esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### PCM-Incorporated Building Model
#### 3.2.3 Optimal PCM Configuration  
Evaluating the best PCM configuration among the external wall layers is carried out by investigating a single PCM layer in the middle, closer to the interior side, and closer to the exterior side of the external wall. In the comparison between the baseline building model with extruded polystyrene and the building after incorporating a single sheet of BioPCM(r) at different positions within the external wall, notable reductions in electrical energy consumption were observed. Placing the PCM in the middle of the external wall  
Figure 19: HVAC energy comparison chart for the tested PCMs with different melting points.  
resulted in a 34.38% reduction in heating and a 23.33% reduction in cooling, contributing to an 18.23% total end-use energy reduction. Positioning the PCM closer to the interior side led to even greater reductions, with a 38.79% decrease in heating and a 24.76% decrease in cooling, resulting in a substantial 20.14% total end-use energy reduction. Similarly, placing the PCM closer to the exterior side resulted in a 32.53% reduction in heating and a 16.18% reduction in cooling, contributing to a 15.61% total end-use energy reduction.  
As clearly observed from the simulation results, the building incorporated with BioPCM(r) closer to the interior surface of the external wall reached the highest energy-saving rate compared with the baseline simulation results and the other PCM configurations, as illustrated in Figure 21. These results comply with the literature [37; 39], as they proved that the optimal PCM layer configuration should be closer to the interior surface of the external wall. Figure 22 illustrates an HVAC energy comparison chart for the tested PCM configurations.
# Research on Short-Term Wind Power Forecasting by Data Mining on Historical Wind Resource
## 2 Lomaofski Optimization Model  
In this section, we describe the improved Lomaofski model for data pre-processing. The power output of wind turbines is stochastic, which is determined by wind and wind turbine generators. At the same time, the bad points and missing values of historical wind sources will be produced by wind turbines and meteorological monitoring equipment. It is necessary to effectively eliminate the spoiled data and supplement missing data in order to fully study the correlation between meteorological data and power data.  
The Lomaofski norm is a criterion to distinguish the singular point of measured data, which can establish basic composite numerical statistical analysis theory and eliminate abnormal data. Considering the characteristics of wind speed and correlation of time series, the Lomaofski norm is selected to preprocess meteorological data such as wind speed.  
The Lomaofski norm is based on the measurements conformed to the t-distribution. In a certain number of measurements, \(n\), the equal-precision measurement obtains a group of data, \(\mathbf{X}=(x_{1},x_{2},\cdots x_{n})\). If there is spoiled data, \(x_{k}\), which needs to be supplemented after eliminating, then delete it. The optimization model is discriminated and processed as follows:  
1. Remove the spoiled data, \(x_{k}\), then calculate the arithmetic average \(\overline{X}\) in the measurements without \(x_{k}\). \(\overline{X}\) is written as: \[\overline{X}=\frac{1}{n-1}\sum_{i=1}^{n}x_{n}\] (1)
2. Calculate the standard deviation \(s\), except the residuals of \(x_{k}\), which is expressed as: \[s=\sqrt{\sum_{i=1}^{n}v^{2}/n-2}\] (2)
3. The coefficient value, \(K(A,n)\), is obtained from the t distribution table according to the selected significant level A and the number of measurements \(n\), \(D=K(A,n)s\).
4. If \(x_{k}-\overline{X}>K(A,n)s\), then \(x_{k}\) is determined to be an abnormal point and should be eliminated; if \(x_{k}-\overline{X}<K(A,n)s\), it means that \(x_{k}\) is normal data and should be retained.
5. The data which is eliminated or missed is supplemented by using the value before and after the spoiled data \(x_{k}\) in the same sequence, which is expressed as \(x_{k}=\frac{x_{k-1}-x_{k+1}}{2}\). If the value before and after the spoiled data \(x_{k}\) is also missed, then it is supplemented according to the spatial correlation sequence obtained by the synchronization monitoring, which is expressed as \(x_{k}=\alpha*x_{k}\), where \(\alpha\) is the correlation coefficient of two spatial sequences, and \(x_{k}\)' is the corresponding point of the spatial correlation sequence.  
