esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Various Types of Functional Bread on Postprandial Glycemia in Healthy Adults
## 2 Materials and Methods
### Calculation of Glycemic Index  
The incremental area under the glucose response curve (IAUC) above the fasting glucose concentration was calculated using the trapezoid method. The IAUC of each study participant after each test food was expressed as a percentage of the mean IAUC  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Tested Products**} & \multirow{2}{*}{**Kcal**} & \multirow{2}{*}{**Protein (g)**} & \multirow{2}{*}{**Dietary Fiber (g)**} & \multicolumn{2}{c}{**Available**} & \multirow{2}{*}{**Fat (g)**} & \multirow{2}{*}{**Salt (NaCl, g)**} \\  & & & & & & **Carbowdrate (g)** & \\ \hline
1 & wholemeal rye bread & 220 & 5.0 & 6.8 & 45.0 & 0.9 & 1.2 \\
2 & wheat-oat bread & 252 & 9.3 & 6.0 & 45.6 & 2.3 & 1.3 \\
3 & wholemeal rye bread 2 & 195 & 5.2 & 6.4 & 37.0 & 1.5 & 1.3 \\
4 & wholemeal rye bread 3 & 201 & 4.1 & 4.4 & 42.0 & 0.8 & 1.5 \\
5 & wholemeal spelt bread & 219 & 7.6 & 4.3 & 37.0 & 3.5 & 1.1 \\
6 & wholemeal rye bread 4 & 216 & 4.6 & 7.2 & 43.0 & 1.2 & 1.2 \\
7 & wheat bread with seeds & 289 & 14.2 & 9.1 & 33.8 & 8.8 & 1.0 \\
8 & wheat bread with bran and seeds & 253 & 9.6 & 6.0 & 45.0 & 2.2 & 2.4 \\
9 & wheat bread with seeds 2 & 311 & 12.95 & 7.0 & 37.6 & 10.5 & 1.2 \\
10 & wholemeal spelt bread 2 & 255 & 11.0 & 4.8 & 37.0 & 5.9 & 1.6 \\
11 & buckwheat bread with seeds & 244 & 7.7 & 8.2 & 26.2 & 10.2 & 1.2 \\
12 & wholemeal rye bread with seeds & 257 & 8.5 & 5.2 & 34.0 & 8.5 & 1.5 \\
13 & wheat bread with seeds 3 & 313 & 13.4 & 2.7 & 39.7 & 10.6 & 0.9 \\
14 & wheat bread with seeds 4 & 297 & 14.2 & 6.0 & 33.9 & 10.3 & 1.7 \\
15 & wholemeal gluten-free or bread & 256 & 8.1 & 6.6 & 28.0 & 11.0 & 1.2 \\
16 & wholemeal rye bread with seeds 2 & 277 & 7.9 & 4.8 & 32.0 & 12.0 & 1.2 \\
17 & wholemeal oat bread & 209 & 9.1 & 5.5 & 33.1 & 3.2 & 1.0 \\
18 & rye bread & 220 & 4.4 & 7.4 & 41.0 & 2.7 & 1.3 \\
19 & rye bread with seeds & 271 & 8.3 & 7.7 & 35.0 & 9.2 & 1.4 \\
20 & wheat bread with grains and seeds & 250 & 9.3 & 9.0 & 37.0 & 5.0 & 1.4 \\
21 & multiseed bread & 348 & 12.1 & 9.3 & 19.9 & 22.4 & 1.45 \\
22 & rye bread with seeds and spices & 239 & 7.1 & 10.5 & 34.0 & 6.2 & 1.5 \\
23 & wheat and oat bread with seeds & 244 & 9.4 & 6.1 & 35.0 & 5.9 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nutritional value of the tested products per 100 g serving.  
glucose induced by the reference food (glucose solution) in the same study participant as GI = (IAUC test food/mean IAUC glucose) \(\times\) 100. The mean value for all the subjects was the GI of a tested food. The GI values were categorized into low (<55), medium (55-69), or high (\(\geq\)70) glycemic response [16]. The glycemic coefficient of variation GI values after ingestion of a reference meal was assumed not to exceed 30% [29].
