esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Zinc oxide nanoparticles (ZnO-NPs) are commonly used for industrial applications. Consequently, there is increasing exposure of humans to them. The in vitro analysis of cytotoxicity and genotoxicity is commonly performed under standard cell culture conditions. Thus, the question arises of how the results of genotoxicity and cytotoxicity experiments would alter if human plasma was used instead of cell culture medium containing of fetal calf serum (FCS). Human mesenchymal stem cells (hMSCs) were cultured in human plasma and exposed to ZnO-NPs. A cultivation in expansion medium made of DMEM consisting 10% FCS (DMEM-EM) served as control. Genotoxic and cytotoxic effects were evaluated with the comet and MTT assay, respectively. hMSC differentiation capacity and ZnO-NP disposition were evaluated by histology and transmission electron microscopy (TEM). The protein concentration and the amount of soluble Zn\({}^{2+}\) were measured. The cultivation of hMSCs in plasma leads to an attenuation of genotoxic and cytotoxic effects of ZnO-NPs compared to control. The differentiation capacity of hMSCs was not altered. The TEM showed ZnO-NP persistence in cytoplasm in both groups. The concentrations of protein and Zn\({}^{2+}\) were higher in plasma than in DMEM-EM. In conclusion, the cultivation of hMSCs in plasma compared to DMEM-EM leads to an attenuation of cytotoxicity and genotoxicity in vitro.  
ZnO-NP; mesenchymal stem cells; genotoxicity; cytotoxicity; human plasma Article
## 5 Challenges of the OWC in the 5G/6G and IoT Solutions
Furthermore, we can use reinforcement learning to enhance the data rate, implement network switching and manage network traffic, among other factors, of the ultra-dense OWC networks for 6G [14]. Integrating machine learning in 6G OWC networks enables intelligent network assignment, auto error correction, efficient decision making, and network re-assignment, among others. Moreover, machine learning approach is a core demand in indoor mobile robot-based dense OWC small networks to perform fast and efficient tasks.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 5 Parameters Affecting the Performance of a Submerged Hollow Fiber System
### Membrane Properties and Module Configurations  
#### 5.1.1 Membrane Materials and Surface Morphology  
Membrane characteristics such as material, surface charge, hydrophilicity, pore size, and pore morphology significantly impact membrane performance and the fouling potential. Membrane surfaces can be modified to combat and mitigate adhesive fouling [25]. The membrane material largely influences the initial rates of deposition of foulants due to the tendency of some materials to adsorb certain solutes or particulates more readily, as quantifiable by the Gibbs free energy of foulant-membrane interaction [154; 155; 156; 157]. When the adsorption becomes such that the effective pore size of the membrane is reduced, the flux is adversely affected [158; 159; 160].  
It is well-known that the severity of fouling increases as the hydrophobicity of the membrane increases, because organic molecules have a higher affinity for hydrophobic materials [47; 161; 162; 163; 164; 165]. Most commercial MF and UF membranes are made from relatively hydrophobic polymers (e.g., polysulfone, polyetherselofue, polypropylene, polyethylene, and polyvinylidene-fluoride (PVDF)), due to their excellent chemical resistance, and thermal and mechanical properties [83; 166; 167; 168; 169; 170]. In some cases, the membranes are modified by additives to confer increased hydrophilicity for water applications. The charge on the membrane surface also plays a role in either exacerbating or mitigating fouling. Attractive electrostatic forces between a charged surface and the co-ions in the feed solution increase fouiling [161], while repulsive electrostatic forces mitigate fouling. In general, a low surface charge or electrically neutral surfaces tend to show better anti-fouling properties during the initial stages of membrane fouling [163; 171]. Since most colloidal particles, such as those in natural organic matter (NOM), that tend to deposit are negatively charged, membrane surfaces are usually negatively charged [161; 168; 169], although positively charged membrane surfaces are also used to repel positively charged colloids and cations [172]. Compared to having just a positively or negatively charged membrane surface, the addition of a zwitterionic charged material, one composed of neutral molecules that have a positive and a negative electrical charge, has been shown to confer better anti-fouling properties [173]. Inorganic nanoparticles (such as SiO\({}_{2}\)[174; 175], Al\({}_{2}\)O\({}_{3}\)[176; 177], clay [178; 179], ZrO\({}_{2}\)[180; 181; 182], TiO\({}_{2}\)[183; 184], and ZnO [185; 186]) also have been used to improve polymeric composite membranes, conferring improvements in the mechanical properties, thermal stability, hydrophilicity, permeation, and antifouling performance of membranes [165].
