esa-sceva/satcom-chunk-collection huggingface dataset:
# Micro-Slit Cutting in an Aluminum Foil Using an Un-Traveling Tungsten Wire
## 3 Experiments  
The experiments were carried out on a specially designed machine tool, as shown in Figure 4. The fundamental component is a three-axis stage fixed on a platform. Its maximum travel length is 50 mm (X-axis) \(\times\) 25 mm (Y-axis) \(\times\) 100 mm (Z-axis). The tungsten wire is put in a small cube made by (polymethyl methacrylate) PMMA where a trench is pre-processed. Meanwhile, the aluminum foil is put on a fixture made by epoxy resin and a PMMA pad is employed to avoid surface scratch and deformation.  
In this work, the aluminum foil is 100 \(\upmu\)m in thickness and the tungsten micro-wire is 50 \(\upmu\)m in diameter. In previous trials, a tungsten wire with a diameter of 25 \(\upmu\)m is easily broken while a tungsten wire with a diameter of 100 \(\upmu\)m is too thick and tends to bend aluminum foil. Further, a set of parameters is selected to investigate their effects on slit cutting performances. The chemical composition  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Size of aluminum foil & 300 \(\times\) 250 \(\times\) 100 \(\upmu\)m \\ Diameter of tungsten wire & 50 \(\upmu\)m \\ Poisson’s ratio of tungsten & 0.28 \\ Poisson’s ratio of aluminum & 0.33 \\ Elastic modulus of tungsten & 380 GPa \\ Elastic modulus of aluminum & 70 GPa \\ Yield strength of aluminum & 30 MPa \\ Rupture strength of aluminum & 90 MPa \\ Initial contact force & 2.01 N \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 4: Experimental apparatus: (**a**) machining tool; (**b**) fixture enlargement.  
Figure 3: Simulation result: (**a**) stress; (**b**) deformation.  
of aluminum foil is listed in Table 2 and primary machining conditions used for experimental tests are shown in Table 3.  
Afterward, the slit dimensions were measured using a 3D profilometer (DVM5000, Leica, Wetzlar, Germany) and a scanning electron microscope (SEM: S-3400N, Hitachi, Tokyo, Japan) was used to observe 3D views. In addition, a 3D measurement laser microscope (OLS4100, OLYMPUS, Tokyo, Japan) was employed to measure surface roughness.
# European Geostationary Navigation Overlay Service Performance Prediction +
## 2 EGNOS Performance Prediction  
### The EGNOS System  
EGNOS improves the performance of GPS L1 signal accuracy by providing range corrections and associated quality metrics to achieve the required level of reliability for Safety of Life applications. To do so, it provides users with protection levels as upper bounds of their position errors. The current version of EGNOS is called EGNOS v2 and has been declared 'Safety of Life' capable since 2011. Since then, several EGNOS v2 releases have been deployed to improve the EGNOS service. The next version of EGNOS called EGNOS v3 is under development and will provide Dual-Frequency Multi Constellation (DFMC) services by augmenting GPS L1/L5 and Galileo E1/E5a signals to enable more demanding service levels beyond what EGNOS v2 can provide. The work presented in this paper addresses EGNOS v2 and not EGNOS v3.  
EGNOS v2 uses L1C/A and L2P(Y) GPS measurements, collected by accurately located reference stations (RIMS) deployed across Europe and remotely. All GPS measurements are transferred to a central computing centre hosting the Central Processing Facility (CPF), which runs the navigation algorithms to calculate differential corrections and integrity messages are calculated. Two types of corrections are provided: corrections for satellite orbit and clock errors, and corrections for ionospheric delays. Consequently, two types of integrity indicators are provided:  
* The User Differential Range Error Index (UDREI). An integer in the range [0-15], which characterizes the residual range errors due to orbit and clock inaccuracies after applying the clock and orbit corrections. Below index 13, a lower UDREI value means that the GPS signal is more accurate, while a higher UDRE value means that there is a greater chance of error in the GPS signal. When the satellite is not observed by sufficient RIMS, a UDREI value of 14 is broadcast to indicate that the satellite is Not Monitored, while a UDREI value of 15 indicates that the satellite is not to be used by the users (Do Not Use status as per MOPS) because a problem has been detected on this satellite (for example, the presence of a failure);
* The Grid Ionospheric Vertical Error Index (GIVEI), an integer in the range [0-15], which characterizes the residual error of the ionospheric delays after application of the ionospheric corrections. As for the UDREI, The lower the GIVEI value, the more accurate the ionosphere corrections and vertical position measurement from the GNSS signal.  
