esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Virtual Sensor Fault-Detection Performance Metrics  
The fault-detection performance of the developed virtual sensor was evaluated using classification performance metrics: accuracy, precision, recall, and F-1 score. The fault-detection result values of the virtual sensor were divided into True Negative (TN), False Negative (FN), False Positive (FP), and True Positive (TP) (Figure 7). TN represents the cases in which the virtual sensor correctly identifies the actual normal data as normal. FN represents the cases in which the virtual sensor incorrectly identifies the actual normal data as a fault. FP represents cases in which the virtual sensor incorrectly identifies the actual faulty data as normal. TP represents the cases in which the virtual sensor correctly identifies the actual faulty data as a fault.
# Terpenes as Potential Anti-Alzheimer's Disease Agents
## 2 Activities of Terpenes on the Mechanisms Associated with AD
### Inhibition of Acetylcholinesterase (AChE)
#### 2.4.4. Triterpenenes
\(\mathbf{R_{1}}\)\(\mathbf{R_{7}}\)\(\mathbf{R_{2}}\)\(\mathbf{R_{1}}\)\(\mathbf{R_{2}}\)\(\mathbf{R_{1}}\)\(\mathbf{R_{2}}\)\(\mathbf{R_{3}}\)\(\mathbf{R_{4}}\)\(\mathbf{R_{5}}\)\(\mathbf{R_{6}}\)\(\mathbf{R_{7}}\)\(\mathbf{R_{8}}\)\(\mathbf{R_{9}}\)\(\mathbf{R_{10}}\)\From the branches and roots of _Malpighia emarginata_ DC, cultivated in China, three friedelanes, pentacyclic triterpenes, were isolated, namely, nonfriedelins A (**203**), B (**204**), and C (**205**). They presented AChE inhibition with IC\({}_{50}\) values of 10.3, 28.7, and \(>\)50 \(\upmu\)M, respectively. The higher activity against AChE of nonfriedelin A (**203**) when compared to nonfriedelin B (**204**) might be due to the presence of the \(\alpha\)-oxo-\(\beta\)-lactone group on compound **203** instead of the lactone group on the compound **204**[114; 133]. 3\(\beta\)-Hydroxy-24-nor-urs-4(23)-12-dien-28-oic acid (**206**) was isolated from _Patrinia scabiosaefolia_ [PERSON], from China. Compound **206** exhibits activity against AChE with an IC\({}_{50}\) value of 10.0 \(\upmu\)M [113; 114]. Three other moderate inhibitors of AChE were extracted from the leaves of _Callicarpa mainayi_ K & G in Malaysia, namely, euscapic acid (**207**), arjunic acid (**208**), and ursolic acid (**209**). These compounds inhibit AChE with IC\({}_{50}\) values of 35.9, 37.5, and 21.5 \(\upmu\)M, respectively. These results demonstrate that ursolic acid (**209**), with only one hydroxyl group at C3, has more potent AChE inhibition than euscaphic acid (**207**) and arjunic acid (**208**), which have three hydroxyl groups at C2, C3, and C19 [114; 134]. This suggests that the two other hydroxyl groups (at C2 and C19) will prevent the molecule from docking into the active pocket of AChE by hydrogen bonding. Other triterpenes were isolated from the bark of _Garcinia lombroniana_ [PERSON], in Malaysia. They were identified as 2-hydroxy-3\(\alpha\)-O-caffeoyltaraxar-14-en-28-oic acid (**210**), taraxerol (**211**), betulin (**212**), and betulinic acid (**213**). Compound **210** is the most potent inhibitor of AChE exhibiting an IC\({}_{50}\) value of 13.5 uM, while compounds **212** and **213** exhibit IC\({}_{50}\) values of 28.5 and 24.2 uM, respectively. The IC\({}_{50}\) value for compound **211** was not determined. Molecular docking analysis showed that 2-hydroxy-3-O-caffeoyltaraxar-14-en-28-oic acid (**210**) interacts with the catalytic and peripheral binding sites of AChE by forming three hydrogen bonds, one with the amino acid Tyr 334, one with His 440, and the last with Ser 200 [114,135].
