esa-sceva/satcom-chunk-collection huggingface dataset:
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 3 Results and Discussion
### Application on Data Sets
#### 3.1.2 Thruster 2  
The data set with 162 samples for model identification included shaft speed in the range 296.14-748.40 rpm. All the samples were accepted in quality control and the selected shaft speed was 296.14-459.94 rpm, which consisted of 113 samples. The identified parameter values of this operational area are shown in Appendix A.  
The application of the models in monitoring is demonstrated in Figure 12 where the first 113 samples were used to train the model. Samples 114-418 are new independent samples in the selected shaft speed range and they correspond to a 370-day period. The defects were confirmed based on videoscope inspection after sample no. 272. The periods marked with negative and positive classes were used for the classification in Section 3.2.  
The monitoring results for thruster 2 shown in Figure 12 have similarity to the behavior in Figure 11. The monitored \(D\) values started to increase after the training period. Especially, the time domain and bearing models showed high values regularly on the 81-day period shown by samples no. 170-272 before the videoscope inspection. The gear model gave clearer indications of change during the period marked as positive class.
# Automated Processing of fNIRS Data--A Visual Guide to the Pitfalls and Consequences
## Methods
### Pre-Processing  
The pre-processing steps are outlined in Figure 4b. Explanations for the decision of which results to present are outlined in the method and result section. Preprocessing was done, using MATLAB-based software packages (MATLAB 6.1, The MathWorks Inc., Natick, MA, USA, 2000). Raw data was trimmed so only the finger-tapping experiment data was analyzed, to mimic the length of a typical experiment, measuring a specific task. Conversion from raw fNIRS time courses to optical density and then to oxy- (\(\Delta\)HbO), deoxyhemoglobin (\(\Delta\)Hb) was done with HOMER2 (v2.2) routines (hmrIntensity2 OD and hmrOD2 Conc, respectively) using the modified Beer-Lambert law [44] with age-dependent differential path length factors (DPF). The typical DPF value of 6, as is suggested in HOMER2, or the often cited DPFs of 6.38 and 7.25 for 850 nm and 760 nm respectively [45], may not necessarily be suitable for participants of all ages. It has been shown previously that the path length of NIR light can change as a function of age [46]. Since we evaluated pre-processing methods  
Figure 4: NIRS set-up and processing steps. (**A**) Head-cap configuration, based on the 10–20 system. Sources (red) and detectors (green) are shown on the right hemisphere of the configuration. The symmetrical configuration is presented on the left hemisphere, with channel number rather than source or detector (small green and red circles). Channels are in the same color when the source is identical for these channels. The green arrow on the left hemisphere marks the left primary motor region (M1 coordinate). (**B**) Diagram of processing steps evaluated here. The GLM results later shown in this paper are based on the bolded pre-processing choice. The reasons for these choices are further described in each paragraph in the method and discussion section. Blue represents processing on raw data, yellow on optical density data, and green on concentration data.  
on participants within a large age range, we also implemented age as a factor in determining the DPF, as previously shown [47]. The code used to determine age-adjusted DPF is in the Appendix A and was sourced from [PERSON] and [PERSON] [47]. We should note that this code was validated for frontotemporal regions and may not be accurate for locations as the occipital cortex.
# Antimicrobial and Odour Qualities of Alkylpyrazines Occurring in Chocolate and Cocoa Products
## 2 Pyrazines in Chocolate and Cocoa
### The Way of Formation of Pyrazine in Cocoa and Cocoa Products
#### 2.1.3. Conching Stage of Chocolate Manufacturing  
Conching is the most significant final process in the creation of chocolate from a technological standpoint, influencing both its texture and aroma, which typically causes a change in colour as a result of tannin oxidation and emulsification. The flavour profile is enhanced while the content of free acids and some other aromatic coco byproducts is decreased throughout this process [13; 15]. It involves mixing hot liquids for at least eight hours in conch-style tanks. The effect of conching on the concentration of volatile chemicals during chocolate manufacture is still up for debate, but comparisons between before and after conching show that, despite the heating method not producing any new important odourants, levels of branched pyrazines increase while the majority of Strecker aldehydes are lost through evaporation [20; 21; 24]. There is general agreement that following conching, chocolates exhibit noticeably less overall off flavour from polyphenols and other byproducts. Heterocycles only saw an increase in concentrations of the least volatile substances, including tri- or tetramethylpyrazine, isobutyl and isopentylpyrazines [13].
