esa-sceva/satcom-chunk-collection huggingface dataset:
_Article_  
**Impact of Historical Software Metric Changes in**  
**Predicting Future Maintainability Trends in**  
**Open-Source Software Development**  
**[PERSON] \({}^{\star}\)\({}^{\square}\), [PERSON]\({}^{\square}\) and [PERSON]\({}^{\square}\)**  
Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroska cesta 46,  
2000 Maribor, Slovenia; [EMAIL_ADDRESS] (T.B.); [EMAIL_ADDRESS] (S.K.)  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +386-2-220-7269  
Received: 29 May 2020; Accepted: 1 July 2020; Published: 3 July 2020  
**Featurered Application: The paper shows that the additional layers of historical changes of software metrics from previous software releases contributes to a better prediction of future software maintainability.**  
**Abstract:** Software maintenance is one of the key stages in the software lifecycle and it includes a variety of activities that consume the significant portion of the costs of a software project. Previous research suggest that future software maintainability can be predicted, based on various source code aspects, but most of the research focuses on the prediction based on the present state of the code and ignores its history. While taking the history into account in software maintainability prediction seems intuitive, the research empirically testing this has not been done, and is the main goal of this paper. This paper empirically evaluates the contribution of historical measurements of the Chidamber & Kemerer (C&K) software metrics to software maintainability prediction models. The main contribution of the paper is the building of the prediction models with classification and regression trees and random forest learners in iterations by adding historical measurement data extracted from previous releases gradually. The maintainability prediction models were built based on software metric measurements obtained from real-world open-source software projects. The analysis of the results show that an additional amount of historical metric measurements contributes to the maintainability prediction. Additionally, the study evaluates the contribution of individual C&K software metrics on the performance of maintainability prediction models.  
**Keywords:** machine learning; software maintainability; maintainability index; maintainability prediction; software metrics; history of software releases
# Recognition of Cross-Language Acoustic Emotional Valence Using Stacked Ensemble Learning
## Materials and Methods  
The methodology of this research work follows a 5-phase cross-language valence emotion recognition process of the fusion of various speech emotion corpora, feature extraction, feature selection, ensemble creation, and recognition of valence. The first phase involves the fusion of five speech emotion corpora of EMO-DB, SAVEE, RAVDESS, EMOVO, and CREMA-D that constitute the materials for this study. These speech emotion corpora were deliberately chosen because they consist of speech files spoken in different languages and accents of North American English (RAVDESS), British English (SAVEE), German (EMO-DB), Italian (EMOVO), and a variety of other English accents (CREMA-D). The phase was subsequently followed by the feature extraction process to extract discriminating acoustic features that would help to improve the performance of the proposed RALOG ensemble algorithm. The extracted features include two main categories of prosodic and spectral features because of their proven effectiveness for the problem of single-language speech emotion recognition. The feature extraction phase was thereafter followed by a feature selection process to reduce the dimensionality that can ultimately impact both the training time and accuracy of the proposed algorithm. This phase was subsequently followed by ensemble creation based on statistical analysis of the correlation of receiver operating curve (ROC) and area under the curve (AUC) scores. The learning algorithms are logistic regression (LR), decision tree (DT), random decision forest (RDF), gradient boosting machine (GBM), multi-layer perceptron (MLP), support vector machine (SVM), K-nearest-neighbor (KNN), and AdaBoost with random forest (ARF).  
The last phase of the study methodology concerns the actual recognition of valence emotion with a comparative evaluation of the proposed RALOG algorithm against four other existing ensemble algorithms. All experiments were implemented on a computer with an i7 2.3 GHz processor and 8 GB of RAM to test the effectiveness of the ensemble algorithms investigated in this study.
# Enabling Horizontal Collaboration in Logistics Through Secure Multi-Party Computation
## 1 Introduction
### Our Contribution  
In this work we aim at overcoming this central limitation by developing a distributed and provably secure variant of the heuristic by [PERSON] et al. [3]. This is achieved by deploying secure multi-party computation (commonly shortened to MPC) for executing the assignment of orders to trucks, following a heuristic designed to support \"MPC-friendly\" implementations.  
More specifically, similar to previous work, we consider a weighted graph in which each node constitutes a potential origin or destination of an order, and the weights correspond to distances (or journey times) between these locations. Each order specifies its origin and destination, size (e.g., required deck space or weight), as well as the owning haulier. Furthermore, each haulier owns a set of trucks, each having a specified capacity and starting position. Logistic companies can now contribute their orders to the CTN platform, and will receive a set of orders they are responsible for carrying out, together with (suggested) assignments for trucks. The developed heuristic only slightly deviates from that of [PERSON] et al. [3] to allow for better compatibility with MPC.  
