esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In the general case and in an area independent of the jamming attack, the method based on general sensor routing is performed. However, detour nodes at the boundary of the jamming area immediately detour normal traffic destined for the jamming area to forward nodes in neighbor zones. Multiple paths prevent flooding of the normal traffic on one route at the boundary of jamming area. The rest of the paper is organized as follows. Section 2 discusses related work. Section 3 describes the detouring service using multiple paths. Section 4 presents our simulation evaluation of the proposed scheme, and finally Section 5 concludes this paper.
# Effect of Endodontic Irrigation on the Bond Strength of Composite Restoration Repair
## 3 Results  
The results of the study were analyzed using the Shapiro-Wilk test to evaluate the normality of the data distribution. After confirming the normal distribution of the data, a two-way ANOVA was performed to evaluate the effects of aging and different irrigation solutions on the composite-composite bond strength. The analysis revealed that irrigation solutions significantly affected the bond strength (\(p\) < 0.001), while aging (\(p\) = 0.208) and the interaction between irrigation solutions and aging (\(p\) = 0.935) did not.  
When pairwise comparisons were performed, the control group showed significantly higher bond strength compared to the NaOCl (\(p\) = 0.000) and NaOCl + CHX (\(p\) = 0.026) groups. In contrast, no significant difference was found between the control group and the NaOCl + EDTA (\(p\) = 0.611) and NaOCl + EDTA + CHX (\(p\) = 0.123) groups. The NaOCl group exhibited significantly lower bond strength compared to all other groups (NaOCl + EDTA, NaOCl + EDTA + CHX and NaOCl + CHX) (\(p\) < 0.05). No statistically significant difference was found in bond strength between the presence and absence of aging (\(p\) = 0.276).  
When both aged and non-aged specimens were considered separately, intragroup comparisons showed that the NaOCl group exhibited a statistically significantly lower bond strength than all other groups, while no significant differences were observed among the remaining groups. Means and standard deviations for all groups are presented in Table 2.  
Figure 3: Representative SEM images of thermally aged specimens following different irrigation protocols. Each row corresponds to one experimental group: Group 1 (Control), Group 2 (NaOCl), Group 3 (NaOCl + EDTA), Group 4 (NaOCl + EDTA + CHX), and Group 5 (NaOCl + CHX). From left to right in each row, images are displayed at increasing magnifications: \(\times\)100, \(\times\)500, \(\times\)1000, and \(\times\)2000.  
The failure modes observed after the shear bond test varied among the groups (Table 3). Overall, the most frequently observed failure type was mixed failure, particularly in groups where more than 50% of the restorative material was involved. Adhesive failure was only detected at a low rate (5.5%) in the NaCl(aged) group. The groups containing NaCl + EDTA + CHX exhibited an increased rate of cohesive failure, whereas aged groups generally demonstrated a higher prevalence of mixed failures. These findings suggest that surface treatment protocols and the aging factor can influence the mode of failure.
## 1 Introduction
Based on the Recurrent Neural Network (RNN), encoder-decoder architecture [11] has become popular due to their success in machine translation. However, the potential problem is that the neural network model using an encoder-decoder structure needs to represent the necessary information in the input sequence as a fixed-length vector, and it is difficult to retain all the necessary information when the input sequence is too long, especially when the length of the input sequence is longer than the length of training dataset. The attention-based encoder-decoder network employs an attention mechanism was proposed by [PERSON] et al. [12]. The basic idea of the attention mechanism is to break the limitation of the traditional encoder-decoder structure that relies on a fixed length vector inside the codec. The attention mechanism is implemented by retaining the LSTM encoder's intermediate output of the input sequence, then training a model to selectively learn these inputs while correlating the output sequences with the model output. The attention mechanism has been widely applied to many research aspects, such as text classification [13], sentiment analysis [14], recommendation system [15], and time series prediction [16; 17]. We use Chl-a as an indicator of the red tides in this paper. Considering that red tides are related to many other dimensions of meteorological factors, such as pressure, light, wind speed, wind direct and air temperature, it is necessary to establish a multidimensional spatial attention mechanism to catch the dynamic correlation between such factors and Chl-a. We also apply a temporal attention to model the dynamic temporal correlation between different time intervals in the target time series, which can solve the issue of the performance of encoder-decoder architecture will degrade rapidly as the encoder length increases. In this paper, the dual-stage attention based RNN (DA-RNN) [16] was applied to predict the Chl-a and forecast the fluctuation of model errors. Our main contributions are as follows: (1) First, we developed the DA-RNN to predict the Chl-a values, we found that dual attention mechanism performed better than other models both on RMSE and mean absolute error (MAE), and DA-RNN could predict the mutation of Chl-a value better than other models, which is significant for red tide prevention. (2) Secondly, we also made a prediction about the forecast reliability, to have a basic understanding of the range and fluctuation of model errors, which could provide a reference to describe the range of marine disasters. The remainder of this paper is organized as follows: Section 2 formulates the problem and proposes the model in detail. Section 3 describes the experiments on Fujian Marine Forecasts Station's HABs dataset and analyses the results. Section 4 shows our discussion about the experiment results. Finally, Section 5 presents the conclusions.
