esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods Used  
### Geographic, Demographic and Territorial Features of the Study Area  
The study area predominantly covers the urban area of Durres. The city lies along the Adriatic coast of western Albania and is one of the oldest cities in the country (known as \"Dyrrachium\"), with a history of over 2500 years [20].  
From a physical-geographical standpoint, Durres is situated in the western Lowlands, on a flat alluvial plain between the Erzeni and the Ishmi rivers, and the surrounding hills. Its highest peaks are found in the Rodon-Erzeni Hills (at 272 m) and in the Durresi Hills (at 178 m). Most of the territory is a plain shaped by the reclaim of the Durresi swamp in the last century. The area of Durres covers only 1% (332 km\({}^{2}\)) of the Albanian territory, defined by a north-south longitudinal extension of about 36 km and a width of 19 km [21]. Durres is one of Albania's most significant urban and economic centers, representing one of the most important nodes of national and international transport, crossed by many important national and international road axes and maritime links. As the second largest metropolitan area in Albania, it has a population of approximately 202,000 [22].  
According to the Institute of Statistics (INSTAT) census, up to 2011, the highest percentage of constructions in the city of Durres consisted of 1-2 story buildings [20]. It is noted that during the period of 1991-2011, the number of buildings with over 6 floors has increased by 79%, and currently constitute about 3.5% of the total number of constructions [20]. Building typologies in Durres comprise of masonry buildings, predominantly residential structures up to 1993, consisting of unreinforced load bearing masonry structures up to 5 stories, and confined masonry structures of 3-6 stories; prefabricated concrete panel buildings built between 1970-1990, of 4 and 6 stories; and concrete-frame buildings with in-fill masonry walls. The development of reinforced-concrete frame systems and infill masonry walls began after 1993, rising from 2-3 stories up to more than 10 stories [23].
## 4 Experiment and Analysis
### Experimental Results and Analysis of Recognition Performance under Different Learning Rates  
According to the experimental settings presented in the previous section, a different learning rate is provided to test the performance of the algorithm and determine the influences of the various learning rates of the SGD optimizer on the performance of the algorithm. The SGD optimizer minimizes the cross entropy learning rate, set to 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, and 0.10. The statistical results and the boxplot of the recognition rate under each of the 10-time learning rates are shown in Figures 8 and 9.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Size of Training Set** & **Size of Test Set** & **Number of Image Category** \\ \hline MNIST & 60,000 & 10,000 & 10 \\ HCL2000 & 2000 & 1000 & 10 \\ EnglishHand & 4200 & 1520 & 26 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test and training datasets.  
Figure 8: Recognition value under different stochastic gradient descent (SGD) learning rates.  
Figure 7: Sample of handwriting character images. (**a**) MNIST dataset; (**b**) HCL2000 dataset; (**c**) EnglishHand dataset.  
For the three datasets tested, the recognition rate of the model increases with the learning rate and the learning and recognition rates show a positive correlation when learning is in the [0.02, 0.05] range (Figure 8). When the learning rate distribution of the SGD optimizer is 0.05-0.07, the recognition rate of the model is in a relatively stable state. At this time, the recognition rate of the model shows a weak growth trend with the learning rate. When the learning rate is increased to more than 0.06, the recognition rate decreases significantly with any increase in learning rate. In particular, when the learning rate reaches 0.1, the recognition rate reaches the lowest level of approximately 20%. According to Figure 9, when the SGD learning rate is 0.06, the midline of the box diagram is higher and the rectangular area is the smallest, and it has no outliers. Analysis and testing based on three data sets show that tests conducted on this dataset show that the introduction of the SGD optimizer can affect the recognition rate. When the learning rate is 0.05-0.07, the performance of the system is enhanced. In the comparison experiment presented in the subsequent section, the learning rate of the SGD optimizer is set to 0.06.
# Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement
## 4 Speech Enhancement Experiment
### Subjective Evaluation  
The subjective evaluation was carried out through a series of blind AB listening tests ([5], Section 3.3.4). To perform these tests, we used the same noisy speech data set (Section 4.3). In this test, the enhanced speech produced by six SEAs as well as the corresponding clean speech and noise corrupted speech signals were played as stimuli pairs to the listeners. Specifically, the test was performed on a total of 112 stimuli pairs (56 for each utterance) played in a random order to each listener, excluding the comparisons for the same method.  
The listener gave the following ratings for each stimuli pair: prefers the first or second stimuli, which is perceptually better, or a third response indicating no difference was found between them. For a pairwise scoring, 100% is given to the preferred method, 0% to the other, and 50% for the similar preference response. The participants could re-listen to stimuli if required. Ten English speaking listeners participated in the blind AB listening tests. The average of the preference scores given by the listeners is termed the mean preference score (%), which was used to compare the efficiency among the SEAs.
## 3 Optimum Parameters Selection  
In this work 75 different wavelet types have been tested [25], but simulation results (average of 100 realizations each) are presented first only for the simplest wavelet: Haar wavelet or Daubechies 1. Once the trends are understood and the optimum parameters are found for the Haar wavelet, the optimum performance for the each signal type and each wavelet type is presented.
## 4 Data Acquisition System for High Repetition Rate Performance
Furthermore, it controls the precision of the overlap between ion traces, detected with the TPS imaging system, and simulated traces of the ion species that are most commonly present in experiments with solid targets (e.g., protons and carbon ions with charge states from C\({}^{+1}\) to C\({}^{+6}\)). If necessary, the developed optimization toolkit corrects the preset parameters responsible for the position and orientation of ion traces in the image and removes possible artificial image noise caused by light scattered from the interior of the diagnostic device. The bulk of the code transforms the image in such a way that parabolic traces with different charge-to-mass ratios are converted into straight stripes, which in turn greatly simplifies the following data analysis. Ultimately, the automatically separated stripes representing individual ion species with defined charge-to-mass ratios are converted into corresponding ion energy distri  
Figure 6: Average time-of-flight (TOF) spectrum and standard deviation calculated from 10 consequent signals recorded by a TOF diagnostic device when an Al target is irradiated with ns-class laser (4 J laser energy) at 10 Hz. Figure 7: MATLAB-based graphical user interface used for analysis of the Thomson Parabola Spectrometer (TPS) images: (left GUI subfigure) example of the RAW image obtained by the TPS imaging system together with simulated ion traces; (right bottom GUI subfigure) corresponding ion energy distribution of the hydrogen ions as calculated by routine. butions, and subsequently raw data together with calculated energy spectra are plotted in a user interface windows for visual control. Storing of the calculated results enables provision of simple online statistics (maximum ion energies and their cumulative charge per shot) and control of the shot-to-shot stability in real-time. As a result, this routine can provide complete and quick information on the ion species and the underlying physical processes in the plasma for a shot sequence acquired at a repetition rate of 1 Hz.
# Numerical Simulation of Thermo-Hydro-Mechanical Coupling of Model Test for Nuclear Waste Disposal
## 4 Results and Discussion
### Swelling Pressure Change Process  
As shown in Figure 9a, there was a strong correlation between expansion stress and water saturation distribution; the stress source of the buffer layer was thermal stress produced by temperature change and expansion stress produced by the saturation process. There was no obvious stress concentration in the high-temperature area around the heater, which may have been due to the relatively weak thermal stress caused by the small range of temperature change. The expansion force began to increase near the boundary of the surrounding rock and decreased near the heater. The reason is that the boundary of the surrounding rock was more strongly affected by water flow and the water saturation was higher, which led to higher expansion stress. The maximum stress of the buffer layer was 4.25 MPa.  