## 2 Materials and Methods
### Predictive Reliability Availability and Maintainability Assessment Using Timed Colored Petri Nets
#### 2.2.2 Reliability and Maintainability Using Timed CPN  
We can extract reliability and maintainability data using mean times collection:  
Let us take a place \(\mathrm{P_{i}}\) of the CPN describing the indicator \(\mathrm{I}\) to be evaluated. \(\mathrm{P_{I}}\) is the subset of all these places. \(\mathrm{K^{*}}(\mathrm{P_{i}})\) the average time elapsed at \(\mathrm{P_{i}}\). It is obtained by the Little formula [15], constituting the ratio of the place \(\mathrm{P_{i}}\) average marking, \(\mathrm{M^{*}}(\mathrm{P_{i}})\), and the sum of the input arcs coming from the transition \(\mathrm{T_{j}}\) to \(\mathrm{P_{i}}\): \(\mathrm{w}(\mathrm{T_{j}},\mathrm{P_{i}})\), multiplied by the average frequency \(\mathrm{F_{a}}\) of input transition \(\mathrm{T_{j}}\) of \(\mathrm{P_{i}}\). This sum includes all input transitions of the place \(\mathrm{P_{i}}\), denoted \(\circ\mathrm{P_{i}}\).  
\[K^{*}(P_{i})=\frac{\mathcal{M}^{*}(P_{i})}{\sum_{T_{j}\in\ \circ P_{i}}w(T_{j},P_{i})F_{a}*(T_{j})} \tag{3}\]  
Let \(\mathrm{L}\) be a list where we can store \(K_{I}=\sum_{P_{i}\in P_{i}}K^{*}(P_{i})\), the different average values of the time elapsed in the place(s) characterizing the indicator \(\mathrm{I}\):  
The mean time between failures indicator MTBF, \(\mathit{KPI_{MTBF}}\) is given by the following:  
\[\mathit{KPI_{MTBF}}=hd\ L(K_{MTBF})-L_{\mathit{prec}}(K_{\mathit{MTBF}}) \tag{4}\]  
where:  
* hd L(K) is the head of the list where the last value of failure time is stored;
* \(L_{\mathit{prec}}\)(K) is the precedent value of the failure time; The mean time to the first failure is the value of \(\mathit{KPI_{MTBF}}\) where the value of \(L_{\mathit{prec}}(K_{\mathit{MTBF}})\) is equal to 0.  
The mean time to repair is given by the average repair time values collected in the paranne state:  
\[\mathit{KPI_{MTTR}}=\sum_{P_{i}\in\ P_{\mathit{panne}}}K^{*}(P_{i}) \tag{5}\]  
where:  
* \(\mathrm{P_{panne}}\) is the paranne places subset.
# A Tutorial on Retroreflectors and Arrays Used in Satellite and Lunar Laser Ranging
## 2 SLR Link Equation
Table 1 lists the relevant properties and/or advantages of (1) aluminum back-coated solid retroreflectors, (2) uncoated solid Total Internal Reflection (TIR) retroreflectors, and (3) hollow retroreflectors. For normally incident light, a single unspoiled retroreflector (cube corner) has a peak, on-axis, optical cross-section defined by  
\[\sigma_{cc}=\rho A_{cc}\left(\frac{4\pi}{\Omega}\right)=\rho\frac{4\pi A_{cc}^ {2}}{\lambda^{2}}=\frac{\pi^{3}\,\rho D^{4}}{4\lambda^{2}} \tag{2}\]  
where the reflectivity of the cube corner, \(\rho\), is typically equal to 0.78 or 0.93 for aluminum-coated back faces and uncoated Total Internal Reflection (TIR) surfaces, respectively, \(A_{cc}\) is the collecting aperture of the corner cube, \(D\) is the cube diameter, \(4\pi/\Omega\) is the on-axis reflector gain, and \(\Omega\) is the effective solid angle occupied by the far-field diffraction pattern (FFDP) of the retroreflector. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Type** & \begin{tabular}{l} **Al Back-Coated** \\ **Solid** \\ \end{tabular} & \begin{tabular}{l} **Uncoated Solid** \\ **(TIR)** \\ \end{tabular} & **Hollow** \\ \hline Frequency of Use & Most Common & Occasional Use & Not currently used in the visible range \\ \hline Satellite Examples & Most satellites & Apollo, LAGEOS, AJISAI, ETS-VIII & ADEOS RIS, REM, TES \\ \hline Reflectivity, \(\rho\) & 0.78 & 0.93 & Can approach 1.0 \\ \hline Polarization Sensitive & No & Yes & No—metal coating \\  & & Yes—dielectric \\  & & & coating \\ \hline Weight & Heavy & Heavy & Light \\ \hline Far-Field Pattern & Wide & Wide & Narrow \\ \hline Issues & Metal coatings absorb & \
\begin{tabular}{l} Fewer thermal \\ problems, but TIR \\ “leaks” at incidence \\ nodes well \\ shielded at high \\ orbital altitudes. \\ \end{tabular} &
\begin{tabular}{l} Thermal heating \\ and gradient effects \\ on joints \\ by a factor of 4. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Three types of cube corners and their properties. Figure 1: _Cont._  
As illustrated in Figure 2, the peak optical cross-section rises rapidly as the retroreflector diameter to the fourth power. For the popular 1.5 in (38 mm)-diameter cube with a physical cross-section of 0.001 m\({}^{2}\), the peak optical cross-section is about 5.8 \(\times\) 10\({}^{7}\) m\({}^{2}\), an increase of over ten orders of magnitude.
