esa-sceva/satcom-chunk-collection huggingface dataset:
# Bioengineered Skin Substitutes: Advances and Future Trends
## 3 Cell-Based Skin Substitutes
### Bioengineered Dermo-Epidermal Skin Substitutes (Under Development)
#### 3.2.1 Cell-Laden Hydrogels as Wound Dressings
When designing a hydrogel scaffold for tissue engineering purposes, pore-related parameters including morphology,  
Figure 3: Schematic representing the preparation process of a cell-laden hydrogel in which cells from an isolated donor are placed and then cultured in a 3D hydrogel matrix and grafted to a skin defect as a skin substitute. volume, size, distribution, throat size, wall roughness, and the interconnectivity of pores are important [93]. It has been demonstrated that the optimum pore size is 5-15 \(\upmu\)m for ingrowth of fibroblast, 20 \(\upmu\)m for hepatocyte ingrowth, and 20-125 \(\upmu\)m for the regeneration of adult mammalian skin [94; 95]. Thus, pore size and distribution should be taken into consideration while engineering hydrogels for skin tissue engineering and to mimic native skin structure. On the other hand, several scaffolds have been developed using stimuli-responsive materials that can release the encapsulated cells and biomolecules into the host tissue when triggered by different internal or external stimuli. To synthesize such a scaffold, first, a stimuli-responsive polymer is mixed with cells; second, the pre-gel solution is applied via injecting/spraying on the wound site [96]. Then, due to the external stimuli like thermal or photo stimulators, the polymer forms a physical gel leading to cell encapsulation within a 3D scaffold. The in situ formation of cell/hydrogel scaffold structure facilitates the delivery of encapsulated cells, growth factors, and necessary nutrients at the wound site via using negligibly invasive techniques [97]. Various studies have been conducted using stimuli-responsive materials. For instance, in the study of [PERSON] et al. [98], a UV-crosslinked biodegradable hydrogel was employed as a scaffold containing adipose-derived stem cells (ADSCs) to stimulate vascularization in difficult-to-head wounds. In this study, methacrylate gelatin (GelMA) and methacrylate hyaluronic acid (HAMA) were used to synthesize the hydrogel network. Afterward, a photoinitiator, to induce photo-crosslinking, and cells were added simultaneously into the pre-hydrogel solution (Figure 4a). The mechanical stability of composite hydrogels was engineered by varying the GelMA and HAMA concentrations and ratios. Although the modulus of this hydrogel (6 kPa) is lower than that of native skin, it proved to be easy to handle and manipulate, which is important in the laboratory and in the clinics. Further, in vitro results showed that those hydrogels provide an appropriate microenvironment for the proliferation of ADSCs. Additionally, in vivo studies demonstrated that stem-cell-loaded hydrogel scaffolds significantly improved vascularization at the wound site compared to their cell-free counterpart groups (Figure 4b) [98].
# Risk Assessment of UAV Cyber Range Based on Bayesian-Nash Equilibrium
## 3 Application of Attack-Defense Tree Model in UAV Cyber Range
### Quantification of Strategic Benefits of Both Sides  
#### 3.2.1 Attack Payoff Function  
An important attribute, Attack Payoff Function (_APF_), is assigned to each attack event that attacks the defense tree. It indicates that during the attack and defense game between the attacker and the defender, after the defender has deployed targeted defensive measures, the attacker will be able to perform the following operations. The benefits obtained from successful attack can still be calculated using the formula shown in the following figure.  
\[APF=\frac{G\times(1-RM)-(cost_{a}+cost_{uw})}{cost_{a}+cost_{uw}} \tag{1}\]  
where \(G\) indicates the profit that the attacker expects to obtain from a successful attack. \(cost_{a}\) represents the cost of the attacker to carry out the attack. The rate \(RM\) at which the risk of an attack on a UAV system is reduced due to the deployment of a defensive measure at a UAV range ranges from 0 to 1. \(cost_{uw}\) means that after the defender has deployed targeted defense measures, the attacker has to increase the attack cost in order to continue to breach these defense measures.
