esa-sceva/satcom-chunk-collection huggingface dataset:
# Static Analysis for ECMAScript String Manipulation Programs
## 2 Background
### Abstract Interpretation  
Abstract interpretation establishes a correspondence between a concrete semantics and an approximated one called abstract semantics [9; 13]. In a Galois Connection framework, if \(\mathsf{C}\) and \(A\) are complete lattices, a pair of monotone functions \(\alpha:C\to A\) and \(\gamma:A\to C\) forms a Galois Connection(GC for short) between \(C\) and \(A\) if for every \(x\in C\) and \(y\in A\) we have \(\alpha(x)\leq_{A}y\Leftrightarrow x\leq_{C}\gamma(y)\). \(\alpha\) and \(\gamma\) are called abstraction function and concretization function, respectively.  
Let \(L\) be a complete lattice. \(X\subseteq L\) is a Moore family of \(L\) if \(X=\mathcal{M}(X)\triangleq\{\ \bigwedge S\ |\ S\subseteq X\ \}\) and \(\top\) (top element) \(\in\mathcal{M}(X)\). If any concrete object in \(C\) has a best abstraction in the abstract domain \(A\) implies that \(A\) is a Moore family of \(C\) and so there exists a Galois connection between \(C\) and \(A\).  
Weaker forms of correspondence are possible, e.g., when \(A\) is not a complete lattice or when only \(\gamma\) exists [14]. In all cases, relative precision in \(A\) is given by comparing the meaning of abstract objects in \(C\), i.e., \(x_{1}\leq_{A}x_{2}\) if \(\gamma(x_{1})\leq_{C}\gamma(x_{2})\). If \(f:C\to C\) is a continuous function and \(A\) is an abstraction of \(C\) by means of the GC \(\langle\alpha,\gamma\rangle\), then \(f\) always has a best correct approximation in \(A\), \(f^{A}:A\to A\), defined as \(f^{A}\triangleq\alpha\circ f\circ\gamma\). Any approximation \(f^{\sharp}:A\to A\) of \(f\) in \(A\) is sound if \(f^{A}\sqsubseteq f^{\sharp}\).  
In abstract interpretation, there exist two notions of completeness: _backward completeness_ and forward completeness. The former is the best known form of completeness and focuses on complete abstractions of the inputs, while the latter is forward completeness [15; 16; 17] and it focuses on complete abstractions of the outputs, both w.r.t. an operation of interest. When we do not have a GC, namely when only the concretization \(\gamma\) exists, we need to focus only on forward completeness, as we will do in this paper. Given a GC \(\langle\alpha,\gamma\rangle\), a concrete function \(f:C\to C\) and an abstract function \(f^{\sharp}:A\to A,f^{\sharp}\) is forward complete w.r.t. \(f\) if \(\forall a\in A.\,f(\gamma(a))=\gamma\big{(}f^{\sharp}(a)\big{)}\).  
\(A\) satisfies the ascending chain condition (ACC) if all ascending chains are finite. When \(A\) is not ACC convergence to the limit of the fix-point iterations can be ensured through widening operators. A widening operator \(\
abla:A\times A\to A\) approximates the least upper bounds, i.e., \(\forall x,y\in A\.\ x,y\leq_{A}(x\
abla y)\) and it is such that for any increasing chain \(x_{1}\leq x_{2}\leq\dots\leq x_{n}\leq\dots\) the increasing chain \(w_{0}=\bot\) and \(w_{i+1}=w_{i}\
abla xi\) is finite.
