esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
* Object-based CD approaches generally require more complex frameworks. This results in a lower degree of automation of the entire CD framework due to the need to individually perform image segmentation algorithms and select appropriate segmentation parameters. With the popularization of deep learning technology, the methods based on deep neural networks have been widely used in change detection. In particular, the graph neural networks (GNNs) have been noticed due to their excellent performance in unstructured data classification. Recently, GNNs have been successfully applied to image classification [33, 34] and change detection [35, 36]. Specifically, in [37], graph convolutional networks (GCNs) are utilized to extract the features of different types for hyperspectral image classification. [PERSON] et al. proposed a semisupervised CD approach based on GCNs [38], which adopts multi-scale parcel segmentation to encode multi-temporal images as a graph. However, there are still few studies on GCN-based CD at present. Therefore, GCNs-based CD still needs continuous and further research. Considering the excellent performance of GCNs in solving image classification, we are able to model the change detection task as a graph node classification task for improving the performance of CD. With this motivation, the paper proposes a novel multi-scale superpixelgraph attention network (MSGATN), which can process unstructured data natively and extract valuable features. In the proposed method, a difference image (DI) is firstly obtained by Euclidean distance between bitemporal images. Then, an SLIC algorithm is employed to divide the DI into many homogeneous superpixels. Subsequently, these superpixels are exploited to model the problem by graph theory to build a series of nodes with Based on this, the multi-scale features of each node are captured by a graph attention network (GATN). Finally, the binary change map (BCM) is generated by classifying each node using some fully connected layers. The contributions of the proposed MSGATN approach are summarized as follows:  
1. We propose a network model based on graph theory, which can process the unstructured data of objects with irregular boundaries in OBCD and consider the adjacency relationship between objects. 2. The proposed method is inductive, which can simultaneously adapt to graphs of different scales. Therefore, our proposed MSGATN can exploit the constructed graphs of various scales, thus improving the abilities of representation and generalization. 3. Experiments on several real datasets obtained from different sensors demonstrate that the proposed MSGATN has high efficiency and performance, as well as certain generalization. The rest of this paper is organized as follows. Section 2 briefly introduces some related works. In Section 3, our method is described in detail. Section 4 provides the experimental settings and results. Finally, the conclusions and future works are given in Section 5.
## 3 FSS Analysis
### Angle Stability  
Due to the uncertainty of the direction of the electromagnetic wave, the FSS, if sensitive to the incident angle, will have a large deviation at its resonant frequency when it resonates. If the deviation is too large, the FSS cannot guarantee to keep the proper spatial filtering function within the working frequency band. Therefore, the resonant frequency of the designed FSS must have good angle stability under different polarized oblique incidences to ensure that the FSS can achieve stable operation for a fixed frequency band.  
Figure 10 shows the simulated reflection coefficients for various oblique angles of incidence. Here, the angle mean deviation is defined to analyze the angle stability of the proposed FSS and calculated as follows:  
\[\delta f_{p}=\frac{\left|f_{p}^{\text{normal}}-f_{p}^{a_{1}}\right|+\left|f_{ p}^{\text{normal}}-f_{p}^{a_{2}}\right|+\cdots+\left|f_{p}^{\text{normal}}-f_{p}^{a_ {n}}\right|}{nf_{p}^{\text{normal}}}\times 100\% \tag{5}\]  
where \(f_{p}^{\text{normal}}\) is the resonant frequency at normal incidence; \(f_{p}^{a_{i}}\) is the resonant frequency at \(a_{i}\) angle; \(n\) is the total number of oblique incident angles. The lower the value, the better the angle stability.  
