esa-sceva/satcom-chunk-collection huggingface dataset:
# SENSE: A Flow-Down Semantics-Based Requirements Engineering Framework
## 6 Related Work  
Much of the research has focused on automation, with regard to translation and analysis, as an attempt to deal with natural languages and requirement variety. The common approach, in this context, is to translate a document written in a specific natural language to another formally defined language [10; 24]. However, the ambiguity of natural languages makes translation difficult and the results of the process are in many cases doubtful. As a result, researchers acknowledge the need to shift from translation approaches to approaches that use standard (semi-) formal languages without the need for further translations, minimizing the use of natural language.  
Additionally, there is another shift from the machine learning techniques that are used in the majority of the literature approaches to the procedures based on semantics. Semantics, namely the meaning of the requirements, allows the enhancement of accuracy and completeness, leading to a better understanding of hidden connections [8; 10; 25; 26; 27]. Inthe literature, however, there are even domain-specific approaches, such as in [28], that deal with a specific case, and such as with security requirements that propose visual notations in order to express requirements. In [28], the authors propose the use of business process management notation (BPMN), a graphical representation for business requirements to define business processes. In [29], a domain-independent tool that uses a UML model Generator provides diagrams and conceptual models. It actually uses NLP techniques and XML in order to provide a UML visualization. In [30], the approach is based on NLP in order to provide conceptual models using Extended Entity Relationship (EER) diagram notations. Finally, in [31] the authors propose an approach that transforms the requirements from natural language into semi-structured specifications. This attempt is based on Cerno, which is also a semantic annotation environment.  
SENSE, on the other hand, provides an approach that allows engineers to develop, validate, and manage requirements in a well-defined way by using boilerplates and validity (SPIN) checks, minimizing the need for natural languages or machine learning techniques. Furthermore, once equipped with the appropriate ontology it can be used in any potential domain rather than in a few specific cases.
# Identification of Land Use Mix Using Point-Based Geospatial Data in Urban Areas
### Related Works
#### 1.1.2 LULC Classification
The use of POI data in combination with Place2 Vec allows the system to capture the spatial patterns of land use and the underlying functional relationships between urban regions, demonstrating the integration of POI data into the deep learning model, which enhances the ability of the system to capture the functional connections between urban areas. In conclusion, LULC classification has been a popular research topic in recent years, focusing on utilizing various data sources such as satellite imagery, social media, and POI data to improve accuracy and efficiency. While many studies have shown promising results, the quality and timeliness of social media data, the quality of geotagged images, and the level of participation in VGI information can limit the performance of some methods. The studies reviewed in this section have demonstrated the potential of integrating social media and POI data into deep learning models for LULC classification, focusing on improving accuracy and efficiency. Further research is needed to address the limitations of these approaches and to continue exploring the potential of incorporating various data sources for improved LULC classification. However, LUM is a different concept than LULC, and the challenge of identifying LUM is much more complex than LULC.
