esa-sceva/satcom-chunk-collection huggingface dataset:
# A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security
## 3 Isfl-Sys Architecture
### Overall Framework  
As shown in Figure 2, a system named ISFL-Sys is designed. The overall architecture of the system consists mainly of the blockchain ledger module and the federated learning module within the edge nodes. In the following, we will provide a detailed explanation of the functionalities of each component.  
The blockchain ledger module is primarily responsible for functions such as creating, authenticating, and publishing transactions within the system. Within each edge node, a ledger based on a local directed acyclic graph is maintained. This ledger is used to store transactions that the node can receive. The transactions store various pieces of information, including publishing nodes, authenticating nodes, and local models, among others, as shown in Figure 3. Within the system, different nodes can communicate with each other through wired or wireless networks, updating their local transaction information and broadcasting transactions to the rest of the nodes in the system.  
Figure 2: Architecture of the ISFL-Sys system.  
Within the federated learning module, we introduce a novel and efficient trustworthy federated learning method named ETFL. This method incorporates a new federated learning aggregation rule. During the training iteration process, the security of client model parameters is validated by a trusted benchmark dataset model (server model) before being uploaded. After completing the training, the relevant information is packaged into transactions.  
In this system, for the sake of managing the process of federated learning tasks, smart contracts in the blockchain can be utilized to control the system. By deploying smart contracts on the network, any node can act as an initiator for federated learning tasks, and by issuing rewards and other means, nodes actively participating in the training task can be incentivized, thus enhancing the enthusiasm of nodes within the network to engage in training, to prevent laziness and harmful nodes (engaging in poisoning attacks) from disrupting the learning task.
## 3 Results
### Validation of DEGs Expression  
A GEPIA2 database and Western blot were performed to validate the expression level of DEGs. The GEPIA analysis results showed that the expression level of COL1A1, CXCL8, SPP1, MMP13, SERPNE1, MMP1, MMP3, and MMP10 was significantly upregulated in ESCA (Figure 8a, \(p<0.05\)). In addition, the Western blot results showed that MMP13, SPP1, MMP10, and COL1A1 expression were upregulated in ESCC cell lines (KYSE-150 and KYSE-510) compared with that in the normal cell (Het-1A) (Figure 8b,c).  
Consistent with these results, DEGs were found to be significantly over-expressed in ESCC by using distinct ESCC datasets via pooled analysis in the Oncomine database (Figure S1), and significant over-expression was also found in the TIMER database (Figure S2a-d).  
Figure 7: Survival analysis and ROC curve for overall survival in ESCC. (**a**) Survival analysis of ten differential expression genes (\(p<0.05\) for prognostic significance); (**b**) ROC analysis of the four genes that were significant in survival analysis (area \(>0.9\) under the curve is of high diagnostic value).
## Materials and Methods
### Performance Metrics
#### 2.5.2 Quantitative Measure  
We used model-agnostic metrics reviewed in [PERSON] et al. [30] to quantitatively measure the similarity of the distribution between real and generated images in our experiments. The 3 metrics \(\rho\) used in this experiment measure the similarity between \(\mathbb{P}_{r}\) and \(\mathbb{P}_{g}\). It has been reported that the feature space is more advantageous for measuring the similarity of the distribution than the pixel space, and the selection of features to be extracted is also crucial [30]. Thus, for an arbitrary feature extractor \(\Phi(.)\), the metric can be described as follows:  
\[\rho(\Phi(\mathbb{P}_{r}),\ \Phi\left(\mathbb{P}_{g}\right)). \tag{12}\]  
MMD measures how different \(\mathbb{P}_{r}\) and \(\mathbb{P}_{g}\) are for a given empirical kernel function k. The higher the measured value, the more the two inputs are interpreted as being different [58]. We used Gaussian functions as kernel functions,  
\[\begin{array}{rl}MMD^{2}\Big{[}\mathbb{P}_{r},\ \mathbb{P}_{g}\Big{]}&=E_{x,\ x^{ \prime}\sim\mathbb{P}_{r}}\ \big{[}k(x_{i},\ x^{\prime}_{i})\big{]}+E_{x,\ x^{\prime}\sim \mathbb{P}_{r}}\ \big{[}k\Big{(}\overline{x}_{i},\ \overline{x}^{\prime}_{i}\Big{)}\big{]}-2E_{ \overline{x},\overline{x}^{\prime}\sim\mathbb{P}_{g}}\big{[}k\Big{(}x_{i},\ \overline{x}^{\prime}_{i}\Big{)}\big{]},\\ &\qquad\qquad=\frac{1}{mn}\sum_{i=\ 1}^{n}\sum_{j=\ 1}^{m}\Big{[}k(x_{i},\ x^{ \prime}_{i})+k\Big{(}\overline{x}_{i},\ \overline{x}^{\prime}_{i}\Big{)}-2k\Big{(}x_{i},\ \overline{x}^{\prime}_{i}\Big{)}\Big{]}.\end{array}\]  
