esa-sceva/satcom-chunk-collection huggingface dataset:
# Impacts of Diversity Technique Application in Order to Reduce the Fading in IEEE 802.15.4 Networks
## 4 BER in the Case of MRC Combiners
### BER for MRC Combiner in the Presence of Rayleigh Fading  
The PDF of the signal at the output of the MRC combiner where Rayleigh fading is present at the input can be represented as [26]:  
\[f_{MRC,Ray}(\gamma)=\frac{\gamma^{L-1}e^{-\frac{\gamma}{\gamma_{0}}}}{(L-1)! \gamma_{0}^{L}}. \tag{26}\]  
The BER of the WSN transceiver based on ZigBee technology operating in an AWGN environment and with a signal under the impact of Rayleigh fading, previously passed through the MRC combiner with \(L\) branches, is obtained when (26) and (2) are included in (4):  
\[BER_{Zigbee,MRC,Ray}=\int\limits_{0}^{\infty}BER_{AWGN}(\gamma)f_{MRC,Ray}( \gamma)d\gamma\]  
\[=\frac{1}{(L-1)!\gamma_{0}^{L}}\left[\int\limits_{0}^{\infty}\frac{1}{2} \gamma^{L-1}e^{-\frac{\gamma}{\gamma_{0}}}erfc\big{(}\sqrt{T\gamma}\big{)}d \gamma-\int\limits_{0}^{\infty}\frac{1}{8}\gamma^{L-1}e^{-\frac{\gamma}{ \gamma_{0}}}erfc^{2}\big{(}\sqrt{T\gamma}\big{)}d\gamma\right]\]  
\[=\frac{1}{(L-1)!\gamma_{0}^{L}}[I_{27.1}-I_{27.2}].\]  
By using the transformation for the erfc(\(\cdot\)) function [22] Eq. (06.27.26.0006.01) and the transformation for the exponential function [23] Eq. (8.4.3), as well as by using [24] Eq. (07.34.21.0011.01), integral \(I_{27.1}\) obtains the following solution:  
\[I_{27.1}=\frac{1}{2\sqrt{\pi}T^{L}}G_{2,2}^{1,2}\bigg{(}\frac{1}{T\gamma_{0}} \bigg{|}\begin{array}{cc}1-L,&\frac{1}{2}-L\\ 0,&-L\end{array}\bigg{)}. \tag{28}\]  
By using the transformation for the erfc(\(\cdot\)) function [22] Eq. (06.27.26.0006.01) and the transformation for the exponential function [25] Eq. (1.211.1), as well as by using [24] Eq. (07.34.21.0011.01), integral \(I_{27.2}\) obtains the following solution:  
\[I_{27.2}=\frac{1}{8\pi}\sum\limits_{n=0}^{\infty}\frac{1}{n!}\bigg{(}\frac{-1 }{\gamma_{0}}\bigg{)}^{n}T^{-(n+L)}G_{3,3}^{2,2}\bigg{(}1\bigg{|}\begin{array} []{cc}1-n-L,&\frac{1}{2}-n-L,\\ 0,&\frac{1}{2},&-n-L\end{array}\bigg{)}. \tag{29}\]  
After substituting the integral solutions (28) and (29) in (27), the final expression for the BER of the transceiver of the IEEE 802.15.4 network operating in an environment with AWGN and with a signal impacted by Rayleigh fading, previously passed through the MRC combiner with \(L\) branches, is obtained:  
Figure 2: MRC combiner with \(L\) inputs.  