We implemented our heuristic in MPyc ([[https://mpyc.readthedocs.io/](https://mpyc.readthedocs.io/)]([https://mpyc.readthedocs.io/](https://mpyc.readthedocs.io/)) (accessed on 30 June 2025)), and provide benchmarks for different instance sizes from a publicly available reference dataset [5], proving the efficiency and effectiveness of our approach. Specifically, we show that the objective value for the CFDP achieved by our implementation is almost identical to that of previous work (0.1162 vs. 0.1126, cf. Section 4.4.3). Furthermore, for arguably sufficiently large instance sizes (cf. also Section 3.3), our implementation achieves runtimes in the orders of a few hours on standard hardware, allowing, e.g., for daily overnight executions.  
Finally, we want to stress that, similar to [3], we followed a simplified modeling, not covering all real-world constraints in collaborative logistics, ignoring, e.g., physical constraints (such as temperature) or timing requirements (such as just-in-time-delivery). This design choice was necessary to enable direct comparison of our results with previous work. However, in Section 5, we explain how our modeling can be extended to also cover more complex external constraints, for which impacts on runtime are to be expected.  
Overall, the presented work represents a valuable first step toward the realization of collaborative transport networks based on secure multi-party computation, enabling joint optimization while safeguarding the participants' critical business information. Despite current limitations in terms of efficiency and the scope of modeling, we believe that this approach can contribute meaningfully to the development of future product-grade solutions in this domain.
# Evaluating the Feasibility of Euler Angles for Bed-Based Patient Movement Monitoring
## 2 Methods
### Procrustes Analysis  
To differentiate the aforementioned measurements, it is essential to perform a comparative analysis of these measured curves. Hence, they first need to be aligned in an optimal  
Figure 1: Illustration of pitch, roll, and yaw relative to a patient in both supine and sitting positions.  
manner prior to any comparison process. This can be achieved through Procrustes superimposition, which entails finding the optimal translation, rotation, and uniform scaling for each object (Figure 2). In essence, this involves adjusting both the spatial placement and dimensions such that similarities in size and arrangement can be attained while minimizing shape differences quantified by calculating the Procrustes distance separating said curves.  
Procrustes shape analysis is a statistical technique used to compare and analyze shapes. The goal of Procrustes shape analysis is to remove the effects of translation, rotation, and scaling from shapes, allowing for meaningful comparisons. The analysis begins with a set of shapes represented by landmark points. The first step is to align the shapes by removing the effects of translation, rotation, and scaling. This is achieved by minimizing the sum of squared differences between corresponding landmarks across all shapes. The result is a set of aligned shapes that are scaled, rotated, and translated to best fit each other. Next, the aligned shapes are subjected to Procrustes superimposition, where further adjustments are made to remove the effects of size and orientation. This is performed by finding a consensus shape, known as the Procrustes mean, which represents the average shape of the aligned set. Each aligned shape is then individually transformed to match the Procrustes mean by scaling, rotating, and translating. After alignment and superimposition, statistical analyses can be performed on the shape data.  
Procrustes shape analysis is a useful tool for the quantification and comparison of shape dissimilarities between individuals or groups, and is commonly used in biomedical studies [8]. This technique has been employed in diverse fields, such as biology, anthropology, paleontology, and computer vision. There are various potential uses for this tool, including its application in the assessment of classification systems that categorize designs according to shape data sets [9]. Hence, in this study, it is employed to differentiate patients' movement patterns while in bed based on Euler angle time series curves.
