esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analyses
### Discussion
Figure 11 complements the analysis providing a quantification of the information loss for the different signals at various time resolutions. The two complementary perspectives allowed to determine critical conditions during turbine operations. Various signals show a large fraction of the aggregation error concentrated for the part load and upper part load regions of the power curve. Moreover, while the aggregation error might be negligible for some signals, such as the main bearing and ambient temperature, for others, in particular active power, currents, and gearbox related temperatures, this error must not be ignored. Decreasing the length of the aggregation period to a value between 60 to 150 s greatly helps reducing the maximum extent of information loss range, maintaining a limited discrepancy between aggregated and raw signal. Moreover, knowing the link between wind speed and error provides relevant knowledge for improving the design of models aiming to describe turbine behavior.
# Optimization of a Centrifugal Compressor Using the Design of Experiment Technique
## 3 Design of Experiment
### Box-Behnken Technique  
Different types of designs are being used for creating the design space. The Full Factorial Technique is a useful technique for exploring all of the design space. With \(n\) parameters, each at \(k\) level, the full factorial creates \(k^{n}\) design points. Two-level factorial techniques are very efficient but they result in first-order models and cannot detect curvature. At Three-level, the \(3^{n}\) factorial, is not efficient and generates huge design space points [20].  
Box-Behnken is a technique which makes an efficient design space and it requires only the fraction of full factorial points to estimate the second-order effects of the response surface. For \(A\), \(B\) and \(C\) factors each at three levels \(\{-1,0,1\}\), Box-Behnken design offers the points as shown in Figure 2 which can be compared with 27 design points obtained from the full factorial technique.
# An Overview on Deep Learning Techniques for Video Compressive Sensing
## 5 Video Single-Pixel Imaging and Video Snapshot Compressive Imaging
### Single Pixel Imaging  
Single-Pixel Imaging (SPI) is a novel paradigm that enables a device, equipped only with a single point detector called single pixel camera (SPC), to produce high-quality images. The general implementation of the SPI can be schematized as in Figure 9.  
Technically, the single-pixel camera essentially detects the inner product of the video and a set of patterns [4]. Then, need to solve an inverse problem to reconstruct the original scene from the raw measurement.  
Mathematically, let \((X_{t})_{t\in\mathbb{N}}\in\mathbb{R}^{N\times 1}\), where \(X_{t}\) is the t-th frame of the detected video. The SPC enables the access to the measurement vector \((y_{t})_{t\in\mathbb{N}}\in\mathbb{R}^{M\times 1}\), then the acquisition step can be modeled by:  
\[y=\Phi X_{t}\Delta_{t},\forall t \tag{14}\]  
where \(\Phi\in\mathbb{R}^{M\times N}\) is a dense matrix that encode the list of patterns (one row represents one pattern of the modulator) and \(\Delta_{t}\) defines the integration time for each pattern. At each time step, \(\Phi\in\mathbb{R}^{M\times N}\) is a matrix containing a set of \(M\) patterns. Generally, it is an orthogonal basis (e.g., Fourier, Wavelet, Hadamard). Indeed, using these structural matrices enables to accelerate the computational process because random matrices require huge storage resources which affect the computational mechanism (Figure 10).  
The most challenging part \(\mathfrak{ain}\) single pixel imaging is the reconstruction paradigm. Therefore, many approaches were proposed in the last decade. These reconstruction approaches can be categorized into two groups: traditional approaches and deep learning based model.  
In traditional strategies we find \(l_{2}\)-regularized approaches [64] and \(l_{1}\)-regularized approaches [4; 65] called also Total-variation approaches. Each approach has its advantages and drawbacks. For \(l_{2}\)-regularized approaches: they are faster but they lead to decreased frame quality. However, \(l_{1}\)-regularized approaches are much slower but they lead to better image quality.  
