esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Solving Strategy
### Parametric Sweep  
The metaheuristics were configured for solving each problem as follows. (i) Starting from default values, (ii) a parameter of the algorithm is selected to be tuned. (iii) Then, 31 independent runs are performed for each value of the parameter in a range, while the others remain fixed. (iv) The configuration providing the best average behavior based on hypervolume is selected, overwriting the default parameter value. (v) Next, a non-configured parameter is selected going to step (ii). This configuring methodology ends when all parameters were selected in step (v). Table 2 shows the configurations selected (_2 Obj_ and _3 Obj_ fields for the bi-objective and three-objective approach, respectively) and the range studied.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{2}{c}{**Fitness Values**} & \multicolumn{2}{c}{**Fitness Values**} & \multicolumn{2}{c}{**Hyp. Reference Points**} & \multirow{2}{*}{**Test Cases**} \\  & \multicolumn{2}{c}{(\(\mathbf{\xi_{r}=0,r_{c}=30}\))} & \multicolumn{2}{c}{(\(\mathbf{\xi_{r}=0,r_{c}=60}\))} & \multicolumn{2}{c}{(\(\mathbf{\xi_{r}\gg 0}\))} \\ \cline{2-9}  & \(\mathbf{f_{1}}\) & \(\mathbf{f_{2}}\) & \(\mathbf{f_{3}}\) & \(\mathbf{f_{1}}\) & \(\mathbf{f_{2}}\) & \(\mathbf{f_{1}}\) & \(\mathbf{f_{2}}\) & \(\mathbf{f_{3}}\) & \\ \hline \(50\times 50\) & 0.0353 & 0.9175 & 0.9964 & 0.0353 & 0.9175 & (0.02,0.04) & (1.00,0.60) & (1.00,0.50) & 1 \\ \(100\times 100\) & 0.1091 & 0.8924 & 0.9567 & 0.1484 & 0.8663 & (0.02,0.10) & (1.00,0.60) & (1.00,0.50) & 2,3 \\ \(200\times 200\) & 0.2791 & 0.8710 & 0.9323 & 0.3871 & 0.8243 & (0.10,0.30) & (1.00,0.60) & (1.00,0.50) & 2,4,6,9 \\ \(300\times 300\) & 0.4225 & 0.7644 & 0.8528 & 0.6295 & 0.8122 & (0.04,0.50) & (1.00,0.60) & (1.00,0.50) & 6,12,18,24 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset description.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **2 Obj** & **3 Obj** & **Range** \\ \hline
**NSGA-II** & & & \\ \(ps_{n}\) & **100** & **50** & [25,50,...300] \\ \(cr_{n}\) & **0.50** & **0.80** & [0.05,0.1,0.15,...0.95] \\ \(mult_{n}\) & **0.50** & **0.80** & [0.05,0.1,0.15,...0.95] \\ \hline
**SPEA2** & & & \\ \(ps_{s}\) & **100** & **50** & [25,50,...300] \\ \(\overline{ps}_{s}\) & **100** & **50** & **50** \\ \(cro_{s}\) & **0.50** & **0.60** & [0.05,0.1,0.15,...0.95] \\ \(mult_{s}\) & **0.50** & **0.70** & [0.05,0.1,0.15,...0.95] \\ \hline
**MO-VNS** & & & \\ \(neigh_{v}\) & **7** & **9** & [4,5,6,...14] \\ \(ns_{v}\) & **2** & **2** & [1,2,3,4,5] \\ \hline
**MO-VNS*** & & & \\ \(neigh_{v}\) & **11** & **10** & [4,5,6,...14] \\ \(ns_{v}\) & **3** & **2** & [1,2,3,4,5] \\ \(per_{v}\) & **0.10** & **0.10** & [0.05,0.1,0.15,...0.95] \\ \hline
**MO-ABC** & & & & \\ \(ps_{a}\) & **100** & **50** & [25,50,...300] \\ \(Se_{a}\) & **0.50** & **0.25** & [0.30,0.35,0.40,...0.70] \\ \(limit_{a}\) & **30** & **15** & [10,15,20,...60] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter selection.
