esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Visible lasers have become ubiquitous in our society for numerous applications ranging from scientific research to industry and entertainment. For instance, the 488 nm wavelength can be matched with hundreds of fluorophores specifically tailored to excite at, or close to, 488 nm in life science research applications [1]. Similarly, orange lasers with wavelengths near 600 nm play a key role in biomedical applications [2], and laser projection displays [3]. Nonlinear conversion processes such as second harmonic generation (SHG) and sum frequency generation (SFG) are equally efficient approaches for realizing such visible light sources with periodically poled ferroelectric crystals. On the other hand, quasi-phase-matching (QPM) technique in a nonlinear optical crystal like lithium niobate (LN) provides a fascinating route towards implementation of such laser sources due to its distinct advantages which include access to the largest nonlinear coefficient and high damage threshold [4]. So far, the various approaches to implementing QPM range from uniform structures to Fibonacci optical superlattice [5], aperiodic [6], linearly chirped [7], apodized [8], and multi-segmented [9]. The non-uniform structures are in general, more suitable for broadband wavelength conversion since they provide many spatial vectors compared to uniform structures which are suited to narrowband applications. This development has led to the simultaneous generation of multiple wavelengths in the visible range within the same crystal which has the advantages of high brightness and large depth of focus highly suitable for laser-based color displays. Various techniques to generate red (R), green (G), blue (B), and even yellow (Y) colors have been previously reported. These techniques include utilizing several crystals each for different nonlinear process [10], self-frequency doubling and self-frequency mixing in Nd\({}^{3+}\)-doped bulk aperiodically poled lithium niobate [11; 12], and cascaded nonlinear interactions in aperiodically poled lithium tantalate (LT) [13; 14] or stoichiometric LT [15; 16]. As an alternative to these approaches which make use of complicated domain distribution, it is desirable to continue to look for simple, compact, and efficient solutions utilizing single-pass simultaneous processes in uniformly poled structures.  
Here we report the coincidental single-pass SFG and SHG phase-matching for the simultaneous generation of CW blue and orange light in a single magnesium oxide doped periodically poled lithium niobate (MgO:PPLN) crystal. The orange light is produced by frequency mixing 974 nm laser diode (LD) and a C-band amplified spontaneous emission (ASE) laser source (1525-1565 nm). The blue SHG light is generated by second-order QPM frequency doubling of 974 nm LD. We utilized the broadband nature of ASE for wavelength conversion into the visible range. Our device has the potential as two technologically interesting laser sources in MgO:PPLN in a simple and practical manner without the need for an aperiodic design.
## 4 Proposed Architecture  
As illustrated in Figure 2, the proposed architecture consists of five main modules, each playing a crucial role in the fall-detection system. First, the Gaussian module ensures the protection of individual privacy by applying a video obfuscation technique to the video input. Next, the OpenPose module utilizes the OpenPose network to extract key points from the video. The STFT module serves a dual purpose. It optimizes computational resources and provides insights into the contextual information regarding fall events. This contextual information helps understand the sequence of events and detect any unusual motions or patterns preceding the sudden fall. The extracted frames are then fed into the classification module, which learns and identifies key point patterns associated with falls and non-fall movements. Lastly, the GradCAM module employs convolution layers to generate sequence heat visualization. This visualization aids in understanding the reasoning behind the fall-detection outcomes. It highlights the temporal variation in the key points contributing to the classification decisions. Further details on each module are discussed in the subsequent section.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 4 Challenges in Indoor Localization Systems
### Scalability and Infrastructure Limitations  
Deploying localization systems in large environments, such as hospitals, warehouses, or industrial plants, requires significant infrastructure investment. Wi-Fi-based RTLSs can leverage existing infrastructure, but as the number of tracked assets increases, network congestion and bandwidth limitations can degrade performance [62]. Similarly, BLE and ZigBee systems may need a dense deployment of anchors or beacons to achieve reliable coverage, which can increase installation costs and complexity [22].  
In military applications, deploying infrastructure in unknown or hostile environments is impractical. Systems like distributed UWB or ZigBee networks are used to minimize reliance on pre-installed infrastructure [36]. Additionally, RFID systems in industrial settings face challenges related to the need for frequent recalibration and the placement of readers to cover large, dynamic areas [26].
