esa-sceva/satcom-chunk-collection huggingface dataset:
# Memory-Non-Linearity Trade-Off in Distance-Based Delay Networks
## 3 Methods
### Task Capacities  
To obtain insight into where the 'useful' regions of the IPC were, we used the notion of task capacity. To compute task capacity, instead of using a dynamical system to reproduce the basis functions of past inputs, we measured the correlation with the outputs of the task system itself and the basis functions of its past inputs. As such, for first-degree basis functions, we had an indication of points at which lags in linear memory were needed. For higher degree basis functions, peaks in the task capacity showed where in time non-lineartransformations were needed. By comparing the task capacity with the optimized ESN and DDN IPC, we can have a better understanding of how well memory and non-linearity are balanced in a task-specific context. Thus, we here define task capacity similarly to Equation (6) as  
\[TC_{l}^{\{d_{i}\}}=r(z_{l}^{\{d_{i}\}},y(n))^{2} \tag{8}\]  
where \(y(n)\) is simply the state of the task system (i.e., the system to be predicted by the reservoir) at time \(n\), and \(z_{l}^{\{d_{i}\}}\) is the same as in Equation (7). Large task capacities indicate a large correlation between the current system state and the function, \(z\), of its past input, meaning that \(z\) is a task-relevant (useful) IPC component. As such, measuring task capacity allowed us to compare a reservoir's IPC profile with the task profile. Moreover, to quantify the portion of the task-relevant IPC, we defined an overlap measure as an inner product between the task capacity profile and the equivalent reservoir capacities, formalized as  
\[o=\sum_{\{d_{i}\}}\sum_{l}C_{l}^{\{d_{i}\}}TC_{l}^{\{d_{i}\}} \tag{9}\]  
This is simply a weighted sum of the IPC, where capacities have a higher weight if the corresponding task capacity is high.  
If the task IPC overlap is indeed a good predictor for task performance, the two should strongly correlate. Following this, for tasks where DDNs outperform ESNs, DDNs should achieve a better task overlap than ESNs. In Section 4, we show that this is true for both unoptimized and optimized reservoirs.
## 4 Results and Discussion
### CNN Architecture Optimization  
There are many hyperparameters in the CNN framework, including the number of convolutional-pooling layers, convolutional kernel size, and the number of fully connected layers. To improve the model performance, we use the balanced set (Subset 2) to adjust and optimal the CNN architecture. According to the control variable method, eight cases are set for trial calculation. Each case is conducted in five runs taking the detection ability on the validation set as the criterion. The mean values of evaluation metrics are tabulated in Table 3. In the table, Time/Epoch denotes the training time required  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subset.** & **Training and Validation Set (Arching: Normal)** & **Testing Set** & **Total** \\ \hline
1 & 8922 (2974:5948 = 1:2) & 20818 \\
2 & 11896 (5948:5948 = 1:1) & 23792 \\
3 & 11896 (2974:8922 = 1:3) & Arching-5948 & 23792 \\
4 & 11896 (1700: 10196 = 1:6) & 23792 \\
5 & 23792 (5948:17844 = 1:3) & 35688 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Five subsets established for training and testing.  
for each epoch. The convolution layer parameters are represented as \"kernel size @ the number of kernels\"; the fully connected layer parameters are represented as \"FC number of hidden nodes in the layer\".  
We can observe that with the increase of convolution-pooling layers (Case 1, 2, and 4), the values of metrics improve. In contrast, the Time/Epoch only slightly changes. The model performance with different convolutional kernel sizes (Case 3, 4, and 5) are also compared. It can be seen that as the kernel size increases, the error metrics first increases and then decreases, whereas the Time/Epoch grows obviously. Furthermore, different fully connected combinations are applied to the model (Case 4, 6, 7, and 8). One fully connected layer with 128 nodes has the highest efficiency.  
Comprehensively considering the detection accuracy and calculation time, we choose the architecture of Case 4, whose F\({}_{1}\)-score is 98.0%. In the model, the number of convolution-pooling layers is three, the convolutional kernel size is \(5\times 5\), and the number of kernels is 6, 12, and 24. Moreover, one fully connected layer with 128 nodes is adopted.
## 3 Data Acquisition
### Ferrara Site  
We acquired two high-resolution seismic lines, the location of which can be seen in Figure 1a, while the UTM coordinates of the two lines are reported in Table 1. For both lines, we deployed 150 active channels, spaced every 2 m, and we shot every 4 m. The length of both lines is therefore approximately 300 m.  
