esa-sceva/satcom-chunk-collection huggingface dataset:
# Finite Element Simulation of HELICOIL(r) Inserts
## 8 Discussion
### Mesh Size Sensitivity  
For the resulting curve (Table 15, Final), the effect of the element size around the cohesive zone was tested. Three different meshes (hereafter called Mesh 1, Mesh 2, and Mesh 3) having 630, 7350, and 26,323 elements and 975, 4728, and 29,508 nodes, respectively, were tested. The contact area between the specimen and the screw--where the cohesive zone was used--was divided on the \(z\)-axis into 7, 14, and 28 parts, and along the perimeter into 6, 10, and 20 parts for Mesh 1, Mesh 2, and Mesh 3, respectively; see Figure 17.  
The displacement in the \(z\)-axis direction was almost the same for all calculated variants; see Figure 18.  
For comparison, the stress on the specimen (Equivalent Von Mises Stress) is shown in Figure 19, with the stress values varying (40-60 MPa) by approximately 30%.  
Comparison of shear stresses (axis \(yz\)) in the specimen and the screw showed much more favourable results (\(-22\) to \(-25\) MPa), with a difference of approximately 8%; see Figure 20.  
When comparing the measured quantities and forces on the screw versus time (output curve), the differences between the variants were negligible; see Figure 21.  
Figure 16: The decrease in the COF value during the solution process.  
In general, the forces and displacements defined over a reasonably large area are less dependent on the quality of the mesh than on the stresses and strains. If the values of the objective function were calculated from stress values, it would be necessary to approach the mesh with great care. Although, in our case, the input to the cohesive zone was the shear stresses, which varied by approximately 8% around the test site for the test meshes, the effect on the resulting curve was negligible. The input curve (Shear stress-Displacement) was the same for all contact elements, of which there were dozens, even in the case of a coarse mesh. Therefore, in our opinion, the input curve (Shear stress-Displacement) was the same for all contact elements, and the results are similar for all contact elements.  
Figure 19: The Equivalent von Mises stress for the specimen and the three meshes.  
Figure 17: Testing meshes. Spec. C indicates the area where the cohesive zone is used in the hole.  
behaves similarly to the output curve (Force-Displacement), and the mesh density has negligible influence with respect to the given problem.
###### Abstract  
The technical development of diode lasers with regard to wavelengths, output powers and pulse durations in the order of microseconds is expanding the range of medical applications. The 445 nm wavelength, in particular, offers a promising approach. Such a laser system is now available, and its technical and optical properties are introduced here. To characterize the diode laser with a wavelength of 445 nm, laser settings were recorded in both CW and pulse modes in the range \(\upmu\)s to ms, with duty-cycles of 1% up to 40% (partially also up to 90%). At the same time, the temporal behaviour of the laser pulses was documented. In addition, the influence of different irradiation parameters for pulse durations of 10 \(\upmu\)s, 100 \(\upmu\)s and 1 ms at duty-cycles of 50%, 25% and 15% on porcine tissue was investigated. With regard to the cutting depth and thermal damage zones, cuts were carried out in the contact mode using an optical fiber with a core diameter of 375 \(\upmu\)m and a cut speed of 3 mm/s. The average power was set between 2 W and 8 W. Output powers of up to 12.7 W in CW mode and peak powers > 30 W, with pulse durations of 5 and 10 \(\upmu\)s, were achieved. Cuts with a depth of up to 1 mm could be created using 100 \(\upmu\)s pulses with different duty-cycles and also in CW mode. The width of the thermal damage zone was between 200 and 350 \(\upmu\)m for all settings. The 445 nm wavelength is characterized by a large number of possible advantages in surgical applications. Pulse durations down to the \(\upmu\)s-range with peak power approximately three times higher than average power could open up new applications.  
output power; duty-cycle; pulse repetition rate; pulse train; laser incisions Article
###### Abstract  
The equalization enhanced phase noise (EEPN), caused by the interaction of the chromatic dispersion (CD) with the phase noise of the local oscillator (LO), has been extensively studied for single-mode optical communication systems. Few-mode fiber (FMF) transmission systems introduce a new channel impairment, the differential mode delay (DMD), which also creates EEPN and hence limits the maximum transmission distance of those systems. In this work, we numerically investigate the optical signal to noise ratio (OSNR) penalties caused by the EEPN in a 3-mode FMF transmission system at 25 GBd for quadrature phase-shift keying (QPSK), 16-quadrature amplitude modulation (QAM), 32-QAM and 64-QAM modulation formats when using the blind phase search (BPS) carrier phase recovery (CPR) algorithm, which has been demonstrated to be both robust and suitable for optical communication systems. Our numerical study assumes a short-span of FMF, modeled in the weakly-coupled regime, and includes two cases; the use of ideal mode-selective de/multiplexers at both ends of the FMF span (model A), and the use of ideal non-mode-selective de/multiplexers (model B). The results show that the EEPN has almost no effect in model A. However, EEPN produces a severe penalty in model B with the onset of the OSNR degradation starting for a DMD spread of the impulse response of about 100 symbols for all modulation formats investigated. The distribution ratio of the amount of phase noise between the transmitter and receiver lasers is also assessed for model B and we confirm that the degradation is mainly due to the phase noise of the LO.  
