esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Determination of the Cement Paste Volume in the Air Entrained Concrete  
The tests were carried out for concrete made with Portland cement (C), silica fume (SF), natural sand 0/2 mm (S) and basalt aggregate fractions 4/8 and 8/16 mm (B). The information about the concrete composition is compiled in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Diameter**} & \multicolumn{4}{c}{**N\({}_{\mathbf{a}}\)(i), 1/mm\({}^{2}\)**} & \multicolumn{2}{c}{**N\({}_{\mathbf{v}}\)(i)**} \\ \cline{3-7}
**Class** & \(\mathbf{\mu m}\) & **1** & **2** & **3** & **Average** & **1/mm\({}^{3}\)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 1: Number of particles N\({}_{\mathrm{a}}\)(i) per 1 mm\({}^{2}\) of the specimen area and the corresponding calculated number of particles N\({}_{\mathrm{v}}\)(i) per 1 mm\({}^{3}\).  
Figure 4: Comparison of the results from calculations and sieve analysis of sand.  
A single \(150\times 100\times 40\) mm specimen was sawn, ground and polished in the same way as described in Section 3.1. Air voids in the specimen were filled with a fluorescent powder.  
The large image was made by gluing together \(18\times 9\) frames with dimensions of \(1280\times 960\) px and a resolution of \(3.37\)\(\upmu\)m/px, which produced a measurement area of \(78\times 28\) mm = \(22\) cm\({}^{2}\). Calculations were made for three areas of measurements (AOM) on the surface of the concrete specimens.  
The volume of aggregate BS\({}^{\text{2D}}\) (basalt + sand) is a ratio of the determined aggregate surface area to the area of measurement (AOM). The volume of air voids A\({}^{\text{2D}}\) is a ratio of the air void surface area to the AOM. The volume of paste (P) in the concrete was determined using the basic assumption in stereology according to which the volume equals the ratio of the paste surface area to the surface measured.  
The procedure involved taking three photographs of the same area under the ordinary light, under the light provided by the new attachment, and under the UV light. Examples of the photographs are shown in Figure 5. The volume of cement paste P\({}^{\text{2D}}\) was determined by subtracting the surface area of the aggregate particles BS\({}^{\text{2D}}\) and that of the air voids A\({}^{\text{2D}}\) from the measured area. Results for the individual phases in concrete are shown in Table 3. The paste volume as an average of the three results was \(26.7\%\). Good agreement was obtained with the paste volume indicated by the concrete mix composition (\(26.9\%\)).  
Figure 5: Image of the test surface: (**a**) ordinary light, voids filled with fluorescent powder, (**b**) binary image of aggregate particles, (**c**) binary image of voids and (**d**) binary image of cement paste, marked in blue
# Periodic Surface Defect Detection in Steel Plates Based on Deep Learning
## 3 Periodic Defect Detection Algorithm Based on CNN + LSTM
### The Improved Algorithm Based on CNN + LSTM + Attention  
#### 3.3.1 The Principle of the Attention Mechanism  
In recent years, attention mechanisms have been widely used in various fields of deep learning [18]. The uniquely human visual attention mechanism is a brain signal processing mechanism: human  
Figure 8: Correspondence between extracted features and the original image.  
Figure 9: LSTM structure.  
vision quickly scans the global image and obtains the focused target area, then pays more attention to this area to obtain more detailed useful information and suppresses other useless information.  
The attention mechanism was used in the encoder-decoder framework in this study. However, the attention mechanism can be seen as a general idea that does not depend on an encoder-decoder framework.  
