esa-sceva/satcom-chunk-collection huggingface dataset:
# Directional Core Drilling as an Approach to Reduce Uncertainty in Tunneling Construction
## 3 Methodology for the Uncertainty Economic Valuation
### Costs Related to Drill and Blast Excavation
#### 3.2.1 Support and Lining  
A worsening in a rock mass's quality is related to the need for more robust supports, increasing the use of shotcrete, bolts, ribs, or thicker linings. In this regard, Equation (2) considers the additional costs of support materials:  
\[C_{s}=C_{st}\cdot M_{st}+C_{c}\cdot M_{c}, \tag{2}\]  
where \(C_{s}\) is the total cost (EUR), \(C_{st}\) is the price of the steel used for t ribs and rock bolts (EUR/t) (with an average cost of 1750 EUR/t), \(C_{c}\) is the price of concrete (EUR/t) (with an average cost of 85 EUR/t), \(M_{st}\) is the mass of the steel (t), and \(M_{c}\) is the mass of the concrete (t).  
The typical supports applied in drill and blast excavations are mainly formed by a combination of three elements (bolting, steel sets, and shotcrete), where the quantities and qualities of these elements define their maximum load capacities [44]. Thus, there are many different combinations of these elements, with some well-known and established recommendations depending on rock mass conditions. In this study, we used the approach established by [PERSON] [45], which is based on the approach proposed by [PERSON] [46].  
The property definitions of the materials used in the supports were necessary for the analysis, and we used the parameters defined by [PERSON] [47] in the Spanish tunnels studied. For instance, one of the more common material combinations was rebar steel bolts with diameters ranging from 25 to 36 mm and maximum tensile strengths ranging from 482 to 844 kN; concrete with compressive strengths ranging from 25 to 45 MPa; and ribs with a TH profile and a yield strength of 240 MPa.  
The mass of the steel and concrete used for a tunnel support can be determined following the procedure detailed by [PERSON] and [PERSON] [44]. Thus, a simplified procedure for tunnel excavation is proposed using Equations (3) and (4), as follows:  
\[M_{st}=m_{st}\cdot(L_{f}-L_{0}) \tag{3}\]  
\[M_{c}=m_{c}\cdot(L_{f}-L_{0}) \tag{4}\]  
where \(M_{st}\) is the steel used for the support (t), \(M_{c}\) is the concrete used for the support (t), \(L_{0}\) is the initial length of the excavated tunnel (m), \(L_{f}\) is the final length of the excavatedtunnel (m), \(m_{st}\) is the specific steel consumption (t/m), and \(m_{c}\) is the specific concrete consumption (t/m).  
Regarding lining usage, the thickness typically applied varies between 35 and 45 cm in most Spanish tunnels, with no variations based on rock mass quality [44]. Therefore, lining was not considered in this analysis.
# Heat Transfer Enhancement of Nanofluids with Non-Spherical Nanoparticles: A Review
## 3 Convective Heat Transfer of NSN Nanofluids
### Experimental Data Evaluation
#### 3.1.1 Different Analysis Parameters and Values  
Research findings on nanofluid CHT document controversial arguments. The first is whether the CHT, not just the factor of TCE, is enhanced by suspending nanoparticles in the base fluid. The second is whether the thermohydraulic performance of nanofluids is better than that of the base fluid, relating to the efficacy of nanofluids for practical cooling applications. The different views could be caused by different analysis parameters. Studies perform the standard dimensionless analysis of CHT based on the Nusselt number and Reynolds number.  
Nonetheless, researchers believe that the constant Reynolds number basis can be misleading since the net result for the constant Reynolds number comparison combines the nanofluid property effect and the flow velocity effect [90]. In the interpretation of nanofluid CHT, [PERSON] et al. [17] argued that no anomalous phenomena are involved in the forced convection heat transfer of nanofluids. Their results show that heat transfer enhancement provided by nanofluids equals the increase in thermal conductivity of nanofluids compared to the base fluid. The CHT coefficient rather than the Nusselt number is more suitable to evaluate the intensity of the CHT of nanofluids. A notable exception was the experiments performed with non-spherical particles showing CHT enhancement of NSN nanofluids. Therefore, it is necessary to discuss the CHT of NSN nanofluids.  
