esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
(1) Background: To analyze the fracture resistance of endodontically upper premolar teeth restored with glass fiber reinforced posts, glass fiber elastic posts, conventional composite resin (CR) and glass fiber reinforced composite (FRC) resins as restorations. (2) Methods: Seventy premolars were submitted to root canal treatment and restored with the following restorative materials (\(n=10\)): A. FRC posts restored with resin; B. Elastic FRC posts restored with resin; C. FRC posts restored with FRC resin; D. Elastic FRC posts restored with FRC resin; E. Direct restoration with resin; F. Direct restoration with FRC resin; G. Untreated teeth. The teeth were embedded in an epoxy resin model, thermal cycling fatigued in distilled water and mechanical cycling fatigued inducing 80 N load. Loading was applied axially on the center of the occlusal surface with a vertical displacement. The fracture was produced by a universal machine at a crosshead speed of 0.5 mm/s with a 5000 N load cell. The results were analyzed by ANOVA and Tukey's test and Weibull characteristic strength and modulus were calculated. (3) Results: The group that obtained the greatest fracture resistance was D (3620 \(\pm\) 470 N) and the least resistant was group A (2420 \(\pm\) 1010 N). Statistically significant differences were observed between the groups restored with Elastic FRC posts-CR versus FRC post-CR and only CR (\(p\) = 0.043 and \(p\) = 0.008). (4) Conclusions: The glass fiber reinforced restorative materials increase the fracture resistance of endodontically treated teeth.  
post and core technique; fiber-reinforced composite; fracture resistance; everX posterior; polyethylene fiber ribbon; elastics post
# Exploiting the Rolling Shutter Read-Out Time for ENF-Based Camera Identification
## 2 Related Works and Concepts
### Camera Read-Out Time (\(T_{ro}\)) and ENF Estimation  
The \(T_{ro}\) is the duration during which a camera captures the rows of a video frame. It is camera-specific and is not often listed in the user manual or specification catalog [46]. The \(T_{ro}\) is a sensitive parameter that can be exploited for several applications, including camera forensics. Camera forensics has become an important area of study for a variety of applications, including verifying whether a query image and video pair originated from one source camera and verifying the origin of a questionable image or video [52,53].  
The author of [30] proposed a method that can extract a flicker signal and the camera \(T_{ro}\) from a pirated video and utilize them to identify the LCD screen and camera used to create the pirated movie. The authors of [21] presented a method that exploits the ENF and the \(T_{ro}\) for camera forensics of rolling shutter cameras. They essentially estimate the \(T_{ro}\) of the camera for each frame using vertical phase analysis modeled as:  
\[T_{ro}=\frac{L\omega b}{2\pi f_{e}} \tag{6}\]  
where \(L\) denotes the frame's number of rows, \(\omega_{b}\) represents the vertical radial frequency, which is calculated from the slope of the vertical phase line, and \(f_{e}\) represents the ENF component that oscillates around the nominal frequency. The slope of the vertical phase line may be acquired by estimating the ENF phases \(\phi[\ell](\ell\in\{1,2,3,4,5,...,L\}\) for every one of the rows. \(\phi[\ell]\) is extracted by applying the Fourier transform to the time series of the \(\ell\)th row, which can be generated by calculating the mean intensity of the \(\mathrm{l}\mathrm{l}\mathrm{t}\mathrm{n}\mathrm{w}\) of every video frame. The authors of [29] made use of the \(T_{ro}\) to compute the number of zeros  
Figure 4: Rolling shutter mechanism video acquisition process [48].  
that correspond to the idle time for the specific camera [30] or the specific video resolution and frame rate [31] that captured the video, which enabled a better estimation of the ENF signal with no distortion compared to the method described in [25], where the \(T_{ro}\) was not exploited.
