esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case
## 7 Assessment of the Available Architectures  
### Methodology  
In this section, we discuss the suitability of the architectures presented in Section 5 to support the use cases reported in Section 4. Since each architecture may be implemented using different combinations of communications technologies, for each architecture we also discuss the resulting implementation choices. The assessment considers the complexity of installation, the compliance to regulatory constraints, and the ability to meet the requirements of the use cases in terms of latency and sampling frequency. We will assume that the SM is able to achieve sampling periods on the order of seconds. Therefore, the limiting factors stem from the bandwidth and latency of the different communications architectures and technologies. Since most use cases in each of the categories identified in Table 2 share similar requirements, in the following we discuss each category independently. If necessary, we highlight those use cases that are an exception and require more in-depth analysis.
## 5 Evaluation of the Security Aspects of the Designed Protocol
### Formal Analysis of the Presented Method and the Results of the Reviews  
The following section provides a detailed examination of our proposed method through formal analysis. Our proposed method underwent comprehensive security validation employing the AVISPA tool. This efficient official tool has been used by security researchers in extensive research for security evaluations of security protocols as well as analysis of security protocols used on the Internet. An automated security analysis system is employed, complemented by specialized servers like On-the-Fly Modeler (OFMC) and Constraint-Logic (Cl-AtSe). Moreover, AVISPA can conduct assessments of protocols in the presence of various attack scenarios. Acknowledging the capabilities of this software, we utilized the AVISPA tool for examining the security of our designed protocol against diverse attack scenarios and to guarantee the confidentiality of private information [42]. The results of the security evaluations performed with the AVISPA tool are illustrated in Figure 4.  
Stemming from the formal scrutiny and the findings presented in Figure 4, it is apparent that the offered method exhibits robustness against both categories of active and passive network attacks. The results derived from the OFMC and CL-ATSE outputs within the official AVISPA tool indicate that the confidentiality of private parameters that are exchanged among communication parties during the protocol's execution is maintained. Also, network attackers or unauthorized nodes are incapable of calculating or accessing these parameters. Table 2 outlines a comparative assessment of the proposed design against similar protocols, emphasizing their respective security features and resilience to security threats. According to the information in Table 2, related protocols aimed at securing communications within the Internet of Things (IoT) network exhibit notable security vulnerabilities. Recognizing the security requirements within the IoT network and the observed attacks, we effectively addressed these vulnerabilities by introducing a comprehensive protocol.  
Figure 4: AVISPA results. (**A**) ATSE (**B**) OFMC.
# _remote sensing_
###### Abstract  
This paper presents the HORUS mission, aimed at multispectral and multiangle (nadir and off-nadir) planetary optical observation, using Commercial Off-The-Shelf (COTS) instruments on-board a 6-Unit CubeSat. The collected data are characterized by a sub-kilometer resolution, useful for different applications for environmental monitoring, atmospheric characterization, and ocean studies. Latest advancements in electro-optical instrumentation permit to consider an optimized instrument able to fit in a small volume, in principle without significant reduction in the achievable performances with respect to typical large-spacecraft implementations. CubeSat-based platforms ensure high flexibility, with fast and simple components' integration, and may be used as stand-alone system or in synergy with larger missions, for example to improve revisit time. The mission rationale, its main objectives and scientific background, including the combination of off-nadir potential continuous multiangle coverage in a full perspective and related observation bands are provided. The observation system conceptual design and its installation on-board a 6U CubeSat bus, together with the spacecraft subsystems are discussed, assessing the feasibility of the mission and its suitability as a building block for a multiplatform distributed system.  
CubeSat; Earth Observation; optical; multiangle; multispectral; remote sensing Article]HORUS: Multispectral and Multiangle CubeSat Mission Targeting Sub-Kilometer Remote Sensing  
[PERSON]; [PERSON], [PERSON]; [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON]; F.; and Multiangle CubeSat Mission Targeting Sub-Kilometer Remote Sensing Applications. _Remote Sens._**2021**, 13, 2399. [[https://doi.org/10.3390/rs13122399](https://doi.org/10.3390/rs13122399)]([https://doi.org/10.3390/rs13122399](https://doi.org/10.3390/rs13122399))  
1
# Smart Road Traffic Accidents Reduction Strategy Based on Intelligent Transportation Systems (TARS)
## 4 Performance Evaluation
### Performance Evaluation Metrics
#### 4.2.1 Message Delivery Ratio (MDR)  
The MDR shows the percentage of messages that are successfully delivered to the vehicles. The basic objective of each routing protocol is to gain a high delivery ratio. The following mathematical formula is used to calculate the MDR.  
