esa-sceva/satcom-chunk-collection huggingface dataset:
# Model-Based Test Suite Generation Using Mutation Analysis for Fault Localization
## 2 Mutation-Based Fault Localization
This approach is statistically significantly more effective than the statement-based approach, despite using only 10% of the mutants for fault localization. Almost all of the existing fault-localization approaches require that the test-case execution priority be set to ensure the efficiency of fault localization [17], or that the fault locations be estimated based on test results [23]. One common characteristic of these techniques is that a sufficient number of test cases is required. It can be difficult to apply them to systems in which it takes a long time to run tests. [PERSON] et al. [24] used a search-based testing technique to improve the test cases and optimize fault localization in the Simulink model. The test-generation algorithm employed featured three fitness functions: coverage dissimilarity, coverage density, and the number of dynamic basic blocks. Coverage dissimilarity focuses on variegation of test execution slices and uses the Jaccard distance [25] to calculate dissimilarity. Coverage density is the mean ratio of all elements covered by the test cases. [PERSON] et al. [26] reported that more accurate statistical rankings were obtained as the coverage density approached 0.5. A dynamic basic block (DBB) [27] is a set of statements that are covered by the same test case. Statistical debugging becomes more effective as the number of DBBs increases. The cited authors established three indicators that increase the variety of tests available for fault  
Figure 1: An example of search-based mutation testing. localization. However, the technique is applicable to Simulink models rather than to state charts; the guard condition is not considered. [PERSON] et al. [17] presented a method using [PERSON]'s information theory [28] to calculate the expected value passed by each test case and the expected value when a test case fails; this was used to prioritize a test. The work developed tests with high summed suspiciousness expectations for both the next test to be performed and passing or failing. This is a good example of the use of information theory to improve fault-localization prioritization. However, this requires a large number of test suites, such as those of Metallaxis-FL. In this study, we propose a test-suite generation technique for fault localization at the test-generation stage, which is intended to improve the effectiveness of fault localization. Our method is applied during the test-suite generation phase, and suggests which test suite should be executed next for fault localization. The proposed method aims to estimate fault locations by executing a small number of tests compared to existing fault-localization methods. Our study is a good example of how a test suite for fault localization can be improved using state charts in the presence of a guard condition.
# IMPA versus Cloud Analysis and IDA: Different Methods to Evaluate Structural Seismic Fragility
## 1 Introduction
In contrast to IDA and MSA (multiple-stripe analysis), the cloud method [26; 30; 31] involves nonlinear analysis of the structure subjected to a sample of different distance/intensity combined unscaled as-recorded ground motion, which may reduce the number of analyses, uncertainties in seismic input with intensity, computational effort in defining a seismic fragility curve [32], and is based on a regression in the logarithmic space of structural response versus seismic intensity. To respond to the need for simplified, faster and/or approximate methods, from the late 1990's, many studies have been published regarding the use of pushover analysis procedures to assess seismic vulnerability, mostly on bridges. In this case, the capacity of the system is evaluated by using nonlinear static procedures (NSPs) (i.e., the capacity spectrum method [33; 34; 35; 36], the N2 method [37; 38; 39], and modal pushover analysis [20]), while the demand is estimated by response spectra. To assess the reliability of these analytical procedures, different authors have compared developed fragility curves to those obtained by nonlinear time history analysis [34; 36; 38]. Among NSPs, a novel procedure called incremental modal pushover analysis (IMPA) has been proposed in recent years by [PERSON] and his co-workers [40]. IMPA requires the execution of modal pushover analysis (MPA) and the evaluation of structural performance within a range of different seismic intensity levels to develop a multimodal capacity curve in terms of base shear versus top displacement. This approach is suitable for performing a displacement-based design procedure and structural analysis of existing structures, yet authors have not suggested the analytical estimation of fragility. This paper aims to evaluate the reliability of structural fragility derived by the methods mentioned, advancing an \(IM\)-based derivation of structural fragility, strikingly similar to IDA, based on IMPA. It is known that IDA has a small sensitivity to record-to-record variability compared to other methodologies. Nevertheless, the results indicate that, amidst its slightly higher sensitivity, IMPA has the advantage of requiring considerably smaller computational effort to perform the structural analysis. The authors argue that scaling response spectra at a higher range of intensities might introduce less uncertainties than a simple amplitude scaling of ground motions. Furthersteps of this research will address how the uncertainties in the seismic input affect the reliability of IMPA versus IDA seismic fragility for strong ground motions. In the following paragraph, analyses are carried out on a real RC frame belonging to a school building located in Norcia (Italy). Each nonlinear dynamic and static procedure is briefly presented, including a step-by-step computational procedure of IMPA. Finally, the reliability of pushover-based estimation of seismic vulnerability is assessed by comparing these curves to those obtained by IDA.
