esa-sceva/satcom-chunk-collection huggingface dataset:
# Imperceptible-Visible Watermarking to Information Security Tasks in Color Imaging
## 5 Comparison with Similar Algorithms  
In this section, the performance of the proposed algorithm is compared with the algorithms [23; 24] under the same characteristics such as hardware capabilities, watermark, and image dataset. To obtain a trustworthy result, 600 images randomly selected from the UCID dataset were watermarked by the algorithms [23; 24], and the proposed watermark used in this evaluation is the QR code, because it is considered the most sensible watermark. Table 9 shows the evaluation results of quality and similarity of the watermarked images and the exhibited watermark patterns, respectively; this evaluation is obtained after treating the watermarked images by the image processing attacks listed in Table 3. As can be seen, the algorithm [23] shows better quality results due to its embedding strength being a fixed and empirical value of 2; in the case of the proposed algorithm, and in [24], the embedding strength is not a fixed value and could be selected by the corresponding strategy in the range of [0,6]. With respect to the similarity evaluation, [23] shows a poor performance due to the algorithm exhibiting the watermark in color. Finally, the proposed algorithm shows a better performance than [23] and one that is a little bit higher than [24]. Figure 11a,b shows a BER comparison of the exhibited QR watermark after compression by JPEG and JPEG 2000, respectively; in both images L, M, Q, and H correspond to the QR error correction level.
# Performance Analysis of P2P Networks with Light Communication Links: The Static Managed Case
## 5 System Description  
Now that the LiFi 2.0 and WiFi 7 scenarios and architectures have been explained, in this section, we describe the variables considered in the mathematical model and simulations developed in this work, namely the download and upload rates in the system, \(\mu\) and \(c\) packets per second, respectively (as shown in Equations (5) and (6)), and the transmission rate \((Transmission_{rate})\) provided by each technology.  
\[c:download\ bandwidth\left[\frac{Files}{s}\right] \tag{5}\]  
\[\mu:the\ uploading\ bandwidth\left[\frac{Files}{s}\right] \tag{6}\]  
Figure 8: WiFi centralized case: maximum coverage range (green circle with dotted line) is strictly required for connections where a server (green node) reaches all the clients (blue nodes).  
Figure 9: The centralized scenario proposed for LiFi environments requires that all the leechers (blue) have a LOS towards the server (green) channel, in which they will establish upload and download links.  
we assume \(c>=\mu\) (since it is usually the case in much practical hardware and equipment where the download of data is more frequent than the upload of data [38]). In our case, we consider in Equation (7) the relation of \(c\) and \(\mu\) for our tests:  
\[c=5\mu \tag{7}\]  
From this, we can calculate the average time required to download a file, which depends on the file size (bits), as we can see in Equation (8):  
\[Download_{time}=\frac{size}{Transmission_{rate}}[s] \tag{8}\]  
Applying (8) for the case of a LiFi system for a 8 Tb file size at 1 Tbps, we obtain:  
\[Download_{time}=\frac{8 Tb}{1 Tbps}=8[s] \tag{9}\]  
Then, the download rate is calculated as:  
\[c=\frac{1}{8[s]}=0.125\bigg{[}\frac{files}{s}\bigg{]} \tag{10}\]  
Finally, applying (7) we calculate \(\mu\):  
\[\mu=\frac{c}{5}=0.025\bigg{[}\frac{files}{s}\bigg{]} \tag{11}\]  
For the WiFi system, we can calculate \(c\) and \(\mu\) similarly, obtaining:  
\[time=\frac{8 Tb}{32 Gbps}=250[s] \tag{12}\]  
\[c=\frac{1}{250}=0.004\bigg{[}\frac{files}{s}\bigg{]} \tag{13}\]  
\[\mu=\frac{c}{5}=0.0008\bigg{[}\frac{files}{s}\bigg{]} \tag{14}\]  
Table 3 shows the system parameter values used to obtain numerical and simulation results in the following sections.