MDR = (Total No. of Received Messages/Total No. of Sent Messages) \(\times\) 100
# A Review of the T-Stub Components for the Analysis of Bolted Moment Joints
## 2 Analytical Model
### Component Approach (EC9) Aluminium T-Stub in Tension  
The aforementioned component approach of the EC3 formulae are only applied for steel mild materials and not for other types of materials. Therefore, the standard EC9 [13] covered the design of aluminum structures and there is found the equations that predict the failures modes of aluminium T-stub components. These failure modes are quite similar to those proposed by EC3, but with a difference in Mode 2, which is divided into two failure modes:  
1. Mode 1: the flange failure by developing 4 plastic hinges, 2 of them at the web-flange intersection (\(w\)) and the other 2 at the bolt location (\(b\)), see Equation (24).
2. Mode 2a: flange failure by developing 2 plastic hinges with bolt forces at the elastic limit, see Equation (25).
3. Mode 2b: bolt failure with yielding of the flange at the elastic limit, see Equation (26).
4. Mode 3: bolt failure and the flange is in the elastic zone, see Equation (27).  
\[F_{Rd,1}=\frac{2(M_{u,1})_{w}+2(M_{u,1})_{b}}{m}, \tag{24}\]  
\[F_{Rd,2a}=\frac{2M_{u,2}+n\sum B_{o}}{m+n}, \tag{25}\]  
\[F_{Rd,2b}=\frac{2M_{u,2}+n\sum B_{u}}{m+n}, \tag{26}\]  
\[F_{Rd,3}=\sum B_{u}, \tag{27}\]  
\[M_{u,1}=0.25t_{f}^{2}\times beff_{1}\times\rho_{u,huzz}\times f_{u}\times\frac{ 1}{k}\times\frac{1}{\gamma_{M1}}, \tag{28}\]  
\[M_{u,2}=0.25t_{f}^{2}\times beff_{2}\times\rho_{u,huzz}\times f_{u}\times\frac{ 1}{k}\times\frac{1}{\gamma_{M1}}, \tag{29}\]  
\[M_{o,2}=0.25t_{f}^{2}\times beff_{2}\times\rho_{o,huzz}\times f_{o}\times\frac{ 1}{\gamma_{M1}}, \tag{30}\]  
\[\frac{1}{k}=\frac{f_{o}}{f_{u}}(1+\Psi\frac{(f_{u}-f_{o})}{f_{o}}, \tag{31}\]  
\[\Psi=\frac{(e_{u}-1.5e_{o})}{1.5(e_{u}-e_{o})}, \tag{32}\]  
\[\varepsilon_{o}=\frac{f_{o}}{E}, \tag{33}\]  
\[B_{o}=\left\{\begin{array}{cc}0.9f_{y}\times A_{s}/\ \gamma_{M2},&for\ steel\ bolts\\ 0.6f_{o}\times A_{s}/\ \gamma_{M2},&for\ aluminium\ bolts\end{array}\right., \tag{34}\]  
In Equation (24), \(\left(M_{u,1}\right)_{w}\) should be determined by considering \(\rho_{u,huzz}<1\) and \(\left(M_{u,1}\right)_{b}\) should be determined by considering \(\rho_{u,huzz}=1\). From Equation (28) to Equation (30)\(\rho_{u,haz}\) and \(\rho_{o,haz}\) are equal to 1 if not welded in a section. Furthermore, \(f_{o}\), \(f_{u}\), \(f_{y}\) are the characteristic values of 0.2% proof strength, the characteristic value of ultimate tensile strength and the characteristic value of yield strength, respectively. \(B_{o}\) and \(B_{u}\) are the conventional bolt strength at elastic limit and the tension resistance of a bolt plate assembly. \(\varepsilon_{u}\) and \(A_{s}\) are the ultimate strain of the flange material, and the stress area of a bolt. Finally, \(\gamma_{M1}\), \(\gamma_{M2}\) are the partial factors for design and take the values of 1 and 1.25, respectively.