The magnitude of parameters selected for study is another point of view in nanofluid research. For experimental studies, the values are practical. However, for some numerical studies, the range of parameters could be beyond the scope of practical applications. [PERSON] [91] first evaluated different mechanisms in the convective transport in nanofluids. [PERSON] et al. [92] claimed that the use of parameter values should represent an actual physical situation. Therefore, the experimental and numerical studies are discussed separately in the next section.
## 2 Materials and Methods
### Deep Learning Segmentation Approach
#### 2.3.3 CNN Semantic Segmentation  
In this paper, semantic image segmentation will be carried out through CNN. Semantic segmentation will classify all the meaningful pixels into the same object classes. There are six basic steps in the basic CNN algorithm, including input layers, convolutional layers, rectified linear unit (ReLU), pooling layer, fully connected layer, and classification layer. In the input layer stage, each image needs to resize and be converted from grayscale to color image if necessary. At the convolutional layer, each image is convolved with k number of generalized kernels to create activation maps. The ReLU layer, also known as an activation layer, is used to transfer the activation maps to a nonlinear plane. Next, the pooling layer is used to reduce the number of parameters especially when the input image is too huge. A fully connected layer provides the correlations of the particular class to the high-level features. The number of outputs of the last fully connected layer must be the same as the number of classes. The classification layer is the final output layer in the CNN algorithm, also defined as a softmax layer that can perform multi-class classification [22].  
The CNN used for deep learning focused on the U-Net model. U-Net was designed specifically for medical image analysis to segment the images accurately with a limited amount of training data [23]. The encoder part of the U-Net model is substituted bypretrained backbone models, which are VGG and ResNet. The backbone model acts as a feature extractor and it is used without the dense layers. In this paper, three backbones were proposed for evaluation, which are VGG-16, ResNet-18, and ResNet-34.  
Then, the data split ratio was used for parameter tuning and was split into two groups, which are the training and validation datasets. The training dataset is the dataset on which the model is trained, these data are seen and learned by the model. The validation dataset is the dataset that is used to provide an unbiased evaluation of the model. In this paper, the data split ratio are adjusted to 0.1, which means 10% of the dataset is utilized as the validation dataset while 90% is considered as the training dataset. The 10% of the validation dataset acts as a holdout dataset to properly assess the performance.
## 2 Issues in the Conventional P and O Algorithm
As shown in Figure 3, the P and O with step-size, 0.1 V presents a smaller steady-state power oscillation compared that of the P and O with step-size, 2.0 V. On the other hand, the P and O with step-size, 2.0 V shows a fast tracking speed, and the P and O with step-size (0.1 V) requires much more time for pushing the operation point away from the low power region (less than 80 W). As a successful PV system, the tracking time should be as short as possible, meanwhile the steady state power oscillation is supposed to be as small as possible. To simultaneously achieve the above two goals, many authors [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23] have been proposing  
Figure 3: P and O: movement locus of the PV operation point. concepts of adaptive perturbations in PV voltage or PV current and estimated optimal operation point. In these studies [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23], it can be recognized that implementations of the more advanced algorithms require more powerful control units and complicated calculations. In some cases, the control strategy for PVs may be too expensive to be adopted. In this paper, two input signals, PV voltage and PV current, are fed into the FLC. Moreover, a self-evaluation mechanism is designed and combined with the multi-rule table so that no extra computation loop is needed to add into the control algorithm. It is worthwhile to note that the PV DC-DC boost system can be generally modeled by the Figure 6. The system consists of the power-stage and control module. In Figure 6, the DC link is presented by a battery. The voltage level of the DC link of a PV system is stable. In the topology shown by Figure 6, the voltage level of the solar panel is related to the voltage level of the DC link and the duty-ratio of the switching signal. Assuming that the voltage level of the DC link is constant, the perturbation in duty-ratio of the switching signal (G\({}_{\rm s}\)) will cause a variation in the voltage level of the solar panel so that the operation point of the solar panel could shift. The key of the MPPT algorithm is to figure out the appropriate value for the duty-ratio, which can lead the solar panel to achieve its maximum power point. Figure 4: The operation point moves among P1, P2, and P3. Figure 5: P and O: tracking traces with different perturbation offsets. Figure 6: The general structure of PV MPPT system in a power converter.