# Experimental Methods to Evaluate the Carbonation Degree in Concrete--State of the Art Review
## 2 Qualitative Methods
### Estimation of the Carbonation Degree Based on pH Indicator
Among them, fiber optic chemical sensors have presented considerable advantages in terms of size and electromagnetic immunity interference in comparison with common sensors. Optical fiber, which acts as the light wave transmission and sense part of the sensor, is used to manufacture fiber optic sensors. The pH sensor is the most important among chemical sensors [41]. This sensor could be inserted in a concrete block in both fresh [42; 43] and hardened states [44; 45] of the sample. Several kinds of sensors have been used to detect the sensitive change in pH scale although they presented some disadvantages such as response time, sensibility and durability when embedding in the harsh and corrosive environment of concrete. For example, recently, a new opt ode, which is developed on a fluorescent probe, was successfully designed to determine pH value in concrete in the study of [PERSON] et al. [46]. This probe, prepared by very thin sensing films made from polyvinyl alcohol-glutaraldehyde matrix and synthesis of efficient fluorophores [47], has presented changeable and reliable reflection of the changes of pH. It is believed that this pH optode has a fast response time and is sensitive in the early hydration phase of low-pH cementitious matrix, and is manufactured by mixing silica fume and cement with the ratio of 1:1. The fluorescence intensity is lower at higher pH values due to the fluorescent protonated form and lower deprotonated form [48]. Furthermore, [PERSON] et al. [42], who used a pH sensor made from sol-gel probe, indicated that the pH value determined by means of chemical sensor is insignificantly different to those observed by the use of pH indicator when they carried out this experiment in a cement mortar sample, immersed in 5% CO\({}_{2}\) concentration over two weeks. Nevertheless, the drawback of this sensor is a slow response time of at least five minutes based on the dimension of the probe matrix, while the time for the sensor membrane and fluorescent probe is only about 50 and 100 seconds, respectively [41; 46]. On the contrary, the sol-gel probe has shown a sensibility and significantly greater durability in comparison with the other probes. It could serve for more than one year, while the disc type probe lasts for a maximum of only one month in the high-pH environment of concrete. Several drawbacks of using a fiber optic system are, however, the requirement of a complicated set up scheme with the sensor and a wide range of accessory devices [49], along with a short service lifespan caused by the chemical insubstantiality of the chromophore in the high alkalinity environment of cementitious composite. Therefore, further examination and investigation should be carried out for better assessments to open promising applications in this field.
## 3 Results
### Analysis and Selection
#### 3.4.2 Motor Selection  
Based on the maximum force required for grasping, the rack/pinion equation, and the torque equation, the motor was selected. The force required to compress the flexible elements is approximately equivalent to the force applied to the rack. Based on this assumption, the minimum torque from the motor to grasp any object can be determined from Equation [1].  
\[T_{p}=F_{r}\times r_{p} \tag{1}\]  
\[T_{p}=\textit{Torque on Pintion }(\textit{from the Motor})\] \[F_{r}=\textit{Force on }\textit{Rack}(\textit{include the Factor of }\textit{ Safety})\] \[r_{p}=\textit{Pinion Radius}\]  
By performing the following calculations, it is possible to determine the necessary motor torque for grasping objects.  
\[F_{r}=\textit{Force on }\textit{Rack}=186\;\mathrm{N}\times 1.183=220\; \mathrm{N}\] \[r_{p}=\textit{Pinion Radius}=0.02222373\;\mathrm{m}\]  
\[T_{p}=F_{r}\times r_{p}=220\;\mathrm{N}\times 0.02222373\;\mathrm{m}=4.89\; \mathrm{N}\cdot\mathrm{m}\]  
According to the result of the calculations provided, it is determined that a motor with a maximum torque of 5 N\(\cdot\)m or greater is necessary. However, if the handle material is  
Figure 10: Flexible element displacement in the grasping process (top view). The diagonal bars are designed to be easily folded during maximum displacement.  
