esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Trends on UV filters
## 3 Results
### Galenic Forms in Adults' and Children's Sunscreens  
The correct choice of vehicle is also very important for the acceptance of the formulation by the consumer. For people to maintain a regular photoprotective routine, and therefore enjoy its beneficial effects, the application of the product should be easy and lead to a pleasant sensation on the skin. The application of an appropriate amount of the cosmetic formulation and the renewal of the application are key factors for maximizing protective effects. Formulations come in various physical forms. When analyzing the different sunscreen products, the galenic form designations that were used were those attributed by the brands and mentioned on the packaging labels.  
Various forms were found, namely cream, fluid, lotion, spray, emulsion, mist, gel, mousse, gel cream, oil gel, oil, and stick forms. An overlap in the names used by the different commercial brands was also noticed. More specifically, names such as milk, lotion, cream, cream-gel, and emulsion are all emulsified preparations, except for lotion, which can be an emulsion or another type of liquid preparation, such as a suspension or solution. Given this categorization, emulsified preparations appear as the main galenic form of sunscreens for adults, while spray is the most frequent for children (Figure 4). The galenic forms most commonly found in adult and children's sunscreens were cream (24.5% and 15.4%), fluid (18.7% and 29.2%), spray (16.6% and 32.3%), lotion (11.4% and 6.15%), and gel (6.33% and 615%). Oil, stick, and oil gel were only present in adults' sunscreens, with a usage frequency under 6%.  
Sprays are easy to apply on children, hence they were the forms most common in children's photoprotectors, while creams are the best for dry skin conditions [41]. The main disadvantage of using a spray form are inadequate application, which could diminish the photoprotective action towards UVR, and toxic effects after inhalation [42]. For this reason, in 2019, the European Union cosmetics regulations amended a restriction to the use of nano form TiO\({}_{2}\) in spray products in the list of European approved UV filters [13; 30].  
Figure 3: Usage frequency of sunscreens containing only organic, only inorganic, or a combination of organic and inorganic UV filters in (**A**) adults’ and in (**B**) children’s sunscreens.
# Centralized vs. Decentralized Cloud Computing in Healthcare
## 7 Results
### RQ5: What Are the Research Gaps within Centralized Cloud Healthcare Solutions?  
This section identifies the current research gaps in centralized cloud healthcare solutions. The first gap concerns data privacy and security. While existing research endeavors in this domain have aimed to bolster information security and fortify data confidentiality measures in centralized cloud healthcare solutions, gaps remain in ensuring robust protection against unauthorized access, data breaches, and privacy violations. Therefore, further research is required to develop advanced encryption techniques, access control mechanisms, and privacy-enhancing algorithms to enhance the security of patient data stored and processed in the cloud.  
The second gap concerns the lack of coordination and integration in the exchange of data. There are still drawbacks in the interoperability and exchange of data between healthcare information systems and cloud information systems. Further, more effort needs to be placed into identifying the right correspondence for use in patient data exchange that allows the proper exchange of healthcare information across the varied systems while simultaneously preserving data consistency and semantic integration.  
The third gap is associated with the opportunity to optimize performance. Centralized cloud healthcare solutions should be effective in catering to real-time applications and cited healthcare services with a large capacity and quick response time. However, new studies have to be conducted in order to increase the efficiency of supplied resources, data processing algorithms, and network topologies to exclude delays and enhance the effectiveness of the systems, especially in those applications where they are utilized, like telemedicine, remote patient monitoring, and many others.  
Last of all, cost optimization and sustainability are the fourth are of a gap in the research in this field. Returning to cost optimization, this concerns healthcare centralized cloud solutions achieving sustainable results. More work must be conducted in cost models, optimization techniques, and energy-conscious computer architectures and protocols to reduce infrastructural costs, the costs of storing data, and energy usage while concurrently enhancing the system and functional ability, as well as addressing data security issues.
## 3 The CFD Settings
### Boundary Conditions for the CFD Simulations  
The boundary conditions at the primary flow inlet and the secondary flow inlet were both pressure inlet, while the outlet was set to pressure outlet. All walls were assumed  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Properties** & **Values/Equations** \\ \hline Density, \(\rho\) & Ideal gas \\ Specific heat, \(c_{p}\) & \(1006.43\,\mathrm{J/kg.K}\) \\ Thermal conductivity, \(\kappa\) & \(\kappa=1.039718\times 10^{-3}+8.985199\times 10^{-5}\ T-\) \\  & \(2.609688\times 10^{-8}\ T^{2}\ \mathrm{[W/m.K]}\); where \(T\) is in Kelvin \\ Viscosity, \(\mu\) & \(\mu=-1.874901\times 10^{-6}+1.024389\times 10^{-7}\ T-\) \\  & \(1.568282\times 10^{-10}\ T^{2}+1.415525\times 10^{-13}\ T^{3}\ \mathrm{[Pa.s]}\); where \(T\) in Kelvin \\ \hline \hline \end{tabular}
\end{table}
Table 1: The properties of air used in the CFD simulation.  
smooth with no-slip condition and adiabatic. Axisymmetric boundary condition was applied at the axis of the ejector.  
