esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Data Sharing in Federated Learning through Blockchain-Based Aggregation
## 3 EIFFeL Framework and Security Analysis  
The EIFFeL framework [16] presents an innovative FL approach that effectively tackles the challenges of data privacy and integrity. Moving beyond the conventional server-client architecture, as outlined in Section 2.1, EIFFeL incorporates a public bulletin board, which broadcasts intermediary information for secure aggregation and integrity checks. This unique architecture positions all clients as verifiers for each other, with the server playing a collective role. In more detail, secure aggregation enables each client to conceal its parameter update within the aggregated value, by leveraging secret sharing and encryption techniques. The integrity property is ensured through non-interactive proof techniques and the adoption of a public bulletin board. In summary, EIFFeL is designed to tolerate multiple malicious client interventions while ensuring the proper aggregation of model parameters.
## Appendix A About the Liver
### The Most Important Diseases of the Liver from an Image Processing Perspective
#### a.1.3 Focal Nodular Hyperplasia (FNH)  
Focal nodular hyperplasia is a tumor-like lesion characterized by a central fibrous scar surrounded by bile ducts and hyperplastic hepatocytes. The cells it contains are normal, but their distribution is not.  
Male-to-female ratio is 1:8. It is usually asymptomatic, but sometimes it can cause some abdominal pain or hepatomegaly.  
Typically, FNH has no capsule, is often located beneath the liver surface (subcapsular), and has well-demarcated borders. Usually it is solitary, but multiple lesions can occur. The typical central scar is not always seen in CT images, especially in small lesions. This central scar is composed of connective tissue, feeding arteries, and draining veins. One very characteristic feature is that the feeding arteries are distributed from the scar to the periphery, having a spoke-wheel appearance. It has a rather regular, slightly elliptical shape. In the arterial phase, the lesion is enhanced rather homogeneously, except for the central scar, which has a marked hypodense appearance according to its connective tissue content. The spoke-wheel pattern is often diagnostic for FNH, although it is not seen in every case. The main differentiation between FNH and hepatocellular carcinoma is in their vascularity. Both FNH and hepatocellular carcinoma lack sinusoids, which are typical in normal liver tissue, but their hemodynamics differ. In the case of FNH, no washout can be seen, instead it is isodense or slightly hypodense in portal phase CT. Sometimes direct draining of the lesion to the hepatic vein is observed.
# Effect of Slot Opening Width on the Air-Gap Magnetic Field of a Direct Drive Permanent Magnet Motor
## 2 Analytical Model
Particularly, the values of the third and fifth harmonics increased with the slot opening width. Figure 9a describes the air-gap magnetic field distribution at different slot opening widths at position 1. When the slot opening width was less than 9 mm, the air-gap magnetic field at the corresponding position of the slot opening was concave. The width and depth of the concave also increased with the slot opening width. When the slot opening width was greater than 9 mm, the magnetic field on both sides of the slot opening was agglomerated and the value of the magnetic field was significantly higher than that in other areas. Figure 9b shows the Fourier analysis of the air-gap magnetic field at different slot opening widths at position 1. When the slot opening width was less than 9 mm, the harmonics of air-gap flux density included not only odd harmonics, but also fewer even harmonics. However, all harmonic amplitudes were small. Figure 9c,d reveals the air-gap magnetic field distribution and its fast Fourier transform (FFT) analysis at different slot opening widths at position 2, respectively. The air-gap magnetic field distribution in Figure 9c shows the same trend as that in Figure 9a. However, the positions that correspond to the magnetic field concaved and agglomerated are different from that in Figure 9a. The comparison between Figure 9a,c indicates that the third harmonic content in Figure 9c was reduced and the second harmonic content was increased. Similar conclusions can also be obtained from Figure 9e,f. The variations of air-gap magnetic field distribution and its FFT analysis with different slot opening widths at position 2 were analyzed, as shown in Figure 9g,h. As opposed to the air-gap magnetic field distributions at positions 1-3, the air-gap magnetic field distribution at position 4 was less affected by the slot opening width. As can be seen from Figure 9h, the fifth harmonic was the main harmonic component. In addition, when the slot opening width was 0 mm, the total harmonic content was low. When the slot opening width was greater than 21 mm, the third harmonic content was significantly increased.
