esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 RR-FRC Under Chloride Ingress
### Corrosion Initiation and Corrosion Propagation in RR-FRC
#### 3.1.1 Effects of Cracks on Corrosion Initiation and Propagation
[PERSON] et al. [70] investigated the chloride diffusivity and corrosion initiation of uncracked and pre-cracked RR-SFRC specimens exposed to wetting and drying cycles in a solution containing dissolved chlorides. The study considered two values of _w/c_ and  
Figure 4: Experimental setup [84]. Figure 3: Corroded bars extracted from RC (specimen C2) and RR-HyFRC (specimen H2) [84]. two values of \(c\). Elements were pre-cracked using a three-point bending test (3 PBT). Pre-cracked elements were simultaneously exposed to sustained flexural loads and chloride ingress. The authors observed the negative effect that cracks have on chloride-induced corrosion of rebar, since, in all cases, corrosion was almost instantaneous in cracked specimens regardless of the fibres, \(w/c\) or concrete cover. Delayed corrosion initiation in uncracked elements was observed for the lower \(w/c\) ratio (no corrosion after 560 days of exposure) compared to 70-140 days in the case of a higher \(w/c\) ratio. Increased cover depth was found to be only effective in the case of uncracked specimens. Fibres were found to enhance performance under corrosion (resulting in a lower chloride content) of cracked RR-SFRC specimens with a lower \(w/c\) ratio of 0.45, whereas in other cases, their effect was inconclusive. [PERSON] et al. [77] specifically focused on the relationship between damage at the steel-concrete interface and the initiation of chloride-induced corrosion. Beam elements were pre-cracked in 3 PBT and instrumented rebar (with sensors placed \(\pm\)10, \(\pm\)30 and \(\pm\)70 mm from the main transverse crack) to monitor \(E_{\text{corr}}\) and \(i_{\text{corr}}\). The authors observed a good correlation between crack widths and interfacial damage with the extent of chloride-induced corrosion over the bar length. The authors reported delayed corrosion initiation in RR-SFRC specimens with \(V_{\text{f}}\) of 0.5% (36 h), whereas in RC and in RR-SFRC specimens with \(V_{\text{f}}\) of 1.0%, corrosion initiation was observed after 24 h.
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 3 Communication Technologies and Protocols
### Serial Communication and Modbus  
Serial communication (the process of transmitting/receiving one bit at a time sequentially) via serial interfaces, such as RS232, RS422 and RS485, is the de-facto standard for wired inter-device communication today, which is attributed to its simplicity, high data rates (1-10 Mbps), range (up to 1200 m for RS422 and RS485, and 15 m for RS232), and reliability. RS232 has a single-ended mode of operation and allows for one driver and receiver on each line, whereas both RS422 and RS485 have differential modes of operation and allow for one driver/10 receivers and 32 drivers/32 receivers, respectively, making this medium well suited for nearly every present-day electronic device involving data communication [90].  
Modbus, a serial communication protocol that supports RS232, RS422, RS485 and Ethernet, was first developed by Modicon (now Schneider Electric) in the mid-1970s for point-to-point communications between its programmable logic controllers (PLC). Decades later, it has now become one of the most frequently adopted methods for communication over serial lines between electronic devices, which is ascribed to its reliability, open-source availability and a simple design protocol based on Master/Slavecommunication where the Master is in full control on the bus and the Slave only responds when a request is sent by the Master. The standard Modbus design includes three related protocols, i.e., Modbus-RTU (Remote Terminal Unit), Modbus TCP (Transmission Control Protocol), and Modbus-ASCII (American Standard Code for Information Interchange), and in terms of typical applications, the three most common ones include: (1) controller/monitor to one smart device, (2) controller/monitor to multiple smart devices from the same vendor, and (3) remote monitoring of information from one smart device [91].
# Privacy-Aware Cloud Architecture for Collaborative Use of Patients' Health Information
## 3 Proposed Architecture
### System Instantiation
#### 3.3.2 Updating Patient Information  
Assuming that the nurse has made an update on [PERSON]'s information, such as information related to current medication. The nurse clicks on the **UPDATE** button on her UA interface. As illustrated in Figure 5:  
Figure 4: Releasing information to the nurse application.  
Figure 5: Updating patient information.  
**Stage 1**: The nurse's UA encrypts doc-1 and doc-2 using their secret keys obtained from the trapdoors. The encrypted information (doc-1 and doc-2) is forwarded to the RA.  