Wind speed and wind direction data sequences are mainly preprocessed in this paper. The preprocessed data obtained by the optimized model is shown in Figure 1. As can be seen in Figure 1, the wind farm has obvious characteristics of randomness, fluctuation and nonlinearity. The samples can be preprocessed effectively by the Lomaofski optimization model.
# Projected Augmented Reality Intelligent Model of a City Area with Path Optimization
## 2 Model Proposed
### Use of GA and A* for Solving the Travel Salesman Problem
#### 2.5.1 Genetic Algorithms  
GA are inspired by the theory of natural selection from Biology. The algorithm starts with a population, created at random. The population contains a number of individuals, defined by their individual genes organized in a chromosome. Solutions for the problem are encoded as chromosomes, so that each individual represents one possible solution. The fitness of each chromosome is calculated using a fitness function. In the present research the fitness function is the A*, as explained in Section 2.5.2. The best chromosomes of each population are selected for crossover, therefore passing their genes to the next generation. The worst chromosomes are discarded, or in the best case they receive a very low probability of being selected for crossover. After crossover, some individual genes may be subject to mutation. Mutation consists in randomly changing some genes. The result of mutation is often bad, but sometimes it allows to escape local minima. Afterwards, the next generation is created with the new individuals. Sometimes a number of the best individuals of the previous generation are allowed to pass unchanged, in a process called elitism. Elitism guarantees that the best individuals are never lost.
# Overlap in Automatic Root Cause Analysis in Manufacturing: An Information Theory-Based Approach
## 7 Conclusions  
This work presents a new measure of overlap. Overlap is a synchronization in the manufacturing process that makes all products that pass through a given machine in a certain step also pass through another specific machine in a step further ahead in the process. With data generated in the presence of this phenomenon, it becomes impossible to discern the influence of the machines that processed all these products on the products' quality.  
We propose a novel measure of overlap that uses information theory concepts such as Positive Mutual Information (PMI). This measure considers two critical aspects, namely whether the associations are positive or negative, and whether it is appropriate to detect overlap among step-machine tuples. This measure is the basis of a factor-ranking algorithm that is used to detect root causes, in an ARCA solution.  
To validate this new method, three experiments were conducted: (i) using mockup data, (ii) using simulated data that emulates a case study, (iii) using real data from the case study itself. It was possible to conclude that the proposed algorithm achieved better performance with simulated data, competing with the benchmark algorithms in the other two experiments.  
This paper contributes to the literature by presenting a robust measure of overlap, which allows for a better understanding and analysis of this characteristic of the problem. In addition, a new factor-ranking algorithm is presented with positive results. A visualization tool was also developed that eases the analysis by practitioners, by depicting the relevant overlaps between tuples in a manipulable graph.  
This work allows researchers and practitioners to have an improved comprehension of a new concept, which can lead to the development of improved ARCA solutions, making the management of manufacturing operations faster and reducing the associated workload.  
**Author Contributions:** Conceptualization, E.e.O.; methodology, E.e.O.; software, E.e.O.; validation, E.e.O., V.L.M. and J.L.B.; formal analysis, E.e.O.; investigation, E.e.O.; data curation, E.e.O.; writing--original draft preparation, E.e.O.; writing--review and editing, E.e.O. and V.L.M.; visualization, E.e.O. and J.L.B.; supervision, V.L.M. and J.L.B.; project administration, E.e.O., V.L.M. and J.L.B.; funding acquisition, E.e.O., V.L.M. and J.L.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Funds through the Portuguese funding agency, FCT--Fundacao Ciencia e Tecnologia within the project SFRH/BD/138228/2018.  
Not applicable.  