# Spatialization and Analysis of China's GDP Based on NPP/VIIRS Data from 2013 to 2023
## 3 Methodology
### Method for Estimating GDP Using NTL Data
#### 3.2.2 Model Regression Accuracy Evaluation Method  
The accuracy evaluation mainly includes three methods: the coefficient of determination of the regression model-\(R^{2}\), the root mean square error (RMSE), and the relative error (RE).  
The coefficient of determination is also called the goodness of fit, which reflects how much of the fluctuation of \(y\) can be described by the fluctuation of \(x\). The greater the goodness of fit, the higher the degree of explanation of the independent variable to the dependent variable. The correlation coefficient \(R^{2}\) indicates the correlation between the NTL index and GDP data. The value range of \(R^{2}\) is 0 to 1. The closer to 1, the stronger the correlation.  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{\text{m}}{\left({f(x_{i})-y_{i}}\right)^{2}}}{ \sum\limits_{i=1}^{\text{m}}{\left({f(x_{i})-\overline{y}_{i}}\right)^{2}}} \tag{1}\]  
where \(f(x_{i})\) is the true label; \(y_{i}\) is the predicted result; and \(\overline{y}_{i}\) is the sample mean. The closer \(R^{2}\) is to 1, the better the model fits the data.  
\begin{table}
\begin{tabular}{c c} \hline
**Attribute** & **Definition** \\ \hline \(DN_{\text{i}}\) & The pixel whose gray value is i in the area. \\ \(DN_{\text{M}}\) & The pixel with the maximum gray value in the area. \\ \(\text{n}_{\text{i}}\) & The gray value in the area is the number of i pixels. \\ \(N\) & The total number of pixels in the area. \\ \(N_{\text{L}}\) & The total number of pixels whose gray value is not 0 in the area. \\ Total nighttime light, TNL & \(TNL=\sum\limits_{i=0}^{DN_{\text{i}}}{\text{DN}_{\text{i}}\times\text{n}_{ \text{i}}}\) \\ Mean nighttime light, MNL & \(MNL=\frac{1}{n}\times\sum\limits_{i=0}^{DN_{\text{M}}}{DN_{\text{i}}\times n_{ \text{i}}}\) \\ Light area ratio, S & \(S=\frac{1}{N}\) \\ Average relative light intensity, I & \(I=\frac{1}{N_{\text{L}}\times DN_{\text{M}}}\times\sum\limits_{i=0}^{DN_{ \text{M}}}{DN_{\text{i}}\times n_{\text{i}}}\) \\ Compounded nighttime light index, CNLI & \(CNLI=I\times S\) \\ \hline \end{tabular}
\end{table}
Table 1: NTL indices and economic parameter list [19].  
_RMSE_ is the square root of the ratio of the square of the deviation between the predicted value and the true value to the number of observations n. It measures the deviation between the predicted value and the true value and is more sensitive to outliers in the data.  
\[RMSE=\sqrt{\frac{1}{N}{\sum\limits_{i=1}^{\text{n}}{(GDP_{p}-GDP_{s})}^{2}}} \tag{2}\]  
Among them, \(GDP_{p}\) is the predicted value of GDP; and \(GDP_{s}\) is the actual value of GDP.  
_RE_ refers to the degree of closeness between the predicted value and the statistical value, and the closer the RE value is to 0, the more accurate it is.  
\[RE=\frac{GDP_{\text{p}}-GDP_{s}}{GDP_{s}}\times 100\% \tag{3}\]
## 2 Theoretical Background and Hypotheses Development
### The Stimulus-Organism-Response (SOR) Theoretical Framework  
Recent empirical studies increasingly emphasize that HRI extends beyond functional engagement and is shaped by psychological, relational, and interactive dimensions [18; 32; 33; 34]. Drawing on this HRI-centered perspective, the present study adopts the Stimulus-Organism-Response (SOR) theoretical framework to examine how cognitive and affective mechanisms influence users' continuance intention to use MOCCA, a social robot equipped with episodic memory. Originally developed in environmental psychology by [PERSON] and [PERSON] [26], the SOR framework explains how external stimuli affect individuals' attitudes and behaviors by activating internal cognitive and affective processes [35].  