The boundary condition values were based on the experimental results of [PERSON] and [PERSON] [9; 11], either from the direct measurements that they did or from calculated values based on these measurements.  
The primary flow inlet pressure, \(P_{0}\), was varied from 308 kPa to 618 kPa. The secondary flow inlet pressure, \(P_{2}\), was ambient pressure (100.8 kPa). The ejector outlet pressure, \(P_{4}\), was also near ambient pressure. The temperature of the primary and secondary flow inlet (\(T_{0}\) and \(T_{2}\)) was 294 K while and ejector outlet, \(T_{4}\), was around 292 K in average.  
The values of the turbulence intensity, \(TI\), at the primary inlet, secondary inlet, and ejector outlet (denoted as \(TI_{0}\), \(TI_{2}\), and \(TI_{4}\), respectively) were calculated from the experimental data. Since the mass flow rates were all measured and the dimensions of the ejector were available, the turbulence intensity values can be estimated by using Equation (4) [29].  
\[TI=0.16 Re_{D_{h}}^{-\frac{1}{8}}\times 100\% \tag{4}\]  
where \(Re_{D_{h}}\) was the Reynolds number based on the hydraulic diameter. The turbulence intensities obtained through Equation (4) were ranging from 3.44% to 4.66%.
## 4 MHAR-OLSR Protocol
### CPN MHAR-OLSR Model
#### 4.2.2 Sub-Model \"Path_Calculation\"  
[PERSON]'s algorithm provides the shortest path calculation. The researchers created its multi-path version, which they applied in OLSRv2 [29] to obtain the MPOLSR multi-path routing protocol [30].  
This new algorithm maintains QoS metrics by supporting frequent changes in the network topology using two auxiliary functions: route recovery and loop verification. It also improves the flexibility and scalability of [PERSON]'s original model [31] by adding link metrics and cost functions into its principle:  
1. fp increases the costs of arcs belonging to the previous path Pi (or of opposite arches belonging to it), which gives us the possibility to have routes with disjoint links by penalizing only the links used. For this, the cost functions will be defined as follows: fp (c) = 3c and fe (c) = c,
2. fe gives paths with disjointed links and nodes. It grows costs of arcs affected to the vertices of the previous path Pi, which penalizes the links and nodes, setting this by: fp (c) = 3c and fe (c) = 2c.  
The [PERSON] multi-path algorithm is only active when data is present to be sent (it can be said to be an on-demand path calculation).  
Given the complexity of the [PERSON] algorithm, several researchers [32] have been able to model it in Petri net to make it more manipulatable. [PERSON] [33] has used multi-path Dijkstra in his study and has modeled it with High-Level Petri nets. Based on his model, I was able to design my own.
# A Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv8
## 1 Introduction
The authors of [15] developed an improved algorithm based on YOLOv8 called LAR-YOLOv8. They designed an attention-guided bidirectional feature pyramid network to generate more discriminative information by efficiently extracting features from the shallow network through a dynamic sparse attention mechanism and adding top-down paths to guide the subsequent network modules for feature fusion. Reference [16] proposes a BSS-YOLOv8 network model, which incorporates the BoTNet module into the backbone network of the YOLO-v8 model. The completeness of feature extraction was improved by connecting global and local features through its multi-head self-attention mechanism. The authors of reference [17] proposed an enhanced road defect detection algorithm, BL-YOLOv8s, based on YOLOv8s, which was optimized by integrating BiFPN concepts and reconstructing the neck structure of the YOLOv8s model. This optimization reduces the parameters, computational load, and overall size of the model. From the above researchers' work, it can be seen that the YOLO series has been widely used in many kinds of target detection tasks and that there exists a lot of room for improvement for detection jobs in specific domains. This work provides a remote sensing image target detection method based on the enhanced YOLOv8 algorithm. We achieved good detection results on the DOTA remote sensing dataset. The primary contributions of this paper are summarized as follows:  
1. The addition of an extra detection layer on the basis of the original multi-scale feature fusion network structure to additionally generate a feature map of larger size, improving the network's ability to learn feature information about small targets. 2. A C2f-E structure based on the Efficient Multi-Scale Attention Module (EMA) [18] is proposed, which enhances the network's detection ability for targets of different sizes through a cross-space learning approach. 3. The use of Wise-IoU [19] to replace CIoU in the original network, making full use of the wise gradient gain assignment strategy to improve the generalization ability of the model based on the improvement of the overall performance of the detector.
