esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Method
### Time Series Encoding  
As we all know, the historical data of time series plays a pivotal role in predicting its future trends. In this paper, we use the LSTM [45] model to encode the historical data of time series. Compared with the vanilla RNN, the LSTM model has a long-term memory function, which could alleviate the problems of gradient vanishing and exploding for long-term sequence modeling. At the \(t\)-th time step, we input the feature vector \(\mathbf{x}_{t}\) into the LSTM model. Formally, the LSTM model performs the calculations as follows:  
\[\begin{split}&\mathbf{f}_{t}=\sigma(\mathbf{W}_{f}[\mathbf{h}_{t-1},\mathbf{x}_{t}]+ \mathbf{b}_{f})\\ &\mathbf{i}_{t}=\sigma(\mathbf{W}_{t}[\mathbf{h}_{t-1},\mathbf{x}_{t}]+\mathbf{b}_{i}) \\ &\mathbf{o}_{t}=\sigma(\mathbf{W}_{o}[\mathbf{h}_{t-1},\mathbf{x}_{t}]+\mathbf{b}_{o}) \\ &\mathbf{\bar{c}}_{t}=\tanh(\mathbf{W}_{c}[\mathbf{h}_{t-1},\mathbf{x}_{t}]+\mathbf{b} _{c})\\ &\mathbf{c}_{t}=\mathbf{f}_{t}\odot\mathbf{c}_{t-1}+\mathbf{i}_{t}\odot\mathbf{\bar{c }}_{t}\\ &\mathbf{h}_{t}=\mathbf{o}_{t}\odot\tanh(\mathbf{c}_{t})\end{split} \tag{4}\]  
Here, \(\mathbf{h}_{t-1}\) is a hidden state containing all the information up to the \((t-1)\)-th time step. \(\mathbf{h}_{t-1}\) and \(\mathbf{x}_{t}\) are concatenated and converted into a forget gate \(\mathbf{f}_{t}\), an input gate \(\mathbf{i}_{t}\), and an output gate \(\mathbf{o}_{t}\), respectively. In addition, \(\mathbf{h}_{t-1}\) and \(\mathbf{x}_{t}\) are also used to generate a candidate cell state \(\mathbf{\bar{c}}_{t}\) which represents the new information to be added. Then, \(\mathbf{c}_{t-1}\) and \(\mathbf{\bar{c}}_{t}\) are combined to form \(\mathbf{c}_{t}\), and \(\mathbf{f}_{t}\) and \(\mathbf{i}_{t}\) serve as the balance factors in this procedure. Finally, \(\mathbf{o}_{t}\) is multiplied by \(\mathbf{c}_{t}\) to output the current hidden state \(\mathbf{h}_{t}\). In Equation (4), \(\mathbf{W}_{f}\), \(\mathbf{W}_{i}\)  
Figure 1: The overall framework. In part (**a**), the input signal of a time series is encoded through a recurrent neural network, and then a self-attention mechanism is introduced to adaptively determine the weight of the data at each time step to calculate the \(SAMA\) indicators. Multiple self-attention heads are used to model multiple moving average indicators of different scales. In part (**b**), the bilinear fusion method is used to fuse information between the dimensions of any two indicators.  
\(W_{o}\), and \(W_{c}\) are the parameters to be learned, \(\odot\) represents the Hadamard product, and \(\sigma(\cdot)\) and \(\tanh(\cdot)\) are the sigmoid and \(\tanh\) activation functions, respectively.
# Kernel-Density-Based Particle Defect Management
## 3 The Proposed Method
### New Monitoring Statistics Using the Proposed Map  
As mentioned in the previous section, the conventional method uses \(PD\) for monitoring. This chapter describes new monitoring statistics that can replace the conventional statistics. The kernel-density-based particle map shows the probabilities at all the points on the wafer, as shown in Figure 8b. While the original particle map shows only the location of the particles,  
Figure 8: The conventional and new particle maps: (**a**) particle map; (**b**) kernel-density-based particle map.  
the kernel-density-based particle map includes the density of the particles at a location. In the case of a kernel-density-based particle defect, the expected value \(E\) of the particle defect is calculated as  
\[E=\frac{\hat{f}_{H}(x,y)}{\sum_{i=1}^{n}\hat{f}_{H}(x_{i},y_{i})}\times n \tag{6}\]  
where \(n\) is the total count of the particles, \(\hat{f}_{H}(x,y)\) denotes the density of the particle map, and \(\frac{\hat{f}_{H}(x)}{\sum_{i=1}^{n}\hat{f}_{H}(x_{i})}\) is the probability based on the density. The expected value indicates a new particle defect at the location of the particle. Therefore, according to the new monitoring statistics, the kernel-density-based particle defect (\(KDPD\)) is calculated as  
\[KDPD=PD+\sum_{i}E_{x_{i},y_{i}} \tag{7}\]  
where \(x_{i}\) and \(y_{i}\) are \(i\)-th particle coordinates and \(E_{x_{i},y_{i}}\) indicates the expected value of each particle at the designated coordinates.  