**Data Availability Statement:** Mockup and Stochastic Simulation data are available from the corresponding author upon reasonable request. Real Case-Study data are not available due to commercial restrictions.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# A Survey on Satellite Communication System Security
## 7 Future Research Directions  
Following our examination of attacks and defenses, this section explores potential security issues in future SCSs within fields such as 6G, various environments and standard protocols, AI-integrated SCSs, and quantum communications.
# Blockchain-Based Cloud-Enabled Security Monitoring Using Internet of Things in Smart Agriculture
## 2 Background
### IoT Smart-Agriculture Background  
IoT technology offers many benefits in agriculture, including improving productivity and high yields [20]. There are number of prior works discussing the role of IoT in agriculture, IoT applications in agriculture and the advantages of IoT in the agriculture field [5; 9; 10; 20; 21; 22; 23]. [PERSON] et al. [24] presented a review of IoT integration with smart agriculture and data-analytics benefits and challenges. The future trends and opportunities are categorized based on technologies, applications, business, and marketing. [PERSON] et al. [23] performed a detailed study of the technologies involved when using IoT in smart agriculture. The technologies covering the network protocols, architecture, cloud computing, and big-data analytics were leveraged to discuss the existing works in IoT-based smart agriculture. Additionally, the security issues, the policies proposed by different countries to support smart agriculture, and the existing smart-device-based applications are presented in the article. [PERSON] et al. [9] presented a review of the various advanced technologies used in IoT-based smart agriculture solutions. Additionally, various smart agriculture applications using IoT are categorized in the article. Furthermore, the existing blockchain solutions for supply-chain management in agriculture are discussed. The articles [25; 26] discussed the usage of wireless sensor networks to regulate water and monitor water level in agriculture. The authors emphasized that IoT plays a major role in water management. However, those articles focused on specific water-management applications in agriculture.  
The authors of [10] performed a survey of different applications used in smart agriculture with technologies, and cloud computing is used as a backend technology stack for application implementation. [PERSON] et al. [5] also performed a survey on using IoT in smart agriculture. The taxonomy is defined using a smart-agriculture architecture and technologies and classified the literature works based on the taxonomy categories. The authors of [20] also reviewed smart agriculture based on IoT architecture, application, software, hardware, principal advantages, and open research and challenges in the future. [PERSON] et al. [7] highlighted the data governance, security, and privacy requirements and concluded that a cultural shift is needed to adopt the IoT solutions in agriculture. Overall, based on the state-of-the-art IoT-based smart-agriculture survey, we can conclude that IoT can have many applications in agriculture. IoT technology has already been integrated with smart-agriculture applications. However, there are still many challenges and research issues regarding security and privacy, network issues, regulations, scalability, reliability, and resource optimization.
# The Role of PIXE and XRF in Heritage Science:
## 2 Brief Overview of the Particle Induced X-ray Emission and X-ray Fluorescence Techniques  
PIXE and XRF techniques have in common the emission of X-rays equal in energy to the difference between atomic shells. In the first, the probes are ions, typically few MeV protons provided by an accelerator, whereas in the latter they are X-rays, indicated as primary X-rays, and are provided by synchrotrons, radioactive sources, or X-ray tubes [17].  
Due to the specific energy levels of each atomic species, X-rays emitted from a target are characteristic of the impinged atoms; therefore, different elements can be simultaneously identified, acquiring an X-ray spectrum. It is worth mentioning that elements lighter than sodium cannot be normally detected with those techniques because of the absorption of low energy X-rays between their emission and their detection [18]. Therefore, they are best-suited for detecting pigments made of medium/high Z elements. Some examples can be cinnabar, lead white, lead-tin yellow, and bismuth black. On the contrary, other techniques, such as FORS (Fiber Optics Reflectance Spectroscopy) and RAMAN spectroscopy may also be used for detecting organic pigments or dyes and may be successfully used in combination for a comprehensive analysis of the materials [19].  
