esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of Terahertz Link Modulation in Atmospheric Weather Conditions
## 2 Channel Model
### Atmospheric Model
#### 2.1.3 Wind  
Tall towers for communication base stations have been increasing with the requirement for reliable data transmission and wide coverage. They are generally sensitive to wind loads. Wind excitation induces fluctuating stresses on the tower leading to antenna tilting. This results in a beam deviation between transmitter and receiver, and then problems such as beam jitter will occur [21]. Therefore, wind-induced attenuation is also introduced into the loss assessment as one of the atmosphere-induced factors. The wind speed in the natural environment is generally between 1.6 m/s and 13.6 m/s. By calculating the attenuation caused by the wind-induced inclination angle, the path loss in the wind environment can be obtained by equation \(g(\theta)=20 log[u^{\prime}/(2)_{1}(u^{\prime}))\). Parameter \(u^{\prime}\) is a variable related to signal wavelength, antenna diameter, and tilt angle [21]. For terahertz antennas, their diameters are set to be a common value of 0.16 m. Parameter \(J_{1}\) refers to solving the first kind of Bessel function for the variable. As shown in Table 1, the simulated link distance is set to be 1 km, as with other weather conditions. In the introduction of wind-induced attenuation, wind speed is set to be 15 m/s in this model as strong wind. This wind speed will be considered as a constant to obtain static (averaged) performance.
## 6 Conclusions and Future Lines of Research  
This paper introduced an optimization approach for a WSN-based hierarchical network with the focus on topology control as a function of the transmission range and power. A new approach to cluster head selection (in both classical and quantum computing) attributed to transmission power will help to establish an energy-efficient network. The factors (the node degree, average distance between nodes, and energy consumption) which are essential for CH selection and proposed for the CWCA algorithm are scalable in terms of weight. MATLAB simulations of the classical algorithm show that around 86% and 97% of the energy efficiency is achievable in the whole network for intra- and inter-cluster communications, respectively. On the other hand, by reducing the time complexity and with a faster search for CH selection, an improved weighted target-based quantum search algorithm was proposed. By implementing the QWSA algorithm, the cluster head can be selected within two iteration steps, which is a favorable agreement between the mathematic approach and the QISKIT simulation result.  
A probable limitation of the CWCA technique is that it requires a computationally expensive system when taking into account a large number of cluster heads. It will also be necessary to evaluate the end-to-end reliability and the delay in the sensor data. In addition, it is essential to ensure security in the cluster head selection process such as  
Figure 7: (**a**) QasmSimulator results when the number of shots = 1024, and (**b**) when the number of shots = 8192.  
identify selfish and duplicate nodes. Future research directions will focus on implementing the proposed QWSA algorithm in significant networking systems. Now, the performance is bounded because of the limited number of qubits. For instances, if we increase the number of qubits by two times, we can easily implement this algorithm for about 256 (\(2^{8}\)) nodes, and increasing it by three times the number of qubits in the simulation can cover up to 4096 (\(2^{12}\)) nodes in the whole network.  
We also demonstrated that qubit selection can have a significant impact on how the quantum algorithms are implemented, and researchers will need to pay attention to this when they are trying to accurately implement algorithms in the future. IBM and other experts in quantum computing are continuously enhancing and introducing new quantum devices. In the future, it will be essential to follow the development of these new and advanced devices to gain a sense of how rapidly quantum computing technology is progressing.  
[PERSON] obtained the analytical results and wrote the paper; [PERSON]-[PERSON] supervised creation of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the 2022 Research Fund of the University of Ulsan.  
Not applicable.  
The authors declare they have no conflict of interest related to the publication of this paper.
## 3 Fundamentals: Workpiece and Moving Head Kinematics
### Axis and Control of the Moving Head  
The moving head has two principal rotational movement axes called _pan_ and _tilt_, which control the position of the light beam. Furthermore, it has _zoom_ and _focus_ parameters that control the size and sharpness of the light spot, as shown in Figure 2. These parameters are comparable to the intrinsic camera parameters within camera calibration, which will be further explained in the calibration section, Section 3. For effects, moving heads are typically equipped with a color filter wheel, one or more so called \"Gobo wheels\" with custom shapes, and a prism.
