esa-sceva/satcom-chunk-collection huggingface dataset:
# Artificial Intelligence-Empowered Doppler Weather Profile for Low-Earth-Orbit Satellites
To ensure a reliable satellite-to-ground 471 MHz link for LEO small satellites, the authors in [24] calculated a link budget for the LoRa satellite with a transmission power of 17 dBm and reception sensitivity of \(-\)123 dBm. It is important to address the performance due to Doppler, which presents a significant challenge for LoRa uplink connectivity. Our approach aims to address gaps in the literature, aiming for novelty by accomplishing the following tasks:  
* The study examines a space surveillance radar system for tracking and monitoring the trajectories of low-Earth-orbit (LEO) satellites, specifically targeting the Globalstar and International Space Station (ISS) satellites. The analysis utilises two strategically chosen locations, Perth and Brisbane, to ensure comprehensive East-West coverage. * The study creates models for LEO constellation satellites, assessing satellite-ground-station connectivity, determining latency, and developing a hybrid and computationally efficient model integrating Iterative Input Selection and Long Short-Term Memory Network (IIS-LSTM). This model seeks to accurately predict the Doppler profile for the weather data of LEO satellites with the highest level of precision. We consider both the Doppler rate and Doppler shift to gauge the influence of each on packet loss. * The IIS algorithm acts as a feature selector to minimise the dimensionality of the input space by selecting the relevant input variables or features that contribute to the model'sperformance. The LSTM algorithm acts as the learning model for understanding and predicting patterns. * The research aims to examine the impact of the capacity constraints of LoRa modulation in response to a significant effect of the Doppler effect caused by the movement of LEO satellites. The investigation involves an evaluation of various communication parameters, such as the spreading factor, altitude of the satellite orbit, frequency offset, and bandwidth of the signal, which affect the receiver's susceptibility to this occurrence. * Benchmarking is conducted using the machine and deep learning models--Convolutional Neural Network (CNN) and Extreme Gradient Boosting (XGBoost)--to enhance efficiency and performance. Based on our current understanding, this study represents a pioneering analysis of the effects of deep learning involving IIS-LSTM on a comprehensive set of parameters about the resilience of LoRa technology to the Doppler effect within a low-Earth-orbit (LEO) environment. The structure of this paper is as follows: Section 2 provides a vast overview of the Methodology, including the study area and LEO satellite tracking radar system, carrier frequency offset estimation, AI model framework for doppler weather profile analysis, impact of doppler on LoRa satellite and satellite link budget perspective. Following that, the results and discussion are examined in Section 3. Finally, Sections 4 and 5 present the concluding remarks and suggestions for future research.
## 5 Experimental Results and Discussion
### Numeral Spotting Results and Discussion  
The registers used in the case study are from the Nicaea district. All 50 pages are divided into 80% training and 20% test. The pixel-wise precision, recall, f-measure, IoU, and high-level numeral spotting error results are presented in Table 2. Note that the first four metrics are presented for 2-class classification (background vs. numeral). The last metric is the accuracy of spotting the numerals in the manuscripts. We successfully spotted the numerals in the documents with 96.06% (\(1-\text{NSE}=\%3.94\)) high-level accuracy. Although the IoU metric is relatively low, the performance of the spotting system shows that the documents are suitable for automatic segmentation processes after the red color mask. We further tested a 3-class classifier (see Table 3). When we added register update class, we obtained lower f-measure classes as expected. The lowest performance is achieved when recognizing the register updates. However, since our main focus is to spot numerals, numeral spotting performance is more important. We obtained 0.61 f-measure score while recognizing only numerals and 0.67 f-measurescore while recognizing numerals with updates vs. background which are close to the 2-class f-measure score 0.72.
# Acoustic Localization for a Moving Source Based on Cross Array Azimuth
## 2 Localization Analysis of Single Array
### Precision Analysis for Localization  
The algorithm for localizing the noise source, as described by Equations (2) and (5) in Section 2.1, and the associated accuracy depend on sound velocity \(c\), array size \(D\) and, in particular, the error involved in estimating the time delay \(\sigma_{\tau}\). Since \(D\) and \(c\) remain constant, for any particular array and measurement environment, the dominant factor affecting the precision of the proposed method is associated with the error involved in measuring \(\sigma_{\tau}\). Due to the symmetric arrangement of the sensors with regard to the central sensor, the standard errors for the time delay of all sensors were assumed to be equal, such that \(\sigma_{\tau}=\sigma_{\tau 1i}\).  