## 3 Results and Comparative Experiments
### Evaluation Metrics  
The main evaluation criteria for the experiments include the following:  
* **Effective Coverage Area:** The total geographical area covered and effectively scanned by the survey lines, measured in square meters.
* **Coverage Rate [25]:** The ratio of the effective coverage area to the total area of the survey region, serving as a key indicator of the model's actual coverage within the marine area.
* **Total Survey Line Length:** The sum of all survey line lengths, measured in meters, reflecting the efficiency of the survey model. The formula for calculating the average survey line length is: \[\text{Average survey line length}=\frac{\text{Total line length}}{\text{Number of data sets}}\]
* **Weighted Average Overlap Rate:** The weighted average value of the overlap rates between each survey line. It indicates the redundancy in the overlapping regions during the survey. The formula for calculating the weighted average overlap rate is: \[\text{Weighted Average Overlap Rate}=\frac{\sum(\text{Overlap Rate}\times \text{Overlap Region Length})}{\text{Total Survey Line Length}}\]
# Mechanical Characterisation and Modelling of Elastomeric Shockpads
## 3 Finite Element Simulation of AAA  
A FE model of the experimental AAA test on the shockpads was created in Abaqus CAE (Figure 6). The purpose of the model was to try and replicate the shockpad response from different height drops after calibration of a material model to a single drop height. The shockpad layer was modelled as a deformable part and was placed on top of a fixed rigid concrete block. The shockpad was meshed using 3D 8-node reduced integration elements with hourglass control (C3D8R) with an increased mesh density around the impact area. A mesh sensitivity test was completed to ensure results were not sensitive to mesh design. A tangential friction was specified between the shockpad and concrete using a penalty formulation with a friction coefficient of 0.8. This value was measured as the coefficient of sliding friction in an experimental slip test performed prior to AAA testing. The AAA was modelled as a rigid steel test foot with point mass placed directly above the centre point. The test foot and point mass were joined by a deformable spring element with a 2000 N/mm stiffness as per the experimental test [21]. All parts of the AAA were restrained to only allow translation in the z-axis. Starting conditions for the test placed the impact foot 1 mm above the shockpad top surface with an initial velocity matching those seen in the experimental tests. Acceleration, velocity and displacement of the point mass were outputted from the simulation and used to calculate the stress-strain response as well as the FIFA test outputs. Similarly, to the stress-strain calculations for the experimental data, engineering stress was based on a fixed cross-sectional area throughout contact corresponding to full test foot contact area (38.5 cm\({}^{2}\)). Due to the symmetrical nature of the problem only a quarter portion of the model was constructed to save on computing time.
# Optimization of Laser-Assisted Polypropylene Aluminum Joining
## 3 Results and Discussions
### Testing
#### 3.3.3 Tensile-Shear Test  
Figure 15 shows the result of the tensile-shear test for laser-joined samples with different modulated powers. It should be noted that the results of the tests are reported in max shear load; therefore, by increasing the power, the joints show higher loads due to the increase in the joint area until a certain point where the extensive degradation of the materials shows a negative effect that cannot be compensated for by increasing the joint area anymore. Therefore, in the case of small, separated bubbles, which were reported for 290 and 300 W modulated power, the shear load of the joints was not significantly affected, and an increasing trend was observed as a result of increasing the joint area. However, by the escalation of PP thermal decomposition in the form of bulges or through holes (310 W and above), the shear load of the joint decreased. Such a trend was also observed in the case of Al-to-polyamide laser joining [15]. More evaluations on the presence of small bubbles need to be performed in order to identify their effect on the performance of the joints. In general, the max shear strength achieved between Al and PP is approximately 20 MPa. Table 6 presents different types of failure during the tensile-shear tests.  
Figure 14: Cross-section of the weld area in transversal direction (**a**); and longitudinal direction (**b**); at the modulated power of 280 W; cross-section of the weld area in transversal direction at the modulated power of 310 W (**c**).  
In addition to the modulated power, the mentioned failures are tightly linked with the engraving speed, as seen in Figure 16. As already discussed in Section 3.2, a higher range of speed needs to be evaluated together with the optimized laser power for the joining process. In other words, the ultimate optimum point should be achieved where the weakening of  
\begin{table}
\begin{tabular}{c} \hline \hline Failure of the PP, which remains stuck in the engravement \\ \hline \hline Failure of the joint, All left an imprint, but PP is not getting stuck in the grooves \\ \hline \hline \end{tabular}
\end{table}
Table 6: Different failure modes of the PP-Al joint.  
