esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimal Sensing Settings for SU Spectral Efficiency
### Optimal Sensing Settings of the Novel ST-CSS Framework with Maximal SU Spectral Efficiency  
As analyzed in the novel CSS scheme, the \(i\)-th SU equivalent compressed sensing time amounts to \(\tau+(i-1)\,t_{r}\). To overcome the adverse effects on detection performance in time-varying channels, time diversity gain can be achieved through combining multi-slot sensing results. It is assumed that individual sensing time is divided into \(L\) slots, and interval of each slot is \(\Delta t_{i}=(\tau+(i-1)t_{r})/L\) The compressed samples at the \(l\)-th slot of \(i\)-th SU is given as: \(y_{i,l}=\eta\Phi_{i,l}h_{i,l}s_{i,l}+\Phi_{i,l}n_{i,l}\), where \(\eta=0,1\) denotes the absence and presence of PU, \(\Phi_{i,l}\) is the measurement matrix at \(l\)th slot of \(i\)-th SU, \(s_{i,l}\) is the signal received at \(l\)-th slot of \(i\)-th SU from PU and the signal power is constant over the \(L\) slots, i.e., \(\sigma_{s,i}^{2}=\|s_{i,l}\|_{2}^{2}\), the same as noise power \(\sigma_{n}^{2}=\|n_{i,l}\|_{2}^{2}\), \(h_{i,l}\) is the channel gain between \(l\)th slot of \(i\)-th SU and PU. The output of compressive detector is given as: \(\theta_{i,l}=y_{i,l}^{T}\Phi_{i,l}h_{i,l}s_{i,l}\). The aggregate compressed statistics \(v_{i}\) used for local decision of \(i\)-th SU is obtained by summing \(\vartheta_{i,l}\) with its corresponding weight \(\omega_{i,l}\) as follows:  
\[v_{i}=\sum_{l=1}^{L}\omega_{i,l}\vartheta_{i,l} \tag{47}\]Note that \(\sum\limits_{l=1}^{L}\omega_{i,l}^{2}=1\). Then, the mean and variance of \(v_{i}\) at \(\eta=0\) and \(\eta=1\) are derived as:  
\[\left\{ \begin{array}{l}E(v_{i}|\eta=0)=0\\ Var(v_{i}|\eta=0)=\frac{(\tau+(i-1)t_{r})f_{s}}{NL}\sigma_{n}^{2}\sigma_{s,i}^{ 2}\sum\limits_{l=1}^{L}\omega_{i,l}^{2}\big{|}{h_{i,l}}\big{|}^{2}\\ E(v_{i}|\eta=1)=\frac{(\tau+(i-1)t_{r})f_{s}}{NL}\sigma_{s,i}^{2}\sum\limits_{l =1}^{L}\omega_{i,l}\big{|}{h_{i,l}}\big{|}^{2}\\ Var(v_{i}|\eta=1)=\frac{(\tau+(i-1)t_{r})f_{s}}{NL}\sigma_{n}^{2}\sigma_{s,i}^ {2}\sum\limits_{l=1}^{L}\omega_{i,l}^{2}\big{|}{h_{i,l}}\big{|}^{2}\end{array} \right. \tag{48}\]  
From now on, we call the above proposed scheme spatiotemporal CSS (ST-CSS), which combines the space diversity gain with time diversity gain.
# Transfer Accuracy of Two 3D Printed Trays for Indirect Bracket Bonding--An In Vitro Pilot Study
## 5 Conclusions  
* The present study found that intraoral scanning may severely impede measurements to assess the accuracy of bracket transfer, whereas micro-CT was shown to be a highly reliable alternative for in vitro settings.
* We demonstrated that linear discrepancies were below the ABO-range of 0.5 mm, most of the angular discrepancies were not within the clinical acceptable limit of 2\({}^{\circ}\).
* Severe crowding and transfer tray hardness have an impact on transfer tray accuracy, and bonding with the soft transfer tray was more accurate in cases of severe crowding.
