esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results and Analysis
### Analysis of Experimental Results
#### 6.3.2 Results of Hybrid Feature Selection  
First, the Pearson correlation coefficients between each feature and the target variables are calculated, and values with absolute values of the correlation coefficients greater than or equal to 0.3 are filtered out. Then, the feature importance of the candidate subset is computed using the out-of-bag estimation random forest algorithm. In the meantime, the individual features are ranked according to their importance from highest to lowest, and the final results are shown in Table 5, where \(\rho_{X_{1}X_{2}}\) represents the Pearson correlation coefficient and number is the sorted sequence number.  
According to Table 5, there are 23 candidate features with Pearson correlation coefficient greater than 0.3. The 23 features are screened using the iterative RF-OOB algorithm, and the results of each feature combination are shown in Table 6. The model classification score of the optimal feature combination is 0.96837, and there are 17 features in the feature combination. Therefore, these 17 features are used as the final input values of the breast cancer classification model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compactness\_Mean** & **Concavity\_Mean** &
\begin{tabular}{c} **Concave** \\ **Points\_Mean** \\ \end{tabular} & **Radius\_se** & **Perimeter\_se** \\ \hline
3.283515 & 2.652874 & 2.532475 & 2.489734 & 2.833031 \\ \(-\)0.48707 & \(-\)0.02385 & 0.548144 & 0.499255 & 0.263327 \\
1.052926 & 1.363478 & 2.037231 & 1.228676 & 0.850928 \\
3.402909 & 1.915897 & 1.451707 & 0.326373 & 0.286593 \\
0.53934 & 1.371011 & 1.428493 & 1.270543 & 1.273189 \\
1.244335 & 0.866302 & 0.824656 & \(-\)0.25507 & \(-\)0.3213 \\
0.088295 & 0.300072 & 0.646935 & 0.149883 & 0.15541 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of data standardization.  
From Figure 4, it can be seen that the loss function values of the training and test sets decrease with each iteration of gradient descent, and with the increase in the number of iterations, the loss function values gradually converge to reach their respective minimum values. Since the loss function values of the train and test sets are low and the difference is between 0.023 and 0.0402, the difference is small. Therefore, from the point of view of the loss function, it can be seen that the difference between the prediction results of the BGD-LR model and the real labels is relatively minor, in other words, the fitting effect is great.
## 1 Introduction
### Geo-Location Tagging  
Geo-location tagging in social media over the past five years has revolutionized how Twitter users share information with their followers including their exact location using latitude and longitude coordinates which tells anyone exactly where the tweeter is located. Geo-location information can be gathered in many different ways, with photographs providing the most data. In almost all newer cameras geo-location data is collected automatically regarding where a photo was taken and then stored within the metadata EXIF (Exchangeable Image File Format) records of the image [21].  
All smartphones have geo-tagging functionality built into them. Just like the social media site Twitter, smartphones have to specifically be given permission by the user to access their current GPS location [22]. With over three quarters of users using their mobile devices to access Twitter and many other applications, the geo-location settings on smartphones are easily turned on and forgotten about which gives rise to a lot of information leakage and it is this automated service that gives the most cause for concern regarding the disclosure of private information [23]. Once uploaded to Twitter anyone can pin point exactly where that photograph was taken. It is the automated service that seems to be highlighting the most concerns for the exposure of private information, even though it is driven by the end users, who may be unaware that this can result in unexpected disclosures [23]. Although geo-location tagging has many privacy issues it is still marketed \"as a useful way to stay connected to others in real time\" [24].