## 2 Related Works  
### Colony Detection and Segmentation  
Interest in automated colony counting dates back to the late 1950s [16, 17] and since then, several tools have been developed to support microorganism detection using traditional computer vision methods [1, 2]. However, these tools often rely on handcrafted features, which limits their level of automation and still demands expert knowledge for effective colony detection, similar to manual counting methods.  
More recently, machine learning-based techniques have become increasingly popular in medical applications, providing more advanced automation options. Researchers have explored convolutional neural networks (CNNs) for bacterial classification [18] and methods for foreground-background segmentation [19], enabling either classification or direct colony counting. Many approaches [9, 20] have adapted U-Net architectures [21] for colony segmentation, while Mask-RCNN [22] has been leveraged for microorganism detection and segmentation in agar dishes [10, 23].  
[PERSON] et al. [4] introduced a patch-based method, dividing high-resolution images into smaller, overlapping sections to detect individual colonies using RCNN models [24; 25], later merging the resulting bounding boxes. This method was extended by [PERSON] et al. [3], who applied synthetic image augmentation to train similar models on limited data.  
However, most approaches focus on either low-resolution images or small image patches, which reduces the accuracy for detecting small colonies or increases computational requirements. By contrast, the AttnPAFPN framework [8] uses a transformer-enhanced version of the Path Aggregation Feature Pyramid Network (PAFPN) [26], allowing for accurate detection even in high-resolution images. With advanced backbones [7] and detection heads [25; 27], AttnPAFPN delivers state-of-the-art performance in high-resolution colony detection benchmarks [4]. However, despite their potential, transformer-based architectures require large datasets due to their extensive parameters and lack of local inductive bias.
## 4 Future Directions
### Build Energy-Efficient MAC and Routing Protocols to Reduce Energy Consumption in UIoT Networks
#### 4.8.1 Auto Device Selection in UIoT Routing Mechanism  
UIoT devices are operated in a constrained UIoT environment, therefore it is necessary to adapt AI based selection module in UIoT devices, that can select appropriate devices and suitable mediums for routing based on the criteria such as distance, turbulence, temperature, etc. This approach can control excess energy consumption in UIoT networks.
# Production Planning Problem of a Two-Level Supply Chain with Production-Time-Dependent Products
## 2 Problem Description
### Decision Variables  
\begin{tabular}{l l} \(Q_{ijlt}\) & production quantity of semi-finished product type \(l\), the quantity that is being processed, at production line \(j\) in supplier \(i\) on period \(t\) \\ \(Y_{ijlt}\) & \(=1\) if production line \(j\) of supplier \(i\) starts processing semi-finished product \(l\) in period \(t\), and 0 otherwise \\ \(Z_{ijt}\) & \(=1\) if production line \(j\) of supplier \(i\) starts producing semi-finished products in period \(t\), and 0 otherwise \\ \end{tabular}  
Now, we give a mixed integer programming formulation for our problem below.  
\[[\text{P}]\text{ Minimize}\quad\sum_{i}\sum_{l}S_{ij}^{U}Z_{ijt}+\sum_{i}\sum_{j} \sum_{l}\sum_{t}\big{(}C_{il}^{p}+C_{il}^{T}\big{)}Q_{ijlt} \tag{1}\]  
\[\text{subject to}\quad\sum_{i}\sum_{j}Q_{ijlt}\geq D_{l,t+p_{l}}\qquad\forall l,\ t \tag{2}\]  
\[\sum_{l}Q_{ijlt}=K_{ij}\cdot Z_{ijt}\qquad\forall i,\ j,\ t \tag{3}\]  
\[\sum_{j}Z_{ijt}\leq 1\qquad\forall\ i,t \tag{4}\]  
\[\sum_{l}Y_{ijlt}\leq L\cdot Z_{ijt}\qquad\ t=1,\ldots,\ T-p_{l},\ \forall i,\ j,\ l \tag{5}\]  
\[\sum_{t^{\prime}=l+1}^{t+p_{l}}Z_{ijt^{\prime}}\leq M\cdot\Big{(}1-Y_{ijlt} \Big{)}\qquad\ t=1,\ldots,\ T-p_{l},\ \forall i,\ j,\ l \tag{6}\]  
\[Q_{ijlt}\leq M\cdot Y_{ijlt}\qquad\ \ \forall\ i,\ j,\ l,\ t \tag{7}\]  
\[Q_{ijlt}\geq 0\qquad\forall\ i,\ j,\ l,\ t \tag{8}\]  
\[Y_{ijlt},Z_{ijt}\in\{0,1\}\qquad\ \ \forall\ i,\ j,\ l,\ t \tag{9}\]  
The objective function (1) is to minimize the total costs incurred in the supply chain, including setup costs of suppliers to start production, production costs at the suppliers, and transportation costs from suppliers to the manufacturing plant. Constraint (2) ensures demand is satisfied, i.e., the sum of production quantities of suppliers for each semi-finished product type is larger than demand. Constraint (3) ensures that the total production quantity is equal to the capacity of the operated production line of the supplier operated in each period. Constraint (4) ensures that only one production line can be selected and operated for a supplier at each operating period. Additionally, constraint (5) ensures that the selected production line can produce up to \(L\) types of products. This constraint defines relationship between \(Y_{ijlt}\) and \(Z_{jlt}\). In addition, constraint (6) prevents a supplier from starting a new batch in the middle of processing a batch. Constraint (7) defines the relationships between \(Q_{ijlt}\) and \(Y_{ijlt}\). Constraints (8) and (9) define the domain of decision variables.