# Lithium Metal: The Key to Green Transportation
## 4 Discussion
### LIB Swapping Stations  
With the growing number of e-mobility vehicles on the road, the charging process is becoming a challenge for drivers. It takes 30 to 40 min to recharge batteries [113]. Charging time is considered a weak point of EVs compared to fuel vehicles, which discourages or delays some people from shifting to EVs. This issue was addressed by building large-scale charging facilities, including fast-charging stations, with the goal of publicizing the development of EVs. People who are still hesitating to use EVs and some of those who are already using them have developed range anxiety (fear of getting an insufficient charge before completing the designated duty) and fear of the cost of purchasing additional batteries (as backups) that is expensive.  
Apart from conventional fast-charging stations [114], the new charging infrastructure of battery swapping stations has been integrated into the charging system [115] and operates as an alternative to charging stations for reducing charging time and EV anxiety. It is a method of removing the empty batteries and replacing them with fully charged ones. This technology reduces the times wasted while charging from 40 to 5 min maximum.  
It is a widespread technology in China, and Chinese firm \"Nio\" takes the lead of this technology in China, where it owns more than 130 battery swapping stations in 58 cities in 2020 [116]. It is also expanding power swap stations in Europe (i.e., Norway) [113]. Other companies like Tesla, Honda, KYMCO, Immotor, Gogoro, etc., are also doing battery swapping systems [117; 118].  
The battery swapping system has broad advantages for EV drivers, including the ability to resume journeys in a minute, extending the lifetime of batteries due to slow-charging mode, always being equipped with fully charged batteries, releasing the burden of cost to replace old batteries for EV owners, getting batteries with the latest technology without additional cost, etc. The system targeted the public transportation in the beginning [114], however, it is becoming popular in all EVs. Particularly, it can be performed by a robotic system that takes less than one minute to complete the swapping operation [119]. Battery swapping stations charge the batteries in a centralized manner and ensure the high efficiency of swapping operations. Both the battery swapping system and the range-improved LIB of up to 300 miles [120] reduce EV range anxiety. It gives a choice to drivers with depleted batteries; they can either choose a charging station (if empty space is available) or go to a battery swap station. Particularly, LIB that can travel long distance up to 300 miles could help reduce EV charge anxiety (fear of not finding or accessing a charging station point), which is also growing among EV owners. It is also ranked as one of the best methods to collect old batteries for recycling.
# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 3 Materials and Methods
### Spatial-Semantic Integrated Model for Tourist Attraction Representation
#### 3.1.1 Spatial and Semantic Feature Representation of Tourist Attraction Entity
\(\mathit{v}n_{i}^{(x)}\) refers to the count of viewpoint-bearing phrases associated with theme \(T^{(x)}\) in the reviews of \(\mathit{Attr}_{i}\). This process systematically constructs a TDV for each tourist site, facilitating quantitative comparison of intrinsic features and experiential feedback across attractions. The method effectively eliminates bias arising from variations in review volume and establishes a standardized analytical framework for cross-attraction comparative studies. 3. Tourist attraction sentiment feature representation Emotional feedback plays a pivotal role in tourist decision-making. Nevertheless, conventional sentence-level sentiment analysis overlooks the complexity of sentiment targets and contextual variations. To address this, a thematic-aware sentiment modeling framework is proposed that maps viewpoint phrases from tourist reviews into multidimensional sentiment vectors, aligned with the attraction's thematic profile. Formally, the sentiment feature _Emotion_ is defined as follows:  
\[ \begin{split}& Emotion=\{\textit{Topic},\textit{weight}\},\\ &\textit{weight}=\left[w^{(1)},w^{(2)},\cdots,w^{(x)},\cdots,w^{ (n_{t})}\right],\end{split}  \tag{5}\]  