# Assessing the Security and Privacy of Baby Monitor Apps
## 4 Evaluation of the Privacy Policies
### Data Retention  
Article 5(1)(e) of the GDPR requires that personal data are stored for no longer than necessary. Additionally, recital 39 invites data controllers to limit the storage time of personal data [31]. In their privacy policies, three of the inspected apps (Dormi, Saby, and Babyphone Mobile) state that they store personal data, mainly to perform services. The Babyphone Mobile app stores users' IP addresses while permanently storing unique identifiers. This app also describes the need to temporarily store encrypted audiovisual data in a data cluster to transfer them between two devices. BabyCam explicitly states not to store any personal data on their servers, while none of the remaining apps describe whether audiovisual data is stored temporarily for audiovisual transmission.
## 4 Parameterization and Analysis of the Proposed TDL Model
### Statistic of the Proposed TDL Model  
In the considered rural area scenario, the statistical parameters of the UAV A2G channel are greatly affected by distance between transmitter and receiver, UAV altitude, and velocity. The obtained statistical parameters based on the measurement are listed in Table 4 to demonstrate the dependence of statistical parameters on the impact factors. It can be observed that the K-factor values depends on the UAV altitude and distance. For instance, the K-factor value is the largest at short distance scenario. For the long range Tx-Rx distance scenarios from FS4 to FS6, UAV altitude is the main factor affecting the K-factor values, where the value varies between 15.96 dB and 17.23 dB for altitudes from 300 m up to 800 m. These results also show that the channel dispersion in the time-frequency domain increases with distance and UAV altitude. The resolvable MPCs change as the UAV altitude and distance, as seen from FS2 to FS3. The observable number of MPCs becomes less when the UAV altitude increases, as depicted in Figure 6d,f, which also confirms the conclusions made based on narrowband evaluation [11; 34].
# Influence of Uneven Subgrade Frost Heave on Deformation and Damage of CRTSIII Slab Track
###### Abstract  
The problem of uneven frost heave deformation of high-speed railway subgrade in seasonal freezing areas seriously affects the service state and service life of slab track structures and threatens the safe operation of high-speed vehicles. Based on the damage plasticity theory of concrete, in this study, an analytical model is established for CRTSIII slab track with damage plasticity and reinforcement. The influences of various frost heaving parameters on track structure deformation, interlayer seam, as well as the stress and damage behaviors were analyzed in detail. The results show that, as compared with a linear elastic model, the damage plastic model better reflects the softening behavior and stress attenuation of track structure concrete after reaching its tensile strength. The deformation and the interlayer seam reach their maximum values when the frost heaving wave peak acts at the position that is one-sixth of the base length from the edge of the concrete base. When the frost heaving wave peak acts on grooves at the base center, initially, the track structure is seriously damaged. The interlayer seam and damage decrease with an increase in frost heaving wavelength and increase with an increase in frost heaving wave amplitude. The interlayer seam of the track structure is more sensitive to frost heave deformation having a wavelength of less than 10 m. The extent of damage to the concrete base gradually increases with a decrease in the wave/amplitude ratio. It undergoes three stages of damage evolution: (i) no damage, (ii) upper surface bending damage, (iii) reverse bending damage. In order to improve efficiency, it is recommended, during track maintenance, to focus on the position of the central groove of the base and the position that is one-sixth of the base length from the edge of the concrete base.  