## 3 Results and Discussion
### The Nature of the Materials and Their Preservation State
The much narrower and strongly diminished band from 3954-3922 cm\({}^{-1}\)/w, together with the very weak peaks from 3696 and 3620 cm\({}^{-1}\), which belong to the different forms of water (hygroscopic, crystallization and constitution), present as neutral waters, weakly basic or slightly acidic (H\({}_{2}\)O, -OH, -O-H\({}^{+}\)) eliminated by prolonged drying over time, have a very low spectrum, being barely perceptible/noticeable. \begin{table}
\begin{tabular}{l l c} \hline \hline \multicolumn{1}{c}{**Specific Group Frequencies (cm\({}^{-1}\))**} \\ \cline{3-4} \multicolumn{1}{c}{**Samples**} & \multicolumn{1}{c}{**Functional Groups**} & \multicolumn{1}{c}{\begin{tabular}{c} **Valence Vibration Region (v)** \\ **4000-1500 cm\({}^{-1}\)** \\ \end{tabular} } & \multicolumn{1}{c}{\begin{tabular}{c} **Deformation Vibration Region (\&)** \\ **1500-580 cm\({}^{-1}\)** \\ \end{tabular} } \\ \hline P2 & \begin{tabular}{l} Dried and aged boiled flax oil \\ Animal glue \\ H\({}_{2}\)O, -OH, -O\({}^{-}\)H\({}^{+}\) \\ Chalk powder \\ \end{tabular} & \begin{tabular}{l} 3470/i, 2964/i, 18274/i, 1942/w, 1747/i \\ 2210/w, 2081/w, 1986/w, 1678/w 1, 1573/i \\ 3978-3945/w, 3764/w, 3620/m, 1678/w \\ 2514/w \\ 369/m \\ 342/i, 2958/i, 1265/w, 1743/i, 1477/i, 1885/i, 185/i, 1797/m, 727/m \\ 2518/m, 2093/i, 1515/m \\ 3944-3915/w \\ 3253/w \\ 3318/m, 3300/m, 205/w \\ 3542/m \\ 3547/i, 2975/i, 2877/i, 2675/i, 2602/i \\ 1468/i, 887/m,797/m, 697/m, \\ 1747/i \\ 2093/i, 1998/m, 1958/m, 1394/i, 1286/i, 1196/i, 1138/i \\ 3954-3922/w \\ 2512/w \\ 352/m, 1098/m, 1054/m, 1000/m, 907/m, \\ 853/i, 655/i, 622/w \\ \end{tabular} \\ \hline P4 & \
\begin{tabular}{l} Dried and aged boiled flax oil \\ Animal glue \\ H\({}_{2}\)O, -OH, -O\({}^{-}\)H\({}^{+}\) \\ Chalk powder \\ \end{tabular} &
\begin{tabular}{l} 3470/i, 2964/i, 18274/i, 1942/w, 1747/i \\ 2210/w, 2018/w, 1986/w, 1678/m, 155/i, 916/i, 1834/m, 723/i, 923/m \\ 3978-3945/w, 3620/m, 1678/w \\ 2514/w, 1688/m, 907/m, 713/w \\ 1468/i, 887/m,797/m, 697/m, \\ 1394/i, 1286/i, 1196/i, 1138/i \\ 3954-3922/w \\ 2512/w \\ 1352/m, 1049/m, 875/i, 713/w \\ 1352/m, 1098/m, 1054/m, 1000/m, 907/m, \\ 853/i, 655/i, 622/w \\ \end{tabular} \\ \hline \hline \end{tabular} Note: **i.** intense; **m.** medium; **w.** weak.  
\end{table} 
Table 2: Specific group vibration values of the bands for the three samples of pictorial material (preparation-P2, red pigment-P3 and blue pigment-P4).
