esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
This degradation is driven by medium absorption, leading to plasticization and chemical reactions that cause the leaching of components and the formation of voids, fractures, and cracks, compromising the structural integrity of the polymer matrix [48; 49; 50; 51]. This occurrence aligns with [PERSON]'s fracture theory, which suggests that any imperfection in the microstructure of a material can behave like a crack in brittle materials such as dental composites [52]. Thermal and chemical aging in composites leads to noticeable microstructural changes, including macro filler fracturing and the surface delamination of the resin (Figures 1-3, A16 and A17). These results suggest that progressive degradation undermines the integrity of composite microstructures, particularly at the filler-matrix interface. Glass fillers are leached out, creating voids and releasing degradation products that may alter the pH within the polymer network structure, thereby accelerating hydrolysis [53]. The studied composite materials differ in filler content, which affects their performance. Key processes include resin matrix plasticization and degradation from environmental factors. Strength decreased by about 25% for Resin F. The resin matrix exhibited sorption values nearly 2.5 times higher than those of Arkon, which contained the most filler [9]. The extent of change in filled materials is influenced by the degradation of the filler and the coupling agent connecting the matrix and filler. High-filling materials require modified filler surfaces for stable interactions with the polymer matrix [54; 55]. Considering the selected protocols, NaOH medium induces accelerated hydrolysis in the tested materials; however, it should be noted that substances present in the oral cavity, such as enzymes and bacteria, accelerate and cause hydrolysis [56]. Nevertheless, clinical conditions also involve physical parameters, such as variable temperature, which can additionally induce stresses at the interface of matrix-filler phases characterized by significantly different thermal conductivities [57]. Our findings suggest that this complex method accelerates aging processes in the tested material and can be valuable for assessing clinical performance in vitro [58; 59]. However, an analysis should be conducted for a wide range of products available on the market. This may allow for identifying a protocol that will be an accessible method for researchers and will standardize the approach to evaluating new dental composites.
# Equalization Methods for Out-of-Band Nonlinearity Mitigation in Fiber-Optic Communications
## 4 Equalization Algorithms
### Turbo Equalization
Figure 6: BER as a function of the number of Turbo-equalization iterations, using different equalizer types. The curves represent results obtained using the Virtual Lab tool [8], whereas the dots correspond to split-step simulations. Note that the case of zero iterations (leftmost point in the graph) corresponds to the case of no adaptive equalization.
## 5 Discussion
### Limitations  
We acknowledge several limitations in this study. First, the experiment was conducted with a single VR application on old hardware (HTC VIVE with Leap Motion), thus limiting the generalizability of the results to a certain type of VR experience. Moreover, Leap Motionis known to have tracking issues [49; 50] that also affected some participants' user experience. Further studies are required to test whether the same potential presence factors can be identified in other gesture-based VR systems and state-of-the-art immersive VR hardware with built-in hand tracking. Second, the identified potential presence factors were based on the subjective opinions of the participants, which were sometimes conflicting. Although this relates to presence as a subjective psychological experience, it makes formulating a consensus challenging. Third, the experiment was conducted using a fairly homogeneous group of participants. Outcomes may be different for participants in other age groups and those coming from different cultural backgrounds. Fourth, this study used data collection instruments that were not based on existing well-known scales but were crafted for the purpose of this study. This limits the comparability of the results. Fifth, although most participants (76.2%) had previous VR experience, this variable was not controlled in the experiment, which may have influenced the results. Finally, the identified presence factors are potential; thus, comparative validation studies with a control group and an experiment group are required to determine whether the existence and absence of each identified factor affect presence in different immersive VR experiences.
## 4 Discussion
### Degree of Confidence of the Sample within the Period of Experimentation  
In order to show one week is enough, we calculate 95%, and then we calculate the maximum likelihood estimator. Figure 7 shows the one-week sampling of the HR of three persons. The visual statistical analysis (Figures 8 and 9) shows that heart rate distribution is highly Gaussian. We observe that the histogram, Figure 8, follows a Gaussian distribution. The Q-Q plot, Figure 9, also shows the histogram is a high normal distribution, with very few outliers. Moreover, statistical analysis using the Shapiro-Wilk test fails to reject the hypothesis that the histogram is non-Gaussian.  
