esa-sceva/satcom-chunk-collection huggingface dataset:
# Can Defect Prediction Be Useful for Coarse-Level Tasks of Software Testing?
## 2 Reviews of Related Works
### Software Testing  
ST is an effective process in the develop cycle to reduce the defects in software. it can be divided into different ways. According to testing types, software testing includes unit testing, system testing, graphical user interface (GUI) testing, performance testing, user acceptance testing, and other types [3]. ST will be referred to unit testing (aka, module testing), integration testing, function testing, system testing, acceptance testing, and installation testing [25] from the perspective of development processes.  
According to testing techniques, it can be classified as functional testing, structural testing, and code reading [28]. In addition, WBT and BBT are two common methods of ST [3; 25]. The ST process costs around 45% budget of the software develop cost [29]. Therefore, many practitioners or researchers reported approaches to make resource allocation more reasonably.  
Several researchers analyzed software to assist on ST [29; 30]. For example, [PERSON] proposed a model, called dynamic error flow analysis (DEFA), about the semantic behavior of a program in fine-level tasks and produced a testing strategy from DEFA analysis [30]. [PERSON] et al. proposed a test analysis method for BBT by using a test category and organizing the classification based on fault and application under test knowledge [29].  
Several authors proposed TCP methods using white- or black-box information [26; 27]. For example, [PERSON] et al. improved the coverage-based TCP method by considering the fault-proneness distribution over code units for fine-level tasks [26]. [PERSON] et al. extended the original additional greedy coverage algorithm for TCP called Additional Greedy method Call sequence) by using dynamic relation-based coverage as measurement [31]. [PERSON] et al. used considered three prioritization objectives for TCP in Highly-Configurable Systems [32]. [PERSON] et al. leveraged black-box related information in order to propose a TCP approach with adaptive random sequence and clustering [27].  
Several researchers reported test case selection (TCS) or test case minimization (TCM) approaches, such as literature [33; 34; 35]. [PERSON] took low memory consumption into consideration that is suited for medium to large projects and proposed a dynamic programming approach for TCS issues [33]. [PERSON] et al. proposed a cost-effective approach, which defined six effectiveness measures and one cost measure relying on black-box data [34]. [PERSON] et al. applied the Uncertainty Theory and multi-objective search to test case generation (TCG) and TCM [35].  
However, if there does not exist execution information or coverage data, their methods cannot work well for resource allocation.
## 9 Coverage and Connectivity
For estimation the visual range of the camera and overlap between other cameras, Snapshot assumes that the size of the internal camera CMOS sensor and the focal length are known offline, and from projection the coordinates of the corners of the CMOS sensor through the camera lens (its location determined from the previous process), the coordinates of a polyhedron or pyramid can be determined using the geometry principles. An object in the volume of the polyhedron is in the visual range of the camera. In order to find the overlapping area between the other cameras, an intersection between any edges of the calculated polyhedron with any other polyhedrons from other cameras is considered as overlapping area. It is shown that this calibration technique gives an error of 1-2.5 degrees in estimating the camera orientation and 5-10 cm error in determining the camera location. In [87], a scheduling method based on cooperation among nodes for object detection in WMSNs is proposed where the network is clustered based on the overlapping coverage areas of the multimedia sensor nodes. To calculate the overlapping area, the proposed algorithm computes the vertex points of the FoV of each multimedia node by assuming the FoV of a node as an isosceles triangle. A multimedia node's location represents one of the three vertexes of the FoV and the other two vertexes will be found by the algorithm by knowing the coverage direction angel, orientation angel, and the sensing range of each node. Then the overlapping area is calculated using a decomposition method for intersection polygons. A cluster consists of a subset of multimedia nodes with high overlapping FoV areas where the size of the overlapping area between FoVs of two nodes determines whether they can be in the same cluster. In each cluster, nodes are scheduled to sequentially wake up, capture an image and monitor presence of object, applying object detection procedures such as background subtraction or motion detection and finally go to sleep.