where _weight_ is the Sentiment Weight Vector (SWV), each element \(w^{(x)}\) is the sentiment weight of the corresponding theme category \(T^{(x)}\). To amplify the discriminative power of positive sentiment in evaluating service performance, we adopt an asymmetric weighting scheme: positive = 2, neutral = 1, and negative = 0. For a given theme, the sentiment weight is derived as follows via weighted averaging over all relevant opinion phrases:  
\[w^{(x)}_{i}=\overline{V\textit{Senti}_{k=1}^{w^{(x)}}}, \tag{6}\]  
where \(i\) is the attraction identifier. \(V\textit{Senti}_{k}\) indicates the sentiment weight of each opinion phrase. Consequently, \(w^{(x)}_{i}e[0,2]\), with higher values reflecting stronger positive emotional resonance in that thematic domain. The method constructs a structured sentiment profile across themes, enabling precise quantification of affective user experiences. It mitigates context bias caused by neglecting thematic relevance in traditional sentiment analysis. The asymmetric weighting mechanism further emphasizes the strategic importance of positive sentiment, thereby improving the fidelity of emotional signals in capturing experiential quality. Additionally, the normalized sentiment scale enables cross-attraction benchmarking, empowering destination managers to pinpoint high-performing themes and prioritize improvements in underperforming areas.
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 4 Experimental Evaluation and Results Analysis
### End-to-End Assessment
#### 4.3.2 Analysis per Security Approach  
Figure 21 summarises the E2E results, focusing on the analysis of the protocol impact.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network Segment** & \multicolumn{2}{c}{**Communication Flow**} & **No Security [ms]** & **DLAPP [ms]** & **MFSPV [ms]** \\ \hline G5 & OBU & \(\rightarrow\) & RSU & **11.63** & 13.24 & 13.55 \\  & RSU & \(\rightarrow\) & OBU & 12.24 & 13.61 & 13.97 \\ \hline \multirow{3}{*}{Cellular} & RSU & \(\rightarrow\) & Smartphone X & **24.59** & 27.71 & 27.19 \\  & Smartphone X & \(\rightarrow\) & RSU & 31.72 & 34.99 & 34.98 \\  & Smartphone X & \(\rightarrow\) & Smartphone Y & 32.76 & 34.77 & 35.16 \\ \hline \multirow{3}{*}{Hybrid} & Smartphone X & \(\rightarrow\) & OBU & 42.18 & 46.46 & 46.75 \\  & OBU & \(\rightarrow\) & Smartphone X & **34.94** & 38.81 & 38.53 \\ \hline \hline \end{tabular}
\end{table}
Table 11: E2E times for the various prototype flows with different security approaches. Communication flows are divided according to the network segment they use.  
Figure 21: Average E2E times of communication per security approach. The study is also divided according to the network segment.  
As observed in the experiments conducted so far, the security protocols have a low impact on performance compared, for instance, to the extension to hybrid networks. In the E2E results, the same happens. For example, the additional E2E delay imposed by the protocols in the hybrid segment workflows is approximately 11% in both protocols. MFSPV proves to be more efficient in local processing, and DLAPP attains slightly shorter latency values. Nonetheless, looking at the big picture (Figure 21), both impose a similar additional E2E time.
## 5 Simulation Evaluation and Numerical Results
### Numerical Results
On the other hand, energy consumption at nodes located at a far distance is larger using the S3 model, leading to a more balanced energy distribution compared with the conventional S2 model. This is because the S3 model utilises dynamic sink proxies to balance energy consumption, and the sink node's operations are distributed to sensor nodes. Figure 7: Energy consumption at each sensor node in data collection. Figure 9 (**A**) and (**B**) show the energy consumption of data queries at each sensor node. S2 queries have high energy consumption at most sensor nodes. The proposed S3 based query has much lower energy consumption for most sensor nodes. The largest energy consumption of the S3 query among sensor nodes is about 0.13 J, which is much smaller than that of S2 (0.2 J). Figure 8: Energy consumption with regard to hop-distance. Figure 9: Energy consumption distribution among nodes. Figure 9: _Cont._  
Figures 9 (**C**) and (**D**) show the distribution of energy consumption among sensor nodes in the data collection (reply) operation. S2-based data collection has high and non-uniform energy consumption at sensor nodes. Nodes near the sink node have the highest energy consumption: up to 0.42 J. In contrast, S3-based data collection has much lower energy consumption at sensor nodes. The highest energy consumption for S3 is 0.17 J, which is about 40% of that in S2-based data collection. Further, S3 data collection has more uniform energy distribution among sensor nodes.