## 2 Background
### Mgtt  
MQTT is an OASIS standard messaging protocol designed to be lightweight and to have a small code footprint [12]. MQTT is one the main protocols that is used for the IoT and for the implementation of middleware services, alongside COAP, AMQP and HTTP [49]. It is nowadays used in many application scenarios, such as smart homes and smart buildings [50; 51], smart cities [52], smart grids and energy management [53; 54], smart agriculture [55], environmental monitoring and early warning [56], and so on. Furthermore, the main cloud platforms for the IoT use MQTT as their middleware service and brokering protocol [9].  
MQTT is a publish/subscribe message protocol that decouples data producers and data consumers through the use of a central entity called \"broker\", a server responsible for dispatching messages among MQTT clients. brokers contain a list of topics, which are strings that are organized hierarchically. The MQTT client can either subscribe to a topic, publish a message on a topic, or both. When a client publishes a message on a certain topic, the broker will forward it to every client that has subscribed to that topic. MQTT includes a series of security mechanisms, including password and username authentication, and authentication with transport layer certificate exchange. Data encryption can be guaranteed with protocols such as the transport layer security protocol (TLS), as is performed in AWS, or it can be implemented at the application layer to provide end-to-end encryption.
## 3 Design of Multi-Objective Fuzzy Continuous Damping Controller
### Control Objectives and Strategies  
#### 3.1.1 Sky-Hook Control  
Sky-hook control assumes that two ends of the damper are not fixed between two masses; instead, it assumes that one end is fixed on one mass and that the other end is fixed in the sky. In this way, the sky-hook shock absorber does not have the opposite effect on both masses at the same time (Figure 5).  
Figure 4: Suspension model. The unsprung mass of the suspension components and wheels is replaced by the equivalent unsprung mass in this model.  
CDC is a semiactive suspension with real-time control of damping force. The power of the CDC shock absorber must be positive under the theory of semiactive suspension. Given the absence of an external energy injection in semiactive suspension, the form of CDC energy transfer can only be from mechanical energy into thermal energy; it can be written as  
\[P_{Dij-CDC}=\left\{\begin{array}{cc}F_{Dij-CDC}\Big{(}w-Z_{uij}^{\cdot}\Big{)}= C_{Dij-CDC}w\Big{(}w-Z_{uij}^{\cdot}\Big{)},&w\Big{(}w-Z_{uij}^{\cdot}\Big{)}>0\\ 0,&w\Big{(}w-Z_{uij}^{\cdot}\Big{)}\leq 0\end{array}\right. \tag{25}\]  
\[F_{Dij}\text{Sign}\Big{(}Z_{Dij}^{\cdot}\Big{)}\geq 0 \tag{26}\]  
where the function \(\text{Sign}(x)\) is described as:  
\[\text{Sign}(x)=\left\{\begin{array}{c}1,x>0\\ 0,x=0\\ -1,x<0\end{array}\right. \tag{27}\]  
\[Z_{Dij}^{\cdot}=w-Z_{uij}^{\cdot} \tag{28}\]  
where \(P_{Dij\cdot CDC}\) is the power of the CDC shock absorber of each wheel, \(F_{Dij\cdot CDC}\) is the force of the CDC shock absorber of each wheel, \(C_{Dij\cdot roll}\) is the damping coefficient of the CDC shock absorber of each wheel, \(Z_{Dij}\) is the displacement of the shock absorber of each wheel, and \(x\) is a variable.  
Figure 5: Abridged general view of sky-hook control.  
Sky-hook damping control provides a damping force opposite to the sprung mass speed in real time. Hence, it can produce minimal spring mass vibration and ensure the comfort of passengers to the maximum extent.  
In addition to the fact that CDC has no external energy constraint, it is affected by its own external characteristics, and it has maximum and minimum damping force limits (Figure 6).