\[BER_{Zigbee,MRC,Ray}=\frac{1}{(L-1)\gamma_{0}^{2}} \tag{30}\] \[\cdot\left[\frac{1}{2\sqrt{\pi T^{L}}}G_{2,2}^{1,2}\left(\frac{1}{T \gamma_{0}}\left|\begin{array}{cc}1-L,&\frac{1}{2}-L\\ 0,&-L\end{array}\right.\right)-\frac{1}{8\pi T^{L}}\sum\limits_{n=0}^{\infty} \frac{1}{n!}\left(\frac{-1}{T\gamma_{0}}\right)^{n}G_{3,3}^{2,2}\left(1\left| \begin{array}{cc}1-n-L,\frac{1}{2}-n-L,1\\ 0,&\frac{1}{2},&-n-L\end{array}\right.\right)\right].\]
## 5 Results and Discussion
### Scenario 2: Optimal Scenario
Additionally, the current driver prioritization rule--which first assigns tasks to Driver A, then to B, and finally to C was not followed in the optimized model, where assignments were based solely on minimizing total distance. This further highlights the need for a more flexible tasking approach, where efficiency takes precedence over driver prioritization. Another important aspect is the cost structure considered in this study. The final service cost of the company includes multiple variables--such as airport fees and out-of-zone travel charges, which were not available for analysis. Instead, the study focused on two measurable cost components: the cost per kilometer traveled and warehouse storage costs, which were EUR 5 per day for up to 24 volumes. Additionally, it is crucial to note that the cost values presented in Table 5 are directly linked to the respective orders, meaning that the distances traveled by drivers when returning to the warehouse were not included unless they were directly related to storage needs. This occurred in three cases, where deliveries required temporary warehouse storage. From a sustainability perspective, reducing travel distances directly contributes to lowering CO2 emissions, as the company's fleet consists of non-electric vehicles. The company's original operations covered 233 km, whereas the optimized scenario reduced this by approximately 38%, leading to substantial fuel savings and emission reductions. This reinforces the environmental benefits of integrating optimization and ABS methodologies, aligning logistics efficiency with sustainable transportation practices. Additionally, the ABS assumed a fixed speed of 30 km/h, reflecting urban traffic conditions in Lisbon, where speed limits and frequent stops influence delivery times. While this assumption provides a reasonable approximation, future enhancements could incorporate dynamic speed adjustments based on real-time traffic data, further improving the model's accuracy. These insights validate the integrated modeling approach, illustrating how optimization and ABS can complement each other to assess and enhance urban logistics efficiency from both operational and sustainability perspectives, reinforcing the importance of data-driven decision-making in logistics.
## 2 Dynamic Health Intelligent Evaluation Model
### Principle Description and Evaluation Index
#### 2.1.1 Definition of Evaluation Index  
* Failure Probability \(\xi(t)\):  
The failure probability refers to the instantaneous probability of failure of the components when the components run to a certain time. The failure probability value is affected by temperature and satisfies the equation:  
\[\xi(t)=f(T,dT/dt) \tag{1}\]  
where, \(\xi(t)\) is the failure probability value at a certain time; \(T\) and \(dT/dt\) are the temperature and its difference of the moment respectively; Function relation \(f\) is fuzzy inference.  
* Health Degree \(H(t)\):Health degree is to evaluate the health of the whole system through the statistics of the damage of each component of the system. Health degree refers to the percentage of the number of units in the system that are not damaged at the current moment when the system runs to a certain time \(t\).  
\[H(t)=1-\frac{N_{failure}}{N_{total}} \tag{2}\]  
where, \(N_{total}\) is the total number of the underlying component units constituting the system, and \(N_{failure}\) is the number of failure failures of the underlying component units in the system at the current moment.  
* Failure Factor \(F(t)\):  
Failure factor describes the deterioration speed of system failure. Failure factor refers to the ratio of the increment of failure sub-units in the system within unit time \(t\) to the number of healthy component units at time \(t\).  
\[F(t)=\frac{N_{failure}(t+\Delta t)-N_{failure}(t)}{\Delta t\cdot[N_{total}-N_{ failure}(t)]} \tag{3}\]  
where, \(N_{failure}(t+\Delta t)-N_{failure}(t)\) is the number of newly added fault failure unit in time; \(N_{total}-N_{failure}(t)\) is the number of remaining underlying component units, that is, the number of component units that have not failed by time \(t\); \(\Delta t\) is the time interval taken.
## 2 Volatiles in Heritage Environments
### Volatiles Emitted by People  
Organic volatiles are emitted not only from objects or furnishings but also from activities and the personal care products people use. Human biogenic volatile organic compounds (HVOCs) can have various sources ranging from respiration to emissions through the skin [19; 48].  
Personal care products, including cosmetics, perfumes, deodorants and hair care products, often contain diverse VOCs. Several studies have demonstrated that the main volatiles measured in public spaces such as offices, schools, cinemas and museums are strongly influenced by the previous consumption of food or alcohol and by the use of personal care products [49; 50; 51; 52].  