The sum of \(E_{x_{i},y_{i}}\) indicates the number of particles considering the densities of the wafer and defines the new monitoring statistics. Thus, the monitoring chart utilizes \(KDPD\) to detect potential particle defects. Figure 9 depicts the comparisons between the results of certain \(PD\) and \(KDPD\). While Wafers A, B, and C exhibit identical \(PDs\), \(KDPD\) assumes different values for each wafer. As \(KDPD\) is derived using the particle distributions on the map in Figure 9, it indicates a density-based \(PD\). The dense particles, shown at the bottom of the Wafer C map, influence monitoring performance. Thus, \(KDPD\) dynamically changes in accordance with the particle distribution.
# SNR-Based Receiver-Type Decision Using Deep Learning for Multiple-Input Multiple-Output Detection
## 2 System Model
Similar to the ZF receiver, for the given MIMO channel \(\mathbf{H}\), we can express the symbol error probability of the ML receiver as follows [20]:  
\[\hat{P}_{S}^{ZF}(\rho,\mathbf{H})=\frac{1}{N_{T}}\sum_{p=1}^{N_{T}}\sum_{s\in \mathcal{S}}\Pr\!\left[x_{p}=s\right]\Pr\!\left[\hat{x}_{p}^{ML}\
eq x_{p}|x_ {p}=s,\mathbf{H}\right]\!. \tag{6}\]  
Considering (5), \(M^{N_{T}}\) searches through all candidates in \(\mathcal{S}_{ML}\) are necessary, and this results in exponentially increasing computational complexity with \(N_{T}\). Instead, the ML receiver can achieve an \(N_{R}\) diversity order, potentially resulting in better error performance compared to the ZF receiver.
## 3 Results and Discussion
### Health Risk Assessment of PAHs and Heavy Metals in Settled House Dust  
#### 3.5.1 Cancer Risk Assessment of PAHs in Settled House Dust  
A risk assessment using the concentration data of carcinogenic PAHs was conducted in order to assess the potentially detrimental effects associated with exposure to B2 PAHs. For the moderate exposure scenario (0.05 g day\({}^{-1}\)) the results indicated that the risk of carcinogenesis was 1.2 \(\times\) 10\({}^{-7}\), while for a residence, in which the highest concentrations of B2 PAHs were observed, the risk was 5.4 \(\times\) 10\({}^{-7}\). These values were found to be one order of magnitude smaller than those of [PERSON] et al. [18] (1.6 \(\times\) 10\({}^{-6}\)) and [PERSON] et al. [40] (7.8 \(\times\) 10\({}^{-6}\)). The results of the present study were comparable but slightly higher than those of [PERSON] et al. [7]. For the high exposure scenario (0.1 g day-1), the risk of carcinogenesis was estimated as 2.5 \(\times\) 10\({}^{-7}\) and compared with the values reported in [PERSON] et al. [18] (3.2 \(\times\) 10\({}^{-6}\)) and [PERSON] et al. [40] (1.6 \(\times\) 10\({}^{-5}\)) was one and two orders of magnitude lower, respectively. However, in the case of the residence, where the highest concentrations of carcinogenic PAHs were observed, the risk was 1.1 \(\times\) 10\({}^{-6}\), and it was comparable to the one of [PERSON] et al. [18] and an order of magnitude lower than that of [PERSON] et al. [40]. Although a strict threshold for risk of carcinogenicity that corresponds to safe exposure has not been established yet, one cancer case per million people (1 \(\times\) 10\({}^{-6}\)) is commonly used as the baseline level of acceptable risk. The values that were obtained by the present study are lower than the above limit for both exposure scenarios. As for the house with the highest levels of exposure, the risk of carcinogenesis for the high exposure scenario marginally exceeded the acceptable risk.