equalization enhanced phase noise; few-mode fiber optical communication systems; time-domain MIMO equalization; mode-division multiplexing +
Footnote †: This paper is an extended version of our paper published in Advanced Photonics Congress(APC), New Orleans, LA, USA, 24-27 July 2017.
## 6 Experimental Results and Discussion
### Change in Position of the Cooling Fan  
The hot air in the MCR is discharged using a cooling fan. Thus, the airflow and internal flow rate can be varied according to the position of such a cooling fan, and this leads to present a difference in the heat transfer performance. As illustrated in Figure 9, the experiment was performed by adjusting the position of the cooling fan from a maximum of 40 mm to a minimum of 20 mm, with an interval of 5 mm referenced on the condenser.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case**Item** & **Power Original (kWh/Month)** & **Power Modified (kWh/Month)** & **Reduction (\%)** \\ \hline Case \#1 & 50.1 & 49.7 & 0.8 \\ Case \#2 & 51.4 & 51.0 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reduction in power of original and modified models.  
Figure 8: Original and modified model of the machine room.  
In the experimental results of the monthly power consumption, as shown in Figure 10, the power consumption decreased when the position of the fan was closer to the condenser. Thus, it can be seen that the interval determined by 20 mm represented the highest efficiency. However, it is necessary to configure the position of the fan by considering the structure of the MCR as it is not possible to reduce the distance due to its structure excessively.
## 4 Implementation and Results
### Field Test Results  
To implement the design of the system proposed in this paper and evaluate its performance, the agricultural environment monitoring server system was installed at a real outdoor location to monitor the environment.  
Figure 19 is a graph that represents environmental data measured by the proposed agricultural environment monitoring server system that is installed to be operational in the real agricultural environment, and it could be seen from this graph that the agricultural environment monitoring server system normally processes information sensed from WSN sensor nodes installed outdoors without malfunction.  
In addition, according to the power consumption field tests, the agricultural environment monitoring server system could be operated for about 24 hours by charging with the solar cells for about 10 hours, and the proposed system could be operated with only the solar cells installed on the system, without requiring a wired power supply connection or any additional charging process. Figure 20 is a graph that represents the results of the power consumption field test of the proposed system. Through the above results, it could be found that the proposed agricultural environment monitoring server system collects information even in the a real outdoors environment without malfunction and provides real-time monitoring and various application services based on the information.  
Figure 18: Web GUI for Agricultural Environment Monitoring Server System.  
Figure 19: Measured Environmental Data Graph. **(a)** Temperature Graph (2010.02.23); **(b)** Humidity Graph (2010.02.23); **(c)** Luminance Graph (2010.02.23).
# Research on Slope Stability Prediction Based on MC-BKA-MLP Mixed Model
## 5 SHAP Analysis
Given the practical significance of these indices, it is clear that the effective stress and shear strength of the slope's rock mass have the most significant influence on slope stability. Additionally, the inherent properties of the slope itself play a crucial role in determining its strength and stress characteristics. Moreover, the slope angle significantly affects slope stability. Therefore, in practical engineering applications for slope design and stability control, measures such as grouting to improve soil properties, installing drainage systems to reduce pore water pressure and increase effective stress, grading the slope to disperse loads, and setting up retaining walls can be implemented to ensure effective landslide prevention.
# Advanced Metal Matrix Nanocomposites
## 2 Fabrication Methods
### Friction Stir Processing  
Friction stir processing (FSP), as a derivative of friction stir welding, is widely used by many researchers and industrialists [54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64] to incorporate nano-particles within a metallic matrix to produce bulk or surface nanocomposites. Initially, a groove is made with the base metallic material which is then filled with the desired volume fraction of the nanoparticles. Using a non-consumable rotating pin-less tool, which is plunged into the surface of the work piece, the groove is closed with the particles being encapsulated within the closed grove. In the next step, a tool with pin is used to disperse the particles, through a stirring process, with the bulk of the metallic substrate (Figure 11). The main challenge of this method is final surface finish of the processed product and uniform dispersion of the nano-sized particles within the matrix. This procedure still under development and has been used in fabrication of aluminum and magnesium based nanocomposites [63; 64]; [PERSON] et al. [65] have published a review paper on composite and nanocomposite fabrication using FSP.  