## 1 Introduction
Moreover, aging leads to changes in temperature distribution, thermal impedance, fatigue resistance, and the shearing of the solder joints as well [11]. One of the most important quality factors that can influence thermal management in these components is gas void formation (empty spaces in the solder joints) [11]. Due to their high surface tension, lead-free solder alloys are more prone to void formation, since the outgassing of volatile organic compounds (e.g., fluxes) from the melted solder is limited [12]. The main influence of void presence could be the change in the thermal and electrical parameters of the components, since they can significantly affect the homogeneity of the connection. As a result, \"hot spots\" on the chip can appear, which limit the useable power or could damage  
Figure 1: A simplified diagram of the heat flow through the component to the FR4 substrate. The arrows show an example of the direction of heat dissipation. the component as well. Assembly of the power components with a big thermal pad (like transistors or LEDs) encourages problems owing to the limitation of voids. As was proven by [PERSON], voids that have already formed in the solder joint cannot be removed by re-soldering, as, by merging some of the smaller voids into bigger ones, their final size could increase [13]. The other important quality factor is the microstructure of the solder joint. The intermetallic compounds (IMC) layer thickness at the solid interfaces and the dispersed IMC phases in the solder joints are most likely to have an effect on the thermal and electrical parameters of the solder joints [14]. The microstructure of the solder joints is determined by the heating method of the soldering technology used and the thermal profile as well. The amount and duration of the inflowing heat (so-called heating factor) into the molten solder influences the IMC formation significantly. [PERSON] et al. [15] have proven that the increase in the heating factor facilitates the growth of the IMC layer. If the solder joints are of poor quality, heat transfer from the power semiconductor element to the FR4 substrate could be ineffective. As a consequence, this can lead to it being damaged as a result of exceeding the maximum working temperatures of the power semiconductor element. Therefore, this study aimed to investigate the influence of the soldering thermal profile on void formation and microstructure of the solder joints and, consequently, the thermal parameters of IGETs.
## 2 Methodology
### Loss Functions, Optimizers, and Schedulers
#### 2.3.1 Loss Functions  
We used three different loss functions to tackle the challenges of class imbalance and encourage more reliable learning. These loss functions bring unique strengths to the training process.  
* Binary Cross Entropy (BCE) Loss: BCE loss is a standard choice for classification tasks, and it measures the difference between the predicted probabilities and the actual labels. It effectively penalizes incorrect predictions as it takes the full probability distribution into account.
* Focal Loss: Focal Loss is very useful when the dataset is highly imbalanced. Traditional loss functions tend to be influenced too much by examples that are easy to classify, which can cause the model to overlook the minority class. Focal Loss addresses this problem by reducing the importance of those easy examples and putting more focus on harder, misclassified examples, which makes it particularly useful for improving performance on underrepresented classes [26; 27].
* Label Smoothing Loss: This loss was used to tackle the issue of overconfident predictions, which often harm generalization, especially in noisy or imbalanced data [28]. Instead of treating labels as absolute, this method softens them slightly. By applying a smoothing factor of 0.1, the model spreads a small portion of the label's certainty across other classes. This encourages the model to be less certain in its predictions, helping to reduce overfitting and improve its ability to generalize.
# Simplified Welch Algorithm for Spectrum Monitoring
## 2 Fourier Transform and Welch Algorithm
### Welch's Method
Larger \(K\) values help the system to estimate each frequency component based on more observations. Second, if there are fast variations in a frequency component (like a carrier with a hopping center frequency), large averaging can have some drawback on estimation and stop the system from monitoring the fast variations in spectrum. Thus, a robust monitoring system needs to have a variable \(K\) to adjust itself from time to time based on the situation. For example, if the target is to detect a weak frequency component under noisy conditions, a \(K\) with a larger value can help the monitoring system to recognize it more clearly. In other situations, when the system is trying to track a fast-hopping carrier frequency, smaller \(K\) values are more useful because the averaging over time can filter fast variations. Implementing this moving average requires \(K\) number of Random Access Memory (RAM) blocks with \(N_{F}\) size that save observed FFT vectors for averaging. Thus, the complexity and required space on FPGA depends directly on the considered number of FFT packets for averaging. Another drawback is that to support a device with variable average order \(K\), one has to provide \(K_{max}\) RAMs in FPGA and use as much as is required depending on the situation.