## 4 Discussion
### Comparison with the Literature  
Table 4 presents a comparison of the AGD values obtained in this study with those in the literature [28; 29; 30; 31; 32; 33; 34; 35]. The CC, MLO, and per woman AGD values obtained in this study were 1.36, 1.19, and 5.10 mGy, respectively.  
Our results can be compared to the results of a study by [PERSON] et al., who reported the AGD values of 1.8 mGy, 1.95 mGy, and 3.7 mGy in the CC and MLO projections and per woman, respectively, in Spain. In another study in Malaysia, the AGD values were 1.54 mGy, 1.82 mGy, and 3.37 mGy, respectively. Similarly, [PERSON] et al. in a study on patients in Australia reported AGD values of 1.6 mGy and 4.6 mGy in per view and per woman, respectively, whereas, in Iran, [PERSON] reported AGD values of 2.0 mGy, 2.4 mGy, and 4.4 mGy in CC and MLO views and per woman, respectively.  
Our AGD values are almost half the doses reported by [PERSON] et al. in Serbia, who reported AGD values of 2.8 mGy, 4.3 mGy, and 7.1 mGy in the CC and MLO projections and per woman, respectively. This study reported an average breast thickness of 49.5 mm and 56.0 mm for the CC and MLO projections, respectively, compared to the average breast thickness of 38 mm and 43 mm in our study. Several studies have shown that MGD increases with breast thickness (assuming comparable exposure factors). Among the different studies presented in Table 4, only the study by [PERSON] et al. in Malta showed a high average breast thickness of 53.8 mm and 63.4 mm but a relatively low AGD of 2.13 per view.  
Among the centers included in this study, the AGD per CC projection was higher than that presented for the MLO projection in six (75%) of the hospitals studied. Although there is a consensus that AGD increases with CBT, breasts with a similar average thickness may show differences in granularity, which is a significant determinant of radiation-induced risk. Pectoral muscle was denser than the rest of the breast muscles, which may be a reasonable explanation underlying this phenomenon [36]. Based on the 75 th percentile of average hospital dose distributions, Sudan's NDRLs based on CC and MLO projections were 1.12, 1.13, and 4.65 mGy. Our values were comparable to those used in the United Kingdom (2.5 mGy) [37; 38] and Belgium (2.46 mGy) [39].  
Our study has several limitations. Owing to significant variations in compressed breast thickness, the ICRP recommends a minimum sample size of 50 women per mammography device for dosimetric studies to minimize dose variations. Furthermore, most hospitals were not utilizing the AEC, meaning that exposure factors were not tailored to the patient's habitat. Therefore, the current study's results did not show a significant correlation between mAs and CBT in terms of AGD values, as expected.
# Collaborative Indoor Positioning by Localization Comparison at an Encounter Position
## 3 Proposed Method
### Localization Comparison
#### 3.3.3 Evaluation Metric for Localization  
Estimated positions are evaluated using a metric \(M\) defined in Equation (3). Specifically, we use the Dynamic Time Warping distance \(D\)[37] to measure the distance between the estimated positions \(\textbf{S}=\{(x_{i},y_{i})\mid i=1,\cdots,n\}\) and the ground truth (GT) position \(\textbf{T}=\{(x_{i},y_{i})\mid i=1,\cdots,n\}\). \(\textbf{T}\) is created by selecting points on the walking path with a fixed interval, which divides the path equally. We used sampling points of the GT path to match estimated positions to appropriate points on the GT. For example, as shown in Figure 6, the estimated positions in the red dashed circle should be matched to the two points, respectively. If the GT path is used, the estimated two points will be matched to close points on the GT path, resulting in incorrect matches. \(N_{\text{match}}\) is the number of matching elements of the series \(\textbf{S}\) and \(\textbf{T}\). A smaller metric is better.  