The abstract model of the attention mechanism is shown in Figure 10. The elements in Source are a series of <Key, Value> data pairs. An element in the target area is queried. By calculating the similarity between _Query_ and _Key_\({}_{i}\), each weight coefficient (_Similarity_) corresponding to _Value_\({}_{i}\) is obtained, and then the weighted sum of _Value_\({}_{i}\) is calculated to obtain the final _attention_ value. So, essentially, the attention mechanism is a weighted sum of the _Value_ of the elements in the _Source_, and _Query_ and _Key_ are used to calculate the weight coefficient of the _Value_. That is, the essential idea can be rewritten as the following formula, where \(Lx=\|Source\|\), which represents the length of the _Source_:  
\[Attention(Query,Source)=\sum\
olimits_{i=1}^{Lx}Similarity(query,Key_{i})*Value _{i}\]  
Conceptually, attention can be understood as filtering out a small amount of important information from a large amount of information, and then focusing on the important information and ignoring the unimportant information. The process of focusing is represented in the calculation of the weight coefficient. The larger the weight, the more focus is placed on its corresponding _Value_, that is, the weight represents the importance of the information, and the _Value_ is its corresponding information.
# Cascaded Frequency Selective Surfaces with Matryoshka Geometry for Ultra-Wideband Bandwidth
## 1 Introduction  
Research and development on frequency selective surfaces (FSSs) using microstrips in a wide variety of applications has been addressed in many works [1; 2; 3; 4; 5]. The most well-known example of FSS application is the common microwave shield, but there are also uses such as radomes [6; 7] to protect the antenna system (typically radar antennas) from atmospheric agents such as wind, rain, snow, hail, and hot temperatures while also optimizing the signal reception. Another example of FSS application is in sub-reflectors [8], which optimize the signal received by an antenna.  
More recent uses of FSSs include smart windows [9; 10], which favor the signals by attenuating the noise and redirecting only the spectral components that are within desired frequency ranges, and wearable microstrips [11], which are circuits printed on fabrics like jeans to be used in clothes. For 5G applications, FSSs are used to improve the signals of reception and transmission, either by blocking unwanted signals or reflecting them to improve antenna performance. Other potential uses of FSSs include dual-band operation, the provision of electromagnetic shielding, and potential near-field applications [12].
## 4 Results and Analysis
### Free Fall Impact  
[PERSON] et al. [26] performed free fall experiments considering heave and roll. Figure 15 shows the results for the experimental condition \(\beta\) = 20\({}^{\circ}\), B = 0.61 m, A = 2.44 m, M = 124 kg, Vcg = 0.216 m, h = 1.22 m, \(\phi_{0}\) = 5\({}^{\circ}\), and inertia of section I\({}_{\mathrm{xx}}\) = 8.85 kg.m\({}^{2}\). The results of angular acceleration, vertical acceleration, vertical velocity, and heel angle are shown in Figure 15. The numerical results from the mathematical model have good agreement with CFD simulation and experiments reported by [PERSON] et al. [26] in terms of the displacements, velocities, and accelerations.  
Figure 14: Pressure distribution during the impact with three degrees of freedom, w = 5 m/s, v = 5 m/s, \(\omega=-2.5\) rad/s, \(\beta=45^{\circ}\), Vcg = 0.25 m, and \(\phi_{0}=-10^{\circ}\). (**a**) \(z_{0}\) = 0.3 m. (**b**) \(z_{0}\) = 0.4 m. (**c**) \(z_{0}\) = 0.5 m. (**d**) \(z_{0}\) = 0.6 m.  
Figure 15: _Cont._Impact with three degrees of freedom (heave, sway, and roll) was simulated according to a wedge evaluated by [PERSON] et al. [26]. The test conditions are: \(\beta=20^{\circ}\), B = 0.61 m, A = 2.44 m, m = 124 kg, I = 8.85 kg.m\({}^{2}\), Vcg = 0.216 m, h = 0.61 m, and \(\phi_{0}=5^{\circ}\). Figure 16 shows the results of accelerations and velocities for heave, sway, and roll motions. The results of the mathematical model were compared with CFD simulation and the numerical solution by [PERSON] et al. [10], in terms of the accelerations and velocities. The results show good agreement with the values reported by [PERSON] et al. [10].  