# Reliability and Maintenance Analysis of Unmanned Aerial Vehicles +
## 2 RAMS Assessment  
The _Reliability, Availability, Maintainability, and Safety (RAMS)_ assessment is an important study in the development of UAVs. This kind of analysis is mandatory if you want to increase the reliability of a drone, its availability, and to reduce repair and maintenance costs [13]. Once an architecture has been chosen, the RAMS assessment is very useful to identify all the critical elements that could increase the failure rate [14]. It also allows us to characterize all the most stressed (or undersized) areas of the project. Furthermore, the reliability prediction, for example, makes it possible to decide whether to duplicate a safety critical system or to put it in _derated_ conditions, with great savings in terms of weight and power consumption [15; 16; 17; 18; 19]. A comparison between the well-known but always efficient technique of redundancy and the improvement of reliability must consider important remarks such as norms, costs, limitations of spaces, and so on. The reliability analysis helps us to assess the value of failures [20]. For instance, if some failures of a specific component happen in a wider system, the failure rate, preventively predicted, is useful to establish if the number of failures which is adequate to the overall number of components present in the system. Alternatively, it can individuate a particularly  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class 1** & **Definition 1** \\ \hline
**AUF** & Environmentally uncontrolled areas, which cannot be \\ Airborne, Uninhabited, Fighter & inhabited by an acircrew during flight. Environmental \\  & extremes of pressure, temperature and shock may be severe. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Environment definition.  
problematic section [21; 22; 23; 24]. Finally, this kind of evaluation can be used to assess the probabilities of findable damage events in a FMECA (Failure Modes, Effects and Criticality Analysis).
# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
## 3 Proposed Method
### Model Architecture
#### 3.1.2. Byte Tokenizing  
Byte tokenizing is the process of separating preprocessed data into bytes and converting the separated bytes into tokens. There are two parts to this process: First, we split the preprocessed data into bytes to use as the input. Second, the process of converting the extracted bytes of data into tokens is performed. In this process, it is crucial to determine the number of tokens to be used for organizing the data. If the number of tokens is too high, it may increase the data processing load, while too few tokens can result in the loss of  
Figure 3: Data composition of the pre-processed data for the three tasks: (**a**) encapsulation task (two classes), (**b**) category task (six classes), (**c**) application task (sixteen classes).  
essential information for classification, leading to performance degradation. Additionally, considering that BERT can handle a maximum of 512 tokens, selecting an appropriate number of tokens is essential. After experimenting with various combinations, we ultimately chose 63 bytes for the first eight packets, which can accommodate a total of 506 tokens, including two special tokens [CLS] and [SEP]. We present a performance comparison based on input shape in Section 5.3.
## 5 Performance Evaluation
### Performance Analysis
#### 5.3.2 Impact of Communication Range  
In this set of experiments, we want to find out how increasing the Tx-Rx distance impacts the V2X communication performance. Figure 5 reveals the result of changing the communication range of a transmitting vehicle from its receiver(s). Interestingly, we found that the NR V2X performance does not degrade until the 1000 m communication range in terms of PIR, PRR, and throughput performance. We found that \(\mu=0\) shows a slightly better performance than \(\mu=1\) in terms of achieving a lower PIR, higher PRR, and higher throughput. However, as discussed in the previous set of results, due to having a lower resource selection window length, \(\mu=2\) shows a distinguished better performance than \(\mu=0\) and \(\mu=1\).  
Figure 4: Cumulative distribution functions of random variables representing the performance metrics: (**a**) average PIR, (**b**) average PRR, and (**c**) average throughput for different numerologies.  
Figure 5: \(Cont\).
# Hand Posture Recognition Using Skeletal Data and Distance Descriptor
###### Abstract  
In this paper, a method for the recognition of static hand postures based on skeletal data was presented. A novel descriptor was proposed. It encodes information about distances between particular hand points. Five different classifiers were tested, including four common methods and a proposed modification of nearest neighbor classifier, which can distinguish between posture classes differing mostly in hand orientation. The experiments were performed using three challenging datasets of gestures from Polish and American Sign Languages. The proposed method was compared with other approaches found in the literature. It outperforms every compared method, including our previous work, in terms of recognition rate.  
Keywords:hand posture recognition; sign language; finger alphabet; skeletal data; leap motion +
Footnote †: journal:  
this type of data. Therefore, there is a need to develop new recognition algorithms based on hand skeletons.  
In this paper, a problem of hand posture recognition based on skeletal data extracted by a depth sensor was tackled. The method is based on a novel hand descriptor combined with another one that was previously developed. The experimental tests were performed using four different classification methods and a proposed modification of the nearest neighbor classifier.  