[22] reported that wines obtained from grapes grown in richer soil and with a minor coarse fraction presented a lower total phenolic content and color intensity, but a higher stilbene concentration [22]. Moreover, the cultivation site can influence the quality of Grenache grapes. In an experimental trial carried out in Terra Alta (Catalonia, Spain) investigating optimal grape ripeness in two different terroirs--named \"early\" and \"late\" ripeness terroirs (in reference to their usual date of harvest), characterized by warm and temperate climates, respectively--Grenache grapes from late terroirs were able to maintain higher levels of acidity--assuring wine quality--compared with those cultivated in early terroirs [23]. In relation to Cannonau in Sardinia, the first study assessing the volatile fraction of Cannonau wine entailed grapes cultivated in a single restricted area: the Jerzu area (NU) of central Sardinia [24]; the study was specifically focused on assessing the effects of different agronomic practices and different winemaking technologies on VOCs, which were only assessed qualitatively [24]. More recently, commercial Cannonau wines have been evaluated for their antioxidant activities and vasodilatory properties [25], as well as their content of free amino acids and biogenic amines [26]. Cannonau grape morphological and agronomical parameters have also been characterized on 85 Cannonau biotypes cultivated in a single vineyard in south-west Sardinia [27]. Therefore, our knowledge about the VOC content of berry skins and juice from \"Cannonau di Sardegna\" is very limited. Finally, the sensory aspects of Cannonau have never been studied in any depth and no studies have reported on the possible correlations between volatile composition and sensory evaluations of the wines obtained from the Cannonau variety. Hence, the aim of the present study was to investigate the volatile chemical composition of Cannonau grapes collected in different areas of Sardinia (the primary Italian region for Cannonau cultivation) and their relative wines. Both qualitative and quantitative analyses of free and bound volatiles from grapes were investigated by means of GC-FID/MS. Moreover, in order to provide a first insight into the relationship among the Cannonau volatile composition and sensory descriptive analysis the presence of any correlations between volatile composition and sensory attributes used to describe this red wine was explored, with the aim to contribute to fill the gap of knowledge on this important worldwide variety.