###### Abstract
The topology optimization method adopted in this paper, as a structure optimization design concept, has been rapidly developed in weight optimization design in recent years [17; 18; 19]. Topology optimization is used to determine the relative placement of the star sensor and the laser communication load while taking other engineering considerations into account. The reference bracket adopts an I-section design based on the structural topological relationship found through topological optimization, and size optimization establishes the critical dimensions. The thermal deformation between the optimized laser communication load and the star sensor installation angle decreased to 14.25\" according to the entire satellite simulation analysis of the modified structure. By comparing the optimized design scheme with the unoptimized design scheme, the maximum angle variation induced by temperature change fell from 117.73\" to 10.73\" during the ground temperature deviation test and prism calibration test. The on-orbit alignment test confirms that the required capture time of 30 s following the entire test is met. The in-orbit acquisition time test results show that the satellite's uncertain region satisfies design requirements and that the thermal deformation stability between the satellite's laser communication load and star sensor satisfies requirements.
# An Earthquake Forecast Model Based on Multi-Station PCA Algorithm
## 4 PCA Extraction for Single Station  
### Introduction  
The data are complex and full of noise and redundant information, which is the reason why a feature dimension reduction process is important. In our work, the effective information that is related to an earthquake is not obvious, making the noise reduction process more important. Dimension reduction could reduce noise in data. On the other hand, the complexity of common algorithms increases as \(KlogK,K^{2},K^{3}\), or \(a^{K}\) when the dimension \(K\) increases. Therefore, reducing the dimension of the input data can greatly reduce the calculation cost.  
PCA is an algorithm commonly used in dimension reduction and feature extraction. It could reduce the duplicate data, and it shows the main components of original input data.
## 3 Methodology
### Algorithm Flow  
The algorithm presented in this study takes raw user ratings as input and generates predicted ratings as output. The detailed algorithmic steps are described as follows:  
Input: Target user \(i\), user rating matrix \(M\).  
Output: Predicted ratings for target user \(i\).  
Step 1: Incorporate the rating variable \(x\) from matrix \(M\) into the hidden layer processing of Equation (7), introducing random noise for constraint. This yields transformed data \(y\).  
Step 2: Introduce the category of items, denoted as \(c\), as auxiliary information in CVAE. This supervises the encoding and decoding of item data. Input the transformed data \(y\) into the improved encoding part of CVAE for training. Obtain the probability distribution of latent space z and express its Kullback-Leibler (KL) divergence with the posterior probability using Equation (8). To maximize the output sample probability and minimize KL divergence, the modified CVAE training loss function is derived, as shown in Equation (9).  
Step 3: Decode through the decoding part of CVAE to reconstruct missing values of rating data, ultimately obtaining the reconstructed rating matrix \(M_{2}\).  
Step 4: Initialize the user compensation matrix \(Y\), constraint matrix \(W\), and project latent space feature matrix \(V\) based on the reconstructed matrix \(M_{2}\).  
Step 5: Assuming that the observed matrix \(M_{2}\) and the approximated rating matrix \(M_{2}\)' follow Gaussian distributions with mean 0, the conditional distribution of the rating matrix is expressed in Equation (11).  
Step 6: Based on the objective function of Probabilistic Matrix Factorization [26], the loss function of CPMF is derived, as presented in Equation (15).  
Step 7: Employ stochastic gradient optimization algorithm for training. First, compute the negative gradient of the loss function according to Equations (16)-(18). Then, update the variables based on the gradient change, as depicted in Equations (19)-(21).  
Step 8: Halt training when the maximum number of iterations is reached or the loss function value stabilizes. Ultimately, derive user latent space feature matrix \(U\) and item latent space feature matrix \(V\). Output the final user-item rating matrix \(M_{3}\) using the formulation \(M=U^{T}V\).