## 1 Introduction
Furthermore, operating these techniques in the field is costly in terms of time and labor [9]. Interferometric synthetic aperture radar (InSAR) is a powerful technique that measures ground surface deformation over a wide area with high precision [10; 11]. Satellite-based SAR systems provide very high spatial and high temporal resolution (4-6 days nowadays) over a large area at all weather conditions, day and night. Cost of the InSAR technique, in terms of measurement points per square kilometer, is much lower than other deformation measurement techniques such as GPS and leveling [12]. Advanced InSAR techniques enable time series analysis of multi-temporal SAR data and provide a good set of tools for ground deformation measurements with millimeter precision [13; 14; 15]. Previous studies used multi-temporal InSAR technique for analysis of land subsidence due to over exploitation of groundwater [16; 17; 18; 19; 20], urban area deformation [21; 22; 23; 24], mining region stability [25; 26; 27], landslide phenomena [28; 29; 30], sinkhole detection [31; 32] and seismic events [33; 34]. The aim of this study is to understand the mechanism of ground deformation in the study area. To do this, we use multi-temporal InSAR technique with Sentinel-1 TOPSAR data. Sentinel-1 products that were acquired in both ascending and descending orbits between October 2014 and October 2018 were processed using Persistent Scatterer Interferometry (PSI) technique. The velocity fields obtained with PSI are compared with geology, groundwater level and water surface area of Eber Lake nearby.
## 2 Materials and Methods
#### 2.1.3 MOORA (Multi Objective Optimization on the Basis of Ratio Analysis) Technique  
MOORA is an MCDM technique that was first developed by [PERSON] and can be successfully applied to solve various complex decision-making problems [63]. The biggest advantage of the technique is that it offers the opportunity to evaluate the criteria where the maximum value is better and the criteria where the minimum value is preferred together. The MOORA technique is implemented with four steps by using Equations (14)-(16) [64].  
**Step 1. Creating the decision matrix:** In the ratio method, the initial (decision) matrix shown in Equation (1) is created.  
**Step 2. Matrix normalization:** Normalization is performed by using Equation (14), where i = 1, 2, \(\ldots\), m is the number of alternatives, j = 1, 2, \(\ldots\), n is the number of criteria (objectives).  
\[{x_{\text{ij}}}^{*}=\frac{{x_{\text{ij}}}}{\sqrt{\sum_{i=1}^{m}{x_{\text{ij} }}^{2}}} \tag{14}\]  
**Step 3. Weighing normalization matrix:** Weighted normalized matrix values (\(v_{I}\)) are created using Equation (15) by multiplying each of the normalized matrix values by the weight of the criterion (\(w_{I}\)). \(w_{j}\) criteria weights were calculated using the PSI method.  
\[v_{ij}=w_{j}*{x_{ij}}^{*} \tag{15}\]  
**Step 4. Determination of utility function of the options:** In the decision matrix, the minimum goal values are subtracted from the maximum goal values of the criteria specified at the beginning. Equation (16) is used for this operation.  
\[{y_{i}}^{*}=\sum_{j=1}^{g}v_{ij}-\sum_{j=g+1}^{n}v_{ij} \tag{16}\]  
\(y_{i}^{*}\) i are the values that the options take according to the criteria evaluation. The process is completed by sorting the \(y_{i}^{*}\) values from biggest to smallest.
## 3 Results
### Dependency between CSO-PVS Severity and Age  
The feature importance was analyzed for the risk factors using a heatmap and individual feature analysis using partial dependence analysis (Figure 3). Among the risk factors, age and CSO-PVS severity highly impacted the de novo hygroma status (Figure 3A). The dependence analysis results show the relationship between CSO-PVS severity and age, as shown in Figure 3B. Most patients aged more than 86 years had a high correlation (62%) with CSO-PVS severity for the development of hygroma. Also, patients who had no or mild CSO-PVS severity showed lower dependency (mean 18.03%) over the entire age, but patients who had high CSO-PVS severity showed higher dependency across age groups (mean 37.97%).