In Equation (5), quadratic function was included in the expression of coordinates \((x,y)\), which makes it different to calculate the transmission. Then, after the precision calculation of coordinates was transferred into angular coordinates, the localization was described with azimuth \(\varphi\) and range \(R\) as illustrated in Equation (5).
# Performability Evaluation of Autonomous Underwater Vehicles Using Phased Fault Tree Analysis
## 3 Performability Evaluation Method Based on Phased Fault Trees
### Performance Evaluation Method
#### 3.2.1 Ranking Method Based on an FT  
There are several ways to establish the ranking of FT events based on the FT structure, including \"Top-Down-Left-Right\" (TDLR), a \"Depth-First Search\" (DPS), a \"Breadh-First Search\" (BFS), the level technique and AND gate counting. TDLR ranks every event by searching through the FT from left to right and top to bottom [60]. Each event is added to an event list by the method, which works left to right and top to bottom. An event will be disregarded if it reappears after already being added to the event list. Once the ranks of all the events within each sub-tree are specified, the DFS technique continues on to the next sub-tree [61]. It determines the ranking of events from left to right in units of sub-FTs. The BFS method specifies the ranking of each event in chronological order for the whole FT rather than breaking it down into sub-FTs [62]. By defining the number of gates that connect each event to the top event as the level, the level technique establishes the order of events [63]. The number of AND gates that separate each event from the top event determines the ranking according to the AND criterion [64]. The highest rank is given to the event with the fewest AND gates. When events have the same number of AND gates, the TDLR technique is used.
## 2 Materials and Methods
### Methods
#### 2.3.1 Land-Use-Related Indices  
The land use transition matrix depicts the volume and directionality of shifts between land classes, revealing the stock and flow of a specific land class [42]. This study establishes transition matrices for two periods, 2000-2010 and 2010-2020, through cross-analysis of land type data.  
Land use dynamics are employed to characterize the rate of LUC, and include the following two types [49].  
The specific land use dynamics indicate the rate of area alteration for a given land use category, while the overall land use dynamic degree signifies the aggregate pace and extent of shifts across land use types. The calculation formulas are as follows (Formulas (1) and (2)):  
\[K_{u}=\frac{U_{n}-U_{m}}{U_{m}}\times\frac{1}{T}\times 100\%, \tag{1}\]  
in this equation, \(Ku\): the specific land use dynamics, \(U_{\text{m}}\) and \(U_{\text{n}}\): the extent of a land use category at the study's commencement and conclusion, \(T\): the study period.  
\[Lc=\frac{\sum_{i=1}^{n}\Delta LU_{i-j}}{2\sum_{i=1}^{n}LU_{i}}\times\frac{1}{ T}\times 100\%, \tag{2}\]  
\(Lc\): the overall land use dynamic, \(\Delta LU_{i:j}\): the magnitude of the area for land use type \(i\) that has transitioned to another land use type, \(LU_{i}\): the initial area of land use type \(i\).  
The Comprehensive Index of Land Use intensity categorizes the degree of land use (Table 2), thereby describing the level and changes of LUCs in the region [50].  
\[LA=100\times\sum_{i=1}^{n}A_{i}\times C_{i}, \tag{3}\]  
Figure 3: Methodological flowchart; (**a**) the methods in Sections 2.3.1 and 2.3.2; (**b**) the methods in Section 2.3.3.  
In the equation, \(LA\): comprehensive index of land use, \(A_{i}\) and \(C_{i}\): the \(i\)-_th_ class land use grading index and the percentage of area.  
Bivariate spatial autocorrelation is used to explore and analyze the spatial correlation characteristics between different variables [51]. The commonly used indicator, _[PERSON]'s_\(I\) index, suggests that when _[PERSON]'s_\(I>0\), there is a positive correlation between the two variables in space; conversely, a negative correlation exists when _[PERSON]'s_\(I<0\); if _[PERSON]'s_\(I=0\), it means they are not correlated.
