esa-sceva/satcom-chunk-collection huggingface dataset:
# Quality Assessment of 2.5D Prints Using 2D Image Quality Metrics
## 4 Results and Discussion
### Are 2D IQMs Responsive to the Quality Variations in 2.5D Prints?
#### 4.1.1 Naturalness Set  
The differences are expected to be in elevation and fine details from surface roughness. Shadows might be introduced due to elevation and thus more differences on the edges are expected, especially in the MS captures because it captures the prints under multiple angle directional illumination.  
All selected IQMs are responsive to this set's images on the edges, on the background, and on the elevated parts' surfaces in both set-up captures. More specifically, the highest response is found on the edges. This shows that they are responsive to the differences between unnatural elevation and surface roughness prints (i.e., the significant differences between the two were in elevation and shadows induced from it and the secondary difference was in fine details). The differences in the prints are better captured by the IQMs in the MS captures than in the SS ones for the three images. Figure 4 illustrates the above-mentioned observations by iCID's lightness difference map on the tiles image where the difference comes from shadows that can appear on both sides of the grout line, which is better captured in the MS captures than in the SS ones. In addition, iCID's lightness structure map is responsive to both edges and fine details in the three images in this set in both set-up captures.  
Figure 4: iCID’s lightness difference (mean) quality maps for the tiles image with unnatural elevation print used as test image and surface roughness print used as reference image from the MS (**top**) and the SS (**bottom**) captures. It shows that iCID’s lightness difference map is the most responsive (i.e., detects larger differences) on the edges in both set-up captures and that the responsiveness is better captured in the MS captures compared to the SS ones. Zero means there is no difference.
## 4 Results and Discussion
### Consensus on Benefits
#### 4.2.6 Work Remotely  
Working remotely without spending time traveling to the site is another advantage of AR application. Five research papers reported that AR allowed site quality managers and trade managers to inspect construction works from the office without visiting the site [35; 36; 46; 47; 48]. This highlights the importance of communication supported by AR in the construction industry. Whatever the contexts of the users or the type of project in which the study has been conducted, researchers showed consensus about these benefits reported during the use of AR for communication.
# Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning
## 3 System Model
### Offloading Computing Model  
Let \(R_{j}\) denote the computing task size (bit) of the \(j-\)th UAV. Since communication overhead is generated during the offloading process, the number of offload task bits received by the AP can be expressed as:  
\[R_{j}^{off}=\theta\ R_{j},\ j\in U_{1} \tag{7}\]  
where \(\theta>1\) is the communication overhead coefficient, such as the message header or encryption.  
According to the Shannon formula of communication engineering knowledge, the offloading transmission power \(P_{j}\) is  
\[P_{j}=\frac{G}{c_{j}}(2^{\frac{\theta\ R_{j}}{T}\frac{\theta\ R_{j}}{B}}-1),\ j\in U_{1} \tag{8}\]  
where \(B\) denotes the communication bandwidth, \(P_{j}\) denotes the offload transmission power from the \(j-\)th UAV to the AP, and \(G\) denotes the Gaussian noise power in the channel from the \(j-\)th UAV to the AP. Therefore, the residual energy of the \(j-\)th UAV in a single time block is:  
\[E_{j}^{res}=E_{j}-E_{j}^{off}=\mu\ P\ c_{j}\ a\ T-\frac{Gb_{j}T}{c_{j}}(2^{ \frac{\theta\ R_{j}}{T}\frac{\theta\ R_{j}}{B}}-1),\ j\in U_{1} \tag{9}\]
## 4 Numerical Results  
### Angular Protection with Distance Protection: Exclusion Zone  
Figure 11 presents the reduction in the exclusion zone when using the proposed angular protection where different antenna types are involved with RMa (a), UMa (b), and UMi (c). A higher beam shut-off range implies a lower BS interference, leading to a smaller exclusion zone. For a 4 \(\times\) 4 antenna array, when 45\({}^{\circ}\) beam angles are sacrificed, the size decrease is negligible. In contrast, when using 8 \(\times\) 8 and 16 \(\times\) 16 antenna arrays, the 45\({}^{\circ}\) beam angle shut-off leads to a significant decrease in the exclusion zone.  