# A Novel Visual SLAM Based on Multiple Deep Neural Networks
###### Abstract  
The current visual simultaneous localization and mapping (SLAM) systems require the use of matched feature point pairs to estimate camera pose and construct environmental maps. Therefore, they suffer from poor performance of the visual feature matchers. To address this problem, a visual SLAM using deep feature matcher is proposed, which is mainly composed of three parallel threads: Visual Odometry, Backend Optimizer and LoopClosing. In the Visual Odometry, the deep feature extractor with convolutional neural networks is utilized for extracting feature points in each image frame. Then, the deep feature matcher is used for obtaining the corresponding feature landmark pairs. Afterwards, a fusion method based on the last and the reference frame is proposed for camera pose estimation. The Backend Optimizer is designed to execute local bundle adjustment for a part of camera poses and landmarks (map points). While LoopClosing, consisting of a lightweight deep loop closure detector and the same matcher as the one used in Visual Odometry is utilized for loop correction based on pose graph. The proposed system has been tested extensively on most of benchmark KITTI odometry dataset. The experimental results show that our system yields better performance than the existing visual SLAM systems. It can not only run in real-time at a speed of 0.08 s per frame, but also reduce estimation error by at least 0.1 m.  
SLAM; deep feature matching; pose estimation; loop correction; pose graph optimization +
Footnote †: journal: Applied sciences
# Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction
## 3 Methodology  
### CNN-BiLSTM-AT  
CNNs can extract local salient features from a company's financial data through convolutional layers. BiLSTM can capture bidirectional dependencies in the time series, and, by adding a bidirectional LSTM layer on top of the features extracted by the CNN, the performance of the company's financial metrics for each quarter before financial distress can be learned. The attention mechanism (AT) can identify key features in the financial data of each quarter and add these features to the output results after weighting them to improve the model's performance. Based on these features, the development of a CNN-BiLSTM-AT model for financial risk prediction is reported in this article. The structure of the model is shown in Figure 1, and mainly includes an input layer, a CNN layer, a BiLSTM layer, an attention mechanism layer, two fully connected layers, and an output layer.  
Figure 1: CNN-BiLSTM-AT model structure diagram. Notes: The symbol * in the attention layer indicates element-wise multiplication.  
The model processes a series of consecutive financial indicators from multiple quarters with an input tensor shape of (batch_size, time_steps, features), where time_steps represents the continuous time steps in each sample. The convolutional layer extracts local patterns along the feature dimension, while keeping the time dimension intact, resulting in an output shape of (batch_size, time_steps, filters). Max pooling is then applied to compress the time dimension, producing an intermediate representation of (batch_size, time_steps/pool_size, filters). This reduces the computational burden while preserving essential information. The pooled sequence is passed into a BiLSTM layer, which models the temporal dependencies of financial indicators bidirectionally, outputting a shape of (batch_size, time_steps/pool_size, 2 \(\times\) lstm_units). An attention mechanism is used to weight and integrate time step information, generating contextual representations that emphasize key features. Finally, the model applies feature transformation through two fully connected layers, resulting in output shapes of (batch_size, dense_units_1) and (batch_size, dense_units_2). The sigmoid activated output layer then predicts the financial risk of the enterprise, with an output of (batch_size, 1). This approach combines convolutional feature extraction, cyclic time series modeling, and attention to focus on critical information, enhancing the accuracy and robustness of financial risk identification.
## 3 Experimental Results and Theoretical Analysis
### Energy Dissipation Characteristics
As the flaw inclination angle increases, the corresponding total work and energy dissipation rate of stress drop point 2 gradually decrease, which indicates that the flyrock phenomena caused by postpeak instability of the selected samples gradually become weaker with increasing flaw inclination angle.
# Spectral Shaping of a Superluminescent Diode for Terahertz Cross-Correlation Spectroscopy
## 2 Experimental Setup
### Experiment Setup
In blue, standard THz-TDS components are used with a photomixer as the emitter. In green: lock-in amplifier for data acquisition (DAQ). A typical terahertz time-domain trace is depicted in Figure 3a. As expected for a THz-QTDS trace, it consists of equally spaced pulses with a maximum peak-to-peak amplitude at the zero-delay position of the cross-correlation measurement. From here, the peak-to-peak amplitude decreases due to the incoherent nature of the used light source. To quantify the amplitude variation, we calculate the envelope of the signal (red and green) and use the FWHM of the lower envelope as a parameter for the coherence. Figure 3b depicts the terahertz spectrum which was retrieved by a Blackman window in the time-domain and FFT. The slope parameter was calculated from a linear fit through the detected peaks of the spectrum in the range between 0.2 THz and 1.2 THz. Figure 2: (**a**) Terahertz time-domain trace with a \(\Delta\)pp = 62.5 nA and a FWHM = 0.9 ps. (**b**) Terahertz spectrum after applying a Blackman window and the FFT algorithm to the time-domain with m = \(-\)35 dB/THz and a peak dynamic range at 200 GHz DR = 59.5 dB. (**c**) Optical spectrum from the SLD after applying a bandpass filter \(\Delta\)BP = 2 THz.