# Novel Microfluidics Device for Rapid Antibiotics Susceptibility Screening
## 7 Conclusions  
In this paper, we presented the engineering of a novel microfluidic device for rapid screening for bacterial antibiotic susceptibility. We utilized an innovative approach for growth chamber inoculation with vacuum, which allowed for the application of a simple single-layer design. The latter was successfully fabricated in a low-cost setup based on resin 3D printing. Our experimental results show that the difference between the microbial growth within the chambers fed with pure medium and antibiotic-enriched medium becomes clearly distinguishable after the sixth hour of cultivation. The latter was also confirmed by directly analyzing the pixel intensity of the images captured by the microscope camera. Consequently, our method is prone to automation not only of the inoculation but also of the cell AMR screening process. Additionally, the time frame for results observation is somewhere around the average of previous reports of such devices.  
Overall, the presented device and the conducted experiments are a good example of a microfluidic system that could be utilized as an antibiotic resistance detection system, which exemplifies a reliable, reproducible and, most importantly, cost-effective experimental method for microbial cell screening.  
The vacuum-loading approach of the growth chambers proved to be quite successful, efficient, and seemingly independent of the chambers' connections (shape and size) to the main feeding channel. The latter represents a significant paradigm shift in microfluidic device design with new horizons yet to be discovered and appreciated.  
Finally, in our endeavor to optimize the design, we have stripped the proposed device from all unessential functions and requirements for additional instruments and specialized knowledge. After all, in our setup, it is completely possible to exchange the vacuum pump with manual suction with a syringe, the driving force of the syringe pump with gravitational flow (i.e., attach the syringe to the wall at sufficient height with adhesive tape), the plate heater with any source of body temperature heat (including a human body itself) and, finally, the cultivation medium with a simple urine sample.  
Conceptualization, B.K. and E.G., methodology, S.P.; writing--original draft. All authors have read and agreed to the published version of the manuscript.  
This research was funded by America for Bulgaria Foundation, grant num.  
Not applicable.  
The totality of the experimental and theoretical work for this research was conducted on the premises of the BioInfotech lab of Sofia Tech Park (Sofia, Bulgaria).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 8: Naked-eye view of the microfluidic device after the experimental part was conducted.
## 3 Results
### Accuracy Analysis of the Integrated STP Pseudo-Observations
#### 3.1.1 Influence of the Sampling Interval  
To analyze the influence of the sampling interval on the accuracy of the STP, we first set up the relationship \(\sigma_{\frac{-}{r}}\ =\ \frac{0.01}{\Lambda^{2}}\) based on Equation (3), corresponding to an accuracy requirement of 0.01 m on the STP. According to \(\sigma_{\frac{-}{r}}\ =\ \frac{0.01}{\Lambda^{2}}\), the accuracy requirement for a priori accelerations increases as the sampling interval increases. If the a priori acceleration errors are less than \(10^{-5}\) ms\({}^{-2}\), the error of the computed STP is less than 0.01 m, even for a sampling interval of 30 s. For a sampling interval of 10 s, the accuracy of the a priori accelerations should be better than \(10^{-4}\) ms\({}^{-2}\). Fortunately, the errors or magnitudes of the accelerations of many dynamic models of LEOs are below \(10^{-4}\) ms\({}^{-2}\), which will be discussed in the following section.