###### Abstract  
The backfilling of lime soil in ultra-deep and ultra-narrow foundation trenches is a difficult construction link, and ordinary-cemented soil has drawbacks, including poor strength, impermeability, and frost resistance. To solve these problems, fly ash (FA)-water glass (WG)-composite-cemented soil is developed based on a background project. The three-factor orthogonal tests are conducted on the unconfined compressive strength (UCS) of the composite-cemented soil, and the optimal engineering mix proportion is proposed for the FA-WG-composite-cemented soil. Its UCS is compared with that of cemented soil only doped with FA or WG (FA- and WG-cmented soil). In addition, the cyclic wetting-drying tests, cyclic freeze-thaw tests, and impermeability tests are carried out to study the endurance of the composite-cemented soil in cold regions rich in water. The hydration products of the composite-cemented soil are investigated through scanning electron microscopy (SEM) and X-ray diffraction (XRD) analysis, and the curing mechanism of the composite-cemented soil is discussed from the microscopic perspective. The research results indicate that the mixing ratio of cement is crucial to the strength development of the cemented soil; the mixing ratio of FA greatly influences the strength development of the cemented soil in the middle and late stages; the mixing ratio of WG only slightly affects the strength. The ratio of cement, FA, and WG of 9%:12%:3% is the optimal engineering mix proportion of the composite-cemented soil. Compared with ordinary-cemented oil and FA- and WG-cemented soil, the composite-cemented soil shows significantly improved compressive load-bearing capacity. The permeability coefficient of the composite-cemented soil is always obviously lower than that of the ordinary-cemented soil after any curing period. Despite the mass loss, the composite-cemented soil is superior to the ordinary one in overall endurance after wetting-drying and freeze-thaw cycles. Through SEM and XRD analysis, the content of hydration products of the composite-cemented soil is found to be obviously higher than that of ordinary-cemented soil after any curing period, and the hydrates exert stronger cementing action on soil particles in the composite-cemented soil. The contents of C-S-H gel and Aft crystals in the composite-cemented soil are apparently larger than those in the ordinary-cemented soil. Under the alkali activation of WG, the FA produces free SiO3\({}^{2-}\) and AlO\({}^{2-}\), which undergo the polymerization reaction with Ca\({}^{2+}\) to generate C-S-H gel and C-A-H gel, further promoting the hydration of cement.  
Keywords:foundation trench; composite-cemented soil; unconfined compressive strength; impermeability; endurance; microscopic property; curing mechanism +
Footnote †: journal: Applied Sciences
# ULYSSES: Automated FreqUentLY ASked QueStions for KnowlEdge GraphS
## 2 Related Work
### Question Answering over KGs  
In addition, numerous works in recent years have focused on answering the textual questions of users on KGs or using embeddings to provide answers to given queries [10].  
Early systems focused on answering natural language questions by transforming them into logical forms that could be directly executed over a knowledge base [11]. Then, template-based approaches exploited predefined templates, to convert natural language questions into structured queries, such as SPARQL [12]. While efficient, challenges like handling the vast variability in natural language expressions and the explosion of the required templates for different question types made these approaches hard to use.  
Recent advancements have leveraged machine learning, especially neural networks, to improve question answering over KGs. These models, including convolutional neural networks and transformer-based models, such as BERT, enhance entity recognition and relation detection, providing more accurate answers. Multicolumn convolutional neural networks, for example, have been used to handle the complexity of freebase data, improving the extraction of relevant information to answer questions [13].  
The process that ULYSSES is using to construct the FAQs could be complemented by these approaches. ULYSSES identifies the most important queries and then directly uses the corresponding SPARQL endpoints to answer them. Then, LLMs transform the answers/questions into text. Another direction that could be explored would be to directly answer the selected queries over the Knowledge Graph, using the aforementioned techniques, which could also directly provide the resulting textual description of the answer.