## 4 Results and Discussion
### VIIRS DNB Direct Lunar Radiance Comparison between Suomi NPP and NOAA-20
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \(\mathbf{C_{0}}\) & \(\mathbf{C_{1}}\) & \(\mathbf{C_{2}}\) & \(\mathbf{C_{3}}\) & \(\mathbf{C_{4}}\) & **Residual** \\ \hline Suomi NPP Waving & 4.27789E+01 & \(-\)1.52492 & 1.97774E-02 & \(-\)1.10252E-04 & 2.23666E-07 & 0.8905 \\ \hline NOAA20 Waving & 3.99329E+01 & \(-\)1.39217 & 1.76487E-02 & \(-\)9.61640E-05 & 1.90863E-07 & 1.1531 \\ \hline Suomi NPP Waning & 4.50942E+01 & \(-\)1.00846 & 7.43709E-03 & \(-\)1.82268E-05 & 1.45129E-09 & 1.1917 \\ \hline NOAA20 Waning & 4.38059E+01 & 1.00959 & 8.08330E-03 & \(-\)2.51526E-05 & 2.16557E-08 & 0.9988 \\ \hline \end{tabular}
\end{table} 
Table 1: Regression Coefficients for the 4 th Order Polynomial Curve Fitting (7 November 2018 to 6 December 2018). Figure 7: Radiometric Bias between VIIRS DNB on NOAA-20 and Suomi NPP. VIIRS DNB operational data production: a \(\sim\)2% bias due to spectral response differences (Figure 1) between VIIRS DNB on NOAA-20 and Suomi NPP, and a 1.1% bias due to solar irradiance spectrum differences used in the operational data processing systems (outdated version used for Suomi NPP processing) [34]. The remaining \(\sim\)2% bias is not well understood, except that it is noted that the low bias in NOAA-20 VIIRS DNB is consistent with other independent studies which concluded that NOAA-20 VIIRS solar band calibration are systematically lower than that of the Suomi NPP by \(\sim\)2%, likely due to prelaunch characterization uncertainties according to recent investigations [35; 36; 37]. The absolute radiance differences become smaller with increasing lunar phase angle, mainly because the lunar radiance also decreases significantly, e.g., down to 1-2 nW/cm\({}^{-2}\)-sr at 90 degrees lunar phase angle as shown in Figure 6. As a result, a small difference in radiance can be a significant percentage of the radiance, which leads to larger uncertainties in percentage at very low radiances. Analysis of other months from July 2018 to March 2019 showed similar results, and all of them followed a 4 th order polynomial functional form despite the coefficient differences.
# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 2 Study Sites and Datasets
### Datasets
#### 2.2.3 Temperature Data  
In situ temperatures acquired from the nearest weather station network to each study site (approximately 3 and 17 km from the study sites A and B, respectively) were used to help derive the thresholds for the freezing detection algorithm. For each S1 SAR image, over the two study sites, we matched air temperatures from the time of the S1 SAR acquisition and up to three hours before the acquisition. Then, these temperatures were averaged because freeze/thaw events could have happened at the same time, or a few hours before the image acquisition.  
In addition, the ERA5-Land dataset, provided by the European Centre for Medium Range Weather Forecasts (ECMWF) was also used. ERA5-Land is obtained by the modelling of in situ and satellite observations, and provides a large number of meteorological and oceanographic variables [36], ERA5-Land data are available at a resolution of 9 km globally, in hourly intervals, and it is produced in near real time (three months after acquisition), with quality-assured monthly updates, and preliminary daily updates published within five days of acquisition. For the validation of our approach of frozen area mapping, we used the soil temperature and the 2 m air temperature. The ERA5-Land soil temperature products are composed of four layers, and go to 289 cm in depth. In this study, we used the upmost layer, which goes up to 7 cm in depth (C-band microwaves can penetrate the soil to only a few centimeters). The spatial matching between our plots and the ERA5-Land grid has been achieved by a nearest neighbor approach.
## 2 Study Area  
The Ganges-Brahmaputra-Meghana (GBM) Delta, covering approximately 100,000 square kilometers of subaerial land, is located at the northern boundary of the Bay of Bengal and constitutes one of the world's largest and most complex deltaic systems [46; 47]. The coastline of this delta within Bangladesh is our study area, as shown in Figure 1. Much of the terrain lies less than 3 m above mean sea level, allowing tidal effects to penetrate nearly 100 km inland, rendering about one-fourth of Bangladesh a coastal region [48; 49]. The GBM rivers collectively drain around 75% of the Himalayan range [46; 50; 51].  
Figure 1: Study area.  