high-speed railway; uneven frost heave of subgrade; CRTSIII slab track; frost heaving position; interlayer seam; concrete damage +
Footnote †: journal: Applied Sciences
# Multidisciplinary Applications of AI in Dentistry: Bibliometric Review
## 4 Discussion
For instance, in the literature in periodontitis, CNNs are used for segmenting images to identify periodontal cysts and then classifying them as either having cysts or no cysts [49]. Similarly, CNNs are employed to detect approximal dental caries in bitewing radiographic images and classify them according to the severity of the lesion [50]. This aligns with the observed bibliometric trends where AI's role in image segmentation and classification has markedly contributed to enhancing diagnostic precision and optimizing treatment outcomes. Such advancements reflect a concerted effort within the dental research community to leverage AI not merely as a supplementary tool but as an integral component of modern dental practice, significantly influencing both current methodologies and future directions in dental care. Future research should explore integrating AI with widely used digital tools, like smartphone applications and daily use software. For instance, combining AI with smartphone apps could make AI-driven diagnostics more accessible and practical in daily practice [51]. Similarly, using AI with software for pediatric dental radiographs could further validate its reliability and ease of use in clinical settings [52]. Addressing these areas will enable the dental research community to more easily test the reliability of AI-based programs in daily clinical practice, ensuring that they are both effective and user-friendly in real-world environments, which is crucial for enhancing dental care. By synthesizing the empirical data from our bibliometric analysis with the theoretical insights from the literature, it is evident that AI's capabilities in dental diagnostics are both evolving rapidly and proving indispensable. This dual approach not only supports but also enhances our understanding of AI's critical role in advancing dental diagnostics and treatment planning, paving the way for further innovation and application in the field. However, several limitations should be acknowledged. At the study and outcome levels, there is a potential risk of bias in the selection of studies, possibly leading to an over-representation of positive outcomes, especially those involving advanced AI techniques. Methodological differences across studies may also affect result comparability. At the review level, incomplete retrieval of research is a concern. Despite an extensive search, some studies may have been missed due to language barriers, publication delays, or access issues. Additionally, reporting bias may have skewed the analysis toward studies with significant or favorable results.
## 7 Thermodynamic Interpretation
### Gibbs Free Energy  
This thermodynamic function defines the spontaneity of a system. It is determined using the following formula [43]:  
\[G_{a}=\mu Q_{a} \tag{11}\]  
where, \(\mu\) is the chemical potential of the receptor sites and \(Q_{a}\) is the adsorption amount.  
According to Figure 7, which depicts variation in the Gibbs energy as a function of concentration, we notice that the totality of the Gibbs energy values is less than zero, confirming the spontaneity of the adsorption phenomenon. Moreover, we note from this figure that when dexamethasone and hydrocortisone concentrations are low, adsorption is more favorable while at saturation. However, for elevated concentrations, the \(G_{a}\) of the two adsorption systems reach stable values despite these concentration increases. In addition, we observe that the Gibbs free energy values of the dual-template MMIPs towards the dexamethasone and hydrocortisone templates are significantly lower than those of the dual-template MNIPs. This difference can be attributed to the molecular imprinting process of the MMIPs, which ensures a tailored fit for the target molecules, and thus stronger and more selective binding compared with the MNIPs. This heightened specificity reduces the overall Gibbs free energy required for the interactions between the MMIPs and the template molecules, making the process more thermodynamically favorable. Consequently, the dual-template MMIPs exhibit lower Gibbs free energy values, indicating more stable and efficient interactions with dexamethasone and hydrocortisone compared with their MNIP counterparts.  
Figure 6: Evolution of internal energy versus hydrocortisone and dexamethasone concentration.  
Figure 7: Evolution of Gibbs free energy versus hydrocortisone and dexamethasone concentration.
## 3 Proposed Method
### Loss Functions
#### 3.4.2 Bin Center Distribution Loss  
Similar to primary AdaBins, the chamfer distance [31] is adopted as the bin center distribution loss in order to supervise the distribution of predicted bin centers. Let \(\mathcal{C}_{pred}=\{c_{1},c_{2},c_{3},c_{4}\}\) denote the set of predicted bin centers at different scales and \(\mathbf{d}_{gt}\in\mathbb{R}^{HW}\) denote the set of valid GT values. The loss term is given by  
\[\mathcal{L}_{bins}=\sum_{b\in\mathcal{B}}CD\left(c,\mathbf{d}_{gt}\right), \tag{13}\]  
where the chamfer distance is defined as  
\[CD(X,Y)=\frac{1}{|X|}\sum_{x\in X}\min_{y\in Y}\|x-y\|+\frac{1}{|Y|}\sum_{y \in Y}\min_{x\in X}\|x-y\|. \tag{14}\]  
This loss term promotes the alignment between the distribution of bin centers and the distribution of GT depth values by pushing the bin centers to match the actual depth values, on average.