* Front teeth were most frequently affected by bonding errors, followed by canines and molars.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11136013/s1](https://www.mdpi.com/article/10.3390/app11136013/s1)]([https://www.mdpi.com/article/10.3390/app11136013/s1](https://www.mdpi.com/article/10.3390/app11136013/s1)). Table S1: Intra- and interrater reliability of the different workflows.  
R.J.: Conceptualization, methodology, investigation, formal analysis, writing--original draft. J.B.: investigation, writing--review and editing. A.S.: methodology, investigation, writing--review and editing. M.H.: investigation, writing--review and editing. R.K.: investigation, writing--review and editing. N.R.: investigation, writing--review and editing. P.P.: resources, supervision, writing--review and editing. D.D.: resources, supervision, writing--review and editing. K.B.: conceptualization, investigation, formal analysis, writing--original draft. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data will be provided upon reasonable request.  
The authors thank Dentaurum and C3D.digital for donating the brackets/tubes and the DLP printed transfer trays utilized in the present investigation.
## 4 Simulation Results and Discussion
### Case Studies
#### 4.1.2 Case Study 2 (8 \(\Omega\) Shunt Resistor)  
In this case, the shunt resistor was set to 8 \(\Omega\). The fault current and the corresponding voltage profile of the single line-to-ground fault are depicted in Figure 4. The r-SFCL with the 8 \(\Omega\) shunt resistor limited the fault current to 3.1 and 2.87 kA for the first and second peak, respectively, representing a reduction of 50.4% for the first peak compared to the case without an r-SFCL. The voltage dropped to about 11.59 kV\({}_{\mathrm{rms}}\). Looking at the previous case study, it is clear that increasing the shunt resistor increased the percentage limitation of the fault current and decreased the associated voltage drop.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Conductor type & AMSC-8602 \\ Conductor width (mm) & 12 \\ Critical current (A) & 241 \\ SC layer thickness (\(\upmu\)m) & 1 \\ Substrate thickness (\(\upmu\)m) & 75 \\ Substrate material & Ni-5 at.\%W \\ Stainless steel thickness (\(\upmu\)m) & \(75\times 2\) layers \\ Stainless steel material & (SUS)316L \\ Tape length (m) & 300 \\ \hline \end{tabular}
\end{table}
Table 2: Specifications of AMSC-8602 superconducting tape.  
Figure 3: Fault response without and with r-SFCL using 4 \(\Omega\) shunt resistor: (**a**) fault current; (**b**) voltage drop; (**c**) r-SFCL temperature; (**d**) current analysis.  
Figure 4: Fault response without and with r-SFCL using 8 \(\Omega\) shunt resistor: (**a**) fault current; (**b**) voltage drop; (**c**) r-SFCL temperature; (**d**) current analysis.  
The r-SFCL's temperature is shown in Figure 4c; it reached 294 K at its peak in this case study. The r-SFCL completely recovered at T = 7.78 s, 5.78 s after the fault occurred, and returned to the coolant boiling temperature of 77 K. The r-SFCL needed more time to fully recover in this case than in the previous case, taking 5.78 s compared to 3.77 s with the 4 \(\Omega\) shunt resistor. Before the fault occurred, the current only flowed through the r-SFCL and no current flowed through the shunt resistor, as shown in the current distribution in Figure 4d. After the fault occurred, both the temperature and the r-SFCL's resistance increased, causing most of the current to flow through the shunt resistor, while the remaining current still flowed through the r-SFCL.