This region's physical and climatic setting, governed by monsoonal systems, low elevation, and the convergence of major river systems, makes it especially vulnerable to hydro-meteorological hazards [48]. In fact, more than 60% of Bangladesh was inundated during past extreme flood events [44; 52]. The country regularly experiences five primary types of natural floods: river floods, rainfall-induced (or pluvial) floods, flash floods, tidal floods, and storm surge events. Key flood-prone zones include the northwestern districts (e.g., Kurigram and Gaibandha) and central floodplains (e.g., Siraiganj and Jamalpur), which are predominantly affected by riverine flooding; the northeastern haor regions, which are prone to flash floods; and the coastal districts (e.g., Satchira, Khulina, and Barguna), which experience a combination of pluvial, fluvial, and tidal inundation. This study focuses specifically on the coastal belt, a region that has experienced multiple extreme weather events in recent years and remains highly vulnerable to compound flooding hazards. For example, Cyclone Renal (May 2024) made its landfall near Mongla and Khepupara coasts, causing surges up to 5-8 feet, impacting major parts of the western and central coast [53]. Alongside cyclonic threats, monsoon-induced inundation, increasingly driven by extreme rainfall and waterlogging, poses severe challenges for low-lying coastal zones [54; 55]. In the four largest major river basins, namely the Ganges, Brahmaputra, Meghna, and the Southeastern Hill basin, monsoon rainfall typically ranges from 1000 to 2900 mm, with an average of approximately 2300 mm [44]. Shifting monsoon patterns, marked by fewer light rain events and more intense downpours, have exacerbated flooding and water stagnation, especially in southern districts [56].
# Manufacturing of Micro-Lens Array Using Contactless Micro-Embossing with an EDM-Mold
###### Abstract  
Micro embossing is an effective way to fabricate a polymethyl methacrylate (PMMA) specimen into micro-scale array structures with low cost and large volume production. A new method was proposed to fabricate a micro-lens array using a micro-electrical discharge machining (micro-EDM) mold. The micro-lens array with different shapes was established by controlling the processing parameters, including embossing temperature, embossing force, and holding time. In order to obtain the friction coefficient between the PMMA and the mold, ring compression tests were conducted on the Shenzhen University's precision glass molding machine (SZU's PGMM30). It was found that the friction coefficient between the PMMA specimen and the mold had an interesting change process with increasing of temperature, which affected the final shape and stress distribution of the compressed PMMA parts. The results of micro-optical imaging of micro-lens array indicated that the radius of curvature and local length could be controlled by adjusting the processing parameters. This method provides a basis for the fabrication and application of micro-lens arrays with low-cost, high efficiency, and mass production.  
micro-EDM models; micro-lens array; contactless embossing; friction coefficient +
Footnote †: journal: Applied Sciences
# On Analyzing Routing Selection for Aerial Autonomous Vehicles Connected to Mobile Network
## 3 Motivation  
Our solution is based on multi-criteria routing selection. In this section we argue that such a solution is required when we assume communication of the drone through commercial mobile network. In Section 3.1 we describe the scenario taken into account and provide message delay measurements of drones communicating in commercial mobile network. The results show the necessity of multiple routing paths for assuring reliable communication. In Section 3.2, we present the solution adopted in this paper.
# Detection and Quantification of Damage in Metallic Structures by Laser-Generated Ultrasonics
## 4 Artificial Damage Detection and Quantification for Aluminum Alloy Plates  
### Ultrasonic Responses in Time Domain  
The damage detection and quantification process contains two steps. First, a single laser pulse irradiating a fixed excitation point on one side of the damage is used to generate ultrasonic waves. The ultrasonic waves are recorded by a transducer placed on the other side of the damage. The distance between the excitation and detection point is 40 mm in this study, as shown in Figure 6c. Using the nonlinear state-space-based predictive feature NNPE, the differences among the ultrasonic responses can be detected, and thereby the damage. Next, the laser scanning technique is used and all the ultrasonic responses from the irradiated points are recorded by the data acquisition system. According to the NNPE values calculated from each excitation point, the damage can be quantified successfully. The schematic of the laser scanning progress is shown in Figure 6d.  
Using the laser-generated ultrasonics damage detection system, the ultrasonic responses of the aluminum alloy plates under the excitation of a single laser pulse were recorded and plotted in Figure 7.  
Figure 6: The dimension of the plates, damage, laser excitation, and detection arrangement:(**a**) Reference undamaged plate; (**b**) currently undamaged plate;(**c**)damage width: 0.05 mm; (**d**) damage width: 0.1 mm; (**e**) damage width: 0.2 mm; (**f**) damage width: 0.4 mm; (**g**) damage width: 0.6 mm; (**h**) damage width: 0.8 mm.