FID is the Frechet distance (d) between the Gaussian distribution with mean \((m_{r},C_{r})\) obtained from \(\mathbb{P}_{r}\) and the Gaussian distribution with mean \(\left(m_{g},C_{g}\right)\) obtained from \(\mathbb{P}_{g}\)[59; 60]. FID uses features extracted from a trained network structure, such as an inception network, to measure the similarity between the distributions. The FID is defined as  
\[\begin{array}{rl}FID^{2}\Big{[}\mathbb{P}_{r},\ \mathbb{P}_{g}\Big{]}&=d^{2} \Big{(}\big{(}m_{r},C_{r}),\ \big{(}m_{g},C_{g}\big{)}\Big{)},\\ &=\big{\|}m_{r}-m_{g}\big{\|}_{2}^{2}+T\Big{(}C_{r}+C_{g}-2\Big{(}C_{r}C_{g} \Big{)}^{1/2}\Big{)}.\end{array}\]  
The 1-NN classifier proposed by [61] as a binary classifier for two sample test statistics sets the label of the real image to 0 and the label of the generated image to 1, and can measure the similarity of the generated images by estimating the LOO accuracy. The closer the LOO accuracy is to 50%, the closer \(\mathbb{P}_{r}\) is to \(\mathbb{P}_{g}\). As shown in [30], the LOO accuracy of 1-NN can be used to detect the tendency of mode collapse, which is difficult to detect with the human eye without special training and careful model selection. It can also robustly measure the similarity between distributions with small transformations in the feature space.
## 4 Discussion
### Comparative Assessment  
The results of this work can be compared to other research studies to demonstrate its advantages in terms of the actuator reduction ratio as well as the operational workspace of the manipulator that was designed based on the presented method. In [21], a method is proposed to reduce the number of actuators to \(n+1\) for an \(n\)-DOF multi-link, cable-driven robot (MCDR). Applying this method to a two-segment SCRaM employing six actuators reduces them to four, resulting in a reduction of 33.3%. In comparison, applying the method presented in this paper reduces the actuators to three, resulting in a 50% reduction. In [22], the crossing of cables is presented as a method to reduce the number of required actuators to drive a planar continuum robot. Moreover, [22] used eight motors to control six joints. In contrast, the current method can be used to control the same robot in a 2D workspace using only two actuators, which presents a 75% reduction in the number of employed actuators, albeit with reduced dexterity. In [23], a method is proposed to reduce the number of driving cables in MCDMs. However, the method manages to maintain the workspace when only one cable is co-shared, thus reducing the number of actuators by 16.7% in a two-segment SCRaM with six actuators, or 11.1% in a three-segment SCRaM employing nine actuators. In contrast, the method presented in this paper manages a 50% reduction for SCRaMs employing six actuators, and a 66.7% reduction for three-segment SCRaMs employing nine actuators. Hence, the presented method achieves a 150% improvement over [21], a 400% improvement over [22], and at least a 300% improvement over [23].  
The manipulator proposed in this work has a fixed-length body containing one variable length-bending segment. With respect to other FLSS-SCRaMs such as [14; 27], the proposed manipulator demonstrates a much larger volumetric workspace in comparison to the  
Figure 10: The manipulator’s volumetric workspace based on the simulation results.  
shell-shaped workspace offered by these systems. This is mainly due to the proposed manipulator's 3-DOF in comparison to the 2-DOF offered by FLSS-SCRaMs. Variable-length, single-segment (VLSS) SCRaMs, such as [13; 28], offer a volumetric workspace; however, the reported workspace is limited in [13] and contains an unreachable space in [28], making them inferior in comparison to the workspace provided by the proposed manipulator. Multi-segment SCRaMs, such as [10; 15; 25], also offer a volumetric workspace. In comparison to these SCRaMs, the workspace offered by the manipulator proposed in this work is fuller, lacking the voids and unreachable spaces exhibited by the former. The ability of the proposed manipulator to provide a continuous and uniform workspace is mainly attributed to the proposed manipulator's thin body profile as well as its ability to relocate the ACTR anywhere along the manipulator's body, thus providing access to all locations within the manipulator's reach.