## 5 Results and Discussion
### Discussion  
We analyzed the characteristics of vulnerable extraction functions that can be utilized during the OTP generation process and proved their security from a distinguishability perspective.  
Ref. [1] constructed a sufficient amount of OTP datasets as extraction methods. Based on numerous datasets, Ref. [1] demonstrated that as the OTP extraction method used in actual systems becomes more similar, the randomness of the data decreases. On the other hand, this paper demonstrated the adversary's advantage and the corresponding provable security with theory alone, without a sufficient amount of practical datasets. This vulnerable extraction function has experimentally increased predictability and theoretically increased distinguishability by compromising the randomness that is the source of security in all crypto and OTP systems.  
From the perspective of generating \"secure OTP values\", the solution we propose to mitigate the problem is as follows:  
Use static or dynamic algorithms: Not only should non-improved algorithms be used, but the specifications related to this algorithm should be removed from the standard. If this algorithm continues to be specified in the standard, the institution developing OTP may mistakenly use this vulnerable algorithm as the strongest algorithm by seeing the phrase \"improved\". If this algorithm is used in the institution using OTP, the algorithm is easily analyzed based on the adversary's attack model presented in this paper. This increases the probability of predicting OTP values. OTP is widely used because it is notreused, has randomness, and cannot predict the next value based on current information. However, as in the results of this paper, if the value is distinguished by the algorithm, the OTP output can be predicted. In this case, the advantages provided by OTP disappear. Therefore, improved algorithms that eliminate these advantages should be removed from the standard, and static and dynamic algorithms should be used instead.
# Freshness Monitoring of Packaged Vegetables
## 3 Factors Indicating the Freshness of Packaged Vegetables
### Humidity  
Undoubtedly, water content inside the package is considered an important factor to monitor. This is because the relative high humidity accompanied with temperature fluctuations will lead to water condensation, which in turn will induce growth of yeast, mold, fungus, and bacteria on the surface of fresh-cut vegetables or even might spread inside through the places where tissue injuries are present [16]. Furthermore, in the case of using perforated plastic packages, blockage of these small holes might occur with the condensed water drops inside, leading to reduced respiration rate than the desired and hence faster deterioration and spoilage of vegetables. The fog resulting from having high humidity inside the package would not give a clear vision of the product quality inside, which may affect the consumer's decision to buy the product [16].
# Optimization of Inspection Period in Natural Stone Claddings
## Materials and Methods
### Formulation of the Optimization Problem
\begin{array}{ll}\textbf{Find:}&\text{The (optimal) time interval between inspections, $t_{ insp}$}\\ \textbf{So that:}&\text{Minimization of the maintenance costs}\\ &\text{Maximization of the efficiency index}\\ &\text{Maximization of the service life}\\ &\text{Minimization of the number of replacements}\\ \textbf{Subject to:}&t_{insp,min}\geq t_{insp}\geq t_{insp,max}\end{array}  \tag{3}\]  
The main interactions among the different models of the optimization procedure are presented in Figure 3. In this procedure, the optimization model is the most important characteristic. Herein, multi-objective genetic algorithms are employed. One distinctive feature of the GA is the use of a population of solutions, allowing searching within several Pareto optimal solutions simultaneously [53]. Figure 2: Example of the non-dominated front of a multi-objective optimization problem. Furthermore, the GA does not need any further information about continuity or differentiability [40]. The management of cladding degradation, maintenance interventions, and maintenance costs are governed by the maintenance model. The initial severity of degradation of the cladding, \(S_{w}\); the degradation rate vector, \(\Theta\); the time horizon of the analysis, \(t_{h}\); the maintenance action details; the cladding characteristics are the input data of the optimization procedure. The optimization procedure begins by setting a value for the design variable (time interval between inspections, \(t_{\text{insp}}\)). In the maintenance model, considering the input data and the time interval between inspections set, the degradation and cumulative maintenance cost profiles are computed. An extensive description of the procedure followed in the maintenance model can be found in [PERSON] et al. [54]. In the oncoming step, the objective functions (the maintenance cost of the maintenance strategy, the efficiency index, the service life, and the number of total replacements performed during the time horizon) are computed. This procedure is iteratively repeated until the predefined stopping criteria in the GA are satisfied (the algorithm stops if the average relative change in the best fitness function value over 100 generations is less than or equal to \(10^{-4}\)[26]). In the following sections, the theoretical background of the objective functions is provided.
