esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Carbon Dioxide (CO\({}_{2}\))  
The CO\({}_{2}\) concentration model used two short regions of Raman shifts (1450.3-1398.8 cm\({}^{-1}\) + 1374.8-1297.6 cm\({}^{-1}\)), which comprise two peaks attributed to vibrational modes of CO\({}_{2}\), specifically 1388 cm\({}^{-1}\) and 1285 cm\({}^{-1}\)[42]. The spectral pre-processing involved MSC, and cross-validation revealed a good prediction accuracy within a large CO\({}_{2}\) concentration range (3.0-16.7 g L\({}^{-1}\)), as indicated by the cross-validation results (R\({}^{2}\)CV = 90.0%, RMSECV of 0.96 g L\({}^{-1}\); RPDCV = 3.2; Table 1). In fact, Figure 3b shows excellent correlations between Raman spectroscopy and reference analysis, highlighting Raman spectroscopy as a potentially useful tool for providing real-time information on the CO\({}_{2}\) concentration. Real-time knowledge on the CO\({}_{2}\) level in a photo-BNR reactor is essential for understanding whether photosynthesis, and thus oxygen production by microalgae, is limited by inorganic carbon availability and when it happens, whether it increases the CO\({}_{2}\) feed to the system. Compared to regular CO\({}_{2}\) sensors, this can be advantageous because no further correction, based on pH, is necessary to know the real CO\({}_{2}\) concentration, reducing the delay time and improving the overall nutrient removal efficiency of the photo-BNR.
###### Abstract  
In dynamic temperature measurement, thermocouple sensors are widely used, and their dynamic characteristics directly affect the accuracy of the test results. So before applying thermocouple sensors to dynamic temperature measurement, their dynamic characteristics should be obtained, and their dynamic performance parameters should be analyzed. The time constant is the most important dynamic parameter, which reflects the response speed of a thermocouple sensor. Therefore, it is necessary to measure the time constant. The time constant is closely related to the heat transfer mode, so the heat transfer environment of the time constant measurement system should be similar to the application environment of thermocouple sensor. When using the thermocouple to measure the temperature in various kilns, the heat transfer is mainly through the radiative mode, and existing equipment, such as constant temperature water/oil tank, shock wave tube and other devices, cannot be used to measure the time constant to reflect scenarios in actual measurement applications. Therefore, this paper proposed a new method to measure the time constant of the thermocouple by improved high temperature furnace. In the system, the high temperature furnace was used to generate the stable temperature field, and the fast feed device was used to insert the thermocouple into the high temperature furnace and generates the ramp temperature excitation. The temperature can reach 1500 \({}^{\circ}\)C in the furnace, and the temperature error in uniform temperature field is \(\pm\)1 \({}^{\circ}\)C. Finally, the time constant of a K-type thermocouple was measured, and the uncertainty of the measurement result was analyzed.  
thermo couple; time constant; high temperature furnace; ramp excitation signal 2018 8 2585; doi:10.3390/app8122585
## 3 Results and Discussion  
### Biochar Production and Characterization  
The physicochemical and textural properties of the biochar (B) and activated biochar (AB) prepared in this study are summarized in Table 1. The biochar made from wood residues did not present a higher developed porosity than the activated biochar because of the thermal conditions used in the fast-pyrolysis reactor: low temperatures (i.e., 450 \({}^{\circ}\)C) and the short residence time (approximately 2 s). The activation of biochar in presence of superheated steam maximized the reactions between the steam and carbon causing the gasification of carbon atoms at high temperatures (900 \({}^{\circ}\)C). Indeed, low-weight carbon molecules are formed during the latter reaction, generating some gaps or pores in the structure of the material [29]. In this context, the surface area of the activated biochar (590 m\({}^{2}\)/g) was much higher compared to the biochar (177 m\({}^{2}\)/g). Also, the activated biochar presented a total pore volume of 0.34 g/cm\({}^{3}\), essentially micropores. Carbon, oxygen, and hydrogen contents were significantly different after the thermal treatments:75.4%, 3.5%, and 19.7% for B while 89.1%, 10.1%, and 0.6% for AB, respectively. Nitrogen and sulfur were very low (less than 1%) for both materials.
