esa-sceva/satcom-chunk-collection huggingface dataset:
# LSTM Attention-Driven Similarity Learning for Effective Bug Localization
###### Abstract  
Objective: The complexity of software systems, with their multifaceted functionalities and intricate source code structures, poses significant challenges for developers in identifying and resolving bugs. This study aims to address these challenges by proposing an efficient bug localization method that improves the accuracy and effectiveness of identifying faulty code based on bug reports. Method: We introduce a novel bug localization approach that integrates a Long Short-Term Memory (LSTM) attention mechanism with top-K code similarity learning. The proposed method preprocesses bug reports and source code files, calculates top-K code similarities using the BM25 algorithm, and trains an LSTM-Attention model to predict the most relevant buggy source code files. Results: The model was evaluated on six open-source projects (Tomcat, AspectJ, Birt, Eclipse, JDT, SWT) and demonstrated significant improvements over the baseline method, DNNLoc. Notably, the proposed approach improved accuracy across all projects, with average gains of 18% in prediction accuracy compared to the baseline. Conclusion: This study highlights the efficacy of combining similarity learning with attention mechanisms for bug localization. By streamlining debugging workflows and enhancing predictive accuracy, the proposed method offers a practical solution for improving software quality and reducing development costs.  
LSTM Attention mechanism; BM25 algorithm; code similarity learning; bug localization; software evolution 2025
## 3 Results  
EROD activity in the solvent control did not significantly differ from control conditions (EROD activity of _A. anguilla in vivo_ exposed to 4 mg/kg BNF (control) (_p_ > 0.05, Student's _t_-test)). The average of the activities of controls in both assays was 7.02 \(\pm\) 0.22 pmol/min/mg of protein, whereas for the solvent control, activity was 7.33 \(\pm\) 0.16 pmol/min/mg of protein. Thus, the latter was used as the control. In the presence of FL, a biphasic EROD activity response was observed, as the addition of 0.1 and 0.3 uM FL significantly increased EROD activity (64% and 26%, respectively), whereas 0.9 and 2.7 uM FL led to significantly lower activities (14% and 35%, respectively) (Figure 1).  
Exposing liver microsomes to cortisol significantly increased EROD activity, corresponding to a 64% increase compared to control levels (Figure 1). The presence of cortisol protected EROD activity from inhibition by FL. In the presence of cortisol, microsomes exposed to the tested concentrations of FL showed significantly higher EROD activities than those of individual exposures to FL. A complete recovery of this enzymatic activity to control (with cortisol) levels was observed for 0.3 and 0.9 uM FL, with the highest increase (86%) being achieved for 0.9 uM FL.  
17\(\beta\)-estradiol (E\({}_{2}\)) significantly increased hepatic EROD activity (Figure 2). 17\(\beta\)-estradiol (E\({}_{2}\)) showed no interaction with co-exposure to lower concentrations of FL (0.1 and 0.3 uM), with enzymatic activities presenting levels very close to those with FL individual exposure. However, E\({}_{2}\) was able to partially revert the _in vitro_ inhibition caused by 0.9 and 2.7 uM FL. The highest protection was observed at 0.9 uM FL, resulting in a 45% increase in activity when compared to FL alone. Despite this increase, it was not sufficient to reach the respective control levels (Figure 2).  
Figure 1: _In vitro_ effects of fluoranthene (FL) on _A. anguilla_ liver EROD activity in the presence and absence of cortisol (5.997 ng/mL). Bars represent mean \(\pm\) SE (\(n\) = 4). Asterisks (*), significant differences between the presence and absence of cortisol (**_p_ < 0.01; ***_p_ < 0.001); a, differences from control without cortisol; b, differences from control with cortisol. One-way ANOVA followed by [PERSON]’s post hoc test.