In this study, within the SOR paradigm, stimuli (S) are defined as external factors that initiate changes in an individual's internal state. Specifically, the stimuli are represented by various features of MOCCA, including perceived anthropomorphism, agency, and risk. These features serve as external cues that shape users' experiences with MOCCA, influencing the way they evaluate the social robot both cognitively and emotionally. This  
Figure 1: Capabilities of MOCCA.  
Figure 2: An example of a conversation with MOCCA.  
evaluation leads to psychological responses and perceptions. These stimuli affect the organism (O), which refers to the individual's internal psychological processes, including both cognitive and affective states. In this study, the organism includes perceived trust and parasocial interaction, which represent users' cognitive and affective evaluations of MOCCA. Users' thoughts regarding MOCCA's reliability and functionality, as well as emotional experiences such as comfort and trust during interactions, reflect these cognitive and affective processes. These internal states function as mediating mechanisms between external stimuli and behavioral outcomes. Finally, the response (R) refers to the behavioral outcomes resulting from exposure to the stimuli and subsequent internal processing [36; 37]. In this study, the response is operationalized as users' continuance intention to use MOCCA, reflecting their willingness to maintain ongoing engagement with the social robot.
# Multimodel Phishing URL Detection Using LSTM, Bidirectional LSTM, and GRU Models
## 3 Proposed Models
### Proposed Model III: Gated Recurrent Unit (GRU)-Based RNN  
The third model, the GRU-based RNN, is similar to regular LSTM. As in the normal gate, three gates were used; the GRU has two gates, namely the \"reset gate\" and \"update gate\" [34; 35]. Here, \(r_{t}\) forgets the LSTM cell gate. It is a combination of the previous hidden  
Figure 3: Detailed structure of the bidirectional long short-term memory (Bi-LSTM) model.  
state and current input and determines how much of the past information is neglected. A general representation of the GRU is shown in Figure 4. See Algorithm 3.  
The GRU model is implemented using the following equations [30; 36; 37]:  
\[r_{t}=\ \sigma\ (W_{r}\cdot\ [h_{t-1},x_{t}]) \tag{11}\]  
\[z_{t}=\ \sigma\ (W_{2}\cdot\ [h_{t-1},x_{t}]) \tag{12}\]  
\[\widetilde{h}_{t}=\ \tanh\ (W\cdot[\ r_{t}*h_{t-1},\ x_{t}]) \tag{13}\]  
\[h_{t}=(1-z_{t})*h_{t-1}+z_{t}*\widetilde{h}_{t} \tag{14}\]  
where \(r_{t}=\) reset gate, \(z_{t}=\) update gate, \(\widetilde{h}_{t}=\) intermediate memory, and \(h_{t}=\) output.  
```
Input : URL \(\{x_{1},x_{2},x_{3}\ldots\ x_{L}\}\), \(L=\) length of URL Output : Phishing or legitimate (Y) (Y = 0: Legitimate, Y = 1: Malicious) Step 1 : Begin Step 2 : For each URL \(x_{1}\).\(x_{L}\) do Step 3 : \(h_{t-1}\) and \(x_{t}\) merge and pass through a sigmoid function and the result stored in \(r_{t}\), Equation (11). Step 4 : Compute \(z_{t}\) from \(h_{t-1}\) and \(x_{t}\) (different biases and weights Equation (12) Step 5 : Compute \(\widetilde{h}_{t}\) by combining the new input \(x_{t}\) with the reset \(h_{t-1}\) and pass their output through a tanh function Equation (13) Step 6 : Subtract \(z_{t}\) from a vector containing all 1s and multiply it with the previous hidden state. Step 7 : The output from Step 4 \(z_{t}\) multiplied with the output from Step 5 \(\widetilde{h}_{t}\). Step 8 : Combine the output from Step 6 with the output from Step 7 and store in \(h_{t}\)  Equation (14) Step 9 : End for Step 10 : Obtain Y from \(h_{t}\) Step 11 : End
```  
**Algorithm 3** GRU-based RNN for phishing URL detection
## 4 Experiments and Results  
### Object Detection  
We performed several experiments to train and assess object detection. Measures commonly used in the field were used for evaluation over the test subset C: precision vs. recall curve (PvR), average precision (AP) for each articulator, mean average precision, and top F1 score. We also summarize the IoUs to show individual-match fidelity by measuring the spatial overlap between the target and detected ROIs in a single image.  