## 5 Studies on the Effects of _M. oleifera_ Leaf Extracts on Male Reproductive Function
### Electromagnetic Radiations  
There is evidence the long-term exposure to electromagnetic radiation causes an increase in the production of reactive oxygen species (ROS) in organs of the reproductive system [7]. These radiations have been reported to reduce sperm motility, cause sperm defects, increase peroxidation, histological aberrations in the testicular tissue and testicular tissue atrophy [97]. Additionally, these radiations affect the HPA axis by increasing the adrenocortic hormone (ACTH) secretion from the anterior pituitary gland, which stimulates the production of cortisol form the adrenal cortex [7]. Cortisol then suppresses testosterone secretion from the Leydig cells [98].  
Studies on the effects of _M. oleifera_ leaf extracts on male Wistar rats exposed to electromagnetic radiation have demonstrated a reduction in oxidative damage in vivo (see Table 1), as indicated by the reduced MDA, increased CAT and SOD activities [78; 79]. In addition, _M. oleifera_ leaf extracts also increased serum testosterone levels [79], increased sperm parameters (epididymal sperm count and motility) and reduced sperm defects (pyriform head, detached head, coiled tails and multiple abnormalities) [78].  
In another study, _M. oleifera_ leaf extracts on adult white albino male rats exposed to an electromagnetic field reduced the degeneration of some parts of the seminiferous tubules. As a result, they increased the number of Leydig cells [80]. In addition, the testes' morphology was almost normal, as seen in the histoarchitecture of the seminiferous tubules and Sertoli cells [80].
# E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol
## 2 Related Works
### Improvement of MQTT  
Numerous research works have tailored MQTT to suit their distinct objectives, appending bespoke functionalities. For example, MQTT-S [2] adapted MQTT to cater to the needs of smaller sensor-actuator (SA) devices within sensor networks, while MQTT-G [4] infused geolocation data into an existing MQTT. [PERSON]'s study [14] introduced a priority-QoS flag within the MQTT packet's fixed header to incorporate the priority concept. The research of [PERSON] et al. [15] involved developing a TCP-based MQTT protocol with retransmission rules to improve content delivery probability, ensuring reliable and secure Internet of Everything (IoE) services for smart city applications. [PERSON] et al. [16] addressed the optimization of a wireless sensor network (WSN) using the MQTT-SN protocol for IoT applications by introducing a dynamic QoS controller.  
Various investigations concentrate on enhancing MQTT's security, as exemplified by PICADOR [19], the incorporation of encryption features into the Narada Brokering System, along with separate security nodes [18] and the application of Secure MQTT [17]. Recently, research efforts are more focused on performance improvement. [PERSON] et al. [21] proposed a QoS dynamic adaptation method (DAM) for WSN to ensure better message delivery. The main idea was to select the proper QoS level based on network latency conditions. [PERSON] et al. [20] established a unified architecture and proposed a delay-reliability-aware MQTT quality of service selection algorithm to effectively reduce delay and packet loss, meeting the diverse quality of service requirements in electric IoT. [PERSON] [22] implemented a real-time remote patient monitoring system utilizing IoT technology to ensure the precision of vital real-time signals. These signals are transmitted from the proposed system to a website through MQTT.