## 1 Introduction
This research study addresses a significant knowledge deficiency by incorporating human elements such as psychosocial factors and socioeconomic conditions and demographic characteristics into evacuation modeling systems, which engineering-based approaches tend to ignore. We present an innovative MCDM framework that integrates CRITIC with WASPAS and TOPSIS for the first time in this specific field. Our model uses objective criteria weighting to improve emergency preparedness decision accuracy. The interdisciplinary methodology connects engineering with social sciences and psychology to provide a more complete approach. This study investigates the insufficient consideration of vulnerable groups, including elderly people disabled individuals and low-income communities. The model demonstrates its practical use through its application to real data from all regions of Chile. This study identifies specific emergency areas that require immediate support for creating more balanced emergency planning. The approach can be duplicated by nations that experience comparable risk profiles. This study enhances both theoretical knowledge and practical tools used in human-centered disaster management. The contribution of the present study can be summarized as follows:  
* The pioneering exploration of interdisciplinary relationships between social science, psychology, and engineering;
* A mathematical approach based on a novel MCDM method for blending several disciplines related to human factors;
* National statistics reports and a literature review, which identify the emergency management knowledge framework and sociodemographic, socioeconomic, and psychosocial factors influencing the process. The findings are then used to guide the establishment of a new risk analysis framework to prioritize vulnerable regions, where human factors affect the consequences of evacuation plans in emergencies. The remainder of this paper is structured as follows: Section 2 presents a detailed literature review. Section 3 explains the methodology of this study. Section 4 details a case study that applies the proposed methodology. Our results are presented in Section 5 and discussed in Section 6. Finally, our conclusions are presented in Section 7.
# The Nonlinear and Threshold Effect of Built Environment on Ride-Hailing Travel Demand
## 2 Literature Review
### Impact of the Built Environment on Ride-Hailing
The results indicated that high land use mix can create environments conducive to walking and encourage non-motorized travel [30], which differs from the conclusions of [PERSON] et al. [33] and [PERSON] et al. [26]. [PERSON] et al. [20] utilized ride-hailing order data from Shenzhen, China, to develop a Geographically Weighted Quantile Regression (GWQR) model, exploring the temporal, spatial, and trip count variations of built environment factors. They discovered that ride-hailing services diminish the appeal of buses, subways, bicycles, and taxis, a trend particularly noticeable during weekdays. High land use mix and dense commercial areas attract ride-hailing trips, although the correlation between land use mix and ride-hailing trip counts exhibited no consistent pattern of growth. [PERSON] and [PERSON] [22] utilized ride-hailing trip data from Texas and applied the Geographically Weighted Panel Regression (GWPR) model to examine the spatial relationship between the built environment and ride-hailing travel demand. They discovered substantial geographical variations in the influence of built environment factors, which were comprehensive in nature. Land use mix positively impacted ride-hailing travel demand throughout the study area. Previous research has employed diverse models to investigate the interplay between the built environment and ride-hailing travel demand, encompassing linear relationships and spatial effects. However, they have neglected the nonlinear and threshold effects between these factors.
# An Auto-Adjustable Semi-Supervised Self-Training Algorithm
## 2 A Review of Semi-Supervised Classification Via Self-Labeled Approach
### Semi-Supervised Classification  
In the sequel, we present the definitions and the necessary notations for the semi-supervised classification problem. Let \(x_{p}=(x_{p1},x_{p2},\ldots,x_{pD},y)\) be an example, where \(x_{p}\) belongs to a class \(y\) and a \(D\)-dimensional space in which \(x_{pi}\) is the \(i\)-th attribute of the \(p\)-th sample. Suppose \(L\) is a labeled set of \(N_{l}\) instances \(x_{p}\) with \(y\) known and \(U\) is an unlabeled set of \(N_{u}\) instance \(x_{q}\) with \(y\) unknown, where \(N_{l}\ll N_{u}\). Notice that the set \(L\cup U\) consists the training set. Moreover, there is a test set \(T\) composed of \(N_{t}\) unseen instances \(x_{t}\) which has not been used in the training stage. The aim of semi-supervised classification is to obtain an accurate and robust learn hypothesis using the training set \(L\cup U\) and in following evaluate its performance using the test set \(T\).  