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## Appendix A Proof of Proposition 1  
Consider the set of all active nodes \(x\in\Phi_{a}\) generated by the homogeneous PPP, the node \(x\) is in the typical cluster \(\Phi_{c}(0)\) if the origin is o is the closest point to \(x\) among all the other collectors \(y\in\Phi_{b}\), i.e., \(|x|<|y-x|\), \(\quad\forall y\in\Phi_{b}\). We can note that the following indicator function  
\[\prod_{y\in\Phi_{b}}\mathds{1}_{\{|x|<|y-x|\}}=\begin{cases}1&x\in\Phi_{c}(0), \\ 0&\text{otherwise}.\end{cases} \tag{121}\]  
The average number of users in this typical cell is,  
\[\mathbb{E}\Big{[}\mathds{1}_{\{x\in\Phi_{c}(0)\}}\Big{]}=\sum_{x\in\Phi_{a}} \left[\prod_{y\in\Phi_{b}}\mathds{1}_{\{|x|<|y-x|\}}\right] \tag{122}\]  
Using Campbell Theorem,  
\[\sum_{x\in\Phi_{a}}\left[\prod_{y\in\Phi_{b}}\mathds{1}_{\{|x|<|y-x|\}}\right] =\int\left[\prod_{y\in\Phi_{b}}\mathds{1}_{\{|x|<|y-x|\}}\right]\lambda_{a}dx. \tag{123}\]  
For a given \(x\in\Phi_{a}\), we use the PGFL property on the PPP of \(\Phi_{b}\),  
\[\mathbb{E}\Big{[}\prod_{y\in\Phi_{b}}\mathds{1}_{\{r<|y-x|\}} \Big{]} = \exp\Big{(}-\lambda_{b}\int_{\mathbb{R}^{2}}(1-\mathds{1}_{\{r< |y-x|\}})dy\Big{)} \tag{124}\] \[= \exp\Big{(}-\lambda_{b}\int_{|y-x|^{2}<r^{2}}dy\Big{)}.\]  
Note that \(|y-x|^{2}=|y|^{2}+|x|^{2}-2|y||x|\cos(\theta_{z}-\theta_{y})\) where \(|y|\) and \(\theta_{y}\) (resp. \(r=|y|\) and \(\theta_{x}\)) are the polar coordinates of \(z\) (resp. \(y\)). The condition \(|y-x|^{2}<r^{2}\) in (124) is then equivalent to \(|y|<2|x|\cos(\theta_{y}-\theta_{x})\) with \(0\leq(\theta_{y}-\theta_{x})\leq\pi\) to guarantee a positive range of \(r_{y}\). The corresponding integral is then,  
\[\int_{|y-x|^{2}<r^{2}}dy=\int_{0}^{\pi-\theta_{x}}\int_{0}^{2r\cos(\theta_{y}- \theta_{x})}r_{y}dr_{y}d\theta_{y}=\int_{0}^{\pi-\theta_{x}}r^{2}\cos^{2}( \theta_{y}-\theta_{x})d\theta_{y}=\pi r^{2} \tag{125}\]  
independently of \(x\). Replacing in (123), Equations (124) and (125), we have,  
\[\mathbb{E}\Big{[}\mathds{1}_{\{x\in\Phi_{c}(0)\}}\Big{]}=\int_{0}^{\infty} \int_{0}^{2\pi}\exp(-\lambda_{b}\pi r^{2})\lambda_{a}drdr\theta=\frac{ \lambda_{a}}{\lambda_{b}}. \tag{126}\]  
Finally, the pdf of \(r\) can be deduced using the CDF of \(r\),  
\[\text{Prob}\{x\in\Phi_{c}(0)\}=\text{Prob}\{\prod_{y\in\Phi_{b}}\mathds{1}_{ \{r<|y-x|\}}\}=\exp(-\lambda_{b}\pi r^{2}) \tag{127}\]
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 3 State-of-the-Art
### State-of-the-Art in the Encryption Aspect  
The last aspect of chaotic image encryption is the encryption aspect, which is about symmetric versus asymmetric cryptography, block versus stream ciphers, and cryptographic primitives. The state-of-the-art in this aspect is studied below.
# R&D of an Innovative OFDM Communication Payload for Small-Size AUV
## 2 Systems and Methods
### Doppler Estimation and Compensation Implementation
#### 2.3.1 Doppler Estimation Strategy  
Considering the Doppler effects on the received OFDM packets, the received signal can be rewritten as  
\[r(t)=s((1+\varepsilon)t), \tag{17}\]  
If \(T_{sam}\) represents the sampling period, the discrete received version of \(r(t)\) can be formulated as  
\[r(n)=s((1+\varepsilon)kT_{sam}), \tag{18}\]  
As the Doppler spread is equivalent to the contraction or expansion of the received OFDM packet in time domain, herein the time amount variation of the preamble sequence containing ten linear frequency modulation (LFM) signals is transformed into the Doppler factor as follows:  
\[\varepsilon=\frac{L_{r}-L_{t}}{L_{t}}. \tag{19}\]  
where \(L_{r}\) and \(L_{t}\) are the length of the received and the transmitted preamble signal at the time domain, respectively. After Doppler estimation is accomplished, the Doppler compensation is carried out to facilitate the demodulation of the OFDM data frame.