**Stage 2**: The RA receives the request from the nurse and does the following:  
1. It searches for [PERSON]'s information to identify him.
2. It searches through the list of authorized users to access [PERSON]'s information and finds the nurse listed as a temporarily authorized user to access doc-1 and doc-2 of patients' information.
3. It forwards the encrypted information, indexes for doc-1 and doc-2, and [PERSON]'s system ID to the CSP.  
**Stage 3**: When the CSP receives the information from the RA, it takes the following actions:  
1. It searches for [PERSON]'s encrypted documents using indexes and the system ID.
2. It identifies the documents using the indexes and replaces them with the new ones.
3. It deletes the indexes received from the RA.
# Recent Advances in Optical Injection Locking for Visible Light Communication Applications
###### Abstract  
The introduction of visible light communication (VLC) technology could increase the capacity of existing wireless communication systems towards 6G networks. In practice, VLC can make good use of lighting system infrastructures to transmit data using light fidelity (Li-Fi). The use of semiconductor light sources, including light-emitting diodes (LEDs) and laser diodes (LDs) are essential to VLC technology because these devices are energy-efficient and have long lifespans. To achieve high-speed VLC links, various technologies have been utilized, including injection locking, Optical injection locking (OIL) is an optical frequency and phase synchronization technique that has been implemented in semiconductor laser systems for performance enhancement. High-performance optoelectronic devices with narrow linewidth, wide tunable emission, large modulation bandwidth and high data transmission rates are desired for advanced VLC. Thus, the features of OIL could be promising for building high-performance VLC systems. In this paper, we present a comprehensive review of the implementation of the injection-locking technique in optical communication systems. The enhancement of characteristics through OIL is elucidated. The applications of OIL in VLC systems are discussed. The prospects of OIL for future VLC systems are evaluated.  
optical injection locking; visible light communication; laser diode; Li-Fi +
Footnote †: journal: _Photonics_
## 4 Experiment
### Evaluation Metrics  
To objectively evaluate the detection performance of the algorithm on blade damage in complex environments, this experiment uses precision (P), recall (R), average precision  
Figure 6: Dataset samples of WTBs: (**a,b**) are peeling samples. (**c,d**) are crack samples.  
(AP), mean average precision (mAP), parameter count (Params), and the number of giga floating-point operations per second (GFLOPs) as the evaluation metrics for the model.  
\[\mathrm{P} =\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}} \tag{18}\] \[\mathrm{R} =\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}\] (19) \[\mathrm{AP} =\int_{0}^{1}\mathrm{P(R)\,dR}\] (20) \[\mathrm{mAP} =\frac{1}{n+1}\sum_{i=1}^{n}\mathrm{AP}_{i} \tag{21}\]  
In the aforementioned formulas, TP denotes the count of instances that the algorithm properly classified as positive, FP denotes the count of instances erroneously classified as positive by the algorithm, and FN denotes the count of instances falsely classified as negative by the algorithm. Precision refers to the ratio of correctly classified positive instances among all samples predicted as positive by the algorithm, while recall refers to the ratio of accurately identified positive instances out of all actual positive instances. AP quantifies a model's effectiveness through the calculation of the space under the precision-recall curve. AP denotes improved model performance across various thresholds. mAP represents the average value of AP for each class; in the experiment, a higher mAP value indicates better performance of the model in detecting damage to WTBs. The Params refers to the total number of parameters that need to be trained within the model. GFLOPs refers to the number of floating-point operations performed by the model in one second. Lower values of parameters and computational load contribute to making the model more lightweight.
# Clinical, Research, and Educational Applications of ChatGPT in Dentistry: A Narrative Review
## 4 Discussion
### Future Directions  
As AI technologies continue to advance, ChatGPT is expected to become more integrated into dental practice, offering advanced diagnostic tools, personalized treatment options, and real-time predictive analytics.  
It could become an even more effective tool to assist dentists in preliminary diagnoses and in providing personalized advice to patients; it could be trained with dentistry-specific datasets to recognize symptoms, suggest diagnostic tests, and offer preliminary treatment options based on the most up-to-date practices. As GPTs become more advanced, if a version specifically trained for dental offices were integrated into the management software, it could help with patient management, appointment scheduling, and even managing prescriptions and communications with other healthcare professionals. Despite notable successes, AI technology and specifically models like ChatGPT have numerous limitations. The quality and reliability of responses may be inconsistent, particularly on topics with a wide variety of opinions or less documented knowledge. Furthermore, these models can generate plausible but incorrect responses, a phenomenon known as \"hallucination\" in the generated texts [115].  
