esa-sceva/satcom-chunk-collection huggingface dataset:
# On Ghost Imaging Studies for Information Optical Imaging
## 3 Ghost Imaging Studies for Information Optical Imaging
### Mapping Higher-Dimensional Light-Field Information into Lower-Dimensional Domain
By the spatial-dimensional second-order correlation between this intensity distribution and the pre-measured impulse response \(I_{r}(\mathbf{r}_{t};\mathbf{r},k)\) corresponding to a monochromatic point source \(\delta(r,k)\) where \(k=1/\lambda\), the object's spatial-spectral information \(T(\mathbf{r},k)\) can be achieved, namely [75]  
\[\Delta G^{(2)}(\mathbf{r},k)=\left\langle\Delta I_{t}(\mathbf{r}_{t})\Delta I _{t}(\mathbf{r}_{t};\mathbf{r},k)\right\rangle_{\mathbf{r}_{t}}\propto T( \mathbf{r},k)*g^{(2)}(\mathbf{r},k) \tag{13}\]  
Figure 2: Schematic diagram of the GI LiDAR system. It uses light fields with designed spatiotemporal fluctuations, which are generated by modulating a pulse laser with a diffuser to illuminate the object, then acquires the return signal with a bucket detector, and retrieves the 3-D spatial-depth image information by the second-order correlation between illuminated light fields and the return signal. where \(g^{(2)}(\mathbf{r},k)\) is the normalized second-order correlation function that characterizes the resolving ability of high-dimensional light-field information. In addition, the GI camera that enables to realize a higher-dimensional spectral-polarization imaging is also demonstrated [78]. Unlike the typical traditional camera that only maps the object's 2-D spatial information into the detection plane by a \"direct point-to-point\" mode, in the GI camera, the desired information in the high-dimensional light-field domain is encoded with randomly distributed speckle patterns and mapped into the 2-D detection plane. Hence, the GI camera is able to transmit high-dimensional image information with less degeneracy, namely, it can much more efficiently make use of the imaging channel capacity compared to the traditional camera. For example, besides the spectral imaging, the spectral-polarization GI camera has also been demonstrated [78]. Further, to improve the efficiency, the GI camera with the super-Rayleigh modulator has been proposed by designing the distribution of those encoded speckle patterns [79], which exhibits a better anti-noise imaging performance compared to the vanilla one. Additionally, the issue of extending the super-Rayleigh modulator to a broad spectral band for hyperspectral GI camera has been considered, and some progress has been made by combining with dispersion control techniques [80].
## 2 Materials and Methods
### The 3 PG-MT-LSTM Hybrid Modelling Approach
#### 2.2.2 Multi-Task Learning and LSTM  
MTL (Multi-task learning) is a machine learning technique that enables the joint training of multiple related tasks [45; 46]. In this study, MLT is used to jointly predict the individual organ and total biomass changes in forest stands. LSTM is a type of recurrent neural network that can process sequential data by selectively remembering or forgetting past information [47; 48]. The key process in LSTM is mathematically shown below.  
\[f_{t}=\sigma_{g}\Big{(}W_{f}x_{t}+W_{f}m_{t-1}+b_{f}\Big{)} \tag{2}\]  
\[i_{t}=\sigma_{g}(W_{i}x_{t}+W_{i}m_{t-1}+b_{i}) \tag{3}\]  
\[o_{t}=\sigma_{g}(W_{o}x_{t}+W_{o}m_{t-1}+b_{o}) \tag{4}\]  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\sigma_{c}(W_{c}x_{t}+W_{c}h_{t-1}+b_{c}) \tag{5}\]  
\[m_{t}=o_{t}\odot\sigma_{h}(c_{t}) \tag{6}\]From Equation (2) to Equation (6), \(\sigma_{g}\), \(\sigma_{c}\), and \(\sigma_{h}\) represent logistic sigmoid function, the input activation function, and the output activation function, respectively. \(f\), \(i\), \(o\), and \(c\) represent the forget gate, input gate, output gate and cell activation vector, respectively. \(m\) depicts the hidden state vector, also known as output vector, of the LSTM units. \(W\) denotes the weight matrix (for example, \(W_{i}\) represents the weight matrix of input gate). The \(\bigodot\) stands for element-wise multiplication, and \(b\) denotes the bias term.  