These corrections and integrity indicators are broadcast to users using geostationary satellites that serve as an augmentation to the original GPS signals [3]. Over the period considered in this paper, between 34 and 38 Reference and Integrity Monitoring Stations (RIMS) were deployed to monitor the GPS constellation. This number varies because of new RIMS deployments or RIMS decommissioning over the last decade.
## 6 Discussion
### Broader Implications  
The success of the DRKG has significant implications for applications requiring reliable and explainable reasoning. In healthcare, for instance, clinicians could trace diagnostic reasoning paths to validate conclusions. In finance, transparent multi-hop inference could enhance auditability for regulatory compliance. Furthermore, the DRKG's robustness under low-temperature configurations (Section 5.7) suggests its suitability for mission-critical systems where deterministic outputs are paramount.
# Extraction of Visible Boundaries for Cadastral Mapping Based on UAV Imagery
###### Abstract  
In order to transcend the challenge of accelerating the establishment of cadastres and to efficiently maintain them once established, innovative, and automated cadastral mapping techniques are needed. The focus of the research is on the use of high-resolution optical sensors on unmanned aerial vehicle (UAV) platforms. More specifically, this study investigates the potential of UAV-based cadastral mapping, where the ENVI feature extraction (FX) module has been used for data processing. The paper describes the workflow, which encompasses image pre-processing, automatic extraction of visible boundaries on the UAV imagery, and data post-processing. It shows that this approach should be applied when the UAV orthoimage is resampled to a larger ground sample distance (GSD). In addition, the findings show that it is important to filter the extracted boundary maps to improve the results. The results of the accuracy assessment showed that almost 80% of the extracted visible boundaries were correct. Based on the automatic extraction method, the proposed workflow has the potential to accelerate and facilitate the creation of cadastral maps, especially for developing countries. In developed countries, the extracted visible boundaries might be used for the revision of existing cadastral maps. However, in both cases, the extracted visible boundaries must be validated by landowners and other beneficiaries.  
Keywords:land plot; land cadastre; cadastral boundaries; cadastral maps; UAV; image processing; image segmentation; feature extraction +
Footnote †: journal:
## 1 Introduction
### Innovative Contributions
#### 1.1.4 Lightweight Cipher for Secure Communication  
Our model incorporates the Lightweight Cipher PRESENT algorithm as a cornerstone for secure communication. This ultra-lightweight block cipher, with options for 80-bit and 128-bit key lengths, strikes an optimal balance between robust encryption and minimal resource consumption. The PRESENT algorithm operates on 64-bit plaintext blocks, providing efficient encryption without compromising the processing speed. Its streamlined design enables swift execution, making it particularly suitable for resource-constrained IoT devices.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Paper/Article** & **Type Study** & **Architectures** & **Implementation** & **Communication Protocols** & **Open Issues** & \
\begin{tabular}{c} **Conceptual Design Frame-** \\ **work/Solution to Cloning** \\ **Attacks** \\ \end{tabular} &
\begin{tabular}{c} **Highlights** \\ **Cloning** \\ **Attacks** \\ \end{tabular} \\ \hline
[16] & Survey & Yes & No & No & Yes & No \\ \hline
[17] & Survey & Yes & Yes & No & No & No \\ \hline
[18] & Taxonomy & Yes & Yes & No & No & No \\ \hline
[19] & Taxonomy & No & Yes & No & Yes & No \\ \hline
[20] & Survey & Yes & Yes & No & No & No \\ \hline
[21] & Review & Yes & No & No & Yes & No & No \\ \hline
[22] & Survey & Yes & Yes & Yes & Yes & No & No \\ \hline
[23] & Survey & Yes & Yes & No & No & No & No \\ \hline This paper &
\begin{tabular}{c} Survey and Taxonomy \\ \end{tabular} & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Contributions of previous surveys and comparison.
## 2 Related Work
### OFDM-Based Image Communication System
Additionally, they have not evaluated different DL models nor have they exploited any model training techniques, such as data augmentation, to improve the DL model accuracy. Therefore, for this study we consider multi-tier analysis of various OFDM-based image communication systems and robustness analysis of multiple DL models on images recovered from these systems to enable CV applications in ITS. The interoperability and seamless integrability of OFDM systems with various modulation techniques makes it the ideal choice for hybrid systems, such as massive MIMO [32]. Therefore, the focus of this paper is on the physical layer of OFDM communication systems, and the MIMO systems are left for future work.