## 4 QoS Aware Multipath Routing Protocols for WMSNs
### Conclusion and Future Trends  
Multipath routing protocols play an important role in the QoS provision and multimedia data delivery in WMSNs. Interestingly, most multipath routing protocols for WMSNs have their single path counterparts in traditional WSNs. DGR and GEAM are based on GPSR [122]. ARA ant-colony-based routing [124] is the basis of AntSensNet. Z-MHTR is an extension of the Zigbee cluster-tree routing [125]. LCMR is built on AODV [123]. Compared to their single path counterparts, the multipath routing protocols are able to distribute the bulk multimedia data traffic more evenly among sensors and thus improving load balancing and fair energy consumption. However, for the routing protocols to obtain maximum gain from multiple concurrent paths, designs like [49; 50] with inter-path interference awareness are favored in order to solve the route coupling problem [126]. Nonetheless, most multipath routing protocols put there emphasize solely on load balancing and energy conservation. For example, only AntSensNet and LCMR explicitly considers QoS and/or prioritization and service differentiation while, in fact, QoS should be a necessity of routing in WMSNs. Moreover, none of the multipath protocols consider about the nature of camera sensors in terms of directional sensor coverage and correlation within the multimedia data from neighboring nodes, which are important to reduce data redundancy and to improve quality of multimedia services [49; 50; 126].  
We argue that the multipath routing protocol that will best fit the demands of WMSNs needs to be energy efficient, QoS-aware, and interference-aware. In the meantime, it also should consider the existence of heterogeneous traffics and correspondingly provide prioritization and differentiated services. Last but not the least, congestion control mechanisms, route recovery methods, and consideration of the nature of camera sensors are also merits in order to provide steady and quality multimedia services.
## 3 Case Study
### POCS Design  
The design of the proposed structure was carried out on the basis of radial geometry. Eight struts, equally distributed around a central pipe, are periodically repeated along the axial coordinate of the geometry. With regard to the field of application under investigation, the central pipe plays a key role in the achievement of a proper heat distribution all over the POCS. In order to maximize the wetted surface area, inclined structs connecting the different periodic sections of the geometry are created. Table 1 reports the main geometrical data of the proposed geometry.  
The obtained POCS is presented in Figure 2, referring to its frontal section and a longitudinal clip of the structure. The achieved geometry is featured by a module that is axially repeated the entire length of the considered duct. The radial strut axial spacing identifies the module length. In particular, the geometrical properties of the identified module can be further evaluated.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strut Diameter (mm)** & **Radial Strut Axial** & **Internal Pipe** & **External Diameter** \\  & **Spacing (mm)** & **Diameter (mm)** & **(mm)** \\ \hline
2.0 & 15.0 & 6.0 & 40.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: POCS geometrical data.  
The porosity of the structure, defined as the ratio between the available area open to the flow over the theoretical cross-section of the pipe, results as dependent on the axial coordinate along the module. The computed porosity all over the module length is reported in Figure 3. As expected, the initial and last sections are featured by the lowest values, given the presence of the eight radial struts. The intermediate values are of approximately 0.94, due to the lower impact of the inclined struts on the available axial cross-section. A visualization of the corresponding sections as function of the axial coordinate is shown in Figure 4, highlighting the available area for the flow circulation.  
Given the electrical heating application, the inner pipe of the structure has been elongated so that it can be connected to the power supply in the external parts of the manifolds. Such geometrical detail can be observed in the bottom portion of Figure 5. To preserve the desired electrical circuit, an insulating material is supposed to be interposed between the metallic duct and the elongated inner pipe.  
In addition, the obtained structure is modified to be fitted inside a given exhaust manifold. As a consequence, the geometrical properties previously evaluated are slightly modified. A visualization of the achieved geometry is reported in Figure 5. With respect to the straight configuration, the module is repeated six times. The inclined struts are not parallel everywhere and periodicity is not strictly preserved.  
Figure 3: POCS geometrical characterization: Porosity trend with respect to the module axial coordinate.  
Figure 2: POCS geometrical characterization: Straight configuration: (**a**) Frontal section; and (**b**) Longitudinal clip.