esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermal Conditions
Furthermore, during the summer months (June, July and August), the indoor T levels approached those  
Figure 4: Mean monthly average temperature (\({}^{\circ}\)C) values within the museum along with the respective limits. Figure 5: Mean monthly relative humidity (%) values within the museum along with the respective limits. of the respective of the external environment, depicting an insufficient heating-cooling system, poor thermal insulation, and the necessity for deep energy retrofitting in this building. On the contrary, Figure 5 illustrates that the RH levels within the whole building remained at relatively adequate levels for the tenant's thermal comfort and for the preservation of exhibits, as the majority of measurements did not surpass the respective limit range (30-60%). An exception to this behavior was found during November 2019, when the RH surpassed the limits in many exhibition rooms, such as in the Applied Sciences and Philosophy exhibition rooms (second floor), which was mainly due to frequent local rainfall during that time. In the same month, the mean monthly external RH values were found to be very close to the RH levels inside the building. Table 2 summarizes the percentages of all of the indoor measurements of T, which were found to be within the proposed limits for the whole building and for each examined room separately. The results are divided into the cold (October-April) and the warm (May-September) monitoring periods of the experiment. It is clear that indoor T does not favor thermal comfort levels, as the percentages for almost all of the cases are relatively decreased. More precisely, more than half of the T values within the comfort range were calculated for the offices of the second floor (51%) during the cold period of 2019. This room generally demonstrates higher percentages compared to the rest of the building, which is mainly because of the frequent use of HVAC systems from the permanent staff during working hours. The inadequate thermal conditions of the building are depicted during the measurements from the cold period, especially for the cases of the Presentation room and the Dentistry room, where the percentage of the T values within the limits was 0%. In total, the museum demonstrated acceptable percentages from 7 to 33% for the entire experimental period, a result that highlights its poor thermal behavior. Furthermore, significant T differences within the museum during the entire year are likely to negatively affect its exhibits. Therefore, renovation actions related to the thermal protection and/or upgrading of the heating-cooling system were considered to be necessary. On the contrary, regarding RH levels, the comfort conditions for the visitors, employees, and exhibits were found to be adequate, as almost all of the measurements were found to be within the respective limit range during the entirety of the experimental procedure. However, it should be noted that RH values are strongly proportional to T fluctuations, meaning that when T decreases, RH tends to increase; the opposite is also true.
## 3 Comparison Traditional Access Control vs. Security Access Control Based on Three Layers
### Security Access Control Based on Three Layers (Ideal Security Policy)
#### 3.2.3 Strategic Layer  
The third layer, referred to as the strategic layer, encompasses a risk control policy known as risk-based access control. This policy focuses on safeguarding both BYOD devices and organizational resources from malware attacks, unauthorized access, and attacks originating from or transmitted through BYOD devices. It plays a critical role in detecting, preventing, and monitoring risks. Intrusion prevention systems (IPS) and intrusion detection systems (IDS) are examples of systems utilized within this layer to achieve these objectives [1].
## 2 Materials and Methods  
### Samples Collection  
The samples used in the experiment included a standard solution of COD and real water. The standard solution was obtained by diluting 1000 mg/L stock solution with distilled water in proportion. The real water was taken from Qianhu Lake in the center of Nanjing City. From June 2022 to June 2023, lake water samples were collected continuously for one year, with a sampling frequency of once a day (excluding holidays), and a total of 240 samples were obtained. Each collected water sample was divided into two parts, one measuring the standard values of water parameters and the other collecting UV-Vis spectroscopy data. To maintain the characteristics of the original water sample, relevant post-experiment procedures should be conducted immediately after water sample collection. Before the experiment, the water sample was not subjected to any other pre-treatment, such as filtration, to simulate the real situation of the water sample in the lake.
## 3 Methodology
### Thermal Experiments  
The thermal experiments were performed using pulsed thermography (PT), schematically represented in Figure 6, via a flash excitation source and a thermal camera. This active-thermography NDE technique is based on an analysis of the response of the surface temperature of the structure to an emitted thermal wave initiated by an excitation pulse [28]. It is one of the most popular methods in active thermography due to its simplicity and fast operation.  