Figure 16: _Cont._  
Figure 15: Free fall impact with two degrees of freedom, m = 124 kg, h = 0.61 m, \(\beta=20^{\circ}\), \(\phi_{0}=5^{\circ}\), \(\mathrm{V_{cg}}\) = 0.216 m, and \(\mathrm{I_{xx}}\) = 8.85 kg.m\({}^{2}\). (**a**) Angular acceleration during the impact. (**b**) Heel angle during the impact. (**c**) Vertical acceleration during the impact. (**d**) Vertical velocity during the impact.  
Simulations of the free fall impact with three degrees were performed with a processor Intel I7-4510U [EMAIL_ADDRESS] GHz and 6 GB RAM memory, the computational time of the current model was \(1.83\times 10^{2}\) s while the CFD simulation with four parallel cores was \(2.508\times 10^{5}\) s. The current model has a low computational cost when compared to CFD.
# Methods of Atmospheric Coherence Length Measurement
## 2 Retrieval of \(r_{0}\) Based on \(C_{n}^{2}\)  
The atmospheric coherence length \(r_{0}\) can be easily derived from its definition, which is closely related to the value of \(C_{n}^{2}\). If known \(C_{n}^{2}\), one can calculate the value of \(r_{0}\) by dint of the definition. This method is referred to as the retrieval of \(r_{0}\) based on the atmospheric refractive index structure parameter, \(C_{n}^{2}\). There are multiple methods to measure \(C_{n}^{2}\), including the temperature structure measurement method, scintillation method and radar measurement method.
## 3 Results and Discussion
### Effect of the Presence of Graphene-Based Nanomaterials in Adsorbents on Methylparaben Adsorption  
Modification of activated carbon and activated olive stones by the addition of graphene oxide or reduced graphene oxide leads to an increase in the adsorption capacity of methylparaben on both adsorbents (Figure 7).  
In the case of activated carbon, the addition of graphene oxide or reduced graphene oxide leads to increases in the methylparaben adsorption capacity of 16.6% and 5.1%, respectively, while in the case of activated olive stones, the increases are 11.8% and 4.0%, respectively. These increases must be the result of the modifications that the addition of GO and rGO produces in the surface characteristics of activated charcoal and activated olive stones [63].  
On the one hand, graphene-based nanomaterials are characterized by their high porosity and high surface area [63], so their presence on the surface of the adsorbents under study should increase their surface area and adsorption capacity.  
On the other hand, GO is the oxidized derivative of graphene that maintains the polyaromatic p-electron system of graphene and contains oxygenated polar functional groups, including hydroxyl, epoxide, carbonyl and carboxyl groups, with the carbonyl and carboxyl groups located at the edges and the epoxide and hydroxyl groups located in the basal plane of GO. Reduced graphene oxide (rGO), as a reduced form of GO, contains the same basic structure as GO but with fewer oxygenated polar functional groups [64; 65; 66]. The additional contribution of oxygenated functional groups and polyaromatic p-electron systems that GO and rGO make to the surface of AC and AOS increases both the hydrogen bridge interactions (oxygenated functional groups of adsorbents/hydroxyl group of MPB) and the hydrophobic interactions (polyaromatic p-electron system of adsorbent/aromatic ring of MPB) between the adsorbents and MPB.  
All this leads to the above-mentioned increase in the adsorption capacity of methylparaben by the modified adsorbents, which, for the reasons indicated, is higher in the modification with GO than with rGO. The greater increase in adsorption capacity observed in the CA with respect to the AOS must be a consequence of the greater amount of nanomaterial retained by the former due to its greater surface area.