Recently, deep neural networks have been used successfully in signal pixel imaging reconstruction problems. In [66], [PERSON] et al. have exploited the spatio-temporal features of video and proposed a Convolutional Gated Recurrent Units (ConvGRU) based algorithm to reconstruct video frames already captured by a single pixel camera. [PERSON] et al. [67] defined a generic convolutional network to recover the original video. In addition, in [47], an auto-encoder network is proposed for SPI reconstruction purposes. However, this ap  
Figure 9: Single Pixel Camera diagram.  
proach does not exploit the temporal features of video scenes since it enables to reconstruct the video frames independently.
## 3 Data Processing and Kinematic Analysis
### Object Tracking and Management
#### 3.4.4 Mode Probability Update  
The mode probabilities are updated in this step using the best-fitting measurements and the uniform mixture model likelihoods \(\lambda_{j,k}\).  
\[\mu_{q,k}=\frac{\mu_{q,k}^{-}\lambda_{q,k}}{\sum_{i=1}^{r}\mu_{i,k}^{-}\lambda_ {i,k}}. \tag{18}\]  
where \(\lambda_{q,k}\ \overset{\text{asm}}{\cong}\ \mathcal{N}\Big{(}\widetilde{z}_{q,k};0,S_{q,k} \Big{)}\)
## 7 Module III: Temporal Privacy
Let us presume \(\psi\) donates one adversary strategy for breaching the anonymity of the system among a set of strategies. Let us presume \(\mathrm{P_{r}}\) is the probability that the adversary succeeds using strategy\(\psi\). The anonymity A as defined in [34] with the existence of a strategy\(\psi\), is presented in Equation (31):  
\[\mathrm{A_{\psi}}:=1-\mathrm{P_{r}}\text{, where }0\leq\mathrm{P_{r}}\leq 1 \tag{31}\]  
If we presume that \(\Sigma\) represents all possible strategies for the adversary to breach the anonymity of the WSN, the accumulated anonymity will be as in Equation (32):  
\[\mathrm{A}:=\mathrm{min}\ (\mathrm{A_{\psi}})\text{, where }\psi\in\Sigma \tag{32}\]  
It is very important to increase anonymity for every individual SN in the network especially with the presence of multi-local or global adversaries. Presence of colluding adversaries could cause the anonymity to drop exponentially [34]. Take Figure 9 as an example, where WSN has a moving Panda from point \"a\", to \"b\", to \"c\", then finally to \"d\" where each location has a SN to report the Panda's movement. If the anonymity of each sensor is \(\mathrm{A}=0.8\), then the anonymity at node \"b\" is \(\mathrm{A}=0.8^{2}=0.64\) and at point \"d\" is \(\mathrm{A}=0.8^{4}=0.41\). Having global adversary makes it super necessary to design a strong anonymity model which can resist the time correlation attack [42]. In this work, we assume the worst case for time correlation attacks which is a global or laptop-class adversary attacks [17]. Having an anonymity scheme to protect against the global adversary will be very expensive solution in terms of energy preservation and thus the lifetime of the network. In the following two subsections, we propose two schemes, the simple global anti temporal (SGAT) and the energy controlled anti temporal (ECAT).