## 5 Case Study
### Data Set and Test Description
NWP variables include windspeed from four different altitudes which are 10, 30, 100 and 170 m. Data of 20 wind farms in Jilin Province are used for training and prediction. So, \(X_{p}^{(0)}\) is a (20 * 40) matrix and \(X_{nup}^{(0)}\) is a (20 * 80) matrix. The hidden state for the wind power GCN module is 60 and for the NWP GCN module is 40. The adjacent matrix in the graph convolution network is calculated by the distance between wind farms. The dimension of variable is labeled in Figure 5. The prediction error is calculated according to the RMSE after normalization and the specific calculation method can refer to the previous description. The iteration epoch of model training is 200 and the training batch size is 256. The optimizer is Adadelta [34] and the learning rate is 0.1. The five-fold cross-validation is used for verification.
## 5 Discussion
### The Coupling of Geometric Information and Spatial Distribution Information  
The fourth advantage of the method proposed in this paper lies in its ability to couple geometric information with spatial distribution information. In the study of spatial information measurement, geometric information and spatial distribution information have traditionally been treated as two separate types of information for individual measurement, and the quantitative relationship between the two has not been effectively demonstrated or analyzed. Treating them in parallel can lead to issues such as inconsistent dimensions and significant fluctuations in information content, as exemplified by the significant differences in the two types of information observed in Experiment 2 using the method of Li and Liu. Additionally, map users or cartographers typically combine geometric and spatial distribution information to analyze map information, as abstracting spatial structures and distributions among objects on a map is a cognitively costly task. Therefore, the cognition of spatial information on maps should be a coupled and comprehensive process. The method proposed in this paper uses geometric information as the primary information source and couples spatial distribution information into the various weight factors of geometric information. This approach enables the measurement of the diversity of spatial distribution information while maintaining consistent dimensions of the eigenvalues, thus achieving a coupling of the two types of information. By adopting a coupled perspective, this method avoids the uncertainty caused by the unclear quantitative relationship between the two types of information.
## 2 Related Work
They formulated the collaborative task offloading problem that minimizes the time-average delay and energy consumption of devices while ensuring data security. [PERSON] et al. [29] discussed the importance of securing communication data and thus proposed a security scheme based on trust and evaluated D2D users for D2D communications data access control in LTE based on Attribute-Based Encryption (ABE) mechanism. In [30], data security, identity privacy and system scalability are explored. The authors proposed a robust and scalable data access control scheme in D2D communications with scalable system attributes and each base station controls the attribute universe individually. The core network server acts as the central authority, and the complicated decryption can be offloaded from an inadequate device to a more capable one. The discussed related works address some of the security issues that are affecting different D2D communications applications providing mutual authentication, session key generation, access controls and data security. However, they do not cover D2D communications from an abstractive point of view, whereby we explore D2D security at the network and service levels of D2D communications. We address entity authentication and access, caching, sharing authorization, data security and user's access right delegation for D2D users in 5G. Most of the related works address authentication or service access control for D2D communications, but not both. In addition, data dissemination, multiple shareholders and supporting technologies in 5G such as Content Delivery Network (CDN) [31], edge services by third party SP and end users' demand for high reliability and high availability have not been considered. Our proposed solution provides robust authentication and authorization mechanisms that can be integrated with the next generation mobile network. Moreover, in our proposed solution, two security protocols are designed to address security in two D2D communications scenarios. We consider the changes in the mobile system and integration with security mechanisms specified in [1] at network and service levels of the 5G and D2D communications systems [2] as well as address security of the UE, transmission and the data.
## 2 Background Theory
### Channel Attention Mechanism  
The channel attention mechanism was introduced to modify the features extracted by convolution; the modified features can keep the valuable features and suppress the non-valuable ones [41]. The main idea of the mechanism is to use some network structures to calculate the attention weights, which are combined with the feature map to build an improved attention feature map. The channel attention module has a dual-channel feature that uses the global average pooling and global maximum pooling, respectively, to obtain different feature information through two different pooling channels. The obtained features then are input into the same MLP, where they are applied to generate channel attention weights using sigmoid, which are finally multiplied by the input feature map to produce enhanced attention. It is crucial to highlight that the CA module does not change the input data's dimensions; hence, both the input data's dimensions and the output data's dimensions remain the same. Figure 3 depicts the whole process of the channel attention mechanism, where \(F_{in}\) denotes the input feature matrix, \(\sigma\) denotes the  
Figure 1: Time series conversion principle.  