The 95% confidence intervals of the heart rate for these three persons are shown in Table 7.  
Similar behavior is observed for the other persons and measurements.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**ID Node** & **\%RE without the Algorithm** & **\%RE with the Algorithm** \\ \hline
1 & 83\% & 92\% \\
2 & 72\% & 86\% \\
3 & 75\% & 85\% \\
4 & 80\% & 91\% \\
5 & 77\% & 88\% \\
6 & 78\% & 86\% \\
7 & 75\% & 86\% \\
8 & 88\% & 94\% \\
9 & 90\% & 96\% \\
10 & 86\% & 94\% \\
11 & 84\% & 90\% \\
12 & 77\% & 88\% \\
13 & 96\% & 98\% \\
14 & 75\% & 84\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Remaining energy (RE) in each sensor with and without the application of algorithm.  
\begin{table}
\begin{tabular}{l} \hline \hline
**95\% Confidence Interval** \\ \hline \hline
[66.27625,67.95041667] \\
[88.83875,90.82416667] \\
[99.03395833,101.63125] \\ \hline \hline \end{tabular}
\end{table}
Table 7: Heart rate 95% confidence intervals for each of these three persons.  
The maximum likelihood estimator is the sample mean: \(X_{M}LE=(1/N)SUM(x_{i})\), where SUM is over all samples. Additionally, \(x_{i}\) is the sample data set of length \(N\). Table 8 shows the mean estimators for each of these three persons.  
Figure 8: Heart rate histogram of three persons.  
Figure 7: Time series of heart rate of three persons.  
Figure 9: Heart Rate Q-Q plot of three persons.  
Similar behavior is observed for the other persons and measurements.
## 6 MRI vs. Other Imaging Modalities and Diagnostic Cystoscopy  
As mentioned above, the staging of BC traditionally relies on TURBT integrated with cross-sectional imaging (CT and MRI) to assess for nodal involvement and distant metastases. mpMRI plays a crucial role in the local staging of BC, whilst additionally assessing extravesical spread of the tumour to lymph nodes, bones and the upper urinary tract, through the use of functional sequences and its high contrast resolution [27; 28; 29]. However, other imaging modalities preserve an additional role in MIBC patients: bone scintigraphy is recommended in cases of suspected bone metastases and PET/CT shows higher sensitivity than CT in the assessment of nodal staging. Furthermore, PET/CT is highly recommended for restaging and the assessment of therapy response. PET/MRI is a novel imaging technique in BC imaging and its role is promising [30]. A small retrospective cohort study compared the local tumour staging in BC using mpMRI with VI-RADS scoring system and dual-phase contrast-enhanced CT. It reported that muscle invasion was overestimated in MRI and underestimated with CT [31]. [PERSON] et al. compared the diagnostic accuracy of VI-RADS with diagnostic cystoscopy, proving the superiority of VI-RADS, particularly for tumours located on the bladder neck, trigone, dome, posterior and anterior walls [32]. However, different concerns have been raised regarding the inability of the MRI to diagnose carcinoma in situ (CIS) [33], which incidence ranges between 5-19% [34] among patients with NMIBC, making diagnostic cystoscopy still an incontrovertible option in the bladder-cancer diagnostic pathway.
# BERT4 Loc: BERT for Location--POI Recommender System
## 5 Results and Analysis
### Comparison of Cold-Start Approaches  
Our BERT4 LOC model can combat the cold-start issue inherent to recommendation systems. It does so effectively by considering the contextual information related to the users' preference and POI items' data. The model employs rich embeddings from user-item features to grasp the nature of POI and relies on the BPR loss function to interpret implicit feedback and thereby observe user preferences. This consolidation of different strategies enables our model to provide relevant recommendations, despite limited user or item data.  
To evaluate the performance of our model in cold-start scenarios, we conducted experiments using three different approaches: collaborative filtering, content-based filtering, and a hybrid approach; these are well-known recommender system approaches, as reported in the literature [3; 48]. For collaborative filtering, we used a matrix factorization approach to recommend items based on similar users or items. For content-based filtering, we used the textual description of the items to recommend similar items to the user. For the hybrid approach, we combined the matrix factorization and content-based filtering approaches. We evaluated the performance of these approaches in the cold-start scenario, where there is limited information about new users or items.  