The thermal data were stored for several time steps. A 3D matrix with the XY-direction containing the temperature of each pixel and the Z-direction capturing each time frame was delivered [4; 29].  
Figure 5: A Xenon flash excitation source output of 6.4 kJ.  
Figure 6: Schematic view of a pulsed thermography inspection, modified with permission from MiViM.  
1: Flash lamps, 2: specimen, 3; camera, 4: processing computer.  
Local material properties, thickness, or experimental set-up parameters, such as excitation power and distance, could be defined [29]. The measurement accuracies were dependent on ambient temperature, emissivity, reflecting temperature, and humidity. These parameters were fixed when measurements were taken. An empirical rule of thumb was used to determine the minimally detectable diameter/depth ratio (should be equal to 2) [30].  
Measurements were post-processed using pulsed phase thermography (PPT), which is a popular signal transformation technique for PT measurements. Considering its relation with thermal wave theory, as described in [7], the (dirac) pulse excitation of a PT measurement can be decomposed into a multitude of individual sinusoidal waves with different amplitudes and frequencies [31]. The benefit of a short dirac equivalent pulse is found in the flat frequency distribution of the discrete Fourier transformed equivalent spectrum. This method is a combination of the advantages of lock-in thermography with the speed of PT. As the dirac pulse is a combination of multiple sine waves, a fast examination can be performed for multiple depths through the structure. By computing the blind frequency \(f_{b}\) and using Equation (1), it is possible to quantitatively estimate the defect depth.  
\[z=C_{1}\sqrt{\frac{\alpha}{\pi\cdot f_{b}}}=C_{1}\cdot\mu\text{ with }\alpha=\kappa/C_{p}\rho. \tag{1}\]  
\(f_{b}\) is the blind frequency, and \(C_{1}\) is a constant value typically equal to 1.82 [32].  
The maximum penetration depth in a material is defined by the thermal diffusion length \(\mu=\sqrt{\frac{\alpha}{\pi\cdot f_{b}}}\), which can be translated to the measured depth according to Equation (1). Defects are defined by their blind frequency \(f_{b}\). The lower the \(f_{b}\) value is, the deeper into the material one can probe for defects.
# A State-of-Art-Review on Machine-Learning Based Methods for PV
## 7 Other Applications in the PV Field
In [124] authors introduce a supervised ML approach to predict and schedule the real-time operation mode of the next operation interval for residential PV/battery systems controlled by mode-based controllers. The performance of the mode-based economic model-predictive control approach is used as the benchmark. The optimal operation mode for each control interval is first derived from the historical data used as the training set. Then, four ML algorithms (i.e., ANN, SVM, logistic regression and RF algorithms) are applied. Simulation results show that using the ML approach can effectively improve the performance of the mode-based control system and reduce the computation effort of local controllers because the training can be completed on a cloud-based ML engine.
## 4 Discussion
### Laser-Assisted Procedures
Additionally, preventing trauma to the oral cavity and maintaining good oral hygiene can help reduce the risk of developing oral mucoceles in the first place [53]. The choice between surgical and non-surgical approaches often depends on factors such as the size and location of the mucocele, the patient's pain tolerance, the potential for scarring, and the risk of recurrence. Larger or recurrent mucoceles are more likely to benefit from surgical interventions, as these methods offer a more permanent solution. Non-surgical methods are generally preferred for smaller and less bothersome mucoceles [54]. While surgical approaches can be highly effective, non-surgical methods can still provide relief and resolution for many individuals with oral mucoceles, especially in cases of smaller or less symptomatic lesions [54]. The recurrence of oral mucoceles after a surgical approach is possible, but the likelihood of recurrence varies depending on several factors, including the surgical technique used, the skill of the surgeon, and individual patient characteristics [54]. The recurrence rate exhibited notable variations based on both location and age. Notably, oral mucoceles had a significantly higher recurrence rate on the ventral mucosa of the tongue (50.0%) when compared to the labial/buccal mucosa (8.8%). Furthermore, recurrence was notably more frequent among younger patients (aged below 30 years, 16.0%) in contrast to older patients (aged above 30 years, 4.4%). However, there was no significant variance in recurrence rates between surgical techniques employing scalpels and those employing lasers [53; 54]. Regular follow-ups with a healthcare provider are recommended to monitor the surgical site and address any concerns promptly. In summary, the risk of recurrence after the surgical treatment of oral mucoceles exists but can be minimized with the proper surgical technique, a skilled surgeon, and attentive post-operative care. The specific risks and outcomes can vary from case to case. As demonstrated by the results of our review, there is no indication in exclusive favor of laser-assisted surgery which is to be considered one of the methods of treatment of the lesion. In fact, to date, there are no clinical trials that justify the superiority of laser-assisted treatment compared to traditional techniques, in terms of limiting the recurrence of the lesion. It would be necessary to conduct randomized clinical trials with adequate follow-ups to evaluate the effectiveness of the technique on long-term outcomes.