## 2 Methods and Hardware for Application of subTHz Instruments
### Gyrotrons: High-Power subTHz Sources and Amplifiers
The sensitivity of heterodyne receivers [23], which have been developed and used in applications by leading international groups, including the authors, has reached 1-2 quantum limits in the subTHz range, and the sensitivity of the developed subTHz superconducting detectors [29] cooled down to 0.1 K is essentially determined by the background level only.
## 1 Introduction
This advantage arises from the considerably lower internal impedance of EDLCs compared with batteries (see Section 2). * Environmental and operational benefits: EDLCs have a lower environmental impact due to easier recycling processes and greater adaptability to varying weather conditions. Additionally, their simplified engineering models reduce design complexity. When used in parallel with batteries, EDLCs enhance load balancing due to their rapid response times [9]. The application of OESD requires detailed models, such as models of railway units, but also a more detailed model for EDLC and other devices to take advantage of its advantages. The main contributions of this paper can be summarized as follows:* It presents a non-linear model aimed at enhancing the understanding of EDLC dynamics to balance energy requirements using Physical-Based methodologies (PBLM), in this case, an electrical circuit model. In terms of accuracy and parameter evaluation, this approach surpasses other practical models, particularly due to its simplicity in evaluation and its straightforward physical interpretation. * The paper also proposes an alternative for repurposing underutilized diesel-electric and electric locomotives on low-traffic lines, leveraging the fact that energy storage and partial electrification are both cost-effective and technically feasible in certain scenarios. This hybridization is particularly applicable to freight services and serves as a viable alternative during the necessary transition period to meet decarbonization targets for 2030-2050. To support this, a specific model for railway units has been calibrated for operations on partially electrified tracks. Furthermore, this approach sustains railway freight operations while avoiding the substantial investments required for full electrification, a significant challenge for several countries like Spain. * Additionally, the paper illustrates how freight traffic, which typically operates at low speeds and experiences long standby periods, can function as a distributed energy resource. This enables the optimization of energy management (e.g., energy recovery and peak demand management) in electrified routes, making the system more efficient overall. The rest of the paper is organized as follows. Section 2 depicts the characteristics of the selected track and potential locomotives for hybridization, as well as the literature review of energy storage models for supercapacitors and batteries. Additionally, it develops the proposed model for EDLC and the different laboratory tests. Section 3 deals with the calibration of the model parameters and the analysis of the experimental and simulation results. Finally, the main findings and recommendations of the study are presented in Section 4.
# Conductive Textiles for Signal Sensing and Technical Applications
## 5 Application of Conductive Textiles
### Supercapacitor  
With the rising demand for fast charging as well as high energy storing devices and the growing concern for human comfort in the wearable smart textiles industry, textile-based supercapacitors are making a revolutionary step due to some of their unique features, such as their extreme wearability, flexibility, comfort, durability, effective electrochemical redox performance, and cost-effective production process [340].  
The energy storage performance of supercapacitors is mainly dependent on selecting the electrode and electrolyte materials; moreover, the development of supercapacitors concerning flexibility with comfort and a combination of supercapacitors with textile fabric is resulting in a revolution [341; 342; 343]. Consequently, a large amount of research has been done to develop advanced electrode materials for flexible supercapacitors with suitable structural properties with deformation, stretching, bending, and washability [344; 345].  
[PERSON] et al. reported textile-based electrodes for supercapacitors composed of PANI/G/HCL through a simple dipping and drying process of graphene on fabrics followed by in situ polymerization of aniline, which exhibited an impressive areal specific capacitance of 1601 mF cm\({}^{-2}\) at the current density of 1 mA cm\({}^{-2}\). In contrast, over 75% of initial area-specific capacitance was preserved after 10,000 cycles [346]. The deposition of rGO and SnO\({}_{2}\) on textile fabric via supersonic spraying showed the highest specific capacitance of 1008 mF cm\({}^{-2}\) at a current loading of 1.5 mA cm\({}^{-2}\), with a capacitance retention of 93% after 10,000 cycles [347].  