In the literature, a variety of self-labeled methods has been proposed, each following a different methodology on exploiting the information hidden in the unlabeled data. Next, we present a brief description of the most popular and frequently used semi-supervised self-labeled methods.
# A Model-Free Online Learning Control for Attitude Tracking of Quadrotors
## 2 Model Description and Problem Statement
### Problem Statement  
The control objective can be stated as follows. Consider the rigid quadrotor attitude system given by (2) and (3), design a control law \(\mathbf{u}\) such that:  
1. the closed-loop attitude error system given by (6) and (7) is globally stable under Assumptions 1-3.
2. the attitude and angular velocity error converge to a small region.
# Concept Justification of Future 3 DPVS and Novel Approach towards its Conceptual Development
## 3 Conceptual Design Approach for 3 DPVS
### Potential Methodology and Tools Supporting 3 DPVS Conceptual Design
#### 3.4.3 Methodology and Tools for Concept Evaluation and Solution Selection  
Here we discuss the potential methodology and tools for 3 DPVS concept evaluation and solutions. The chief task for concept evaluation is to evaluate the generated concepts in a theoretical and philosophical way, such as using the concept-ranking and solution-weighting tool. QFD can help to establish a weight-criteria system for evaluating conceptual solutions produced via the TRIZmethodology, comparing different criteria based on the design attributes or characteristics of 3 DPVS, as well as selecting the optimized solution and providing feedback for the design in the next stage. Additionally, mathematic means like the Paugh Matrix might also be available for conceptual evaluation and selection, weighting concepts via criteria in a quantitative way. If necessary, computer simulation tools like Solidworks and Cosmol Multiphysics will be used, one for simulating geometric and mechanical properties, another for testing the possible vibration properties. In brief, the conceptual design of 3 DPVS can, therefore, be seen as a novel, integrated process of three stages corresponding to three tasks respectively. Since there has been no formal methodology proposed for scaffold design, in our study, several methods and tools have been recommended which could conduct and improve the design efficiency of these tasks. Table 1 briefly summarizes these potential tools.
## 4 Experimental Validation
### Slice Creation  
The iFusion architecture enhancement proposed for this paper performs the management of services and resources through the use of information models that capture the definitions of managed entities in terms of attributes and supported operations. Hence, a set of Yang Data models has been defined to render and realize a network slice between  
Figure 3: Network Design, including the Logical and Physical Infrastructure to support the test cases.  
each of the control entities. The workflow proposed used postman to simulate, the network slice creation requests. The YANG data-model used for this request was defined in [37]. It includes the endpoints, the customer information and the service level agreement of the slice requests. The PE-CE and the end-to-end connectivity protocols for the network slice realization was automatically expanded by the SDTN controller and received by the IP SDN controller to properly configure the network elements. Each network slice, The workflow can be seen in the Figure 4.  
A capture of the messages exchanged between the control items is depicted in Figure 5. The Figure illustrates in Orange the messages exchanged for the Network Slice Creation using the IETF network slices data model. The subsequent statements show how the SDTN controller unwrapped the creation request message to the optical and IP domains. It illustrates in red the IP network masses using gRPC, including the access ports, routing protocols, and PE-CE connectivity parameters. In purple, the T-API messages have the connectivity and optical service requirements.  
The time consumed in the Slices creation was 10.34 s on average. The left part of the Figure 6 depicts a cumulative histogram of the time consumed for the slice creation in the controller layer using ten samples. Additionally, the rigth side Figure 6 shows the percentage of time spent for the VPN-instantiation in the Virtual routers. The most time-consuming task is the BGP Neighbours creation (13 s), followed by the BGP instance redistribution (10 s). In contrast, the device implemented the primary VPN configuration parameters (Rute-Distinghuiser, Route Target and Router ID) in less than a second.  
