esa-sceva/satcom-chunk-collection huggingface dataset:
# Disaster Recognition and Classification Based on Improved ResNet-50 Neural Network
## 3 Results and Discussion
### Evaluation of Model Classification Performance
In addition to evaluating classification performance, this study further investigates the training dynamics and stability of both models. As shown in Figure 9, the proposed ResNet-50-based model exhibits significantly smoother and faster convergence during training. Specifically, in Figure 9a,c, the training accuracy of ResNet-50 increases rapidly and stabilizes above 94%, while the validation accuracy consistently remains around 82%, with minimal fluctuation. Simultaneously, the training loss steadily decreases and converges to a low value, and the validation loss remains relatively stable, indicating robust generalization and no signs of overfitting. In contrast, the VGG16 model, illustrated in Figure 9b,d, shows slower convergence in the early training stages, with greater fluctuation in both accuracy and loss. Although the training loss continues to decline, the validation loss remains relatively high and oscillatory, suggesting limited learning capacity or possible underfitting. These results demonstrate that the residual connections in ResNet-50 not only alleviate the vanishing gradient problem but also promote more stable and efficient training, ultimately contributing to superior performance in disaster classification tasks.
# A Novel Hybrid Recommender System for the Tourism Domain
## 3 Content-Based Recommender Component
### Hierarchy Similarity Measure-Based Recommendations  
The _Hierarchy SMbR_ is a sub-component of the CB recommender algorithm which is capable of providing recommendations via a hierarchy similarity measure called _XWP_. This metric is based on a similarity measure introduced in [16], which is an extended version of _Wu-Palmer_ (_WP_) [35]. More specifically, the semantic similarity between two concepts \(X,Y\), by employing \(WP\), is described as:  
\[WP(X,Y)=\frac{2N}{E_{1}+E_{2}}\quad with\;\;X\
eq Y \tag{1}\]  
where \(E_{1}\), \(E_{2}\) represent the number of edges between the _root node_ and the ontologies \(X,Y\) respectively, while \(N\) is the number of edges between the _root node_ and the _Least Common Ancestor_ (_LCA_) of \(X\) and \(Y\). When the _WP_ similarity score is close to 0, ontologies \(X,Y\) are very distant, while when it approaches 1, \(X\) and \(Y\) are quite similar.  
The extended version of _WP_, or _XWP_ as we denote it, computes the semantic relation between the ontologies \(X,Y\) and is defined as:  
\[XWP(X,Y)=WP(X,Y)\cdot e^{\frac{-1\lambda}{H}}=\frac{2N\cdot e^{\frac{-1 \lambda}{H}}}{E_{1}+E_{2}} \tag{2}\]where \(H\) is the height of the hierarchy structure and \(L\) represents the minimum distance between ontologies \(X\) and \(Y\). Furthermore, the \(\lambda\) factor is zero when the compared ontologies' LCA is in their neighborhood, otherwise it equals one. In our instantiation of the \(XWP\) measure, we define the neighborhood of a particular node to be the set of all other nodes which have a distance less than or equal to 2 from it.  
The left part of Figure 5 depicts an overview of the _Hierarchy SMBR_-based recommendation process.  
Specifically, the \(u_{L}\) vector of \"liked\" images is given as an input to the _Hierarchy SMBR_ sub-component and for every \"liked\" generic image contained in this set we compute the \(XWP\) similarity distance between it and each POI in our database. Now, the \(M\) most relevant POIs to each generic image \(i\) according to \(XWP\) are stored in a set, denoted as \(u_{M}^{i}\), alongside their similarity scores. For instance, as Figure 5 illustrates, if the user has \"liked\" \(N\) generic images during the elicitation process, our system will create one set for every \"liked\" image, i.e., \(u_{M}^{1},u_{M}^{2},\ldots,u_{M}^{N}\). The POIs included in all \(u_{M}\) sets are grouped together, and their corresponding \(XWP\) similarity scores are transformed into a probability distribution by a normalization process--i.e., the _Normalization Process_ part of the _Hierarchy SMBR_ component (illustrated in blue) in Figure 5. Finally, if the standalone operation of this sub-module is desired, we sample out 20 POIs in order to be presented to the user.