## 2 Materials and Methods
### Clinoptilolite Zeolite, Rice Straw, and Paddy Husk Composts Characterization
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Rice Straw Compost** & **Paddy Husk Compost** \\ \hline Cation exchange capacity (cmol(+) kg\({}^{-1}\)) & 86 (\(\pm\)0.07) & 176 (\(\pm\)3.17) \\ pHwater & 7.6 (\(\pm\)0.07) \({}^{\dagger\ddagger}\) & 7.9 (\(\pm\)0.03) \\ Phenolic (OH, cmol(+) kg\({}^{-1}\)) & 300 (\(\pm\)6.42) & 350 (\(\pm\)7.90) \\ Carboxylic acid (cmol(+) kg\({}^{-1}\)) & 450 (\(\pm\)10.39) & 400 (\(\pm\)11.35) \\ Total acidity (cmol(+) kg\({}^{-1}\)) & 750 (\(\pm\)8.08) & 750 (\(\pm\)6.90) \\ E\({}_{\ddagger}\)/E\({}_{\epsilon}\) ratio \({}^{\dagger}\) & 7.73 (\(\pm\)0.07) & 7.78 (\(\pm\)0.02) \\ Humic Acid (\%) & 15.2 (\(\pm\)0.32) & 5.7 (\(\pm\)0.03) \\ Electrical Conductivity (ds m\({}^{-1}\)) & 1.1 (\(\pm\)0.02) & 1.2 (\(\pm\)0.02) \\ Carbon (\%) & 27.3 (\(\pm\)0.42) & 28.2 (\(\pm\)0.52) \\ Organic Matter (\%) & 47.1 (\(\pm\)0.73) & 48.6 (\(\pm\)0.55) \\ Nitrogen (\%) & 1.8 (\(\pm\)0.06) & 1.6 (\(\pm\)0.03) \\ C/N Ratio & 15 & 17 \\ Ammonium (mg kg\({}^{-1}\)) & 294 (\(\pm\)2.84) & 362 (\(\pm\)2.92) \\ Nitrate (mg kg\({}^{-1}\)) & 161 (\(\pm\)5.23) & 172 (\(\pm\)1.85) \\ Phosphorus (mg kg\({}^{-1}\)) & 458.2 (\(\pm\)5.50) & 1097 (\(\pm\)0.88) \\ Calcium (mg kg\({}^{-1}\)) & 14,080 (\(\pm\)6.91) & 15,080 (\(\pm\)0.88) \\ Magnesium (mg kg\({}^{-1}\)) & 15,350 (\(\pm\)3.92) & 15,189 (\(\pm\)1.45) \\ Potassium (mg kg\({}^{-1}\)) & 27,720 (\(\pm\)2.72) & 27,150 (\(\pm\)0.88) \\ Sodium (mg kg\({}^{-1}\)) & 13,326 (\(\pm\)1.03) & 14,001 (\(\pm\)0.14) \\ Copper (mg kg\({}^{-1}\)) & 12.4 (\(\pm\)0.21) & 2.4 (\(\pm\)0.11) \\ Iron (mg kg\({}^{-1}\)) & 13.1 (\(\pm\)0.42) & 3.6 (\(\pm\)0.14) \\ Manganese (mg kg\({}^{-1}\)) & 2.1 (\(\pm\) 0.05) & 1.1 (\(\pm\)0.12) \\ Zinc (mg kg\({}^{-1}\)) & 11.8 (\(\pm\)0.15) & 11.2 (\(\pm\)0.17) \\ Ash (\%) & 6.4 (\(\pm\)0.33) & 7.9 (\(\pm\)0.29) \\ Moisture Content (\%) & 43 (\(\pm\)0.10) & 44 (\(\pm\)0.71) \\ \hline \hline \end{tabular} \({}^{\dagger}\) E\({}_{\ddagger}\)/E\({}_{\circ}\) (optical density) is the absorbance at two arbitrary selected wavelengths (extinction at 465 and 665 nm); \({}^{\ddagger}\) Standard error.  