[PERSON] et al. [54], for instance, filled multi-walled CNTs (diameter of 20-50 nm) into a groove (1 mm \(\times\) 2 mm) on the AZ31 (rolled plates with thickness of 6 mm) and employed FSP to fabricate surface composites; good distribution of the nanotubes within the matrix at 1500 rpm and traverse speed of 25 mm/min was observed after exploring various parameters along with great grain refinement (less than 0.5 \(\upmu\)m) in the FSPed specimens. Using the FSP principles, with a constant traverse speed of 45 mm/min and a rotational speed of 800 rpm, [PERSON] et al. [55] fabricated an AZ61/SiO\({}_{2}\) (average diameter of 20 nm) nanocomposite with volume fractions of 5 and 10%. Multi-pass FSP trials showed that the clustering phenomenon becomes inferior with increasing the number of the FSP  
Figure 10: Schematic of the ball milling process (**a**). The used balls are hitting the CNTs and the matrix powder material (**b**), welding and integrating the two components (**c**), and the bounce of the powder particle to restart this process again at another spot (**d**) [53].  
passes in addition to significant grain refinement and improvement in the mechanical properties in the FSPed samples versus the starting materials.
###### Abstract
Conversely, it has been demonstrated that steppe meadows in permafrost zones experience \"sink-source\" or \"source-sink\" transitions in response to the seasonal distribution and interannual fluctuation of external conditions such as temperature and precipitation [31; 32]. For example, [PERSON] et al. discovered that permafrost in high-elevation grassland ecosystems may change from a carbon sink to a carbon source under late 21 st-century warming [33]. Climate variations often restrict grassland development and are the primary driver of interannual change in carbon sinks, as they can dramatically lower GPP and NEP [31]. Since previous research has mostly concentrated on single-point and short-term studies [34; 35], the carbon budget of the TRSR has not been thoroughly characterized at regional and long-term scales. Therefore, the impact of climate change on the source/sink dynamics of alpine grassland ecosystems is not well understood, especially the effect of changes in the combination of hydrothermal factors on the interannual variability of carbon fluxes in alpine grassland ecosystems, which is more uncertain and needs to be studied in depth. The boreal ecosystem productivity simulator (BEPS) model is an ecological remote sensing model developed on the basis of the forest biogeochemical cycles (forest-BGC) process model and was originally applied to Canadian boreal forest ecosystems [36; 37]. This model is made up of modules for photosynthesis, energy balance, hydrology, and soil biogeochemistry, and it considers the coupling of terrestrial carbon, water, and nitrogen cycles [38]. The BEPS model introduces advanced radiative transfer theory and a sophisticated photosynthesis module, sorts the vegetation canopy leaves into sunlit and shaded leaves, and simulates the photosynthesis and evapotranspiration of these two types of leaves, thereby improving the accuracy of the simulation. Additionally, the model uses the Farquhar model to solve the compatibility problem of different types of data, as well as the spatio-temporal scale transformation of remote sensing data [39; 40]. In recent years, the model has been widely used to simulate terrestrial carbon fluxes on regional and global scales [41; 42; 43; 44]. In this study, the interannual dynamics of the carbon budget of the TRSR based on a long time series from 1985 to 2018 and its influencing factors were compared using the BEPS model. The objectives of this study were as follows: (1) to analyze the GPP and NEP variations within the TRSR and its subregions from 1985 to 2018; (2) to investigate the spatio-temporal distribution of carbon sources and sinks in the TRSR; (3) to evaluate the major mechanisms controlling carbon source and sink variability in alpine ecosystems.
## 2 Background Projects and Database Construction
### Project Overview
#### 2.1.2 Monitoring Data  
In this paper, the data from more than 400 monitoring sections of the Yangjiawopu Tunnel, South Fire Road Tunnel, Khan Gou 1, and Khan Gou 2 tunnels are selected. The arrangement of measurement points for the standard section is shown in Figure 2. The monitoring point A is the displacement of tunnel arch. The monitoring points B-E are the convergence deformation monitoring points. The data for tunnel arch settlement and convergence are shown in Figure 3. The deformation gradually transitions to the stabilization stage with time. The deformation prediction model targets the convergence deformation as a predictor. The distribution of deformation values for the four tunnels is shown in Figure 4. Positive values of vertical deformation represent the settlement of the vault, and positive values of convergence measurements represent smaller distances. The minimum and maximum values of settlement of the vault are 22.2 mm and 0.5 mm, respectively. The maximum and minimum values of horizontal convergence deformation of the initial support structure are 20.1 mm and 1.9 mm, respectively.  