## 5 Implementation and Application  
In this paper, we have developed a vital sign analysis system based on AB Broker. This study executed the evalQRSDetection Function developed in MATLAB as shown in Figure 6.  
1 in Figure 6 is an AB Adapter used for executing the low-pass filter function developed in R programming, as shown in Figure 7. The evalQRSDetection function is an algorithm that analyzes the value of R Peak on the electrocardiogram (ECG). The evalQRSDetection function has two input values, electrocardiogram data, and sampling frequency. The two result values are the R peak index and R peak value.  
Figure 5: Vital sign analysis system architecture based on AB broker.  
function[maxIdx, maxVal] = evalORSDetection(data, FS)  
ifnargin<2  
FS=100;  
end  
fs=FS;  
fh=5;  
maxIdx = [];  
maxVal = [];  
rawData = data;  
dcRemData = rawData-mean(rawData) | | AB Adapter|  
lpData = [adapter('lowpassfilter.R',dcRemData,60,200);  
hpData = hpassfilter(lpData,fh,fs);  
diffData = diff(hpData);  
sarData = diffData.*diffData:  
window= ones(1,30);  
integral= medifit(filter(window,1,sarData),10);  
delay = ceil(length(window)/2);  
integralData = integral(delay:length(integral));  
max_h=max(integralData);  
thresh = 0.3;  
peak_re = integralData>(thresh+max_h);  
sIndex = find(diff([0 peak_reg'])==1);  
eIndex = find(diff([peak_reg' 0])==-1);  
for i=1:length(sIndex)  
[maxVal(i)maxIdx(i)] = max( hpData(sIndex(i):eIndex(i)));  
maxIdx(i) = maxIdx(i)-1+sIndex(i);  
end  
ifisempty(maxIdx)  
maxIdx = [1];  
end
## 6 Research Areas
### Autonomous Navigation
#### 6.1.1 Localization  
In the localization stage, the robot estimates its current position in the target space. [PERSON] et al. [201] used the local navigation system (LNS) for the localization of any robot inside a building. The LNS uses preinstalled anchor nodes for calculating the location of the robot, similar to how the Global Positioning System (GPS) works, though GPS is ineffective in an indoor setting. Traditional LNS requires a direct line of sight with the anchor nodes and produces an accuracy of up to 11 m in a non-line-of-sight setting, which is not very useful. However, [PERSON] et al. [201] developed an algorithm to compensate for non-line-of-sight and achieved an accuracy of 2 m. This is a significant improvement but may not be sufficient for reliable inspection in small spaces.  
In GPS-denied environments, such as inside large buildings or areas surrounded by high-rise structures, additional technologies such as Real-Time Kinematic (RTK), Differential GPS (DGPS), and Ultra-Wide Band (UWB) technologies are used to augment the capabilities of GPS and improve the positioning accuracy to within the centimeter range [200]. However, these technologies require additional infrastructure to set up and operate [53].  
The Simultaneous Localization and Mapping (SLAM) algorithm is used to create a digital map of the surroundings using a camera, LiDAR, or other sensors. [PERSON] and [PERSON] [89] combined measurements from an IMU sensor, wheel odometry, and GPS (whenever available) and used the extended Kalman filter (EKF) for estimating the position of a Clearpath Jackal robot, a medium-sized UGV. This technique is called dead-reckoning, which can enable localization without any external infrastructure, but can be subject to drift over time and needs to be re-calibrated repeatedly [53]. Parameters required for localization of the robot are categorized into four categories--interior orientation parameters (e.g., camera focal length, the principle point, and length distortion), exterior orientation parameters (e.g., orientation from IMU), ground control points (e.g., any known natural or artificial targets), and tie/pass points (e.g., unique feature points on the scene that can be tracked in multiple images) [202]. The scale-invariant feature transform (SIFT) technique was used for feature extraction in the surrounding environment and comparison with a CAD model for estimating the position of the robot in the frame of reference of the building [203; 204; 205].  