# Implementation of a Cryogenic Facility for Space Debris Analysis
## 4 Prototype Implementation
### Data Acquisition and Control  
Data acquisition and control for thermal cycling were performed by the LabVIEW program using a DAQ (Data acquisition system) from LabJack and a SERIAL protocol.  
LabJack U12 is a USB-based measurement and automation peripheral. It has eight analogue input signals, which can operate as eight single-ended channels or four differential channels. Each input has a 12-bit resolution, and the input range for a single-ended measurement is \(\pm\)10 V.  
Due to the need for nine digital output pins and an analogue voltage reader, LabJack U12 was chosen as the control unit to completely monitor and control the thermal cycles in this project.
# Power Efficient Random Access for Massive NB-IoT Connectivity
## 2 Literature Survey Random Access in NB-IoT
### NPRACH Fundamentals  
Similar to legacy LTE/LTE-A networks, RAP in NB-IoT is a four-step process [14]. However, unlike LTE in step one, an NB-IoT device starts RAP by transmitting a newly designed preamble in the NB-IoT physical random access channel (NPRACH) [15]. NPRACH is the time-frequency resource for the transmission of random access preambles in NB-IoT. While PRACH in LTE uses a bandwidth of 1.08 MHz, NB-IoT uplink bandwidth is limited to only 180 KHz, which is far less. Thus, NPRACH is focused on a newly designed preamble format such that each preamble consists of four symbol groups [14]. Thereupon, each symbol group comprises of five symbols and a cyclic prefix. The NB-IoT preamble transmission comprises of 4 symbol groups transmitted without gaps [3]. While each group uses a single subcarrier, it can hop across 12 subcarriers as shown in Figure 2 which facilitates uplink timing estimation at the eNodeB (eNB) [16]. The device can repeat the preamble transmission for up to 128 times in each RAP attempt. The exact number of repetitions is based on the CE level of the device [3; 17]. Each repetition consists of symbol groups transmitted at four different subcarriers (within a band of 12 subcarriers) that follow a predefined frequency hopping pattern [18]. The preamble is uniquely identified by its hopping signature and therefore can be differentiated based on the choice of the initial subcarrier. In Figure 2, the hopping pattern of PRACH index #0 is highlighted in blue. With the subcarrier spacing of 3.75 KHz, the NPRACH is a set of 48 subcarriers on anchor carrier with a basic sub-carrier allocation unit of 12 sub-carriers [9]. Thus, \(12,24,36\) or 48 orthogonal preamble sequences are possible on an anchor carrier which are shared by up to three CE levels [19]. It is to be noted that one subcarrier can be assigned to only one CE level.  
To initiate RAP, the device transmits its preamble using randomly chosen subcarriers mapped to its initially selected CE level. The device identifies its initial CE level by measuring reference signal received power (RSRP). Devices with the highest value of RSRP are in the lowest initial CE level (CE0), while the devices that measure the lowest RSRP are in the highest initial CE level (CE2). Each NPRACH resource configuration, corresponding to different CE levels, is characterized by its own parameters like periodicity, number of repetitions, starting time, frequency location and number of subcarriers [20]. The choice of the subcarrier for preamble transmission is random and independent of the choice made by other devices. If more than one devices from the same CE level select the same initial subcarrier in the same PRACH occasion, it results in a collision.
## 3 Numerical Simulations of Gas Explosion along Pipelines with Abrupt Cross-Section Area Changes
### Change Laws of Vorticity and Kinetic Energy  
Figure 9 shows the distribution of peak vorticity along the pipe axis for three fuel volumes in different pipe configurations. Vorticity displays an obvious peak and tends to flatten out either side of the peak. This indicates that the turbulent effect of explosive gas is obviously strengthened after it passes through the abrupt cross-section change of the pipe. Taking simulation results with a fuel volume of 0.18 m\({}^{3}\) as an example, as the diameter of the middle section of pipe increases from 0.2-0.36 m, the peak vorticity of gas explosion increases from 1834-3650 s\({}^{-1}\). Considering Figure 9a,c, if the diameter of the middle section of pipe is held constant, a greater volume of explosive fuel produces a higher peak vorticity.  