## 4 Exploring the TES Performances Improvement by a Numerical Parametric Study
### System Sizing Sensitivity Analysis  
The size of the nodules also affects the storage performance, but to a lesser extent (Figure 13). Indeed, the reduction in the diameter of the nodules from 98 mm to 77 mm (the two sizes of commercial nodules from the industrial partner's technology) made it possible to decrease the storage time by 27%. This reduction was induced by the increase by 28% in the exchange surface between the HTF and the nodules (from 3.65 m\({}^{2}\) to 4.68 m\({}^{2}\)), reducing the convective thermal resistance and consequently increasing by 26% the heat transfer between these two entities (from 228 W/m\({}^{2}\)-K to 287 W/m\({}^{2}\)\(\cdot\)K). However, the envelop material ratio also increased, reducing the system energy density. Thus, while the charging time was reduced to \(\sim\)1 h 30 with 2.5 cm-diameter nodules, the system energy density fell by 33%. This loss could be limited with thinner nodule walls (e.g., of 1 mm instead of 2 mm), but they should remain thick enough to guarantee their mechanical integrity.  
Besides this, the quantity of valuable energy also rises when the nodules diameter is reduced, until reaching what seems to be a _plateau_. This attenuation in the rising could be even more pronounced if a full energy balance is considered, including the pumps' consumption, which would be increased by the greater pressure drop due to the smaller nodule diameters.  
Finally, the influence of the dimensions of the tank was also analyzed, emphasizing the conservation of the performance of the storage with different lengths of tank and identical volumes.
## 4 _P. gingivalis_ and Systemic Diseases
### Other Diseases  
The oral pathogen _P. gingivalis_ is associated not only with periodontitis but also with diseases in other parts of the body. The mechanisms by which it travels from the oral cavity to other organs of the body have been identified in four mechanisms (Figure 4 from [77]). First, proteolytic enzymes secreted by _P. gingivalis_ degrade adhesion molecules between tissue cells and the extracellular matrix. This weakens the structural integrity of the mucosa and allows _P. gingivalis_ to penetrate the tissue. The second is transcytosis: bacteria actively enter tissue cells and transfer to the next layer or the extracellular space. Traveling from one cell to another, _P. gingivalis_ reaches deeper structures. Third, professional phagocytes absorb _P. gingivalis_ and travel to the bloodstream, where _P. gingivalis_ is released. Fourth, _P. gingivalis_ can adhere to hyphae, forming _Candida albicans_. These hyphae can penetrate mucosal tissue, allowing _P. gingivalis_ to reach deeper structures.  
The following is a schematic overview of the four putative mechanisms for _P. gingivalis_ to translocate the oral mucosa or the endothelium of blood vessels: (1) Proteolytic enzymes known as gingipains (Rgp/Kgp) are secreted by _P. gingivalis_ (green) and degrade cell-cell adherence molecules (yellow circles) and the extracellular matrix (ECM; light blue). The structural integrity of the oral mucosa is weakened so that _P. gingivalis_ is able to pass in  
Figure 4: Mechanisms by which _P. gingivalis_ travels from the oral cavity to other organs of the body. ECM (extracellular matrix), Rgp (arginine-specific gingipain), Kgp (lysine-specific gingipain). From [PERSON] et al., 2023 [77], under the terms of the Creative Commons Attribution License.  
between the cells. (2) _P. gingivalis_ can enter the cell after adherence and subsequent endocytosis and exit the cell on the other side of the epithelial layer. _P. gingivalis_ needs to have a mechanism to be able to survive within the cell and not be degraded by phagolysosomes. It can then travel from cell to cell, migrating deeper into the tissue, eventually reaching the basal membrane and finally the endothelial cells that line the blood vessels. (3) Phagocytic cells of the host are able to pick up _P. gingivalis_ within the tissue and transfer it across the endothelial barrier. They can then travel back into the bloodstream, taking the bacterium with them. Again, _P. gingivalis_ needs to have a mechanism to survive degradation by the phagocyte. (4) Interactions with other microbes such as C. _albicans_ (blue) could allow for translocation across the oral mucosa due to the hyphae of C. _albicans_ that can insert themselves between cells. _P. gingivalis_ can attach to these hyphae. Macrophages may also play a role in this mechanism as they are attracted to the hyphae of C. _albicans_ and can phagocytose the attached _P. gingivalis_.
# Experimental Evaluation of the Packet Reception Performance of LoRa
## 5 Results and Analysis  
### The Impact of SF on the Packet Reception Performance of LoRa  
In this experiment, we studied the LoRa packet reception performance with different SF values. We used the software radio platform to effectively generate LoRa signals with noise and accurately control each parameter. We let each data packet have a fixed sending cycle. All data packets were generated by random sequences. First, we kept the SF, BW, CR, and packet length unchanged, and gradually increased the noise intensity value, until the PER was equal to 1, and all the results were obtained. The information included SNR and PER. Then, the SF value was increased by one, the BW and packet length remained unchanged, and the noise intensity value was gradually increased until the PER was equal to 1. In this way, the experiment was conducted with all SF values.  