For this study, LSTM was used to model the temporal dynamics of biomass change. The combination of multi-task learning and LSTM allows the model to capture the complex relationships between thinning rules and climate change. By sharing the LSTM layer and jointly training the model on multiple related tasks, the MT-LSTM model can learn to generalize better and make more accurate predictions [49; 50].
## 4 Results and Discussion
### Structural Equation Modeling (SEM) and Hypothesis Testing
Hypothesis H2b, which posited a significant positive relationship between effort expectancy (EE) and behavioral intention (BI), was not supported based on the evidence collected. While the regression analysis yielded a parameter estimate, the \(p\)-value was not statistically significant across datasets, including the United Kingdom, Turkey, and the combined data. The participants might already perceive robot technologies as inherently easy to use, particularly if they have prior exposure to similar systems. This is consistent with [PERSON] [69], who noted that the significance of effort expectancy (EE) decreases as users become more familiar with the technology. Robots designed for office settings often emphasize user-friendly interfaces, minimizing the cognitive load required to operate them. As a result, participants may not view effort expectancy (EE) as a significant determinant of their intention to use robots. The collected data indicate that performance expectancy (PE) had a strong and significant influence on behavioral intention (BI). This might overshadow the role of effort expectancy (EE), as participants prioritize the robot's utility and outcomes over the ease of learning or using it. For office professionals, practical utility may outweigh concerns about the effort involved in adoption. Differences in cultural expectations or workplace environments between the UK and Turkey might also play a role. For instance, in cultures emphasizing productivity, ease of use might be assumed or taken for granted, leading to a reduced impact on behavioral intentions. The robots demonstrated in the study, such as ADA7, exhibit a high degree of autonomy. Participants may perceive these robots
# Adam Algorithm with Step Adaptation
## 2 Problem Statement and Background
\(p_{k}=\beta_{1}p_{k-1}+(1-\beta_{1})g_{k}\). 5. \(b_{k}=\beta_{2}b_{k-1}+(1-\beta_{2})g_{k}^{2}\). 6. \(\hat{p}_{k}=p_{k}/\left(1-\beta_{1}^{k}\right)\). 7. \(\hat{b}_{k}=b_{k}/(1-\beta_{2}^{k})\). 8. Get the descent direction \(s_{k}=\frac{p_{k}}{\sqrt{b_{k}+\epsilon}}\). 9. Find a new approximation of the minimum \(x_{k+1}=x_{k}-\alpha s_{k}\). 10. If the stopping criterion is met, then stop, else go to step 3. ```  
**Algorithm 1** (Adam)Step 4 gives us the exponential average for the gradient. Step 5 gives us the exponential average for the squares of the gradient components. Steps 6, 7 and 8 form the descent direction. Steps 6, 7, 8 and 9 can be represented as follows:  
\[s_{k}=\frac{p_{k}}{\sqrt{b_{k}}+\varepsilon_{1}},\quad x_{k+1}=x_{k}-\alpha \frac{\sqrt{1-\beta_{2}^{k}}}{(1-\beta_{1}^{k})}\,s_{k} \tag{5}\]  
where first the descent direction is found and then the step multiplier is determined. To assess the importance of the presence of the parameters \(p_{k}\), \(b_{k}\) in the algorithm, we first use only \(p_{k}\) and then this together with \(b_{k}\) to organize the algorithm with step adaptation.
# Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network
## 2 Training Principle of the RVFLNN Model  
The RVFLNN has a good ability to quickly model a nonlinear process. Its basic structure is shown in Figure 1[17].  