## 4 Results and Discussion
### Slope Length (L) and Slope Steepness (S) (LS Factor)  
The LS factor was calculated by considering the slope and flow accumulation as inputs, although it depends on slope more than on flow accumulation (Equation (3)). The lowest LS values occupy the low-lying southern lands, reflecting the slope effect on the LS outcomes. In contrast, the highest LS values are situated in the complex northern and eastern landforms of the study area with steep slopes [61] between the anticlines and synclines (Figure 5). The LS factor of the basin has five classes: 0 to 2; 2 to 10; 10 to 25; 25 to 50; and \(>\)50, representing 63.2, 21.2, 11.8, 2.9, and 0.9% of the basin's area, respectively.  
Figure 4: Spatial distribution of K factor in the study area.
# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 3 Inform@Risk Monitoring System
### Test Site Near Regensburg, Germany  
To test these LoRa nodes, we created a number of prototypes in order to test the functionality and longevity in a field installation. Because of the pandemic, it was not possible to do this in Colombia, so we chose a site in southern Germany near Regensburg, which has similar properties. A rotational landslide in fine-grained material (sand, silt and clay in varying proportions) started to develop some years ago and is slowly moving towards railroad tracks. This slope is already being monitored by two inclinometers. In parallel to these, we have installed six of the Inform@Risk LoRa nodes: Two IN on a wall (Figure 6d) and four variations of the SN and LCI in different depths and sensor constellations. The data are being collected by a LoRa gateway which is about 100 m from the sensors. The sensors, which were installed in July 2020, show a robust and continuous data transmission. However, no inclination measurements could be detected right now which could be confirmed by the inclinometer measurements. This installation helped us in improving the hardware, software and installation procedure to be more reliable. In Figure 7, the inclination data of the IMU in one node is shown as an example. Although only temperature cycles are visible, they show that even the low-cost sensor is capable of replicating these slight changes in inclination.  
While the presented subsurface sensors offer an effective method of directly measuring subsurface deformations, it must be emphasized that they are mainly applicable to shallow rotational landslides. Especially the SN might not detect the movement if other landslide mechanisms (e.g., purely translational sliding) are present. In general the application of the proposed sensors has to be critically checked based on the geological process and hazard analysis. While the presented systems have been tested for about half a year now, still further testing is required to prove the full functionality of the system. This includes tests to determine the sensitivity of the SN for different geometrical setups and direct comparisons of LCI measurements with classic inclinometer measurements.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Type** & **Depth [m]** & **Pipe** & **Installation** & **Data Quality** & **Cost** \\ \hline IN & - & - & ++ & RD; Wide range of sensors; & \(\sim\)150 \(\in\) \\ SN & \textless{}2.5 & Steel & + & SQ (Tilt sensor); GW & \(\sim\)200 \(\in\) \\ LCI & 3–6 & PVC & - & AD (Tilt sensor); GW & \(\sim\)250 \(\in\) \\ Chain Inclinometer & \textgreater{}5 & PVC/Alu & – & AD & \(>\)\textgreater{}5000 \(\in\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the LoRa sensor nodes, in comparison with a regular drilling and installation of a chain inclinometer. Installation: (++): easy, (+): intermediate, (-): difficult, (-): very difficult. RD: relative deformation measurements, AD: absolute deformation measurements, SQ: semi-quantitative measurements, GW: groundwater measurements.
# Towards an Automated Design Evaluation Method for Wire Arc Additive Manufacturing
## 3 Methods  
### Medial Axis and Medial Surface Transformation  
To assess the manufacturability of a geometry, it is necessary to define a set of indicators that describe the geometry features specified by the design rules for a specific manufacturing technology. In this study, the 3D-MAT method is applied and refined to assess the manufacturability with WAAM similar to Heuvers' spheres method.  