Table 3 counts the three resonant frequencies and \(\delta f_{p}\) when TE and TM waves are incident on the proposed FSS at different angles. It can be seen from Table 3 that the average angle deviation of the proposed FSS does not exceed 1% when the TE wave is incident, and the average angle deviation does not exceed 3% when the TM wave is incident. All the results show the proposed FSS has high angle stability characteristics.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Nodes** & \multicolumn{3}{c}{**TE (GHz)**} & \multicolumn{3}{c}{**TM (GHz)**} & \multicolumn{3}{c}{\(\overline{M}_{a}^{\text{TE}}(\%)\)} & \(\delta f_{p}^{\text{TE}}(\%)\) & \(\delta f_{p}^{\text{TM}}(\%)\) \\ \hline
**Angle (deg.)** & **0** & **30** & **45** & **60** & **0** & **30** & **45** & **60** & & \\ \hline \(f_{1}\) & 7.5 & 7.5 & 7.5 & 7.3 & 7.5 & 7.7 & 7.7 & 7.7 & 2.60 & 0.89 & 2.67 \\ \(f_{2}\) & 17.5 & 17.5 & 17.5 & 17.3 & 17.5 & 17.5 & 17.5 & 17.3 & 0 & 0.38 & 0.38 \\ \(f_{3}\) & 24.3 & 24.5 & 24.3 & 24.1 & 24.3 & 24.5 & 24.3 & 24.3 & 0.21 & 0.55 & 0.27 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The resonance frequency and offset of the proposed FSS for different waves incident modes at different angles.  
Figure 9: Reflection coefficients for various oblique angles of incidence under TE- and TM-polarized waves.
# Analysis of Elastic Nonlinearity Using Continuous Waves: Validation and Applications
## 3 Validation
### Feasibility of the Separation of Modulus and Damping Contributions
#### 3.1.4 Hysteresis  
Finally, the MoDaNE approach in presence of hysteretic nonlinearity was also validated. In this case, as a consequence of memory effects, the material parameters at a given amplitude of excitation depend not only on the actual nonlinearity, but also on the previous stress history. It follows that a hysteretic loop is observed when considering the dependence of velocity (and attenuation) on strain applying a protocol with increasing and then decreasing amplitudes of excitation [15].  
To prove the capability of the MoDaNE approach in separating velocity and attenuation nonlinearities also in the presence of hysteresis, numerical simulations were performed using a phenomenological model. The model, described in [49] and validated in [46,50], is based on a Preisach-Maiergoytz approach in which hysteresis in modulus and damping are linked together. Results of the analysis of the numerical data are reported in Figure 6 and allow observing that the MoDaNE approach is sensitive to memory effects.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 6 Local Experiments Performance Evaluation  
### Topology 1 and Fixed Message Size  
A first set of experiments has been run by combining Topology 1 and fixed message size, being the results for MQTT, CoAP, and OPC UA respectively presented in Tables 6-8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiments** & **Message Size** & **Number of Messages** & **Topology** \\ \hline EX1 & Fixed & 5000 & 1 \\ \hline EX2 & Fixed & 500 & 1 \\ \hline EX3 & Fixed & 5 & 1 \\ \hline EX4 & Fixed & 5000 & 2 \\ \hline EX5 & Fixed & 500 & 2 \\ \hline EX6 & Fixed & 5 & 2 \\ \hline EX7 & Variable & 5000 & 1 \\ \hline EX8 & Variable & 500 & 1 \\ \hline EX9 & Variable & 5 & 1 \\ \hline EX10 & Variable & 5000 & 2 \\ \hline EX11 & Variable & 500 & 2 \\ \hline EX12 & Variable & 5 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Local experimental scenarios with combination of the different parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **IAT (ms)** & **Message Sent** & **Number of Senders** & **Number of Receivers** \\ \hline MQTT 1 & 1000 & 5 & 1 & 1 \\ \hline MQTT 2 & 1 & 5000 & 1 & 1 \\ \hline MQTT 3 & 1000 & 5 & 1 & 94 \\ \hline MQTT 4 & 1 & 5000 & 1 & 94 \\ \hline MQTT 5 & 1000 & 470 (5 each) & 94 & 1 \\ \hline MQTT 6 & 1 & 4700 (50 each) & 94 & 1 \\ \hline MQTT 7 & 1 & 5000 (100 each) & 50 & 47 \\ \hline CoAP 1 & 1000 & 5 & 1 & 1 \\ \hline CoAP 2 & 1 & 5000 & 1 & 1 \\ \hline CoAP 3 & 1000 & 5 & 1 & 94 \\ \hline CoAP 4 & 1 & 5000 & 1 & 94 \\ \hline CoAP 5 & 1 & 470 (5 each) & 94 & 1 \\ \hline CoAP 6 & 1 & 4700 (50 each) & 94 & 1 \\ \hline CoAP 7 & 1 & 5000 (100 each) & 50 & 47 \\ \hline \hline \end{tabular}
\end{table}
Table 5: FIT-IoT experimental scenarios.  