Figure 9: (a) Displacement of the flexible element grasping a small square-shaped object and (b) displacement of the flexible element grasping a large rectangular-shaped object.  
changed from NYLON Carbon 12 to Aluminum 6061 and the flexible element's thickness is increased in future iterations of the design, the required torque for the motor will also increase. Consequently, a motor with significantly higher torque was chosen to accommodate potential future applications or design modifications. This motor is equipped with a gearbox capable of withstanding a maximum holding torque of 25 N-m while still fitting the designed gripper body.
# Interpretable Variational Graph Autoencoder with Noninformative Prior
## 5 Experimental Results and Analysis  
In this section, we assess the effectiveness of our model (NPIVGAE) on two classic downstream tasks: (i) link prediction and (ii) node classification. The processor used in the experiments is Intel(R) Core(TM) i5-9400 CPU @ 2.90 GHZ. The memory is 8G and the Windows version is WIN10. Our experiments are implemented with Python's Tensorflow, and the version of Tensorflow is 1.12.0. The weights \(\mathbf{W}_{0}\), \(\mathbf{W}_{1}\), and \(\mathbf{W}_{2}\) of the two-layer GCN are initialized to be uniform distribution using the random_uniform function in Tensorflow, and the weights are updated in the back propagation process. In order to show the effectiveness of using the noninformative prior, we design a variant of our model, namely NPVGAE. It employs \(p(\mathbf{Z})\propto\mathbf{\sigma}^{-2}\) as the noninformative prior of the hidden variables \(\mathbf{Z}\) without introducing block-block correlation matrix \(\mathbf{II}\).
# An Optimal Authentication Scheme through Dual Signature for the Internet of Medical Things
## 6 Informal Security Analysis
### Integrity  
An adversary might modify the contents of plaintext, which will result in loss of integrity. In our scheme, IoMT devices compute \(\mathcal{Q}\), which is the hash of plaintext \(m\) and send that to the intended receiver along with the cipher text C and signature DS. Suppose an adversary tries to modify the contents of the cipher text. In that case, the resulting hash value will be completely different due to the collision-resistant property of the hash function. Hence, our scheme effectively provides integrity.
# Personalized Contextual Information Delivery Using Road Sign Recognition
## 1 Introduction
By leveraging a CoT-based reasoning approach, extracted text and symbolic information are linked with user-specific data such as location, speed, and destination. This enables the system to generate context-aware and user-adapted outputs rather than static text extractions. Additionally, this study demonstrates the effectiveness of semantic inference in road sign recognition by comparing the proposed approach with traditional image captioning models. Through this comparative analysis, the system is shown to significantly enhance real-time decision making and usability in navigation applications. The remainder of this paper is structured as follows. Section 2 reviews existing research on object detection, text recognition, and context-based reasoning in road sign recognition. Section 3 details the proposed modular system, including the integration of user context and semantic inference. Section 4 presents experimental results, including comparisons with traditional image captioning models and performance analysis. Section 5 discusses limitations and future research directions, particularly in optimizing real-time processing and expanding semantic inference applications. Finally, Section 5 provides the conclusion and final remarks.
_Article_  
Changing Preference of One- Vs. Two-Stage Implant Placement in Partially Edentulous Individuals:  
An 18-Year Retrospective Study  
[PERSON] 1,1,2,3, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29  
Dental implants were traditionally inserted into the bone by means of a two-stage surgical protocol ad modem Branemark, in order to reduce the risk of failure. Primary stability of the implant in the bone and the absence of its mobility during the healing period were considered two key factors essential to success [3]. Implant mobility during the healing period may lead to lack of osseointegration, similar to non-healing following fragment movement in bony fractures [4]. The two-stage surgical approach significantly reduces the risk of transfer of unwanted forces to the implant/bone interface during healing, however, a second surgical intervention is required to connect the implant's healing caps. In addition, this second surgical intervention hinders the continuation of rehabilitation for several weeks required for complete healing of the soft tissue around the healing caps [2].  
Simultaneously, Straumann AG. (Basel, Switzerland) developed an implant system using a single surgical stage [5]. That system had an implant neck around 3 mm longer than the implants used for the two-stage system. The edge of the rough surface, located about 3 mm apical to the implant edge (and not the tip of the implant) was placed at the height of the alveolar ridge. This obviated the need for a healing cap whose function was now carried out by the cover screw with the longer neck of the implant. In this one-stage method, the surgical flap was sutured around the implant neck, thus avoiding the need for a second surgical intervention. Long-term studies have demonstrated the potential of reaching an even higher rate of success with this method [6; 7]. Additional controlled studies [8; 9; 10; 11; 12; 13; 14], including implant brands and types using a two-stage surgical protocol similar to Branemark system with internal or external hex, compared one- vs. two-stage implant placement also showed that one-stage implants yielded high success rates.  