(1) Input layer: \(X_{d,N}\in R^{d\times N}\) is a given training set and is a \(d\times N\)-dimensional vector. \(W_{L}\) and \(B_{L}\) are weights and biases from the input layer to the hidden layer, with \(W_{L}\in R^{L}\) and \(B_{L}\in R^{L}\). Here, \(d\), \(N\), and \(L\) are the dimension of input variables, the number of samples, and the number of hidden layer nodes, respectively. \(W_{L}\) and \(B_{L}\) are chosen in the beginning of the learning process, independently of the training data. In particular, \(W_{L}\) and \(B_{L}\) are chosen randomly from a predefined probability distribution in an RVFLNN. Hence, the input sets, weights, and biases of RVFLNN are as shown in Equations (1) and (2):  
\[X_{d,N}=\left(\begin{array}{ccc}x_{1,1}&\cdots&x_{1,N}\\ \vdots&\ddots&\vdots\\ x_{d,1}&\cdots&x_{d,N}\end{array}\right) \tag{1}\]  
\[W_{L}=(w_{1},\ldots,w_{l},\ldots,w_{L}),\ B_{L}=(b_{1},\ldots,b_{l},\ldots,b_{ L}). \tag{2}\]  
(2) Hidden layer: The purpose of the hidden layer is to establish an activation function, \(H_{L,N}\), for the hidden layer nodes. From Equation (3), each \(h_{l}\) is a fixed nonlinear function known as a hidden function. In our paper, the sigmoid basis function is given by the following equation:  
Figure 1: Random Vector Functional Link Neural Network (RVFLNN) structure.  
\[h_{l}=\frac{1}{1+\exp\{-w_{l}Tx_{l}+b_{l}\}}. \tag{3}\]  
Finally, the hidden matrix, \(H_{N,L}\), is as shown in Equation (4):  
\[H_{N,L}=\left(\begin{array}{ccc}h_{1,1}&\cdots&h_{L,1}\\ \vdots&\ddots&\vdots\\ h_{1,N}&\ldots&h_{L,N}\end{array}\right). \tag{4}\]  
(3) Output layer: A desired output, \(\gamma_{N}=\left(y_{1},\ldots,y_{j},\ldots,y_{N}\right)^{\prime}\), is an \(N\)-dimensional vector. The output of an RVFLNN can be described as  
\[f(x)=\sum_{l=1}^{L}\beta_{l}h_{l}(x). \tag{5}\]  
Therefore, Equation (4) can be expressed in the form of the matrix  
\[Y_{N}=H_{N,L}\beta_{L}. \tag{6}\]  
The output weights, \(\beta_{L}=\left(\beta_{1},\ldots,\beta_{l},\ldots,\beta_{L}\right)\), are the purpose of the training, so the learning problem can be formulated as minimizing both the training error and the output weight norm as follows:  
\[\underset{\phi}{\min}\frac{1}{2}\|H_{N,L}\beta_{L}-Y_{N}\|_{2}^{2}+\frac{ \lambda}{2}\|\beta_{L}\|_{2}^{2},\lambda>0 \tag{7}\]  
where the factor \(\frac{1}{2}\) is added for successive simplifications, and \(\lambda\) is the complexity coefficient. We obtain the well-known solution for \(\beta_{L}\):  
\[\beta_{L}=\left(H_{N,L}^{T}H_{N,L}+\lambda I_{L}\right)^{-1}H_{N,L}^{T}Y_{N} \tag{8}\]  
where \(I\) is the identity matrix of proper dimensions to prevent numerical instabilities. Thus, the learning process can be completed using Equations (1)-(8) [18; 19; 20].