Looking into the results obtained for the time-to-completion (TTC, in milliseconds), CoAP provides the lowest values for frequent messages (short and average IAT). However, for larger IAT, CoAP is the protocol that provides the largest TTC (1051 ms). An explanation for this pattern concerns the acknowledgement behaviour of CoAP. For messages that have a large interarrival time (IAT), lack of synchronization may occur. However, we have also observed some peaks in TTC derived from the shared usage of the Internet access connection between the local testbed, and the Internet campus access.  
A similar behavior, showing more variability, is also observed in MQTT.  
OPC UA is the protocol that provides the most stable results for TTC, when the IAT is varied, even though the resulting TTC is slightly, but not significantly, higher. In the experiments carried out at the local testbed, the experiments incurred zero packet loss, for all rounds of execution in the three protocols tested.  
As for packet loss, none of the protocols showed any packet loss for the scenarios run, as expected, given that the generated message rate does not fill the available link capacity.
# Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area
## 1 Introduction  
### Background  
Land cover mapping is a fundamental tool for understanding complex land-use patterns across the globe. It provides key information for such critical issues as eco-system health and biodiversity conservation, and it monitors changes over time. By accurately classifying various types of land covers, like forests, agricultural lands, wetlands, andurban areas, land cover maps offer valuable insights. Advancements in remote sensing technology have increased the availability of satellite imagery with diverse characteristics, making it a cornerstone in land cover mapping [1].  
Vineyards contribute significantly to biodiversity by supporting habitats that provide critical ecosystem services [2]. Grasslands, herbaceous zones, and forested areas surrounding vineyards act as ecological corridors, promoting species richness and fostering pollinators and natural predators essential for pest regulation and crop production [3,4]. These habitats also improve soil health, reduce erosion, and support water retention, thereby enhancing overall ecosystem resilience [5]. By maintaining a diverse landscape, vineyards can help preserve local flora and fauna, thereby supporting ecological stability and productivity [6]. Mapping surrounding land cover types is indispensable for biodiversity conservation in vineyard ecosystems. High-resolution maps generated through advanced technologies, such as satellite imagery and UAVs, allow the precise identification and monitoring of diverse habitats. These maps facilitate the understanding of spatial patterns, enabling informed decisions to optimize agricultural practices while conserving biodiversity [7]. For instance, detailed land cover maps guide the management of cover crops, monitor pest-prone zones, and assess the ecological benefits of natural habitats within and around vineyards [8].  
Traditionally, classical machine learning techniques, such as SVM [9], decision trees [10], and random forest [11] have been used for land cover mapping from satellite images. However, these methods have limitations, such as the need for expert analysis [12] and challenges with high-resolution imagery [13]. An example is maximum likelihood classification (MLC), which is based on pixel probabilities but struggles with resolution limitations and expert input [14].
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
### Literature Search and Selection Methodology  
This paper specifically surveys graph-based RA algorithms categorized in each of these network types in a separate Section. This classification is based on the significant number of relevant papers published in highly influential journals and conferences in recent years (60% of those since 2017). The overall number of papers (out of 130 relevant works, published after the year 2006) chosen to be included in this review, after filtering of their contents and topics in regards to relevance and influence, is 62, which shows that the topic of graph-based RA methods is still very open to further investigation. The process of relevant literature selection for this survey follows the PRISMA protocol [13], and is illustrated in Figure 1. This is further highlighted by the analysis of recent relevant surveys in Section 2.