# Resource Allocation in Unmanned Aerial Vehicle (UAV)-Assisted Wireless-Powered Internet of Things
## 4 Optimal Solutions for Energy Transfer
\begin{array}{l}\left[ \begin{array}{l}\sum\limits_{i=1}^{N_{L}}\biggl{[}-\frac{1}{4}\pi_{L}^{2}(t) \frac{l_{H_{i}^{2}(t)}^{2}}{\delta_{L_{i}^{2}(t)}^{2}}-\pi_{L}(t)\gamma_{th} \frac{l_{L_{i}^{2}(t)}}{\delta_{L_{i}^{2}(t)}}\biggr{]}-c_{L}p_{L}^{2}(t)\\ -c_{L}^{L}\sum\limits_{i=1}^{N_{L}}\biggl{[}\frac{1}{2}\pi_{L}(t)\frac{l_{H_{ i}^{2}(t)}^{2}}{\delta_{L_{i}^{2}(t)}^{2}}+\gamma_{th}\frac{l_{L_{i}^{2}(t)}}{ \delta_{L_{i}^{2}(t)}}\biggr{]}\\ -c_{L}^{H}\sum\limits_{i=1}^{N_{H}}\biggl{[}\frac{1}{2}\pi_{H}(t)\frac{l_{H_{ i}^{2}(t)}^{2}}{\delta_{H_{i}^{2}(t)}^{2}}+\gamma_{th}\frac{l_{L_{i}^{2}(t)}}{ \delta_{H_{i}^{2}(t)}}\biggr{]}-\mu_{L}x(t)\\ +V_{x}^{L}(t,x)\end{array}\right\}\end{array} \right. \tag{14}\]  
Calculating the partial derivative for \(\pi_{H}(t)\) and \(\pi_{L}(t)\) in (13) and (14), the optimal unit price for the wireless power transfer can be given as follows:  
\[\pi_{H}^{*}(t)=V_{x}^{H}(t,x)e^{rt}-\varepsilon_{H}^{H}-2\gamma_{th}\sum \limits_{i=1}^{N_{H}}\frac{\delta_{H,i}(t)}{I_{H,i}(t)}+\sum\limits_{j=1,j \
eq i}^{N_{H}}\frac{l_{H,j}^{2}(t)}{\delta_{H,i}^{2}(t)}\cdot\frac{g_{H,i}^{2 }(t)}{l_{H,i}^{2}(t)}\Bigl{[}V_{x}^{H}(t,x)e^{rt}-\varepsilon_{H}^{H}\Bigr{]}, \tag{15}\]  
\[\pi_{L}^{*}(t)=V_{x}^{L}(t,x)e^{rt}-\varepsilon_{L}^{L}-2\gamma_{th}\sum \limits_{i=1}^{N_{L}}\frac{\delta_{L,i}(t)}{I_{L,i}(t)}+\sum\limits_{j=1,j \
eq i}^{N_{L}}\frac{l_{L,j}^{2}(t)}{\delta_{L,j}^{2}(t)}\cdot\frac{g_{L,i}^{ 2}(t)}{l_{L,i}^{2}(t)}\Bigl{[}V_{x}^{L}(t,x)e^{rt}-\varepsilon_{L}^{L}\Bigr{]}. \tag{16}\]  
Meanwhile, solving (13) and (14), the optimal allocated resource for wireless energy transfer \(p_{H}(t)\) and \(p_{L}(t)\) can be given as follows:  
\[p_{H}^{*}(t)=\frac{\eta_{H}V_{x}^{H}(t,x)e^{rt}}{2c_{H}}, \tag{17}\]  
\[p_{L}^{*}(t)=\frac{\eta_{L}V_{x}^{L}(t,x)e^{rt}}{2c_{L}}. \tag{18}\]  
**Theorem 1**. : _The value functions \(V^{H}(t,x)\) and \(V^{L}(t,x)\) given in (11) and (12) can be obtained as follows:_  
\[V^{H}(t,x)=[A_{H}(t)x+B_{H}(t)]e^{-rt}, \tag{19}\]  
\[V^{L}(t,x)=[A_{L}(t)x+B_{L}(t)]e^{-rt}, \tag{20}\]  
_where \(A_{H}(t)\) is given by_  
\[A_{H}(t)=\biggl{(}\frac{\mu_{H}}{r-\delta}+s_{H}\biggr{)}e^{(r-\delta)(t-T)}- \frac{\mu_{H}}{r-\delta}, \tag{21}\]\[A_{H}(T)=s_{H}, \tag{22}\]  
_and \(A_{L}(t)\) is given by_  
\[A_{L}(t)=\left(\frac{\mu_{L}}{r-\delta}+s_{L}\right)\!e^{(r-\delta)(t-T)}-\frac{ \mu_{L}}{r-\delta}, \tag{23}\]  
\[A_{L}(T)=s_{L}. \tag{24}\]  