Figure 9 shows the precision vs. recall curves with two different IoU thresholds \(t_{IoU}\) of 0.50 and 0.75. Table 2 presents the class-wise and overall average precision and F1 score. The IoU box plots of successful detections used for the segmentation training are given in Figure 10. In general, the detection of lips is the most efficient of all classes, with an almost perfect PvR shape and AP and F1 measures. It is also the least sensitive to the IoU thresholding and output confidence level. The individual IoU rarely falls below 0.8, with a median of 0.862, indicating excellent detection agreement. That meets our primary goal, as the lips/mouth ROI is essential for further processing, e.g., by defining the input data for the segmentation pipeline. The other two articulators are detected at a solid level for the DRLSE-based preparation of weak labels. The PvR curves show that the top recall approachable is between 0.8 and 0.9 in both cases, so the model always misses some instances of teeth and tongue. Perfect precision is available in tongue with recall below 0.3 and practically unavailable in teeth. Overall, teeth detection performs better, as the average precision and F1 score are consistently greater than in tongue, though, once detected, both are identified with similar accuracy (the IoU of ca. 0.73 in Figure 10). The differences between results obtained with two \(t_{IoU}\) thresholds are not significant. Hence, we decided to apply the lower threshold of 0.50 to the general workflow as it supports detecting smaller objects, which is vital in spotting teeth and tongue.  
Figure 9: Precision vs. recall curve obtained by our YOLOv6 model with the IoU threshold \(t_{IoU}\) equal 0.50 (**a**) and 0.75 (**b**).  
Our experiments slightly favor YOLOv6 architecture over other versions of YOLO (v3, v5-v7) [72; 73; 74]. Table 3 compares their performances in similar settings. The two oldest YOLOs (v3, v5) were outperformed by the two most recent ones in almost all measures. YOLOs v6 and v7 compete in particular articulators, with v6 being significantly better in tongue and v7 more accurate regarding the F1 score in teeth. With a similar overall F1 score and better average precision, YOLO v6 is our selection for object detection.
# RSCS6D: Keypoint Extraction-Based 6D Pose Estimation
## 1 Introduction
Furthermore, to accelerate the convergence of the pose estimation network, we employ keypoint cloud and residual structures. This approach is more efficient than time-consuming processes like PnP-RANSAC. Our method not only enhances the accuracy of pose estimation but also improves the efficiency of the network's convergence. This paper makes the following key contributions:  
* RSCS Algorithm for Keypoint Cloud Extraction:  
We propose a novel keypoint cloud extraction algorithm called RSCS, which integrates RGB images and depth images to extract 3D keypoint cloud from both edge information and depth information. This approach effectively reduces redundancy and enhances the quality of correspondences for pose estimation. * RSCS6D Method for Pose Estimation:  
Building on the G2L-Net method [27], we introduce RSCS6D, which leverages semantic segmentation via PIDNet [28] and the keypoints extracted by RSCS. By incorporating these keypoints into an improved pose estimation network, we achieve more accurate 6D pose predictions. * RSCS6D_Pruned: A Lightweight Version:  
To meet the diverse needs of industrial applications, this paper presents a lightweight version of RSCS6D, namely RSCS6D_Pruned. Considering the feature redundancy in the pose estimation network of RSCS6D, we utilize a coarse-grained pruning algorithm, Network Slimming (NS) pruning [29], to optimize the network and reduce model complexity and computational cost. Our method improves the accuracy of pose estimation while accelerating network convergence through the use of keypoint cloud and residual structures. Additionally, unlike direct pose regression methods, our approach retains a high level of interpretability, making it easier to understand and debug. These contributions provide a robust solution for 6D pose estimation from RGB-D images. The overview of our method is illustrated in Figure 1.  
Furthermore, we introduce a lightweight version of our method to meet the needs of various industrial applications. This lightweight version inherits the advantages of the original method while reducing computational complexity and resource requirements.