Figure 6 shows the SNR and PER for different SF values, as well as the distribution of the experimental results, rather than just the median and mean values. One can easily notice a certain regularity in PER behavior as a function of SNR, which allows us to make two conclusions:  
Figure 5: USRP B200 software radios were used to generate signals, which were superimposed by the LoRa signal and noise signal. SX1276 and RN2483 were used to receive signals.  
1. As the SNR decreases (the noise intensity becomes larger), the PER value increases, that is, the noise signal covers the LoRa signal. The LoRa signal is distorted and demodulation errors occur. In addition, as the SF value increases, the PER value becomes smaller. Therefore, a higher SF has better noise immunity. For example, for SNR = \(-12\) dB, when SF = \(7\), PER = \(1\), but when SF = \(12\), PER = \(0.034\).
2. Each time SF increases by one, SNR in the high PER area (PER \(\approx 1\)) decreases by \(2\) or \(3\) dB. For instance, from SF = \(9\) to SF = \(10\) for BW = \(125\) kHz, the high PER region's SNR changes from \(-16\) to \(-18\) dB.  
In addition, we also studied the influence of SF on the packet reception performance of LoRa when the BW is different. It can be seen from Figure 6 that when the BW is different, SF still has a great influence on the packet reception performance of LoRa.  
Figure 6: The measurement results of the impact of different SFs on the packet reception performance of LoRa: (a) \(\text{BW}=125\) kHz, measurement results of different SFs; (b) \(\text{BW}=250\) kHz, measurement results of different SFs; (c) \(\text{BW}=500\) kHz, measurement results of different SFs.
## 3 The Multilevel Approach
### Third Level  
Topology optimization assumes a fundamental role in the definition of the compliant structural configuration that can supply the desired deformation. Genetic algorithms represent a global method that can explore a large portion of the space of solutions for defining a suitable topology. However, it must be followed by a local optimization to determine a more accurate sizing of the mechanism and to refine the optimal solution selected on the Pareto front. The third level consists of a sizing and geometric optimization based on a constrained nonlinear optimization. A Sequential Quadratic Programming (SQP) approach is chosen at this stage.  
The scalar objective function is defined by choosing it among all of the objective functions considered in the second level. All of the other objective functions become constraint functions because they must at least preserve the same value that they assume at the end of the second level. Stress constraint functions are added to account for the mechanical limitations of the material that the compliant structure is made of. To correctly evaluate the stress, the topology solution is specialized while considering the actual material and manufacturing details [37]. Geometry and sizing constraint functions can be added to account for the manufacturing requirements, as described in Section 3.2. The objective and constraint functions are computed under the loads computed during the first level. Optimization variables are the same as in the previous level except for the topological variables that are not present.  
Objective and constraint functions are evaluated as a result of the finite element analyses performed by Abaqus. A medium/high-fidelity model obtained by a constant-section extrusion is used at this level, and a more accurate finite element model of the skin, also in terms of material behavior, is introduced and allows the modeling of the stiffeners. The length of the skin in the span-wise direction is equal to the actual rib pitch, and a model is made of a single rib in the middle or a pair of ribs at the ends of this portion. The goal is to not exceed the elastic properties of the actual material while considering a model with higher fidelity than the model used at the previous level. The generation of the model is automatic and exploits the Abaqus Scripting Interface (API), which is an extension of the Python object-oriented programming language [38]. Since large deformations are expected, static nonlinear analyses are used.  
This level can be repeated for different sections along the wing span, or to change the Pareto-optimal solution if considering actual materials or manufacturing details or if a more accurate model makes the results too much worse. From this point of view, the choice of the Pareto-optimal solution and the third level can represent an iterative design loop.
# Speech Emotion Recognition through Hybrid Features and Convolutional Neural Network
## 2 Related Work
### Feature Extraction
[23] presented a technique to combine handcrafted and automatic features. The method was evaluated on well-known datasets, including IEMOCAP, EMO-DB, and RADVES. Initially, acoustic features and spectrograms were derived from audio files. Then, data enhancement was used to create additional training data. Next, deep features were derived from spectrogram images using pre-trained SqueezeNet, VGG16, DenseNet201, ResNet18, ResNet50, and ResNet101 CNNs. Finally, acoustic, and deep features were combined to get hybrid features, and linear SVM was used to identify emotions. The authors achieved 85.4% accuracy for IEMOCAP, 90.1% for EMO-DB, and 79.4% for RADVES dataset.