The changes in swelling pressure with time are shown in Figure 9b; the expansion force at each point increased with the increase in time, and the time history curve of the expansion force underwent a rapid expansion, a slow expansion and a gradual stabilization. The closer the water injection boundary, the steeper the curve slope was and the greater the expansion force growth rate. The closer to the heater, the slower the slope of the curve, and the smaller the rate of expansion force growth. The moisture content decreased gradually from the heater to the buffer layer, and the redistribution of moisture resulted in the increase in local expansion force. The water injection pressure constantly increased because the water was continuously injected into the buffer zone, the saturation value of the buffer zone increased continuously, and the pore pressure was formed around the buffer zone; the pore pressure and saturation of the buffer layer were constantly re-distributed and adjusted, so the curve of the swelling force had a small fluctuation.
# Mechanical Performance Characterization of Lignin-Modified Asphal Mixture
## 4 Results and Discussion
### Moisture Susceptibility  
Moisture susceptibility of asphalt mixture is mainly influenced by the characteristics of mineral aggregates, the asphalt-aggregate interaction behaviors, and the air void of asphalt mixture. Generally, it is easier for moisture to infiltrate aggregate surface than asphalt binder. This will reduce the adhesion between asphalt and aggregates and lead to moisture damage-related distresses (e.g., raveling) [17]. The indirect tensile strength (ITS) test results of samples at different states are summarized in Table 4. It shows that both SMA-LP and SMA-LF had a higher ITS than the control mix, which demonstrates that lignin in asphalt mixture has a reinforcing effect when it is under the tensile stress. However, after the freeze-thaw cycle, the ITS of different asphalt mixtures degrades differently. As reflected by the TSR values, the ITS of SMA-LF degrades the most after the treatment of freeze-thaw. It implies that it had the worst moisture stability. The reason for the lower TSR value of SMA-LF could be because of its relatively large air voids after the addition of fibers. The increased air voids provide more possibilities to contain water and form ice at low temperatures, resulting in greater expanding forces inside the aggregate skeleton structure. Additionally, different thermal contraction coefficients of asphalts and fibers may cause interfacial sliding between aggregates and fibers [18]. Comparing to SMA-C, SMA-LP seems to improve the moisture stability, as indicated by the higher TSR value of 89.6%. The reason for this is due to improved adhesion properties between lignin-modified asphalt binders and aggregates. Based on the specified limit of TSR value in the mix design specification, all three asphalt mixtures in this study meet the requirement of minimum TSR of 80%.
# On the Use of a Feedback Tracking Architecture for Satellite Navigation Spoofing Detection
## 4 Using ECADLL Information for Spoofing Detection
### Classification as Spoofer Presence
#### 4.2.1 Detection of Negative Delays  
A simple check that can be performed in order to decide whether or not a spoofer is present is to observe if the relative delay is negative, such as:  
\[\hat{\delta}\tau=\tau_{1}-\tau_{0}<0 \tag{10}\]  
In this case, a spoofing attack can be declared safely assuming that the Unit0 is tracking a non-zero level satellite signal. Given the physical nature of the multipath and assuming that the satellite signal has a non-zero power level in the receiver, it is not possible for a multipath reflection to have a delay smaller than the LOS signal, given that, by definition, the LOS is the minimum distance between the receiver and satellite.  
On the other hand, a spoofing signal could perfectly arrive into the receiver before the satellite signals, thus giving away its presence. A spoofer could avoid this by managing its delay correctly, but this simple check is a first hard detection to exclude these cases. Following this first discrimination, the impairment will always be declared as a spoofer when (10) is true, and we can observe in Figure 5 how it is implemented as a second level of discrimination.
# Influence of Soil Characteristics on Wood Biodeterioration by Brown Rot Fungi
## 2 Materials and Methods
### Influence of Wood Decay on the Stiffness of Wood  
For evaluation of the impact of brown rot fungi on stiffness (Young's modulus of elasticity) of Scots pine wood, seventy-four specimens with dimensions of \(15\times 1.2\times 0.25\) cm\({}^{3}\) (47 longitudinal strips) and \(12\times 1.2\times 0.25\) cm\({}^{3}\) (27 transversal strips) were excised and prepared for free-free vibration tests of beam elements at the first natural frequency. Wood specimens were impregnated by vacuum-pressure according to EN 807 [33]. Based on former studies with utility poles [16], three different concentrations of CC wood preservative were used (0.0, 3.8 and 37.2 kg m\({}^{-3}\)). After impregnation, all specimens were sterilized with ethylene oxide, and then incubated with _A. serialis_, _S. himantioides_ and _R. placenta_ (Table 1). Autoclavable plastic containers (\(40\times 60\times 15\) cm\({}^{3}\)) with vermiculite/sawdust were used as described above for the incubation of wood specimens. Three wood specimens impregnated with each preservative concentration were incubated with all fungi for a further 12 weeks and monitored as previously described. After incubation, the specimens were removed from the containers, cleaned with a brush and dried at 50 \({}^{\circ}\)C for 1 week.  