## 4 Experimental Results
### Resources Usage  
The last set of tests has been designed to analyze the resources usage of the sidecar object during runtime. The used configuration takes into account the following parameters: (i) six full message processing chains (five telemetry topics and one state topic, from smart object to the cloud); (ii) delay between telemetry messages (for each topic): random in the range 100-1000 ms; (iii) delay between state messages: random in the range 500-5000 ms; (iv) message payload of 4 KB and (v) test duration of 30 min. Memory and CPU usage data were collected using the Java profiling tool VisualVM.  
The first execution has been performed setting the maximum heap memory size for the sidecar object at 256 MB (using the -Xmx256m option), that is largely enough for the correct functioning of the system. Figure 11a,b show the results related to memory and CPU usage. From the memory graph emerges that the 256 MB limit configured in this test is overabundant, as the JVM automatically  
Figure 10: (**a**) State messages pass-through delays; (**b**) publisher queue sizes; (**c**) queue sizes adopting the NoRecovery Policy; (**d**) queue sizes adopting the AllRecovery Policy; (**e**) queue sizes adopting the AllRecovery policy during the disconnection event; (**f**) queue sizes adopting the AllRecovery policy during the reconnection event; (**g**) Queue sizes adopting the OneEveryIRrecovery policy; (**h**) queue sizes adopting the OneEveryNRecovery policy during the disconnection event; (**i**) queue sizes adopting the AllRecovery policy during the reconnection event.  
reduces the max heap size in order to optimize the resource usage; it also important to note how the garbage collector shrinks the memory used by the sidecar object intervening periodically. Regarding the CPU usage, most of the measurements were \(\sim\)0.4%: those extremely low values were due to the high computational power available on the testing machine.  
For the second execution the Heap Memory size limit has been set to only 16 MB (using the -Xmx16m option): the results are shown in Figure 11c. In this case, memory available is sufficient to guarantee the correct functioning of the system: the maximum heap is reduced only for extremely short times, and the Java garbage collector intervenes at a much higher rate that the previous case. Despite the \"constrained\" configuration, the measured behavior does not highlight any problem or error proving that the sidecar object is able to remain operational.
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 5 Towards Algorithm Development: Propagation Concepts, Path Loss and Communication Graph
### Air-to-Ground Channel (A2G)
#### 5.3.2 3 GPP Models  
Reference models of the path loss for aerial vehicles are provided in 3 GPP TR 36.777 (Table B-2) [69]. Even though they are too complicated for use in performing theoretical analysis or for the offline optimisation of UAV trajectories, they are very useful for numerical simulations [68]. The models target three scenarios: Rural Macro-cell with the model Rural Macro (RMa-AV) in LoS conditions, Urban Macro-cell with the model Urban Macro (UMa-AV) in NLoS conditions and Urban Micro-cell with its corresponding model, Urban Micro (UMi-AV). The specific usages of each scenario are depicted in Table 3. In this paper, only the Rural Macro-cell scenario is envisioned, considering a use case for the Danube Delta or another isolated area where the rural environment prevails.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Usage** \\ \hline RMa & Large areas with radii up to 10 km and low building densities; BS antenna \(>\) average buildings height [70] \\ UMa & BS antenna above buildings heights, NLoS or obstructed-LoS, BS–BS distance \(<3\) km [71] \\ UMi & Higher LoS probability than UMa [70], BS–BS distance \(<1\) km [71] \\ \hline \hline \end{tabular}
\end{table}
Table 3: UAV-to-ground models using 3 GPP.