# Emotion Detection Using Facial Expression Involving Occlusions and Tilt
###### Abstract
The Viola-Jones algorithm is presumed to be comprised of four stages which can be stated as follows:  
* Haar-like features;
* Integral image;
* AdaBoost algorithm;
* Cascade of classifiers. Or just use a pre-trained cascade to detect an object or facial images within an image. However, with the advancements in technology, it is thereby recommended that the scope of human-computer interaction is widened, and challenges such as occlusions, illumination variations, and changes in physical appearance should be taken into account before considering more novel and practical solutions for detecting emotions with good accuracy. Therefore, this paper proposes a new architecture of convolutional neural networks (CNN) for facial emotion recognition systems. In the proposed framework, face detection utilizes the Viola-Jones cascade followed by face-cropping and image re-sizing. The proposed model is based on five convolution layers, one fully connected layer, and a SoftMax layer. Furthermore, feature map enhancement is employed to accomplish higher precision and the detection of more emotions. Several experiments are performed to detect anger, disgust, fear, happy, neutral, sad, and surprise. Performance is compared with two test models selected for experiments. The rest of the paper is organized as follows. We present the related work in Section 2, where recent trends in composition studies over the past research papers are compared on the basis of attributes such as face detection, preprocessing, feature extraction, classification, database, and number of emotions, and the accuracy and motivation of our research are established. The proposed framework is discussed in detail in Section 3. The implementation of the proposed framework and results are presented in Section 4. Finally, we present the conclusion and future directions of this research work in Section 5.
## 2 Materials and Methods
### Measurements
The optical cables measuring upwelling radiance were pointed at nadir, and were positioned at 1.5 m above the canopy throughout the measurement period, viewing a 25\({}^{\circ}\) field of view (2.6 m diameter at ground level). The FLoX is designed for high temporal frequency (at approximately 1 min time interval) acquisition of continuous top of canopy (TOC) optical properties with a focus on measurements of SIF, using an Ocean Optics FLAME S, which covers the full range of visible and near-infrared wavelengths (VNIR, 400-900 nm) and an Ocean Optics QEPro, with high spectral resolution (full width at half maximum, FWHM of 0.3 nm) in the range of the fluorescence emissions (i.e., 650-800 nm). Each spectrometer's optical input was split between two fiber optics, one leading to a cosine receptor measuring the incoming solar irradiance, and a bare fiber measuring the vegetation reflected and emitted radiances. Both spectrometers were housed in a Peltier thermally regulated box, keeping the internal temperature lower than 25 \({}^{\circ}\)C in order to reduce dark current drift. The thermoelectric cooler of the QEPro was set to 20 \({}^{\circ}\)C in order to provide stable measurements. The signal was automatically optimized for each channel at the beginning of each measurement cycle, and two dark current spectra were collected. The key settings, such as the interval between measurement cycle and the maximum integration time, were saved in configuration files. The data and metadata (e.g., spectrometer temperature, detector temperature and humidity, GPS position and time) were also stored. The system was designed for long-term fully automatic field measurements, requiring only minimal user input.
## II.1 2.1. A Compact Laser Plasma Accelerator  
The Ti:Sa system operating at ILIL currently generates pulses as short as 40 fs with a peak power exceeding 2 TW at the repetition rate of 10 Hz. Here we focus on some key results obtained earlier with a 67 fs duration FWHM and a second, low energy probe pulse. The temporal and spatial properties of the femtosecond pulses were characterised in detail using custom developed second-order autocorrelator [22]. The contrast of the laser pulse, _i.e._, the ratio between the peak power and the low intensity pedestal originating from pre-pulses and amplified spontaneous emission (ASE), was measured with a third-order cross-correlator (SEQUOIA). In this system, the full power pulse, attenuated by reflections off high quality uncoated glass flats (\(\sim 10^{2}\) attenuation), is split in two equally intense pulses using a beam splitter. One of the pulses is sent through an optical delay line and is frequency doubled in a BBO crystal. The frequency doubled pulse is combined with the remaining fundamental pulse in a non-linear crystal to generate third harmonic radiation. The signal is detected in the forward direction by a photomultiplier.  