# Ultra-Broadband Bending Beam and Bottle Beam Based on Acoustic Metamaterials
## 3 Results
### Bottle Beam and Particle Manipulations  
The design for the bending beams are then extended for the generation of a three-dimensional bottle beam by rotating the metastructure in Figure 3c along \(y=0\) axis. The simulated three-dimensional sound field for the bottle beam at 3500 Hz is shown in Figure 5a. From the result, the sound waves propagate along a curved path, and eventually form a focal point. There exists a local 'empty' bottle inside the beam, where the sound intensity is almost zero. The sound field profile \(|p|^{2}\) along the central \(z\) axis is shown in Figure 5b, where the pressure remains near 0 for quite a long distance (\(\sim\)20\(\lambda\)). The pressure maximum happens at \(z\approx 25.5\lambda\), and the focal length, obtained by the \(-3\) dB locations (marked by two vertical red dashed lines), is about \(3.7\lambda\).  
We further examine the field distribution along the transverse direction \(r=\sqrt{x^{2}+y^{2}}\) at \(z\approx 5\lambda\) (black solid line in Figure 5c), where the diameter of the 'empty' region is about \(7\lambda\) (see inset for the two-dimensional field at \(x-y\) cross section). The corresponding force potential (Equation (3)) for small rigid particles is computed based on the simulated field, and the particles will be trapped to the potential minimum [red dots in Figure 5c]. From the results, the location to trap the particles is near the first peak of the pressure maximum. The field distribution and force profile near the focal point (\(z\approx 25.5\lambda\)) are also examined (Figure 5d). In this case, the particles cannot be trapped to the focal point (pressure maximum) at the center but at a location near the center where \(r\approx 0.4\lambda\).
## 3 Results
### Characteristics of the Included Studies  
The primary characteristics of participants and intervention details are summarized in Table S2. The included studies comprised 1492 HF patients in the exercise groups and 1254 HF patients in the control groups. Across the various studies, the sample sizes ranged from 15 to 313 participants. The mean age of patients ranged from 48 to 77.6 years, with 16 studies involving patients whose mean age was between 45 and 59 years [36; 37; 47; 49; 50; 51; 52; 54; 57; 62; 64; 70; 77; 78; 79], and 19 studies involving patients with a mean age of 60 years or older [36; 37; 42; 43; 45; 46; 53; 55; 56; 58; 61; 62; 63; 65; 66; 67; 69; 76; 81].  
Among the 47 included studies, 38 provided data on aerobic capacity [36; 37; 38; 40; 41; 43; 44; 45; 57; 58; 59; 61; 62; 63; 65; 67; 68; 69; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81], with one study measuring VO2 max [68] and the remaining 37 studies assessing VO2 peak. Additionally, 34 studies provided data on QOL [36; 37; 39; 40; 42; 43; 44; 47; 50; 51; 52; 53; 54; 55; 56; 61; 63; 64; 65; 66; 68; 70; 74; 75; 79; 80; 82], all of which were evaluated using the MLHFQ. In terms of intervention types, 26 studies involved aerobic exercise [37; 38; 40; 41; 43; 44; 45; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 63; 65; 67; 69; 72; 75; 78; 82], two studies focused on resistance exercise [42; 79], and 16 studies combined aerobic and resistance exercise [39; 47; 48; 49; 50; 52; 61; 62; 64; 67; 70; 71; 73; 77; 80; 81]. Due to limited data on resistance exercise, it was not included in the subgroup analysis. The session duration ranged from 15 to 70 min, with an average of 47.6 min per session. The frequency varied from two to seven sessions per week, with an average of 3.2 sessions per week. The weekly time ranged from 90 to 300 min, with an average of 141.9 min. Finally, among the 47 included studies, 29 provided data on interventions conducted under medical supervision [36; 37; 39; 41; 42; 44; 46; 48; 49; 50; 52; 54; 56; 58; 61; 62; 63; 65; 67; 68; 74; 75; 76; 80; 82].