In addition, using a 16 \(\times\) 16 antenna array leads to a higher exclusion zone decrease because the antenna array can steer a narrower beam when more elements are in the antenna array. Without angular protection, the exclusion zone of the 8 \(\times\) 8 and 16 \(\times\) 16 arrays are larger than that of the 4 \(\times\) 4 array. However, when the beam shut-off range is 45\({}^{\circ}\), the exclusion zone of the 8 \(\times\) 8 array 16 \(\times\) 16 arrays become smaller than that of the 4 \(\times\) 4 array. Therefore, when the antenna array has more elements, the angular protection becomes more effective. As for UMi, a minor improvement is achieved when using 90\({}^{\circ}\), 180\({}^{\circ}\) and 270\({}^{\circ}\) beam shut-off ranges, compared with the 45\({}^{\circ}\) beam shut-off range. Without angular protection, the exclusion zone of the 8 \(\times\) 8 and 16 \(\times\) 16 arrays are greater than that of the 4 \(\times\) 4 array. However, when the beam shut-off range is set to 45\({}^{\circ}\) or higher, the exclusion zone of the 8 \(\times\) 8 and 16 \(\times\) 16 arrays become smaller than that of the 4 \(\times\) 4 array. In conclusion, the advantage of increasing the number of elements in the antenna array in terms of angular protection efficiency is more significant for UMi than RMa. Finally, a higher beam shut-off range results in a lower BS interference, which leads to a smaller exclusion zone.  
Figure 10: Combination of the exclusion and restriction zones: (**a**) angular protection and (**b**) flux gram.
## 4 Examples and Simulation
### Comparison of Training Time  
The three models were run in MATLAB. In Model 1, it takes 0.095 s for initialization and 0.052 s for online study. In Model 2, it takes the same time as that of Model 1 to initialize, but only 0.049 s for online study. It takes 0.076 s to train each time in Model 3. Therefore, it can be seen that online study can save about 30% of retraining time. However, it takes 6% more time in Model 1 than in Model 3 for every time online study is conducted.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Season** & **Model** & _nRMSE_ & _MAPE/\%_ \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Spring \\ (April–June) \\ \end{tabular} } & FOS-ELM & 0.0953 & 15.492 \\  & OS-ELM & 0.1041 & 16.730 \\  & ELM & 0.1126 & 18.483 \\  & FOS-ELM & 0.0892 & 14.329 \\  & OS-ELM & 0.0933 & 15.883 \\  & ELM & 0.1083 & 16.032 \\  & FOS-ELM & 0.0974 & 15.289 \\  & OS-ELM & 0.1018 & 16.325 \\  & ELM & 0.1219 & 17.933 \\  & FOS-ELM & 0.0876 & 15.245 \\  & OS-ELM & 0.0945 & 16.319 \\  & ELM & 0.0983 & 17.703 \\ \hline \end{tabular}
\end{table}
Table 1: Monthly average accuracy comparison.  
Figure 5: Comparison of three models on a cloudy/rainy day. (**a**) FOS-ELM; (**b**) OS-ELM; (**c**) ELM.
# Combined Access Barring Scheme for IoT Devices Using Bayesian Estimation
## 3 The Proposed Technique: Combined Access Barring
### Estimation on \(u\)
Let us denote the event \(F_{a}\) be the event and \(P(F_{a})\) its corresponding probability that \(a\)-th of the \(R-i-s\) channels is selected by at the most one IoT device, then we can write \(P_{col}(R-i-s,v-s)\) as:  
\[P_{col}(R-i-s,v-s)=1-P\left(\bigcup_{a=1}^{R-i-s}F_{a}\right) \tag{18}\]  
where \(P\left(\bigcup_{a=1}^{R-i-s}F_{a}\right)\), by using inclusion-exclusion principle [21], can be written as follows:  
\[P(u|(i,s))=\frac{P(u,i,s)}{P(i,s)} \tag{10}\]0  
where \(S_{b}(R-i-s,v-s)\) represents the probability that \(b\) out of \(R-i-s\) preambles are selected by at the most \(v-s\) devices. We can evaluate (19) by using portion of (17) as follows:  