## 3 Results and Discussion
### Purification of APs from KPH
#### 3.2.2 Anion-Exchange Chromatography  
Figure 2A showed the anion-exchange chromatographic chart of six fractions (AC-1 to AC-6) from UF-1. According to their adsorption capacity with the DEAE-52 cellulose, AC-1, AC-2 and AC-3, AC-4 and AC-5, and AC-6 were gradually eluted out by an eluent of DW, 0.1 NaCl solution, 0.5 NaCl solution, and 1.0 M NaCl solution, respectively.  
The data in Figure 2B suggested that the EC\({}_{50}\) value of AC-4 (2.58 mg protein/mL) was significantly less than those of UF-I (3.24 mg protein/mL) and other five fractions (EC\({}_{50}\) values of 10.65, 4.37, 5.69, 3.18, and 7.26 mg protein/mL for AC-1, AC-2, AC-3, AC-5, and AC-6, respectively) (\(p<0.05\)). Amino acid residues in bioactive peptides with the acidic and hydrophobic groups can be easily adsorbed to the anion-exchange resin (Q Sepharose FF, DEAE, DEAE-52 cellulose and XX 260) and separated from other peptides [PERSON] et al. (2018); [PERSON] et al. (2018). Moreover, amino acid residues with acidic and/or hydrophobic groups, such as Glu, Met, His, Asp and Pro, are recognized as one of the vital factors for the activity of APs [PERSON] et al. (2018); [PERSON] et al. (2018). Thus, AC-4 isolated should contain APs with high potential activity and was selected for the following experiment.  
Figure 1: The half elimination ratio (EC\({}_{50}\)) of the hydrolysate (KPH) and its four active fractions (defined as UF-1 to UF-4) by ultrafiltration on the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical. The data are presented as the mean \(\pm\) SD (\(n=3\)). \({}^{a-d}\) The same superscripts indicate no significant difference (\(p>0.05\)).  
Figure 2: Elution diagram of UF-I in the diethylethanolamine (DEAE)-52 cellulose column of (**A**) and EC\({}_{50}\) values of UF-I and its active fractions (defined as AC-1 to AC-6) on the DPPH radical (**B**). The data are presented as the mean \(\pm\) SD (\(n=3\)). \({}^{a-d}\) The same superscripts indicate no significant difference (\(p>0.05\)).
# Mobility-Multihoming Duality
## 6 Results of Evaluation
### UDP Packet Delivery Statistics  
Figure 9c on page 26 shows the UDP packet delivery statistics. Both the misordering and loss observed were less than 1% across all delay configurations and in both directions.