These limitations raise significant concerns, especially in critical fields such as medicine, where the accuracy of information is crucial. To overcome these obstacles, AI developers are working on multiple fronts: improving learning algorithms to reduce the risk of errors, increasing the size and quality of training datasets to better cover the breadth and depth of human knowledge, and developing new methodologies for validating the responses generated to ensure their reliability. Furthermore, the implementation of transparency systems in AI answers is another crucial field of development, which will help build confidence in its use in professional systems and contexts, including dentistry and medicine in general.
## 3 Results  
The results of the basic physicochemical analyses of the examined wines are presented in Tables 2 and 3. The selected technology does not have a significant effect on the content of the basic physicochemical parameters in wine. For the wine from the Welschriesling and the Rheinriesling varieties, in both monitored years, we found lower contents of free and total SO\({}_{2}\) in variants that were processed by targeted oxygenation, while that fact did not negatively affect the sensory profiles of the wine. It was confirmed that, with the targeted oxidation technology, it is possible to spread wines more economically. The content of total Sulphur dioxide in the variants with targeted oxygenation was from 11-16% lower than in the reductive variants. These values were measured, after the clarification of the wine, with technological auxiliaries. For the Chardonnay variety from 2015, using both variants, it was necessary to use twice the high dose of SO\({}_{2}\) in the production plant when bottling the wine for \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Phenolic Substance**} \\ \hline Variant & **W Control Variant** & **W Oxygenation** & **RIE Control Variant** & **RIE Oxygenation** & **CH Control Variant** & **CH Oxygenation** \\ \hline
**Galic Acid** & \(3.174\pm 0.25\) & \(1.719\pm 0.3\) & \(1.712\pm 0.38\) & \(1.403\pm 0.21\) & \(2.021\pm 0.35\) & \(1.742\pm 0.25\) \\ \hline
**Protocatechic Acid** & \(0.518\pm 0.02\) & \(0.593\pm 0.01\) & \(0.520\pm 0.01\) & \(0.548\pm 0.012\) & \(1.414\pm 0.01\) & \(1.182\pm 0.01\) \\ \hline
**Vanillic acid** & \(0.093\pm 0.013\) & \(0.108\pm 0.015\) & \(0.443\pm 0.02\) & \(0.430\pm 0.01\) & \(0.426\pm 0.03\) & \(0.410\pm 0.02\) \\ \hline
**Galic Acid Ethylester** & \(0.262\pm 0.015\) & \(0.140\pm 0.018\) & \(0.281\pm 0.013\) & \(0.263\pm 0.017\) & \(0.524\pm 0.01\) & \(0.488\pm 0.01\) \\ \hline
**Total Caffeic Acid** & \(47.229\pm 0.16\) & \(37.013\pm 0.11\) & \(46.344\pm 0.02\) & \(44.432\pm 0.03\) & \(35.217\pm 0.02\) & \(32.847\pm 0.11\) \\ \hline
**Total Kumaron Acid** & \(13.610\pm 0.4\) & \(11.476\pm 0.2\) & \(5.147\pm 0.1\) & \(4.752\pm 0.1\) & \(6.421\pm 0.2\) & \(4.489\pm 0.3\) \\ \hline
**Total Ferulic Acid** & \(2.826\pm 0.062\) & \(2.216\pm 0.025\) & \(5.840\pm 0.018\) & \(5.414\pm 0.029\) & \(5.623\pm 0.075\) & \(5.329\pm 0.081\) \\ \hline
**Catechin** & \(4.157\pm 0.35\) & \(3.629\pm 0.28\) & \(2.321\pm 0.45\) & \(2.178\pm 0.25\) & \(6.782\pm 0.52\) & \(4.578\pm 0.32\) \\ \hline
**Epicatechin** & \(1.526\pm 0.01\) & \(1.214\pm 0.0\) & \(1.078\pm 0.0\) & \(1.019\pm 0.0\) & \(2.358\pm 0.01\) & \(1.891\pm 0.0\) \\ \hline
**Rutin** & \(0.298\pm 0.0\) & \(0.382\pm 0.02\) & \(0.307\pm 0.01\) & \(0.327\pm 0.0\) & \(2.789\pm 0.0\) & \(2.577\pm 0.01\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Content of 10 monitored substances in mg\(\cdot\)L\({}^{-1}\) from individual variants in 2016 with the standard deviations.