Figure 1: (**a**) Location of the lines acquired in the area of Ferrara, Italy. (**b**) Location of the lines acquired in Kástela, Croatia. Images created on Google Earth Pro on 30 March 2022.  
Along Line 1 (highlighted in red in Figure 1a), we acquired surface-wave data by deploying 48 4.5 Hz receivers, which were spaced every 4 m. As a source, we used a 100 kg weight-drop. We shot four times: twice at the beginning and twice at the end of the line.  
The data of Line 1 were acquired along a gravel road. The main sources of noise were the agricultural activities ongoing in the nearby fruit farms (tractors, walking, irrigation pumps) and the car traffic.  
The data of Line 2 were acquired in a fruit farm along a line of pear trees. Therefore, the geophones were planted in the soft soil. The main sources of noise are, similarly to Line 1, the activities going on in the fruit farm, including tractors and irrigation pumps.
# A Novel Slicing Strategy to Print Overhangs without Support Material
## 1 Introduction
### RotBot: 4-Axis Printer with Rotational Printing Head and Conic Slicing  
A brief overview on the basic idea of the 4-axis kinematics is given here as the basis of the new slicing strategy. This publication only covers the slicing and the path generation for the 4-axis RotBot printer. Details on the basic idea and the hardware setup are given in a previous publication on the \"RotBot\" 4-axis approach [20].  
Having a 45\({}^{\circ}\) tilted nozzle and a rotational axis around the z-axis enables 90\({}^{\circ}\) overhangs without support. In contrast to the conventional FDM printer, where the layers are parallel to the printing plane, the layers become cone shaped with this new system. Figure 1 shows this cone shaped layers and 90\({}^{\circ}\) overhangs towards the outside of the part. Printing a given geometry with this kinematics requires a geometry with layers having a cone shape.  
Depending on the direction of the overhangs, the cone is an outward cone for overhangs towards the outside or an inward cone for overhangs towards the inside as shown in Figure 2. The presented slicing strategy only slices parts with overhangs in one direction. Therefore, the possible geometries are limited at the moment. More complicated geometries with overhangs to the outside and overhangs to the inside, can be split into segments with overhangs in only one direction. A theoretical outline of this printing approach is also discussed in [20]. As soon as the splitting up and slicing of individual segments is solved, a big variety of the real-world parts becomes printable without support.  
Figure 1: Printing strategy with the cone shaped layers.  
[PERSON] [21] presents on his blog another important result with a conic slicing approach, where conic slicing approach is used to print 90 deg overhangs with a conventional 3-axis printer. He adapted the conic slicing with a much lower angle (15-25 deg), such that the non-tilted nozzle does not interfere with the already printed part.
## 6 Experimental Validation of the Calculation Result
### The Axial Stiffness Test of the Aerostatic Spindle  
To verify the calculation result, the axial stiffness of the spindle with different thrust plate thicknesses is tested in this section. The stiffness test is conducted by applying different loads along the axial direction of the spindle and measuring the axial displacement of the spindle by a displacement sensor. The experimental apparatus mainly consists of a computer, a displacement sensor, and an aerostatic spindle, which is shown in Figure 10. The displacement of the thrust plate can be acquired by the displacement sensor with varying axial loading.  
The experimental result is compared with the simulation result, as shown in Figure 11. It can be seen that the experimental curve and the simulation curve show the same trend. According to the test date, the axial stiffness of the spindle with the thrust plate thickness  
Figure 10: Experimental apparatus for the stiffness test.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Test Point** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline
**Air film thickness (\(\upmu\)m)** & 12.86 & 12.72 & 12.89 & 12.78 & 12.93 & 12.84 & 12.86 & 12.84 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measuring result of the air film thickness.  
Figure 9: Experimental setup for air film thickness measuring and test result. (**a**) The Experimental setup for air film thickness measuring and (**b**) a test result of the air film thickness test.  
of 10 mm is 176.2 N/\(\upmu\)m, which is much lower than the ideal stiffness of 209.6 N/\(\upmu\)m. It proves that the stiffness will be declined due to structural deformation. By increasing the thrust plate thickness to 20 mm, the stiffness increases to 201.8 N/\(\upmu\)m. It indicates that the structural rigidity of the thrust plate can be improved by increasing its thickness. With further increase of the thrust plate thickness to 30 mm, the axial stiffness of the spindle increases slightly to 204.2 N/\(\upmu\)m. It means that the stiffness is close to the ideal stiffness with the further increase of the thrust plate thickness. The experimental result validates the accuracy of the FSI model.