# Wind-Load Response and Evacuation Efficiency Analysis of Marine Evacuation Inflatable Slide
## 2 Wind-Load Response Analysis Method of Inflatable Slide and Establishment of Numerical Model  
### Formulation of Weak Coupling Solution Scheme  
Because the geometric size of the inflatable slide is small relative to the wind field, the effective response we need to obtain is the displacement deformation and wind pressure distribution on the surface of the inflatable membrane structure. Based on the above, the interface between fluid domain and structure domain is mainly studied. That is to say, information acquisition is carried out when the solution of interaction transfer calculation in the two domains of structure and fluid reaches stable convergence state [11]. Therefore, the selection of weak coupling solution strategy can greatly reduce the computational workload. The basic scheme of weak coupling solution method is shown in Figure 3. In the process of this research, the force factor is transferred to the surface of the inflatable film through the fluid-solid coupling surface through the setting calculation of wind load in the fluid analysis software, and the response results of displacement and wind pressure are formed in the structure domain [12]. It is necessary to simplify the process by establishing a simplified information transfer model.  
To illustrate the basic idea of this method, we consider only the case of pressure transfer from the fluid domain to the structural domain. \(p_{f}\) represents the fluid pressure at the interface and \(p_{s}\) represents the pressure on the structure, we expect:  
\[p_{s}(x)=p_{f}(x). \tag{1}\]  
This equation can be satisfied by using the weighted residual method. Multiply both sides by a set of weight functions \(\left\{W^{i}\right\}\), and then integrate on the interface \(\Gamma\) to get:  
\[\int\Gamma W^{i}p_{s}\mathrm{d}\Gamma=\int\Gamma W^{i}p_{f}\mathrm{d}\Gamma. \tag{2}\]  
The finite element method can be used to calculate the pressure:  
\[p_{s}(x)=N_{s}^{j}(x)\hat{p}_{sj} \tag{3}\]  
\[p_{f}(x)=N_{f}^{j}(x)\hat{p}_{fj} \tag{4}\]  
where, \(\hat{p}_{sj}\) and \(\hat{p}_{fj}\) respectively represent the estimated pressure of the structure and fluid at the corresponding unit node j.  
The [PERSON] method \(W^{i}\)= \(W_{s}^{i}\) is adopted in Equation (2), and the calculated value of pressure is inserted:  
\[\int\Gamma N_{s}^{i}N_{p}^{j}\hat{p}_{sj}\mathrm{d}\Gamma=\int\Gamma N_{f}^{i }N_{f}^{j}\hat{p}_{fj}\mathrm{d}\Gamma. \tag{5}\]  
After further substituting each term into the solution, a non-zero pressure value can be obtained at all structural integral points, and then the preliminary calculation of pressure transfer on the FSI (fluid-structure-interaction) surface can be completed.  
In this paper, the wind-load response research was carried out by ANSYS Workbench simulation tool, and the fluid part was simulated by CFD (Computational Fluid Dynamics) module. It is expected that the surface wind pressure, velocity vector and wind field streamline of the inflatable film slide can be obtained.
# Alginate Silver Nanoparticles and Their Effect on Sperm Parameters of the Domestic Rabbit
###### Abstract  
Silver nanoparticles possess valuable physical, chemical, and biological properties, rendering them widely applied as bioactive agents in the industry. Nonetheless, their influence on the natural environment and on living organisms remains unclear. Therefore, this study aims to investigate the impact of polymer composites containing silver nanoparticles on sperm cells. The nanosilver polymer composites were chemically synthesized, employing sodium alginate as the stabilizer. The reducing agents employed were solutions comprising sodium borohydride and xylose. The concentration of silver nanoparticles obtained after synthesis was 100 parts per million. The examined biological species were rabbit sperm cells. The impact of nanosilver on the sperm was assessed through the elucidation of the toxicity profile, comet test, and analysis of morphological characteristics of the animal cells. The results of the study demonstrate a twofold impact of polymer composites infused with silver nanoparticles on domestic rabbit sperm when obtained through chemical synthesis using two reducing agents (xylose and sodium borohydride) at a 10 ppm concentration. The comet test showed no harmful effect on the DNA integrity of rabbit sperm by the tested compounds. Twenty-four-hour exposure of rabbit spermatozoa to silver nanoparticles, obtained by reducing xylose and borohydride, induced significant secondary changes in the morphological structure of male reproductive cells. These findings indicate the potential reproductive toxicity of silver nanoparticles.  