## 2 State-of-the-Art Overview and Problem Formulation
### Overview of Handover Schemes  
The implementation of a handover procedure is necessary in any wireless network because of the mobility of the users within. However, such implementation depends on various factors including the average coverage of the serving entities e.g., AP or base station (BS), the trajectory and the velocity of the users, the multiple access scheme (orthogonal or non-orthogonal multiple access), carrier frequency and the type of the network (mmWave based or cmWave based, Homogeneous Networks or HetNets, etc.). Handovers are also classified based on the decision entity, decision criteria, and performance metrics. A comprehensive overview of handover schemes can be found in [21, 22], where the authors paid particular attention to the so-called vertical handover. The latter is a switch between different RATs or standards, which is a key mechanism to enable the HetNets. In cellular networks, handover decisions are usually made in a centralized manner. In other words, the handover is in general carried out based on the downlink RSSI measurements together with the corresponding hysteresis and timer, during which the RSSI of the target cell should be higher than the RSSI of the original cell. However, in vertical handover it is not always efficient to use traditional metrics [23].  
In addition, handovers can be classified based on the target performance. For example, a handover scheme allowing to switch the cell without a service interruption (minimized packet loss) is called \"seamless\" handover, while in \"fast\" handover the latency of packets is minimized. In the case of mmWave access, additional issues arise: due to high channel penetration loss, the zone where a handover may be performed without session interruption is usually very small [24]. This problem also appears in the case of LoS-non line-of-sight (NLoS) boundary crossing, where mmWave signal level drops significantly and rapidly, and the traditional RSSI-based handover schemes cannot react fast enough.
## 4 Results and Discussion
### Data Preprocessing Results
#### 4.1.3 Spectral Dimensionality Reduction Results
CARS has better prediction results than SPA; LLE is superior to PCA. When the two are combined, the prediction effect of the model is improved to some extent. Among them, CARS-LLE obtained the best prediction results, and the correlation coefficient reached 0.8498 in the larch test set. Compared with the single variable selection method, the combination method can make use of the complementarity between different algorithms to first select the wavelength variable or wavelength interval, and then select fewer and more effective variables, which can eliminate the redundant information in the high-dimensional spectrum to the greatest extent. Figure 14: Validation curve of PLS principal component number: (**a**) Before dimension reduction; (**b**) After dimension reduction with CARS-LLE. Figure 13: Learning curve of training samples: (**a**) Before dimension reduction; (**b**) After dimension reduction with CARS-LLE. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{
\begin{tabular}{l} **Number of** \\ **Features** \\ \end{tabular} } & \multicolumn{2}{c}{**Cross-Validation Set**} & \multicolumn{2}{c}{**Test Set**} \\ \cline{3-6}  & & **RMSECV: MPa** & **RCV** & **RMSEP: MPa** & **RP** \\ \hline SPA & 10 & 1.9881 & 0.7879 & 2.0378 & 0.7852 \\ CARS & 66 & 1.9006 & 0.8106 & 1.9158 & 0.8033 \\ PCA & 10 & 2.0214 & 0.7852 & 2.0554 & 0.7827 \\ LLE & 7 & 1.9121 & 0.8078 & 1.9189 & 0.8025 \\ CARS + PCA & 13 & 1.7293 & 0.8319 & 1.6970 & 0.8382 \\
**CARS + LLE** & **6** & **1.6031** & **0.8523** & **1.6658** & **0.8498** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of results of spectral dimensionality reduction methods.
