonding sheets during the ultrasonic bonding is hindered. This may cause the relative motion between the horn tip and the upper sheet, causing the enhancement of edge penetration and the thinning of the bonding part. The resonant frequency measured in this study is affected by the second moment of the area of the bonded part, because the specimen vibrates with a bending motion. Thus, the deviation of resonant frequency in the base material fracture is considered to be due to the thinning of the bonded part.  
Figure 11: Frequency response of bonded specimen. (**a**) Driving frequency from 10 to 1000 Hz; (**b**) Driving frequency from 1000 to 10,000 Hz.  
Figure 10: Relation between bonded area and bonding strength.  
Figure 12: Relation between resonant frequency at each of vibration mode and bonding strength. The resonances were observed at the driving frequency of **(a)** 173 Hz; **(b)** 626 Hz; **(c)** 1485.9 Hz; **(d)** 1717.9 Hz, and **(e)** 2013.7 Hz.  
Figure 13: Indentation at contacted area with the horn bonding tip. **(a)** Vibration time is 200 ms; **(b)** Vibration time is 1000 ms.
## 3 Analysis of the Final Equilibrium State  
### Boundary Conditions  
The main problem to deal with is the size of the toxin ions, which is huge compared to atomic ions. Moreover, these molecules are not compact, nor they have a defined shape. Therefore, the first assumption made is that these proteins are spheres with effective radius mentioned in Table 1. A second assumption considered is that, during their movement, toxin ions are not noticeably deformed, losing entirely their spherical shape. Consequently, the electric field applied should have a relatively small intensity. By considering them approximately spheres, their center cannot approach the duct wall at a distance less than their radius, so the Stern model can be used. According to this model, near to each electrode, a double layer is formed (the area that the ions are cornered) consisting of two parts [27, 28]:  
1. The compact part (Stern layer), which consists of one layer of ions in direct contact with the walls of the duct. This is simulated with a Helmholtz capacitor, as presented in Figure 4, with effective width \(\lambda_{S}\), approximately equal to the radius of the ion (Table 1). Thus, the capacity of the compact part per unit area \(c_{H}\) is given by \[c_{H}=\frac{\varepsilon}{\lambda_{S}}\] (1)
2. The diffuse layer, which is formed besides the compact part in the inner side of the duct which is simulated with a Gouy-Chapman-type capacitor with effective width \(\lambda\) (Figure 4) and capacity of the diffuse layer per unit area \(c_{D}\) is given by \[c_{D}=\frac{\varepsilon}{\lambda}\] (2)  
Figure 3: Change of toxin concentration C\({}_{+}\) across the duct width L, supposing that it has positive charge. C\({}_{\text{M}}\): the toxin concentration at the middle of the duct after the electric potential application. The top illustration represents the duct after applying the electric field showing the accumulation of charged toxins on one side of it.  
Firstly, we should declare the boundary conditions. Considering that for \(y=\frac{L}{2}\) (the center of the duct), the potential is equal to zero, \(\varphi\left(y=\frac{L}{2}\right)=0\).  
Thus, the potential at the point with \(y=0\) is \(\varphi(0)\) and \(-\varphi(0)\) at \(y=L\). Still, considering the relationship between potential and distance linear within the compact layer, we have:  
\[\varphi=\pm\varphi(0)\pm\lambda_{s}\frac{\partial\varphi}{\partial y}\text{ for }y=0,L \tag{3}\]  
At this point, it should be clarified that in the model there is an external electric field which is created by the application of the voltage V on the two electrodes externally to the duct simultaneously with an opposite electric field which is created by the double layer of ions according to the Stern model. Therefore, the external electric field causes the movement of toxin ions and results in the creation within the solution of a total internal electric field. This internal electric field is mathematically expressed by the creation of potential \(\varphi\) (V), which varies spatially along the y axis.
## 4 Experiments and Results
### Learning Capacity  
To validate the learning capabilities of the proposed HPANet network, comparative experiments were conducted under the experimental configurations outlined in the previous section. The HPANet was rigorously evaluated on the Kvasir dataset against four traditional method-based models, including U-Net, as well as the PVT-based Poly-PVT approach. Quantitative results in Table 1 highlight HPANet's superior performance across key metrics, including mIoU, mDice, recall, precision, and F2-score.  
As shown in Table 1, HPANet demonstrates enhanced learning capabilities for colonoscopy image segmentation through comprehensive benchmarking on the Kvasir dataset. Compared to the five selected models, this model achieved excellent performance in multiple metrics. Notably, when evaluated against the PVT-based Polyp-PVT model sharing the same backbone architecture (PVTv2), HPANet exhibits a 0.6% improvement in both mIoU and mDice metrics, which means that the segmentation results have higher spatial overlap and better pixel-level alignment accuracy with the real polyp region. It is helpful for doctors to clearly observe the boundary and shape of polyps and reduce the occurrence of misdiagnosis. The enhancements extend to recall (0.33% higher), precision (0.69% elevation), and F2-score (0.47% increase), indicating balanced precision-recall optimization. The improvement of metrics reduces the missed detection of small polyps or polyps with complex morphology. Furthermore, comparative analysis with PraNet reveals more substantial advancements: 5.00% mIoU, 4.02% mDice, and 6.05% recall improvements, alongside 0.31% precision and 5.37% F2-score gains. These metrics collectively validate the model's robustness in handling complex colonoscopic feature representations under clinical simulation conditions.
## 3 Results and Discussion
### Case Study-Winter 2014-2015
Figure 7: Shows spatial distribution of PM\({}_{10}\) mass concentrations (\(\mu\)g/m\({}^{3}\)) measured at three different sites in Lahore city: (**a**) Lahore University of Management Sciences; (**b**) University of Engineering and Technology (UET), Kala Shah Khaku (KSK) campus; (**c**) University of Engineering and Technology (UET), Lahore campus, during the winter months. The red line in each graph represents the Pak-NEQS for PM\({}_{10}\) in ambient conditions; (**d**) shows the correlation between MODIS-retrieved AOD and average PM\({}_{10}\) concentration at all monitoring stations. Only days were selected when both MODIS and any of the three monitoring sites had data.