## 3 Results
### Resonant Column Test
Figure 8: Material damping curve for different moisture \(m\) contents: (**a**) \(m=8.1\%\), (**b**) \(m=9.4\%\), (**c**) \(m=10.8\%\), and (**d**) \(m=12.9\%\). For the test results, the modulus reduction curve was fit. For the purpose of accurately modeling the soil stiffness degradation, the upper and lower limit curves were derived. The soil pseudoreference stress was estimated from the test results, and based on this, and the curvature parameter was fit to the data. The value of \(\alpha\) was equal to 1.15 for all test results. The results of the calculations are shown in Figure 9a. The pseudoreference stress could be estimated based on the empirical equation proposed by [PERSON] [42] (11):  
\[\gamma_{t}=(\varphi_{1}+\varphi_{2}\cdot I_{P}\cdot OCR^{\varphi_{3}})\cdot \sigma^{\prime}_{3}\varphi_{4}, \tag{11}\]  
where \(\varphi_{1}\) = 0.0352, \(\varphi_{2}\) = 0.0010, \(\varphi_{3}\) = 0.3246, and \(\varphi_{4}\) = 0.3483. The pseudoreference stress for non-cohesive soils reduced to the simpler Equation (12):  
\[\gamma_{t}=\varphi_{1}\cdot\sigma^{\prime}_{3}\varphi_{4}, \tag{12}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample No. ** & \(\mathbf{e_{0}}\) & \(\mathbf{S_{r}}\) & \(\mathbf{\sigma_{3}}\)  
For the presented power function, we found the value of the constants by regression analysis, which were equal to \(\varphi_{1}=0.0565\) and \(\varphi_{4}=-0.182\) with the coefficient of determination R\({}^{2}\) equal to 0.238, which corresponded to low correlation, although the calculations with the constant curvature parameter and calculated \(\gamma_{1}\) based on Equation (12) showed good correlation with test data (Figure 9b). Modeling of the damping gave us information about energy dissipation, which could occur even when the soil strain was at a low level. The soil damping could be related to the \(G\)/\(G_{0}\) relationship. We modified the proposition of [PERSON] and [PERSON] [43] and we was able to approximate the shape of the damping characteristic (13):  
\[G=\rho\cdot v_{s}=\rho\cdot\frac{\omega_{n}\cdot l}{\beta}, \tag{7}\]0  
This simple relationship between the soil stiffness and damping characteristics was able to calculate the damping without special effort to perform the damping tests. \(D_{norm}\) is the normalization damping parameter. We analyzed the damping characteristics, and based on the test data, we found that for the compacted sandy silt soil tested in this article, \(D_{norm}\) did not have a constant value, but changed with the normalized shear modulus progress. This characteristic can be observed in \[G=\rho\cdot v_{s}=\rho\cdot\frac{\omega_{n}\cdot l}{\beta}, \tag{7}\]1 Based on these results, the relationship between \(D_{norm}\) and \(G\)/\(G_{0}\) was established as follows (14):  
\[G=\rho\cdot v_{s}=\rho\cdot\frac{\omega_{n}\cdot l}{\beta}, \tag{7}\]2  
where \(\delta_{1}\) and \(\delta_{2}\) are constants equal to 0.094 and 0.0196, respectively, for this function's coefficient of determination, R\({}^{2}\) was equal to 0.988.
# Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa
### Main Air Pollutants
#### 1.1.3 Particulate Matter (PM)  
PM refers to a type of air pollutants which consist of a complex mixture of particles suspended in the air, with various sizes and compositions. They are produced by both natural and anthropogenic activities. The main sources of particulate pollution are industrial activities, power plants, motor vehicles, construction activity, fires and natural windblown dust. The major industries in Kampala include the following: sugar, brewing, tobacco, cotton textiles, cement and steel production [19]. PM mass concentration is typically tracked as both PM\({}_{10}\), the total mass of PM with a diameter of 10 micrometres or less, and PM\({}_{2.5}\), the total mass of PM with a diameter of 2.5 micrometers or below (and a subset of PM\({}_{10}\)). This paper concentrated mainly on PM\({}_{2.5}\), nitrogen dioxide and sulphur dioxide.