## 4 Validation
### Sensitivity Analysis  
To analyze the influence of noise and change intensity, we utilized asymmetric Gaussian function to simulate a set of time series representing stable and changes [34,35]. TIMESAT [34,35] was used to analyze the phenological characteristics for each class and nine seasonality parameters were estimated based on the time series in each class. With the estimated seasonality parameters, we simulated time series representing six land-cover classes. Furthermore, we generated land-cover changes with various types and change intensity levels. For each time series generated with asymmetric Gaussian function, we added white noise generated with a normal distribution of zero mean and a series of variance varying from 0.005 to 0.07 with an increment step of 0.005, in order to simulate the noise in NDVI observations. In addition, we added cloud contamination by randomly replace white noise with \(-\)0.1 NDVI. Totally, we simulated 600 stable time series with 100 for each of the six land-cover classes. The performance was evaluated by the ratio of false changes. Meanwhile, we simulated 1800 changed time series with 600 for each of the three change intensity levels. The RMSE of change date, RMSE of number of changes, and omission errors were calculated to analyze the sensitivity to noise at different change intensity levels.  
The pre-processing step of SCD offers a smoothed time series by identifying cloud conditions and removing white noises with wavelet decomposition. To test the effect of the smoothing techniques used in SCD, we selected a range of decomposition levels, which were used to reconstruct time series from wavelet decomposition, and tested on the 244 independent time series. The decomposition levels range from 0 (no wavelet decomposition applied) to 20, where a greater decomposition level offers smoother time series.
## 3 Circuit Implementation
### Linearity Enhanced Method
In order to compensate for these distortions, a nonlinear capacitance compensation technique is employed using PMOS transistors alongside NMOS transistors in the second stage, since the change of PMOS gate capacitance is opposite to NMOS. Under appropriate size and biasing voltage, the total capacitance seen at the NMOS gate will remain approximately constant, thereby compensating for the distortions. Figure 11 shows the NMOS gate capacitance, PMOS gate capacitance, and total capacitance. It can be seen that PMOS can compensate for the changes in NMOS gate capacitance in a wider input voltage range, thus achieving high linearity. Figure 12 shows the comparison of IMD3 between with- and without-PMOS compensation, indicating better IMD3. Figure 10: Simulated comparison of the IMD3 according to the output power with/without gm. Figure 9: Simulated \(g_{m3}\) versus bias voltage.
## 1 Introduction  
Studies have shown that poor indoor air quality (IAQ) affects human health in long-term exposure [1]. In the INDEX project [2, 3], several chemicals, their concentration levels, and their toxicity information were analyzed and evaluated in indoor environments. It was concluded that volatile organic compounds (VOCs), such as benzene, toluene, and xylene, together with aldehydes, should be considered as priority pollutants regarding their health effects. Several studies related with IAQ have indicated that VOCs are emitted by indoor sources such as building materials, furnishings, and cleaning products [4, 5, 6, 7, 8]. In 1998, [PERSON] and [PERSON] published a review on VOC emissions from newly built houses [9]. They stated that building material emissions are the sources of VOCs in the indoor environment, especially during the first six months after construction [9]. Among the indoor pollutants, VOCs are ubiquitous and have harmful effects on human health, such as asthma, wheezing, allergic rhinitis, and eczema.  
VOCs are frequently classified according to their boiling point [10]: very volatile organic compounds (VVOCs), such as formaldehyde; VOCs, such as solvents and terpen; semi VOCs (SVOCs), such as pesticides; and particulate organic matter (POM), such as biocides. Regarding IAQ, VOCs and VVOCs are the pollutants most frequently found indoors [11]. Some of them are toxic and carcinogenic, such as formaldehyde, and, in general, exposure to formaldehyde is higher indoors than outdoors [12; 13; 14]. Formaldehyde (CH\({}_{2}\)O) is a highly reactive aldehyde. It is a ubiquitous pollutant and it is a component of different chemical and industrial products [14]. Because of its occurrence indoors and the evident impact on human health, the study presented focused on the reduction of indoor formaldehyde concentrations.