In underwater inspection, such as inside water tanks, visual sensors become ineffective. An underwater robot needs ultrasonic acoustic and inertial navigation sensors [8]. GNSS and underwater acoustic position systems have been used together to provide reliable localization underwater [206]. The other approach is to use a USV to carry a submersible vehicle. The USV being above water can receive GNSS signal, and the submersible vehicle's absolute position can be calculated from its position relative to the USV [137].
## 3 Experimental Setup  
To explore the characteristics of the SC induced by filamentation of BG and LG beams at different locations in water, we design an experimental setup shown in Figure 1. The laser pulses are generated by an amplified Ti: Sapphire laser system. The central wavelength, duration and repetition rate of the laser pulses are 800 nm, 50 fs and 1 kHz, respectively. The maximum pulse energy is 3.0 mJ, and the pulse energy is adjusted by an energy attenuator composed of a half-wave plate H and a Glan prism G to the desired value. The beam spot size is adjusted by an iris (which is 10 mm in the experiment), and consequently, the light beam can perfectly enter the liquid crystal screen of a spatial light modulator (SLM, Hamamatsu LCOS-SLM, X13138). The SLM can generate LG beams by loading the phase diagram (see Figure 2a\({}^{\prime}\)-\({}^{\prime}\) in Ref. [39]). In this experiment, we attempt to compare the characteristics of the SC generated by higher-order BG and LG beams, and thus two kinds of optical lenses are used to focus the vortex beams: One is a focusing (plano-convex) lens (fused silica), whose focal length is \(f=75\) mm, and the other one is an axicon lens, whose top angle is 170 degrees. As the generated LG beams with different TC values pass through the axicon lens, higher-order BG beams can be generated. The dashed rectangle suggests the axicon lens can be replaced by a focusing lens. The vortex beams are focused into a quartz cuvette C (with dimensions: 50 mm \(\times\) 10 mm \(\times\) 40 mm) filled with water and form filament generating the SC. The transmitted spectrum (i.e., the SC) is firstly collected by an integrating sphere IS and then guided to a spectrometer (Avantes, avaspec-ulis2048l), and thus the transmitted spectral data are obtained. Both the cuvette and integrating sphere are fixed on an electric translation stage, and by moving the electric translation stage, the SC induced by femtosecond laser filamentation at different locations in water is recorded. This method is somewhat similar to the Z-scan technology [57; 58; 59]. The propagation of laser beams in water is affected by the water attenuation and water-induced turbulence (e.g., bubble generation), which affect the stability of supercontinuum. To reduce the error, each spectrum is obtained by an average of a hundred measurements.
# An IVTIFN-TOPSIS Based Computational Approach for Pipe Materials Selection
## 2 Materials and Methods  
### An Indicator System for Pipe Materials Selection  
An indicator system for pipe materials selection is established, as shown in Figure 1. These indicators are classified into three categories, indicating materials' functional, economic and environmental attributes, to reflect the most crucial criteria that should be considered in materials selection for a piping-system design.  
The functional attribute reflects the specific performance that certain material must satisfy [21]. In this study, a number of typical criteria are identified to examine functional performances regarding pipe materials, in order to ensure reliability and reduce environmental risk during waste-water collection and transport, including tensile strength (C1), elastic modulus (C2), linear expansion (C3), scaling resistance (C4), and corrosion resistance (C5). C1 reflects materials' capability to withstand a maximum load of tensile stress [43]. C2 reflects the ability of materials to resist elastic deformation, while C3 shows materials' resistibility to deformation caused by temperature change [44; 45]. Scaling and corrosion resistance play key roles in pipe materials' functional performance to decrease piping failure [46].  
Cost plays an important role in materials selection, by which three criteria are considered, consisting of the materials' marketing price (C6), density (C7) and Hazen-Williams roughness (C8). It is worthy of note that C7 and C8 have indirect impacts on the costs of pipeline construction and operation. The pipe material with a larger density may entail a heavier cost on pipeline installation [47]. In addition, the degree of roughness of pipe material is positively correlated with the maintenance cost in sewage transportation [48].  