# Improving Juvenile Age Estimation Based on Facial Landmark Points and Gravity Moment
## 1 Introduction
The age prediction phase: This phase aims to address the estimation task as a classification or regression problem. With the classification problem, facial images are organized into age-groups with defined age ranges such as juveniles or adults. In contrast, with the regression problem, ages are taken to be numeric values. In some proposals, the two prediction methods are combined hierarchically, first to classify age into the adult or juvenile group, and then estimate age as a specific number within the target age-group [26; 27; 28]. Recently, the artificial neural network approach has been gaining ground in age estimation [29]. Although they have a unique ability to combine both the feature extraction and prediction phases into a single pipeline, the approach comes with a higher dependency on very large datasets, which may not be readily available [28; 30]. Consequently, traditional methods continue to attract attention [31; 32]. Irrespective of the approaches considered for the age estimation proposals, Mean Absolute Error (MAE), Cumulative Score (CS), and Classification Accuracy (Acc) are the favored metrics for evaluating the performance of the system. Challenges: Considering the variability of the aging face and the different aging patterns and features, age estimation continues to challenge computer vision researchers. Moreover, when the age estimation task is within the same aging subspace such as juveniles (0 to 17 years), it becomes even more difficult to measure subtle changes in features that correspond to the age-group. These challenges are compounded by the general lack of a juvenile dataset. We hope to address these problems by offering different value combinations that serve the juvenile age estimation goals. Contributions: In this paper, we present the first-time use of the combination of facial landmark points and Term Frequency Inverse Gravity Moment (TF-IGM) to contribute a juvenile age estimation scheme we name LaGMO. The implementation of LaGMO took place through the following efforts:* Utilizing the 68 facial landmark points to build high-level terms that describe the shape and appearance features. By exploiting the implicit ordinal relationships among the frequencies of the terms (features) in the various ages, we aggregated the features into a weight matrix for the entire ages. * The weights of the features and their contributions to the age prediction task were computed by TF-IGM. * We demonstrated the effectiveness of LaGMO on a discriminating dictionary of juvenile age descriptors, which we obtained from the FG-NET dataset. * With a MAE of 4.42 and a SC of 89.8%, LaGMO advances juvenile age estimation. The rest of the paper is organized as follows: Section 2 reviews previous work and introduces the various components of our proposal. Section 3 discusses the fundamental concepts of our proposal. We present details of the proposed scheme in Section 4. In Section 5, experimentation and results are discussed. Finally, Section 6 concludes our contribution.
## 3 Test Data Analysis
### Z Vibration Level Analysis  
According to \"Measurement Method of Environmental Vibration in Urban Areas\" GB10071-1988 [32], the tunnel wall vibration data were analyzed according to ISO 2631/1-1997 [33] vertical (Z-direction) weighting method (1-80 Hz). The Z vibration level (V\({}_{\text{LZ}}\)) refers to the Z vibration level measured when the train passes through the measuring segment, and is the most commonly used evaluation index. However, since the running speed of the train will be different under different line conditions, it is necessary to correct the data of the Z vibration level according to the data of the train to balance the influence of the train speed on the result.
## 3 Related Works
### The Benchmarks
#### 3.3.2 Xrpl  
XRPL [21] is our second benchmark, which takes a cross-layer approach to jointly adjusting the transmission power and the routes. Their proposal integrates an OF called the Minimum Expected Transmission Power OF (METOF), which basically multiplies ETX with the transmission power. XRPL defines a new Information Element (IE) in the 802.15.4 frame that notifies the receiver of the transmitter's transmission power. The receiver utilizes this information to calculate its OF and route selection. XRPL is fast to respond to evolving link qualities, as it adapts to the environment upon receiving each packet. On the other hand, XRPL only allows two transmission power settings to simplify the exploration.