As we can see from Table 4, BERT4 LOC outperforms the other models in all evaluation metrics in the cold-start scenario. It has the highest recall, precision, F1-score, HR, and NDCG, indicating that it is the most effective model in recommending items for new users or items with limited information. This is probably because BERT4 LOC leverages the power of Transformer-based architectures, which can handle sequential data and contextual relationships effectively. By integrating both location information and user preferences, BERT4 LOC can provide more personalized and relevant recommendations, even in situations where there is limited information about the user or the item.
## 4 Experimental Results and Discussion  
The proposed method was implemented in the language of MATLAB 2014b on a personal computer with a 2.30 GHz CPU and 8.00 GB RAM on a Windows 8 operating system. To assess the quality of the proposed technique, public databases were utilized to extract features based on Gabor filter with several orientations and frequencies, as described in this section.
## 3 Proposed Congestion Management Approach
### Price Analysis  
The LMPs are the key factors in both managing the transmission congestion and identifying the spot price. The LMPs are utilized by using a linear programming (LP) approach to compute the power  
Figure 3: MOPSO procedure.  
dispatch schedules while simultaneously maximizing the social welfare function. Network congestion causes LMP differences at the buses. These should be minimized to relieve the congestion while also keeping their values as low as possible. LMPs are commonly associated with both the bidding costs and the dual variables (Lagrange multipliers) of the power flow equation [25]. The Lagrange multiplier is the shadow price for the load flow constraints of the OPF, and it describes each congested transmission line on the network. A higher multiplier means, in general, a greater influence of the corresponding congested transmission line on the prices at each location. Using Equations (10)-(27), the expressions for the LMPs are computed to be  
\[\begin{split}& LMP_{S_{i}}=\phi_{P_{S_{i}}}=C_{S_{i}}+\mu_{P_{ Smar_{i}}}-\mu_{P_{Smar_{i}}}-\phi_{cP_{S_{i}}}(1+\lambda_{c}^{*}+k_{C_{C_{c}}}^{*})\\ & LMP_{D_{i}}=\phi_{P_{D_{i}}}=C_{D_{i}}-\phi_{Q_{D_{i}}}\tan( \varphi_{D_{i}})-\mu_{P_{D_{max}}}+\mu_{P_{D_{min_{i}}}}\\ &\qquad\qquad\qquad\qquad-\phi_{cP_{D_{i}}}(1+\lambda_{c}^{*})- \phi_{cQ_{D_{i}}}(1+\lambda_{c}^{*})\tan(\varphi_{D_{i}})\end{split} \tag{40}\]  
Equation (40) contains terms related to the loading parameters, which consider the generation marginal cost of the electricity responsible for both supply and demand, including the system losses of each transmission line. Note that the LMPs have additional terms that rely on the voltage security constraints. Thus, these equations represent the manner in which the voltage stability coordinates the existing prices.  
This paper also proposes NCPs to both maintain network security parameter limits and send an appropriate signal based on the price of electricity. Equation (40) can be used to perform a decomposition calculation in order to obtain NCPs that are associated with the transmission line limits. The equation to determine the NCP is given as  
\[NCP=\left(\frac{\partial f^{T}}{\partial X}\right)^{-1}\frac{\partial Z^{T}}{ \partial X}(\mu_{max}-\mu_{min}) \tag{41}\]  
which shows that the NCPs not only involve transmission congestion but also estimate the degree of congestion severity. After clearing the electricity market from the P-VSCOPF solution, our work uses an LMP payment mechanism for the CM as a cost settlement process that is performed according to the market participants' contributions to both the system congestion and the system losses [26]. In doing so, the total ISO payment is computed as the difference between the supplier and consumer payments as  
\[Pay_{ISO}=\sum_{i}C_{S_{i}}P_{S_{i}}-\sum_{j}C_{D_{j}}P_{D_{j}} \tag{42}\]  
Overall, the proposed approach is implemented sequentially, as shown in Figure 4.  