With environmental considerations being gradually immersed into product design, selection of materials focuses on environmental impact throughout a product's entire lifecycle [49]. In such a context, three indicators are proposed to indicate the environmental performance of pipe material: energy consumption (C9), human health risk (C10) and material recyclability (C11). Energy consumption here focuses on the power consumption related to pipeline construction and maintenance, which may contribute to indirect carbon emissions [49]. Health risk indicates the hazard of toxic components released to the workers in the pipe materials' processing and welding [12]. The material recyclability is used to reflect the potentials of waste prevention, thus to reduce landfill disposal of waste materials [50].
# Automatic Quantitative Coronary Analysis Based on Deep Learning
###### Abstract  
As a core technique to quantitatively assess the stenosis severity of coronary arteries, quantitative coronary analysis (QCA) is urgently supposed to become more automated and intelligent, especially for regions lacking expertise and technology. The existing QCA methods highly depend on manual operation, which is time-consuming and subject to personal experience. This study innovatively proposes a fully automatic QCA workflow based on artificial intelligence (AI-QCA), which can quickly and accurately make a quantitative assessment of stenosis severity. The whole AI-QCA workflow mainly consists of three parts: the boundary-aware segmentation on the coronary angiogram (CAG) images, the AI-enabled coronary artery tree construction, and the diameter fitting and stenosis detection. Experiments show that the precision, recall, and F1 score of the segmentation, evaluated on 1322 CAGs, are 0.866, 0.897, and 0.879, respectively. Furthermore, the RMSE between diameter stenosis assessed by AI-QCA and manual QCA served by senior experts, evaluated on 249 CAGs, is 0.064, and the Pearson coefficient is 0.765. Meanwhile, the operation time can be reduced from tens of minutes to several seconds by AI-QCA. As a conclusion, the proposed AI-QCA is able to quickly quantify stenosis parameters as accurately as senior experts, which is significant for the intelligent diagnosis and treatment of coronary artery disease.  
q mtiatative coronary analysis; deep learning; vessel segmentation; coronary angiogram; artificial intelligence; vessel tree +
Footnote †: journal: Medical Image Analysis  
2023
# Multi-Objective Decision-Making Meets Dynamic Shortest Path: Challenges and Prospects +
## 6 Applications
### Intelligent Transportation Systems  
Traffic networks comprise a problem addressed to the transportation systems that involves numerous variables to be satisfied, e.g., time travel, distance, safety (drainage problems, weather, and lack of illumination), and travel costs [70; 71; 72]. All those variables can be updated dynamically with a real-time system, as indicated in the work of [PERSON] and [PERSON] [73]. Therefore, we see the traffic networks as an MODSP problem since the problem can receive real-time updates regarding the objectives cost and changes in the graph topology due to incidents that can close or open roads at any time. Moreover, the optimal shortest path result will be subjected to user preferences where a user selects a fast travel time rather than driving a short distance.  
Works addressed to transportation systems consider the traffic networks dynamic when the weight changes over time or the objectives' cost follows a pattern. However, as [PERSON] and [PERSON] [73] listed in their work, the desired requirements for a safe-route-finding system regard the capability to update the graph information in real-time with the support of sensors and dealing with trade-offs over the travel time. Then, following this future of  
Figure 5: Venn diagram representing the taxonomy of the MODSP problem class and its relationship with the DSP, MOSP, SP, and MODM problem classes.  
the traffic network problem, the MODSP problem fits as a better model for developing new solutions.  
Moreover, another current traffic network problem that calls our attention is the navigation system used by self-driving cars. In such a problem, vehicles are also subject to dynamic environmental changes. Therefore, they need to accommodate multiple objectives regarding the optimal route choice, such as travel time, comfort, energy consumption, obstacles, distance to be covered, and threats to passengers and pedestrians [60; 73; 74; 75].