Figure 15: The result of tensile-shear tests measuring the maximum force in the function of the modulated power.  
Al due to the laser engraving process is minimized while the engraves are effectively filled with PP with minimum thermal decomposition.  
For the experiments resulting in Figure 16, the modulated power of 300 W is implemented as it showed the highest shear load (see Figure 15).  
At the low engraving speed of 50 mm/s, losing the Al cross section is quite considerable due to the presence of deep grooves or cavities discussed in Section 3.3.1. At the range of 100 to 150 mm/s, the optimum range is achieved, which is compensated for between weakening the Al and promoting the mechanical interlocking between Al and PP. Finally, from 200 mm/s onward, the damage on the Al is significantly reduced, however, at the expense of mechanical interlocking.
## 5 Service Delay Statistical Model
Therefore, the number of idle events during \(\overline{BO}\) can be approximately equal to the number of busy events (counted in a slot time), \(M\), and namely, \(N\cong M\). Since the occurrence of the busy events is caused by the successful transmission of the data frames or data frame transmission collision, \(M\) is divided into two parts, namely the number of busy events due to the successful sending of the data frames, \(n_{s}^{\prime}\), and the number of busy events due to the data frame transmission conflicts, \(n_{c}^{\prime}\). Therefore,  
\[\textbf{Conclusion 4}:\ n_{s}^{\prime}=Np_{s}^{\prime},\ \ n_{c}^{\prime}=N\times \big{(}1-p_{s}^{\prime}\big{)}. \tag{24}\]  
For the last term, \(T_{idle}\), the idle time in (18), since the backoff counter is frozen when the channel is busy, can be expressed as \(\overline{BO}\) minus the number of busy slots, \(M\), i.e., \(T_{idle}=\overline{BO}-M=\overline{BO}-N\). In addition, since \(n_{c}\), \(n_{c}^{\prime}\), and \(n_{s}^{\prime}\) are independent random variables, the PGF of \(T_{w}\) can be obtained by applying \(z\)-transformation to Equation (18), as follows:  
\[E\Big{(}z^{T_{w}}\Big{)}=E\Bigg{(}z^{\big{(}T_{s}+\sum_{j=1}^{s_{c}}T_{cj}+ \sum_{j=1}^{s_{c}^{\prime}}T_{cj}^{\prime}+\sum_{j=1}^{s_{c}^{\prime}}T_{cj}^{ \prime}+Idle_{T_{inv}}\big{)}}\Bigg{)}\]  
\[G_{T_{w}}(z)=G_{T_{s}}(z)E\Bigg{(}z^{\sum_{j=1}^{s_{c}}T_{cj}}\Bigg{)}E\Bigg{(} z^{\sum_{j=1}^{s_{c}^{\prime}}T_{cj}^{\prime}}\Bigg{)}E\Bigg{(}z^{\sum_{j=1}^{s_{c}^{ \prime}}T_{cj}^{\prime}}\Bigg{)}E\Big{(}z^{Idle_{T_{inv}}}\Big{)}. \tag{25}\]  
Combining the above results, using the properties of the conditional PGF [27], we can obtain:  
\[E\Bigg{(}z^{\sum_{j=1}^{s_{c}}T_{cj}^{\prime}}\Bigg{)}=\sum_{i=-1}^{m}\frac{p _{s}(1-p_{s})^{i}}{1-\big{(}1-p_{s}\big{)}^{m+1}}G_{T_{s}}(z)^{i}. \tag{26}\]  
\[p(n_{c}=i)=\frac{{p_{s}(1-{p_{s}})}^{i}}{{1-{(1-{p_{s}})}^{m+1}}}i=0,\ \ \ldots,\ m. \tag{19}\]0  
\[p(n_{c}=i)=\frac{{p_{s}(1-{p_{s}})}^{i}}{{1-{(1-{p_{s}})}^{m+1}}}i=0,\ \ \ldots,\ m. \tag{19}\]1  
\[p(n_{c}=i)=\frac{{p_{s}(1-{p_{s}})}^{i}}{{1-{(1-{p_{s}})}^{m+1}}}i=0,\ \ \ldots,\ m. \tag{19}\]2  
where \(p_{n_{s}^{\prime}}(i)=\delta(i-n_{s}^{\prime})\), \(n_{s}^{\prime}=Np_{s}^{\prime}=\frac{\overline{BO}\cdot p_{s}}{1-p_{s}}p_{s} ^{\prime}\). So far, we obtain a closed expression for the PGF of the service delay \(T_{w}\) by Formulas (25)-(29). Since all the protocol details have been considered in the discussion, the PGF can reasonably describe the service delay behavior of the 802.11 network. Furthermore, we obtain the mean and the variance of \(T_{w}\) by the first-order derivative and second-order derivative, i.e.,  
\[E\Big{(}T_{w}^{i}\Big{)}=\frac{d^{i}}{dz^{i}}\big{(}G_{T_{w}}(z)\big{)}|_{z=1} \Rightarrow\Bigg{\{} \begin{array}{c}\overline{T_{w}}=\frac{d}{dz}\big{(}G_{T _{w}}(z)\big{)}\Big{|}_{z=1}\\ Var_{T_{w}}=\frac{d^{2}}{dz^{2}}\big{(}G_{T_{w}}(z)\big{)}\Big{|}_{z=1}+ \overline{T_{w}}-\overline{T_{w}}^{2}.\end{array}  \tag{30}\]