# Methodology for the Efficient Progressive Distribution and Visualization of 3D Building Objects
## 3 Methodology
### Storage Methods of the BuildingTree  
Figure 7 shows the storage system architecture. Since city models can be generated from different sources, the storage system should support multiple formats for data input. In this study, CityGML is selected as the standard for 3D city dataset integration. Meanwhile, a 3D visualization format such as Collada and X3D are also supported. 3D city/visualization data in different formats are first read into the system with specific parsers; for example, we use citygml4j for CitygML files, xj3d for X3D files and j3d for KML and Collada files. Then, the geometry content and semantic features (if any) of the input 3D models are extracted and stored in the NoSQL database in JSON format which can support different schemas for different models (for more details, refer to Section 4). In this paper, we employ the polygon set to present the geometry data which is consistent with CityGML. For example, all buildings have common features such as ID, type, centroid, height and so on, but one building may have the feature \"owner\" while the others do not. The data on all these buildings can be stored in a collection of NoSQL, which is not supported in a table of relational databases since its columns have to be predefined. Furthermore, to simplify the 3D data storage, we export the 3D polygons as raw text which can be easily imported into programming languages (Python or Java) as polygon list objects.
## 5 Evaluation
### Performance Evaluation  
This section presents a performance evaluation of the proposed system. We adopted two different metrics to measure that performance: the throughput and the total time. The aim is to show the impact of changing data size on the system throughput and on the time taken to encrypt and decrypt the JSON data. We generated dummy JSON data (that resemble real-life JSON data) by using the Faker library ([[https://fakerjs.dev/](https://fakerjs.dev/)]([https://fakerjs.dev/](https://fakerjs.dev/)), accessed on 12 March 2024).
## 6 Results
### Error Metric  
Comparing and evaluating the forecasting models requires an error metric. The symmetric mean absolute percentage error (SMAPE) measures the average absolute error relative to the mean between the actual and forecast values. This measure is expressed as a percentage. SMAPE is always non-negative and bounded between 0 and 200%; values closer to zero indicate a smaller forecast error. Therefore, when comparing two models, the one that produces the smaller SMAPE is preferred. SMAPE is defined in Equation (13).  
\[\text{SMAPE}=\frac{100}{n}\sum_{i=0}^{n}\frac{\mid y_{i}-\hat{y}_{i}\mid}{( \mid y_{i}\mid+\mid\hat{y}_{i}\mid)/2} \tag{13}\]  
where \(n\) is the length of the test set, \(y_{i}\) is the \(i\)-th actual value, and \(\hat{y}_{i}\) is the \(i\)-th predicted value. We use SMAPE because it is a relative error metric that enables us to compare the error between time series in different units. SMAPE is a bounded quantity and minimizes (to a certain extent) the problem of having a 0 in the denominator of (13).
## 3 System Model
### UAV Data Collection  
Notably, data are gathered from the deployed sensor by a number of \(N\) UAVs. It is considered that the UAVs and deployed sensors communicate via uplink-based orthogonal frequency division multiple access (OFDMA). \(K\) continuous links are supported by the UAVs for data collection. Additionally, we provide a distributed system where UAVs share data with surrounding fog sites. Since the inserted sensors may be portable, there may be sporadic communication between the sensors and UAVs with significant packet loss.  
Therefore, the line of sight (LoS) must be maintained for communication to be successful. The LoS probability is calculated as in [64].  
\[p_{LoS}=\frac{1}{1+o\times exp(\gamma[\psi-\
u])} \tag{7}\]  
where \(\psi\) is the elevation angle between the sensor \(S_{xy}\) and the UAV \(U_{xy}\), and \(o\) and \(\gamma\) are constants and are determined by the communication frequency and range. The probability increases with increasing UAV height; therefore, deployed sensors may only be allocated to the \(j_{th}\) UAV if the chance of LoS is near to 1. Consequently, the condition of the connection between the sensor and the UAV is:  
\[d_{ij}=\frac{a_{j}}{sin(p_{LoS})} \tag{8}\]  
where \(d_{ij}\) is the distance between the \(i_{th}\) ground node and the \(j_{th}\) UAV and \(a_{j}\) is the height of the UAV from the ground node.  
Considering there are a \(\times\) b sensors spread out over the farm, the coverage time \(t_{cov}\) of the UAV is calculated as:  
\[t_{cov}=\Sigma_{a}\Sigma_{b}T_{ab}+\frac{2}{N}\Sigma_{i}\Sigma_{j}S_{ij} \tag{9}\]  
where \(T_{ab}\) is the time required for the UAVs to travel from one ground node to the other node, and \(N\) is the total number of UAVs that were utilized to collect the data.  