## 2 Materials and Methods
### Monthly Variation, Seasonality and Relation with PM\({}_{10}\)  
In order to understand seasonality, we here simply defined the seasonality index (_SI_) for visibility as the difference between the two seasonal averages, normalized by the dry-season average, i.e.,:  
\[SI\left(\%\right)=\left(\frac{\overline{VIS}_{\text{wet}}-\overline{VIS}_{ \text{dry}}}{\overline{VIS}_{\text{dry}}}\right)\times 100. \tag{3}\]  
The seasonality index for visibility was calculated for both met-screened visibility data and original visibility data. Additionally, we calculated the monthly average for visibility and other meteorological variables based on daily data. The understanding of seasonality and monthly variation helped us determine that visibility degradation is mainly a problem in the dry season (discussed in Section 3.1) and hence was chosen for this study. The relationship between visibility and PM\({}_{10}\), was analyzed in three ways: (a) correlation analysis; (b) mean PM\({}_{10}\) under different visibility classes; and (c) weekday and weekend differences. Visibility classes (e.g., poor and good) have often been used in visibility assessment [14; 45], offering convenience in public communication and also partially reducing the effect of changed visibility targets. As suggested by the US EPA (1987) [43], visibility classes were selected based on yearly frequency, with the lowest upper limit (of visibility target) found over a period of time used as the best visibility
# Demand Control Strategies of a PCM Enhanced Ventilation System for Residential Buildings
## 2 System Description
The average heat pump heating coil COP is 2.87, and the average cooling coil COP is 1.87. The supply fan total efficiency is 0.7. Figure 7 shows a sketch of the heat pump system [27]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Constructions**} & **Solar** & **Solar** & **Thickness** & **Conductivity** \\  & **Transmittance** & **Reflectance** & **(mm)** & **(W/m-K)** \\ \hline External shading for PCM energy storage & 0 & 0.5 & 5 & 0.01 \\ Between glass shading & 0.3 & 0.6 & 2 & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Shading properties. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Location**} & **Living Room** & **Living Room** & **Living Room** & **Bedroom 1** & **Bedroom 2** & **Bedroom 3** \\  & **PCM Facade 1** & **PCM Facade 2** & **PCMVW** & **PCMVW** & **PCMVW** & **PCMVW** \\ \hline PCM amount (kg) & 93 & 93 & 40 & 40 & 40 & 40 \\ PCM surface area (m\({}^{2}\)) & 23.38 & 23.38 & 10 & 10 & 10 & 10 \\ Total heat capacity & 7.35 & 7.39 & 3.16 & 3.16 & 3.16 & 3.16 \\ [from 10-30 °C] (MJ) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The PCM amount for each room. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Constructions**} & **Material** & **U Value (W/m\({}^{2}\)K)** & **g Value** \\ \hline Windows and glass doors & 3 glass panels & 0.8 & 0.63 \\ VW out layer glass & 2 glass panels & 1.1 & 0.63 \\ VW inner layer glass & Single glass panel & 5.7 & 0.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Window constituents and properties. Figure 7: The sketch of the heat pump system [27]. The room temperature set points are 22 \({}^{\circ}\)C and 26 \({}^{\circ}\)C for summer and winter, respectively. The weather data of New York from Typical Meteorological Year is used for the simulation. The data can be found in the EnergyPlus homepage [28]. Figure 8 shows the yearly outdoor air dry bulb temperature and solar radiation.
# An Accurate GNSS Spoofing Detection Method Based on Multiscale Eye Diagrams
## 2 Signal Models
### Received Signal Model  
The signals received from a satellite are frequently affected by many types of interference. In particular, when spoofing signals are present, they are often a significant source of error in signal processing. For example, consider the GPS L1 signal. When spoofing signals are present, the received signal model, after processing by the RF front end, can be expressed as follows [27]:  
\[\begin{array}{l}S(t)\\ =S_{a}(t)+S_{s}(t)+n(t)\\ =\sqrt{2P_{a}}C_{a}(t-\tau)D_{a}(t-\tau)\cos(2\pi f_{a}(t-\tau)+\theta_{a})\\ +\sqrt{2P_{s}}C_{s}(t-\tau-\Delta\tau_{s})D_{s}(t-\tau-\Delta\tau_{s})\cos(2\pi (f_{a}+\Delta f_{s})(t-\tau-\Delta\tau_{s})+\theta_{a}+\Delta\theta_{s})+n(t) \end{array} \tag{1}\]  
where \(S(t)\) is the intermediate frequency signal received at time \(t\); \(S_{a}(t)\) and \(S_{s}(t)\) are the real signal and spoofing signal, respectively; \(n(t)\) is additive Gaussian white noise with mean 0, power spectral density \(N_{0}/2\), and variance \(\delta^{2}\); \(P_{a}\) and \(P_{s}\) are the average powers of the real signal and spoofing signal, respectively; \(C_{a}\) and \(C_{s}\) are the \(C/A\) codes of the real signal and spoofing signal, respectively; \(D_{a}\) and \(D_{s}\) are the data codes of the real signal and the spoofing signal, respectively; \(f_{a}\) is the carrier frequency of the real signal; \(\Delta f_{s}\) is the difference in carrier frequency between the spoofing signal and the real signal; \(\theta_{a}\) is the initial carrier phase of the real signal; \(\Delta\theta_{s}\) is the difference in the initial carrier phases of the spoofing signal and the real signal; \(\tau\) is the propagation delay of the real signal; and \(\Delta\tau_{s}\) is the difference in the propagation delays of the spoofing signal and the real signal.  