# H2O: Secure Interactions in IoT via Behavioral Fingerprinting
## 5 Evaluating the Performances of H2O Network
In this experiment, we considered that \(10\%\) of the nodes was able to create fingerprint models with their acquaintances during the _safe starting phase_. In this case, the \(r\)-redundancy parameter plays a fundamental role, indeed, it imposes that at least \(r\) neighbors of a node \(n_{i}\) must own fingerprint rates of a target node \(n_{j}\) to allow \(n_{i}\) to compute a derived fingerprint rate for \(n_{j}\) and, hence, to assess whether it can be considered reliable or not. In Figure 9, the percentages of assessable nodes over the total number of node in the H2O network, for different values of \(r\) parameter, are shown. In this experiment, the \(r\)-redundancy threshold assumes integer values in the interval \([2,20]\). Observe that, when we talk about assessable nodes, we refer to nodes for which either a direct or a derived fingerprint rate can be computed. This score can be, hence, used to establish the reliability of nodes (i.e., a node will be considered reliable if this rate is greater than the threshold \(1-\epsilon\), see Section 4.2). This result is useful to choose the appropriate value of the redundancy parameter \(r\) basing on the percentage of not reliable nodes tolerated in the H2O. From this figure, we can see that by setting \(r=5\), which is a value in line with the average number of neighbors for a node in the network, with just the \(10\%\) of nodes owning a direct fingerprint model  
Figure 8: Nodes degree distribution in H2O network. Figure 9: The percentage of assessable nodes for different values of \(r\). with others, our approach can estimate the reliability of more than the 50% of nodes in the H2O network. The last experiment focuses on the capability of our approach to inhibit possibly malicious transactions originating from hacked/corrupted nodes in H2O. For this experiment, we considered the following different percentages of hacked/corrupted nodes: \([1\%,2\%,4\%,10\%,20\%,50\%]\). Whereas, the \(r\) parameter assumes the following values: \(r=[2,5,10]\). We measured how many times a transaction towards an hacked/corrupted node has been prevented by means of our solution. Figure 10 shows the absolute number of prevented attacks against the number of hacked/corrupted nodes and with different values of the \(r\) parameter. From the analysis of this figure, we can observe that the number of blocked attacks increases exponentially as the percentage of hacked/corrupted nodes increases. Whereas, the impact of \(r\), once again, depends on the average number of neighbors for a node in H2O. In the setting of this experiment, the best trade off between consensus robustness and number of blocked attacks is 5, which is very near to the most probable neighborhood cardinality in the considered H2O network.
## 3 Results
### CMR Characteristics  
#### 3.2.1 Functional and Geometry  
All volumes were indexed to body surface area (BSA) using the Boyd equation. The measured CMR parameters are presented in Table 2. In general, no significant variation was observed in left ventricle end-systole volume (LVESV), left ventricle end-diastole volume (LVEDV), left ventricle stroke volume (LVSV), and myocardial mass between the DMD patients and controls (all \(p\)-value > 0.05). However, there were significant differences in left and right ventricular EF parameters, with notably lower values in the DMD patients than in healthy controls. The mean of LVEF for the healthy controls was 61.60 \(\pm\) 40.60 (IQR 53-72) %. For the DMD patients, the mean of LVEF was 56.14 \(\pm\) 3.51 (IQR 50-65) %, (\(p\)-value <0.05). A value of 38% of all DMD patients showed a lower LVEF (<55%). Based on the dataset studied, ED wall thickness, ES wall thickness, and ED wall thickening from the basal to the apical level of the LV were calculated for the healthy controls and DMD patients. The mean ED wall thickness was 8.81 \(\pm\) 1.76 mm in the healthy controls and 6.72 \(\pm\) 2.71 mm in the DMD patients. The ES wall thickness was 8.89 \(\pm\) 2.52 mm in the healthy controls and 7.38 \(\pm\) 2.75 mm in the DMD patients. The average value of wall thickening was 72.83 \(\pm\) 7.47% in the healthy controls and 55.85 \(\pm\) 8.98% in the DMD patients.
## 4 Simulation Results
### Key Sensitivity and Key Space Analysis  
The key sensitivity is usually used to test whether the proposed method is stable. For brevity, only the decryption image \"cameraman\" is shown. The dependences of CC on the change of \(r\), \(R\), and \(\lambda\) are shown in Figure 6. When \(r\), \(R\), and \(\lambda\) value changes \(2\times 10^{-6}\), \(4\times 10^{-6}\), \(4\times 10^{-9}\) respectively, CC value would change dramatically. It shows that this method is extremely sensitive to the keys. From the above analysis, the key space of this method is \((10^{6})^{3}=10^{18}\).  
Figure 5: Encryption and decryption results: (**a–d**) original images, (**e**) private key for the first image \(P_{1}\), (**f**) combined image \(f\), (**g**) ciphertext \(C\), (**h**) private key \(PK\), (**a1–d1**) decrypted images.  
Figure 6: Correlation coefficient (CC) values of decrypted images with incorrect keys: (**a**) \(\Delta r/\Delta R\) (mm/100), (**b**) \(\Delta\lambda\) (\(\upmu\)m/100).