The main volatiles emitted by people are acetone, isoprene and methanol [53]. Other components are aldehydes, alkanes and alkenes. In particular, the oxidation of skin oil by reaction with ozone produces 4-oxopentanal (4-OPA) and 6-methyl-5-hepten-2-one (6-MHO) [52]. Slight changes in temperature and relative humidity do not contribute to major changes in the types of volatiles emitted. However, elevated temperature and RH lead to greater dermal emission, sweating and thus the production of more carboxylic acids [19]. The main types produced by human skin are acetic acid, propanoic acid and butyric acid.  
Studies looking at the contribution of personal care products have identified monoterpenes as the main contributors [49; 54] and studied the efficiency of their removal through ventilation. Despite the extensive research on volatiles generated by human activities (respiration and skin emissions), there is a lack of research on the impact of personal care products on the environment at cultural heritage sites.
# Continuous Space Wireless Communication Tower Placement by Hybrid Simulated Annealing
## 5 Experimental Analysis
### Effectiveness of the Hybridization  
The hybrid SA is the hybridization of conventional SA and a local optimization algorithm. To examine the effectiveness of the hybridization, we simply compare the performance of the conventional SA with the local optimization algorithm, i.e., the hybrid SA, to that of the conventional SA without the local optimization algorithm using those test problems that were used for evaluating the exploration and exploitation capacities of the hybrid SA previously.  
For each of the test problems, we used the two SA algorithms to solve them. The quality of the solutions (coverage rate) was recorded. Considering the stochastic nature of the two SA algorithms, we performed each experiment 30 times. Table 2 shows the statistics of the experimental results.  
It can be seen from the statistics of the experimental results that the hybrid SA algorithm (the SA with the local optimization algorithm) found an optimal solution for all the test problems in the 30 runs. In fact, it found an optimal solution in most of the 30 runs for every test problem. On the contrary, the conventional SA algorithm (the SA without the local optimization algorithm) never found an optimal solution to any test problem in any run. The average coverage rate of the wireless communication tower placement solutions found by the hybrid SA algorithm for the test problems is between 0.993 and 1.000, whereas the average coverage rate of the wireless communication tower placement solutions found by the conventional SA for the same test problems is only between 0.741 and 0.806. In other words, the hybrid SA algorithm could always find an optimal or near-optimal solution, but the conventional SA could not. Since the only difference between hybrid SA and conventional SA is whether they use the hybridization technique or not, the experimental results show that the hybridization technique is effective.
## 3 General and Local Implications of Titanium Alloys
### Influence upon the Serum Metal Levels  
As described, the oxide layer formed on titanium alloys materials can suffer over time, and can even be degraded by chemical or electrical corrosion [62]. This specific action can determine the formation and release of active metallic compound into the bloodstream. Although, titanium is considered as biocompatible, this possible consequence has not been yet fully investigated and the response of the human body has not been completely elucidated [63]. Research conducted by [PERSON] et al. [64] outlined the fact that metal debris have been found in patients that underwent orthopaedic surgery--a fact that raised an important doubt regarding the potential unknown consequences [64]. Taking into consideration the fact that the maxillofacial field frequently used titanium-based alloys devices for various pathologies, this aspect in an important actual concern.  
A study conducted by [PERSON] et al. [62] included a number of 30 subjects that had implanted different titanium-based alloys devices, including miniplates and screws, for at least one year. The aim of the study was to identify if any changes existed in the metal concentration of the serum of these subjects. The results showed that in the blood of five subjects included in the study, the levels of cobalt in the serum was higher in three subjects, the level of chromium was higher in two subjects and only one subject showed a higher level of titanium and chromium.  
The existence of metallic debris that are secondary to the implantation of metallic devices has gained attention in the past years. The presence of titanium, chromium, cobalt and other metallic components can play an important role in the failure of the metallic devices by negatively influencing their strength [65]. Studies showed that the contribution of monocytes by their development into osteoclasts can determine local loosening of the metallic devices [66; 67].  