# Audio-Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model
## 1 Introduction
used cross-modality attention computing the AV align, we propose dual cross-modality (DCM) attention that combines two cross-modality attentions calculating the AV align and also the audio context using video query (VA align), in order to increase the role of visual modality to a level of audio modality by fully exploiting input audio and visual information in training attentions. Recently, the transformer model provided better performance than the conventional LSTM-based model because it calculated the global context vector over the entire time of input data [18]. Therefore, we apply our proposed DCM model to the transformer model. Furthermore, we introduce a connectionist-temporal-classification (CTC) loss in combination with our attention-based model to force monotonic alignments, which results in a hybrid CTC/attention architecture to improve the performance of AVSR [19]. Figure 1 shows an overview of our AVSR architecture. The remainder of this paper is organized as follows: Section 2 summarizes related works on the AVSR task, attention mechanism, modality fusion, and hybrid CTC/attention architecture for speech recognition. In Section 3, we propose an AVSR model with DCM attention scheme and the hybrid CTC/attention architecture. Our proposed methods are compared with other attention mechanisms implemented on the transformer model through experiments on LRS2-BBC and LRS3-TED datasets [20] in Section 4. Finally, some concluding remarks are presented in Section 5.
## 4 Result and Interpretation
### Fault Activity and Coupling Degree of Fault-Sand Body  
Hydrocarbon migration is one of the key elements for hydrocarbon accumulation systems outside the oil source [35]. The late-stage activities of the NE-trending oil-source faults in the northern medium-strong compression zone (Structure-Z) and the southern strong compression zone (Structure-N) of the Central Anticline Belt are strong, and they can extend upward to the T16 interface. The coupling degree between the faults and the sand bodies is high, and they have a strong hydrocarbon migration and charging capacity. In comparison, Structure-Q located in the western weakly compressed zone of the Central Anticline Belt has relatively weak late-stage tectonic activities and no late-stage faults, and the preservation conditions of the oil reservoir are good. However, the NE-trending oil-source faults of Structure-Q have weak activities, and the faults only extend upward to the T21 interface, and its main reservoir-forming strata are all located in the strata deeper than H4 of the Huagang Formation.  
Structure-Q has the characteristics of a flower-like structure (Figure 6). Its third-level main control faults develop on the flank of the anticline, with relatively large-scale and strong fault activity. The oil-source faults can extend upward to the T21 interface and downward to the T40 interface, with a strong hydrocarbon migration and conduction capacity. The anticline core of this structure Q is mainly composed of fourth-level adjustment faults, which have a smaller fault scale and limited cut-through horizons (Figure 6). Therefore, the hydrocarbon migration capacity of the fourth-level faults is also relatively limited. Well-Q1  
Figure 5: The well-N1 composite bar chart of the H3 sand group of the Huagang Formation, including the gamma ray (GR) curves, lithology, various logging curves, QGF-index data, gains containing oil inclusions (GOI) data, and gas–water contact (GWC) logging interpretation.  
is located in the core of anticline Structure-Q, and the migration fault is a fourth-level adjustment fault. The gas column of the main layer H5 is 25 m, and the inclusion abundance is less than 5%, indicating that there is no large-scale hydrocarbon charging and that the hydrocarbon migration capacity is limited. In contrast, well-Q2 is located on the west flank of the anticline Structure-Q, and the migration fault is the third-level fault F1. The coupling and matching of the fault and sand body are good, and the gas column height of the gas reservoir is 65 m. By comparing the hydrocarbon accumulation characteristics of well-Q1 in the anticline core and well-Q2 in the anticline flank, it can be determined that the oil-source faults located in the flank of the anticline structure have a higher level, stronger cut-through activity, and stronger hydrocarbon conduction capacity.