## 5 Attributes and Characteristics of FL
### Non-IID Data in FL
#### 5.8.4 Real-World Implications Across Sectors  
The practicality of non-IID data settings in FL is seen in numerous sectors. First, the decentralized nature of FL, combined with non-IID data considerations, permits hospitals to collaboratively train models without compromising patients' confidentiality. Moreover, in the finance sector, FL offers a mechanism to analyze distributed financial datasets securely, ensuring that proprietary and confidential information remains within its respective entities. Lastly, IoT, with its vast array of heterogeneous devices, produces diverse datasets. FL becomes indispensable in such settings, allowing for collaborative model training that respects the unique data's origins.
# Grouped Contrastive Learning of Self-Supervised Sentence Representation
## 5 Further Investigation of GCLSR
### Effect of the Size of Groups  
Grouping the features of the projector and predictor can solve the issue of information loss caused by contrastive loss computing between high-dimensional vectors. Therefore, we conduct an experiment to study the effect of the size of the feature grouping. The results are shown in Table 11.  
Generally speaking, different grouping sizes can achieve comparable performance on STS tasks. Although the performance gap between feature grouping and no grouping is small, as observed in Figure 4, the stability and robustness of the model with feature grouping are better compared with no grouping. This verifies that the usage of local information  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Augmentation/STS** & **STS12** & **STS13** & **STS14** & **STS15** & **Avg.** \\ \hline No Aug. & 54.65 & 70.63 & 65.39 & 72.96 & 65.91 \\ Full Aug. & 55.31 & 70.51 & 65.81 & 73.49 & 66.28 \\ Partial Aug. (ours) & **55.79** & **72.72** & **66.73** & **75.40** & **67.66** \\ \hline \hline \end{tabular}
\end{table}
Table 10: The effect of the data augmentation. No Aug.: no word vectors are subject to the data augmentation. Full Aug.: all of the word vectors of a sentence are augmented with our proposed four data augmentation strategies. Partial Aug.: word vectors are augmented by our proposed PWVA.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Grouping Size/STS** & **STS12** & **STS13** & **STS14** & **STS15** & **Avg.** \\ \hline No grouping & 55.77 & 72.69 & 66.67 & 75.33 & 67.62 \\
4 & 55.77 & 72.62 & 66.61 & **75.50** & 67.63 \\
8 & 55.79 & 72.63 & 66.65 & 75.35 & 67.61 \\
16 (ours) & **55.79** & **72.72** & **66.73** & 75.40 & **67.66** \\
32 & 55.73 & 72.45 & 66.57 & 75.39 & 67.54 \\
128 & 55.75 & 72.63 & 66.59 & 75.26 & 67.56 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The effect of the size of feature grouping. No grouping: feature grouping is not performed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Region Size/STS** & **STS12** & **STS13** & **STS14** & **STS15** & **Avg.** \\ \hline (1,2,3,4,5,6) & 55.36 & 67.92 & 63.33 & 73.55 & 65.04 \\ (2,3,4,5,6) & 53.85 & 62.36 & 59.82 & 70.80 & 61.71 \\ (1,1,1,2,3,4) & 55.52 & 71.72 & 65.79 & 74.90 & 66.98 \\ (1,1,1,4,5,6) & 56.71 & 71.35 & 65.53 & 75.19 & 67.20 \\ (1,1,1,1,1,1) & **57.81** & 71.01 & 65.83 & **75.62** & 67.57 \\ (1,1,1,6,15,20) (ours) & 55.79 & **72.72** & **66.73** & 75.40 & **67.66** \\ (1,1,1,20,30,40) & 57.44 & 70.82 & 66.00 & 75.35 & 67.40 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The effect of the region size of TextCNN.  
by feature grouping can help the model mine more information for contrastive learning to advance the performance of sentence representation slightly (from 66.66 to 66.75).
# A Novel Deep Learning Model to Predict Ultimate Strength of Ship Plates under Compression
## 2 Methodology
### Deep Learning Model Configuration  
Deep learning differs from machine learning in that it can independently learn data. It is an artificial neural network composed of an input layer, a hidden layer, and an output layer, and can be used to model nonlinear relationships [13].  