## 4 Multi Objective Model of Optimal PMU Placement
### The Basic Model of OPP Considering Cost and State Estimation Accuracy  
The first goal is to minimize the cost, so the objective function \(f_{1}(\mathbf{X})\) is the number of PMUs, which can be expressed as Equation (16):  
\[\text{min}f_{1}(\mathbf{X})=\sum\limits_{i=1}^{N_{S}}X_{i}, \tag{16}\]  
where \(N_{S}\) is the number of buses in the compressed network; \(i\) is the bus number; \(\mathbf{X}=\left[X_{1}\text{, }X_{2}\text{, }\cdots\text{, }X_{N_{S}}\right]^{T}\) is the \(N_{S}\)-dimensional vector of placement; \(X_{i}\) is a 0-1 variable, which can be expressed as Equation (17):  
\[X_{i}=\begin{cases}1&\text{if a PMU is placed on bus }i\\ 0&\text{other}\end{cases}. \tag{17}\]  
The second optimization objective is to minimize the state estimation error, that is, to maximize the state estimation accuracy. The objective function \(f_{2}(\mathbf{X})\) is the sum of the error in voltage amplitude estimation and error in voltage phase angle estimation, which can be expressed as Equation (18):  
\[\text{min}f_{2}(\mathbf{X})=e_{amp}+e_{ang}=\frac{\sum\limits_{i=1}^{n}\left|U_{i, true}-U_{i,se}\right|}{n}+\frac{\sum\limits_{i=1}^{n}\left|\varphi_{i, true}-\varphi_{i,se}\right|}{n}. \tag{18}\]  
The constraint is that the distribution network is fully observable, which can be expressed as Equation (19):  
\[\mathbf{M}=\mathbf{A}\mathbf{X}\geq\mathbf{b}, \tag{19}\]  
where \(\mathbf{M}\) is the measurement redundancy vector, which presents measurement redundancy of each bus, and its entry \(m_{i}\) is the number of times that bus \(i\) is observed directly or indirectly; \(\mathbf{b}=\left[1,1,\cdots,1\right]^{T}\) is the minimum measurement redundancy vector, \(b_{i}=1\) ensures each bus observable; \(\mathbf{A}\) is the connectivity matrix, which can be defined as Equation (20):  
\[\mathbf{A}(i,\ j)=\begin{cases}1&\text{if bus }i\text{ is connected to bus }j\\ 1&\text{if }i=j\\ 0&\text{other}\end{cases}. \tag{20}\]  
The more buses the network has, the more solutions with the same number of PMUs required for the full observability exist. Therefore, the network measurement redundancy becomes an important index to select the optimal solution [43]. In order to ensure that the optimization effect of searching the minimum number of PMUs does not be affected, the first objective function \(f_{1}(\mathbf{X})\) can be modified to Equation (21):  
\[\text{min}f_{1}(\mathbf{X})=\sum\limits_{i=1}^{N_{S}}X_{i}+\frac{\sum\limits_{i=1 }^{N_{S}}m_{i}}{(\text{max}\{m_{i}\}+1)N_{S}}. \tag{21}\]
## 6 Results, Analysis and Discussion
### Analysis Spatial Distribution of Performance Metrics
#### 6.2.4 Retransmissions, CSMA Retries and Tries with 2D Representation for 225 Nodes  
In order to check the validity of our results and the behavior of the protocols, we simulated a network with more nodes. Figure 10 shows the results for a grid of 225 nodes. Retransmissions, CSMA retries and tries are simulated: two columns for each of these studied metrics. The left column in each case represents the case when there is no jamming, and the right column shows the