Figure 2: CNN structure.  
sigmoid activation function, MLP denotes the multiple linear perceptron, \(CA_{F_{in}}\) denotes the attention weight matrix of the input features, \(F_{out}\) denotes the output feature map after attention enhancement, and \(*\) denotes the matrix multiplication.  
\[CA_{F_{in}}=\sigma(MLP(Avgpool(F_{in})+MLP(Maxpool(F_{in}))) \tag{7}\]  
\[F_{out}=CA_{F_{in}}*F_{in} \tag{8}\]
# A Sensitivity Analysis of a Computer Model-Based Leak Detection System for Oil Pipelines
## 3 Results and Discussion
### Uncertainty in \(R\) Factor
#### 3.1.1 Viscosity Uncertainty  
In the leak detection system, the friction correction coefficient _FPDC_ is applied to the friction factor \(f\) of a pipe segment:  
\[f_{c}=(1-FPDC)\times f \tag{8}\]  
where \(f\) is the friction factor in the model calculated with the input viscosity, and _f_c is the friction factor after Statefinder adjusts it. As a result, the case with +20% error in \(R\) factor (i.e., +20% error in _f_) required an _FPDC_ of 0.1667 while the case with \(-20\%\) error in \(R\) factor (i.e., \(-20\%\) error in _f_) required an _FPDC_ of \(-0.25\) in order to adjust the friction factor to the correct value. Therefore, the case with \(-20\%\) error required more correction than the +20% error case in order to make full correction. The constraint on _FPDC_ is calculated by Statefinder and is a function of the flow rate and _VER_, a-user specified parameter that is greater or equal to zero [15]. _VER_ can be increased so that full correction can be made to the friction factor.  
It was shown previously in Figure 2 that leak detectability was unaffected by viscosity error if Statefinder was allowed to adjust the friction factor to the correct value, i.e., the bound for _FPDC_ was set greater than or equal to the required _FPDC_. To further investigate how viscosity errors are handled, _VER_ was set to 50% which amplified the _FPDC_ bound to \(\pm 0.23\). This allowed full correction for the +20% error but not for the \(-20\%\) error case. It was found that in both cases the error in the viscosity did not impact leak detectability when simulated leak data were perfect. However, when 1% noise was present in the pressure and flow rate, Figure 3 shows that the _DVB_ curve for the +20% error case collapsed onto the baseline, while the curve for the \(-20\%\) error case deviated from the baseline, indicating that leak detectability was affected when the error in viscosity was not fully corrected. In the latter case, the leak was detected 1.1 and 8.5 min sooner compared to 33.1 and 61.7 min for the baseline case during flow decrease and increase transients, respectively. This improved leak detectability caused by the viscosity error may not be desirable since the resulting larger magnitude diagnostic flow was not due to the leak, but rather due to the uncorrected error in viscosity. The same trend was observed for leaks during steady state flow conditions.
## 2 Materials and Methods
### Method  
#### 2.3.1 Continuous Wavelet Transform  
Continuous wavelet transform (CWT) is a linear transformation method that decomposes hyperspectral data into a series of wavelet coefficients with different scales by using wavelet basis functions [11]. The transformation formula is (Equation (1)):  
\[f(a,b)=\int_{-\infty}^{+\infty}f(\lambda)\varphi_{a,b}(\lambda)d\lambda \tag{1}\]  
where \(f(\lambda)\) is the spectral reflectance, \(\lambda\) is the spectral band in the range of 350-2500 nm, \(\varphi_{a,b}\) is the wavelet basis function, a is the scale factor, and b is the translation factor. In this study, a Meyer function with fast convergence speed was selected as the wavelet basis function, and CWT was performed based on MATLAB software (MATLAB 2021, MathWorks Company, Natick, MA, USA). In order to reduce the redundancy of data, the scale of the continuous wavelet transform was decomposed into 21, 22, 23,..., 210, corresponding to scale \(1,2,3,\ldots,10\).