# Landslide Prediction with Model Switching +
## 5 Experiments
### Evaluation of Landslide Pre-Alarm  
The same datasets as in Section 5.1 were applied to evaluate our landslide pre-alarm method. Table 8 shows the prediction time (PT) for ten different experiments. For each experiment, the minimum PT, maximum PT, and average PT were recorded. From Table 8, the program was able to observe that the prediction time for dataset #5 was the longest (52.2 min); while that for dataset #10 was the shortest (38.4 min). As a shorter prediction time indicates that the change of FS is intense and quick, dataset #10 represented a higher probability of landslide occurrence. Taking the average of all timings, it can be seen that the proposed MoSLaPS method could warn of the occurrence of a landslide an average of 44.2 min in advance.  
Further, we take a best-case example, to demonstrate how the proposed method can warn of landslide occurrence far in advance. At time point 255, the 8 th prediction result is \(FS<1\); that is, a landslide will occur after eight time units, as shown in Figure 14. In our experiments, the interval between two time points is 10 min. Hence, the landslide occurrence could be predicted and warn  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Time (s)** & **CPU Usage** & **Memory** \\ \hline MoSLaPS & 1.205 & 23.90\% & 972 kb \\ ADASYN+BPNN & 0.739 & 20.30\% & 192 kb \\ BPNN & 0.639 & 19.90\% & 148 kb \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparisons of time consumption and resource usage.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Min. PT (min)** & **Max. PT (min)** & **Avg. PT (min)** \\ \hline
1 & 10 & 80 & 40 \\
2 & 10 & 70 & 33.3 \\
3 & 10 & 50 & 35.7 \\
4 & 20 & 80 & 47.6 \\
5 & 10 & 80 & 52.2 \\
6 & 10 & 80 & 51.8 \\
7 & 10 & 80 & 42.6 \\
8 & 10 & 70 & 45.7 \\
9 & 10 & 80 & 43.3 \\
10 & 10 & 80 & 38.4 \\ \hline Avg. & 10.7 & 76.4 & 44.2 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Prediction time in advance for different datasets.
## 1 Introduction
### Outline of Instrument Specifications and Observation Methods  
Table 1 shows the type of spectrometer, spectral range, sampling interval and full-width-at-half-maximum (FWHM) of GOSAT and OCO-2 [8,9]. GOSAT was launched into a 666 km orbit with a ground track repeat cycle of three days. The GOSAT Thermal and Near infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) observes solar radiation reflected by the Earth's surface and atmosphere, and thermal infrared radiation (TIR) emitted from the surface and the atmosphere. It has three narrow bands: Band 1 at 0.76 \(\upmu\)m, Band 2 at 1.60 \(\upmu\)m and Band 3 at 2.06 \(\upmu\)m, and one wide TIR band spanning 5.5-14.3 \(\upmu\)m. The Michelsoninterferometer modulates and divides the scene flux into three bands with two linear polarizations (P and S) and into the TIR band, and acquires interferograms every 4 s. The spectral sampling interval is 0.2 cm\({}^{-1}\), which is determined by a maximum optical path difference of 2.5 cm. The TANSO-FTS instantaneous field of view (IFOV) is 15.8 mrad, which corresponds to about a 10.5 km diameter footprint on the ground at nadir observation. A two-axis pointing mechanism directs the IFOV in the cross-track (CT: \(\pm\)35 degrees) and the along track (AT: \(\pm\)20 degrees) directions. The footprint of the single point FTS observation can be monitored by a two-dimensional (1280 by 1024 pixel) CMOS (Complementary Metal Oxide Semiconductor Image Sensor) camera (CAM) which is aligned with the FTS optical axis. Each TANSO-FTS scene has at least one camera image with better than 100 m spatial resolution. Cloud-contaminated footprints can be filtered out using data from this camera.  
The OCO-2 satellite was launched into the 705 km Afternoon Constellation (A-train). The orbit is near-polar, sun-synchronous and a 16-day ground track repeat cycle. OCO-2 uses a common telescope that illuminates three long-slit imaging grating spectrometers, with diffraction gratings to disperse the incoming light. The three grating spectrometers cover the O\({}_{2}\)A band at 0.765 \(\upmu\)m, a weak CO\({}_{2}\) band at 1.60 \(\upmu\)m and a strong CO\({}_{2}\) band at 2.06 \(\upmu\)m. These three channels are designated ABO2, WCO2, and SCO2, respectively. The footprint dimensions are determined by the cross-track IFOV of 0.1 degree and the integration time (0.333 s). For nadir observations, this yields eight cross-track footprints along the spectrometer slit with dimensions of <1.29 km by 2.25 km [10].