## 3 Results and Discussion
### SPME Time and Temperature Optimization
After 60 min of extraction, the signal for (E)-\(\beta\)-damascenone was acceptable. To obtain the best compromise between a satisfactory signal for all four studied compounds and a cost-effective extraction method, an extraction time of 60 min was chosen. Overall, the optimum conditions for the extraction of volatile compounds from a distilled spirit using HS-SPME were found to be as follows. First, the sample was diluted to reach a concentration of ethanol of 10% (\(v/v\)) and 10% NaCl (\(w/v\)) added. Extraction was then carried out using a DVB/CAR/PDMS fiber, at 35 \({}^{\circ}\)C, with an extraction time of 60 min. Figure 1: Impact of temperature on the peak area (TIC) with standard deviations of four selected compounds of the model mixture. (\(n=3\)).
# Closed-Form Performance Analysis of the Inverse Power Lomax Fading Channel Model
## 4 Simulation and Results' Analysis
### Experimental Verification
Figure 10: Outage probability vs. \(\bar{\gamma}\) for various channel parameters: (**a**) the impact \(\beta\) for fixed small \(\alpha=0.1\), (**b**) the impact \(\alpha\) for fixed small \(\beta=2.1\), and (**c**) the impact \(\gamma_{th}\) for fixed \(\alpha=0.5\), \(\beta=2.1\).
# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 5 Unstructured Background Models
### Kernel-Based Models
#### 5.3.2 Kernel Density Estimate of the Background Distribution Models  
Kernel density estimation (KDE) is the well-known technique for nonparametric estimation of an unknown probability density function (PDF) based solely on the given dataset [93; 94]. For estimation of the background PDF of hyperspectral images, a multivariate KDE can be used [95; 96; 97; 98; 99; 45]. The multivariate KDE can be represented as [94]:  
\[p_{b}(\underline{x}|\mathcal{B})=\frac{1}{N}\sum_{n=1}^{N}\frac{1}{\| \underline{\underline{H}}(\underline{x},\underline{x}_{n})\|}\kappa\Big{[} \underline{\underline{H}}^{-1}(\underline{x},\underline{x}_{n})\cdot( \underline{x}-\underline{x}_{n})\Big{]} \tag{26}\]  
where \(\underline{\underline{H}}(\cdot)\) is the bandwidth matrix (contains the kernel function widths) and \(\kappa(\cdot)\) is the kernel function centered at each of the sample data \(\{\underline{x}_{n}\}_{n=1}^{N}\). A simple strategy in determining the bandwidth matrix could be to set the same bandwidth to all spectral bands. That means that \(\underline{\underline{H}}\) is equal to the scaled identity matrix \(\underline{\underline{H}}=h\cdot\underline{\underline{J}}\), which makes the contours of the kernel function spherically symmetric [17]. With this simplification, (26) becomes [17]:  
\[p_{b}(\underline{x}|\mathcal{B})=\frac{1}{N}\sum_{n=1}^{N}\frac{1}{h^{d}( \underline{x},\underline{x}_{n})}\kappa\Big{[}\frac{\underline{x}-\underline{ x}_{n}}{h(\underline{x},\underline{x}_{n})}\Big{]}. \tag{27}\]  
The respective detector for (27) is given in [11; 17] as the background PDF log-likelihood function:  
\[D_{F\!KDE}(\underline{x}|\mathcal{B})=-\log\left[\frac{1}{N}\sum_{n=1}^{N} \frac{1}{h^{d}(\underline{x},\underline{x}_{n})}\kappa\bigg{[}\frac{ \underline{x}-\underline{x}_{n}}{h(\underline{x},\underline{x}_{n})}\bigg{]} \begin{array}{c}H_{1}\\ >\\ <\\ H_{0}\end{array}\right.\lambda. \tag{28}\]  
Equation (28) represents a fixed form of KDE (FKDE) [94], often called Parzen windowing [11]. It was shown in [100; 101] that FKDE could be seen as a Euclidean distance detector applied in a higher dimensional kernel-induced feature space. The major impact on the performance of the FKDE detector has the selection of the kernel bandwidth \(h\) value. Numerous techniques have been proposed for its selection [94; 96; 98], but a unique \(h\) value that escapes over-smoothing the PDF body and simultaneously under-smoothing PDF tails may not exist. This problem motivated the development of the variable-bandwidth KDE (VKDE) [94; 102]. In [95; 97; 99], it has been shown that VKDE achieves better background estimation in comparison with the FKDE. According to [17], there are two distinct types of variable bandwidth selection techniques: the balloon estimator (BE) and the sample point estimator (SPE). The BE varies the bandwidth for every test pixel \(h(\underline{x},\underline{x}_{n})=h(\underline{x})\) and the SPE varies the bandwidth at each sample data \(h(\underline{x},\underline{x}_{n})=h(\underline{x}_{n})\).