# Emerging Industrial Internet of Things Open-Source Platforms and Applications in Diverse Sectors
## 2 Background
### IoT Communication Protocols  
The importance of communication protocols in the development and utilization of IoT solutions is undeniable. Different sectors and industries have distinct requirements, and the selection of an appropriate communication protocol can address these specific needs. The primary focus of Wireless IoT is the utilization of various communication technologies and protocols to link up smart devices. All of these are short-range protocols [18]. In addition, LoRa is a protocol that offers two-way and long-distance communication. Tables 1 and 2 list various communication protocols and their main characteristics, as well as the behavior of different metrics.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **Zigbee [19]** & **RFID [11]** & **BLE [20]** & **LoWPAN [21]** & **NFC [22]** \\ \hline
**Governing standard** & IEEE802.15.4 [19] & RFID technology [11] & IEEE802.15.1 [20] & IEEE 802.15.4 [21] & ISO/IEC 14443 [22] \\ \hline \multirow{2}{*}{**Operational frequency**} & \multirow{2}{*}{2.4 GHz} & Multiple (125 kHz, & \multirow{2}{*}{2.4 GHz} & Various (866 MHz in EU, & Multiple (125 kHz, & 13.56 MHz, & 13.56 MHz, & 860 MHz) \\  & & & 902-928 MHz) & & GHz (Globally) & \\ \hline
**Network type** & Wireless personal area & Proximity-based & WPAN & WPAN & Peer-to-peer (P2P) \\ \hline
**Network topology** & Multiple & P2P & Star-bus network & Star and mesh & P2P \\ \hline
**Power consumption** & 30 mA, low power & Ultra-low & 30 mA, low power & Low (1–2-year battery & 50 mA, very low \\  & & & & & \\  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of communication protocols in IoT and edge computing.
## 3 Methodology
### Temporal Correlation Prediction Method
#### 3.2.2 GRU Model  
The GRU [46] is a type of Recurrent Neural Network (RNN). Like the LSTM, it was introduced to address issues such as long-term memory retention and the vanishing gradient problem in backpropagation. GRU simplifies the LSTM structure by reducing the number of gates from three (input, forget, and output gates) to two (reset and update gates) and merges the cell and hidden states. This design aimed to reduce complexity and computational resource requirements while preserving the ability to capture long-term dependencies. Compared to LSTM, which uses three gates, GRU uses two gates to regulate the flow and manage long-term memory retention, has fewer parameters, and trains faster (Figure 6).  
For each time step \(t\), the computation process of the GRU was as follows: the update gate (\(z_{t}\)) was computed as shown in Equation (12).  
\[z_{t}=\sigma(W_{Z}\cdot[h_  
The candidate hidden state (\(\widetilde{h}_{t}\)) was computed as shown in Equation (14):  
\[\widetilde{h}_{t}=\tan h(W\cdot[r_{t}*h_{t-1},x_{t}]), \tag{14}\]  
where \(W\) represents the weights of the hidden layer, and \({}^{*}\) denotes element-wise multiplication. This step applied the reset gate to the previous hidden state to determine how the new input \(x_{t}\) will influence the candidate hidden state.  
The hidden state at the current time step \(x_{t}\) was calculated as shown in Equation (15):  
\[h_{t}=(1-z_{t})*h_{t-1}+z_{t}*\widetilde{h}_{t}, \tag{15}\]  
This step combines the previous hidden and current candidate hidden states to generate the current hidden state. The update gate \(z_{t}\) determines how much of the previous hidden state \(h_{t-1}\) to retain and how much of the new candidate hidden state \(\widetilde{h}_{t}\) to incorporate.