## 3 Application and Results
### Fault Diagnosis of Bearing with Fault at the Inner Raceway
With spectrum correction, the fault frequency 155 Hz and 154.9 Hz are almost equal to the theoretical fault frequency 155.3 Hz, as shown in Figure 8b. The noise and spectral intensity of each segment are different from each other, which affects the value of the spectrum correction, as shown in Figure 8b. However, it does not affect the accurate extraction of the fault frequency. Through spectrum correction, the sampling number of signals can be shortened, while keeping an accurate frequency value. Figure 6: Comparison chart of the Hilbert envelope spectrum of \(\mathit{PF}_{1}(t)\) which is from Figure 5a: (**a**) Without spectrum correction; (**b**) with spectrum correction. Figure 7: Comparison chart of the Hilbert envelope spectrum of \(\mathit{PF}_{1}(t)\) which is from Figure 5b: (**a**) Without spectrum correction; (**b**) with spectrum correction.
# Revisiting Label Smoothing Regularization with Knowledge Distillation
## 4 Method
### Gradient Analyses  
In the KD method, a factor \(\tau^{2}\) on the KL-loss is applied to stabilize the back-prop gradient while \(\tau\) is changing. Here, we analyze the KL-loss gradient with \(\tau\) in Equation (8) briefly, similar to [13]:  
\[\begin{split}\frac{\partial L_{KL}(p(\tau),u)}{\partial z_{m}}& =\frac{1}{\tau}(p_{m}(\tau)-u_{m})\\ &=\frac{1}{\tau}(\frac{exp(z_{m}/\tau)}{\sum_{i=1}^{M}exp(z_{i}/ \tau)}-u_{m}).\end{split} \tag{12}\]  
If the temperature is high with respect to the logits' magnitude and the logits of the model output have been zero-meaned, Equation (11) simplifies to:  
\[\begin{split}\frac{\partial L_{KL}(p(\tau),u)}{\partial z_{m}}& \approx\frac{1}{\tau}(\frac{1+z_{m}/\tau}{M}-u_{m})\\ &=\frac{1}{M\tau}(z_{m}/\tau-(Mu_{m}-1))\\ &=\frac{z_{m}}{M\tau^{2}}.\end{split} \tag{13}\]  
From the above, the LSR-OS stage penalizes large and confidence logit values [26] in the high-temperature limit. Since the gradient of the KL-loss is scaled by \(1/\tau^{2}\), it is necessary to multiply the KL-loss by \(\tau^{2}\) to ensure the relative contribution of the two losses (Equation (8)). As Figure 2b shows, the KL-loss drops dramatically with the temperature \(\tau\) growing. However, after \(\tau^{2}\) is multiplied, the KL-loss is stabilized to a roughly similar magnitude (Figure 2c).  
The reader may notice that we used a different temperature value for the second term in Equations (8) and (10). This is because the gradient of LSR-OS with teacher correction acts differently:\[\frac{\partial L_{KL}(p(\tau),p^{\prime})}{\partial z_{m}} =\frac{1}{\tau}(p_{m}(\tau)-p^{\prime}_{m}) \tag{14}\] \[=\frac{1}{\tau}(\frac{exp(z_{m}/\tau)}{\sum_{i=1}^{M}exp(z_{i}/\tau )}-p^{\prime}_{m}).\]  
Similarly, with the high temperature and zero-meaned logit output assumption, Equation (13) simplifies to:  
\[\frac{\partial L_{KL}(p(\tau),p^{\prime})}{\partial z_{m}} \approx\frac{1}{\tau}(\frac{1+z_{m}/\tau}{M}-p^{\prime}_{m}) \tag{15}\] \[=\frac{1}{M\tau}(z_{m}/\tau-(Mp^{\prime}_{m}-1))\] \[=\frac{1/M-p^{\prime}_{m}}{\tau}.\]  
Note that \(Mp^{\prime}_{m}-1\) is a non-zero constant. The result of Equation (14) gives us two inferences:  
1. First, to stabilize the learning of LSR-OS-TC, we need to multiply a factor \(\tau\) with the KL-loss term (Equation (10)) instead of \(\tau^{2}\), which is the traditional choice of knowledge distillation methods (Equation (6)).