\end{table} 
Table 2: Some physico-chemical characteristics of the rice straw and paddy husk composets.
# Defining Feasible Joint and Geometric Workspaces Through Boundary Functions
## 3 Theoretical Framework and Methodology
The relative position between \(a+n\) and \(b+m\) can be expressed as the difference between their global positions:  
\[\|\mathbf{r}_{a+n,b+m}\|\geq d_{\min}. \tag{11}\]0  
Using the transformations for global positions, we write  
\[\|\mathbf{r}_{a+n,b+m}\|\geq d_{\min}. \tag{11}\]1 \[\|\mathbf{r}_{a+n,b+m}\|\geq d_{\min}. \tag{11}\]2  
Substituting these expressions into the relative position equation yields  
\[\|\mathbf{r}_{a+n,b+m}\|\geq d_{\min}. \tag{11}\]3  
Simplifying this expression, we have  
\[\|\mathbf{r}_{a+n,b+m}\|\geq d_{\min}. \tag{11}\]4  
Recognizing that \(R_{0,b}\) can be rewritten as \(R_{0,a}\cdot R_{a,b}\), we substitute and simplify to yield  
\[\|\mathbf{r}_{a+n,b+m}\|\geq d_{\min}. \tag{11}\]5  
Factoring out \(R_{0,a}\), we obtain  
\[\|\mathbf{r}_{a+n,b+m}\|\geq d_{\min}. \tag{11}\]6  
The collision condition \(\mathbf{r}_{a+n,b+m}=\mathbf{0}\) implies that the term in parentheses must also be null and defined as follows:  
\[\|\mathbf{r}_{a+n,b+m}\|\geq d_{\min}. \tag{11}\]7  
We now define the boundary function \(B_{i}(\mathbf{q})\) that encapsulates the condition for collision in terms of the joint variables \(\mathbf{q}\):  
\[\|\mathbf{r}_{a+n,b+m}\|\geq d_{\min}. \tag{11}\]8  
Thus, a collision occurs if and only if  
\[\|\mathbf{r}_{a+n,b+m}\|\geq d_{\min}. \tag{11}\]9This formulation ensures that the collision condition is expressed as a function of the joint variables, linking the geometry of the system to the kinematic configuration. Since the null vector ensures positional coincidence, the theorem is validated. Note that this approach facilitates a straightforward implementation of collision checks and the definition of boundaries, which are critical in the modeling of feasible workspaces and the optimization of manipulator trajectories. A significant simplification is achieved in the analysis of relative kinematic chains by avoiding the inherent complexity associated with homogeneous transformations and the Denavit-Hartenberg (DH) formalism. By focusing exclusively on the geometric relationships between consecutive systems, the need for complete transformation matrices is eliminated. Consequently, a more intuitive and computationally efficient model is provided, which is particularly advantageous in scenarios requiring rapid calculations and analytical clarity.