## 4 Fiber Sensing Technologies
### Optical Sensing for Gas Detection
#### 4.3.2 Gas Sensing System Design
This means that, on channel B of the lock-in amplifier (Figure 3), the amplitude of first harmonic must equal zero, thus tuning the laser wavelength to the targeted gas. The amplitude of the second harmonic (channel A of lock-in amplifier), which is proportional to the gas concentration, was normalized by the average laser intensity read by the photodetector. Figure 5: DFB laser emission wavelength shift in function of current, for a specific temperature, of the detected NH\({}_{3}\) absorption line at 1512.21 nm. Real-time concentration measurement was performed for each gas (NH\({}_{3}\), CO\({}_{2}\), and CH\({}_{4}\)) one at a time by introducing the gas inside the multi-pass cell and sealing it at room temperature and atmospheric pressure. The different gases' (NH\({}_{3}\), CO\({}_{2}\), and CH\({}_{4}\)) concentrations were achieved by mixing a calibrated gas sample in an N\({}_{2}\) environment. Figure 6 illustrates the results obtained. The proposed sensing system shows linear responses in the concentration range from 0 to 10%, and has a limit of detection (LOD) of 0.06%, 0.04%, and 0.09% for ammonia, carbon dioxide, and methane measurements, respectively. The limit of detection was defined according to [45]:  
\[LOD=\mu_{b}+K_{D}\frac{\sigma_{b}}{S_{S}},\]  
where \(\mu_{b}\) and \(\sigma_{b}\) are the mean and the standard deviation of the blank measures (0% of concentration), respectively, and \(K_{D}\) a factor chosen according to the confidence level desired (it was chosen \(K_{D}\) equal to 3 that corresponds to a confidence level of about 90%). The sensitivity of the proposed system S\({}_{\text{S}}\) is the slope (\(\Delta\)S\({}_{\text{WMS}}\)/\(\Delta\)C) of the calibration curve, where C is the gas concentration and S\({}_{\text{WMS}}\) is the normalized signal measured by the sensing system that is proportional to gas concentration. This signal (S\({}_{\text{WMS}}\)) was obtained through division of the lock-in amplifier signal by the photodetector signal. In this case, the sensitivities obtained were 1.80%, 0.89%, and 0.85% for ammonia, carbon dioxide, and methane measurements, respectively.
## 4 Numerical Simulation Applied to the FFF Process
### Melt Flow
By parameterizing the nozzle profile using spline curves and enforcing manufacturability constraints, they identified geometries that significantly reduce pressure drops while maintaining effective flow characteristics. Experimental validation confirmed these findings, demonstrating that optimized nozzles--particularly the organic and PET-G-specific designs--lowered pressure drop by up to 67%, with reduced backflow and improved extrusion force control. These results emphasize that tailoring nozzle design to polymer rheology can directly enhance printing performance, precision, and throughput, employing nozzle optimization as a valuable tool in the advancement of FFF processes (see Figure 10). Figure 10: Nozzle geometries (**a**) and pressure drop reduction per geometry compared to the standard nozzle (**b**), with values obtained from simulations and experimental validation. Adapted with permission from [43]. Figure 9: Sketch illustrating the definition of the solid filament, recirculation region, stagnation point, and melt zone. Adapted with permission from [41]. At high extrusion speeds, melt flow presents additional complexities. [PERSON] et al. (2020) [29], through CFD simulations in ANSYS Fluent (Version Ansys 2020 R1), observed that rapid extrusion reduced the heating period, resulting in incomplete polymer melting, filament distortion, and decreased accuracy. They indicated that current numerical models fall short under high-speed conditions and recommended developing advanced thermal and rheological models to improve predictive accuracy. This limitation is closely tied to the viscoelastic nature of polymer flow at elevated deformation rates, particularly during elongational deformation in the nozzle. Figure 11 presents the pressure drop of PLA melt as a function of shear rate, measured using both capillary and orifice dies. The steep increase in pressure drop with shear rate underscores the dominance of elongational effects in high-speed extrusion and supports the adoption of the Phan-Thien-Tanner (PTT) model to predict flow resistance more accurately under these conditions. These experimental insights validate the simulation observations and highlight the need for rheological models that capture pseudoplastic behavior when aiming to scale FFF processes to higher speeds. Collectively, these studies indicate that melt flow behavior in FFF strongly depends on extrusion conditions, including nozzle temperature, feed rate, and layer height. CFD-based approaches effectively characterize flow instabilities, optimize deposition, and enhance interlayer bonding. Nevertheless, melt flow alone does not solely determine the mechanical properties of printed components. Post-deposition cooling and solidification introduce thermal dissipation and phase transitions that significantly impact residual stresses and structural stability. The subsequent section explores these thermal mechanisms and their role in interlayer adhesion, mechanical strength, and dimensional accuracy in FFF.