2. Second, with an excessively high temperature, the logit loses the participation in the KL-loss gradient. In this case, the KL-loss gradient becomes constant and inoperative. A relatively small temperature \(\tau\) is a more reasonable choice.
## 3 Results
### Test in a Virtual Environment
Positional errors were measured as the distance between the true position of each camera's centre of projection and the estimated one. The orientation parameters, calculated as the difference between the three true axes angles and the estimated ones, were significantly less accurate with eight cameras. These cameras exhibited errors between \(10^{-1}\) and \(10^{-2}\) degrees for all three axes, compared to \(10^{-3}\) to \(10^{-4}\) degrees observed with a triple row of cameras. Figure 9 illustrates the errors distribution for the 8-camera configuration (left) and the 24-camera configuration (right). Errors within \(\pm 3\sigma\) are represented. Green indicates errors within \(\pm 0.02\) mm, red indicates errors up to \(0.5\) mm, and blue indicates errors down to \(-0.5\) mm. The mean distance error was \(0.06\) mm for the 8-camera configuration and \(0.03\) mm for the 24-camera configuration. Figure 10 shows the histogram of the errors ranging from \(-0.5\) mm to \(0.5\) mm. The yellow line represents the reconstruction error for both the 8-camera and the 24-camera configurations obtained using the true camera orientation parameters, serving as our reference for subsequent outcomes. The blue and the red lines display the results obtained by estimating the orientation parameters in the virtual environment using, respectively, 8 cameras and 24 cameras. The black line shows the distribution of the reconstruction error obtained with the full camera's configuration working in a relative frame. The 3D model was scaled within Agisoft using a known distance (scale bar) selected between two background GCPs and aligned by the Iterative Closest Point (ICP) plugin of Cloud Compare. Scaling accuracy was verified by measuring 10 check distances between different pairs of background GCPs, resulting in a mean error of \(0.44\) mm. Figure 9: Reconstruction errors. Error up to \(0.5\) mm are represented in red and error down to \(-0.5\) mm in blue. We tested different setups to minimize the number of cameras and our results indicate that cameras with reduced fields of view, due to their sensor dimensions, require three rows of cameras to create a robust image bundle even in the relative frame.
## 3 Results  
Within the initial study sample of 30 subjects, three individuals were excluded from the data analysis due to technical problems during the recording procedure. Thus, data are reported on a sample of 27 individuals (11 males, 16 females; mean age 28.3 \(\pm\) 1.7 years).  
Mean sleep duration was 7.0 \(\pm\) 1.3 h during the four nights. No considerable sleep interruptions that might have influenced outcomes were reported by any subjects.  
The average SB index over the four recording nights was 3.6 \(\pm\) 1.2 (range 0-9).  
The mean number of phasic, tonic and mixed events/hour was 2.1 \(\pm\) 1.5, 2.4 \(\pm\) 2.3 and 0.3 \(\pm\) 0.4 respectively (Table 1).  
The distribution of contraction types was comparable between the four recording nights (Table 2) and the ANOVA test (Table 3) shows that there are no statistically significant differences in any of the SB event types between the four recording nights.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Outcome Variable** & **Average Values (SD)** & **Range** \\ \hline SB index & 3.6 (1.2) & 0–9 \\ Phasic sMMA events per hour & 2.1 (1.5) & 0.1–6.7 \\ Tonic sMMA events per hour & 2.4 (2.3) & 0–12 \\ Mixed sMMA events per hour & 0.3 (0.4) & 0–1.2 \\ \hline \hline \end{tabular} SD, standard deviation; SB, sleep bruxism; sMMA, sleep-time masseter muscle activities.  