\[M=\frac{D(\textbf{S},\textbf{T})}{N_{\text{match}}} \tag{3}\]
## 2 Materials and Methods
### Data Synthesis Procedures for Meta-Analysis  
Using version 4.0 of Comprehensive Meta-Analysis software (Englewood, NJ, USA), we conducted a meta-analysis. For effect size calculations, we used the standardized mean difference (_SMD_) by quantifying differences in exercise performances between compression garments and control groups (e.g., sample size, mean, and standard deviation values) for the parallel design [63]. For the crossover design, a paired analysis was applied using sample size and mean difference with standard error based on prior suggestions [63; 64; 65; 66]. We defined that greater values of _SMD_ indicate more increases in following exercise performances with wearing compression garments as compared with those for the control group: (1) endurance, (2) speed, (3) power, and strength, (4) functional motor performances, and (5) sport-related performances. Using the random-effect model, we statistically synthesized individual effect sizes into overall effect sizes because of the assumption that common effect size does not exist across the included studies [63; 67]. Finally, we excluded potential outliers that exceeded upper and lower limits (i.e., \(\pm\)2 \(\times\) SD of individual effect sizes). Specifically, potential outliers could distort the overall effect size, increase the distribution of data, decrease statistical power, and elevate the inaccuracy of the model. Thus, removing outliers allows data to be normally distributed and minimizes biased results [68].  
The level of heterogeneity among individual effect sizes was estimated by quantifying \(I^{2}\)[63; 69]. For calculating \(I^{2}\), we quantified Q statistic (i.e., summing the squared differences between the effect size of each study and the overall effect size that is weighted by the inverse variance of each study). Then, the percent of \(I^{2}\) equals (Q-degrees of freedom)/Q \(\times\) 100. Normally, greater values of \(I^{2}\) than 75% denote substantial variability among the included studies [70]. To assess potential publication bias, we provided revised funnel plots after  
Figure 1: Flow chart for study identification procedure.  
trim and fill methods [63], and further performed the Egger regression test. A \(p\)-value for intercept (\(\beta_{0}\)) less than 0.05 represents a significant level of publication bias among the included studies [71]. In addition, we conducted additional moderator variable analyses for each component of exercise performance. Two analyses determined whether different physical activity levels of participants (i.e., athlete, highly trained adult, moderately trained adult, and healthy adult) [72; 73] and garment types (i.e., upper-body, lower-body, and whole-body suits) influence changes in exercise performances.
# Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection
## 3 Methodology
### Task Definition  
Given an audio signal \(S\), the task of SCD is to find a set of change points for the segments, denoted as \(Y=[y_{1},\ldots,y_{m}]\), where \(y_{i}\in\mathbb{R}^{r}\), \(r\) is the total number of frames in each window, and \(m\) is the total number of segments. Let \(X_{i}=\{x_{i}\}_{i=1}^{n},x_{i}\in R^{d}\) represent the sequence of feature vectors of segment \(y_{i}\), where \(x_{i}\) is the features and \(n\) is the sample size, \(d\) is dimension of feature vector. Generally, speaker change point could be detected by calculating a distance or divergence between feature distributions of two adjacent audio segments and it can be represented as:  
* a first segment of \(X_{i}\) feature vector, the sequence of feature vectors from time \(t_{i}\) to \(t_{i+1}\).
* a second segment \(X_{i+1}\), the sequence of feature vectors from time \(t_{i+1}\) to \(t_{i+2}\).  
A speaker change is detected at time \(t_{i+1}\) if the distance between these two segments exceeds a threshold \(\tau\):  
\[d(X_{i},X_{i+1})>\tau. \tag{1}\]  
where \(d\) is a distance function, and \(\tau\) is a threshold to detect changes in speaker.
# Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History
## 5 Evaluation of OLSR-HBP in Mobile Scenarios
### Experimental Framework  
The performance of OLSR-HBP was analysed and compared using several settings and parameters. For each specific set of parameters, we executed ten variants of the tests, and then we computed the average, standard deviation and the minimum and maximum values of the observed variables. The parameters used in the simulations are specified in Table 5.  
The deployment area, the number of nodes participating in the network and their mobility pattern was set simulating a regular (small) urban emergency, like a fire in a building or the partial collapse of a civil infrastructure. In these scenarios the incident commander (who is in charge of the emergency response process) and the team leaders represents the network nodes. Most of them have high mobility in the affected area following a self-similar least action walk (SLAW) pattern. Regarding the confidence, we performed various initial tests, first with 0 bits (no confidence) and then with confidence with 2 bits in order to determine an appropriate value for the simulations. After these tests we discarded the latter case (i.e., the confidence with 2 bits), and the reason was that the small number of errors produced at destination nodes, when no confidence was applied (about 0.5% of the TP messages predicted/generated at destination), were \"amplified\" by the confidence mechanism. This mechanism produced 40 times more wrong predictions at destination and 42% less TP messages omitted (not sent) at origin; therefore, the confidence was set in 0. Finally, the data traffic and the ping periods were set according to the recommendations given in [11].