## 2 Materials and Methods
### Porous Media  
#### 2.2.1 Bentheimer Core Plug for Single-Phase Polymer Flood  
Core plugs with an average diameter and length of 30 and 60 mm, respectively, were used for single-phase polymer flooding. Porosity was measured by using a helium gas porosity meter (Micromerities 1340 pycnometer from Micromerities GmbH Unterschleissheim, Germany), and permeability was measured by using a nitrogen gas permeameter (SYROPERM from GL Test Systems GmbH Heiligenstadt Germany). Brine permeability was measured by injecting synthetic seawater at five different injection rates (0.5. 1.0, 2.0. 5.0, and 10.0 mL/min). Table 3 provides a summary of the used core plugs.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Name** & **Length** & **Diameter** & **Dry wt.** & **Porosity** & **Permeability (g)** & **Permeability (B)** \\ \hline  & & **mm** & **mm** & **g** & **\%** & **mD** & **mD** \\ \hline
1 & C3 & 60.47 & 29.51 & 82.767 & 24.60 & 2988 & 1292.8 \\
2 & C5 & 59.89 & 29.29 & 80.656 & 24.68 & 3046 & 1541.2 \\
3 & C6 & 60.07 & 29.31 & 80.619 & 25.06 & 3226 & 986.7 \\
4 & C8 & 59.97 & 29.35 & 80.988 & 24.71 & 2980 & 1548.6 \\
5 & C11 & 59.84 & 29.56 & 82.686 & 23.83 & 2666 & 1052.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Bentheimer core plug properties.  
A schematic representation of the core-flood setup is shown in Figure 2. It consists of a Hassler cell connected to three ISCO pumps (Teledyne ISCO from Lincoln NE, USA) for multi-flow infusion/withdrawal arrangement. The core plug receives confining pressure of 30.0 bar as an overburden pressure, using an ISCO injection pump. A system pressure of 2.0 bar was established, using a backpressure regulator for each experiment. Experimental preparation for single-phase polymer flooding uses the following steps:  
* Perform the oscillatory, steady shear viscosity, and extensional measurements of polymer solutions before core flood (3000 ppm in B1 and B2) at the selected temperatures (22 and 55 \({}^{\circ}\)C) for rheological characterization.
* Perform polymer flooding with an increasing range of injection rates (37 to 29 mL/min) through the core at the relevant temperatures (22 and 55 \({}^{\circ}\)C), to investigate in situ viscosity.
* Perform the oscillatory, steady shear viscosity and extensional measurements of effluents from core flood at the chosen temperatures (22 and 55 \({}^{\circ}\)C), for loss in rheological properties.
## 2 Conventional Digital Image Correlation (DIC) Applications
### Bending and Buckling Analyses of Composite Conical Shells for Helicopter Tail-Boom Applications  
#### 2.2.1 Synopsis  
In this case study, bucking and bending behaviors of composite conical shells were studied experimentally and theoretically. In the theoretical approach, a first-order shear deformation shell theory was proposed to study buckling and bending behaviors of composite conical shells, and a semi-analytical solution was developed to study buckling under bending of composite conical shells. In the experimental approach, a pure bending test setup was designed and developed to study buckling under bending of composite shells. The setup was equipped with a 3D DIC system to measure three-dimensional deformation and surface strain of the test article. A thermoplastic composite cone, a full-scale section of a helicopter tail-boom, was tested using this setup in pure bending, and the experimental buckling moment was measured, which agreed well with the theoretical one [15].
# Cancelable Multimodal Biometrics Based on Chaotic Maps
## 3 Proposed Approach
### Cancelable Face Features  
To transform the face images, we employ a basic principle based on mathematically convolving a user-specific kernel, which is generated using a chaotic map with biometric data to establish preventative measures against a range of attacks [22]. User face images are scanned for the enrollment stage, and following this, the user-specific kernel is generated with a chaotic map. We proposed using a Logistic map to generate the user-specific kernel. The Logistic map is expressed as  
\[x_{i+1}=\mu x_{i}(1-x_{i}) \tag{5}\]  
where \(x_{0}\) is the initial value. \(\mu\in(3.57,4)\) is the control parameter, which is used to produce the chaotic sequence numbers, which are then used to construct the kernel. To ensure that the user-specific kernel is unique, every user will be assigned a different value \(x_{0}\).  
The face template is first resized to \(8\times 8\) and then convolved by the generated kernel. The convolved images are considered as being the protected templates. The convolution process is defined by  
\[h=F*g \tag{6}\]  
where \(F\) refers to the image, \(g\) to the kernel and the \(*\) symbol refers to the convolution process, which is based on a point-wise multiplication process.  