Figure 10 shows the change in peak kinetic energy with distance from the ignition source for various diameter pipes and fuel volumes of 0.18 m\({}^{3}\), 0.23 m\({}^{3}\), and 0.28 m\({}^{3}\). Taking the simulation results with a 0.18 m\({}^{3}\) fuel volume and 0.36 m diameter pipe, before the abrupt cross-section change, the peak kinetic energy increases and then decreases with distance. The maximum kinetic energy occurs at measuring point 2, with a peak value of 1.2386 MPa. Passing into the middle section of pipe (measuring points 3 and 4), the peak kinetic energy decreases sharply to 0.5728 MPa. In contrast, when passing back into the smaller pipe (measuring points 6 and 7), the peak kinetic energy increases from 0.4384 MPa to 0.5922 MPa. From measuring points 7-10, the peak kinetic energy presents a downward trend. As the gas explosion propagates, kinetic energy is converted into static pressure energy and vice versa. Specifically, when the cross-section area of the pipeline abruptly increases, kinetic energy is converted to static pressure energy, and when the cross-section area of the pipeline abruptly decreases, static pressure energy is converted back to kinetic energy.  
Figure 8: Maximum temperature versus propagation distance from ignition source.
# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
## 4 Evaluation, Result and Analysis
### Evaluation Result
#### 4.3.3 Performance of the Efficiency
The proposed method achieves an efficiency of 30.7, which is higher than ET-BERT but lower than XENTC and FastTraffic. The proposed method seems to exhibit relatively high PT since it processes eight packets within the flow. However, similar to the ST perspective, the proposed method demonstrates high efficiency considering both accuracy and multi-task classification. The efficiency of a model is highly influenced by hardware performance and data structure, and there is typically a trade-off between model performance and efficiency. Considering this trade-off, evaluating the balance between model performance and efficiency becomes crucial. Further discussion is needed based on additional experimental results to better understand this trade-off and assess the overall performance and efficiency of the model. Therefore, in the future, we plan to enhance the model to achieve higher efficiency while maintaining its classification performance.
# Design and Implementation of a Mixed IoT LPWAN Network Architecture
## 2 Related Technologies
### Sigfox
The protocol overload consists of a preamble for synchronization of the receiver, a device identifier for authenticating the message, a message identifier for detecting duplicated elements and a sum of bits for error checksum. It does not include any signalling with the network and messages are not confirmed either. The user is free to distribute as desired the information within the 12 bytes of payload, since these will be stored and will arrive at the client's servers as is [26]. Figure 2 shows this frame corresponding to OSI level 2. The downlink frame in Figure 3 can contain up to 8 bytes of customizable information depending on the application. The content can be updated from the Sigfox backend with constants or variables such as the current date and time, or be generated dynamically from the user's infrastructure. Regarding security, the emitter is authenticated by means of the device identifier and the message authentication is performed by means of its sequence number. As a negotiation with the network, it is not necessary at the moment of sending messages, the device transmits the message without waiting for a response; therefore, trying to interfere with the receiver of the device does not avoid communication. In the radio link there is no encryption of the messages, this task is assigned to the OSI application layer if necessary. A VPN tunnel is established between the base stations and the Sigfox servers and the secure HTTPS protocol can be used to communicate with the user's infrastructure. The topology of Sigfox network is a'star of stars' type, connecting multiple objects to the same base station and all the base stations to the Sigfox servers. The base station works as a sink of data received towards certain IP address. The detection of duplicated elements and the authentication are managed by the network server, not by the base stations. The concept of collaborative network is used. Figure 1: Chronology of a Sigfox communication. Figure 3: Downlink data-link frame. Fields and associated length. Figure 2: Uplink data-link frame. Fields and associated length. Each zone is usually covered by 3 base stations in what is called'spatial diversity'. With 3 base stations in 3 different locations covering each object increases the reliability of message reception.