difference between the no jamming case and the reactive jamming case when the position of the jammer node is at the center of the topology. These results are presented for the three protocols: AODV, DSR and MPH. The results expose that the performance of the three protocols tends to be similar when the number of nodes increases, so we can confirm that our grid of 49 nodes really explains what happens in the network. The AODV protocol presents the greatest average number of retransmissions, CSMA retries and tries. The MPH protocol offers the best resistance to the attack and causes fewer nodes to be affected. These results can be quantified in Table 4 to specifically observe the exact total numbers.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{4}{c}{**Retransmissions**} \\ \hline \hline  & **Near** & **Middle** & **Far** \\ \hline
**AODV** & 30.4 & 30.7 & 32.6 \\
**DSR** & 22.9 & 36 & 31.5 \\
**MPH** & 13.1 & 31.4 & 28.6 \\ \hline \hline \multicolumn{4}{c}{**CSMA Retries**} \\ \hline \hline  & **Near** & **Middle** & **Far** \\ \hline
**AODV** & 32.5 & 42.7 & 31.5 \\
**DSR** & 12.8 & 39 & 36.9 \\
**MPH** & 14.7 & 25 & 31.1 \\ \hline \hline \multicolumn{4}{c}{**Tries**} \\ \hline \hline  & **Near** & **Middle** & **Far** \\ \hline
**AODV** & 42 & 39 & 41 \\
**DSR** & 53 & 43 & 38 \\
**MPH** & 37 & 34 & 34 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sum of the values of differences for Figures 7–9.
# Asymmetric Total Synthesis of Ieodomycin B
## 3 Experimental Section
### (35,5,R,E)-Benyl 3-hydroxy-5-((4-methoxybenzyl)oxy)-8-methylundeca-8,10-dienoate (**1**)  
To a solution of Ti(O\({}^{\prime}\)Pr)\({}_{4}\) (10.1 mL, 33.8 mmol) in toluene (30 mL), TiCl\({}_{4}\) (3.37 mL, 30.7 mmol) was added dropwise. The solution was stirred at ambient temperature for 30 min, and the resultant 1 M Ti(O\({}^{\prime}\)Pr)\({}_{2}\)Cl\({}_{2}\) solution was used in the next step.  
The above freshly prepared Ti(O\({}^{\prime}\)Pr)\({}_{2}\)Cl\({}_{2}\) (2.08 mL, 2.08 mmol, 0.6 equiv) solution was cooled to \(-\)78 \({}^{\circ}\)C to yield a milky white slurry. This slurry was then treated with a solution of aldehyde **3** (1.0 g, 3.47 mmol, 1 equiv) in toluene (25 mL) via a cannula over the course of 10 min at \(-\)78 \({}^{\circ}\)C. The resultant pale yellow homogeneous solution was stirred at \(-\)78 \({}^{\circ}\)C for 15 min before being treated with a solution of the ((1-(benzyl)vinyl)oxy)trimethylsilane **2b** (4.63 g, 20.8 mmol, 6.0 equiv, prepared following [14]) in toluene (6 mL) via a cannula. The bright yellow reaction mixture was then stirred at \(-\)78 \({}^{\circ}\)C for another 40 min before being quenched with a saturated aqueous NaHCO\({}_{3}\) solution (20 mL) when it was observed via TLC that compound **3** was consumed. The mixture was warmed to room temperature and extracted with DCM (3 \(\times\) 50 mL). The organic extracts were combined, washed with brine, dried over anhydrous Na\({}_{2}\)SO\({}_{4}\), filtered, and concentrated in vacuo. The residue was purified via flash chromatography (petroleum ether/EtOAc = 10:1) to yield compound **1** (1.09 g, 72%) as a yellow oil.  