# Improved Ant Colony Algorithm for the Split Delivery Vehicle Routing Problem
## 4 Improved Ant Colony Algorithm
### Customer Selection Method  
#### 4.1.1 Initial Customer Selection  
In the SDVRP, because both the vehicle and the customer have load requirements, the choice of the initial customer will affect the vehicle's subsequent choice of customer points. If the initial customer selection is not good, it is difficult to explore the best solution route. When the ant colony algorithm is used to solve the vehicle routing problem, the initial customer of the vehicle generally comes from two choices:  
1. All coverage methods. The vehicle can search the customer points as the initial customer one by one, thereby expanding the search scope. However, when handling larger-scale problems, the algorithm running time may increase dramatically.
2. Random placement. This method will randomly select the initial customer of the vehicle according to a probability distribution. The advantage of this method is that the running time of the algorithm can be reduced. However, due to the random selection, the best path length generated by each iteration may greatly differ, thus reducing the convergence speed of the algorithm.  
To enable the algorithm to balance convergence speed and global search ability, this paper proposes an initial customer selection method using a combination of best selection and random selection and designs an adaptive selection threshold, as shown in Equation (11).  
\[q_{0}=\lambda e^{-\frac{1}{2}\left(\frac{\lambda q_{0}}{\text{max},\lambda e }\right)^{2}} \tag{11}\]where \(\lambda\) is the scale coefficient, the value range is (0, 1), _iter_ is the current iteration number, and max_iter_ is the total iteration number. The variable \(q_{0}\) is responsible for adjusting the customer selection strategy of the algorithm.  
Additionally, if the path distance between customer points is also taken as an influence factor, it may make it easier for ants to select the customer point closest to the depot as the initial customer point. However, this is not necessarily the initial customer on the global best path, so the selection of the initial customer is affected only by the pheromone on the path. All vehicles are parked in the depot. At time \(t\), the initial customer that vehicle \(k\) can select is \(j\), and the customer selection method is shown in Equation (12):  
\[\begin{cases}j=\operatorname*{argmax}\bigl{\{}\tau_{0y}(t)\bigr{\}},&q\leq q _{0}\\ p_{0j}^{k}=\frac{\tau_{0y}(t)}{\sum\limits_{y\in allov(0)}\tau_{0y}(t)},&q>q_ {0}\end{cases} \tag{12}\]  
where \(\tau_{0y}(t)\) denotes the pheromone content on the path (0, \(y\)) at time \(t\), \(p_{0j}^{k}\) denotes the probability that the initial customer of vehicle \(k\) selects \(j\), \(allow(0)\) denotes the set of customer points with transportation demand when the vehicle is in the depot, \(q\) is a random value in the interval (0, 1), and \(q_{0}\) is the set adaptive selection threshold.
# Micromechanism of the Dispersion Behavior of Polymer-Modified Rejuvenators in Aged Asphalt Material
## 3 Results
### DSR Analysis  
Figure 9 shows the changes in the complex modulus (G*) and phase angle (\(\delta\)) of base asphalt binder (AH-70), polymer-modified asphalt binder (1-D), aged asphalt binder, CR rejuvenated asphalt binder (3%, 6%, 9%), and MR-2 rejuvenated asphalt binder (3%, 6%, 9%), with temperature.  
In Figure 9a, for both rejuvenators, the rejuvenating resulted in a decrease in the G* for aged asphalt binder. Additionally, as the rejuvenator content increases (3%, 6%, 9%), the decrease range of the G* become greater. This indicates that the addition of the rejuvenators can soften the aged asphalt binder in different degree.  
However, it can also be observed that the G* of the MR-2 rejuvenated sample was higher than that of the CR rejuvenated sample at a high temperature (50 to 80 \({}^{\circ}\)C) under the same rejuvenator content. Previous studies showed that the asphalt is a temperature sensitive material. Therefore, the test result indicates that the temperature stability of rejuvenated binder with MR-2 is higher than CR samples, and as the increases of MR-2 content, the stability of G* is more obvious.  