## 3 Results and Discussion
### Image Segmentation Results
#### 3.3.2 Qualitative Analysis  
In this study, an ensemble learning approach was adopted to improve the accuracy of image segmentation. Specifically, we integrated multiple well-trained models to independently segment the same unsegmented image. Subsequently, we calculated the porosity consistency values of these model segmentation results and compared them with a preset consistency threshold. If the consistency value exceeded the threshold, the segmentation result was deemed reliable and used as a pseudo-label; if it was below the threshold, the result was excluded. We set two consistency thresholds: a high threshold of 28 and a low threshold of 18, and Figure 8 visually demonstrates the specific impact of the different threshold settings on segmentation results. The findings reveal that the models trained with a high consistency threshold significantly outperformed those trained with a low consistency threshold in segmentation accuracy. Particularly, in handling fine cracks between particles, the segmentation results with a high consistency threshold were more precise. Furthermore, for identifying clay-filled regions within pores, the segmentation results were robust and showed minimal differences between the two thresholds. However, in identifying large pores, although the high consistency threshold had some advantages in detail handling, both threshold-trained models generally performed well. Through qualitative analysis, we can clearly observe the impact of the different consistency threshold settings on the segmentation results. The high consistency threshold has a distinct advantage in improving the segmentation accuracy, especially in recognizing fine cracks. Although the performance in identifying large pores was similar between the two thresholds, the high consistency threshold models had notable advantages in detail handling. These findings provide valuable insights for image segmentation research and guide further model optimization.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**U-Net**} & **Adaptive** & \multirow{2}{*}{**Pseudo-Label**} & \multirow{2}{*}{**IoU**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**PA**} & **Local** & **Porosity** \\  & & & & & & **Consistency** & **Difference** \\ \hline \(\surd\) & & & 0.9615 & 0.9667 & 0.9624 & 0.9649 & 0.0231 \\ \(\surd\) & & \(\surd\) & 0.9796 & 0.9680 & 0.9724 & 0.9841 & 0.0156 \\ \(\surd\) & \(\surd\) & & 0.9801 & 0.9699 & 0.9843 & 0.9855 & 0.0128 \\ \(\surd\) & \(\surd\) & \(\surd\) & 0.9981 & 0.9927 & 0.9993
## 4 Results and Discussion  
According to the CFD simulation results obtained in Section 3, we can discuss the influence of different parameters on the numerical simulation in three aspects: the NACA0012 models, the far-field distances, and the turbulence models. NACA0012 is a symmetrical airfoil; the \(C_{l}\) is 0 when the AOA is 0\({}^{\circ}\) and the magnitude of the simulation result of \(C_{l}\) is 10\({}^{-6}\), which approximately approaches 0. Therefore, the error ratio of \(C_{l}\) is about 0.00% when the AOA is 0\({}^{\circ}\).
## 3 Methodology
### Relation Instantiation
The arrows in different colors indicate the different rotation between entities, with solid lines representing the determined rotation process and dashed lines representing the indeterminate rotation process. within a given entity's neighbors. Our goal was to ensure that relations can have different rotations in different environmental contexts. Figure 5 shows the specific construction of the relation instantiation module. In relation instantiation, we select a significant number of second-hop, third-hop, and fourth-hop neighbors, represented by \(e_{sec}\), \(e_{thr}\), and \(e_{for}\), to provide contextual information for the source entity. The nth-hop neighbors refer to the nodes that the source entity can reach through n transformations of relations. In this context, each transformation of the source entity to the tail entity through a relation in the triple is considered a first-hop. Neighbors with smaller hop counts, such as 1-hop and 2-hop neighbors, have a closer relationship with the source entity. They can directly reflect relevant information about the source entity. On the other hand, neighbors with larger hop counts, such as 3-hop and 4-hop neighbors, can provide additional information by roughly reflecting the scene where the source entity is located. It is important to note that we do not use the relations of these neighboring triples, as the size of the relation set \(R\) is significantly smaller than the number of neighboring triples we will select. As a result, these relations do not contribute to the feature representation of the source entity. Then, we obtain three \(1\times d\) context vectors by averaging the representations of \(e_{sec}\), \(e_{thr}\), and \(e_{for}\). These context vectors are then inputted into the multi-headed Transformer \(T_{r}\) along with the source entity \(e_{s}\), the relation \(e_{r}\), and the two flag vectors, \(CLS_{0}\) and \(CLS_{1}\). The resulting two relation components that are obtained via \(T_{r}\) are represented by \(CLS_{0}^{\prime}\) and \(CLS_{1}^{\prime}\), which represent the instantiation results of the relation from different perspectives. Using multiple relation components is intended to convey more contextual information and to help balance the weight of information across multi-hop neighbors during decoding. The multi-head Transformer consists of a sub-layer that comprises a multi-head attention network connected in series with a sub-layer of a feed-forward network. The two sub-layers are connected by a connection layer that includes residual connections, dropout, and normalization operations.