The main contributions of this paper are as follows:  
1. The novel hand descriptor encoding information about distances between selected hand points;
2. The modified nearest neighbor classifier, suitable for posture recognition in the case where some of the classes differ mostly in hand orientation;
3. Experimental verification of the proposed methods using challenging datasets.  
The remaining parts of this paper are organized as follows. The related works are characterized in Section 2. The proposed hand posture recognition method is presented in Section 3. Section 4 discusses the used datasets and performed experiments. Section 5 concludes the paper.
## 3 The Proposed Multiple-Signal Joint Estimation Method
### Square Root Unscented Stable Filter
(2016)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Modified federated Kalman filter for INS/GNSS/CNS integration. _Proc. Inst. Mech. Eng. G J. Aerosp. Eng._**2016**, _230_, 30-44. [CrossRef]
* _[PERSON] (2021)_ [PERSON]; [PERSON] A Wearable Pedestrian Localization and Gait Identification System Using Kalman Filtered Inertial Data. _IEEE Trans. Instrum. Meas._**2021**, _70_, 2507908. [CrossRef]
* _[PERSON] and [PERSON] (2001)_ [PERSON]; [PERSON] The square-root unscented Kalman filter for state and parameter-estimation. In Proceedings of the 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, Salt Lake City, UT, USA, 7-11 May 2001; Volume 6, pp. 3461-3464. [CrossRef]
* _[PERSON] et al. (2011)_ [PERSON]; [PERSON]; [PERSON] Square Root Unscented Kalman Filters for state estimation of induction motor drives. In Proceedings of the 2011 IEEE Energy Conversion Congress and Exposition, Phoenix, AZ, USA, 17-22 September 2011; pp. 75-82. [CrossRef]
* _[PERSON] and [PERSON] (2023)_ [PERSON]; [PERSON] Tracking Magnetic Target Based on Internative Multi-Model Square Root Unscented Kalman Filter. _IEEE Trans. Magn._**2023**, _59_, 4000312. [CrossRef]
* _[PERSON] et al. (2016)_ [PERSON]; [PERSON]; [PERSON] A Variational Bayesian-Based Unscented Kalman Filter With Both Adaptivity and Robustness. _IEEE Sens. J._**2016**, _16_, 6966-6976. [CrossRef]
* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Multipath Error Fusion Modeling Methods for Multi-GNSS. _Remote Sens._**2021**, _13_, 2925. [CrossRef]
* _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Indoor Positioning With Adaptive Wavelet Denoise Enhancement and Trend Surface Analysis Based Multipath Map. _IEEE Sens. J._**2022**, _22_, 15191-15198. [CrossRef]
* _[PERSON] et al. (2012)_ [PERSON]; [PERSON]; [PERSON] Tracking low-precision clocks with time-varying drifts using Kalman filtering. _IEEE/ACM Trans. Netw._**2012**, _20_, 257-270. [CrossRef]
* _[PERSON] et al. (2016)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Joint 3D Positioning and Network Synchronization in 5G Ultra-Dense Networks Using UKF and EKF. In Proceedings of the 2016 IEEE Globecom Workshops (GC Wkshps), Washington, DC, USA, 4-8 December 2016; pp. 1-7. [CrossRef]
* _ETSI (2020)_ ETSI. _Physical Channels and Modulation_; Document TS 38.211; 3 GPP: Sophia Antipolis, France, 2020. * _[PERSON] et al. (2020)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Role, path, and vision of \"5G + BDS/GNSS\". _Satell. Navig._**2020**, \(1\), 23. [CrossRef]

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## 3 Energy-Saving Optimal Control Strategy of Refrigeration Station
### Edge Node Fuzzy Neural Network Control Algorithm
#### 3.6.2 Construction of Fuzzy Neural Networks and Their Self-Learning  
Figure 9 is the deep learning and training procedure flow of the neural network. The fuzzy neural network is used to train the error of the collected operation data by variation of the LM (Levenberg-Marguardt), SCG (Scaled Conjugate Gradient), and CGP (PolaeRibiere Conjugate Gradient) algorithms, and then the optimal PID controller under the corresponding operating conditions is obtained.  