Equation (1) shows that the received signal is a superposition of the real signal, the spoofing signal, and Gaussian white noise. The spoofing signal resembles the real signal in terms of its structural composition; however, there are notable discrepancies between the two, particularly with respect to their respective signal power levels, code delays, Doppler frequencies, initial carrier phases, and navigation messages. These differences enable the real and spoofed signals to be identified and distinguished.
# The Subsea Micro-Drilling Vehicle's Dynamic Analysis during Landing
## 3 Simulation Analysis of Landing Dynamics
### Simulation Analysis of SMDV Slope Landing Substrate
The ultimate slipping and overturning angles under various release heights are connected into curves to obtain the relationship between the state of SMDV and the release height and slope under the three landing directions, as shown in Figure 14.  
The solid green line in the relationship graph between the SMDV landing attitude, release height, and slope angle separates the stable state from the slip state. The solid red line separates the slip state from the overturned state, with the overturned state located above the solid red line and the stable state below the solid green line. The slip state is represented by the region between the two solid lines, and it determines the landing attitude of the SMDV. Figure 13 illustrates how the risk of slip and overturning of the SMDV increases with slope inclination when it lands on the substrate. Under the same slope, the risk of slip and overturning of the vehicle is lowest in the forward direction of the substrate, and the risk of backward and lateral direction of the substrate is comparable. The effect of rig release height on the risk of slip and overturn is more complex. Relative to the three landing orientations, it either increases or decreases with changes in height, but the risk of slip and overturn tends to increase as the release height increases. Ensure that SMDV land safely. It is necessary to prevent them from slipping and overturning. When the currently developed SMDV is landing on the substrate in a certain sea area, under the condition that the release height is less than 5 m, the slope of the seabed substrate surface must be less than 10\({}^{\circ}\) in order to prevent slip. If it is permitted to allow a small amount of slip, the slope of the seabed substrate surface must be less than 15\({}^{\circ}\) in order to prevent overturning. This provides a theoretical foundation for securing SMDV and planning and formulating an acceptable landing program.
# Dynamic Simulation Model and Performance Optimization of a Pressurized Pulsed Water Jet Device
## 4 Effect of Key Structural Parameters of the PPWJ Device on Jet Characteristics
### Effect of Nozzle Diameter \(d_{n}\)  
Figure 7 shows the relationship between the jet characteristics and nozzle diameter \(d_{\rm n}\). When \(d_{\rm n}\) is smaller than 0.5 mm, the pulse pressure remains constant at 74 MPa, indicating  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Structural Parameters** & **Initial Value** & **Value Range** & **Gradient** \\ \hline Nozzle diameter \(d_{\rm n}\) (mm) & 0.5 & 0.35–0.7 & 0.05 \\ Piston maximum displacement \(h\) (mm) & 35 & 20–55 & 5 \\ Piston diameter \(d_{\rm p3}\) (mm) & 60 & 57–78 & 3 \\ Variable diameter ratio \(d_{\rm p1}/d_{\rm p2}\) & 20/50 & 12–26/50–50 & 2/0 \\ Fixed diameter ratio \(d_{\rm p1}/d_{\rm p2}\) & 20/50 & 8–36/20–90 & 4/10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The structural parameters used in the simulation model.  
Figure 6: Comparison of the pressure curves obtained by simulation and experiment: (**a**) inlet pressure: 2 MPa, throttle opening: 1, (**b**) inlet pressure: 14 MPa, throttle opening: 1, (**c**) inlet pressure: 8 MPa, throttle opening: k\({}_{1}\), (**d**) inlet pressure: 8 MPa, throttle opening: k\({}_{2}\).  
that the oil inlet pressure equals the maximum input pressure. \(q_{\rm sin}\) increases with \(d_{\rm n}\), leading to an increase in \(v_{\rm sp}\), which in turn results in a higher pulse frequency and a shorter pulse duration. \(q_{\rm sin}\) is limited by the system's rated flow rate; once \(d_{\rm n}\) surpasses 0.5 mm, the pulse pressure drops rapidly, and \(q_{\rm sin}\) reaches its peak and remains steady. This implies that \(v_{\rm sp}\) will remain unchanged, and the pulse frequency and pulse duration are likely to remain constant. \(d_{\rm n}\) is a critical factor influencing the rock breaking ability of a jet. It was found that increasing \(d_{\rm n}\) will increase the jet diameter. Meanwhile, the duration of the impact stress by the jet will be longer, and the area of the impact stress will increase. Due to the increased impact energy of the jet, the rock surface is more susceptible to damage, leading to a reduction in the threshold pressure for rock breaking [12; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46]. Therefore, an appropriate increase in the \(d_{\rm n}\) is beneficial for improving rock breaking efficiency.