Figure 4: Procedure for proposed approach.
# Considering the Optimization Design of Urban Bus Network Scheduling
## 4 Example Analysis
### Validation of Effectiveness
#### 4.2.2 Model Effectiveness Analysis  
To further validate the effectiveness of synchronous optimization of the network and scheduling, this paper also conducts phased optimization on the selected bus network. The synchronous optimization results are compared and analyzed with the phased optimization results to study the effectiveness of the synchronous optimization model for the network and scheduling. The specific steps for phased optimization of the bus network and vehicle scheduling are as follows:  
(1) Consistent with the road network basic data and passenger demand data used in synchronous optimization, optimize the bus network first with the lowest passenger time cost as the objective, considering route length, non-repeating route stations, and non-linear coefficient.  
(2) Keep the optimized network unchanged, and then optimize the bus vehicle scheduling with the lowest operating cost as the objective and departure interval as the constraint, based on the optimized network.  
Both phases of network optimization and scheduling setting use a genetic algorithm for model solving. In the network optimization phase, the solution obtained is the stations of the bus network, with the dimension of the solution set to match the number of network stations, which is 125. In the scheduling optimization phase, the dimension of the solution set is the number of bus routes, set to match the number of routes, which is five. Using the genetic algorithm for solving in this manner, with the model parameters and algorithm parameters consistent with synchronous optimization, the model was solved five times for each phase, resulting in optimized layouts for the bus network and corresponding departure intervals for each route after phased optimization. To ensure result accuracy, the average of the passenger time cost and operating cost from the five optimization results is taken as the phased optimization result.  
By utilizing genetic algorithms to solve the synchronous optimization model, the final optimized result yields an optimal objective function value of 250,835.3 yuan. The data results for the corresponding five bus routes after synchronous optimization are shown in Table 3.  
Figure 2: Optimization process of objective values.  
From the above figure, we can see that in the bus network stations after synchronous optimization: The number of stations existing in all five routes has decreased from two stations before optimization to zero stations. The number of stations existing in three routes has decreased from five stations before optimization to two stations. The number of stations existing in two routes has increased from 10 stations to 17 stations. The highly repeated stations in the optimized network have significantly decreased, and the repeated stations in the network are mainly concentrated on two routes, avoiding excessive route duplication and the waste of bus resources, while meeting passengers' transfer needs during travel.
# Gaze-Guided Control of an Autonomous Mobile Robot Using Type-2 Fuzzy Logic
## 2 Method of Gaze Guided System
### Interval Type2 Fuzzy Control System  
The design and theoretical basis of a type-2 fuzzy model for mobile robot motion control has been presented in this section. This is planned to supply the basic thoughts needed to explain the algorithm using gaze input variables and rule base to determine the value of the output system. Fuzzy type-2 has been verified to be a powerful tool for controlling a complex system because of its robustness for controlling nonlinear systems with characteristic and uncertainties [33; 53]. The concept of the type-2 fuzzy set was proposed by [PERSON] [28; 54] as an extension of type-1 fuzzy logic. It is able to model uncertainties in a much better way for control application. The appearance of uncertainties in nonlinear system control using the highest and lowest value of the parameter extending type-1 (Figure 7a) fuzzy to type-2. (Figure 7b). Uncertainty is a characteristic of information, which may be incomplete, inaccurate, undefined, and inconsistent and so on. These are represented by a region called a footprint of uncertainty (FOU) that is a limited region of upper and lower type-1 membership function.  
An interval type-2 fuzzy set denoted by \(\widetilde{A}\), it is expressed in (1) or (2).  
\[\widetilde{A}=\{(x,y),\ \mu_{\widetilde{A}}(x,y)\big{|}\ \forall_{x}\varepsilon\ X,\ \forall_{u}\varepsilon\ J_{x}\ \subseteq[0\ 1]\}. \tag{1}\]  
Hence, \(\mu_{\widetilde{A}}(x,u)=1\), \(\forall_{u}\varepsilon\ J_{x}\ \subseteq[0\ 1]\) it is considered as interval type-2 membership function as shown in Figure 8.  