## 2 Applications of Biosensors in Bacteria Detection
### Recognition of Bacteria Using Biosensors
#### 2.1.3 Electrochemical Biosensors
With using labels in case of label-based optical biosensors, the sensitivity can be significantly increased, however, the labels may disturb the cells and the measurements can be more complicated and time-consuming, and the labels increase the costs of the assay [63; 166]. In case of mechanical biosensors, the advantage is that mechanical parameters can be easily extractable and high sensitivity can be achieved, opening up novel directions in the rapidly growing field of mechanobiology. However, as their working principle is usually based on monitoring a resonance frequency, these devices are highly sensitive to any vibration noises present in their environment [65]. The advantages of electrochemical methods are the lower manufacturing costs and ease of system miniaturization and integration, and the ease of use in integrated point-of-care devices [23]. In general, the disadvantages of some systems, which should be critically considered, are the irreproducibility, high limits of detection in real-life assays, and problems with nonspecific response [23; 171].
# Design and Evaluation of a Web- and Mobile-Based Binaural Audio Platform for Cultural Heritage
## 2 Background
### Sonic Narratives for Cultural Heritage
A first evaluation of a mobile application for the ancient Athens Agora [37] produced positive results with the users particularly appreciating the chance of freely exploring the environment. In the ARCHES project [38], researchers worked on the inclusivity aspect of storytelling exploring new modalities to design cultural heritage experiences for people with difficulties and/or disabilities. Other examples of tools developed by academia that could be used to create soundscapes and immersive experiences are the Soundscape Renderer [39] and those developed for the CARROUSO project [40]. There are also commercial applications developed for audio narratives like Echoes.XYZ [41]; both are GPS-triggered audio tours with web-based tools for the creation of audio guides and mobile apps to experience them. It is worth highlighting how none of the currently available commercial solutions use sound spatialisation.
# Battery Management System Hardware Concepts: An Overview
## 2 Battery Management System Requirements
### Redundancy  
Redundancy, besides self-tests, signal monitoring, supply monitoring and watchdog timers, is a topic that increases reliability of a system according to the ISO 26262 standard [17]. Not all systems can be designed allowing a high grade of redundancy due to cost, limited time of design process or other limitations. However, cell voltages usually are observed redundantly to a certain level, as mentioned in paragraph \"voltage acquisition\". This means there are at least two separate information sources. Usually, there is a main chip that can measure the cell voltages very accurately. In addition to this chip, there is a companion chip installed that also acquires cell voltages, but only gives binary information on whether even one cell is drifting outside the predefined voltage window. This means the BMS master has to select the most trustworthy signal source--a high resolution value that is sophisticated hence more prone to fail--or a simple technology window comparator signal.  
In addition, in terms of higher level processing redundancy concepts existing, where e.g., in special CPUs, protection mechanisms like lockstep, memory error correction and self test mechanisms implemented in hardware are provided [18]. In the report [19], which covers a topic similar to this paper, a broader overview on functional safety aspects is given.
# Neighborhood Aggregation Collaborative Filtering Based on Knowledge Graph
## 4 Experiments
### Experimental Setup and Parameters  
We selected two classical recommendation algorithms and three recommendation algorithms combined with KG as the baseline to compare the performance of NACF on the three datasets. The baselines are as follows:  
* SVD [31] is a classic CF-based algorithm that uses inner product to model user-item interactions.
* LibFM [32] is a feature-based factorization model widely used for CTR prediction. In this experiment, TransR [33] was used to learn entity embedding for LibFM.
* CKE [7] combines collaborative filtering with knowledge embedding, text embedding, and item image embedding in a unified Bayesian framework. In this experiment, we represent each item only as an embedded vector learned by the TransR because the text descriptions and images in the datasets are not available.
* RippleNet [23] is a hybrid approach that uses KG information to assist recommendation, which spreads the user's potential preferences on KG for CTR recommendations. In this work, we use the TransE algorithm [34] to learn entity embedding.
* KGCN [18] is a recommendation algorithm that uses GCN to convolve item features, which makes use of the knowledge graph to extract item potential associations.  
The code of NACF is implemented under Python 3.7, tensorflow-gpu 1.12.0 and NumPy 1.15.4. Experimental hardware environment includes AMD R5 2600, GTX 1070 and 16G memory. We set the ratio of training, evaluation and testing sets to 6:2:2 and manually adjusted the hyper-parameters of the NACF according to area under curve (AUC). Both the \(\lambda_{1}\) and \(\lambda_{2}\) in the loss function were set to 0.001. The hyper-parameters of NACF are shown in Table 3.