# BER Minimization by User Pairing in Downlink NOMA Using Laser Chaos Decision-Maker
## 1 Introduction
The aim of the base station is to find the pairing that minimizes BER of the entire system under study. In addition, by examining detailed parameter dependencies in the pairing by laser chaos decision-maker, we introduce an adaptive scheme to optimize the operation parameters in the NOMA system. Moreover, the performance achieved by the proposed method is compared with C-NOMA, UCGD-NOMA, and \(\epsilon\)-greedy-based user pairing in terms of BER, where the proposed method accomplishes superior BER than the counterparts. The main contributions of this paper can be summarized as follows:  
* We design a fast user-pairing scheme that can make real-time decisions for downlink NOMA systems based on the laser chaos decision-maker while considering the BER of the communications and user responses on the basis of a realistic system model. * To improve the performance in terms of BER for our proposed scheme, error-correcting codes are introduced in the system. In addition, parameter dependencies in the user pairing by laser chaos decision-maker are investigated to minimize the BER of the NOMA system. Simulation results indicate that our proposed scheme can improve the BER of the NOMA system. * To verify the effectiveness of the proposed scheme, we conduct a performance evaluation for our proposed scheme and compare it to several other conventional schemes. Simulation results demonstrate that our proposed scheme exhibits superior performances in BER compared to other conventional schemes. As the cell radius of the NOMA system gets smaller, the superior on the BER of our proposed scheme gets bigger. Specifically, our proposed scheme can decrease the BER from \(10^{-1}\) to \(10^{-5}\) compared to the conventional schemes when the cell radius is 400 m.  
The rest of the paper is organized as follows. After describing the system model of the present study in Section 2, Section 3 introduces the laser chaos decision-maker. Section 4 presents the proposed method and shows basic numerical results. Section 5 investigatesparameter dependencies and discusses performance optimization. Section 6 concludes the paper.
# A Survey on Bug Deduplication and Triage Methods from Multiple Points of View
## 5 Works Based on Bug Reports  
Throughout the entire life cycle of bug reports, in this paper, we focus on the deduplication and triage stages. Essentially, both deduplication and triage aim to find similarities among bug reports. Common methods include text analysis, information retrieval, and various machine learning techniques. For example, [PERSON] et al. combined TF-IDF vectors, word-embedding vectors, and component information from bug reports to comprehensively assess the similarity between reports. The commonly used methods for assessing similarity are briefly introduced in Section 3.2. During deduplication and triage, bug reports are first preprocessed to eliminate irrelevant information. Common preprocessing methods include word segmentation, which involves standard transformations such as removing punctuation and converting letter cases. Stop word removal is performed to remove insignificant words such as conjunctions and adverbs. Stemming is also applied, which involves extracting words in their base form, regardless of their different expressions.
# Seamless Indoor-Outdoor Localization Through Transition Detection
## Appendix C Outdoor Positioning Algorithm  
```
Input:\(\{X_{k-1},P_{k-1},t_{k-1}\},Z_{k}\) Output:\(\{X_{k},P_{k},t_{k}\}\) Prediction step: \(dt_{k}\gets t_{k}-t_{k-1}\) \(X_{k|k-1}\leftarrow\Phi(X_{k-1})\) \(P_{k|k-1}\gets AP_{k-1}A^{T}-Q\) If measurement input is from AI-IMU:  Measured state vector \(Z_{k}^{ai-imu}=\left[\omega_{k}^{ai-imu},\upsilon_{k}^{ai-imu}\right]^{T}\) Update step: \(K_{k}\leftarrow(P_{k|k-1}H^{T})(HP_{k}H^{T}+R^{ai-imu})^{-1}\) \(P_{k}\leftarrow(I-K_{k}H)P_{k|k-1}\) \(X_{k}\gets X_{k|k-1}+K_{k}(Z_{k}^{ai-imu}-MX_{k|k-1})\) Else if measurement input is from GNSS:  Measured state vector \(Z_{k}^{GNSS}=\left[\theta_{k},\
u_{k}^{x},\
u_{k}^{y}\right]^{T}\) at time \(t\) Calculate measurement noise covariance matrix \(R^{GNSS}\): \(R^{GNSS}_{k}\leftarrow c_{k}R^{GNSS}\) Update step: \(K_{k}\leftarrow(P_{k|k-1}H^{T})(HP_{k}H^{T}+R^{GNSS})^{-1}\) \(P_{k}\leftarrow(I-K_{k}H)P_{k|k-1}\) \(X_{k}\gets X_{k|k-1}+K_{k}(Z_{k}^{GNSS}-HX_{k|k-1})\) End If
```  
**Algorithm A3** Outdoor positioning algorithm.