# A Multi-Path Semantic Segmentation Network Based on Convolutional Attention Guidance
## 3 Method
### Multi-Path and Attention-Guided Fusion Module
This output is further fused with the final results of the two paths to obtain the output of the decoder. The fusion is performed using the AGFM module, with the only difference being the fusion of three paths, as illustrated in Figure 6.  
Here, dp represents the features from the DP, mp represents the features from the main path and bp represents the features from the BP. S denotes the Sigmoid operation, \(\otimes\) represents element-wise multiplication, indicating weight allocation, and \(\oplus\) represents element-wise summation. The BP's boundary spatial information can better optimize the contextual information from the main path and the spatial details from the DP. In this case, the BP is employed to guide the fusion of the DP and the main path. It is important to note that the main path is accurate in contextual semantics but loses too much spatial and geometric detail, especially for boundary regions and small objects. Since the BP is better at capturing boundary spatial details, it forces the model to trust the BP more concerning details and use the contextual features from the main path to fill in other regions. The computation of the AGFM in this context can be expressed as Equations (17) and (18):  
\[S=Sigmoid(bp) \tag{17}\]  
Figure 5: The structural diagram of AGFM. \[\left\{ \begin{array}{l}i_{1}=Conv((1-S)\otimes mp),\\ i_{2}=Conv(S\otimes dp),\\ Out_{AGFM}=Down(i_{1}+i_{2}).\end{array} \right. \tag{18}\]  
where \(Down\) represents the downsampled features, \(\otimes\) denotes element-wise multiplication, \(Conv\) represents the convolution operation and \(mp,dp,bp\) represent the features from the main path, BP and DP, respectively, which are input to the AGFM.
# Assisting Drivers at Stop Signs in a Connected Vehicle Environment
## 6 Conclusions  
This paper proposes a driving assistance protocol to help drivers around road intersections controlled by stop signs. The protocol starts by configuring the architectural design of the road intersection. Then, the existence of the main road scenario is investigated. Each vehicle can determine its relative location to the targeted road intersection. The optimal braking distance and deceleration rate are recommended for each vehicle at the targeted intersection. This is according to braking distance mathematical models. After that, the vehicle is informed of the best time to proceed and the optimal traveling speed according to the driving rules and available gaps. This protocol was extensively tested through simulation in different road intersection scenarios. From the experimental study,  
Figure 7: The performance evaluation of the driving assistance protocol at X-junction.  
we can infer that this protocol successfully extends braking distance by 25% on average. It enhances safety conditions and eliminates accidents at the investigated road intersections with the assumption that all vehicles apply the recommended instructions of the assistance protocol. Finally, the proposed protocol increases the road intersection's throughput by 20% on average. It enhances general traffic efficiency over the road network.  
In future studies, we plan to investigate other scenarios where pedestrians and cyclists are simulated on the tested road intersections. Other simulations of the road intersections that consider the parameters of arrival rate, compliance rate regarding a full stop, and speed profile will be considered. Moreover, the scenarios with noncompliance rates are going to be considered. Weather conditions, such as snow and heavy rain scenarios, on the safe stopping distance and traveling speed, are going to be considered in our future studies as well.  
This research received no external funding. Data Availability Statement: Data will be available upon reasonable request.  
The author declares no conflict of interest.
## 3 Experimental Results and Discussion
### Comparative Analysis of the Optimization Performance of the Parallel EGO Algorithm
#### 3.1.2 Convergence Speed of Optimization
However, for functions F2, F3, and F8 (all belonging to the Griewank family), the GEK-based approach shows no performance advantage and even exhibits degraded convergence compared to the OK-based method. This phenomenon may be attributed to the Griewank functions'  
Figure 8: Average convergence curves of the OK-based parallel EGO algorithm on all tested problems. characteristic local oscillations (illustrated in Figure 6), where gradients exhibit violent fluctuations near local optima. The GEK model's strong dependence on gradient information may cause it to overemphasize local curvature at the expense of global trends, thereby misleading the search direction. Consequently, OK-based parallel EGO is recommended for problems exhibiting such functional characteristics. Furthermore, while the GEK model possesses powerful fitting capabilities, practical applications must consider the accessibility of gradient information, and potential noise contamination in gradient data--both critical factors when selecting GEK-based approaches.