Accordingly, in Figure 9b, the phase angle shows a similar changes law to the G*, with the different rejuvenator content of CR and MR-2. Nevertheless, there are obvious plateau regions are observable in the phase angle curves, which caused by the stable polymer network structure in MR-2 (9%) and 1-D asphalt samples at high in-service temperature. When the MR-2 rejuvenator content was reduced, the plateau decreased (6% MR-2 content) and ultimately disappeared (3% MR-2 content), which was due to the weakened network structure with the gradual reduction of polymer content in rejuvenator.  
Overall, combined with test results of G* and phase angle, the asphalt binder rejuvenated with MR-2 has excellent performance of deformation resistance at a high in-service temperature, as with polymer-modified asphalt. On the contrary, the asphalt binder rejuvenated with CR has a relative general performance of deformation resistance at a high in-service temperature. From these curves, it can also be observed that the changes in complex modulus and phase angle with temperature of the MR-2-9% and CR-9% samples are closest to that of the 1-D and AH-70 samples, respectively.  
Figure 8: SEM-derived morphology for the different asphalt binders: (**a**) aged binder (5000\(\times\)); (**b**) CR rejuvenated binder (5000\(\times\)); (**c**) MR-1 rejuvenated binder (5000\(\times\)); (**d**) MR-2 rejuvenated binder (5000\(\times\)); (**e**) MR-2 rejuvenated binder enlarged (20,000\(\times\)).
# Wave-Shaped Microstructure Cancer Detection Sensor in Terahertz Band: Design and Analysis
## 5 Fabrication of the Proposed HC-PCF  
Another significant issue is the manufacturing of PCF-based devices. In spite of the development of numerous manufacturing techniques, including boiling techniques, capillary packing, multiple thinning, casting, and the sol-gel and stacking and drawing method [38; 39; 40; 41; 42; 43; 44; 45; 46]--all of which have been used to create hollow-shaped processes--3D printing was utilized for creating a number of hollow-shaped core processes [46]. Several PCF structures have been created by [PERSON] et al. using the stack and draw technique [45]. Additionally, [PERSON] et al. [43] presented various stack and draw techniques for fabricating suspended microstructure fibers. Recently, it was reported that a hollow core PCF was developed using the stack-and-draw method, which involves stacking several layers of extruded material on top of each other and then drawing the whole structure into its final shape and was based on sodium-calcium-silicate glass [47]. The HC-PCF-based sensor can be rendered using normal stack-and-drawing technology and includes circle-formed air holes both in the center and cladding [48; 49]. By leaving out some capillaries, it is possible to create a hollow air core and claddings. In light of current fabrication methods and their shortcomings, we proposed an HC-PCF biosensor that could be produced in any extrusion or 3D printing manufacturing environment.  
Figure 14 shows the basic structure for detecting cancer cells using the proposed biosensor. In the figure, light is incident upon the core of the biosensor, which is filled with analyte. The light passes through the analyte in the biosensor and goes into the spectrometer. The spectrometer is used to analyze spectral properties of the fiber, which can provide information about the refractive index of the surrounding medium and any analytes present. The spectrometer can also be used to monitor changes in the transmission spectra of the fiber over time, which can be used for real-time sensing applications. All these data are transferred to a PC for further analysis and comparison.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Ref.** & **Cancer Cell** & **Frequency/Wavelength** & **RS (\%)** & **CL (dB/cm)** & **EML (cm\({}^{-1}\))** & **EA (m\({}^{2}\))** & **B** \\ \hline
# Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems
## 5 Results and Discussion  
For evaluation, the simulation results are obtained using MATLAB software (8.5.0, MathWorks, Natick, Massachusetts, USA). The single-cell downlink NOMA system with the BS's transmission power (\(P_{max}\)) of 41 dBm and the circuit power consumption (\(P_{c}\)) of 1 W is considered. The BS is located at the center of a circle with radius \(R\) = 500 m, and there is a random distribution of \(M\) users in the cell coverage. To reduce computational complexity, NOMA assigns only two users to each subchannel, and OMA assigns a single user to each subchannel. The CSI is known at the BS. The minimum distance between BS and users is 50 m, and the minimum distance between users is 40 m. The bandwidth of the NOMA system (\(B_{w}\)) is 5 MHz and the path-loss exponent \(\beta\) = 2. The noise power \(\sigma_{s}^{2}=B_{w}N_{0}/S\), and the power spectral density \(N_{0}\) = -174 dBm/Hz. The obtained results are compared with OFDMA, FTPA [11] and the DC algorithm [28].