## 3 Results and Discussion
### Possible Mechanisms for Dye Adsorption onto BC550  
The FTIR spectra of the BC550 surface before and after the adsorption of single dye and dye mixtures are shown in Figure 4. After the dye adsorption, the FTIR spectrum of BC550 confirmed the absence of various bands such as carbonates (CO3\({}^{2-}\), 875 cm\({}^{-1}\)), C-O stretching (1049 cm\({}^{-1}\)), and C-H (1430 cm\({}^{-1}\)). As reported in the previous literature, these functional groups may participate in MB sorption [22]. Therefore, it was obvious that BC550 comprises binding sites for the dye adsorption. In addition, a significant increase of peaks at 1036 cm\({}^{-1}\) and 1640 cm\({}^{-1}\) under the adsorption of dye mixtures emerged, compared to those under the adsorption of a single dye. These results imply that MB and CR were simultaneously adsorbed onto BC550.  
XPS results of the BC550 and dye-bounded BC550 were compared to determine the detailed binding matrix related to the adsorption of a single dye and dye mixture by BCs (Figure 5A-F). C1s, Ca2p, N1s, O1s, and Mg1s were visible in the survey scan of BC550. The O1s and Mg1s were associated with the CR adsorption, whereas C1s, Ca2p, O1s, and Mg1s were associated with the MB adsorption (Figure 5A). As shown in Figure 5B, the decrease of functional groups (C-H) mainly appeared during MB adsorption [26]. As mentioned in Section 3.1, it was demonstrated that the dye adsorption was induced by the decrease of Ca\({}^{2+}\) and Mg\({}^{+}\) ions (Figure 5D,E).  
Figure 4: Fourier transform infrared (FTIR) spectra of BC550 before and after sorption of single dye and dye mixtures.  
Figure 5: XPS of (**A**) survey scan, (**B**) C1s, (**C**) O1s, (**D**) N1s, (**E**) Ca2p, and (**F**) Mg1s of BC550 before and after adsorption reaction.  
As reported in a previous study, the MB and CR adsorption may be assigned by ion exchange processes, such as of Ca\({}^{2+}\) and Mg\({}^{+}\) ion [2; 22]. In contrast, the increase of N1s could be related to N source in MB and CR after the adsorption. These results indicate C1s, O1s, Ca2p, and Mg1s as binding sites, indicating functional group and ion exchange influenced the dye adsorption. Thus, maple leaf-derived BCs can be applied for cationic and anionic dye elimination as a low-cost and effective adsorbent.
# Water Level Simulation in River Network by Data Assimilation Using Ensemble Kalman Filter
## 3 General Form of a River Network Hydrodynamic System  
The general form of a one-dimensional hydrodynamic equation formed using the model described above can be expressed as  
\[\textbf{F}(\textbf{X},t)=0 \tag{15}\]  
where \(\textbf{F}\) is defined as the hydrodynamic equation. \(\textbf{X}\) is the hydraulic state variable, including the discharge, water depth, or water level, and \(t\) is time.  
Given the existence of white noise in relation to hydraulic state variables, the following discretized nonlinear hydraulic state equation can be obtained:  
\[\textbf{X}_{k}=\textbf{\Phi}_{k}(\textbf{X}_{k-1},k-1)+\Gamma_{k}(\textbf{X}_{ k-1},k-1)\textbf{W}_{k} \tag{16}\]where \(\mathbf{\Phi}_{k}\) refers to the nonlinear hydraulic state functional from the state at time \(k-1\) to the state at \(k\), including the hydrodynamic model functions written in Section 2, and \(\mathbf{\Gamma}_{k}\) is the weighting matrix of state noise, which is normally taken as a unit matrix. \(\mathbf{W}_{k}\) is a dynamic noise with a mean value equal to zero.  