The _medial axis transformation_ (MAT) is a transformation that returns a medial object, a dual representation of the input geometry. This dual representation is either a medial axis or a medial surface for 2D or 3D input geometries, respectively. It is defined as the set of all radii and midpoints of maximal circles/spheres inscribed in the geometry [43]. The medial object can thus be interpreted as the \"skeleton\" [43] or the local symmetry axis/plane of the transformed shape (see Figure 2a,b) while the associated radius correlates with the geometry's local thickness. In our method for manufacturability assessment, we will use a medial surface representation of the input geometries; however, for ease of visualization, we illustrate the indicators on a medial axis.  
\begin{table}
\begin{tabular}{l l} \hline
**Feature** & **Recommendations and Constraints** \\ \hline min. wall thickness \(w_{\text{min}}\) & \(1.5\ldots 2\,\text{mm}\) (depends on process parameters) [3; 13] \\ min. clearance & \(0.9\,w_{\text{min}}\) (for Al5A06) [36] \\ post-processing allowance & process dependent, e.g., machining \(1\,\text{mm}\)[37] \\ edges, corners & generously rounded [3] \\  & sharp corners either avoided or built as intersection [13] \\ overhangs & fixed torch: only angles \(\geq\)45\({}^{\circ}\) possible [37] \\  & variable torch: arbitrary angles possible [38] \\ T-crossings & sharp, to avoid surface defects [13] \\  & alternative: optimized path planning strategies [39; 40] \\ angles & simple path planning: \(\geq\)58.65\({}^{\circ}\) (for multi-bead walls) [41] \\  & optimized path planning: arbitrary angles [41], \\  & favor \(\geq\)20\({}^{\circ}\) (for Al5A06) [36] \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._The concept of Heuvers' circles is related to the 3D-MAT in so far as the inscribed spheres and their radii are the three-dimensional counterparts of Heuvers' circles and radii, respectively [15].
# Evaluating Deep Learning Techniques for Natural Language Inference
## 5 Experimental Results
### Results on QNLI  
With DAM, the best performance on QNLI was 75.01% accuracy on the development set. The model was trained with a batch size of 32, a hidden layer size of 200, and the AdaGrad optimizer with a learning rate of 0.025 and a dropout rate of 0.2 for 49 epochs. The same accuracy was obtained with the intra-attention model.  
The ESIM model performed higher (81.16%) on the development set of QNLI. It was trained with a batch size of 64, a hidden state size of 300, and the Adam optimizer with a learning rate of 0.0004 and a dropout rate of 0.5 for 8 epochs.  
The BERT-base model raised the accuracy even more, scoring 89.36% on the development set in the 4 th epoch of training, although validation loss increased after the 2 nd  
Figure 5: **(a)** Validation and training accuracy graph and (b) validation and training loss graph of best model (RoBERTa-Large) for MNLI.  
epoch. The hyperparameters used were a batch size of 64, a learning rate of 3 \(\times\) 10\({}^{-5}\), and a warmup ratio of 0.15. The large version performed slightly higher (90.54%) in terms of accuracy, and it was trained with a batch size of 16 and a learning rate of 10\({}^{-5}\) for 4 epochs with a warmup ratio of 0.1. We observed the same pattern on the validation loss with BERT-base.  
RoBERTa-base achieved an accuracy of 90.77% when trained for 4 epochs with a batch size of 32, a learning rate of 2 \(\times\) 10\({}^{-5}\) and a warmup ratio of 0.1. The large version scored an even higher accuracy (93.21%). It was trained for 4 epochs with a batch size of 16, a learning rate of 10\({}^{-5}\), and a warmup ratio of 0.2.  
ALBERT-base achieved 93.80% accuracy on QNLI, 0.6% higher than the large version of RoBERTa. It was trained with a batch size of 32 and a learning rate of 2 \(\times\) 10\({}^{-5}\) for 1 epoch or 3.274 steps, with the first 327 being warmup steps. The large version achieved the same accuracy of 93.80% (Figure 6). This model was trained with a batch size of 16, a learning rate of 10\({}^{-5}\), and 1.309 warmup steps for 13.094 steps.