# A Novel Process Recommendation Method That Integrates Disjoint Paths and Sequential Patterns
## 5 Visual Prototyping System  
To demonstrate the real-life application of the proposed method, we designed and proposed a visual prototyping system that automatically recommends the next node based on the process node entered by the modeler through the process recommendation method that combines disjoint path and sequential patterns, thus better assisting the modeler in rapid modeling. As illustrated in Figure 11, the system comprises two modules: the upper and lower modules.  
Figure 10: Time comparison of five methods: (**a**) real dataset; (**b**) simulation dataset.  
1. The upper module, shown in Figure 11a, is a process builder that offers the following features: 1. Enabling users to add process nodes and build process diagrams freely; 2. Enabling users to recommend, add, edit, delete, and drag and drop each node while connecting them; 3. Enabling users to undo modifications and reverse undo modifications when accidental deletions or other errors occur; 4. Enabling users to save the process with one click once it is built, without any other unnecessary operations.
2. The lower module, shown in Figure 11b, is the recommendation and data interaction module.  
After the user finishes building the current reference model and selects the next node, clicking on the \"Get recommendation node\" button prompts the program to parse the reference model built on the process builder, analyze and match the data in the background, and display the corresponding results in the recommendation module, with support for limiting the maximum number of recommendation nodes displayed.  
When the user selects a recommendation node as a hit, the process builder automatically builds the next process node based on the user's selected results without requiring the user to build it manually.  
As shown in Figure 10b, when \"Message Generator\" is taken as the termination node, the system recommends \"Terminal\" and \"Python3 Operator\" as the recommendation nodes after the matching is completed and displayed on the bottom right side. The system will automatically build the \"Terminal\" node after clicking the \"choose\" button.  
Figure 11: Visual prototyping system: (**a**) process builder module; (**b**) recommendation and data interaction module.
# Towards Harmonious Coexistence in the Unlicensed Spectrum: Rational Cooperation of Operators
## 4 Discussion, Future Topics, and Conclusions  
### Discussion  
So far, we have examined how two LTE operators carry out strategic plans for the unlicensed spectrum under different assumptions of their payoffs. Since the spectrum is scarce and valuable, the unlicensed spectrum should be preserved as common resources so that any wireless networks can operate their own service. After Qualcomm proposed LTE-U to deliver LTE with small cells using the unlicensed 5-GHz spectrum, many companies in the wireless industry and 3 GPP have continued to work towards the standardization of LTE-LAA. To coexist harmoniously with other wireless networks, the LBT mechanism is included in LTE Release 13.  
Figure 6: Average channel utilization with low load. (**a**) potential collusion between LTE operators without mandatory LBT; and (**b**) LTE operators with mandatory LBT towards harmonious coexistence.  
Figure 7: Average channel utilization with high load. (**a**) potential collusion between LTE operators without mandatory LBT; and (**b**) LTE operators with mandatory LBT towards harmonious coexistence.  
Apart from technical standards, we have verified that LTE operators might as well strategically exploit the unlicensed spectrum under dense heterogeneous networks in countries without specific requirements for the usage of LBT. Based on the operator's dilemma game, we highly recommend that telecommunications regulatory bodies mandate the usage of LBT in the 5 GHz band at national level. Otherwise, different LTE operators have an incentive to advance their network strategies to maximize utility in the unlicensed spectrum. Moreover, if possible, it would be better to encourage every LTE operator to unify the LBT mechanism. Different LBT configurations may cause a high collision rate and severe overhead, and consequently the overall spectral efficiency will decrease.  
In addition to regulatory requirements of the LBT mechanism, it is necessary to detect and monitor malicious wireless users who violate power limits or produce jamming signals. During a certain period of time, a monitoring system must look at the behavior patterns in the unlicensed spectrum and inflict punishment on selfish wireless networks in order to achieve harmonious coexistence. Data from the monitoring system could help regulatory bodies to propose a new frequency allocation and channel assignment plan in the unlicensed spectrum. For example, some countries are moving toward allowing an unmanned aerial vehicle (UAV), typically known as a drone, to use the unlicensed spectrum within the 5 GHz band for remote-control communications. Under severe interference conditions, drones and other wireless systems cannot expect reliable communications. For this reason, recently, Korea allowed drones to use a dedicated spectrum 5030-5091 MHz (61 MHz bandwidth) for commercial purposes only, and planned for an additional 160 MHz bandwidth within the 5 GHz band.