To overcome this limitation of PIXE, it is used in combination with other IBA techniques that may provide complementary information on lighter elements as Particle Induced Gamma-ray Emission (PIGE) [20] and on stratigraphy such as Rutherford Backscattering (RBS) [21].  
Both PIXE and XRF analysis are non-destructive (or not deliberatively destructive in the case of PIXE) and non-invasive multi-analytical techniques and are therefore largely in use in HS. An important difference between them is the penetration depths of primary probes. In XRF spectroscopy, in typical conditions, due to the radiation matter interaction, the probed depth is much bigger than in PIXE. Just to give an example, the range of a 3 MeV proton beam in a carbon matrix (somehow simulating light organic medium) is about 74 \(\upmu\)m. Conversely, 20 keV X-rays (half of the maximum energy of the typical X-ray beams used at INFN-LABEC, where beam intensity is still quite high, see e.g., [22]), through the same carbon thickness, have a 100% transmission factor, which slowly decreases down to 92% for a 740 \(\upmu\)m slab and to 48% for 7.4 mm (from [23], see also [24] for a comparison of PIXE and XRF probed depth in the study of metal samples).  
Therefore, roughly, PIXE analysis can be considered a surface technique, or in a way more superficial than XRF, although it depends on the energy of the X-ray considered [25].  
Another crucial difference is the quantitative analysis that can be carried out with PIXE technique, and it is not typically possible with XRF analysis in HS [26]. Experimental aspects of the PIXE and XRF techniques are reported in [27].  
Both techniques are largely in use in many laboratories [28; 29; 30; 31] and, recently, they were combined in a single facility [32].
# Feature Interaction in Terms of Prediction Performance
###### Abstract  
There has been considerable development in machine learning in recent years with some remarkable successes. Although there are many high-performance methods, the interpretation of learning models remains challenging. Understanding the underlying theory behind the specific prediction of various models is difficult. Various studies have attempted to explain the working principle behind learning models using techniques like feature importance, partial dependency, feature interaction, and the Shapley value. This study introduces a new feature interaction measure. While recent studies have measured feature interaction using partial dependency, this study redefines feature interaction in terms of prediction performance. The proposed measure is easy to interpret, faster than partial dependency-based measures, and useful to explain feature interaction, which affects prediction performance in both regression and classification models.  
machine learning; black box model; model interpretability; feature interaction; feature importance +
Footnote †: journal: Applied Sciences
# Networked Roadside Perception Units for Autonomous Driving
## 3 Design and Implementation of the RSPU System
### System Design of AutoC2X  
Cooperative perception denotes a desirably function both at the level of the infrastructure and vehicle, which explains the rationale behind designing AutoC2X for the two nodes. The set of functionalities offered by the RSPU system is similar to that of autonomous vehicles cooperative ITS functions, Figure 2 illustrates this proposed model. We designed a system in both vehicle and RSPU, in which a host realizes perception, whereas a router takes charge of the cooperative ITS function. Put differently, OpenC2X and Autoware operate on different nodes rather than being deployed on one computer. This design was chosen since a vehicle and RSPU typically include some dedicated computers necessitating external connectivity. As a case in point, dedicated computers could be on a vehicle for navigation, mapping, and driving logs. In a similar manner, an RSPU could have separate computers serving in the capacity of traffic monitoring systems, edge servers, as well as for signals/signage. For this reason, it is a better idea to ensure that a router manages all the nodes' external connectivity as opposed to extending all the nodes to have external connectivity, including the antenna. This configuration is also recommended by the ITS station architecture specification [1, 2] also recommends such a configuration.  
A key difference between RSPU and vehicle is equipment pertaining to wired connectivity and mobility. The controller area network (CAN) gateway of the vehicle helps gain control over steering,braking, and acceleration, whereas the RSPU has wired connectivity to the Internet as well as roadside networks. Moreover, the RSPU's functionality is a subset of the autonomous vehicle, i.e., specific functionalities of autonomous driving are found to be absent. These include localization, planning, and CAN control.