## 3 Results and Discussions
### Food Safety  
Based on a literature review and field investigations, it is evident that in the past, _V. kulingiana_ leaves from Guling were commonly used as both wild vegetables and animal feed. Nutritional analysis results demonstrated that _V. kulingiana_ leaves are rich in nutrients, displaying excellent protein content, diverse trace elements, and their respective quantities. The traditional practice of using them as wild vegetables and animal feed holds scientific merit. Furthermore, the high content of vitamin E and flavonoids in _V. kulingiana_ leaves suggests their significant potential for medicinal use. According to Chinese plant records,the entire _V. kulingiana_ plant can be used for medicinal purposes, and further exploration is warranted to understand its underlying principles and therapeutic effects. The analysis of volatile components has revealed that _V. kulingiana_ leaves contain a rich variety of volatile organic compounds, with as many as 165 different types, each contributing to distinct aromas (as shown in Table 3). Additionally, the relative content of each component is relatively low, with the highest content, of \(\beta\)-Ionone, only accounting for 8.24% of the total. This complexity in their aroma composition suggests that it is insufficient to draw definitive conclusions solely based on the relative content or proportion of volatile substances. Further experimental research is needed to confirm the contribution of volatile compounds to the aroma of _V. kulingiana_ leaves. Furthermore, the safety of _V. kulingiana_ leaves has not yet been verified. While no toxic or harmful substances were found among the volatile organic compounds, assessing the safety risks of consuming this wild plant solely based on the results of volatile components testing is insufficient. The following considerations should be taken into account: (1) Volatile components cannot represent all the ingested substances in _V. kulingiana_ leaves, and non-volatile natural toxic components such as alkaloids may be present and require additional testing. (2) Safety assessment should not rely solely on chemical composition; the specific content of each component must also be considered, as even aromatic volatile compounds can exhibit toxicity at low doses. (3) Different individuals
###### Abstract  
The exponential growth of cities has brought important challenges such as waste management, pollution and overpopulation, and the administration of transportation. To mitigate these problems, the idea of the smart city was born, seeking to provide robust solutions integrating sensors and electronics, information technologies, and communication networks. More particularly, to face transportation challenges, intelligent transportation systems are a vital component in this quest, helped by vehicular communication networks, which offer a communication framework for vehicles, road infrastructure, and pedestrians. The extreme conditions of vehicular environments, nonetheless, make communication between nodes that may be moving at very high speeds very difficult to achieve, so non-deterministic approaches are necessary to maximize the chances of packet delivery. In this paper, we address this problem using artificial intelligence from a hybrid perspective, focusing on both the best next message to replicate and the best next hop in its path. Furthermore, we propose a deep learning-based router (DLR+), a router with a prioritized type of message scheduler and a routing algorithm based on deep learning. Simulations done to assess the router performance show important gains in terms of network overhead and hop count, while maintaining an acceptable packet delivery ratio and delivery delays, with respect to other popular routing protocols in vehicular networks.  
v 2019; 9, 5254; doi:10.3390/app9235254
# Analysis of the Impact of Vibrations on the Driver of a Motor Vehicle
## 3 Results
Excitation of the vibrations in this frequency range is probably related to kinematic excitation from road profile unevenness, but importantly it is not dominant in the considered case of impact on the driver's hands. Precise determination of the natural frequency of unsprung mass vibrations would require excitation on a measuring stand (e.g., CUSAMA stand), which was not possible in the type of case of the conducted research. The Y-direction vibrations have lower amplitudes than the X-direction, but the dominant frequencies are 11.6 Hz (0.33 m/s\({}^{2}\)), 34.8 Hz (1.42 m/s\({}^{2}\)), and 46.4 Hz (0.7 m/s\({}^{2}\)). This means that some vibrations may be caused by road irregularities or steering resonances. Furthermore, the decrease in amplitudes with increasing frequency suggests that the lower frequency components (especially 11.6 Hz) may be related to the basic structural properties of the vehicle. The Z-direction vibrations are distinguished by different frequency peaks, with dominant amplitudes seen at 1.8 Hz (0.4 m/s\({}^{2}\)), 34.8 Hz (2.8 m/s\({}^{2}\)), and 70.2 Hz (0.68 m/s\({}^{2}\)). This can be attributed to the vertical movement of the seat, which affects the driver the most. The maximum amplitude at 34.8 Hz coincides with the peak in the X direction, indicating the overall effect of mechanical vibrations. From all three directions, it can be observed that the frequencies 34.8 Hz and 70.2 Hz are dominant. This information is important because 35 Hz is close to the resonant frequency of human hands (8-16 Hz), which can cause discomfort, while 70 Hz can be related to the design features of the vehicle. This means that under adverse conditions, these frequencies can negatively affect driving comfort and safety.
## 2 Materials and Methods  
### Study Area  
Gujrat is the capital of the Gujrat district, in the Punjab region of Pakistan (Figure 1). The selection of the area makes the study idea for the proposed objectives as the population of the study area is increasing and green area is finishing, which creates a problem for the local residents in the form of loss of urban biodiversity.  