For the deep neural network model to predict the ultimate strength of a plate, a total of six parameters were selected through correlation analysis, and a Tensorflow-based Python program was used. The selected parameters are the initial deflection amount, plate slenderness ratio, plate length, plate thickness, curvature, and yield stress.  
In this study, the loss function used in the deep learning model had the smallest mean square error, and the degree of learning was evaluated using this value. The loss function quantifies the difference between the actual and predicted values [14]. Given that the activation function can solve the vanishing gradient problem of the sigmoid function and has a fast convergence speed, the ReLU function used in most neural networks was applied [15]. Although various types of optimization functions are being used, optimization was performed using the most commonly used, the adaptive moment estimation (Adam) [16].  
As presented in Table 3, the model was configured such that the weights from six input features and seven hidden layers received values from the activation function, ReLU, and output the ultimate strength value. Among a total of 4050 cases of data, 80% or 3240 cases were used as training data, and the training was conducted for 1500 epochs. The training data were used in the process of learning the model, and the test data evaluated the final performance of the model and whether the training was successful [17]. An epoch is the number of times the entire training set passes through the neural network. Specifically, one epoch indicates that the entire training set is applied to one neural network and passes through the neural network once through forward propagation and then through  
Figure 4: Mesh convergence study result.  
backward propagation [18]. The results were confirmed using 810 cases as the test data, which excluded the training data from the entire dataset.
# A Novel GPU-Based Acceleration Algorithm for a Longwave Radiative Transfer Model
## 3 Model Description and GPU Overview
### RRTMG_LW Code Structure  
Figure 1 indicates the profiling graph of the original Fortran RRTMG_LW. Here, the subroutine \(rad\_rrtmg\_lw\) is the driver of longwave radiation code. The subroutine \(mcica\_subcol\_lw\) is used to create Monte-Carlo Independent Column Approximation (McICA) stochastic arrays for cloud physical or optical properties. The subroutine \(rrtmg\_lw\) is the driver subroutine for the RRTMG_LW. The subroutine \(rrtmg\_lw\) (a) calls the subroutine \(inatm\) to read in the atmospheric profile from the GCM for use in the RRTMG_LW, and to define other input parameters; (b) calls the subroutine \(cldprmc\) to set cloud optical depth for the McICA based on the input cloud properties; (c) calls the subroutine \(setcoef\) to calculate information needed by the radiative transfer routine that is specific to this atmosphere, especially some of the coefficients and indices needed to compute the optical depths by interpolating data from stored reference atmospheres; (d) calls the subroutine \(taumol\) to calculate the gaseous optical depths and Planck fractions for each of the 16 spectral bands; (e) calls the subroutine \(rtrtmc\) (for both clear and cloudy profiles) to perform the radiative transfer calculation using the McICA to represent sub-grid scale cloud variability; and (f) passes the necessary fluxes and heating rates back to the GCM.  
Algorithm 1 shows the computing procedure of \(rrtmg\_lw\). As depicted in Figure 1, \(rrtmg\_lw\) took most computing time of \(rad\_rrtmg\_lw\), so the study target was to use GPUs for accelerating the \(inatm\), \(cldprmc\), \(setcoef\), \(taumol\), and \(rtrtmc\) subroutines.
## 2 Materials and Experiment
### Test Condition  
A steel plate where there were two holes with a diameter of 20 mm to insert heater cartridges and a hole with a diameter of 13 mm for a bolt in it for a test rig was modeled and manufactured. An M12 \(\times\) 1.75 bolt and nut were modeled in Autodesk Fusion 360 software and printed by using a Zortrax M200 3D printer. A test rig was designed to study self-loosening of the 3D-printed bolt under repeated temperature changes. A high-accuracy compression load cell (manufacturer: Radian QBIO, Seoul, South Korea, model: RCWW-1T, nonlinearity: 1%, hysteresis: 0.5%) was used to measure the clamped force variation of the bolted joint, as shown in Figure 3. A high-accuracy thermocouple (K-type thermocouple with capacity up to 300 \({}^{\circ}\)C) was attached to the plate to indicate the bolted joint's temperature. The bolt was fixed using clamp bench, which was fastened to a stationary desk, and a dial indicator was used to measure the rotation displacement of the nut (see Figure 3).  