[PERSON] et al. [68] in their study used optical microscopy to examine titanium-based alloys devices that had been removed from 28 patients after they underwent maxilla-facial surgery. Their conclusion was that in the nearby connective tissue, the blood cells contained high titanium levels, and that the release of the titanium ended when bone deposits covered the titanium surfaces. Another study performed by [PERSON] et al. [69] aimed to examine the surrounding soft tissue after the removal of titanium plates, and their results revealed that titanium was released into the soft tissue during the osteosynthesis procedure, but its levels remained constant and it was characterized as being inert, as shown in Table 3.
## 5 Case Study  
Workload data acquired from an actual road construction project were used to validate the proposed model. The workload included data created from the planning stage of the construction project derived from the onsite measurement performed in advance. The road construction data utilized in this study were obtained from the planning stage of a highway project conducted by the construction company D. The details of the case study are listed in Table 4.  
The scheduling result for this project's planning stage is shown in Figure 3; as previously mentioned, the level of detail of this result was considerably low for direct utilization at the construction site.  
As is evident from the actual construction project scheduling of this study, the scheduling of earthworks at the planning stage was at a low level of detail (in the case of this study, there were only three activities: site preparation, earth cutting, and soil transportation), thus making construction activities at construction sites (based on these results) challenging. Owing to the previously conducted, low-detail scheduling of the case study project, subsequent scheduling rework or repetitive modifications occurred at the construction site. This resulted in additional costs and time consumption that had a negative impact on the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Information** \\ \hline location & Cheongju-si, Chungcheongbuk-do, Republic of Korea \\ duration & 2520 d (17\({}^{\prime}\)–23\({}^{\prime}\)) \\ load length & 5.64 km \\ load width & 20.0 m \\ \hline \hline \end{tabular}
\end{table}
Table 4: Case study information.  
Figure 3: Case study scheduling result (conventional method).  
project's productivity. To complete a case study on the proposed model, this study utilized data related to the highway construction plan of Construction Company D, as mentioned earlier. The workload data for each highway construction project activity used in the model case study are presented in Table 5.
## 2 Materials and Methods  
### Problem Formulation  
Consider an IoT platform with K distributed nodes. These nodes are end devices of the IoT platform. Each node k has a measurement \(s_{k}\) of a deterministic parameter \(\theta\) given by Equation (1):  
\[s_{k}=\theta+n_{k} \tag{1}\]  
where \(n_{k}\) is the measurement noise with zero mean and variance of \(\sigma^{2}{}_{sk}\). If the node k does not have any child node, it quantizes sk into a discrete value \(x_{k}\). Otherwise, it combines all the received data from its child nodes and \(s_{k}\) together and obtains the quantized discrete output \(x_{k}\). Here, we assume that \(s_{k}\) is bounded by \([-W;W]\). The probabilistic scheme in [49] is used to obtain the \(x_{k}\) with quantization resolution \(L_{k}\). \(x_{k}\) is then transmitted to the sensor \(k\)th parent node. When the quantized output \(x_{k}\) propagates through a wireless channel, the signal is attenuated and contaminated by channel noise. The received signal at the parent node can be given by Equation (2) from our previous work in [11] as  
\[r_{k}=(1-2p_{k})(\theta+n_{k}+w_{k})+\gamma_{k} \tag{2}\]  
where \(w_{k}\) is the quantization noise and the mean of the quantization noise is equal to zeros and its variance is upper bounded by \(\delta_{k}^{2}\), which is equivalent to \(W^{2}/(2^{L_{k}}-1)^{2}\). \(\gamma_{k}\) has zero mean and variance \(\epsilon_{k}{}^{2}=(4W^{2}(2^{L_{k}}+1)p_{k})/(3(2^{L_{k}}-1))\). The bit error probability between the node \(k\) and its parent is denoted by \(p_{k}\).
# Joint Deployment Optimization of Parallelized SFCs and BVNFs in Multi-Access Edge Computing
## 2 Recent Works
### Joint Deployment of SFC and BVNFs with Parallelization
To address the reliability-aware, delay-guaranteed, and resource-efficient SFC deployment problem, [PERSON] et al. [32] proposed a novel SFC sub-chaining method to enhance service reliability. Then, they formulated the reliable SFC deployment problem of minimizing the number of active physical nodes allocated for SFCs deployment. Given the high computational complexity, they proposed a modified stable matching algorithm to provide a near-optimal solution. However, the failure of any VNF of these sub-flows willinterrupt service provision and decrease the service reliability. Moreover, to process these sub-flows and improve reliability, it needs to deploy multiple active VNF instances and VNF replicas consuming massive resources.