Gujrat is an old city. \"Ude-Nagri,\" its ancient name, literally means \"the city of pleasant scents and greenery.\" Before 1993, the nearest district, Mandi Bhawal Din, was part of Gujrat. However, after 1993, this district became separate [27]. Gujrat city then developed as an industrial city of Pakistan since the era of \"Akbar\" (the Mughal Empire in British India, from the 16 th to the 18 th century), resulting in it being one of the fastest-growing capitals of the district, to the point that today it is the 18 th largest city of Pakistan [28]. The neighboring regions around this region are Sialkot, Gujranwala, Jhelum [29], and Bhimber Azad Jammu Kashmir (AJK). The total population of this region, including the peri-urban areas, is 390,533 persons, according to the population census of Pakistan 2017 [30].  
The current area of the city is 65 Km\({}^{2}\)[30]. As an industrial city, its extension is expected to grow in an unsustainable pattern unless wise rules and governance suggestions are applied. The population of the surrounding region, indeed, constantly migrates to this urban area and settles there gradually, resulting in continuous urban sprawl.  
Green areas in Gujrat city (Figure 1) were mapped by retrieving OSM data from the OpenStreetMap Repository through QGIS software. The data validation was done by digitizing the green area with Google Earth to verify and justify the geographical feature present on the land surface. Moreover, Landsat satellite images were used to validate the resulting map of green spaces in figure no. 4 in Gujrat district with reference to the year 2020. To do this, Landsat-8 Enhanced Thematic Mapper scenes, with 30 m resolution per pixel, were used to provide the spatially explicit base for the evaluation of green areas and of the related potential to provide ES.  
Figure 1: Map of the study area. In the upper left corner, the location of Gujrat in Pakistan is shown.  
In the study area, the green spaces can be divided into two major categories, including formal and informal green areas. In addition to the formal green spaces, including parks, green belts along the roads, and green lawns in public intuitions, about four parks (Nawaz Sharif Park, Children Park, Ayub Park, and Inayat Park) are available for the population of Gujrat, while the informal green spaces, including the trees and agricultural fields, are in the study area.
# Quantitative Assessment of the Environmental Impacts of Dredging and Dumping Activities at Sea
## 3 Results
### Calibration and Validation Spectral Wind-Wave (SW) Model
#### 3.1.2 Calibration and Validation of Spectral Wind-Wave in 2D Model  
In the seaside of Nhon Hai ward, the sea is relatively deep, the slope of the shoreline is steep, and the 20 m isobath is 15 km from the shore. In the study area, the wave regime is suitable for the wind regime, and the wave regime is divided into two seasons. In the winter, the direction of the wave is northeast (NE) with an average height between 0.8 and 0.9 m, and it may range from 1.1 to 1.2 m during the first three months. The biggest wave varies from 4.0-4.5 m. In the summer, the direction of the wave is south west (SW) and southeast (SE). The average height ranges from 0.6 to 0.7 m. The largest wave can be 4.0 m.  
The calibration and validation of spectral wind-waves in a 2D model was undertaken using observed wave height data at Ly Son station during 1-30 September 2016 and 1-31 October 2016, respectively. The calculation of the wave characteristics showed that the simulated and observed wave heights were in good agreement in terms of the vibration amplitude, absolute value, and tidal phases during both the calibration and validation processes (Figure 4). The Nash-Sutcliffe efficient (NSE) [49] values ranged from 0.91 to 0.92 in the calibration and validation processes. Root mean square error (RMSE)-observations standard deviation ratio (RSR) values ranged from 0.27-0.3 in both calibration and validation. Percent bias (PBIAS) values for calibration and validation ranged from \(-\)5.6% to \(-\)3.8% (Percent Bias (PBIAS) \(<\pm\) 10%) [50]. During the southwest monsoon, the 2D SW model simulated the general trend of the wave peaks well. The parameters obtained in calibration were maintained in the validation process with the bottom friction, based on Nikuradse roughness, ranged from 0.038-0.04 m. Dissipation coefficients white-capping (Cdis and Delta dis) were 4.5 and 0.5, respectively. The wave breaking coefficient ranged from 0.68-0.73. The validation results showed that the 2D SW model had successfully simulated the wave propagation in the study area (Figure 5). The parameters were reliable enough to conduct the wave field simulations under different scenarios in the study area.  
Figure 3: (a) 2D visualization bathymetry of wave mesh; (b) Detailed mesh of study area.
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 5 Experimental Results and Discussion
### Results and Performance Analysis  
The experimental results presented in Tables 3 and 4 demonstrate coding performance and encoding time reduction of the proposed and state-of-the-art methods against HEVC HM16.1 implementation when using PSNR and VQM as the quality metrics, respectively. The following section discusses the overall performance and implications of using the proposed fast coding framework.