## 3 Proposed Solutions
### The Selection of a Listening Window to Establish a Connection with the Device  
The scenario assumes a single synchronization of the transmitter device clock during its production. Afterwards, for a long period of time \(X\), the device remains disconnected from the network and with unknown clock drift. After this period, we try to reconnect the device to the network by attempting to receive the signal transmitted from this device. We assume the quartz clock error as \(Y\). The receiver sets the listening window according to the strategy defined and appropriately modified. We assume that the receiver should maximize the probability of reception of at least one packet within 3 consecutive transmission intervals (e.g., 3 days for a device transmitting a single packet per day). Traditionally, this is achieved by repetition of the same time window--e.g., if the receiver is expecting that the transmission occurs at 2 a.m. it listens e.g., from 1.59 a.m. to 2.01 a.m.
## 2 Numerical Modelling and Validation  
### DED Process Design Constraints  
DED can be a \"powder blown\" or \"wire feed\" AM process that uses a laser beam or a heat source to melt metal powder/wire and deposit it on the surface of a suitable material. The laser/heat source creates a melt pool and the powder/wire is blown or fed into the melt pool with the help of inert gas. The powder or wire melts as it enters the melt pool, thus increasing the volume of the melt pool. As the laser or heat source moves through the liquid, it solidifies and creates a trace of material. Parts can be created by placing overlapping tracks in a parallel direction to form a layer, and then creating multiple layers to create the desired geometry. In industrial applications, PBF technologies have usually limited work table dimensions to lower than 400 mm \(\times\) 400 mm, otherwise it cannot offer hybrid manufacturing. In cases during hybrid products needing large width or length dimensions, DED can facilitate and optimize costs for manufacturing processes [20; 21; 22].  
Various optimization studies with different methodologies have been investigated by researchers in the literature. Response surface approximation, radial basis functions, Kriging, and artificial neural networks (ANN) are widely used for surrogate modelling. The response surface approximation is a useful method for design optimization problems with high mathematical complexity [23]. The response surface approximation employs a sequence of designed experiments to find an ideal answer. Radial basis functions have the high prediction characteristics of highly non-linear approximation models [24]. The radial basis function method operates by describing itself by the distance from its origin. Kriging is a useful and flexible method for creating a metamodel of simulated response surfaces with various functional structures [25]. Kriging is an approach of interpolation based on a Gaussian procedure controlled by primary covariances. ANN method is a powerful method that can produce complex relationships between nonlinear design variables [26]. For this reason, in this research the ANN method has been preferred in order to optimize the specific energy absorption considering peak force and total mass according to the design parameters which have a significant influence on the performance of semi cross metal parts as shown in Figure 1.
# A Review of Harmonic Detection, Suppression, Aggregation, and Estimation Techniques
## 5 Harmonic Estimation Models
** & **Advantage** & **Disadvantage** & **Applications** \\ \hline \multirow{4}{*}{State-Estimation-Based Method} & \multirow{4}{*}{[69]} & Eliminates need for expensive equipment, accurate even without synchronized measurements & May struggle with high-frequency harmonics, complex model tuning & Large-scale industrial power systems like iron and steel plants \\ \hline Compressive Sensing Harmonics Detector & \multirow{2}{*}{[72]} & Efficient, reduces the number of necessary measurements & May be less effective in high variability environments & Identifying and estimating principal pollution sources in distribution grids \\ \hline