# Thermal Characterization of Phase Change Materials by Differential Scanning Calorimetry: A Review
## 3 Calorimetric Characterization
### Temperature Program (Heating Mode)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Melting Point (\({}^{\circ}\)C)** & **Enthalpy** & **Remarks** & **References** \\ \hline Mercury & \(-38.8344\)\({}^{\circ}\)C & \(11.443\pm 0.004\) J/g & \begin{tabular}{c} Short shelf-life \\ Unsafe \\ \end{tabular} & [41] \\ \hline Water & 0.01 \({}^{\circ}\)C & \(335\pm 0.6\) J/g & \begin{tabular}{c} Liquid water is suitable for Cp \\ measurement, especially for \\ large-scale measurement \\ \end{tabular} & [6,41] \\ \hline Gallium & 29.8 \({}^{\circ}\)C & 80.097 \(\pm\) 0.032 J/g [34] & Incompatible with Al crucibles & [34,39] \\ \hline Biphenyl & 68.97 \(\pm\) 0.01 \({}^{\circ}\)C & \(122.5\pm 0.4\) J/g & Suitable for large sample calibration & [33] \\ \hline Benzoic acid & 122.37 \({}^{\circ}\)C & \(147.4\pm 0.1\) J/g & \begin{tabular}{c} [19,41,42] \\ \end{tabular} \\ \hline Diphenylacetic acid & 147.19 \(\pm\) 0.03 \({}^{\circ}\)C & \(146.8\pm 0.6\) J/g & \begin{tabular}{c} Should be used in hermetic \\ crucibles due to mass losses \\ \end{tabular} & [27] \\ \hline Indium & 156.61 \(\pm\) 0.02 \({}^{\circ}\)C & 28.71 \(\pm\) 0.08 J/g & \begin{tabular}{c} Promising reference to calibrate \\ temperature and heat of fusion \\ Can be re-melted up to ten times \\ \end{tabular} & [27] \\ \hline Tin & 231.928 \({}^{\circ}\)C & 60.6 \(\pm\) 0.2 J/g & \begin{tabular}{c} [41] \\ \end{tabular} \\ \hline Bismuth & 271.442 & 53.07 \(\pm\) 0.58 J/g & \begin{tabular}{c} [38,41] \\ \end{tabular} \\ \hline Zinc & 419.527 \({}^{\circ}\)C & 108 \(\pm\) 0.6 J/g & \begin{tabular}{c} Amalgam with aluminum at 450 \({}^{\circ}\)C \\ \end{tabular} & [41] \\ \hline Solid copper & 1084.62 \({}^{\circ}\)C & 205.4 \(\pm\) 6.6 J/g & \
\begin{tabular}{c} Oxidizes easily and reacts \\ with alumina \\ \end{tabular} & [6] \\ \hline Solid alumina (Sapphire) & Not precise & Not precise &
\begin{tabular}{c} Suitable for heat flow calibration \\ in Cp measurement \\ \end{tabular} & [19,43,44] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Reference materials for DSC calibration. step mode is the complexity of programming and measurement, plus time-consuming analysis [22, 47]. However, in the step mode heating, results dependency to heating rate and sample mass is far lower than that of dynamic mode. In both heating modes, a better temperature resolution can be achieved by lowering heating rate in the dynamic mode and decreasing the step size in the step mode. However, by doing so, the signal to noise ratio increases and affects the accuracy of enthalpy measurement.