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 6 Future Directions and Real-World Challenges
### Lack of Interoperability  
Interoperability is the ability of different systems to work together and exchange information. Without interoperability, FRSs would be unable to share information, limiting their effectiveness. This could lead to a lack of the personalization and accuracy of recommendations. Moreover, this would create a barrier between users and their data, making it difficult for them to find the most-relevant content.  
* **Future directions**: These may include developing standards and protocols for data exchange between systems, as well as utilizing open-source solutions that allow different systems to communicate with each other. In addition, developing technologiessuch as blockchain could provide solutions to ensure data privacy and security while allowing interoperability.
* **Real-world challenges**: The main challenge in achieving interoperability in FRSs is the need for systems to be able to share data without compromising privacy or security. Moreover, different systems may have different data formats, making it difficult for them to communicate with each other. Developing standards and protocols for data exchange could help address this challenge, as could the development of open-source solutions. In addition, ensuring data privacy and security in federated systems could be a difficult task, as data may need to be shared between different systems. Utilizing blockchain technology could help address this challenge, allowing for secure data sharing while ensuring privacy.
## 2 Methodology and Modeling
### Passive Solution Tests
#### 2.6.6 Shading  
Various shading devices were tested. Alternative 1 served as the base case without shading. In alternative 2, internal shading was added with a gray semi-transparent shade, activated when the zone air temperature reached 22 \({}^{\circ}\)C.  
Alternatives 3 and 4 employed overhangs (1.28 m projection) and vertical fins (0.914 m depth), respectively. The overhang length was optimized for different solar angles across Teresina, Sanluurfa, and Evora, ensuring consistent shading performance throughout the day.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Location** & \(u\) **[km/h]** & \(u\) **[m/s]** & **Infiltration Rate [ACH]** \\ \hline Évora & 14 & 3.89 & 2 \\ Sanluurfa & 12 & 3.33 & 1.5 \\ Teresina & 5 & 1.39 & 1 \\ \hline \end{tabular}
\end{table}
Table 7: Infiltration rates and average wind speeds (\(u\)) for each location applied to inclined roofs.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Alternative** & **Description** & **SHGC** & \(U\) **[W/(m\({}^{2}\)-K)]** \\ \hline Alt. 1\_Window & Simple glass without thermal break & 0.86 & 5.89 \\ Alt. 2\_Window & Double glass with thermal break & 0.7 & 2.51 \\ Alt. 3\_Window & Tinted glass & 0.48 & 2.51 \\ \hline \end{tabular}
\end{table}
Table 8: U-value and SHGC for each window solution.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Alternative** & **Glass** & \multicolumn{2}{c}{**Brazil**} & \multicolumn{2}{c}{**Portugal**} & \multicolumn{2}{c}{**Turkey**} \\  & **WH** & **WTWR** & **WH** & **WTWR** & **WH** & **WTWR** \\ \hline Alt. 1\_WTWR & Simple, clear & 2 m & 14.53\% & 3 m & 19.44\% & 1.5 m & 7.15\% \\ Alt. 2\_WTWR & Simple, clear & 1.5 m & 11.05\% & 1.8 m & 11.69\% & 1.05 m & 5.12\% \\ Alt. 3\_WTWR & Simple, clear & 1 m & 7.56\% & 0.9 m & 5.88\% & 0.75 m & 3.73\% \\ Alt. 4\_WTWR & Double, tinted & 2 m & 14.53\% & 3 m & 19.44\% & 1.5 m & 7.15\% \\ Alt. 5\_WTWR & Double, tinted & 1.5 m & 11.05\% & 1.8 m & 11.69\% & 1.05 m & 5.12\% \\ Alt. 6\_WTWR & Double, tinted & 1 m & 7.56\% & 0.9 m & 5.88\% & 0.75 m & 3.73\% \\ \hline \end{tabular}
\end{table}
Table 9: Analysis of the window-to-wall ratio (WTWR) with varying window height (WH) for Brazil (BR), Portugal (PT), and Turkey (TK).  
Due to varying window distribution across facades, shading solutions were applied to all windows in all locations. Table 10 summarizes the shading solutions.