# Laser Beam Welding of CubeSat 1U Structure Parts Obtained by Powder Bed Fusion
## 4 Conclusions  
Titanium CubeSat structures were successfully built using pure titanium tubes and Ti6 Al4V connectors obtained by additive manufacturing. Selective laser melting (SLM) and electron beam melting (EBM) techniques were used to manufacture the connectors. For the union between the connectors and tubes, in order to form the structure of the CubeSat, laser beam welding (LBW) was performed.  
The produced parts showed a similarity to the planned electronic model, with a maximum deviation of 0.2 mm and a surface roughness Ra of 32 \(\pm\) 5 \(\upmu\)m and 42 \(\pm\) 6 \(\upmu\)m for SLM and EBM, respectively.  
An energy of 400 J with a pulse duration of 400 ms from a fiber laser was sufficient for the union between the connectors and the tubes in the present configuration.  
The microstructure of the fused zone showed the characteristics of a martensitic microconstituent with a hardness of approximately 320 HV, which was slightly below the hardness of the base material of the connector. At the interface between the weld zone and the tube, there was a sharp decrease in hardness, from 320 to 130 HV, which caused a rupture in this region in the tensile tests.  
In terms of load, each laser spot weld averaged 2000 N, which was far above the hydrostatic load in a given rocket launch (60 N). Thus, the CubeSat frame is ready for further robustness and reliability tests.  
Conceptualization, R.H.M.d.S. and D.J.C.; methodology, R.H.M.d.S.; validation, R.H.M.d.S. and D.J.C.; formal analysis, R.H.M.d.S.; investigation, M.S.F.d.L.; resources, M.S.F.d.L.; data curation, R.H.M.d.S. and D.J.C.; writing--original draft preparation, M.S.F.d.L.; writing--review and editing, M.S.F.d.L. and D.J.C.; supervision, M.S.F.d.L. and D.J.C.; project administration, M.S.F.d.L. funding acquisition, M.S.F.d.L. and D.J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Sao Paulo Research Foundation (FAPESP), grant number 2019/25229-7. This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001.  
Not applicable.  
Thanks are due to BioFabris National Institute for fabricating the AM connectors.  
The authors declare no conflict of interest.
###### Abstract  
The vibration response of equipment foundation structures is not only affected by the structural stiffness and mass, but also greatly influenced by the degree of a soil-foundation structural interaction. Furthermore, the vibratory performance of equipment foundation structures supported by pile systems largely depends on the soil-pile dynamic stiffness and damping, which are variable in nature within the speed range that machines operate at. This paper reviews a method for evaluating effective soil-pile stiffness and damping that can be computed by [PERSON]'s method or by commercial software (DYNA6, University of Western Ontario). A series of Finite Element (FE) time history and steady-state analyses using SAP2000 have been performed to examine the effects of dynamic soil-pile-foundation interaction on the vibration performance of equipment foundations, such as large compressor foundations and steam/gas turbine foundations. Frequency-dependent stiffness is estimated to be higher than frequency-independent stiffness, in general, and, thus, affects the vibration calculation. This paper provides a full-spectrum steady-state vibration solution, which increases the reliability of the foundation's structural design.  