\[P(u|(i,s))=\frac{P(u,i,s)}{P(i,s)} \tag{10}\]1  
Substituting (20) into (19) gives:  
\[P(u|(i,s))=\frac{P(u,i,s)}{P(i,s)} \tag{10}\]2 \[P(u|(i,s))=\frac{P(u,i,s)}{P(i,s)} \tag{10}\]3 \[P(u|(i,s))=\frac{P(u,i,s)}{P(i,s)} \tag{10}\]4  
Using (22) along with (10), we can evaluate (9) as follows:  
\[P(u|(i,s))=\frac{P(u,i,s)}{P(i,s)} \tag{10}\]5  
The change in backlog can be calculated as follows:  
\[P(u|(i,s))=\frac{P(u,i,s)}{P(i,s)} \tag{10}\]6  
Now, if we put optimal \(p\) from (16) in (24) through (23), and then dividing it by 10, we can get \(\Delta x\) as follows:  
\[P(u|(i,s))=\frac{P(u,i,s)}{P(i,s)} \tag{10}\]7  
The division by 10 is because of the assumption that the incoming devices are uniformly distributed in all of the 10 classes. The procedure involving backlog estimation is shown in Algorithm 2 with \(\gamma\) is used as boosting factor to incorporate bursty nature of traffic [13]. In every slot, \(x\) is updated to find optimal \(p\) by updating \(\Delta x\) in each time slot. ```
1: Extract bitmap from SIB-14, \(p=1\), \(x=0\)
2:if the class is unbarred then
3: Generate random number, \(G\in(0,1)\)
4:if\(G<p\)then
5: Choose preamble among \(R\) and transmit
6:endif
7:endif
8: Observe the number of idle and success preambles among \(R\)
9:if\(i\) idle and \(s\) successful preambles then
10:\(\Delta x=0.13922\times R-0.3922\times i-0.13922\times s\)
11:if\(\Delta x>0\)then
12:\(\gamma=\gamma+1\)
13:else
14:\(\gamma=R\)
15:endif
16:\(x_{t-1}=x_{t-1}+\gamma\times\Delta x\)
17:\(x_{t}=x_{t}+\Delta x\)
18:endif
19:\(x_{t}\)
20:\(x_{t}=\max\left(R,x_{t}\right)\)
21:\(p=\min\left(1,\frac{R}{x_{t}}\right)\) % To be used for the next time slot
22: Repeat the process from Step 2 in every time slot
```  
**Algorithm 2** Combined Access Barring (CAB) Scheme using Bayesian Estimation
## 3 Results
### Spectral Analysis of Dandelion Inulin
#### 3.3.3 FT-IR Spectra of Dandelion Inulin  
The obtained FT-IR spectra of the isolated dandelion inulin after classical, ultrasound-assisted, and microwave-assisted extraction are shown in Figure 4.  
It was noticed that all the spectra contain main bands characteristic of inulin-type fructan. In general, the FT-IR spectra show a broad band at about 3370 cm\({}^{-1}\) typical for the stretching vibrations of OH groups and inter- and intra-molecular hydrogen bonds. The band at 2935 cm\({}^{-1}\) was assigned to the asymmetric stretching vibrations of CH\({}_{2}\) groups (vC-H\({}_{88}\)(CH\({}_{2}\))), and the bands at 2893 cm\({}^{-1}\) to the symmetric stretching vibrations vC-Hs(CH\({}_{2}\)). The 1647 cm\({}^{-1}\) band is due to the absorption of water, while the bands at 1330 cm\({}^{-1}\) and 1255-1240 cm\({}^{-1}\) are typical for \(\beta_{\rm O-H}\) (OH). The band at 1143 cm\({}^{-1}\) is assigned to C-O-C ring stretching vibrations from glycoside linkage. The bands at 1033 cm\({}^{-1}\) and 989 cm\({}^{-1}\) are assigned to C-O stretching vibrations in the furanose ring, and the band at around 937 cm\({}^{-1}\) indicates the presence of \(\beta\)-(2\(\rightarrow\)1) linkage in inulin. The typical bands for fructans were noticed in the fingerprint region: 869 cm\({}^{-1}\) due to \(\beta\)-anomer bendings C1-H, ring vibration of 2-ketofuranose, and 820-819 cm\({}^{-1}\) due to 2-ketose in the furanose ring. Moreover, the bands of dandelion inulin were overlaid with chicory inulin Raftiline for comparison (Figure 3 line d). It was obvious that the bands coincided, which additionally confirmed the inulin structure of isolated polysaccharides. The applied ultrasonic and microwave irradiation did not cause changes in inulin bands.