**Proof.
# Intelligent Computation Offloading Based on Digital Twin-Enabled 6G Industrial IoT
## 2 Related Work
### Edge Computation Offloading Supported by AI  
Integrating artificial intelligence into DT-enabled edge computing and deploying it on edge servers can better optimize offloading policies. The authors of [34] considered service caching and task dependencies and proposed a DT-based mobile edge computing architecture that can support mobile users in offloading dependency-aware tasks. In [35], the authors satisfied the latency tolerance for latency-sensitive tasks by considering both computation offloading and service caching. The authors in [36] proposed an algorithm for learning optimal task offloading and service caching policies with a multi-intelligence reinforcement approach based on graph attention. The authors in [37] solved the computation offloading and resource allocation problems by using the AAC algorithm. They used Liapunov optimization techniques to equivalently transform the original problem into a deterministic per time slot problem. The authors in [38] effectively solved the problem of intelligent computation offloading for UAVs. A new approach was proposed in [39] that allowed distributed mobile edge servers to collaborate on peer-to-peer offloading tasks to balance compute workloads on edge servers.  
By analyzing these previous works, we can summarize the relevant research as follows. First, a DT-enabled network architecture can provide a solution for some risky field environments. Second, an AI algorithm can effectively solve the computation offloading problem in a complex dynamic environment. This is exactly what we will address next. In addition, we consider for the first time the heterogeneity of tasks generated by IIoT devices and how to optimize offloading strategies for such tasks.
# Study on Deformation Characteristics of Low-Highway Subgrade under Traffic Load
## 1 Introduction  
Expressways have become the central infrastructure for interregional transportation, due to their advantages such as fast speed, high efficiency, and large carrying capacity [1, 2, 3]. As the main force-bearing part of the road, the subgrade bears millions of traffic loads over many years [4]. Its strength, rigidity, and long-term stability will directly affect the road's service performance and service life. Under the action of high-speed, heavy-duty, and large-flow traffic cyclic loads, the subgrade-material particles often slide and squeeze to cause the particles to rearrange and move [5, 6]. Under long-term traffic load, the subgrade-filler skeleton is not stable, which leads to the gradual deterioration of the service performance of the subgrade structural layer, resulting in road diseases such as excessive settlement, uneven settlement, and road-surface cracking [7, 6, 8].  
With the innovation of test apparatus, engineers have developed dynamic modules based on static triaxial tests. At present, the dynamic triaxial test has become one of the main methods to research the dynamic deformation characteristics of the soil [9, 10]. Experts and scholars have studied the dynamic characteristics of soil structures to meet the design requirements of actual complex projects. In the early stage, the experimental study of dynamic characteristics is mainly based on seismic load. The test has the characteristics of high confining pressure, enormous strain, and a small number of cycles [11, 12, 13]. However, subgrade fillings are mainly subjected to traffic loads, and their stress state is characterized by low confining pressure and low dynamic-stress amplitude [14]. The strain is mainlyelastic response, and under the action of large long-term cycles of load, it is manifested as plastic-strain accumulation [15; 16]. Therefore, it is necessary to carry out many cyclic triaxial tests to deeply study the deformation behavior of subgrade fillings under long-term traffic loads [17; 18]. On the other hand, many experts and scholars have conducted much research on the dynamic-response characteristics of subgrade under the action of traffic load by using numerical calculation methods based on actual engineering [19; 20; 21]. However, because the attenuation rule of subgrade-filling modulus under the action of traffic load is not clear, the traditional numerical calculation can only qualitatively analyze the rule of subgrade deformation under traffic load.  
Therefore, this paper carried out a dynamic triaxial test to study the time-evolution rule and spatial-distribution rule of the accumulated strain and pore-pressure ratio of subgrade soil under the action of traffic load. The numerical-calculation model of subgrade settlement under traffic load was established, and the deformation characteristics of subgrade under dynamic cyclic load were studied.