Figure 3: Monitoring data curve over time.  
Figure 2: Layout of monitoring points in a standard tunnel cross-section.
# How Resilient Are Kolmogorov-Arnold Networks in Classification Tasks? A Robustness Investigation
## 4 Methodology
### Datasets  
In our experiments, we utilized three different traffic sign recognition datasets to evaluate the performance and robustness of our KAN models: The Belgium Traffic Sign Dataset (BTSD) [60], the Chinese Traffic Sign Database (CTSD) [61], and the German Traffic Sign Recognition Benchmark (GTSRB) [62]. The BTSD includes a total of 62 classes categorized into three superclasses: mandatory, prohibitive, and danger classes. The dataset is divided into 4591 training images and 2534 test images, providing variety in sign appearances to assess model robustness, as depicted in Figure 2(a). China's CTSD comprises 6164 images in 58 categories, with 4170 training images and 1994 test images, offering a well-annotated dataset frequently used in traffic sign recognition research, illustrated in Figure 2(b). Lastly, The GTSRB dataset, widely used to benchmark traffic sign classification models, contains 43 classes with 39,209 training images and 12,630 test images, offering a diverse representation of German traffic signs, as shown in Figure 2(c). We observed that certain classes within the BTSD and CTSD had a limited number of images in both the training and test sets, resulting in class imbalance. Our experiments employed various data augmentation techniques to address this issue, such as rotation, flipping, scaling, and adding noise and adversarial examples during adversarial training.
## 4 Applying SWE
### Example SWE Deployment  
An exemplary case-study of the application of SWE in a real-world hydrological deployment scenario is shown in Figure 5 and is based on what has been described in [104]. The SWE services are applied to manage a network of hydrological sensors (e.g., water gauges, weather stations, or cameras observing critical facilities) by providing access to sensor data (Section 3.3.2), by realizing event handling (Section 3.3.4), and by enabling interoperable tasking of sensors (Section 3.3.3). The described deployment can be easily adapted to other real-world deployments of sensor networks.  
Figure 5: Deployment scenario for SWE services.  
Observations from the various sensor resources out in the field are inserted to an SOS. The figure shows a direct connection between sensor and service. However, in real world applications the raw data measured by sensors is first processed, enriched and encoded as O&M before it can be inserted to the SOS. Hence, in real world deployments there are usually data acquisition systems and middleware components located between sensors and SWE services. Once the observations are uploaded to the SOS, applications can retrieve the data through the standardized interface and can visualize it for example as time series charts or on maps.  
If a client is only interested in particular data which matches some defined filter criteria, e.g., the exceedance of a threshold, it can subscribe to an SES. The sensor data is continuously published to the SES and in case a specified filter criterion is matched, the SES forwards the data to the client. Clients can also register for alarms if certain events occur. In that case, the SES triggers a WNS to notify the client via a defined communication protocol. For example, a user can receive a notification via SMS or email if the water level at a gauge station is above 5 meters.  
Finally, the SPS is utilized to task sensors. For example, the SPS can be used to task cameras at certain points of interest along a river course (e.g., a dam or a water gauge). The cameras can be rotated or zoomed and the real time video stream can be accessed by the client through another means of data access.
# A Microfluidic Approach for Biosensing DNA within Forensics
## 3 Biosensing of DNA
### STR Analysis  
STR analysis is the method within forensic science to generate DNA profiles. After amplification of the DNA, by PCR, the separation and detection of the amplified DNA must be carried out, which is usually accomplished by capillary electrophoresis (CE) [15].  
Chips for PCR can be divided in two categories: well-based and continuous-flow chips [16]. In the first case, a well-based system (Figure 1A), the complete microdevice is sequentially cooled and heated. Drawback of a well-based system is the long time per cycle, caused by the relatively large total thermal mass of the system [16; 17]. Continuous-flow chips can be further split up in three categories: fixed-loop, closed-loop and oscillatory chips. A fixed-loop microdevice (Figure 1B) contains zones with different temperatures through which the sample is moved. The numbers of meanders in the microdevice defines the number of thermal cycles. The time of each step is typically controlled by the length of the meander in a specific temperature zone in combination with the flow rate. In a closed-loop chip (Figure 1D) the circuit is fixed through which the sample is moved, while the number of thermal cycles can vary. Additionally, in an oscillatory microdevice the number of cycles can be varied (Figure 1C). The different zones of the microdevice are held at different temperatures and the sample is shunted back and forth between these different zones [16].  
In order to obtain a STR profile, in forensics there is significant interest in chips for CE to accomplish this [18]. By using such devices the required reagent and sample volumes are decreased, as well as the total analysis time [15]. Typical duration times of the separation are about 5-15 min [19; 20; 21].