## 5 Materials and Methods
### Planar Lipid Bilayer and Electrical Recording:  
Planar lipid bilayer was formed as described in detail elsewhere [28; 35; 36]. An aperture in a Teflon septum with a diameter of 105-130 \(\upmu\)m was pre-painted with hexadecane dissolved in purified n-hexane at 1.5-2% (_v/v_) and the Teflon chambers were dried for 40-45 min. Bilayers were made with 1,2-diphytanoyl-sn-glycerophosphocholine at a concentration of 5 mg/mL in n-pentane [25; 35; 36; 51; 54]. Stock solutions of the outer membrane porins Omp-Pst2/OmpF, were added to the cis side for all the measurements. Standard Ag/AgCl calomel electrodes (Metrohm AG) were employed to perceive the ionic current [35; 36]. The cis side electrode of the cell was connected to the ground, whereas the trans side electrode was connected to the headstage of an Axopatch 700A amplifier, used for the conductance measurements in the voltage clamp mode. Signals were filtered by an on-board low-pass Bessel filter at 10 kHz and recorded onto a computer hard drive with a sampling frequency of 50 kHz [35; 36]. The analysis of the current recordings was performed using Clampfit (Axon Instruments, Union City, CA, USA) and Origin\({}^{\text{R}}\) (Origin-Lab-Corp, Northampton, MA, USA). The current voltage relation of the individual experiments was calculated from single averaged currents at the given voltage [35; 36]. All the experiments were repeated three times minimum. Standard solutions along with bulk conductance and channel conductance are given in Table 2[35; 36; 37]. The relative permeability of cations vs. anions in the bi-ionic case were obtained by fitting the experimental I-V-curves with the Goldman-Hodgkin-Katz current equation [35; 36; 78]. Single-channelconductance in the presence of ammonium chloride and ammonium sulfate was measured for Omp-Pst2/OmpF reconstituted in planar lipid bilayer.
# Advances in Finite Element Modeling of Fatigue Crack Propagation
## 3 Advanced Techniques in Finite Element Modeling for Fatigue Crack Propagation
### Extended Finite Element Method (XFEM)
#### 3.1.5 LS-DYNA  
LS-DYNA is a commonly used explicit finite element analysis software that has been applied in various industries for simulating complex engineering problems. In recent years, LS-DYNA has also incorporated XFEM to accurately model crack growth behavior in structural components. Numerous research studies, such as those referenced in [116, 117, 118], provide detailed insights into the capabilities of LS-DYNA coupled with XFEM for analyzing crack propagation. In LS-DYNA, XFEM employs a partition of unity strategy, as referenced in [119, 120] to depict the displacement field in the vicinity of the crack tip. Through the incorporation of supplementary degrees of freedom into the finite element model, XFEM facilitates precise simulations of crack growth, encompassing intricate crack trajectories, branching, and merging phenomena. LS-DYNA does not have a fully free version, but some universities may provide access through educational licenses for students and faculty.
## 3 Numerical Simulation on Deformation of Embankment Combined with Subgrade
### Settlement Prediction  
Generally, primary consolidation is the main part of total settlement. After primary consolidation settlement, when the excess pore water pressure dissipates, and on the condition that effective stress remains still, some soft clay under load will keep settling over time, and this settlement is called secondary consolidation settlement. The reason for secondary consolidation settlement is the rheology of soil, which includes creep deformation, stress relaxation, long-term strength, and the strain rate effect, and creep deformation is the most frequent factor so that deformation of the soil mass increases with time under the action of a constant load. Therefore, secondary consolidation settlement is treated as volume creep of soil.  
Figure 16 shows the displacement diagram of the vertical increment of the model of the equivalent sand-wall in the first year of the operation. According to Figure 16, in the field test, the vertical displacement of soil layers except for the silt layer under the embankment is small. This means that by preloading for a while after construction of the road crust, settlement happens mainly in the silt clay. By analysis of the numerical calculation, the excess pore water pressure is \(-0.79\) kPa after the pavement construction and dissipates to about 0 after that, as the settlement of the silt layer is creep settlement. Because the settlement later happens mainly in silt clay, after operating for 30 years the creep settlement is the same as the settlement after construction, as shown in Table 8.  
Figure 15: The settlement of the field monitoring test and numerical calculation: (**a**) CJ1-1; (**b**) CJ1-2.