The first resonance frequency (fR) and internal damping (tan8) of each wood specimen was determined before and after fungal treatment by a custom developed free-free resonance flexural vibration test [35; 36; 37]. Each specimen was supported by cotton threads located at the position of the vibration nodes of the fundamental frequency. They were subjected to forced bending vibration by an electromagnet facing a metallic plate of glued at the end of the specimen. Vibration was emitted by means of an electric magnet device and displacements at the anti-node of the first vibration mode were measured with a laser triangulation displacement sensor. The specific modulus of elasticity (_E/p_) was calculated from the first natural frequency of the specimens, according to the Euler-Bernouilli equation as follows:  
\[\frac{E}{\rho}=\frac{48\pi^{2}l^{4}}{m_{n}^{4}l^{2}}f_{Rn}^{2} \tag{1}\]
## 3 Numerical Configuration
### Physical and Numerical Configuration  
Before starting with the setup of the case, the nature of the novelty of the application must be considered, meaning that some hypotheses widely accepted for modelling conventional means of transportation could be invalid in the framework of the current paper. As it was previously stated, the pressure conditions inside the tube of the hyperloop are considerably lower than the atmospheric pressure. At this point, the main hyperloop projects have considered an operating pressure of about 0.1 atm [15; 20]. Therefore, the continuum hypothesis for such air could be questioned. For that, it is common to check the Knudsen number. Previous works in the field of hyperloop research have already confirmed that according to that number, this low-pressure air can be accurately modelled as a fluid continuum [20; 25].  
For the spatial discretization, the finite-volume approach of CFD software SIMCENTER STAR-CCM+ was employed, selecting a second-order implicit scheme solved as a density-based flow due to the remarkable compressible behaviour. With respect to the temporal discretization, the nature of the case allowed the simplification of assuming steady conditions, as the objective point was the compressor design condition, where the flow tends to behave quite stably and steady. As for the turbulence treatment, one could solve some turbulent scales using a detached eddy simulation (DES) [26] or even a large eddy simulation (LES). Nevertheless, a RANS model of the turbulence using a k-w SST configuration [27] should be enough to achieve a reasonable trade-off between accuracy and computational time [28; 29]. Indeed, the usage of such turbulence model has been validated in the framework of axial compressors [28] and internal flow mixing problems [30], which, if combined, would reduce the impact of the lack of experimental validation in the scope of the current problem, since no compressor-assisted hyperloop prototype systems exist. Moreover, with the aim of improving the similarity with real conditions, a laminar-turbulent transition model was added with the objective to capture the behaviour in those zones where, especially in the later 3D model with the compressor, the flow presents a laminar condition. Is it worth mentioning that, traditionally, a turbulent transition model asks for a finer mesh, but taking a look at the geometry of the compressor, this was not the main limitation.