We employed a set of brokers \(B\) to control the fog sites to analyze the collected data. A fog broker was in charge of the effective use of fog services near the target consumers. The broker distributed the resources to other brokers on the site. This supported latency-sensitive IoT applications by reducing the communication time. \(N_{d}=d_{m}+d_{n}\), where \(d_{m}\) is the network delay between the sensors and UAVs, and \(d_{n}\) is the network delay between the UAV and the designated fog site and is dependent upon the distance between ground nodes. The network cost \(C\), on the other hand, is a linear function of distance and is given as:  
\[C=\vartheta\big{(}d_{m,n}+\Sigma_{k\in B\delta n\
eq o}d_{n,o}\big{)} \tag{10}\]  
where \(\vartheta\) is constant, \(d_{m,n}\) is the distance between the \(m_{th}\) UAV and its local broker \(n\) and \(d_{n,o}\) is the distance of the local broker renting the computation resource \(n\in B\).
# HarmoDehazeDiffusion: Multi-Scale Spatial Fusion Network for Dense Haze Removal
## 2 Related Work
This design flaw results in two critical problems: first, the fixed receptive field in feature extraction weakens the model's ability to model the long-range contextual relationships; second, the lack of dynamic coordination between shallow detail information and deep overall illumination features affects the collaborative generation of fine-grained textures and the overall illumination. To address these issues, we propose a depth-guided multi-scale feature spatial fusion network named HarmoDehazeDiffusion. The network first estimates the depth of the input image using an existing open-source depth estimation network [37] and uses this depth information to guide the denoising process. Subsequently, by constructing a hierarchical progressive interaction architecture between the deep and shallow features and incorporating multi-scale feature extraction layers, it achieves dynamic adaptive fusion of the cross-scale features. This network enhances the overall consistency of illumination while preserving the integrity of the local details, thereby improving the semantic coherence and spatial rationality of the diffusion model in complex scenes. Experiments show that this design not only enhances the depth perception through depth guidance but also effectively mitigates issues such as blurring of details and the illumination inconsistency caused by fragmented scale information.
## 5 Engineering Application
### Application Analysis in the Field of Aeronautical Engineering  
The proposed methodology is not only applicable to long-life products in space engineering but also provides a new theoretical tool for the reliability assessment of large-scale aviation systems. Taking civil aircraft as an example, only one aircraft can typically be allocated for a full-scale life test due to limitations in development cycles and test costs. Moreover, the test results may not even show failure [33]. This very small sample size leads to a significant underestimation of the aircraft's safe life. Conversely, abundant component test data--such as those for the fuselage, wings, and tail--have been accumulated during aircraft development. With the new method, component life test data and aircraft life test data can be fused, increasing the available information. Compared to the traditional method of reliable life assessment based solely on aircraft life test data, the proposed method significantly improves the accuracy of aircraft life prediction.  
Regarding life extension, once an aircraft reaches its reliable life, the traditional method often selects one aircraft for a life extension test. However, because of the small number of test samples and limited information, the precision of life extension is not high [34]. As this type of aircraft is put into service, a large amount of service life data will be accumulated. The proposed method can exploit these system-level data for updating life assessment results, enabling low-cost life extension.
# Practical Application of Mesh Opportunistic Networks
## 5 Discussion
### Mesh OppNets in Professional Communication Workflows  
We aimed to test the coverage and effectiveness of a mesh OppNet network to coordinate communications while producing an audiovisual news piece by journalists. With the obtained results in this experiment, we can conclude that mesh OppNets can be implemented for intercommunication processes in the context of professional journalism. These networks do not impede the creation of informative audiovisual content, yet they offer the benefit of network communication when the connection through mobile devices (telephone and Internet) is compromised by saturation, emergency circumstances or the use of inhibitors. In contexts where the use of telephone data is limited, this type of electronic communication can aid professionals who must save their data for the transmission to the cloud (i.e.,: a newsroom and/or social networks). It is a mode of communication that while having many advantages in terms of data saving, and in location and connection integration, regardless of the technical context, it also has some drawbacks that must be borne in mind. Among these, the limited spatial coverage stands out, which might limit the use of mesh OppNets in professional contexts to those events in which all members involved in the communication flow are within a given communication radius. There are currently places like New York where this type of communication can be implemented without considering this drawback, given that their use is more widespread and there are hundreds if not thousands of nodes that increase the spatial coverage of the mesh network. However, in other areas there may be hardly any nodes to amplify the coverage of a single device. That is why intragroup communication at mass events is today considered to be the ideal working context for the use of these mesh networks, particularly in the field of professional journalism. However, a limitation of this work is the lack of use of a system that tracked participants locations at all the times. In a professional context as the one described here, it would be useful to have access to the movement of the users and to analyze the impact of mobility on communications.