# A Cox Model-Based Workflow for Increased Accuracy in Activity-Travel Patterns Generation
## 3 Methodology
**Survival Function for Each Latent Class**  
The survival function for each latent class c is given by:  
\[S_{i,c}(t)=\exp(-\int_{0}^{t}h_{o}(u)\exp(X_{i}\beta_{c}+frailty_{c})du)\]  
where,  
* _ho_(\(t\)): The non-parametric baseline hazard function
* \(X_{i}\beta_{c}\): the inner product of the coefficients \(\beta\)c and the covariates \(X\) for individual \(i\)
* _frailty_\({}_{c}\): The frailty term for each latent class \(c\)  
**Overall Survival Function**  
The overall survival function is a weighted combination of the survival functions for the two latent classes:  
\[S_{i}(t)=\pi_{1}S_{i1}(t)+\pi_{2}S_{i2}(t)\]  
where,  
* \(\pi_{1}=\ Z_{i1}\) (probability of belonging to latent class 1)
* \(\pi_{2}=\ Z_{i2}\) (probability of belonging to latent class 2)  
**The Cox-based machine learning model**  
The third model employed in this study is a Cox-based machine learning model, which extends the classical semi-parametric Cox proportional hazards model by introducing a non-linear predictor through a fully connected neural network. This approach preserves the proportional hazards assumption while allowing the model to learn complex, non-linearinteractions among covariates. Specifically, the hazard function for an individual \(i\) at time \(t\) is defined as:  
\[h_{i}(t|x_{i})=h_{0}(t)\cdot\exp(f_{\theta}(x_{i}))\]  
where \(h_{0}(t)\) denotes the unspecified baseline hazard function \(x_{i}\) is the covariate vector for individual \(i\), and \(f_{\theta}(\cdot)\) is a neural net parametrized by weights and biases \(\theta\). Model training is performed by maximizing the Cox partial log-likelihood function:  
\[L(\theta)=\sum_{i\in E}\left[f_{\theta}(x_{i})-log\left(\sum_{j\in R_{i}}\exp \bigl{(}f_{\theta}(x_{j})\bigr{)}\right)\right]\]  
With \(E\) representing the set of uncensored events and \(R_{i}\) the risk set for subject \(i\). This modeling approach proposed by [PERSON] et al. [50] and further developed by [PERSON] et al. [51], enhances the Cox model's expressiveness while maintaining its foundational structure, making it potentially suitable for capturing complex behavioral patterns in activity-travel data.
###### Abstract  
The size of nanomaterials influences physicochemical parameters, and variations in the size of nanomaterials can have a significant effect on their biological activities in cells. Due to the potential applicability of nanoparticles (NPs), the current work was designed to carry out a size-dependent study of gold nanoparticles (GNPs) in different dimensions, synthesized via a colloidal solution process. Three dissimilar-sized GNPs, GNPs-1 (10-15 nm), GNPs-2 (20-30 nm), and GNPs-3 (45 nm), were prepared and characterized via transmission electron microscopy (TEM), high-resolution TEM (HR-TEM), hydrodynamic size, zeta potential, and UV-visible spectroscopy, and applied against liver cancer (HepG2) cells. Various concentrations of GNPs (1, 2, 5, 10, 50, and 100 \(\mu\)g/mL) were applied against the HepG2 cancer cells to assess the percentage of cell viability via MTT and NRU assays; reactive oxygen species (ROS) generation was also used. ROS generation was increased by 194%, 164%, and 153% for GNPs-1, GNPs-2, and GNPs-3, respectively, in the HepG2 cells. The quantitative polymerase chain reaction (qPCR) data for the HepG2 cells showed up-regulation in gene expression of apoptotic genes (Bax, p53, and caspase-3) when exposed to the different-sized GNPs, and defined their respective roles. Based on the results, it was concluded that GNPs of different sizes have the potential to induce cancer cell death.  
gold nanoparticles; TEM; inverted microscopy; MTT; NRU; ROS Article