# Plugging Small Models in Large Language Models for POI Recommendation in Smart Tourism
## 4 Methodology
### Plugin Model  
#### 4.3.1 User Personal Habit and Novelty Preferences  
A user's personal preferences are pivotal in personalizing the next POI recommendation. Within this module, our aim is to discern each user's habitual preference and novelty preference based on a copy-generation mechanism [34]. Users usually show their habitual behavior patterns in different time slots and may be interested in unfamiliar POIs at any time. For example, [PERSON] drinks a cup of coffee at his favorite coffee shop at 2 p.m. almost every weekday, but occasionally tries other activities. Therefore, we can capture user preferences during specific time slots through user embeddings and the time slot embeddings of the current query context \(q=(u,t,?,d)\). First, we transform \(S_{u}^{d}\) into \(\hat{S}_{u}^{d}\) by  
\[\hat{\mathcal{S}}_{u}^{d}=f(\mathcal{S}_{u}^{d}),\;f(x)=\begin{cases}+\lambda,&\text{if }x>0.\\ -\lambda,&\text{if }x=0.\end{cases} \tag{2}\]  
Then, with the help of \(\hat{S}_{u}^{d}\), the set of familiar POIs is extracted through the copy mode to mine habit preferences, \(v_{hab}\), and unfamiliar POIs are identified through the generation mode to mine novelty preferences, \(v_{nov}\).  
\[v_{hab}=tanh(W_{hab}[u,t]+b_{hab})L^{\top}+\mathcal{S}_{u}^{d}, \tag{3}\]  
\[v_{nov}=tanh(W_{nov}[u,t]+b_{nov})L^{\top}-\mathcal{S}_{u}^{d}, \tag{4}\]  
where \(tanh(\cdot)\) is the activation function. And \(W_{hab},W_{nov}\in\mathbb{R}^{2d\times d}\) and \(b_{hab},b_{nov}\in\mathbb{R}^{d\times d}\) are trainable parameters. \(L\) represents all location embeddings. \([\cdot]\) denotes concatenation.
## 4 Experimental Results and Discussion
### Performance of Proposed Power Control Strategies
#### 4.1.1 P&O with Fixed Step Size  
Figures 9 and 10 present the experimental results of P&O with three different fixed step sizes (2, 4 and 6 V) to target LPP1 and LPP2 for a wind speed of 6.5 m/s. As can be seen in these figures, the power generated (\(p_{WT}\)) for different fixed step sizes is oscillating at around 50 W for both LPP1 and LPP2, whereas the voltage (\(p_{WT}\)) varies close to 15 V and 60 V for LPP1 and LPP2, respectively. This corresponds to the selected case (see Figure 8). In addition, during the transition from LPP1 to LPP2, the current decreases approximately from 3.5 A to 1.5 A (Figures 9d and 10d).  
power oscillations around the \(P_{\rm lim}\) and, hence, less efficiency. In contrast, a smaller step size enhances efficiency, but reduces the convergence speed. This is in agreement with the results found in the literature [35; 36]. In addition, comparing LPP1 and LPP2 reveals that working in LPP2 point leads to more power oscillations around the \(P_{\rm lim}\). This could be related to the rotor speed, which is less braked in LPP2, inducing an increase in the voltage and the rotor speed and thus generating more power oscillations compared to LPP1.  
Figure 9: Fixed step-size P&O for LPP1: (**a**) evolution of step size \(\Delta v\); (**b**) evolution of \(p_{WT}\); (**c**) evolution of \(v_{WT}\); (**d**) evolution of \(i_{WT}\).
###### Abstract  
A mobile ad-hoc network (MANET) is a temporary network of wireless mobile nodes. In a MANET, it is assumed that all of the nodes cooperate with each other to transfer data packets in a multi-hop fashion. However, some malicious nodes don't cooperate with other nodes and disturb the network through false routing information. In this paper, we propose a prominent technique, called dual attack detection for black and gray hole attacks (DDBG), for MANETs. The proposed DDBG technique selects the intrusion detection system (IDS) node using the connected dominating set (CDS) technique with two additional features; the energy and its nonexistence in the blacklist are also checked before putting the nodes into the IDS set. The CDS is an effective, distinguished, and localized approach for detecting nearly-connected dominating sets of nodes in a small range in mobile ad hoc networks. The selected IDS nodes broadcast a kind of status packet within a size of the dominating set for retrieving the complete behavioral information from their nodes. Later, IDS nodes use our DDBG technique to analyze the collected behavioral information to detect the malicious nodes and add them to the blacklist if the behavior of the node is suspicious. Our experimental results show that the quality of the service parameters of the proposed technique outperforms the existing routing schemes.  
MANET; DoS attack; black hole; gray hole; IDS node; CDS; status packet; dual attack +
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