## 1 Introduction
### The Case Study Area  
The Khon Kaen town plan (KKTP) area, covering approximately 228 km\({}^{2}\), is located in the northeastern region of Thailand (see Figure 2). Khon Kaen is located approximately 445 km from Bangkok. In 2022, approximately 380,557 people resided in the KKTP [32]. The general trends for gross provincial product (GPP), employment, population, and registered vehicles for Khon Kaen are shown in Figure 3. Typically, GPP, population, and registered vehicles increased throughout the period 2006-2022. Employment fluctuated over the period but showed overall net growth. The KKTP was selected as the case study area because it is one of the most strategic regional cities in Thailand. Khon Kaen city has been recognized as a hub for transport and logistics, education, medical services, meetings, and incentive travel, in addition to being a convention and exhibition (MICE) city, a low-carbon city, and a smart city. Khon Kaen has been actively working towards becoming a low-carbon city through various initiatives, such as public transport improvement, the integration of renewable energy sources into its infrastructure, waste management, and urban planning to reduce GHG emissions [33]. The Khon Kaen smart city emerged as part of Thailand's broader initiative to develop smart cities across the country. This initiative focuses on leveraging information and communication technology (ICT) systems to enhance urban living [34]. Several previous studies have used it as a case study, ranging from a public transport master plan and detailed design study to a low-carbon society research study [25; 32; 35; 36]. Khon Kaen city could be representative of a medium-sized city in a developing country.  
Figure 2: Location of the Khon Kaen town plan area [37].
## 3 Results and Discussion
Despite the statistical analysis proving TCS as a significant factor, they contribute the least from the three individual factors to the CRI difference variance (with an F-ratio of 0.009 and a sum of squares of 36.54). To further analyse the influence of light modifiers on the colour rendering properties, we took a closer look at individual test colour samples (TCS). Figure 6 shows the averaged CRI differences per TCS, taking into account all light-shaping attachments for (a) xenon and (b) halogen light sources. In Figure 6, we can again see an inverse relationship when we compare the results for xenon and halogen. All medians in Figure 6a are positive except for sample R7 (\(-0.07\)), while in Figure 6b only the median of R7 is positive (0.07). The largest absolute median and the largest deviation between the first and third quartiles are found for xenon in TCS R9, which represents the strong red, and for halogen in TCS R12, which represents the strong blue. Interestingly, in both cases, the most problematic TCS corresponds to the opposite part of the visual spectrum when the light source emits its light. This becomes clear when comparing the results from Figure 1 with Figure 6. Xenon, which peaks in the blue part of the spectrum (\(\sim\)460 nm), interacts worst with TCS R9, which peaks in the red part of the spectrum (\(\sim\)650 nm). The emission spectra of the halogen light source predominate in the red part of the spectrum, while the blue TCS R12, which is shown as problematic in Figure 6, peaks in the blue part of the visible spectrum (\(\sim\)460 nm). This relationship shows the influence of the properties of the light source on the interaction of light and light-shaping attachment, leading to different results in colour rendering quality. Still, the TCS R9 shows the largest deviations in both cases when the minimum and maximum values are taken into account, which corresponds to the widely observed problems with the reproduction of red colour tones. To further determine how light-shaping attachments influence the reproduction of certain colours, we analysed the results in terms of modifier type. Figure 6: Averaged CRI values per TCS with annotated median values and outliers for (**a**) xenon and (**b**) halogen.
# Unsupervised Learning for Concept Detection in Medical Images: A Comparative Analysis
###### Abstract  
As digital medical imaging becomes more prevalent and archives increase in size, representation learning exposes an interesting opportunity for enhanced medical decision support systems. On the other hand, medical imaging data is often scarce and short on annotations. In this paper, we present an assessment of unsupervised feature learning approaches for images in biomedical literature which can be applied to automatic biomedical concept detection. Six unsupervised representation learning methods were built, including traditional bags of visual words, autoencoders, and generative adversarial networks. Each model was trained, and their respective feature spaces evaluated using images from the ImageCLEF 2017 concept detection task. The highest mean \(F_{1}\) score of 0.108 was obtained using representations from an adversarial autoencoder, which increased to 0.111 when combined with the representations from the sparse denoising autoencoder. We conclude that it is possible to obtain more powerful representations with modern deep learning approaches than with previously popular computer vision methods. The possibility of semi-supervised learning as well as its use in medical information retrieval problems are the next steps to be strongly considered.  
Keywords:representation learning; unsupervised learning; deep learning; content-based image retrieval +
Footnote †: journal: Medical Image