Taylor–Kalman–Fourier (TKF) Filter & \multirow{2}{*}{[68]} & Reduces total harmonic distortion (THD), accurate with dynamic components & Complexity in implementation, sensitive to tuning parameters & Estimating dynamic harmonic content in power systems \\ \hline Flat-Top FIR Filter & \multirow{2}{*}{[76]} & Accurate under nominal frequency conditions, computationally efficient & May produce errors under frequency deviations & Embedded systems with fixed harmonic frequencies \\ \hline Time-Domain & \multirow{2}{*}{[75]} & Compact modeling of harmonic distortion in circuits & Limited to specific circuit types & Estimating harmonic distortion in CMOS circuits \\ \hline Dynamic Harmonic Phasor Estimation & \multirow{2}{*}{[81]} & High accuracy with harmonic modulation, zero-error under nominal conditions & Complex mathematical modeling, limited flexibility & Systems with variable harmonic frequencies, improving estimation accuracy \\ \hline Norton Coupled Model & \multirow{2}{*}{[91]} & Effective for estimating harmonics in nonlinear loads & Requires detailed component-level data & Nonlinear loads in distribution networks \\ \hline Frequency Coupling Matrix (FCM) Model & \multirow{2}{*}{[84, 85, 86]} & Can estimate harmonic currents and voltages in complex systems, includes harmonic interactions & Sensitive to variations in grid impedance, complex parameter tuning & Harmonic analysis in AC/DC converters and multi-frequency grid systems \\ \hline Fourier-based Estimation & \multirow{2}{*}{[92]} & Widely used, fast computations & Inaccurate for non-stationary signals & Stationary power systems with constant harmonics \\ \hline Waveform Variation & \multirow{2}{*}{[87]} & Models cross-order supply voltage harmonics & Complex to implement, requires empirical validation & Estimating harmonic current emissions in residential systems \\ \cline{1-1} \cline{3-3} \cline{4-4} \cline{1-1} \cline{3-3} \cline{4
# Chemo-Mechanical Model for the Expansion of Concrete due to Alkali Silica Reaction
## Appendix A Volume Fraction of Evaporable Pore Water and Diffusion Coefficients  
The relation between volume fraction of evaporable pore water \(\phi_{w_{e}}\) and humidity \(h\) can be estimated as:  
\[\phi_{w_{e}}=\frac{Ck_{m}V_{m}h}{(1-k_{m}h)[1+(C-1)k_{m}h]}, \tag{1}\]  
Figure 8: Comparison between measured [54] and simulated ASR expansions, mortar bars.  
where \(C\), \(k_{m}\), and \(V_{m}\) are parameters. For \(t_{e}\geq 5\) days and \(0.3\leq w/c\leq 0.7\), they can be determined as [55]  
\[C = \exp(855/T),\] (A2) \[k_{m} = \frac{C(1-1/n_{w})-1}{C-1},\] (A3) \[n_{w} = (2.5+15/t_{e})(0.33+2.2w/c),\] (A4) \[V_{m} = (0.068-0.22/t_{e})(0.85+0.45w/c),\] (A5)  
where \(t_{e}\) is the age of concrete hydration in days. The unit of temperature is K.  
\[D_{af} = D_{af,gel}^{ref}f_{\rm T}(U_{n},T)f_{h1},\] (A6) \[D_{f} = D_{f}^{ref}f_{\rm T}(U_{n},T)f_{h1},\] (A7) \[D_{Ca} = D_{Ca}^{ref}f_{\rm T}(U_{c},T)f_{h1},\] (A8) \[D_{ui} = D_{wi}^{ref}f_{\rm T}(U_{h},T)f_{h2},\] (A9) \[D_{m} = D_{m}^{ref}f_{\rm T}(U_{h},T)f_{h2},\] (A10) \[D_{g} = D_{g}^{ref}f_{\rm T}(U_{g},T),\] (A11)  
with the reference values at reference temperature: \(D_{f,gel}^{ref}\), \(D_{f}^{ref}\), \(D_{Ca}^{ref}\), \(D_{wi}^{ref}\), \(D_{m}^{ref}\), and the influence of temperature and humidity:  
\[f_{\rm T}(U,T) = \exp\left[\frac{U}{R}\left(\frac{1}{T_{ref}}-\frac{1}{T}\right) \right],\] (A12) \[f_{h1} = \left[1+\frac{(1-h)^{n}}{(1-h^{*})^{n}}\right]^{-1},\] (A13) \[f_{h2} = \alpha_{0}+\frac{1-\alpha_{0}}{1+[(1-h)/(1-h^{*})]^{n}},\] (A14)  
where \(T_{ref}=296\) K is the reference temperature, \(T\) is the current temperature, \(R\) is the universal gas constant, and \(U=U_{n}\), \(U_{c}\), \(U_{h}\), and \(U_{g}\) are the activation energies of the alkali diffusion process, calcium diffusion process, moisture diffusion process, and gel infiltration process, respectively. \(U_{n}=20\) kJ/mol for aggregate, 40 kJ/mol for cement paste [20], \(U_{c}=44\) kJ/mol, \(U_{h}=42.5\) kJ/mol, \(U_{g}=45\)kJ/mol [18], and the value of \(\alpha_{0}\) is between 0.025 and 0.1, representing the ratio of min \(D_{m}\) and max \(D_{m}\). Here, we take \(\alpha_{0}=0.05\).