Figure 4: Workflow used to instantiate the network slices in the network. From Postman, a user sends a slice request to the SDTN controller. The SDTN controller automatically split the request based on technological requirements. The L3 NM and the T-API are used for the IP and Optical domains, respectively.
## 4 Discussion
### Impact of Electrode Localization Error  
The precision of electrode localization can be generally ascribed to two main factors: the single-electrode localization and the overall co-registration of the sensor positions over the head shape [48]. The results of our analyses show that random errors in electrode positioning (i.e., single-electrode localization errors) had generally a larger impact than the systematic errors (i.e., sensor co-registration errors) on the EEG connectivity results (Figure 6). The effect of random and systematic errors was significantly different for average displacements equal to 0.5 cm, with the former one clearly hindering the reconstruction of EEG connectivity patterns. For greater error magnitudes, instead, the effects of the two error types were more comparable, although systematic errors always had a more limited impact on EEG connectivity measures than random errors. Notably, for displacements as low as 0.25 cm there was no significant difference between the random and systematic errors, and relatively high correlations with our benchmark results. This finding is in line with previous literature, suggesting that electrode positioning errors smaller than 0.5 cm are negligible for an adequate brain source reconstruction [71; 72].  
When collecting electrode locations to be included in the individual head model, commonly-used electrode positioning techniques can be affected by both random and systematic errors, but their respective magnitude can vary consistently [41; 42; 45; 46; 48; 65; 73]. In this study, we modeled electrode positioning errors of the 3D scanning, assuming that the related measurements were affected by small systematic errors (0.25 mm) and that random errors were negligible. We also modeled electrode positioning errors of the digitizing technique as prone to both error types. The results of our study clearly show that  
Figure 8: Impact of head modeling on the intra- and inter-network connectivity differences, for each RSN. This was assessed by calculating the correlation with values obtained using the reference analysis workflow. Two electrode localization techniques (_3D Scan_ and _Digitizer_—on the **left**) and three head tissue segmentation methods (_12-layer WTS_, _3-layer WTS_ and _3-layer template_—on the **right**) were tested.  
the different measurement accuracy of these two techniques [44; 45; 48] resulted in evident differences in the EEG-RSN connectivity results (Figure 7 and Table S4).
## 3 Results and Discussion
### The Mechanism of Bayer Red Mud Admixture on the Compressive Strength of AASR
#### 3.2.3 Phase Composition  
1. XRD: The XRD patterns of all samples at 28 days are shown in Figure 7. It can be seen that the products of the control sample are mainly C-A-S-H gels with a disordered tholeite structure [10; 27]. The main minerals of the AASK materials are hydrated calcium aluminosilicate (CaAl\({}_{2}\)Si\({}_{2}\)O\({}_{8}\)-4H\({}_{2}\)O), calcite (CaCO\({}_{3}\)), hematite (Fe\({}_{2}\)O\({}_{3}\)), hard alumina monohydrate (AlO(OH)), and calcium chalcogen (Al\({}_{6}\)CC\({}_{40.4}\)H\({}_{4}\)N\({}_{27.6}\)O\({}_{29.2}\)Si\({}_{6}\)), which are consistent with the main mineral composition of the original red mud. Furthermore, as the content of RM increased, the intensity of diffraction peaks gradually became stronger, indicating that the red mud hardly participated in the alkali activated reaction, which corresponds well to the results from isothermal calorimetry.
2. FTIR: Due to the poor crystallinity of most of the AASK products, which was difficult to detect by the XRD, the FTIR test of each group of paste specimens were then carried out, and the results are shown in Figure 8. The absorption peaks, at around  
Figure 6: Exothermic rate curve of AASK (**a**) and cumulative hydration heat (**b**).  