In addition, the equation for the measured river network state variables \(\mathbf{Y}_{k}\) can be expressed as:  
\[Y_{k}=\mathbf{H}_{k}(\mathbf{X}_{k})+\mathbf{V}_{k} \tag{17}\]  
where \(\mathbf{H}_{k}\) is the linear measurement function at time \(k\); \(\mathbf{V}_{k}\) is the measurement noise with a mean value equal to zero.  
By combining Equations (2) and (3), we obtain the equations describing the hydrodynamic nonlinear dynamic system of a river network:  
\[\left\{\begin{array}{l}\mathbf{X}_{k}=\mathbf{\Phi}_{k}(\mathbf{X}_{k-1},k -1)+\mathbf{\Gamma}_{k}(\mathbf{X}_{k-1},k-1)\mathbf{W}_{k}\\ \mathbf{Y}_{k}=\mathbf{H}_{k}(\mathbf{X}_{k})+\mathbf{V}_{k}\end{array}\right., \tag{18}\]
## 5 Implementation
### Communication between Modules  
The AMQP implementation from RabbitMQ [32] is used for the communication between modules. RabbitMQ was thought to support parallelism and be robust for managing messages. For the connection between Python and the RabbitMQ service, the pika library was used.  
In AMQP, _exchange_ elements provide the message delivery service, according to instructions about how and where to send them. There are four types of exchange elements: direct, topic, fanout, or header. All data in RabbitMQ are in JSON format, a standard, language independent, and simple format for data exchange.  
An exchange of type 'direct' was defined between Recognition and Tracking and Pattern Detection modules. Each instance of the Recognition and Tracking module generates messages that are addressed to a unique queue attended by an instance of the Pattern Detection module. Messages exchanged between the two main modules are of two types: _configuration_, used to attend the instance of recognition and tracking that sends the message; and _data_, which contains precise information about objects of interest. The Pattern Detection module sends its results to an exchange of type topic. Each message contains a key that indicates the message type: commands, state information, and matched patternswarnings. The events generator module binds the exchange with a queue to receive the three types of messages, while the instance launcher module binds the exchange to receive just command messages.
## 3 Results and Discussion
### PCA Analysis of Aroma Compounds Variation During Sotol Must Fermentation  
PCA analysis was performed based on the aroma properties of volatile metabolites reported in Table 1 to investigate the effects of fermentation time on the production of aroma compounds. As exhibited in Figure 4, the first two principal components explained 72% of the total variance (PC1 53.6%, PC2 18.4%, and PC3 16.1% result not shown). There was a clear separation of the aroma compounds of day zero and day one from the rest of the fermentation days, indicating significant changes in the aroma compounds during the fermentation process. In particular, day one was characterized by the presence of metabolites with attributes such as diesel, citric, sulphur, fatty, waxy, and creamy. Day two and day three were described by the attributes pungent and sweet. The final part of the fermentation, represented by day five, was characterized by the attributes floral, vinegar, ethereal, and winey. It was evident that the flavor attributes of the middle part of the fermentation represented by day two and day three and the aroma attributes of the final part of the fermentation described in day four and day five were separated based on PC2. PCA results suggest that the analysis method and data processing are reliable for separating the aroma attributes of the sotol must fermentation, being an accurate approach to discriminate the changes of the volatile metabolites due to the biosynthetic activities of the fermenting microbial consortia.