\end{table}
Table 1: Descriptive statistics of outcome variables during the four recording nights.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Night \#** & **\% Phasic sMMA Events** & **\%Tonic sMMA Events** & **\%Mixed sMMA Events** \\  & **(s.d)** & **(s.d)** & **(s.d)** \\ \hline
1 & 42.5 (20.5) & 51.6 (36.5) & 5.9 (6.1) \\
2 & 49.3 (26.8) & 44.4 (40.5) & 6.3 (5.6) \\
3 & 45.2 (27.8) & 50.3 (39.3) & 4.5 (4.7) \\
4 & 39.3 (22.8) & 53.4 (33.6) & 7.2 (7.5) \\ Mean & 44.1 (24.5) & 49.9 (37.5) & 6.0 (6.0) \\ \hline \hline \end{tabular} sMMA, sleep-time masseter muscle activities.  
\end{table}
Table 2: Percentage distribution of phasic, tonic, and mixed sMMA events over each recording night.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Outcome Variable** & **Night 1** & **Night 2** & **Night 3** & **Night 4** & **Sig.** \\ \hline SB Index & 3.4 (2.2) & 2.9 (1.6) & 4.2 (2.1) & 4.0 (1.9) & 0.084 \\ Phasic sMMA events per hour & 2.2 (1.2) & 2.3 (1.6) & 2.2 (2.0) & 1.8 (1.2) & 0.758 \\ Tonic sMMA events per hour & 2.7 (2.2) & 2.1 (2.4) & 2.5 (2.9) & 2.5 (1.8) & 0.790 \\ Mixed sMMA events per hour & 0.3 (0.4) & 0.3 (0.3) & 0.2 (0.3) & 0.4 (0.4) & 0.711 \\ \hline \hline \end{tabular} SB, sleep bruxism; sMMA, sleep-time masseter muscle activities.  
\end{table}
Table 3: Average values of outcome variables (s.d. in parenthesis) over each recording night and the ANOVA test for significant differences. (statistical significance: \(p\) < 0.05).  
No significant differences were found regarding gender comparison in any outcomes except for mixed sMMA events (\(p<0.05\)) (Table 4).
## 2 Radiation-Induced Second Cancer  
### Site of Origin  
Criteria have been defined for a malignant disease to be characterized as a radiation-induced second cancer [19; 20]. First, the second cancer develops at sites exposed to primary or secondary radiation. Second, the second cancer is not a metastasis, and it has a different histology from that of the irradiated first tumor. Third, there is no presence of the second cancer during RT. Fourth, the second cancer usually appears after a long latent period of years or decades from the RT course. Fifth, the irradiated patient does not have any predisposition for cancer syndromes.  
The site of origin of the second cancer varies widely depending upon the location of the irradiated initial disease. For example, prostate cancer RT leads to an increased risk of developing second cancers in the adjacent urinary bladder and rectum [21]. A significantly elevated probability for lung cancer induction was also observed following pelvic RT. Previous studies have discussed the distribution of the second cancers relative to their position with respect to the treatment volume [12; 13]. [PERSON] et al. [12] focused on second cancers presented in a cohort of 4581 patients who underwent RT for solid tumors or lymphomas. The mean follow-up was 15.4 years. They found that 22% of the second cancers developed at more than 2.5 cm inside the applied treatment field. Sixty-six percent of the secondary malignancies were presented from 2.5 cm inside and 5 cm outside the irradiated target, whereas 12% occurred at distant locations of more than 5 cm from the beam edge. [PERSON] and [PERSON] [13] studied 31,000 patients subjected to RT for various malignant diseases. Almost 50% of the second cancers appeared 2.5 cm within the field border and 5 cm outside the treatment volume, and 40% of the secondary malignancies appeared at distances exceeding 5 cm from the field edge. The above studies showed that a second cancer may appear at any site of the human body receiving ionizing radiation during treatment. The majority of the second cancers develop at the beam bordering the area around the target volume. However, a second cancer can develop at sites located far away from the primarily irradiated area.