# Detecting and Mitigating Attacks on GPS Devices
## 6 Countermeasures and Their Comparison
### Countermeasures for GPS-Denied Environment--Alternate Positioning
#### 6.3.2 Landmark-Based Approaches
Furthermore, many of these approaches rely on ML-based neural network approaches that will place a computational requirement on the UAS. There are several technical challenges associated with visual landmark-based navigation. These methods are typically computationally expensive and may not be suitable for many unmanned systems; visual methods generally do not perform well in low-light scenarios, which may limit the scenarios in which this method may perform well; and visual methods do not perform well when reference scenery or objects change. Causes of change can be manmade, such as in the presence of construction, or natural, such as changes in seasons or time of day.
## 4 Case Studies
### Three-Dimensional Point Cloud of San Nicola in Montedoro Church
#### 4.2.2 Survey of the Terrestrial Laser Scanner of the External Part of the Structure  
Regarding the generation of the model for the external part of the church, the survey was carried out by a terrestrial laser scanning survey. In this case study, FARO FocusS 350 instruments were used because specially designed for outdoor applications. HDR imaging and HD photo resolution (overlay up to 165-megapixel colour) ensure true-to-detail scan results with high data quality (distance accuracy up to \(\pm\) 1 mm). The main features of this scanner are summarized in the following Table 1:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Distance Accuracy** & **Range** & **Measurement Rate** & **Laser Class** & **Integrated Colour Camera** & **Operating Temperature** \\ \hline up to \(\pm\) 1 mm & 0.6 m to 350 m & up to 976,000 points/s & 1 & yes & +5 °C to + 40 °C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main Features of the FARO FocusS 350 terrestrial laser scanner (TLS).  
Figure 4: Images of some ground control points used for the georeferencing of the point cloud.  
In order to cover the entire external surface of the church, three acquisition stations were built.  
The post-processing of the TLS scans was performed in Autodesk Recap software. This software, where the word \"Recap\" stands for Reality Capture, allows a fully automatic recording of the scans. In the case the procedure is partially successful, the software allows manual identification of targets and natural homologous points, to reduce distance among contiguous scans, improving their alignment using the iterative closest point (ICP) algorithm [21].  
2.3 Unmanned Aerial Vehicle (UAV) Photogrammetry to Obtain the 3D Point Cloud of the Upper Part of the Church  
The aerial survey was carried out using a Parrot Anafi, a UAS (unmanned aerial system) quadcopter equipped with a Sony Sensor(r) 1/2.4\" 21 MP (5344 \(\times\) 4016) CMOS (complementary metal-oxide semiconductor), which allows obtaining, thanks also to a 3-axis stabilizer, clear and detailed images (Figure 5a). The distance between the UAV and the building was really close due to the presence of many obstacles in the old town where the church is located. Consequently, the images were acquired with high geometric resolution (Figure 5b). In any case, the photogrammetric survey was carried out with a high degree of overlap between the images. In addition, by varying the tilt angle of the camera, it was possible to acquire images of every part of the building. In this way, it was possible to build a network of the 97 images with a high degree of overlap and convergent image configuration (Figure 5c).  
Taking into account 5 GCPs, the root mean square error (RMSE) for spatial coordinates, evaluated on the cameras used in this dataset, was of 0.009 m; in particular, this RSME refers to the georeferencing process of the images and not to the resolution of the model. In addition, the modelling of the roof was obtained through the insertion of its own and accidental load.
# A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
## 4 Experiments and Results
### LC Classification Performance  
The confusion matrix for our U-Net-based LC classifier is shown in Figure 5. Seventy percent of the _EO-learn_ dataset was used for training and the rest for validation. The learning rate used was 0.001, and the batch contained 32 input patches. The model was trained for 100 epochs.  
Figure 5: LC classification performance: a confusion matrix analysis.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **MAE** & **MSE** & \(\mathbf{R^{2}}\) \\ \hline Linear Regression & 2.007 & 13.4461 & 0.22 \\ \hline Ridge & 2.007 & 13.4461 & 0.22 \\ \hline ElasticNet & 2.0179 & 13.7324 & 0.2038 \\ \hline K-Nearest Neighbors & 1.904 & 13.5501 & 0.2122 \\ \hline Extremely Randomized Trees & 1.1291 & 6.4353 & 0.6279 \\ \hline Support Vector Machine & 1.8269 & 14.5798 & 0.1555 \\ \hline Decision Tree & 1.3719 & 11.1106 & 0.3442 \\ \hline Random Forest & 1.1894 & 6.7416 & 0.6098 \\ \hline Gradient Boosting & 1.511 & 9.4497 & 0.4549 \\ \hline Multi-Layer Perceptron & 1.9467 & 12.7394 & 0.2557 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Result of 10-fold cross-validation with all 3 datasets.  