The purpose of the present study was to assess the changing surgeons' preference of one- vs. two-stage implant placement in partially edentulous individuals over a period of 18 years. The null hypothesis was that one-stage preference increased over time.
# A Simplistic Downlink Channel Estimation Method for NB-IoT
## 5 Measurement Results
### Interpolation vs. Averaging in the Frequency Domain  
As mentioned in Section 2, two approaches are considered for populating channel coefficients, namely time interpolation with frequency interpolation (TIFI) and time interpolation with frequency averaging (TIFA). The latter is preferred due to lower computational complexity. However, its SNR performance needs to be evaluated in the fading channel scenario, where channel response is not as flat as in the case of AWGN.  
Channel estimation performance indicators were evaluated for both approaches, and the results are presented in Figures 7 and 8. As can be seen, frequency averaging, despite providing only one coefficient per OFDM symbol, performs better for both AWGN and EPA channel profiles. The improvement is particularly noticeable for low SNR conditions in the AWGN channel where TIFA demonstrates significantly lower phase RMSE and a higher symbol correlation coefficient. In the fading channel, TIFA performs better in terms of phase SNR. The performance expressed by symbol correlation is similar for TIFI and TIFA.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SNR** & **AWGN Channel** & **EPA 5 Hz Channel** \\ \hline \(-5\) dB & 99.4\% & 90.1\% \\ \(0\) dB & 100.0\% & 93.9\% \\ \(5\) dB & 100.0\% & 98.3\% \\ \(10\) dB & 100.0\% & 99.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Percentage of successful time-frequency synchronizations.  
Figure 6: Algorithm for processing a file with captured NB-IoT waveform.
# WDS-YOLO: A Marine Benthos Detection Model Fusing Wavelet Convolution and Deformable Attention
## 4 Experiments
### Experiment Results
#### 4.2.4 Visual Comparison of Detection Results  
To qualitatively assess the detection performance of the WDS-YOLO algorithm in real-world underwater conditions, we selected three distinct scenarios representing dense distributions, occlusions, and low-visibility conditions. A comparative analysis was performed with the baseline YOLOv8n model, with the results illustrated in Figure 8.  
The detection results revealed that YOLOv8n produces false positives for holduurian targets in high-density scenes, whereas WDS-YOLO effectively eliminates such errors. This demonstrates that the enhanced model exhibits superior object detection capabilities and background interference resistance in complex environments.  
For partially occluded marine benthos objects with incomplete contours, the original model struggles to accurately identify their features, leading to missed detections. In contrast, the proposed WDS-YOLO algorithm addresses the challenge of feature extraction in such cases, successfully identifying these objects and significantly reducing missed detection rates.  
Furthermore, in low-visibility conditions with blurred imagery, YOLOv8n demonstrates limited detection capability, exhibiting numerous missed detections for holdurians and scallops. Conversely, WDS-YOLO maintains superior detection performance even in challenging underwater environments with significant background blur. These comparative analyses across three distinct scenarios substantiate that the proposed algorithm acquires more comprehensive feature representations of marine benthos objects relative to the original YOLOv8n, effectively mitigating both missed detections and false positives for occluded and blurred marine organisms.  
To analyze the limitations of our improved WDS-YOLO, we selected representative error examples from the detection results, as illustrated in Figure 9. Firstly, the misclassification of white rocks as scallops and a brown rock as a holduurian indicates insufficient discriminative power in distinguishing between morphologically similar underwater  
Figure 8: Comparison of detection results. Green bounding boxes indicate correctly detected objects, red boxes denote false positives, and blue boxes represent false negatives.  
objects. Secondly, the model erroneously identified shadows as echinus, suggesting vulnerability to illumination-induced artifacts in complex underwater environments. Thirdly, the partial occlusion of a holdhurian by an echinus led to a missed detection, while the edge contour of the echinus was erroneously segmented as an independent target, exposing the model's inadequacy in handling occluded objects and boundary ambiguity. To address these challenges, future work will focus on integrating a multi-scale context-aware module to enhance occlusion reasoning and on exploring adaptive spectral decomposition techniques to improve illumination robustness in dynamic underwater scenarios.