# Enhancing Density Prediction of Agricultural Land Soil through Void Area Curve Analysis
## 2 Materials and Methods  
### Image Acquisition for Soil Samples  
The soil samples used in this study were collected from agricultural lands. The condition of the soil samples for image analysis is summarized in Table 1. Soil samples were classified as sandy loam (SL), loam (L), silt loam (SiL), and siltly clay loam (SiCL) by the USDA soil classification methods. From SL towards L and SiL, the content of sand gradually decreases and the content of silt increases. SiCL is a soil with a lower content of silt and a higher content of clay compared to SiL. Each soil was oven-dried at 110 \(\pm\) 5 \({}^{\circ}\)C for 24 h and then water was added to adjust the water content to 5, 10, 15, 20, and 25%. The wet soil was then compacted into cylindrical acrylic molds to prepare samples for image acquisition. The size of the mold was 150 mm in diameter and 50 mm in height. The dry density of the soil in the mold was adjusted to 1.1, 1.2, 1.3, and 1.4 t/m\({}^{3}\). The ranges of dry density and water content were determined in consideration of general field soil conditions. Each soil sample was prepared in triplicate under the same conditions. Therefore, the total number of samples was 240, 60 for each soil. A Canon EOS100d camera (Canon Inc., Tokyo, Japan) was installed in the vertical direction of the soil sample. The size of the image was 5184 horizontal pixels and 3456 vertical pixels, and the resolution was 0.018 mm/pixel. Digital images were acquired under a controlled constant light condition.
## 3 Materials and Methods
### Application in the Field  
After validating the described method in the lab, sampling of acetic acid was performed in indoor and livestock settings. Indoor air sampling included two sites: a manure treatment laboratory and an office space at Iowa State University. In the livestock setting, air sampling of acetic acid was carried out inside of the barns. Livestock air samples were taken at two swine farms: a typical swine farm located in Central Iowa (Farm 1) and a new farm with air scrubber and filtration technology for odor reduction (Farm 2). Both the new method (i.e., an SPME fiber exposed inside of a glass liner) and the conventional method (i.e., the sorbent tubes) were used at the sampling sites. The samplers were placed upstream of exhaust fans. The opening of the \"retracted\" fibers and sorbent tubes were pointed in the direction of the exhaust fans.  
Three 85-\(\upmu\)m Car/PDMS fibers were used at each site. Every fiber was thermally cleaned in a GC injector port as described earlier. Then, the fiber was assessed for residuals. For SPME fiber protection in the field, a \"retracted\" SPME fiber was placed inside of a 40-mL thermally cleaned vial. This was done to make an additional barrier between the dusty and odorous environment and the TWA SPME sampler (Figure 8). Thus, only the opening of the glass liner was exposed to theenvironment. Vials with a \"retracted\" SPME fiber were kept in thermally clean aluminum foil to prevent any interaction with the environment before actual sampling. Depending on anticipated concentrations at each monitoring site, the sampling time for the \"retracted\" SPME fiber was adjusted. For the quantification of acetic acid in the indoor setting, a sampling time of 12 h was used. For testing the method in the livestock setting, a sampling time of 40 min was sufficient. The diffusion coefficient was equal to \(1.1\times 10^{-5}\) m\({}^{2}\cdot\)s\({}^{-1}\) at 25 \({}^{\circ}\)C [36].  
Quantification of acetic acid was also performed with Tenax sorbent tubes. The sorbent tubes were thermally cleaned as described earlier. Multiple air samples were taken with two adjacent sorbent tubes, and the results were averaged for the indoor setting. The sampling time was 20 min. For the swine farm setting, a sampling time of 40 min was used.  
After samples were taken, SPME fibers and sorbent tubes were covered with thermally cleaned aluminum foil and placed in clean glass vials and then transported for further analysis. All samples were analyzed within 5 h of sample collection. A \(t\)-distribution was used to test the null hypothesis that the sample means received with the two methods were equal at the 95% CI (two-tailed test).