## 3 Upper-Bound Solution for Different Potential Failure Modes on a Clay Slope
### Trapezoidal-Wedge Failure  
An upper-bound limit analysis can be further performed for a potential trapezoidal-wedge failure on a clay slope. Figure 5b presents a trapezoidal-wedge failure mode with a different range of \(\theta\) values compared to the triangular-wedge failure in the preceding section. Under this failure mode, the onset of failure from the toe of the track system advances to the side of the clay block, which establishes a failure line defined by \(\theta\) with the range of \(\tan^{-1}(L/H)<\theta<\pi/2\). Similar to the case of triangular-wedge failure, the total external power (\(P\)) and the dissipation rate (\(D\)) can be expressed as:  
\[P=\{F_{x}\text{sin}\,\theta+F_{y}(\text{sin}\,\psi\text{sin}\,\theta-\text{ cos}\,\psi\text{cos}\,\theta)\}V, \tag{15}\]  
\[D=\frac{c_{u}LV}{\text{sin}\,\theta}. \tag{16}\]  
It is noted that the slip line is expressed in terms of the length of the clay block, \(L\). By equating the total external power to the internal energy dissipation rate, the equation of soil thrust under trapezoidal-wedge failure can be expressed as:  
\[F_{x}=F_{y}\bigg{(}\frac{\text{cos}\,\psi\text{cos}\,\theta}{\text{sin}\, \theta}-\text{sin}\,\psi\bigg{)}+\frac{c_{u}L}{\text{sin}^{2}\,\theta}. \tag{17}\]  
As in the triangular-wedge failure mode, \(c_{u}\), \(L\), \(F_{y}\), and \(\psi\) are the given constants of the soil-track system. Accordingly, we can obtain the critical value of the soil thrust by the minimization of \(F_{x}\) with respect to \(\theta\) based on the upper-bound limit analysis, that is:  
\[\frac{\partial F_{x}}{\partial\theta}=0\Rightarrow\text{tan}\,\theta=-\frac{2 c_{u}L}{F_{y}\text{cos}\,\psi}, \tag{18}\]  
\[\therefore\,\theta=\text{tan}^{-1}\bigg{(}-\frac{2c_{u}L}{F_{y}\text{cos}\,\psi} \bigg{)}. \tag{19}\]  
Provided that \(c_{u}\), \(L\), and \(F_{y}\) are the given positive values, and \(\text{cos}\,\psi\) is also positive when the slope angle ranges from \(0\leq\psi<\pi/2\), the value of \(\theta\) from Equation (18) is always negative. In other words, the condition of \(\theta\) from Equation (18) is not compatible with the trapezoidal-wedge failure mode, in which the range of \(\theta\) is \(\text{tan}^{-1}(L/H)<\theta<\pi/2\). Regardless of the inclination angle of the clay slope, therefore, a trapezoidal-wedge failure cannot be established on a clay slope associated with a track system.
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 4 Improved Saturation Attack Method
### The Inferring Phase  
In this phase, the attacker triggers a new rule installation and identifies what type of timeout strategy the SDN controller adopts.  
To achieve this, we use the _search_timeout_ primitive to obtain the exact timeout value of new flow rules. The initial timeout values of new flow rules vary for different types of policies. For fixed timeout, all flow rules have the same timeout value (e.g., 10 s); for random timeout, timeout values show a random distribution; for adaptive timeout, all rules are generally assigned with a minimal value. We can simultaneously generate tens of flows and acquire their assigned timeout values to reduce the probing time. Then, we can analyze the timeout distribution and infer the type of timeout strategy. Since the timeout probing for each flow rule is independent, this process can be finished in tens of seconds. When inferring the type of timeout decision policies, the attacker generates only a few packets, making the process difficult to detect.
# Modeling and Analysis of Contactless Solar Evaporation for Scalable Application
## 3 Result and Discussion
### Modelling Validation  
To verify the accuracy of the modelling framework, the simulation results were compared with the experimental results. Two repeated experiments using the test rig shown in Figure 4 were carried out, and the SCE models were solved under the same conditions shown in Table 2. As shown in Figure 5, three steady-state temperatures (\(T_{\text{wat-b}}\), \(T_{\text{wat-t}}\) and \(T_{\text{ae}}\)) were obtained after 40 min from the start of the experiment, which exhibit small difference with the simulation results. The maximum error is less than 5%, indicating the mathematical models and boundary conditions applied to the laboratory SCE is effective. Since the scalable and laboratory SCE models only have differences in the boundary conditions on lateral surfaces, the rationality of the scalable SCE could also be ensured.