## 2 Basic Theories
### Hybrid PSOGSA Optimization Method
Therefore, the hybrid PSOGSA algorithm is suitable for efficiency optimization of SBG, and the basic optimal steps are as follows:  
(1) the initial population is randomly initialized, while best position \(p_{best}\) and \(g_{best}\) are defined;  
(2) the particle fitness should be calculated;  
(3) \(M\), \(G\), \(p_{best}\), and \(g_{best}\) are updated;  
(4) the resultant force \(F\) is calculated;  
(5) the accelerations, velocities, and positions are updated, in which the velocity formula could be updated as:  
\[v_{i}^{d}(k+1)=r_{i}\cdot v_{i}^{d}(k)+c_{1}\cdot r_{k}(p_{best}^{d}-x_{i}^{d }(k))+c_{2}\cdot r_{m}(g_{best}^{d}-x_{i}^{d}(k))+a_{i}^{d}(k) \tag{25}\]  
(6) step 2 to step 5 are iterated and cycle calculated until the convergent conditions are met. It could easily be seen that the values of constant \(c_{1}\) and \(c_{2}\) would affect the effects of PSO on GSA. The main computational formulas are listed from Equation 19 to Equation 25. A series of recent literature has also been summarized for reference. Considering the simulation procedure of SBG efficiency, the flow chart for SBG efficiency optimization based on the hybrid PSOGSA algorithm is shown in Figure 3.
## 3 MIMO Radar Simulation Platform
### Signal Generation Module  
MIMO radar uses a linear frequency modulation (LFM) as a transmitting signal. The _PRI_, \(\beta\), and \(\tau\) parameters represent the pulse repetition interval, signal pulse bandwidth, and pulse width, respectively. The time domain LFM signal is shown in Figure 4. It can be expressed as:  
\[s(t)=\left\{\begin{array}{c}e^{j\pi\beta\left(t-\tau/2\right)^{2}}/\tau,0 \leq t\leq\tau\\ 0,\tau\leq t\leq PRI\end{array}\right. \tag{16}\]  
LFM signal can be categorized as a wide time bandwidth signal and can meet the demand of pulse compression technology. According to the theory of signals and systems, the time width of the signal is inversely proportional to the bandwidth, and the product of the two physical quantities  
Figure 4: Linear frequency modulation (LFM) signal. The blue line and the red line represent the real part and imaginary part of the signal, respectively. LFM signals with large time width will not only improve the radar speed, accuracy, and resolution, but also the radar’s detection range.  
Figure 3: MIMO radar simulation platform schematic. The system noise, channel noise, and clutter are added to the radar target reflected signal, then the sum signal is processed for data analysis.  
is fixed. LFM signaling is widely used because of its simplicity, convenient processing, and mature technology. In order to adapt to different circumstances, the pulse repetition interval of the LFM signal can also be non-uniform. Correspondingly, additional processing is required at the receiver and the algorithm is more complex. In this paper, we will discuss the performance of single RF link MIMO radar with a uniform pulse repetition interval LFM signal.
# Trade-Off Analysis for Array Configurations of Chipless RFID Sensor Tag Designs
## 7 Trade-Off Analysis
### Linear Detectability vs. Spherical Detectability  
Planar array configurations can exhibit different behaviours in terms of linear and spherical detectability. Larger array configurations enhance linear detectability since they can scatter electromagnetic waves better and more efficiently in a specific direction, resulting in more stable resonance frequencies over longer distances.  
In contrast, small array configurations enhance spherical detectability since they can scatter electromagnetic waves more efficiently in multiple directions. This allows for successful tag identification across a broader range of angles. However, increasing the array size also concentrates the radiation pattern, limiting the spherical detection zone at certain angles.
# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 3 State of the Art Anomaly Detection for Spacecraft Telemetry Data
### Artificial Intelligence Methods
#### 3.2.4 Computational Efficiency
\begin{table}
\begin{tabular}{l l l l l} \hline
**Method(s)** & **Datasets(s)** & **Anomaly Type(s)** & **Metric(s)** & **Benefits \& Limit(s)** \\ \hline \hline \multirow{4}{*}{CNNs: TCNs [7, 54]} & & & - Precision & + High performance in capturing \\  & & & - Recall & spatial patterns \\ \cline{1-1}  & \multirow{2}{*}{- NASA SMAP/MSL} & - Point & - \(F_{\rm i}\) score & + Able to model high dimensionality \\ \cline{1-1}  & & - Contextual & - TPR & and complex correlation among \\ \cline{1-1}  & \multirow{2}{*}{- Collective} & - FPR & variables \\ \cline{1-1}  & & - Accuracy & + Low false detection rate \\ \cline{1-1}  & & - Time & + Computational efficiency \\ \hline \multirow{4}{*}{RNNs: LSTMs [20, 22, 57, 58], \(\,\) GNNs [56, 60]} & & + High detection performance \\ \cline{1-1}  & \multirow{2}{*}{- NASA SMAP/MSL} & - Point & - Precision & + Process multiple channels \\ \cline{1-1}  & & - Contextual & - Recall & + Perform very well with short \\ \cline{1-1}  & \multirow{2}{*}{- Power System of a Rocket (PSR)} & - Collective & - \(F_{\rm i}\) score & sequences \\ \hline \multirow{4}{*}{Transformers} & - NASA SMAP/MSL & & + Capture temporal anomalies \\ \cline{1-1}  & - Numenta Anomaly Benchmark & & + Low false positive rates \\ \cline{1-1}  & - HexagonML (UCR) & & - Precision & + Process length sequences better than \\ \cline{1-1}  & - MIT-BIH Supraventricular & - Point & - Recall & CNNs and RNNs \\ \cline{1-1}  & \multirow{2}{*}{- Curvature} & - Contextual & - \(F_{\rm i}\) score & + Suitable for intercorrelated \\ \cline{1-1}  & \multirow{2}{*}{- Secure Water Treatment (SWAT)} & - Collective & - ROC & anomalies \\ \cline{1-1}  & - Water Distribution (WAD) & & - AUC & + Early anomaly detection \\ \cline{1-1}  & - Server Machine Dataset (SMD) & & - Require significant computational \\ \cline{1-1}  & - Multi-Source Distributed System & & & resources \\ \cline{1-1}  & \multicolumn{1}{l}{} & & \\ \end{tabular}  
* The symbol “\(\star\)” is used to refer to the benefits or strengths of the method(s).  
\end{table} 
Table 1: Cont.
# Fast Deployment of a UWB-Based IPS for Emergency Response Operations
## 4 Implementation  
### Testbed Location  
The performance of the UWB-based IPS was extensively assessed in a testbed deployed into and around a two-storey rectangular (20.11 \(\times\) 12.88 \(\times\) 6.20 m) building located in the south of the municipality of Egaleo within the Athens metropolitan area, with a total built area amounting to roughly 520.00 m\({}^{2}\) (see Figure 4a). This building was later employed as one of the pilot scenarios of the RESPOND-A EU project, aimed to provide _next-generation equipment tools and mission-critical strategies for FRs_[64].  
Figure 4: Testbed building. (**a**) Front view. (**b**) 3D model.  
The single main entrance of the building is located on the ground floor. The material of the door is metallic, which totally covers the entrance. As for the facade, it is made up of concrete, whose thickness is 30 cm. The two floors are connected with stairs, share 4 columns, and have plenty of windows surrounding them (see Figure 5). As for the interior of the building, there are no walls, except for those of plasterboard that create two little rooms. However, the ground floor has plenty of tables and chairs, as well as scattered appliances and objects. None of this furniture exceeds 1.5 m in height.  
A 3D model of the testbed building, whose general view can be found in Figure 4b, was designed to better understand potential UWB coverage issues, select the best position of anchors to maximize the network coverage area, and evaluate the accuracy and performance of the localization system on a visual basis.
# Buffeting Response Prediction of Long-Span Bridges Based on Different Wind Tunnel Test Techniques
## 2 Theoretical Analysis
### Buffeting Response Prediction Based on Taut Strip Model Test  
The taut strip model buffeting response test is similar to the full-bridge aeroelastic model test. They both measure the 3D buffeting response of the 3D model under the action of 3D turbulence, but the former eliminates the additional aerodynamic interference of the structural ancillary facilities. The buffeting response spectrum \(S_{h}(y,k_{1})\) of the structure under the simulated turbulent flow field is measured through the taut strip wind tunnel test, and the RMS of the buffeting response can then be calculated by Equation (8):  
\[\sigma_{h}^{2}(y)=\int_{0}^{+\infty}S_{h}(y,k_{1})\mathrm{d}k_{1} \tag{8}\]