# A Multi-Bandwidth Reconfigurable Patch Antenna for Devices in WLAN and UWB Technology Applications
###### Abstract  
This article introduces a process to design, simulate, and measure a novel multi-band patch antenna with different operation modes, i.e., band centers and bandwidths. Switching between operation modes is possible using a pair of PIN diodes to connect different parts of the antenna with the main antenna patch. Such a reconfigurable design allows for individual control of each frequency range. The main operation mode of the resulting antenna has an impedance bandwidth with two bands, one from 2.4 GHz to 2.73 GHz and another from 3.4 GHz to 5.73 GHz, with a maximum gain of 4.85 dBi and stable radiation patterns. The resulting antenna is suitable for applications using both ultra-wideband technologies and wireless local-area network (WLAN) technologies.  
reconfigurable; ultra-wideband; slot; PIN diode; wireless local-area network Article
# Air-Based Contactless Wafer Precision Positioning System
## 5 Wafer Sensing
### Performance Analysis  
Before the implementation of the CCD sensor inside the contactless actuator, a performance analysis experiment was done. In these tests, the linearity of the sensor was tested, as well as the steady-state behaviour. The performance test was performed in an environment where disturbance sources from the external lighting, vibrations, and the electrical side were not removed. The steady-state response shows no drift and high precision, since the position returns to the same position. Figure 20 shows the performance analysis test where the position of the CCD is compared to a stage that has a resolution of 100 nm. The figure shows no drift and follows the position of the stage with an error of 3.0 \(\mu\)m RMS.  
Figure 19: A CCD sensor with 2 LEDs, showing the actual position of the wafer \(x_{w}\), the shadow created by the first led \(x_{s1}\) and second led \(x_{s2}\).
# A Systematic Survey of Multi-Factor Authentication for Cloud Infrastructure
## 4 Analysis of Authentication Factors
### Comparison of Factors for Authentication  
Depending on a particular type of factor or a set of factors used simultaneously, user verification can evolve as a simple but robust mechanism for user authentication. One or more factors listed in Table 2 can be used to authenticate users. As a suggested method to mitigate the inherent weakness of password-based authentication, measures like biometric-based with multi-factor-based authentication are advised [64]. Based on the comparative analysis in Table 3 regarding various types of attack scenarios, it can be concluded that retina analysis and thermal image recognition are the least vulnerable to attacks; hence, they are the most secure approaches. However, they require specialized hardware and corresponding specialized software for their operation. On the other hand, despite being vulnerable to Spoofing attacks and Impersonation attacks, face recognition can use the standard web camera to deal with the attack scenario with a suitable solution involving a liveness aspect in place. This aspect is considered for progressing in our approach for the desired authentication mechanism. Single-factor Authentication (SFA) depends on only one factor. Because of its convenience and user-friendliness, SFA is widely accepted by users. Among the various methods, the most common one employed is a username and password/PIN-based authentication. It is based on the knowledge factor (what-you-know). These password/PIN-based authentications have been considered weak as they are vulnerable to various attacks. Furthermore, malicious users can apply dictionary attacks [65], rainbow tables [66], or social engineering tactics to acquire access. When using this authentication method, a minimum password complexity need is usually considered. Users tend to have the same passwords over multiple websites, and a password leak from one can lead to security threats over all the user accounts on different service providers. Some notable drawbacks of this approach are as follows:To access various services, users must remember the authentication factor. If one common password is used for several accounts, these programs may be affected if the password is compromised. 2. When many passwords are used, the burden of its remembrance, upkeep, and safe-keeping for proof is on the users. Hence, password fatigue is evident when certain users only use one password for authentication. 3. If this factor is penetrated or compromised, the user will be unable to utilize the service until the problem is fixed. It causes a considerable delay in obtaining the desired service or information needed when it is required. 4. If a single element is compromised even without the user's knowledge, the result might be disastrous.