###### Abstract
A high grade of independency from the electric grid was a second goal obtained for one of the two cases. For both the cases presented, the main ideas driving the design phase of the plants were introduced first, and the outstanding elements of the control logic were described successively. The first case study refers to a school building (built and operative since 2009) in Agordo, a mountain resort in Belluno province, North Italy [18; 19]. Different solutions were evaluated with respect to the heating and ventilation systems through dynamic simulation. An integrated source absorption heat pump system was designed to fulfil the needs of the building: the sources were ground, sun, and recovery on ventilation. The control logic conceived by the authors provided that the system works at two different temperature levels: a higher one for the ventilation system and a lower one for the radiant floor space heating system. The two different systems work independently and with different time schedules. For this case study, an analysis of the energy performance was carried out based on the evaluation of data obtained through real-time monitoring of the HVAC system in operation for many heating seasons. For the second case study, a dual source glazed PVT/ground heat pump system coupled to a retrofitted part of an old school building in the north of Italy was presented [20]. Such a configuration allowed the thermal efficiency of the plant and the electric efficiency of the PVT to increase, preventing the risk of cell damage due to overheating. The contemporaneity of different thermal loads during the year (DHW, heating, and cooling) made it necessary to set up a suitable scheme of the HVAC plant and its control logic by dynamic simulation. After describing the modeling hypotheses of the HVAC plant, and the operation control logic of the dual source heat pump system, different solutions were compared to evaluate the energy performance. As the originality of this study, the conceptualization of innovative control logics and their dynamic simulation by means of Trnys\({}^{\text{\textregisteredregistered}}\) are presented for two real HVAC plants, in order to attain very high efficiency and low non-renewable primary energy consumption, and the best configuration in terms of integration and management of renewable energy and multisource heat pump systems.
## 6 Determinant Factors of Obesity
### Genetic predisposition  
Along with the discovery of genetic diseases with implications on weight excess, several further aspects bolster the importance of genetic predisposition in obesity.  
The fact that most obese pediatric patients come from families where one or both parents have excess weight is an expression of the complex interaction between genetic and environmental factors [201]. In addition to the hereditary influence of obesity, the often-encountered familial character of obesity is related to the exposure to risk factors associated with the environment created by cohabitation with family members. Sedentarism, inefficient time management, and unhealthy dietary habits are all responsible for altering the lifestyle of the youngest members of a family and are influenced by a large variety of socio-economic and cultural factors [202]. There are, however, several arguments that underline the importance of genetic determinism in obesity.  
One such argument is in relation to studies on twins. Type 2 diabetes and obesity are more frequently simultaneously encountered in monozygotic twins than in dizygotic twins. Furthermore, some studies have even shown a correlation between the percentages of adipose tissue in monozygotic twins raised in different environments. The simultaneous character of obesity in monozygotic twins seems to take little regard to family environment. In addition, monozygotic twins tend to have similar mechanisms of adapting their weight to the environmental factors they are exposed to [203].  
A further argument is brought forth by studies on adopted children. Frequently, adopted children exhibit a weight pattern more similar to their biological parents rather than the adoptive ones [204]. The importance of epigenetic mechanisms has also been outlined by studies that have shown a strong correlation between the presence of adiposity in males and their descendants [205].  
Finally, another hypothesis worth taking into consideration is the theory of \"thrifty\" genes. The fundament behind it states that across evolution, a genetic arsenal tailored towards creating energy storages, in a time when sources of nutrition were scarce, was a survival advantage. The same genetic configuration has become a major disadvantage in modern times. This theory could explain the puzzling differences in obesity between certain races, as well as the variable influence of the same environmental factors on different ethnicities of people. A relevant example of where this theory could provide an explanation regards the development of weight excess in migrating populations from areas where the prevalence of obesity is low, to locations where it is higher. The prevalence of obesity frequently becomes greater in the migratory population when compared to natives [206].