# Automatic Quantitative Coronary Analysis Based on Deep Learning
## 3 Results
### Performance of CAT  
CAT construction is a crucial step of AI-QCA, which is closely associated with the performance of segmentation. In order to evaluate the performance of CAT construction, the average distance is proposed to measure the difference between the generated CAT  
Figure 6: Representative examples of vessel segmentation. The first column presents the original CAG, and the second column presents the ground truth of the segmentation. Column 3–6 show results of segmentation with different models. Different rows represent different types of vessels, including LAD, LCX, and RCA. CAG = coronary angiography; GT = ground truth; LAD = left anterior descending; LCX = left circumflex artery; RCA = right coronary artery.  
and the ground truth of CAT. In some sense, the performance of CAT construction also represents the edge effect of segmentation, because CAT derives from the centerline of the vessel boundary sides. Due to the existence of different lengths between the two CATs, the dynamic programming (DP) algorithm [44] was adopted to align the vessels by point-to-point matching (Figure 7).  
According to the vessel match by the DP algorithm, the average distance variances of corresponding vessels can be calculated. The average distance variances of CAT generated by BasNet and Pix2 Pix are listed in Table 5. The overall distance variances of CAT generated by BasNet and Pix2 Pix were 0.944 and 1.011, respectively. For every type of vessel, the distance variances generated by BasNet are lower than those of Pix2 Pix, which means the CAT construction of BasNet behaves better than Pix2 Pix. The average distance variance for the RCA main was the lowest among all types of vessels, illustrating that the RCA main is more likely to have accurate CAT results. Figure 8 shows the visualization of the distance variance along the CAT from the root node. It can also be seen that the distances measured by BasNet are mostly lower than those measured by Pix2 Pix.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vessels** & **BasNet (mm)** & **Pix2 Pix (mm)** \\ \hline LAD main & 0.515 & 0.565 \\ LAD branch & 0.962 & 1.089 \\ LCX main & 0.548 & 0.600 \\ LCX branch & 1.176 & 1.307 \\ RCA main & 0.302 & 0.304 \\ RCA branch & 1.303 & 1.418 \\ overall & 0.944 & 1.011 \\ \hline \hline \end{tabular} CAT = coronary artery tree; LAD = left anterior descending; LCX = left circumflex artery; RCA = right coronary artery.  
\end{table}
Table 5: The average distance variances of CAT.  
Figure 7: The application of a dynamic programming algorithm to find the optimal point-to-point matching of two vessels.
# Deep Representation of EEG Signals Using Spatio-Spectral Feature Images
## 3 Methods  
We evaluate our approach to analyzing deep representations on DNN models trained to solve a ternary classification problem. This ternary classification problem is that of predicting whether a one-second long, 14-channel EEG segment from the PhyAAt dataset was recorded during a listening, a writing, or a resting task. The implemented prediction pipeline consists of two stages, as shown in Figure 1. First, one-second long 14-channel EEG segments are extracted and transformed into SSFI arrays. Then, each SSFI array is used as an input to a trained CNN model, which labels the segment as either listening, writing, or resting.  
In this section, we first define SSFI structures and describe the method that we use to generate them from multi-channel EEG signals. Then, we present our chosen CNN architecture and outline how we use the PhyAAt dataset to train and test multiple CNN models and how we carry out our ISD analysis. Our approach to analyzing deep representations in terms of SSFIs is finally described.  
Figure 1: Prediction pipeline consisting of an SSFI processing stage followed by a CNN model. Small windows \(t_{1}\), \(t_{2}\), \(\dots\), \(t_{N}\) are extracted from a 14-channel EEG, transformed into SSFI, and then fed into the CNN model.
## 3 Methodology
### Ensemble Learning  
In recent times, selection of an efficient ML algorithm has become crucial for good performance of any fault diagnostic model. Ensemble learning algorithms are one of the approaches that provide better performance than any single prediction algorithm [59]. In ensemble learning, multiple weak learners work together to produce a better model achieving higher accuracy. Some ensemble learning algorithms such as XGBoost may achieve higher accuracy than artificial neural networks and degree-day ordinary least square regression for energy model loss estimation [60]. In our study, two ensemble learning algorithms known as random forest and XGBoost were investigated for fault classification of IM.