## 2 Related Works
### Global Positioning System (GPS)  
The Global Positioning System (GPS) is a system which is able to give the exact location of an object on the Earth at anytime, in any weather, and in any location. It is a satellite-based, radio navigation system. The satellite used by the GPS system is continuously monitored by ground stations located worldwide. The satellites transmit signals that can be detected by anyone with a GPS receiver. Using the receiver, one can determine the location of an object with great precision [26-29].  
GPS has three parts: a space segment, a user segment, and a control segment. The space segment consists of 24 satellites, each in its own orbit 11,000 nautical miles above the Earth. The user segment consists of receivers which can be hand-held or mounted in a car. The control segment consists of ground stations (five ground stations located around the world) that make sure the satellites are  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bits of Security** & **Symmetric Algorithm** & **RSA** & **ECC** \\ \hline
80 & 2 TDEA & k = 1,024 & f = 160–223 \\
112 & 3 TDEA & k = 2,048 & f = 224–255 \\
128 & AES-128 & k = 3,072 & f = 256–383 \\
192 & AES-192 & k = 7,680 & f = 384–511 \\
256 & AES-256 & k = 15,360 & f = 512+ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Equivalent key sizes for symmetric and asymmetric cryptography.  
working properly. GPS receivers typically work well outdoors, with positioning accuracy within a 15 meter range.
## 3 Applying Predictive Models to an eLearning Messaging System
### The Development Phase
#### 3.1.3 The Prediction Formula or Algorithm  
The prediction model produces a method for calculating predictions for each individual. Some prediction models use algebraic formulas for this purpose. Multiple linear regression, for example, links the input variables to the outcome variable via a linear equation such as the one shown in Figure 1: \(\hat{\Upsilon}=614.8+22.8\mathrm{X}_{2}-0.09\mathrm{X}_{15}\).  
In other cases, the method for producing predictions is algorithmic. Consider the hypothetical case of an online course that prepares students for the CPA exam. Figure 2 shows a simplistic decision tree designed to predict the probability that a student will pass the CPA exam using two input variables: work experience and the final grade in the CPA preparatory course. The prediction algorithm in this case is:  
* If [Experience \(\leq\) 5] and [Grade \(\leq\) 85] then [Probability of Passing = 40.6%],
* If [Experience \(\leq\) 5] and [Grade \(>\) 85] then [Probability of Passing = 51.1%],  
and so forth.  
The prediction formula/algorithm is the output from the development phase of the project that then becomes the input for the next phase: the implementation stage.  
Figure 2: Example of a decision tree.
# Collision Risk Assessment and Operation Assistant Strategy for Teleoperation System
## 3 The Establishment of the Virtual Environment
### The Graphical Modeling of the Robot  
In the system designed in this paper, a 6-DOF lightweight robot (UR5, Universal Robot Inc.) was employed as the teleoperation robot. The master position, acquired from the localizer in a Cartesian coordinate system, was transmitted to control the virtual robot. The position of the localizer needed to be converted to a set of robot joint angles to allow an accurate description of the desired robot posture. In this paper, the D-H method based on the shortest path principle was adopted to solve the inverse-kinematics problem. Based on the calculated joints of the robot and geometric parameter, a visualized interface of the robot arm was realized as shown in Figure 3. OpenSceneGraph 3.4.0 (OSG), an object-oriented, cross-platform 3D graphics toolkit that offers the features and capability of OpenGL, was used to build the graphical modeling of the robot [27].
## 2 MT Forward Modeling and Parameter Setting
### Forward Modeling Parameters Setting  
To assess the ability of various survey parameters to resolve fault zones at different resolutions in MT detection, we devised the following surveys for two model grid scenarios:  
(1) The survey length is 14 km, with a frequency band ranging from 0.001 to 1000 Hz, and station spacings of 200 m, 400 m, 600 m, and 800 m, respectively.  
(2) The station spacing is 400 m, with a frequency band ranging from 0.001 to 1000 Hz, and survey lengths of 8 km, 10 km, 12 km, and 14 km, respectively.  
(3) With a station spacing of 400 m and a survey length of 14 km, the frequency band is divided into two ranges: 0.001 to 1000 Hz for long-period measurements with broadband data, and 0.01 to 100 Hz for relatively narrow-band measurements.  
For all survey designs in the two scenarios, forward modeling and inversion parameters remained consistent, except for those comparing survey systems (refer to Table 1), to eliminate discrepancies in accuracy caused by inconsistent parameter settings. In Scenario 1, the horizontal mesh is set to 100 m, while in Scenario 2, it is set to 200 m. The vertical mesh starts at 100 m at the surface and increases logarithmically to a depth of 15 km. The air layer extends to 10,000 m, and a minimum of 100 iterations were performed.