The experiment started with a 1000 N initial preload of the bolted joint, and to measure the clamped force of the joint, a compression load cell installed between the plate and the nut was used. After taking a measurement of the initial temperature, the desired temperature changes were applied by the cartridge heaters, which were controlled by power controllers in the structure. The experiments were carried out under two different temperature change conditions from 10 \({}^{\circ}\)C up to 40 \({}^{\circ}\)C and 80 \({}^{\circ}\)C. A cycle of temperature changes consisted of two processes of heating and cooling. The preload change of the bolted joint was recorded in LABVIEW software (Version: LabVIEW 2015 SP1, Developer(s): National Instruments, Austin, Texas). A single cycle of temperature changes from 10 \({}^{\circ}\)C to 40 \({}^{\circ}\)C in the test lasted about 50 min (6 min for the heating process and 45 \(\pm\) 5 min for the cooling process). A single cycle of temperature changes from 10 \({}^{\circ}\)C to 80 \({}^{\circ}\)C in the test lasted about 135 min (10 min for the heating process and 120 \(\pm\) 10 min for the cooling process). The rotation displacement of the nut in the loosening direction was also measured simultaneously by a dial indicator. The experiment for all types of bolts was repeated three times and average values were used to illustrate the results.  
Figure 2: The results of maximal preload of bolts printed by use of two different printing methods for all bolt materials.
## 3 RTI Data Acquisition Methods
### Acoustic Methods  
Subsurface features modify the characteristics of propagating soundwaves. Acoustic methods pulse predefined waveforms into the tunnel intrados and analyse resultant distortion and delay to identify audible indicators of defects. Acoustic methods can be subdivided into Ultrasonic Testing (UST) and Infrasonic Testing (IST). In UST, reductions in travelling pulse velocity correspond to elastic deformation of defected regions [25; 26];contrastingly for IST, defects are indicated by high resonant frequency components in returning pulses [27].  
We only encountered two research groups directly applying UST to tunnel subsurface inspection. In [28], UST is extremely time-inefficient, requiring 9-25 min to scan 1m of tunnel wall and necessitating use of a preliminary GPR scan (Section 3.6) to localise suspected features. Likewise, despite robotic automation, UST scans performed by tunnel profiler ROBOSPECT achieve comparably inefficient durations of one hour to scan 6m [29] and are optimised for surface level crack and spall detection only [30; 31]. Evidently, UST can be considered ill-suited for RTSSI, where surveys must be swift to minimise periods of tunnel closure.  
IST proves more useful for RTSSI. Traditionally, hammer-strike emissions are performed by experienced human operatives who detect audible defect indicators 'by ear' alone, but those remaining are few, approaching retirement and are not being replaced. Faster robotic schemes are now preferential, boasting improved high level access achieved by mounting hammers to robotic arms [32; 33; 34; 35] on Variable Guide Frames [36] and UAVs [37; 38]. We notably uncovered a unique non-contact infrasonic UAV system [39] successfully inducing hammer-strike reminiscent flexural vibrations in infrastructure at distances of up to 5 m, for which application to remote-RTSSI presents an interesting research venture.  
Detrimentally, inherent reliance on human interpretation of audio-spectra (which do not physically resemble subsurface features they convey) critically limits the insight non-specialist end-users can draw from IST without costly training or additional contextual metadata (e.g., maps of striking locations).
# Uncertainty Theory-Based Resilience Analysis for LEO Satellite Communication Systems
## 4 LEO-SCS and Its Uncertain Evolution Model
### Uncertain Satellite Network Evolution Model  
#### 4.2.1 Assumptions  
We build the uncertain satellite network evolution model based on the following assumptions:  
* The capacities of satellites, ISLs, ULs, and FLs are adequate, as satellite communication uses high-frequency bands, which have a high transmission rate and large system capacity [25; 26];
* User terminals and gateways are perfect, and only satellite failures, which are affected by aleatory uncertainty [12], are considered, as maintenance is not easy in space;
* Processing delays of an LEO-SCS follow regular uncertainty distributions.
## 3 Results and Discussions
### Low Frequency Mini-Brillouin-Zone Mode
#### 3.2.3 MBZ-Edge Mode Excitation and Detection
(Here, the phonon bandgaps on MBZ center and edge are ignored for simplicity and values of frequency are approximately regarded as \(n\)-times 16 GHz based on calculation, where \(n=1\), \(2\), \(3\),... ).