# CONDA-PM--A Systematic Review and Framework for Concept Drift Analysis in Process Mining
## 8 Discussion  
It is crucial to have a clear understanding of the stages and targets that should be satisfied by an approach to concept drift analysis. This understanding is shaped in dimensions of the CONDA-PM framework. A comprehensive approach to concept drift analysis should have a goals definition phase, a coding and processing phase, an implementation phase and an evaluation phase. In the goals definition phase, the granularity level and the mode the approach is addressing are identified. In the approach coding and processing phase, the drift patterns to be studied as well as the desired input and the perspectives to addressed are identified. As shown, there is a relation between the inputs and the decision about the covered perspectives. Based on the former decisions, the authors make choices of the used techniques and the change form. In the same phase, the study focus is decided. The implementation and evaluation phases demonstrate the outputs of an approach and show the maturity level reached by the approach.  
The results obtained after conducting the SLR, constructing the CONDA-PM framework, and applying it on 19 selected studies highlight some notes. _First_, although concept drift is mentioned as a challenge in the process mining manifesto [1], less studies exist that address this challenge. Compared to other process mining topics, like conformance checking and predictive monitoring, more research efforts should be spent on concept drift analysis. _Second_, to the best of our knowledge, no study exists that reasons about the detected drifts and explains them by contextual analysis neither of the process instance context nor the entire business process context. _Third_, most studies detect and localise concept drifts in offline mode, whereas only few studies enable an runtime online analysis of running processes as well. This research gap disables the use of concept drift analysis approaches in other process mining tasks. As example of missed opportunities for concept drift analysis, consider predictions on a running business process when the data used by the predictions is changing. _Fourth_, there is a lack of studies regarding the effect of a drift on process performance measurements, and the causality between a certain type of a drift (i.e., whether it is a sudden, incremental, recurring or gradual drift) and different performance indicators.
## 3 Methodology
### Preprocessing Raw Data
#### 3.1.5 EEG Band Decomposition  
The normalized EEG signal \(x_{nor}[n]\) was decomposed into different frequency bands using five zero-phase Chebyshev Type-II band-pass filters (BPFs). Each BPF was designed to extract one of the following EEG frequency bands: Delta (\(\Delta\), 0.5-4 Hz), Theta (\(\theta\), 4-8 Hz), Alpha (\(\alpha\), 8-13 Hz), Beta (\(\beta\), 13-28 Hz), and Gamma (\(\Gamma\), 31-49 Hz).  
The sixth-order filter, determined heuristically, provided the optimal balance between the signal attenuation and computational efficiency. For all filters, the stopband attenuation was set to 50 dB to ensure minimal signal distortion. The normalized EEG signal (\(x_{nor}[n]\)) was divided into EEG sub-bands \(x_{BPF}^{\Delta}[n]\), \(x_{BPF}^{\Delta}[n]\), \(x_{BPF}^{\Delta}[n]\), \(x_{BPF}^{\Delta}[n]\), and \(x_{BPF}^{\Gamma}[n]\).  
Figure 3 illustrates a two-second window of each decomposed EEG band, clearly showing that the \(\Delta\) band represents the lowest frequency range, while the \(\Gamma\) band corresponds to the highest frequency range. Additionally, Figure 4 presents the Fast Fourier Transform (FFT) of each decomposed band, demonstrating that the filters effectively separate the bands within their respective frequency ranges.  
Figure 3: A two-second window of the decomposed EEG bands (Delta, Theta, Alpha, Beta, and Gamma) extracted using sixth-order Chebyshev Type-II band-pass filters. The Delta band represents the lowest frequency range (0.5–4 Hz), while the Gamma band corresponds to the highest frequency range (31–49 Hz).