# Model Updating Using Frequency Response Functions Based on Sherman-Morrison Formula
## 3 Implementation Procedure  
Figure 2 is a flowchart of the model updating method using FRFs based on Sherman-Morrison formula, with the steps described below:  
1. Establish the FE analysis model, and extract the mass matrix \(M_{1}\) and the stiffness matrix \(K_{1}\) of the original model to obtain the dynamic stiffness matrix \(\mathbf{Z}(\omega)\) of the system, and calculate the FRFs, that is \(\mathbf{H}(\omega)\);
2. Select several parameters that have an impact on the dynamic characteristics of the structure as the initial variables \(\mathbf{P}_{1}\), and \(\mathbf{P}_{1}\) become \(\mathbf{P}_{2}\) by perturbations, and the variation is \(\Delta\mathbf{P}\); extract the mass matrix \(M_{2}\) and the stiffness matrix \(K_{2}\) of the changed model to obtain the variation of dynamic stiffness matrix of the system, that is \(\Delta\mathbf{Z}(\omega)\), and recalculate the FRFs \(\mathbf{H}^{*}(\omega)\) after parameter perturbations by using Sherman-Morrison formula;
3. Select the appropriate frequency range which is away from the resonance for model updating and obtain the sensitivity \(\mathbf{S}\) of the FRFs to the designed parameters.
4. Conduct the model updating by the obtained sensitivity \(\mathbf{S}\), and the FRFs was iteratively updated by using Equation (27), obtain the variation \(\mathbf{P}_{\text{n+1}}-\mathbf{P}_{\text{n}}\), if the variables \(\mathbf{P}_{\text{n}}\) are converged, stop; otherwise set \(n=n+1\), go to step (2); the stop criterion is \(\|\mathbf{P}_{n+1}-\mathbf{P}_{nn}\|\leq 10^{-6}\);
# The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review
## 5 Challenges and Limitations
### Interoperability and Standardization in Digital Twins  
#### 5.2.1 Limitations  
An excellent example of the best practice requirements can be found in the aerospace and defense industries, where there are numerous different Digital Twin systems that must work together with various complex systems and simulations. OpenMETA is an open-source data management system used to create and manage models and simulations from different tools and/or domains [104]. It achieves this by providing a common data model with a domain-specific language tailored to modeling and simulation. This allows for seamless integration between toolsets, model reuse, and sharing and executing complex workflows for model-based systems engineering. OpenMETA has huge potential to be applied to Digital Twin systems outside the aerospace and defense industries once it is more developed.  
Moving forward, Digital Twin systems must maintain interoperability with other systems via data integration and/or the ability to interface directly with other systems. In order to achieve this, new Digital Twin systems must be designed with open architectures and common data models to allow for easier integration with other systems. Open architectures allow systems to be more easily understood and accessed by external systems, while common data models would provide a common ground for different systems to map, transform, and exchange data.  
Interoperability between different Digital Twin systems is vital for a variety of reasons. It facilitates collaboration on shared projects, allows businesses to operate and innovate more efficiently, and enables a surge in new and improved technologies. Unfortunately, interoperability has almost been an afterthought with the multitude of Digital Twin systems employed by businesses. Existing systems are often overhauled or abandoned for newer systems as business requirements and technologies advance. This has resulted in a disparate landscape of systems where there is little communication between different Digital Twins of the same asset, as well as different Digital Twins of different assets.