[PERSON] et al. designed a flexible electrode with a high specific surface area provided by metal-organic frameworks (MOFs) and flexibility provided by CNTs [348]. The performance of that supercapacitor showed a stable energy supply under cyclic deformations and even wearable conditions. Mechanically strengthened ultralong MnO\({}_{2}\) nanowire composites were developed as a stretchable electrode that showed a specific capacitance of 227.2 mF cm\({}^{-2}\) and can be stretched up to 500% without degradation of electrochemical performance. Around 98% of the initial capacitance after 10,000 stretch-and-release cycles was performed under 400% tensile strain [349].
## 2 Materials and Methods
### Model Evaluation  
Five-fold cross-validation and independent test are used to evaluate the PTG-PLM model and compare it with the other tools. Several evaluation metrics are employed in this work, including accuracy, Matthew's correlation coefficient (\(MCC\)), recall, precision, and \(F1\)-score which are illustrated as:  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{7}\]  
\[MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FP)(TN+FN)}} \tag{8}\]  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
\[Precision=\frac{TP}{TP+FP} \tag{10}\]  
\[F1=\frac{2\times recall\times precision}{recall+precision} \tag{11}\]  
where \(TP\) (True Positive) is the number of positive sites that are correctly predicted, \(TN\) (True Negative) is the number of negative sites that are correctly predicted, \(FP\) (False Positive) is the number of positive sites that are incorrectly predicted, and \(FN\) (False Negative) is the number of negative sites that are incorrectly predicted. In addition, we use the \(AUC\) metric, the area under the receiver operating characteristic curve (ROC), that measures the classifier's ability for separating data of the two classes by plotting the true positive rate (\(TPR\)) against the false positive rate (\(FPR\)).
# A 3D Vision-Based Weld Seam Extraction Method for Arbitrarily Placed Spatial Planar Weldments
## 3 Weld Seam Extraction
### Vertex Validation  
Based on the topology combination analysis described above, every weld seam comprises a line set connecting a starting point and an ending point, both of which are intersected points (vertices) formed by three planes. By iterating through all \(C(n,\,3)\) combinations of the segmented planes, all possible starting and ending points can be calculated. However, not every vertex is a valid start point or end point of a weld seam. As an illustration, Figure 9 shows a point \(p_{1}\) intersected by planes \(S_{1}\), \(S_{2}\) and \(S_{3}\), which obviously cannot serve as a vertex of a weld seam.  
Figure 8: The possible topologies of three planes: (**a**) no intersection lines; (**b**) one intersection line; (**c**) two intersection lines; (**d**) three parallel intersection lines; (**e**) three intersection lines intersecting at one point. The intersection lines are represented with red lines.  
Figure 9: Example of an invalid intersection point (\(p_{1}\)).  
By comparing \(p_{1}\) with a valid vertex \(p_{2}\) in Figure 9, a quick discovery is that invalid vertices are not located in the inliers of any of the planes intersecting. To rule out invalid intersections for weld seam extraction, each vertex is validated by the following criterion:  
\[\min\bigl{(}\text{distance}\bigl{(}p,P_{S_{i}}\bigr{)}\bigr{)}<\sigma_{d},\ \ i\in\{1,2,3\} \tag{17}\]  
where \(P_{S_{i}}\) is the cluster of inliers of plane \(S_{i}\). This criterion would likely fail without adopting Rule 5 proposed in Section 2.2.4 because isolated noisy points will be included in the inliers of a segmented plane, making the criterion satisfied even in case of an invalid intersection.
## 3 System Model
### Optimization Problem  
We consider a discrete-time system, where the entire time is discretized into the \(t\) time slots of equal length, denoted as \(\mathcal{T}=\{1,2,\ldots,T\}\), and the unit length is denoted as \(\delta\). On the basis of equal-length time slots, the time system is abstracted into multiple execution cycles of unequal length. The duration of one cycle is the time it takes to execute a decision, and the length of each time slot depends on the task performed in this time slot. For example, in the \(y\)-th cycle, the UAV performs the data acquisition task of the \(n\)-th device, \(n\in N\), then the execution time is \(T^{i}=T^{i}_{f}+T^{i}_{c}\), where \(T^{i}_{f}\) represents the flight time to the target sensing device in (3), and \(T^{i}_{c}\) represents the time of the data transmission in (4).  