Specifically, the controller of the central refrigeration station consists of a training neural network and a predictive neural network working in parallel. The task of training a neural network is to learn the relationship between different inputs (including control parameters and non-control parameters) and the whole energy consumption, and the network then serves as the excitation function of the prediction network; the function of the predictive neural network is to find the control parameters that minimize the energy consumption.  
As shown in Figure 9, the learning and training steps of the training neural network model are as follows:  
1. Data acquisition: Collecting the related operating parameters of the refrigeration host, cooling water pump, freezing water pump, cooling tower, air-conditioning terminal load, etc.
2. Data collation: Using the automatic collation tool to reduce dimension, reduce noise, filter, and so on.
3. To obtain the characteristic value sensitive to the energy-saving index: Finding out the key parameters related to energy efficiency control objectives (refrigerating capacity, heating capacity, EER, and COP (Coefficient of Performance)) by correlation analysis of the completed table data.  
Figure 8: Fuzzy neural network PID controller.  
Model training: Using high-quality data and a deep neural network, the energy efficiency prediction model is trained. After training, the prediction accuracy of the energy efficiency model can reach 99.5% with an error of 0.005.
5. Reasoning decision-making: The prediction and decision-making models are published in the central control system to provide online decision-making models that can be tuned. The energy-saving scheme can find out the optimal parameter combination under the current outdoor environment and the air-conditioning terminal load from hundreds of thousands of combinations in 1 min and then issue the command and feed the effect back.  
Figure 9: Deep learning and training procedure flow chart.  
Through a large number of trial calculations, as shown in Figure 10, it was found that two hidden layers and 25 hidden layer nodes above can achieve higher accuracy. According to experience, the higher the number of hidden layer nodes, the higher the accuracy of fitting, but this will not only increase the training time, but also produce over-fitting, which will affect the accuracy of prediction.
# Edge Computing for IoT-Enabled Smart Grid: The Future of Energy
## 5 Challenges and Open Issues
### Flexibility  
Flexibility is one of the other challenges of EC-based SGs through the optimal system resource allocation [66] and delivering flexible services according to the demand dynamically and QoS requirements [67].  
Aiming toward real flexibility in an EC-based SG, the system needs to predict the demands of end-users and respond to the ability of service providers and real-time system factors before allocating and offloading computational resources such as storage, bandwidth, and processing power. In [68], dual prediction schemes have been introduced to optimize the data transmissions of sensors.  
In [69], the authors present an autonomic resource provisioning framework that is based on computing methods and machine learning techniques. Auto-scaling solutions can be implemented in horizontal, vertical, or hybrid [70]. The horizontal solutions allow resizing the resource allocation between nodes in a cluster, whereas vertical solutions allow resizing the resource allocation in a node. An advanced hybrid autonomic resource provisioning framework that combines both vertical and horizontal is introduced in [71].
## 7 Conclusions and Future Outlook  
The aim of the study was to compare the environmental impacts of the end-of-life (EoL) of carbon-reinforced concrete (CRC) and steel-reinforced concrete (SRC). To do so, a life cycle assessment using literature data is performed--only focusing on the end-of-Life scenarios. In the background, we describe in detail all possible options for the EoL of CRC. The results show that the emissions of CRC EoL, despite lower input related to mass and an expected higher lifetime, do not necessarily decrease compared to SRC, but are partly higher. The best SRC scenario in terms of GWP is a mobile plant leading to 8.8 kg CO\({}_{2}\) eq., while the best CRC scenario is mechanical recycling (7.0 kg CO\({}_{2}\) eq. best case and 8.6 kg CO\({}_{2}\) eq. worst case), which does not include pyrolysis and does not provide material circularity. Moreover, the concrete grain size is identified as relevant for emissions; the smaller the particle sizes, the higher the environmental emissions. Carbon fibers cannot be completely looped and downcycling is likely. The use of CRC should therefore be carefully considered. While its use may have advantages in some applications, a general advantage may not be established. Even though it is based on secondary data, the study may provide relevant insight into the EoL for both academics and industry--already today focusing on challenges 100 years from now. A study with primary data and a comparison with glass-fiber-reinforced concrete, another alternative for fiber-reinforced concrete, should be performed in future research.  