\[\widetilde{A}=\int_{x\in X}\int_{u\in J_{x}}1/(x,u)\ J_{x}\ \subseteq[0\ 1] \tag{2}\]  
where \(\int\int\) donate the union of all acceptable \(x\) and \(u\). An IT2 FIS can be explained in terms of an upper membership function \(\overline{\mu}_{\widetilde{A}}\left(x\right)\) and a lower membership function \(\underline{\mu}_{\widetilde{A}}(x)\). \(J_{x}\) is just the interval \([\overline{\mu}_{\widetilde{A}}^{-}\left(x\right)\), \(\underline{\mu}_{\widetilde{A}}(x)]\). A type-2 FIS is characterized by IF-THEN rules, where the antecedent and consequent sets are of type-2. The fundamental block used for designing the type-2 controller is the same as used with type-1. As shown in Figure 8, A type-2 FLS includes a fuzzifier, a rule base, a fuzzy inference engine, and an output complement. The output processor includes a type-reducer and defuzzifier; it produces a type-1  
Figure 6: The graphical representation of the robot field.  
Figure 7: (a) Type-1 membership function. (b) Type-2 membership function.  
fuzzy set output (from the type-reducer) or a crisp number (from the defuzzifier) [53]. Type reducer is added because of its association with the nature of the membership grade of the elements [35].
###### Abstract  
This study compared the quality of organic strawberry powders, obtained by convective drying (CD), freeze drying (FD) and spray drying (SD) methods. In the study, such analyses were performed: the content of vitamin C and polyphenols by liquid chromatography method, antioxidant activity using ABTS+\(\bullet\) radicals, microstructure using a scanning microscope, sensory quality by profile method and the determination of the physicochemical properties. The FD powders were characterised by the highest content of vitamin C and polyphenols, obtained values were close to fresh strawberries after conversion to the dry matter content. The content of these ingredients in CD and SD powders was lower by 55%-80% for vitamin C, and 80% for the polyphenols content. Strawberry flavour was most beneficial for the FD powders, while smoothness and homogeneity of appearance were equally rated for the FD and SD powders. Strawberry powders are a concentrate source of bioactive compounds and, therefore, the FD powders should especially be mainly used as a valuable component of high-value foods, especially functional foods, while CD and SD powders can be used to enrich food with vitamin C and as a valuable natural flavour and colouring component, replacing food additives.  
strawberry powders; drying; bioactive compounds; organic food +
Footnote †: journal: Applied Sciences
# Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation
## 3 Results and Discussions
### Resolution vs. System Evaluation  
Results presented above showed that driving the LDAS by either ERA5 or HRES leads to good results monitoring the impact of the summer 2018 heatwave on vegetation, with HRES providing better results. In an attempt to investigate whether the improvement from the use of ERA5 to HRES is due to the resolution only (e.g., better representation of land cover) or to the forcing quality (or both), another experiment was carried out for 2017 (see Table 1). ERA5 was downscaled from 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) to 0.10\({}^{\circ}\)\(\times\) 0.10\({}^{\circ}\) (ERA5_010) spatial resolution to force ISBA and outputs were compared to those of LDAS_HRES open-loop (ran for 2017, with similar initial conditions). A bilinear interpolation from the native grid to the regular grid was made. Figure 8 illustrates monthly scores (R and RMSD values over 2017) for LAI from 2 experiments, namely ERA5_010 and LDAS_HRES open-loop. From the two panels of Figure 8, one may appreciate the score similarities between ERA5_010 and LDAS_HRES open-loop. HRES was upscaled to ERA5 spatial resolution to run ISBA and outputs where compared to these of LDAS-ERA5 open-loop (ran for 2017, with similar initial conditions), and similar results as discussed above were obtained (not shown). Although a longer time period would be required to further test such configurations, it is very interesting to notice than when ERA5 forcing is downscaled to 0.10\({}^{\circ}\)\(\times\) 0.10\({}^{\circ}\) to force ISBA, it performs almost as good as the operational forcing, HRES. These results could justify running longer periods of time of ERA5 at 0.10\({}^{\circ}\)\(\times\) 0.10\({}^{\circ}\) when the operational forcing is not available (e.g., prior to April 2016).