# Strategy for Non-Orthogonal Multiple Access and Performance in 5G and 6G Networks
## 5 MA with Code Separation  
The 3 GPP document also suggests NOMA circuits with processing at the symbol level (after the modulator), which include numerous variations that differ in the way NOMA group signals are produced. These NOMA circuits feature processing at the symbol level [19]. When processed at the symbol level, group signals that are compliant with the 5G requirements can be generated using a number of different methods [20], such as the individual interleaving for each subscriber with the addition of invalid characters or the use of individual code sequences for diverse subscribers using modulation types that are already specified in the 5G standards.
# Multi-Output Based Hybrid Integrated Models for Student Performance Prediction
## 3 Materials and Methods
### Data Preprocessing  
Due to the fact that raw data are \"dirty\" data, in order to reduce the impact of low-quality data on model predictions during the process of model construction, scientific data processing methods such as scaling and one-hot encoding were selected [11]. In this study, the primary issue observed with the data was the presence of missing values. Three methods of filling in missing values--0 fillings, mean filling, and random forest filling--were compared to select the most appropriate method for model construction. Furthermore, the midterm and final exam scores were classified into two categories: scores ranging from 60 to 100 were represented as 1, indicating a passing grade, while scores below 60 were represented as 0, indicating a failing grade. The ratio of samples with scores of 0 and 1 in the midterm exam was 0.54:1, while the ratio in the final exam was 1.26:1, indicating a basic balance between the data. Since machine learning algorithms cannot handle character data, the character types in the data need to be converted accordingly. In addition, since the values in each column attribute are on different scales, the data need to be normalized to the range of [0, 1]. The formula for normalization is as follows: where max represents the maximum value and min represents the minimum value [12].  
\[\mathrm{X^{\prime}}=\frac{\mathrm{x-min}}{\mathrm{max-min}} \tag{1}\]  
Table 3 presents the detailed performance of students in the C++ Programming course. The passing rates for the midterm and final exams were 64.75% and 44.26%, respectively. In the hybrid online and offline learning of this course, the low pass rate in the final exam could significantly reduce students' enthusiasm for learning, indicating the need for corresponding teaching interventions. The overall mean and standard deviation (std) of the original data and missing values, filled by 0 fillings, mean filling, and a random forest algorithm filling, are shown in Figures 2 and 3, respectively. The mean value was lowest when using the 0-filling method, and the standard deviation value was lowest when using the mean-filling method.
# Shielding Grounding Optimization Method for Spaceborne Multi-Cable
## 3 Experiments  
The specific steps of the grounding optimization method for spaceborne multi-cable shields were described in the previous section, and the following experimental tests were conducted on the actual scanning mirror drive control system with the experimental equipment and test environment shown in Figure 6. The reasons for choosing this system as the experimental verification system are as follows:  
1. This system has multiple cables, and the electrical signals flowing through the cables vary in amplitude and frequency. Motor drive cable: internal three-twisted cable, transmitting 30 V, 12 kHz three-phase electricity, and a large source of interference. Field excitation cable: internal twisted cable, transmitting 4 V, and 5 kHz sinusoidal signal. Angle measurement cable: internal transmission of the induction synchronizer returns an angle signal, and in this target analysis cable, the signal is weak and vulnerable to interference.
2. After the upper computer issues the scanning mirror fixed-point pointing command, the scanning mirror will point to a fixed angle, but due to the presence of external interference from the PWM signal to the motor drive and excitation signal to the induction synchronizer, the scanning mirror fluctuates around this fixed angle, affecting the angle accuracy. The magnitude of the external interference is directly reflected in the change in the angle accuracy, which can be used as an indicator to measure the shielding effect of different shield grounding states, and is more intuitive and convenient.
3. This system functions under different conditions and at different sites, the scanning mirror angle accuracy varies greatly, and the shielding effects of different shield grounding states also differ; therefore, it is necessary to analyze and optimize this system.  
Each cable in the scanning mirror drive control system was wrapped in a shield, and each shield had four states--double-ended grounding, ungrounded, single-ended near grounding, and single-ended far grounding--so there were 4\({}^{3}\) shield grounding states for the three cables. The upper computer sent the scanner pointing command to the scanner circuit, tested the angle accuracy in these 64 states, and recorded the data.  
Figure 6: Experimental equipment and test environment.