In the instance where a protected template is disclosed, a new user-specific kernel is assigned to the user through the modification of the initial value \(x_{0}\). It is not possible for an attacker to construct the protected template without knowing the user-specific kernel employed in the enrollment stage. The initial value has a significant role to produce a unique kernel for each genuine user.
# Optical Boundaries for LED-Based Indoor Positioning System
## 5 Results and Discussions
### Effect of Encoding Duration on BER  
By maintaining the receiver noise at the value obtained in Section 5.1, and as the receiver moves on a horizontal plane (Figure 2), the LED data is encoded using BPC for various values of pulse duration \(L\). As \(L\) is increased from 0 to 60 \(\upmu\)s, the BER increases as the mobile receiver moves from  
Figure 9: Validation of the packet delivery ratio (PDR) and bit error rate (BER) relationship in (11) using 4 bit and 12 bit protocols.  
an incidence angle of \(-\Phi\) to \(\Phi\) as shown in Figure 10. Two key pieces of information are drawn from the Figure 10. The first is the effect of the encoding duration on BER. As the value of \(L\) increases, the minimum BER also reduces and the range of incidence angles for which is an acceptable BER increases. The second piece of information is about the range of incident angles with acceptable BER values. From Figure 10, if no threshold is defined at the receiver, as the mobile receiver moves towards regions where the angle of incidence is above \(40^{\circ}\), the BER value becomes greater than \(10^{-2}\) and the PDR is less than 1 (see Figure 9). Therefore according to (14), the positioning time is increased. As the mobile receiver approaches the full angle (\(78^{\circ}\)), the BER increases further, which causes much more of a delay in positioning time. To address this delay, a desired PDR value which corresponds to an optical threshold angle is set. For explanation purposes, let a minimum PDR value be selected such that when packets starts getting discarded (say two out of every 10 so that \(\mathcal{P}=0.8\)), the receiver defines a boundary. A plot of the incidence angle above which the BER does not meet the conditions set out in Section 5.3 is presented in Figure 11.  
Figure 11: Maximum angle of incidence (Max AI) for encoding pulse duration between 0 and 2 ms.  
Figure 10: BER vs. angle of incidence for increasing BPC pulse length L and a minimum PDR of 0.8.  
The result in Figure 11 shows the maximum angular displacement of the receiver from the transmitter at different encoded pulse durations to keep the PDR above 0.8. For a pulse duration of 500 \(\upmu\)s, a threshold angle of about 62\({}^{\circ}\) gives a PDR above 0.8 and for a pulse duration of 600 \(\upmu\)s, the threshold angle for the same PDR is 60\({}^{\circ}\) for the 12-bit protocol and 64\({}^{\circ}\) for the 4-bit protocol. By using this strategy in the design of the positioning system, the positioning time is defined according to (14) thereby reducing positioning delays.
## 3 Preprocessing Methods
### Non-Metric Multidimensional Scaling-Based Load Profile Representation
```
0:\(data1=X_{[K,2]},data2=X_{[K,N]}\)
0:\(BestSol_{[epsilon,minpts,ncluster,RMSSTDindex]},label\)
0:\(Initialisation:\) Use the initial parameters described in [22]
0:\(Modification:\) Change purity metric with RMSSTD index for clustering validation and performance evaluation
1: max epoch = 30
2: max iteration = 100
3:for\(i=1\) to max epoch do
4:for\(ii=1\) to max iteration do
5: run BDE-DBSCAN algorithm for \(data1=X_{[K,2]}\)
6: calculate RMSSTD index as in (4) using \(data2=X_{[K,N]}\)
7:if\(Population_{[i,ii]}.Cost<Population.Cost\)then
8:\(BestSol=Population_{[i,ii]}\)
9:elseif\(RMSSTDindex_{[i,ii]}=RMSSTDindex\)and\(ncluster_{[i,ii]}<ncluster\)then
10:\(BestSol=Population_{[i,ii]}\)
11:endif
12:endfor
13: store \(BestSol_{[i,ii]}\) to a new variable named results
14:endfor
15:return results
```  
**Algorithm 1** Pseudo-code of the proposed outlier detection process based on the binary differential evolution (BDE)-density-based spatial clustering applications with noise (DBSCAN) algorithm [22]