# The Influence of Radial Stress on Mechanical Properties of Anchorage Structure
## 4 Numerical Simulation
### Influence of Different Confining Pressure on the Interfacial Shear Stress  
A large number of studies have shown that, although the shear stress on the two interfaces of anchorage structure is different, the distribution rule of shear stress is the same. Therefore, this numerical simulation takes the interface between grout and rock as the research object to study the influence of confining pressure on the shear stress distribution at the interface.  
A pullout force of 160 kN is applied to the bolt, and confining pressures of 0 MPa, 1 MPa, 2 MPa, 4 MPa, 8 MPa, and 16 MPa are applied to the model respectively. After calculation, the distribution curves of interfacial shear stress between the grouting body and rock are extracted. Under different confining pressures, the distribution curves of shear stress are shown in Figure 11. It is not difficult to see from the figure that the confining pressure has little influence on the distribution form of interfacial shear stress, but it has an obvious influence on the size of interfacial shear stress. With the increase of confining pressure, the peak of interfacial shear stress increases. When there is no confining pressure, the maximum value of interfacial shear stress is 1.16 MPa, and when the confining pressure increases to 16 MPa, the interfacial shear stress increases to 1.72 MPa. Obviously, the confining pressure has an effect on the maximum value of interfacial shear stress. In this study, the maximum value of interfacial shear stress at the confining pressure of 16 MPa increased by nearly 50% compared with that without confining pressure and the influence of confining pressure on the maximum value of interfacial shear stress is obvious.
## 3 Results and Discussion
### The Effects of Load Resistances and Spacing Ratios on Energy Conversion
#### 3.3.3 Optimal Energy Conversion of UTP + DTP
As velocity increased, oscillation was experienced in the VIV lower branch; the active power of UTP + DTP for \(L\)/\(D\)\(\geq\) 6 was much higher than that of STP and increased as velocity increased in this region. For \(R_{L}\) = 11 \(\Omega\), in the range of 10.375 \(\leq U_{r}\leq\) 12.25, if there was an initial displacement applied to the prism, the active power of UTP+DTP increased sharply in all tests due to the increase in amplitude caused by the external excitation for both UTP and DTP. As the velocity decreased, the amplitude and the active power decreased. In the galloping branch (10.375 \(\leq U_{r}\leq\) 12.25), HG occurred, the optimal configuration of UTP+DTP enhanced the active power by around 1.5 times, and the best performance in active power was at a spacing ratio of \(L\)/\(D\) = 6, followed by spacing ratios of \(L\)/\(D\) = 12 (29.10 W), \(L\)/\(D\) = 15 (28.04 W), and \(L\)/\(D\) = 9 (27.10 W); the lowest occured at \(L\)/\(D\) = 3 (25.36 W), as shown in Table 7.
# Trident: A Deep Learning Framework for High-Resolution Bridge Vibration Monitoring
###### Abstract
While the SDS concept proposes an efficient method for processing features from a dense array of sensors and mapping them to a large grid of output damage data, extensive feature preprocessing is still essential. Otherwise, the computational costs of processing raw vibrations from a dense network of accelerometers are restrictive. Some studies use time-series data with different tricks to obtain a measure of structural damage. For example, [PERSON] et al. [18] used several independent 1D CNNs for each acceleration node, and [PERSON] et al. [19] stacked several sequences and reshaped them into an image-like tensor shape structure as input to a 2D CNN architecture. [PERSON] and [PERSON] [20] further extended the concept of grid environment in SDS by proposing an architecture named SCAN that comprises a sequence of a spatial encoder, a temporal encoder, and a spatial decoder to process raw vibrations without manual feature engineering. SCAN was validated on a 2D RC structure case study leveraging unsupervised learning. [PERSON] et al. [21] proposed Long-Short Term Memory Neural Networks to learn from the sequential features of a seismic signal for regional damage assessments. [PERSON] and [PERSON] [22] recently proposed Mel Filter Banks (MFBs) as an alternative signal preprocessing technique that allows deep learning architecture to learn from time and frequency variations for bridge column drift regression [23]. Large-scale vibration-based health monitoring is challenged by how to deal with high-dimensional data. Sensors are often placed on a three-dimensional (3D) structure, and each physical node with sensors might contain several sensor channels, such as accelerations in the three principal directions. To the best of the authors' knowledge, the existing deep learning frameworks in the literature cannot process vibration data without significant manipulations in the vibration input tensor for dimensionality reduction. Furthermore, the structural damage output ideally needs to be represented by maintaining separate dimensions in the 3D physical space, which is missing from the earlier studies. The research presented in this paper proposes Trident, a 3-branched neural network architecture equipped with hybrid recurrent and convolutional layers known as ConvLSTM3D that enable direct feature extraction from 6D vibration input tensors. Trident can also use different network heads designed for various damage prediction tasks, including 3D SDS. The remainder of this paper is organized as follows. First, different components of the deep learning architecture and their purpose are discussed. The following section proposes details on the vehicle passing damage simulations. Subsequently, the truss bridge modeling and the dataset generation process are provided. The next section is dedicated to tuning Trident's hyperparameters and documenting the performance metrics, followed by a conclusion.