According to the model and formulas described above, the central goal of the multi-UAV path-planning problem can be modeled as the following maximization problem  
\[\max_{p^{u}}\sum_{i=1}^{l}\sum_{t=\sum_{j=1}^{j-1}T^{j}}^{\sum_{j=1}^{j}T^{j}}Q (t), \tag{9}\]  
\[s.t.\quad p^{u}_{i}\in\{p_{1},...,p_{N}\},\forall i\in\mathcal{I}, \tag{10}\]  
\[e_{i}>0,\forall i\in\mathcal{I}. \tag{11}\]  
Figure 2: Example diagram of AoI changes.  
The first constraint in (10) indicates that the target point of the UAV can only be selected from the set of specified locations for each decision. The second constraint in (11) indicates that the energy of the UAV during the mission will not be exhausted prematurely, where \(e_{i}\) stands for the remaining energy.
## 3 Factors Governing Survival and Occurrence of Opportunistic Pathogens in Premise Plumbing Systems
### Disinfection
#### 4.3.4 Copper-Silver Ionisation  
Copper-silver ionisation has been used to control _Legionella_ and other bacteria in various settings [164]. However, some researchers argue that it is most effective when concentrations can continually be monitored and adjusted, requiring special equipment and expertise. Both copper and silver have biocidal activity, which results in a synergistic effect in the lysing of bacteria and protozoa cells, and denaturing of their proteins [17; 42]. Recommended effective concentrations of copper and silver range from 0.2 to 0.4 mg/L and 0.02-0.08 mg/L, respectively [165], and they tend to be higher in larger systems. Other studies have shown the ability of these metals to penetrate biofilms. The overall efficiency of copper-silver ionisation is largely impacted by water pH and TDS. Silver will precipitate when subject to waters with high TDS concentrations and become unavailable for disinfection. [PERSON] et al. [166] warns of the potential for microbial resistance to silverover the time span of years. Evidence in support of in-building copper-silver ionisation originates predominately from field studies, with experts calling for additional data from controlled laboratory investigations. For example, _Legionella_ outbreaks have been reported in buildings featuring copper-silver ionisation installations [15]. Application of this method has been severely restricted by authorities in the US and parts of Europe on this basis.
# Implementation and Evaluation of Dynamic Task Allocation for Human-Robot Collaboration in Assembly
## 3 Results
### Human Factors and User Satisfaction
#### 3.3.1 Variables Depending on Task Allocation Mode  
An overview of the results for workload, fluency, and satisfaction is shown in Table 5 for the different task allocation modes. Regarding the workload, the interpretation of the RTLX score shows a medium-level workload (see [65] for summary on interpretation of the NASA TLX score) perceived by the subjects in all setups. In both static task allocation modes, the workload is lower than in manual task allocation and dynamic task allocation modes. An examination of the answers to the individual questions shows that this is mainly due to the higher mental workload in both cases, due to the higher pace of the task in the case of dynamic task allocation, and due to the need for harder work to accomplish performance in the case of manual assembly.  
The subjective evaluation of HRC fluency shows almost identical fluency in both static task allocation modes, while in the dynamic task allocation mode, fluency was reported to be slightly better (cf. Table 5). When analyzing the feedback in detail (see Figure 21a), the intelligence of the robot, the perception of the robot as a team partner, and the flexible reaction to process changes are rated significantly better in the dynamic task allocation mode. However, the need of the human to adapt their own movements to the robot is rated worse in dynamic task allocation. This can be explained by the less clearly defined sequence of robotic tasks resulting in less predictable robot movements.  
The satisfaction assessment reveals a significantly higher satisfaction score in the implemented approach for dynamic task allocation, where in particular the satisfaction with the resulting task assignment is markedly perceived best (cf. Figure 21b).  
Figure 22 shows the subjective user preference regarding the preferred mode of task allocation. A clear preference for the dynamic task allocation can be observed, which is judged as the preferred mode by 75% of the subjects.