nanosilver; nanoparticles; semen; DNA integrity; sodium alginate; nanocomposite +
Footnote †: journal:  
0000-0001-6000-0000][PERSON]  
2 Faculty of Animal Science, University of Agriculture in Krakow, Al. Mickiewicza 21, 31-120 Krakow, Poland; [EMAIL_ADDRESS] (A.G.); [EMAIL_ADDRESS] (M.K.-G.); [EMAIL_ADDRESS] (O.J.); [EMAIL_ADDRESS] (P.N)
# The VIRTUALDiver Project. Making Greece's Underwater Cultural Heritage Accessible to the Public
## 3 Results and Discussion
### The Platform  
#### 3.1.1 Interdisciplinarity  
The VIRTUALDiver project aims at strengthening special forms of tourism, such as: Cruise tourism, diving, science tourism, and others. The platform was based on the interdisciplinary collaborative relationship of experts with the ultimate goal of creating interactive experiences through a scientifically sound approach (interdisciplinary and transdisciplinary design process). The main researchers (with theoretical and technological background) who make up the design team of VIRTUALDiver belong to the following scientific fields:  
1. Scientific disciplines of geology,
2. Humanities with an emphasis on culture (archaeology, history, museology, etc.),
3. Disciplines of theoretical and practical issues of design, audio, and video arts,
4. Information and communication technologies,
5. Other scientific fields that may make a useful contribution, depending on the design strategy.  
The interdisciplinary of the consortium ensures the holistic character of the approach making the VIRTUALDiver platform a pioneering product in the market.
## 2 Equivalent Thermodynamic Model  
### Mechanical Equivalence  
To study the heat effect of high-power lasers and associated failure modes of targets, it commonly requires time-consuming and very expensive tests on the prototype structures [21; 22]. In contrast, the present study adopts the equivalent scaling experiments, which reduce the cost and risk of the tests, while also maintaining accuracy. By establishing an equivalent scaling model, the simulation test parameters can be quickly and clearly defined [22]. The similarity criterion is a dimensionless invariant which can be expressed by \(\prod_{\mathrm{i}}\). To ensure that the equivalent scaling model has the same mechanical characteristics as the original target, the similarity criteria are:  
1. The equivalent scaling model has identical material properties to the prototype structure: \[\Pi_{1}=\sigma_{\theta 0}=\sigma_{\theta 1}\] (1)where \(\sigma\) is the yield stress of material; subscript 0 represents the prototype structure and subscript 1 is for the equivalent model.
2. The internal and external stress conditions of the prototype and the equivalent model are equal: \[\Pi_{2}=\frac{D_{0}}{\delta_{0}}=\frac{D_{1}}{\delta_{1}}\] (2) where \(D\) is the cylinder diameter and \(\delta\) is the cylinder shell thickness.
3. The identity heat transfer speed through the cylinder thickness directions: \[\frac{2(\kappa_{0}t_{0})^{\frac{1}{2}}}{\delta_{0}}=\frac{2(\kappa_{1}t_{1})^{ \frac{1}{2}}}{\delta_{1}}\] (3) where \(\kappa\) is the thermal diffusivity, \(t\) is the irradiation time. \[\Pi_{3}=\frac{t_{1}}{t_{0}}=\left(\frac{\delta_{1}}{\delta_{0}}\right)^{2}\] (4)
4. During the irradiation period \(t\), the energy density of laser absorbed by the per unit thickness material should be equal: \[\frac{a_{0}q_{inc0}t_{0}}{\delta_{0}}=\frac{a_{1}q_{inc1}t_{1}}{\delta_{1}}\] (5) where \(a\) is energy absorption rate and \(q_{inc}\) is incident laser power density. \[\Pi_{4}==\frac{A_{1}q_{inc1}}{A_{0}q_{inc0}}=\frac{\delta_{1}}{\delta_{0}} \frac{t_{0}}{t_{1}}\] (6)
5. The length \(L\) of the cylinders should be long enough that the influence of the boundary conditions at both ends on the spot areas are negligible: \[\Pi_{5}=\frac{L_{1}}{L_{0}}=\frac{\sqrt{D_{1}\delta_{1}}}{\sqrt{D_{0}\delta_{ 0}}}\] (7)
6. The center angle of the targets corresponding to the spot diameter should be equal: \[\Pi_{6}=\frac{d_{z1}}{d_{z0}}=\frac{D_{1}}{D_{0}}\] (8)  
By satisfying the above conditions, the mechanical properties of the prototype structures could be treated as the same before and after the equivalent scaling.