The autocorrelation curve obtained from the second-order autocorrelator yielded a laser pulse duration (FWHM) of 67 fs. The cross-correlation curve over a range of 200 ps gave a contrast of \(10^{9}\) over the entire explored range. Moreover, the intensity of the short pre-pulses before the main pulse is below \(10^{-5}\) times the intensity of the main pulse. According to these measurements, our laser system can be considered basically free from pre-pulse. This was further verified by interferometry measurements taken at different time-steps around the main pulse, showing no detectable plasma formation up to a few tens of femtoseconds before the main pulse. The spatial quality of the laser pulse was studied by means of an equivalent plane monitor (EPM) using a 100 cm nominal focal length optics and a 12 bits CCD camera (a Photometrics Sensys) with a pixel size of 8 \(\mu\)m size. These EPM measurements show that with the 20 cm focal length off axis parabolic mirror used in our experiment, a FWHM focal spot of approximately 10 \(\mu\)m is found. Transmission measurements through pin-holes of known diameters were carried out at low laser intensity (below plasma formation on the pin-hole substrate) to estimate the Strehl ratio, which was found to be as high as 0.9. Considering the pulse length of 67 fs and an energy of 120 mJ, we find that the maximum intensity on the target can exceed \(10^{18}\) W/cm\({}^{2}\).
## 3 Quantification of Uncertain Information
### Motion Intention-Switching by Identifying the Actual Intention of the User
#### 3.3.4 Estimation of Alternative Perceptive Distance (\(D_{a}\))  
The estimation of alternative perceptive distance, \(D_{a}\) is illustrated in Figure 5 considering the two possible cases where \(D_{gesture}>\delta_{max}D_{r}\) and \(D_{gesture}<\delta_{min}D_{r}\). A field angle of \(\alpha\) in the intended moving direction is considered for estimating the \(D_{a}\). The field angle, \(\alpha\), is considered as \(30^{\circ}\) since according to [20] the objects in that region have a higher impact for the human mobility. In case (a), \(D_{a}\) should be a value greater than \(D_{r}\) since \(D_{gesture}>\delta_{max}D_{r}\). Therefore, \(D_{a,max}\) exists and in order to estimate that, a vector parallel to the direction of the intended moving direction (i.e., the vector parallel to \(D_{r}\)) is extended until it reaches another obstruction for the movement inside the considered field. The magnitude of this vector is considered as \(D_{a,max}\) in such cases (i.e., cases where \(D_{a,max}\) is required as a result of \(D_{gesture}>\delta_{max}D_{r}\)). In case (b), \(D_{a}\) should be a value less than \(D_{r}\) since \(D_{gesture}<\delta_{min}D_{r}\). Therefore, \(D_{a,min}\) is required. The distance along a path parallel to the default intended moving path (i.e., parallel to \(D_{r}\)) to an obstacle within in the considered field from the robot is taken as the \(D_{a,min}\) in such cases. If \(\delta_{min}D_{r}\leq D_{gesture}\leq\delta_{max}D_{r}\) or a valid gesture is not detected (i.e., \(D_{gesture}=null\)), the default perceptive distance (\(D_{r}\)) is considered as the perceptive distance (\(D\)) and hence the intention of the robot will not be switched from the default to an alternative intention in such cases.
## 3 Identification of Deformation Modes
### Cross-Section Deformation Modes
#### 3.2.3 Numbering of Identified Deformation Modes  
In the previous section, PDM were assembled to provide cross-section deformation modes, which have clear structural interpretation since they stem from the eigenvectors of the governing equations. Moreover, the order of eigenvectors and the participation of PDM reflect the priority of the identified deformation modes, meaning it is possible to organize the deformation modes in hierarchy. Therefore, it is conducive to a reduced higher order beam model.  