# _photonics_
## 4 Simulation Results
### QKD Link Performance of an LEO Satellite Constellation over Greek QCI  
The satellite constellation considered in this study consists of 10 LEO sun synchronous satellites at an orbital altitude of about 600 km with an inclination angle of 97.4\({}^{\circ}\). Each satellite flies over Greece approximately twice a day. The initial step towards the satellite QKD link modeling involves the estimation of time instances when each one of the ground stations views each satellite with an elevation angle greater than 20\({}^{\circ}\). For this estimation, we used the latitude, longitude, and orbital height data of each satellite that are obtained every 10 s employing the AGI/STK system tool [58].  
By having all the time instances when the satellites are visible by the OGSs, we considered only nighttime communication, as discussed in the previous subsection. The time period between 6:00 am to 6:00 pm was considered as the daylight period; therefore,  
Figure 3: (**a**) Normalized SKR (bps/pulse) over solar radiance (watt/sr m\({}^{2}\upmu\)m) over distance (km). (**b**) Normalized SKR (bps/pulse) over solar radiance (watt/sr m\({}^{2}\upmu\)m) over elevation angle (deg) for a satellite orbit altitude of 600 km.  
no key rates could be distilled during this time. In Figure 4 the satellites route during 24 h is depicted.
# The Legal Framework for Space Debris Remediation as a Tool for Sustainability in Outer Space
###### Abstract  
The growth of orbital space debris is both a consequence of and a potential hindrance to space activities. The risks posed by space debris propagation in the most used orbital regions highlight the need to adequately address the challenges posed to the sustainability in outer space. The preservation of the access to and usability of outer space in the long-term requires that action is taken which has to be the result of both mitigation and remediation measures for existing and future space missions. As the enforcement of such technical measures will depend on adequate regulation, they need to be approached also from a legal perspective. The deficiencies in law for space debris remediation mechanisms originate from the fact that although technical concepts have been developed, the legal framework for space activities does not impose any legal obligations for debris removal and on-orbit servicing. Nevertheless, an overview of the relevant legal framework shows that there is a legal basis for the protection of the outer space environment which can, as has already been the case with space debris mitigation guidelines, be substantiated in more concrete terms by the formulation of voluntary, non-binding instruments and included in national legislation.  
space debris; remediation; sustainability; space law; environmental protection; orbital propagation; reference models; space debris mitigation guidelines; active debris removal Article
###### Abstract  
This paper studies the coupling effect of laser intensity and waist radius on a series of electron radiation characteristics, such as radiation power, electron trajectory, a time spectrum, and a frequency spectrum. Through a large number of numerical simulations, based on the study of all radiation patterns, the influence laws and mechanisms of these two parameters are revealed. The larger the light intensity \(\alpha_{\omega}\), the greater the influence of the waist radius \(\omega_{0}\) on the symmetry of the radiation power. When the light intensity is the same, the larger the waist radius, the greater the radiation power, the better the collimation, and the radiation power approximately satisfies a quadratic function. These results indicate that the radiation power can be rapidly increased by seven orders of magnitude through adjustment. Through a numerical simulation, we have obtained high-energy, high-frequency, high-collimation, good directivity, and supercontinuum X-rays. The above research has a pioneering guiding significance for the modulation of X-rays in optical laboratories.  
laser physics; nonlinear Thomson scattering; chirped laser pulse; radiation characteristics +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods  
### Sample Collection, Preparation, and Analysis  
A total of 96 sediment samples were collected from the estuary and the nearby coastal area (Figure 1). Surface sediments (0-5 cm) were collected from the submerged parts of the estuary using a stainless-steel Van Veen grab positioned on a boat. Approximately 1 kg of  
Figure 1: Study area and sampling stations.  
sediment was collected from each sampling point, homogenized in the field, stored in a double zip-lock bag, and transported to the laboratory in a portable fridge. Each sample was air-dried and divided into different representative subsamples, using a rifle-type sample splitter with a removable hopper. For grain size analysis, an aliquot of each sample was treated with a solution of 3% (\(v/v\)) H\({}_{2}\)O\({}_{2}\) for 48 h to remove most of the organic matter. The sample was then wet-sieved to 2 mm and analyzed with a Fritsch ANALYSETTE MicroTec Plus 22 (Fritsch Company, Markt Einersheim, Germany). Grain size data have been synthesized according to the Spanish maritime regulations in sand-mud (silt + clay) notation. Major and trace elements were determined on 1 g representative samples, which were digested in a microwave using the pseudototal aqua regia (HCI+HNO\({}_{3}\)) method, filtered and analyzed by inductively coupled plasma mass spectroscopy (HP 7700 Agilent Technologies ICP-MS) at the ALS Global Geochemistry laboratory (Sevilla, Spain). The accuracy and precision of the results were verified by comparison against an analysis of the Standard Reference Materials RTC-CRM026-050 (Sandy Loam 9), CRM042-056 (Sandy Loam 2), OREAS 503c (Rock) and MRGe008 (Rock). Quality control samples were prepared with standard solutions (Merck and Icus 3058 Custom Standard, Ultra-scientific), analytical blanks and duplicates of random samples.  