# Durability of Textile Reinforced Concrete: Existing Knowledge and Current Gaps
## 1 Introduction  
A growing body of research has been devoted to understanding the mechanical response and properties of textile-reinforced concrete (TRC) composites (also referred to as textile-reinforced mortar (TRM) or fibre-reinforced concrete matrix (FRCM) in the literature) [1, 2]. These composites are made of continuous fabrics embedded in an inorganic matrix (e.g., cement-based or lime-based mortar) and can show a pseudo ductile response with a high tensile strength when designed appropriately. This makes these composites suitable for the development of novel thin structural components or for the strengthening of existing masonry and concrete structures [3, 4, 5, 6, 7]. A disadvantage is that a large portion of this ductility (or crack development) occurs at service load levels (after mortar cracking), thus threatening the reliability and durability at ultimate limit state loads. The addition of short fibres, additives or fibre with different sizing and coatings to the mix design can help in addressing this disadvantage to some extent [8], but further investigations in this field are still needed. The current understanding of the mechanical response of these composites is reaching an advanced stage, but existing knowledge on the long-term performance and durability of these composites is still limited [9].  
Durability is defined as the ability of material or components to resist loads without failure during their service life [10]. For the structural application of TRCs, long-term strain capacity, transport properties (in both cracked and uncracked states) and resistance to aggressive environments are the most critical durability indicators [10]. It is generally believed that TRCs show better durability than conventional reinforced concrete [8]. However, there is still a lack of sufficient experimental or field evidence for this debate. Considering the variety of existing textile and matrix types, the durability of TRCs is alsoexpected to vary significantly, hence the need for comprehensive investigations in the coming years. The service cracking pattern and width can have a significant influence on the long-term performance of TRCs. As a criterion, limiting the crack widths to less than 50 \(\upmu\)m is suggested to avoid penetrating deteriorating materials into TRC composites [8].  
A growing number of publications can be found in the literature on the evaluation of the durability of TRC (and TRM) composites. These studies are mainly focused on the effect of chemical attacks, freeze-thaw cycles, or high temperatures on the mechanical response of TRC composites. Aiming at highlighting the gaps in this field and uncertainties on the existing data, a brief overview of these studies is presented in this paper.
# Thermal Stability Design of Asymmetric Support Structure for an Off-Axis Space Camera
## 5 Conclusions  
In this paper, a thermal stability design method based on CFRP is proposed for the asymmetric support structure of an off-axis TMA space camera. A complete example of thermal stability design is presented in the detailed design of the CFRP support structure for the camera.  
The relationship between CTE and the ply angle of multi-layer composites is demonstrated through the theoretical analysis of the composites. By adjusting the laying angle of the carbon fiber composite, the expansion of the camera support structure can be re-duced almost to zero in the asymmetric direction. Finite element thermal analysis and optical analysis show that the proposed support structure with low CTE greatly reduces the displacement of the optical elements under temperature load conditions compared with a support structure with isotropic layers, and the influence of a temperature rise of 5 \({}^{\circ}\)C on the MTF of the optical system decreases from 10.41% to 2.67%.  
This proves that it is necessary to fully consider the thermal stability design method in relation to structural asymmetry, a method which has great potential in the thermal stability design of off-axis space cameras and will attract more attention with the improve-ment of the precision of off-axis space cameras.  
Conceptualization, W.S. and P.X.; methodology, W.S. and P.X.; software, W.S., S.L. and Y.X.; validation, W.S. and P.X.; writing--original draft preparation, W.S.; writing--review and editing, W.S. and P.X.; supervision, W.S.; project administration, P.X.; funding acquisition, P.X. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the National Key Research and Development Program of China (Grant No. 2016 YFB0501200).  
The authors declare no conflict of interest.