Since the scattering is a significant channel impairment, Section 2 includes several scattering probabilistic functions (volume scattering function (VSF) and SPF), their data visualisation, and a table listing their associated expressions to ease the comparison. Section 3 reviews the RTE solution options, associated considerations, four simplified analytical solutions, and four numerical solutions, including the MCS. Since this study focuses on the MCS approach, Section 4 explains the MCS framework's mathematical derivations and computational pillars, encompassing the photon initial position, event/impairment set, and photon travelling steps. To provide a strong tutorial element in this communication and pave the way to enhance the hands-on experience, Section 4 provides programming architecture and Python implementation notes, validation methods, and the techniques of producing the results representing the received photons as an output for several use cases. Section 5 explores some MCS-published works and their benefits and shortcomings. Section 6 summarises UOWC security concerns, challenges, and future directions. Section 7 presents the main conclusions, which indicate that the MCS theoretical formulation maturity and the corresponding implementations need to be elevated to take full advantage of the advancement in the computing platform. Also, in this section, the novelty statement summary is noted. This table provides novelty statements mapped to this communication's associated equations, tables, and diagrams. The topics covered in this work are summarised in Figure 1, and the list of acronyms is in Table 1.  
There are several review articles in the literature [29; 30; 31; 32; 33; 34; 35; 36; 37; 38] from which we can identify the main challenges that have been addressed in the field of UOWC systems. Table 2 provides a comprehensive comparison of some of the latest literature. It outlines the focus areas of each study and the challenges addressed, offering readers a concise understanding of how this study aligns with and advances state-of-the-art research in this field.
# Deep Learning Regressors of Surface Properties from Atomic Force Microscopy Nanoindentations
## 3 Results and Discussion
### JKR Regressor  
Due to the absence of fitted experimental retraction curves, the evaluation of JKR FCNNs was confined to synthetic data. Figure 9 illustrates the error distribution for the best model in each of the six splits, focusing on surface properties \(E\) and \(\gamma\). A notable observation is that predictions of [PERSON]'s modulus were consistently more accurate than those of adhesion energy across all splits. However, both parameters exhibited highly satisfactory results, achieving average errors of \(\bar{\varepsilon}_{E}=0.95\%\) and \(\bar{\varepsilon}_{\gamma}=1.68\%\) across all folds. The selected architecture and optimized hyperparameters contributed to a uniform performance across all splits and for both material parameters. The exception was Split 6, where error values were notably higher compared to the remaining splits, yet still within the range of acceptable predictions.  
Once again, we delve into a detailed analysis of the predictions made by our best model, as elucidated in Figure 10. This time, the selected model strikes a balance between the predictive capabilities for the two surface properties. We specifically opted for the model from the fifth split, which exhibited the most favourable results for the stiffness variable (\(\bar{\varepsilon}_{E}=0.50\%\)). Although this model ranks second in predicting adhesion energy (\(\bar{\varepsilon}_{\gamma}=1.37\%\) compared to \(\bar{\varepsilon}_{\gamma}=1.19\%\) in the model from Split 3), the substantial difference in [PERSON]'s modulus prediction errors (\(\bar{\varepsilon}_{E}=0.96\%\)) justifies the preference for the model from Split 5. The predictions of the selected model for both material parameters indicate that, for [PERSON]'s modulus, they closely align with the true values across the entire domain. In contrast, the accuracy for adhesion energy is comparatively lower, with the number of observations deviating from ideal predictions increasing with the rise in \(\gamma\) (see Figure 10a,b).  
Taking as a reference a residual error of 2.5% in surface properties, which holds little significance in the comparison between real curves and those obtained with predicted outputs, we observe that 97.9% of synthetic test set curves had an elastic modulus prediction below that threshold. However, this value decreases to 86.5% for adhesion energy predictions (see Figure 10c,d).  
Figure 9: Results for the best model in each split of the JKR FCNN, for both material parameters: \(E\) (dark orange) and \(\gamma\) (light orange). Lines extending above and below each boxplot represent 1.5 times the interquartile range. \(\bar{\varepsilon}_{E}=0.95\%\) and \(\bar{\varepsilon}_{\gamma}=1.68\%\) were the average percentage error (horizontal lines) values obtained across the 6 splits.
# Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection
## 4 Experiment
### Evaluation Metrics  
Assuming that in the evaluation of experimental results, the number of samples that are truly diseased fruits and detected as diseased fruits is TP, the number of samples that are truly diseased fruits and detected as healthy fruits is TN, the number of samples that are truly healthy fruits and detected as diseased fruits is FP, and the number of samples that are truly healthy fruits and detected as healthy fruits is FN, the calculation formulas for evaluation metrics are as follows:  
\[accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{2}\]  
\[precision=\frac{TP}{TP+FP} \tag{3}\]  
\[sensitivity=\frac{TP}{TP+FN} \tag{4}\]  
\[specificity=\frac{TN}{FP+TN} \tag{5}\]  
When quantitatively evaluating the core browning disease detection model, comprehensive consideration should be given to four metrics: accuracy, precision, sensitivity, and specificity. Taking into account cost sensitivity, the importance of these four metrics is ranked as follows: precision, sensitivity, specificity, and accuracy.
# Deterministic Propagation Modeling for Intelligent Vehicle Communication in Smart Cities
## 4 Statistical Analysis
Figures 14a and 9b,c show the CDF comparison between the ECDF of S1(60 m), S2(60 m), and S3 (26 m) with Gamma, Lognormal, Nakagami and Weibull distributions. Table 4, items (a) and (c) show that S1 and S3 can be described in terms of Lognormal distribution while S2 is better fitted by Weibull distribution. The SCV of each individual segment is significant lower than the joint segment which is an indicator of data homogeneity that can lead to consistent statistical GOF test. Analogous statistical behavior was obtained for the symmetric and equidistant segments S6, S7 and S8 (Table 4, items f, g, h). Figure 14d depicts the CDF for the joint data segment S1 to S3 (146 m), and its comparison with Gamma, Lognormal, Nakagami, and Weibull distributions where the best fit is observed for the Lognormal distribution; however, the AD-GOF test resulted in the rejection of the null hypothesis (H\({}_{0}\)) which means that the RSS statistical behavior of this segment does not fit with any of the tested distribution. The GOF test results for Gamma, Lognormal, Nakagami and Weibull distributions are registered in Table 4, item d. This joint segment S1 to S3 is characterized by a high data dispersion level measured by the SCV index (34.268), indicator of heterogeneity in the RSS data. This heterogeneity could be a possible factor that causes the rejection of the null hypothesis. Consequently, it makes necessary an analysis with smaller segments where the RSS data is distributed homogenously. Analogous results were obtained for the joint segment S5 to S6 (144 m) according the results in Table 4, item e.  
Figure 14: CDF for TX1 (S1-to-S3) along AV2. (a) S1, S2, S3 (x: 1 m to 146 m); (b) S1 (x: 1 m to 60 m); (c) S2 (x: 60 m to 120 m); (d) S3 (x: 120 m to 164 m). When the GOF test indicates the non-rejection of the null hypothesis for two or more distributions, the best distribution fit will be defined for the lower \"AD-statistic\" given that this statistic represents a divergence or discrepancy measurement [43]. The input parameter (shape)--estimated from the 3D-RL RSS data using Maximum Likelihood Estimation MLE--that characterizes the Gamma, Lognormal, Nakagami and Weibull distributions, shows slight variations for the shape parameter when the individual segments are compared; for the joint segments these parameters were not reported given the lack of fit between the data and the proposed distributions.
## 4 Simulation and Results
### Results for the Best Model with Media Noise  
Using (20), we simulated the BPMR with 6% position fluctuation [23; 32], when \(\Delta_{x}\) and \(\Delta_{z}\) are modeled as white Gaussian processes with a standard deviation of 6% of the bit period. The results are shown in Figure 12.  
As shown in Figure 12, the proposed model obviously improves the performance compared to previous methods. The results indicate that the proposed model can achieve a gain of at least \(\sim\)1.5 dB at a BER of \(10^{-5}\). Finally, as shown in Figure 13, we varied the position fluctuation from 6% to 16% at 15 dB to observe the resistance of the proposed model to media noise.  
Figure 11: BER vs. TMR with SNR = 15 dB.  
Figure 12: BER vs. SNR with 6% position fluctuation.  
The results in Figure 13 show that the proposed model can resist the position fluctuation by less than 16%. If the fluctuation position is greater than 16%, the BER performance of the proposed model converges with that of previous studies.