## 2 Materials and Methods
### Proposed Loss Functions
#### 2.3.1 Dissimilarity from False Negative  
Studying the false negative pixels and reducing the number of those will automatically help us increase true positive pixels. The relation between false-negative pixels and DSC is inversely proportional. The lower the number of false-negative pixels, greater the value of DSC.  
\[DSC\ \propto\ \frac{1}{P_{FN}} \tag{6}\]  
where \(P_{FN}\) is the number of pixels of the false negative in the predicted image. Figure 5 shows the process of getting false negative pixels from the predicted output. To get false negative pixels, we first invert the predicted output image. To do so, we subtract each pixel of the output image from a integer value one. Then the inverted image is multiplied by the ground truth image. As both images (ground truth and predicted output) are binary images, only the intersection between them will not be zero. This remaining non-zero area is the false negative pixel, as this area is non-zero in the ground truth. From this pixel value, we can calculate a dissimilarity coefficient. The dissimilarity coefficient for false-negative pixels prediction is calculated as,  
\[DC_{FN}=\frac{\left|I\ \cap\ \mathit{inv}\ (\hat{I})\right|}{\left|\mathit{ inv}\ (\hat{I})\right|} \tag{7}\]  
where \(\left|\mathit{inv}\ (\hat{I})\right|\) is the number of pixels of the ROI from the inverted predicted output. \(DC_{FN}\) is a metric of area overlap between the ground truth and the inverted predicted image. \(\left|I\cap\ \mathit{inv}\ (\hat{I})\right|\) represents the number of pixels that are predicted as background but are not background in the ground truth image. Thus, we consider these pixels as a false negative. The process of calculating a dissimilarity coefficient for false negative values is summarized in Algorithm 1. From Equation (7) we can say that the \(DC_{FN}\) is the ratio of the number of false negative pixels, or false background pixels, to the number of total background pixels predicted. This ratio tells us how many of the predicted background pixels do not match with the actual ground truth background pixels.
# A Compact Multi-Identity Fully Homomorphic Encryption Scheme Without Fresh Ciphertexts
## 2 Preliminaries
### Definition
The challenger \(\mathcal{C}\) runs the private key generation algorithm Extract to generate the corresponding private keys \(\mathbf{s}\mathbf{k}_{id_{1}},\cdots,\mathbf{s}\mathbf{k}_{id_{D}}\) and sends them to the adversary. Challenge Phase: Adversary \(\mathcal{A}\) submits a plaintext message \(msg\) as the challenge. Challenger \(\mathcal{C}\) chooses a random bit \(r\) from \(\{0,1\}\) and a random ciphertext \(c\). If \(r\) equals \(0\), then \(c^{*}\) is defined as \(\mathit{Encrpt}(MSK,id^{*},msg)\); if \(r\) equals \(1\), then \(c^{*}\) equals \(c\). Challenger \(\mathcal{C}\) then dispatches \(c^{*}\) to adversary \(\mathcal{A}\) as the challenge. Guess Phase: The adversary \(\mathcal{A}\) outputs \(r^{{}^{\prime}}\in\{0,1\}\) as their guess. If \(r^{{}^{\prime}}\) equals \(r\), then adversary \(\mathcal{A}\) wins the game and is considered an IND-sID-CPA adversary. Let \(Adv(\mathcal{A})=\left|Pr\Big{[}r^{{}^{\prime}}=r\Big{]}-\frac{1}{2}\right|\) denote the advantage of adversary \(\mathcal{A}\) in attacking the encryption scheme. If for all IND-sID-CPA adversaries the advantage is \(Adv(\mathcal{A})=\left|Pr\Big{[}r^{{}^{\prime}}=r\Big{]}-\frac{1}{2}\right|= negl(n)\), then the encryption scheme is IND-sID-CPA secure.