## 2 Theory and Method
### Time Series Forecasting Model Based on ARIMA-LSTM
#### 2.2.1. ARIMA(p,d,q) Model
Assume the initial values \(\overset{\rightarrow}{y_{0}}=\left(y_{0},y_{-1},\ldots,y_{-p+1}\right)^{\prime}\) and \(\overset{\rightarrow}{\epsilon_{0}}=\left(\epsilon_{0},\varepsilon_{-1}, \ldots,\epsilon_{-q+1}\right)^{\prime}\) is known, then according to \(\left\{y_{1},y_{2},\cdots,y_{r}\right\}\), it can be iterate to this equation:  
\[\varepsilon_{t}=y_{t}-c-\Phi_{1}y_{t-1}-\Phi_{2}y_{t-2}-\cdots-\Phi_{p}y_{t-p }-\theta_{1}\varepsilon_{t-1}-\theta_{2}\varepsilon_{t-2}-\cdots-\theta_{q} \varepsilon_{t-q} \tag{20}\]  
The sequence \(\left\{\varepsilon_{1},\varepsilon_{2},\cdots,\varepsilon_{T}\right\}\) for \(t=1,2,\cdots,T\) can be obtained, then the conditional likelihood function is:  
\[L(\theta)=\ln f_{Y_{T},Y_{T-1},\ldots,Y_{1}|\overset{\rightarrow}{Y_{0}}, \overset{\rightarrow}{\epsilon}_{0}}\left(y_{T},y_{T-1},\ldots,y_{1}\mid \overset{\rightarrow}{Y_{0}},\overset{\rightarrow}{\epsilon}_{0},\overset{ \rightarrow}{\theta}\right)=-\frac{T}{2}\ln(2\pi)-\frac{T}{2}\ln\left(\sigma ^{2}\right)-\sum_{t=1}^{T}\frac{\varepsilon_{t}^{2}}{2\sigma^{2}} \tag{21}\]
# Continuous Latent Spaces Sampling for Graph Autoencoder
## 2 Related Work
### Graph Contrastive Learning  
The primary objective of Graph Contrastive Learning (GCL) is to learn embeddings that bring positive samples closer to one another while simultaneously separating them from negative samples. GCL has been adapted from various domains, such as computer vision and natural language processing. For instance, DGI [17] uses mutual information maximization as a pretext task [25] to train models, while MVGRL [15] is based on graph diffusion [26] and extends CMC [27] to graphs. GCC [8] integrates InfoNCE [28] and MoCo [29] for large-scale Graph Neural Network (GNN) pretraining. Other GCL approaches, such as SimCLR [30], GRACE [4], GCA [10], and GraphCL [20], directly consider other nodes/graphs as negative samples to learn node/graph representations. BGRL [9], inspired by BYOL [31], adopts a negative-sample-free pretext task with complex asymmetric architectures. Additionally, MERIT [32] uses self-distillation and performs contrastive learning, while AFGRL [33] treats nodes as positive samples by considering their context without augmentation or negative sampling.
# Two-Dimensional Topology Optimization of Headtube in Electric Scooter Considering Multiple Loads
## 2 Design Optimization
### Topology Optimization: Multiple-Load Condition  
Methodology  
Enhancing the driving safety of electric scooters through topology optimization requires considering diverse loading conditions. To address this, topology optimization was performed under four representative load conditions (Figure 3). By incorporating multiple loads, the proposed method enables the development of structurally optimized and complex headtube designs.  
The design variables and parameters remained consistent with those described in Section 2.2. Four load conditions were selected to represent each group, and the results  
Figure 3: Load and boundary conditions for topology optimization considering multiple loads. Red and blue arrows indicate the applied forces at F\({}_{1}\) (upper point) and F\({}_{2}\) (lower point), respectively.  
of the 16 load conditions were summarized. Topology optimization considering multiple loads is formulated as follows:  
\[\min_{\rho} f(\rho)=\sum_{k=1}^{M}\sum_{i=1}^{N}c_{k}(\rho_{i})^{3}\mathbf{u}_{i(k )}^{T}\mathbf{k}_{0}\mathbf{u}_{i(k)}, \tag{6}\]  
where \(c_{k}\) is a normalized weighting factor for the \(k\)-th load condition (\(c_{1-4}=0.25\)) and \(M\) is the total number of load conditions.