As the confusion matrix, the model was sufficiently accurate for a number of LC classes. The performance was best for forests, which were detected with 97.7% accuracy. Of particular importance for our task were the cultivated land and grassland classes, for which the performance of the model was still good (they were detected with 75.5% and 80.8% accuracy). It is worth noting that, even when the classification for these two classes was wrong, the model tended to confuse these two classes. The only other class that was confused with these two was the artificial surfaces, but these were likely to be an artifact of our training dataset, which contained a portion of artificial surfaces (mainly roads), which were of dimensions smaller than the resolution of Sentinel-2 and could not be reliably identified in the imagery.  
The most challenging for the model were the _shrubland_ and _wetlands_ classes. This can be attributed to the relative scarcity of training data pertaining to these surfaces in the dataset. They represented just 0.6% of our dataset.  
Since the model was successful in separating the cultivated and grassland classes from the rest of the dataset, we concluded that the latent features extracted will be sufficiently relevant to our ultimate goal.
# Similarity Analysis for Time Series-Based 2D Temperature Measurement of Engine Exhaust Gas in TDLAT
## 4 Statistical-Based Mean Difference Test and Similarity Analysis
### [Step 2] Similarity Analysis
#### 4.2.2 Root Mean Square Error (RMSE)  
RMSE is used to measure the difference between values predicted by a model or an estimator and the value observed. The RMSE is a measure of accuracy to compare forecasting errors of different models for a particular dataset and not between the dataset as it is scale-dependent. RMSE is always non-negative and a value of zero would indicate a perfect fit to the data. In general, a lower RMSE is better than a higher one. The formula is as follows:  
\[T_{RMSE}\ =\ \sqrt{\frac{1}{m}\times\sum_{i=1}^{m}\!\left(T_{Thc(i)}-T_{Rec(i,j )}\right)^{2}}. \tag{13}\]
## 4 Controller Design
### Stability Analysis of Nonlinear Disturbance Observer  
Consider the following system in order to analysis the stability of NDO:  
\[\dot{x}_{i}=g(\overline{x}_{i})\cdot x_{i+1}+f(\overline{x}_{i})+d_{i} \tag{45}\]  
where \(d_{i}\) denotes the disturbance of the system, and \(\overline{x}_{i}\) denotes \((x_{1},x_{2},\cdots x_{i})\). Generally, due to the disturbance is uncertain, we assume that \(\dot{d}_{i}=0\). The NDO for the system represented by the Equation (45) is presented, as follows.  
\[\left\{\begin{array}{l}\dot{d}_{i}=z_{i}+p_{i}(\overline{x}_{i})\\ \dot{z}_{i}=-L_{i}\cdot z_{i}-L_{i}\cdot(p_{i}(\overline{x}_{i})+g(\overline {x}_{i})\cdot x_{i+1}+f(\overline{x}_{i}))\end{array}\right. \tag{46}\]  
where \(z_{i}\) is the internal variable and \(L_{i}\) is the gain for NDO. The estimating error of \(d_{i}\) is defined as  
\[\overline{d}_{i}=d_{i}-\dot{d}_{i} \tag{47}\]The Lyapunov function is chosen in order to analysis the stability of NDO, as follows  
\[V=\frac{1}{2}\overline{d_{i}}^{2} \tag{48}\]  
Differentiating the Lyapunov function (48), Equation (49) is obtained.  
\[\dot{V}=\overline{d}_{i}\cdot\dot{\overline{d}}_{i}=\overline{d}_{i}\cdot( \dot{d}_{i}-\overline{d}_{i})=-L_{i}\overline{d_{i}}^{2} \tag{49}\]  
By choosing positive \(L_{i}\), the stability can be proofed, and the desired exponential convergence rate is related to the value of \(L_{i}\).