Total organic carbon (TOC) was measured using a Shimadzu TOC-V CSH. The TOC analysis proceeded through progressive acidification with HCl (0.1-1.0 M) at a combustion temperature of 920 \({}^{\circ}\)C according to the method set out by [28]. Acetanilide was used as the standard compound for calibration.  
The results of the analytical analysis were treated with free statistical software R and the dispersion models were evaluated by the inverse distance weighted (IDW) method with the commercial Geographic Information System ArcGIS under the license of the University of Oviedo Subsoil and Environmental Research Team. Statistical treatments comprised normality tests such as the Anderson-Darling, Kolmogorov-Smirnov and Pearson's correlation analyses.
# The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines
## 4 Results and Discussion
### Model Complexity and Interpretability  
* Linear Regression: LR is the most elementary of the three models, yielding highly interpretable outcomes with direct coefficients that represent the correlation between features and the target variable. Nonetheless, it may encounter difficulties in capturing intricate, non-linear interactions.
* Random Forest Regression: RFR is an advanced, non-linear model that identifies relationships among variables and accommodates intricate patterns within the data. Nonetheless, it compromises interpretability for enhanced efficiency, as the aggregation of decision trees complicates the understanding of each feature's individual impact.
* Lasso Regression: Employs regularization to penalize insignificant characteristics, hence potentially streamlining the model by removing unimportant variables. This enhances interpretability and mitigates overfitting. Nonetheless, it fails to account for non-linearity in the data.  
Figure 19: Comparison of regression models by metrics (RMSE, MAE, MSE).
## 2 Data and Methods
### Evaluation Criteria  
Three criteria were employed to evaluate the accuracy of the GPP, ET, and eWUE from the coupled PMLv2 and uncoupled MODIS products against the in-situ observation,including the root mean square error (RMSE), the coefficient of determination (R\({}^{2}\)), and the average absolute errors (Bias). They are calculated as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum\
olimits_{i=1}^{N}{(Var_{sim}(i)-Var_{EC}(i)) ^{2}}} \tag{2}\]  
\[\text{R}^{2}=\left(\frac{\sum\
olimits_{i=1}^{N}{(Var_{EC}(i)-\overline{Var_{ EC}})\left(Var_{sim}(i)-\overline{Var_{sim}}\right)}}{\sqrt{\sum\
olimits_{i=1}^{N}{ (Var_{EC}(i)-\overline{Var_{EC}})^{2}}}\sqrt{\sum\
olimits_{i=1}^{N}{(Var_{ sim}(i)-\overline{Var_{sim}})^{2}}}}\right)^{2} \tag{3}\]  
\[\text{Bias}=\frac{\sum\limits_{i=1}^{N}{(Var_{sim}(i)-Var_{EC}(i))}}{N} \tag{4}\]  
where _Var_ represents the variables of GPP, ET, or eWUE; subscript _sim_ represents the GPP, ET, or eWUE estimates from the coupled PMLv2 and uncoupled MODIS products; subscript _EC_ represents the GPP, ET, or eWUE observed by the eddy covariance system; \(i\) represents the ith eight-day data; \(N\) represents the sample number; and the over-bar represents the mean value.
## 2 Theoretical Considerations
This mechanism provides a unique advantage over conventional retroreflectors by exploiting the narrowband nature of SPR and the angular specificity afforded by the two grating pitches.
## 3 Photon Polarization  
When a high-energy electron bunch scatters off a laser pulse of sufficient intensity, high-energy \(\gamma\) photons are produced as demonstrated in the previous section. We focus on photon polarization theoretically. Polarization is an important feature of photons, and high-energy polarized photons show potential applications in many research fields. Correct and high-precision calculations of photon polarization using the QED theory are necessary in the theoretical investigations of upcoming QED experiments. In this section, we theoretically calculate the photon polarization probability and polarization degree.