## 4 Architecture Solution
### Tokenized Non-Fungible Asset Service Content  
The smart contract transaction sequence for the asset service token is represented in Figure 7 for the bidding process similar to the asset component. This is similar to the earlier asset data transaction workflow explained but is in the other sense where the vehicles subscribe to the service offered by RADAR_OEM_A\(\mid\)B. This is explained as follows:  
* Road RADAR Signature Asset Service Interest Collation: A particular RADAR_OEM_A has to build the localization, additional and planning layers of additional information over the processed data as the road RADAR signature map service publishes an event to the blockchain smart contract of its availability. This is then processed by Vehicle_OEM_X\(\mid\)Y, where each one gathers the interested vehicle clients for subscribing to this service.
* Road RADAR Signature Asset Service Creation and Bidding: RADAR_OEM_A creates an asset service NFT ERC 721 token and the characteristics of the service with the acceptable price range limit.
* Asset Service Subscription and Proof Generation: Then, each of Vehicle_OEM_X or Y expresses the interest in the subscription service on behalf of its clients as an encrypted bid. RADAR_OEM_A accepts the bid of Vehicle_OEM_X along with its vehicles, transferring funds from the vehicles consent to the escrow account.
* OffChain transfer of Asset Service OAuth API Access Token: RADAR_OEM_A generates an OAuth access token for its road RADAR signature map service along with the hash proof of the service including raw data location, vehicle speed and quality. The OffChain worker retrieves the encrypted OAuth access token from the ledger and submits it to Vehicle_OEM_X.  
Figure 7: Tokenized Asset and Asset Service Monetization Architecture.  
Fund Transfer and Finalization of Asset Service Transfer: In parallel, the OffChain worker transfers the funds from the escrow for the bid to RADAR_OEM_A. Also, the asset service token ownership is transferred from RADAR_OEM_A to Vehicle_OEM_X and the interested vehicles.
* Review of Transferred Asset Service: The vehicles or Vehicle_OEM concerned for the asset service token can review the improvement in vehicle driving as a result of road RADAR signature map service, positively or negatively connoting its price and fidelity accordingly.
## Appendix B  
With the exception of OMA-based features, most DSFs depend on a pre-defined frequency bandwidth, \(\omega_{m}\). In addition, multiple DSFs (see Table 1) can be derived for short-time windows, as described in Section 2.2. The influence of these two parameters of TAC values for a subset of five earthquake responses, yielding similar EDP values characterizing linear, mildly nonlinear, and strongly nonlinear behavior, respectively, is shown in Figure 11. The frequency bandwidths that include only the first mode (at 6.6 Hz) are not sensitive to noise (see subplot (a) of Figure 11). When comparing the frequency range that is tightly defined around the first two modes (4-25 Hz) with a more generic large bandwidth (1-30 Hz), the average value is found to be similar; yet, a larger frequency bandwidth leads to a larger scatter (see subplot (a) of Figure 11).  
When evaluating the influence of the window size (Figure 11b), longer windows reduce the scatter of linear time histories, while shorter time windows reduce the scatter of nonlinear time histories. This is due to the fact that longer time windows are less prone to the influence of changes in the input signal, and longer time windows are less sensitive to temporary nonlinearity.  
Wavelet decomposition is used to derive DSFs during strong ground motions. In addition to the mother wavelet (in this paper, the Morse wavelet is chosen), the time bandwidth parameter, which governs the trade-off between resolution in time-and-frequency domains, is an important hyper-parameter of discrete wavelet decomposition techniques. As shown in Figure 11c, increasing this parameter greatly benefits the separation between linear and nonlinear building responses. In other words, a higher frequency resolution is more beneficial than a higher time-domain resolution.