# RAC1B: A Rho GTPase with Versatile Functions in Malignant Transformation and Tumor Progression
## 4 Biochemical Properties, Generation and Degradation of RAC1B  
### Biochemical Properties  
The RAC1B protein acts like a fast cycling GTPase in GTP binding and hydrolysis assays [3]. A structural and biochemical analysis has revealed the structures of RAC1B in the GDP- and the GppNHp-bound forms. They show that the insertion induces an open switch I conformation and a highly mobile switch II. As a consequence, RAC1B exhibits an accelerated guanine nucleotide exchange factor (GEF)-independent GDP/GTP exchange and an impaired GTP hydrolysis, which is restored partially by GTPase-activating proteins (GAPs) [4]. The insertion of exon 3b leads to a reduced affinity for GDP and consequently enhanced intrinsic guanine nucleotide exchange, as well as a decreased intrinsic GTPase activity, resulting the intracellular predominance of the active GTP-bound state of RAC1B. Earlier studies showed that RAC1B exhibited the biochemical features of a constitutively activated GTPase [5]. Thus, RAC1B has similarities to the activated melanoma RAC1-P29S protein with respect to spontaneous activation by substantially increased inherent GDP/GTP nucleotide exchange [6]. RAC1B, however, differs from this RAC1 mutant by the reduced intrinsic GTP hydrolysis which in RAC1-P29S is not affected [6]. The mechanisms of RAC1B and RAC1-P29S activation are thus different from the common oncogenic mutations found in Ras-like GTPases that abrogate GTP hydrolysis [6]. Although the regulation of both RAC1 and RAC1B activities is dependent on GAPs, the difference in their activation is mainly determined by the inability of RAC1B to interact with RHO-GDP dissociation inhibitor (RHO-GDI) [7,8]. As a consequence, most RAC1B remains bound to the plasma membrane and is not sequestered by RHO-GDI in the cytoplasm. Although little RAC1B protein is expressed in cells, the amount of activated RAC1B protein may exceed that of activated RAC1, suggesting that RAC1B contributes significantly to the downstream signaling of RAC. However, the specific biochemical properties of RAC1B have severe consequences for signaling and interaction with downstream effectors that initially led authors to suggest that RAC1B may be defective in biological activity.
# Talent Supply and Demand Matching Based on Prompt Learning and the Pre-Trained Language Model
## 3 Results and Discussion
### The Impact of Sentence-Level and Token-Level Feature Embedding from the Pre-Trained BERT Model  
The output of BERT includes both the sentence-level and token-level embedding. To compare the differences between these two types of outputs in terms of their performance on talent supply and demand matching, we used sequence-level and token-level features in the separate models. As sequence-level features already capture sentence-level information, an MLP network was utilized for feature learning. In contrast, token-level features represent word-level information; therefore, a CNN+Bi-LSTM network was used to learn the representations of these tokens. The results of the comparative experiments are presented in Table 3.  
As shown in Table 3, both recommendation and prediction tasks demonstrated superior performance when using token-level feature embedding compared to sentence-level feature embedding. Specifically, for the recommendation task, token-level embedding achieved Acc, M-Pre, M-Rec, and M-F1 of 82.46%, 80.40%, 77.15%, and 77.48%, respectively, outperforming sentence-level embedding by 6.41%, 9.98%, 9.96%, and 11.10%. In the prediction task, token-level embedding achieved values of 78.39%, 77.47%, 72.29%, and 73.21% for these metrics, respectively, surpassing sentence-level embedding by 2.29%, 3.26%, 5.1%, and 3.8%, respectively.  
Furthermore, Figure 4 presents a comparative performance analysis of token-level and sentence-level embedding across different disciplinary categories. The results indicate that token-level embedding consistently outperforms sentence-level embedding in all  
Figure 3: Performance comparison of prompt learning across various LLMs in multilingual text contexts.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mission Type** &
\begin{tabular}{c} **Feature** \\ **Embedding** \\ \end{tabular} & **Acc (\%)** & **M-Pre (\%)** & **M-Rec (\%)** & **M-F1 (\%)** \\ \hline \multirow{2}{*}{Recommendation task} & sentence-level & 76.05 & 70.42 & 67.19 & 66.38 \\ \cline{2-6}  & token-level & 82.46 & 80.40 & 77.15 & 77.48 \\ \hline \multirow{2}{*}{Prediction task} & sentence-level & 76.10 & 74.21 & 67.19 & 69.41 \\ \cline{2-6}  & token-level & 78.39 & 77.47 & 72.29 & 73.21 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison between sentence-level and token-level feature embedding.  
categories. While sentence-level embedding captures the overall meaning of a sentence, it is more challenging to fine-tune for specific downstream tasks using diverse neural network architectures. In contrast, token-level embedding is more effectively fine-tuned for task-specific applications, enabling superior performance across various downstream deep learning models. Thus, token-level embedding offers a greater advantage over sentence-level embedding in this context.