\([\alpha]_{D}^{25}\) = +4.1 (\(c\) = 1, CHCl\({}_{3}\)); \({}^{1}\)H NMR (400 MHz, CDCl\({}_{3}\)) \(\delta\) 7.41-7.35 (m, 5H), 7.28 (d, \(J\) = 7.1 Hz, 2H), 6.88 (d, \(J\) = 8.3 Hz, 2H), 6.59 (dt, \(J\) = 16.9 Hz, 10.6 Hz, 1H), 5.86 (d, \(J\) = 10.8 Hz, 1H), 5.17 (s, 2H), 5.12 (d, \(J\) = 16.8, 1H), 5.02 (d, \(J\) = 10.2 Hz, 1H), 4.54-4.45 (m, 2H), 4.41-4.31 (m, 1H), 3.81 (s, 3H), 3.75-3.70 (m, 1H), 3.39 (brs, 1H), 2.59-2.50 (m, 2H), 2.13-2.09 (m, 2H), 1.78 (s, 3H), 1.83-1.60 (m, 4H); \({}^{13}\)C NMR (CDCl\({}_{3}\), 100 MHz) \(\delta\) 172.2, 159.2, 138.9, 135.6, 133.2, 130.3, 129.5, 128.5, 128.2, 125.6, 114.9, 113.8, 75.6, 71.0, 66.3, 65.2, 55.2, 41.8, 39.9, 35.2, 31.8, 16.6. HRMS (ESI): \(m/z\) calcd. for C\({}_{27}\)H\({}_{34}\)NO\({}_{5}\)Na [M + Na]\({}^{+}\) 461.2298, found 461.2302.
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 4 Energy Conservation Techniques
### Network-Level-Focused Energy Conservation Schemes  
Network-level-focused energy conservation schemes refer to strategies that are specifically designed to optimize energy consumption at the network level in the WSN. These schemes aim to enhance the overall energy efficiency and prolong the operational lifetime of the network by considering interactions and communication patterns among nodes. The rationale behind network-level-focused energy conservation schemes is grounded in minimizing energy wastage, improving communication reliability, and promoting sustainability. Network-level-focused energy conservation schemes are principally focused on reducing the energy consumption of the entire network rather than individual nodes. They may involve duty cycling schemes that can deactivate a subset of sensor nodes (topology control) or energy-efficient routing algorithms that reduce energy consumption by being energy-aware when routing data in the WSN.
###### Abstract
Nevertheless, unlike indoor applications, which imply rather stable conditions with a limited influence from parasitic light sources or from weather phenomena, vehicular VLC applications involve a variety of optical noise sources (i.e., natural and artificial), a greater degree of mobility, higher velocities and also a significant degree of unpredictability. In this context, different requirements and different results are expected. Indoor applications mainly require the ability to provide high-data rate short-range communications. On the other hand, vehicular applications require highly reliable, low latency, short-to-long-range communications. In such conditions, the data rate exigencies become less stringent, as the main purpose is to have an active link, no matter the circumstances.
## 4 Conclusions
Further work still needs to be done on whether the MOT modeling at the observation level of GPS data processing would narrow the gap between global IGS stations' non-linear position and the environmental loading induced displacement time series. Finally, the spectral characteristics of global IGS stations that caused by MOT is analyzed. Our results show that MOT could only cause small periodic difference, although the impact on the coastal stations is slightly bigger than that on the inland stations, and the magnitude of the periodic difference in the vertical component is about 10-times larger than the horizontal components. We find that MOT correction could significantly reduce the annual signal of the global stacked E component, but that of the U and N components exhibit a significant increase, for example, the annual signal of the N component for station ARTU increased by 23%. Furthermore, the near fortnightly periodic signal and the long-term periodic signal with frequency equal to or smaller than 0.5 cpy in the U component show a significant decrease after correcting the MOT effect. This may indicate a more precise data processing strategy for reducing some of the high frequency periodic signals in the GPS height time series, and also support the recent finding that the unmodeled diurnal and sub-diurnal periodic signal would indeed propagate into spurious long-term signals. Finally, we find that MOT could reduce the power of most anomalous harmonics of 1.04 _cpy_\(\pm\) 0.008 _cpy_ until the 7 th draconitic signals to some extent, in particular for the 3 rd draconitic signal of the U component, thus we conclude that MOT may also be a possible source that caused the anomalous harmonics of 1.04 _cpy_.