3460 cm\({}^{-1}\) and 1631 cm\({}^{-1}\), can be assigned to the stretching vibration of [O-H] from water molecules [28]. One important feature in Figure 8 is that the bending vibration absorption peaks and the stretching vibration absorption peaks, at around 982-1007 cm\({}^{-1}\) and 466 cm\({}^{-1}\), which are assigned to the [Si-O] from C-A-S-H [29], became narrower with the increase of red mud content, indicating that the formation of the C-A-S-H gel is reduced. Moreover, bands at 1495 cm\({}^{-1}\) and 872 cm\({}^{-1}\) could be found in all specimens, which can be attributed to the anti-symmetric stretching and out-of-plane bending modes of CO\({}_{3}^{2-}\) ions, indicating the presence of calcite (originated from raw material of red mud). After adding red mud, the intensity of these bands became stronger, and the more red mud that was introduced, the stronger the intensity of these bands. This indicates that the geopolymerization reaction is less as increase of red mud, which is in agreement with the XRD results.  
Figure 8: FTIR spectra of the AASR with different additions of RM.  
Figure 7: The XRD patterns of the alkali slag-red with different additions of RM.
## 3 Results and Discussion  
The goal of the AD plant was constant production of about 450-500 Sm\({}^{3}\)/h of biomethane to be fed into the national distribution network for use in the transport sector provided that the limits imposed by Italian legislation [19; 33] were respected.  
The membrane-based technology was chosen for the biogas upgrading (CO\({}_{2}\) removal) system. Indeed, the advantages of using membranes for upgrading biogas to biomethane compared with other technologies are known [34] and make this technique the most used in Europe. The used membranes are selective for CO\({}_{2}\) permeation, leaving CH\({}_{4}\) in the reentate flow (>98% content) but unable to separate biogas contaminants that remain in the bio-CH\({}_{4}\) stream. Moreover, trace contaminant compounds in biogas can damage membranes and affect the performance of the CH\({}_{4}\)/CO\({}_{2}\) separation process. For this reason, a pre-treatment system was optimized for the purification of biogas before it was sent to the membrane separation unit. The adopted biogas cleaning system consists of several purification technologies placed in series according to Figure 1 (details are discussed in Sections 3.2-3.5). This choice was mainly based on the type of contaminants quantified in the biogas and their national regulatory limits to be respected in the final biomethane fed into the national distribution network. Details of the contaminants determined in the biogas produced by the industrial AD of food waste are discussed in Section 3.1.  
Figure 1: Adopted biogas pre-treatment and upgrading system.
## 3 Simulation Results and Analysis  
In the four-stage induction coil launching device, the armature is initially accelerated from rest by the first-stage drive coil. By changing the triggering position of the armature in the first-stage coil (with the aligned position between the armature and the left side of the drive coil as the zero triggering position), the optimal exit velocity of the first stage is obtained, thus determining the optimal triggering position \(P_{1}\) of the armature in the first stage. The timing of the acceleration of the armature by the subsequent three stages is determined by changing the excitation triggering time of each stage coil \(T_{i}\) (where \(i=2\), \(3\), and \(4\)), and then comparing and analyzing the armature velocity curves obtained by optimizing the triggering timing for both the PP and PN configurations of the launching device.  
Meanwhile, in order to explore the influence of the inter-stage distance \(D\) on the armature velocity of the two configuration launching devices, simulation experiments are carried out for the launching devices with \(D\) = 5 mm and \(D\) = 15 mm.
# Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data
## 3 Results
### Validation of Estimated GLASS GPP
#### 3.2.1 Seasonal Variation in GPP
Generally, estimated GLASS GPP and MODIS GPP could capture the seasonal dynamics of FLUXNET GPP. But estimated and MODIS GPP were a little overestimated during the growing seasons in 2008 and 2012. In conclusion, estimated GLASS GPP in DBF, EBF, ENF and MF could capture the variations of FLUXNET GPP, and their time series are obviously more continuous with smoother trajectories than MOD17 C05 GPP and MOD17 C55 GPP time series. But in CRO and GRA, estimated GLASS GPP, MOD17 C05 GPP and MOD17 C55 GPP underestimated the FLUXNET GPP during the growing season.