## 3 The Deep Learning-Based Approaches
### Optimization Stage  
In the optimization stage, we treat the required SINR and the required harvested energy as fixed constants, and generate a large number of channel realizations \(\left\{\left|h_{ij}\right|\right\}\) following certain distributions (specified in Section 4). Each tuple \(\left(\gamma_{k},e_{k},\left\{\left|h_{ij}\right|\right\}\right);i,j\in \mathcal{K}\) is then fed to optimization algorithms like problem Equation (5), and obtains the corresponding optimized power and PS ratio vectors \(\left\{p_{k},\theta_{k}\right\}\). The functions \(\frac{\theta_{k}^{2}}{\theta_{k}}\) and \(\frac{e_{k}}{\mu_{k}(1-\theta_{k})}\) are convex with variable \(\theta_{k}\) where \(\theta_{k}\in(0,1)\). So, Equations (5a)-(5c) are also convex function. Therefore, the minimum power problem is a convex optimization problem and Matlab's CVX [42] can efficiently solve this problem.
# Underwater Image Super-Resolution via Dual-aware Integrated Network
## 2 Related Work  
### CNN-Based Underwater Image Super-Resolution  
Developing deep learning for various computing tasks shows great benefits [24; 25; 26; 27; 28]. Image SR has been a major focus of research in the area of computer vision. It is focused on increasing the quality of photos and videography. SRCNNs [29] first used CNNs in the SR task, which can learn the relationships between paired images, achieving significant improvements over conventional approaches. In order to push the boundaries of reconstruction accuracy, numerous studies have been carried out in this area [30; 31; 32; 33; 34; 35; 36]. In contrast, less research has been performed on underwater images. Owing to the paucity of extensive underwater image datasets, the execution of the aforementioned underwater SR model has some deficiencies. Thankfully, [PERSON] et al. [37] proposed a dataset termed USR-248 that contains paired LR-HR images and devised an underwater SR generative model named SRDRM. To allow for deeper exploration of the underwater SR domain, they also developed an adversarial version of SRDRM, called SRDRM-GAN, which adopts Markov PatchGAN [38] as the discriminator to reconstruct more texture details. Subsequent to this, an increasing number of CNN-based and GAN-based models have been utilized in underwater tasks. [PERSON] et al. [39] proposed an  
Figure 1: Network architecture of our proposed DAIN.  
approach, called the alpha super-resolution generative adversarial network (AlphaSRGAN), to improve restore accuracy. Improving underwater imagery quality results in higher resolution and more concise details. [PERSON] et al. [40] proposed WaterGAN for synthesizing underwater images based on indoor images and depth maps. [PERSON] et al. [41] proposed an underwater residual convolutional neural network (URCNN) with great depth based on VGG [42]. [PERSON] et al. [43] developed an algorithm for underwater image processing to reduce image blurriness, and they then utilized an end-to-end network to create natural and color-enhanced images. Although these approaches are effective in addressing the undesirable effects of underwater scenes, they lack modeling of global information and they are not conducive to generating more natural and realistic textures.
## 3 Results
### Main Oxidant Production versus Radiation Composition
#### 3.1.2 Alkaline Aqueous Solution  
Figure 3 presents how the addition of base alters the steady state concentrations of O\({}_{2}\), H\({}_{2}\)O\({}_{2}\), \({}^{\bullet}\)OH and HO\({}_{2}\)\({}^{\bullet}\) in the mixed gamma-neutron radiolysis. Simulations have been performed for fraction of \(\gamma\)-rays varying from \(f_{\rm g}\) = 0 (high LET neutron radiolysis) to \(f_{\rm g}\) = 1 (low-LET gamma radiolysis).  
In alkaline solution, the steady-state concentration of HO\({}_{2}\)\({}^{\bullet}\) is significantly reduced, irrespective of the dose rate and radiation composition. The influence of these factors is seen, however, in the case of O\({}_{2}\), H\({}_{2}\)O\({}_{2}\), and \({}^{\bullet}\)OH. Simulation shows that at a small fraction of gamma radiation, base addition may decrease the amount of \({}^{\bullet}\)OH and increase concentration of the stable oxidants (O\({}_{2}\) and H\({}_{2}\)O\({}_{2}\)). This negative effect of base addition is less pronounced at 1 kGy\(\cdot\)s\({}^{-1}\).