Conceptualization: J.G.B., P.D.R. and A.L.; methodology: J.G.B., P.D.R. and A.L.; formal analysis and investigation: J.G.B.; writing--original draft preparation: [PERSON], A.L. and P.D.R.; writing--review and editing: [PERSON], P.D.R., A.L. and M.T.; funding acquisition: [PERSON], [PERSON] and P.D.R.; supervision: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the German Research Foundation (DFG), as part of the Sonderforschungsbereich/Transregio 280 (SFB/TRR 280) 'Konstruktionsstrategien fur materialminimierte Carbonbetonstrukturen'/Design Strategies for Material-Minimized Carbon Reinforced Concrete Structures' (subproject E01, project number 417002380), as well as SFB/TRR 339 (project ID 453596084), and FaBeR (Faser- und Beton-Recycling von Carbon- und Textilbeton), a project funded by the Federal Ministry of Education and Research (BMBF). The financial support from the German Research Foundation (DFG) and the Federal Ministry of Education and Research (BMBF) is gratefully acknowledged.  
All data used is integrated within this article.  
The authors declare no conflict of interest.
# A Novel Index for Detecting Bare Coal in Open-Pit Mining Areas Based on Landsat Imagery
## 3 Methods
### Separability Analysis of BC Spectra  
The design of the BC index should focus on distinguishing BC from background features. Typically, the main background features include bare soil, man-made structures, vegetation, and water, with bare soil and man-made structures being the most challenging to differentiate from bare coal. Correlation analysis between different land cover types reveals that bare coal also has a high correlation with water. To determine the degree of separation between different land cover types and bare coal using the Red, NIR, SWIR1, and SWIR2 bands, a separability indicator [37] was employed. This indicator (M) is defined as the difference in the means (\(\upmu\)) of two land cover types normalized by the sum of standard deviations (\(\upsigma\)), as shown in Equation (1). M \(>\) 1.0 indicates good separability, and M \(<\) 1.0 indicates poor separability.  
\[\text{M}=\frac{\upmu_{1}-\upmu_{2}}{\upsigma_{1}+\upsigma_{2}} \tag{1}\]  
As shown in Figure 6, there is better separability between BC-S (bare coal and soil) and BC-M (bare coal and man-made structures) in the NIR, SWIR1, and NIR + SWIR1 bands; between BC-Water in the SWIR1, SWIR2, and SWIR2-SWIR1 bands; and between BC-Veg (bare coal and vegetation) in the NIR, NIR-Red, and SWIR2-SWIR1 bands. Therefore, the Red, NIR, SWIR1, and SWIR2 bands were selected for the construction of the bare coal index to effectively separate BC pixels.  
Figure 5: The reflectance of sampling BC pixels against BC fraction: (**a**) Blue, (**b**) Green, (**c**) Red, (**d**) NIR, (**e**) SWIR1, (**f**) SWIR2, (**g**) NIR–Red, (**h**) NIR + SWIR1, and (**i**) SWIR2–SWIR1. The \(p\)-value is for the significance test of the linear regression.
## 4 Case Study: AQM Implementation
### Verification and Assessment for the CNS
#### 4.3.1 The RVQA Process at the Agent Individual Level of the CNS  
Figure 9 shows individual agent activities and the implementation of the RVQA to check an agent data confidentiality disclosure status during an agent active period. The individual agent activities include the stage when the agent account becomes active, the dynamic changes of agent decisions toward profile anonymity and presence visibility throughout the agent active period, and the stage when the agent account becomes inactive.  
Algorithm 1 shows the RVQA confidentiality violations detection for the agent individual level of the CNS that checks agent identity and location confidentiality disclosures. As defined by the checking rules for the individual level [51], the agent account active state is a critical state, while the agent decisions for profile anonymity and presence visibility determine its identity and location confidentiality disclosures statuses. An agent dynamic profile anonymity option can be chosen to be either \"real\" or \"anonymous\" and determines whether the agent identity is \"disclosed\" or \"protected,\" respectively. Similarly, the agent presence visibility option can be set to be \"visible\" or \"invisible\" and determines whether the agent location is \"disclosed\" or \"protected,\" respectively.  
Figure 9: The RVQA implementation at the agent individual level of the CNS.