To this end, it is essential to develop a numbering system that orders the identified deformation modes taking into account of their priority. Generally, the numbering system demands a form filled with all identified deformation modes in the following way: The ones originating from the same eigenvector occupy the same column and the sequence of each column is consistent with the order of the corresponding eigenvector. Within each column, the Classic family is placed on the first row, while the Primary and Secondary families follow them. The numbering is carried out row by row, avoiding double counting of the same deformation mode. Figure 7 shows the numbering system for the illustrative thin-walled structure, where seven out-of-plane modes (modes i to vii) and eleven in-plane modes (modes I to XI) are ordered.  
Figure 6: The decomposition result of the second case of decomposing an amplitude function into components (DAFC 2) concerning the third eigen matrix of the illustrative thin-walled structure.  
The proposed approach, although involved with cross-section discretization and shape function interpolation, is different from both the theory put forward in [36] to obtain final deformation modes through GBT cross-section analysis and the model developed in [38] to derive a set of deformation modes representing higher order effects applying the uncoupling procedure. In fact, the authors worked on avoiding both the complex subdivision of deformation modes and the solution of nonlinear eigenvalue problem in identifying higher order modes. Instead, a procedure was developed by uncoupling PDM in modal analysis first and then assembling them for cross-section deformation modes considering their participations in eigenvectors. The process lowers the requirements on users, and the deformation modes naturally possess the features of physical interpretation and hierarchy, which is conducive to develop a reduced model.
## 4 NIST Guidelines for the Secure Deployment of IPv6
### Content Completeness and Depth of the NIST Guide  
For the evaluation of content completeness and depth, the NIST guide was checked for coverage of the RFCs listed in Appendix A. The main categories remain the same as in the survey. Additionally, the weights derived by AHP using the results from the survey from Section 3.3.1. of the Appendix were applied to derive the weighted completeness that for each main category multiplies its result by its weight and then adds them all up. For weighted completeness only the end result of the guide counts while (basic) completeness is shown for every subcategory, category and the whole guide. Depth is calculated as explained in Section 3.1. This depth score is aggregated for every subcategory, category, and for the overall guide. The following list shows the legend for Table 6 and explains the columns: A = Count of covered RFCs, B = Count of relevant RFCs, C = Completeness, D = Weight (gray rows)/Importance Rating (white rows), E = Weighted Completeness (A*D), F = Depth. Category rows are shaded gray and show their subcategories below them (white rows). Values for subcategories are aggregated to the main categories. Totals are shown in the last line of the table. Overall:As the totals of the last line of the table show, the NIST guide covers 174 of the 213 RFCs selected for evaluation. This results in a completeness of 0.82 or 82%. This result does not change much when applying the weights. The weighted completeness is 0.81 or 81%, which is only 0.01 less than completeness without weights. Thus, overall the NIST guide covers many of the existing RFCs regarding IPv6 and seems to be closely aligned to the preferences derived by the opinion of the survey participants. The overall depth is only 0.43 which translates to a little above _low_ when translating it into a depth category. Specification and Address Format:The category _Specification and Address format_ has 57 relevant RFCs, of which 50 are covered in the guide by NIST. This leads to a completeness of 0.88 and thus almost 90%. The missing points come from subcategory _Header_ (4), _Mobile IPv6_ (2) and _Address format_ (1). The completeness of this category is very high and shows that the guide at least mentions many of the relevant RFCs. The depth is at 0.52 which translates to between low and medium with a tendency to medium. When looking into the subcategories, this value results from a low depth of the subcategories _Header_ (0.34) and _Mobile IPv6_ (0.49) while the other two subcategories achieved higher depth values. From the point of view of the importance ratings, _Header_ and _Mobile IPv6_ also got lower scores, therefore a less deep coverage must not be automatically considered as negative. In general,