FEM (Finite Element Method); DYNA6; soil-structure interaction; soil-pile dynamic stiffness +
Footnote †: journal: Applied Sciences
###### Abstract
When evaluating treatment plans, nonoperative options, such as bracing, are used to manage isolated grade 1 and 2 MCL injuries, while grade 3 lesions may require surgical intervention with a repair or reconstruction, depending on the presence of joint instability [6, 7]. However, there are no unequivocal treatment recommendations, probably due to the conflicting classification schemes, especially in chronic injuries [8]. Since the spontaneous healing of ligaments is often incomplete, optimal treatment should induce the healing of injured tissues and the resolution of symptoms. Among non-invasive therapeutic modalities, extracorporeal shockwave therapy (ESWT) has been progressively expanding its field of application to many musculoskeletal pathologies. Particularly, focused-ESWT (f-ESWT) generates a pressure field that is focused to an accurate area, where maximal pressure is achieved, at a determined depth of the tissues. In daily clinical practice, the pathologies commonly treated with ESWT include traninopathies and enthesopathies (calcified and not) and bone, muscle, and skin diseases [9, 10, 11]. The rationale for the use of ESWT is based on the induction of a controlled inflammation in a tissue in order to sustain its regeneration [9]. This has been well demonstrated by several studies of basic and clinical research. For these reasons, besides the known efficacy of ESWT in different conditions, the application of ESWT in ligament injures may be considered. The scientific literature scarcely explored this application and the majority of the studies considered animal models [12, 13]. To the best of our knowledge, there is no study that has described the usefulness of US-guided focused ESWT (f-ESWT) in the treatment and evaluation of ligament injury in human subjects. Herein, we report a case of MCL injury treated with f-ESWT. Moreover, our case shows the importance of using ultrasonography to determine the effect of treatment.
# Web Mapping for Farm Management Information Systems: A Review and Australian Orchard Case Study
## 4 Case Study Results
### Current Imagery Misfit Vectors  
The positional accuracy of available web imagery is expected to vary within the coverage of a farm, between farms, between historical images, and between images from different web imagery providers. This uncertainty will be due to (a) poor resolution of the web imagery, leading to inaccuracy of location of a given feature in the Web Mercator projection, (b) image registration error inherent in the source of data, (c) the use of the spheroid model in the Web Mercator projection, and (d) tectonic shift. Tectonic shift error is introduced as web imagery is typically recorded in the WGS84@ date of image capture.  
The mean misfit vectors were calculated in the WGS84 Universal Transverse Mercator (UTM) coordinate system. The vectors varied between farms and between years of GEI, e.g., varying from 1.976 m to 5.919 m in magnitude and from 42.6576\({}^{\circ}\) to 346.3344\({}^{\circ}\) on the CQ-1 farm (Table 3 and Figure 4). The highest variance in misfit vectors of GCPs occurred on the FNQ-2 farm, which was likely associated with the size of this farm (2 km across), and thus had the potential for the presence of several images. The highest magnitude misfit vector was observed on the FNQ-1 farm. An RMSE of 3.432 m, with an RMSE of 1.391 m and an RMSEy of 3.137 m, was calculated for the difference between GEI and GNSS (WGS84 GDA2020 epoch) for the combined data of all sites.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Farm**} & \multirow{2}{*}{**SCM**} & \multicolumn{2}{c}{**GDA94 Coordinates**} & \multicolumn{2}{c}{**GDA20 Coordinates**} & \multicolumn{2}{c}{**Misfit Vectors**} \\ \cline{3-8}  & & **Latitude (\({}^{\circ}\))** & **Longitude (\({}^{\circ}\))** & **Latitude (\({}^{\circ}\))** & **Longitude (\({}^{\circ}\))** & **Magnitude (\({}^{\circ}\))** & **Bearing (\({}^{\circ}\))** \\ \hline CQ-1 & 147698 & \(-\)23.032350 & 150.629297 & \(-\)23.032338 & 150.629304 & 1.581 & 25.1876 \\ CQ-2 & 046749 & \(-\)23.313954 & 150.517036 & \(-\)23.313941 & 150.517043 & 1.580 & 24.9753 \\ FNQ-1 & 073155 & \(-\)17.115310 & 145.283933 & \(-\)17.115297 & 145.283940 & 1.687 & 28.6620 \\ FNQ-2 & 140459 & \(-\)17.32102 & 145.098745 & \(-\)17.132088 & 145.098752 & 1.687 & 28.5861 \\ FNQ-3 & 208477 & \(-\)17.126252 & 145.425309 & \(-\)17.126239 & 145.425317 & 1.684 & 28.7109 \\ \hline \multicolumn{8}{c}{Mean misfit vector} & \multicolumn{2}{c}{**1.643**} & \multicolumn{2}{c}{**27.2794**} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Estimation of the shift vector between GDA2020 and GDA94 coordinates of survey control marks (SCMs) used as ground control points (GCPs) on each of the five farms.  
Figure 3: Observed horizontal positional shift vectors between GDA94 and GDA2020 for GCPs on five farms (blue arrows) and the mean shift vectors (red arrow).