esa-sceva/satcom-chunk-collection huggingface dataset:
# On Evaluating IoT Data Trust via Machine Learning
## 2 Related Work
### MI-Based Approaches  
The works of [3; 11] are among the first ones that use ML algorithms to assess the trustworthiness of IoT data via binary classification. They consider extracting several features from raw sensor data that contain information related to the trustworthiness of data. The most commonly used features are the DST-based belief and plausibility values [10; 12]. To calculate these features, time-series IoT sensor data are generally replaced by the probability mass function (PMF) of their values. However, the PMF of time-series sensor data may not fully capture all the information, especially regarding the sequential nature of the data. Time-series data typically exhibit temporal dependencies and patterns such as trends, seasonality, autocorrelation, or other temporal relationships. PMF alone does not consider the ordering or temporal dependencies between the data values. Hence, DST-based features do not fully incorporate the temporal information within time-series data and may not capture all the important relevant information.  
Most existing ML-based approaches to evaluating trust in IoT data have been validated through experiments, which can be seen as artificial or contrived. One commonly used experimental setup involves learning an ML model, typically linear SVM, to classify the trustworthiness of the data after labeling it artificially using an unsupervised cluster analysis method, with the \(k\)-means clustering algorithm being the prevalent choice [10; 11; 12]. Such setups are adopted mainly due to the lack of ground-truth labels and the presumption that the trustworthy and untrustworthy data form distinct clusters in the feature space, which can be discovered via simple cluster analysis methods, such as \(k\)-means clustering. The \(k\)-means clustering algorithm assumes multivariate Gaussian distributions with equal covariances for the clusters corresponding to the trustworthy and untrustworthy data. There is no evidence to support the validity of this assumption nor to substantiate the underlying presumption that clustering can provide reliable trust labels.
## 1 Introduction  
Synthetic Aperture RADAR (SAR) is a coherent active microwave remote sensing instrument [1; 2; 3], whose capability to efficiently map the scattering properties of the Earth's surface is well documented [4; 5; 6]. A SAR sensor--which can be mounted on board an aircraft or satellite--has a side-looking illumination direction and can accurately retrieve the location of an object (target) relative to the position of the platform where the sensor is mounted. Both the acquisition geometry and the physical characteristics of the imaged scene contribute to the formation of the received backscattered RADAR signal (echo) which, correctly processed, leads to the reconstruction of a complex-valued high-resolution microwave image of the observed area [2]. SAR is an active imaging sensor, i.e., it does not need an external energy source to work. Moreover, since it typically operates in the microwave region of the electromagnetic spectrum, it can be used efficiently in any meteorological condition (i.e., in the presence of a significant clouds' cover) and with a full day-and-night operational capability. As a consequence of its flexibility, SAR technology mostly improved over the last decades, thus developing further technologies based on the use of SAR data for the detection and monitoring of several geophysical phenomena [7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. Among these techniques, the SAR interferometry [13; 14; 15; 16] plays a major role in the study of the dynamic of Earth's crust, thus permitting the monitoring of the surface movements. Deformations can be induced by several geophysical phenomena, such as the extensive fractures due to big earthquakes [17; 18; 19], pre-seismic deformations [20], slow-moving landslides [21; 22; 23; 24], the eruptions of active volcanoes [25; 26; 27; 28]. Human beings also severely contribute to the changes in the environment. In this framework, InSAR can monitor the modifications over time of highly urbanized zones by measuring the settlements of buildings, private and public facilities [29; 30; 31]. The results of these investigations are useful for city planners and local authorities to mitigate the urban risk and design effective plans of cities development.  
This review paper will first address the fundamentals of InSAR technology and, then, will provide an overview of recently proposed multi-track/multi-satellite InSAR-based methodologies [32], which allow the detection and the continuous monitoring of the three-dimensional shape of deformation phenomena.  
The paper is organized as follows. Section 2 illustrates the InSAR rationale, emphasizing key strengths and potential limitations. Section 3 provides an overview of the multi-pass InSAR techniques and Section 4 describes, with more details, the recently proposed minimum acceleration InSAR combination method [32]. The results of a few experiments will also be presented. Future perspectives on the use of InSAR and multi-satellite InSAR methodologies will be finally provided in Section 5.
## 1 Introduction
[20] developed an adaptive DE technique that integrated changes to the DE technique's governing parameters based on the fitness value. A more advanced and adaptable DE method was introduced in the study conducted in [21] in order to extract PV parameter values. The authors proposed a strategy for population reduction that involves a dynamic approach and a mechanism for sorting crossover rates. In order to forecast the parameters of solar cells, a multitude of researchers have developed multiple iterations of the successful history-based adaptive differential evolution DE (SHADE) algorithm. Ref. [4] introduced a novel technique known as memetic adaptive differential evolution DE (MADE) in order to improve the precision, convergence speed, and robustness of parameter estimation in the PV model. In the study conducted in [22], the modified DE method incorporated the Lambert W function and a meta-heuristic step. This approach aimed to identify the optimal mutation factor and crossover parameters for individual I-V characteristics. In recent times, a variety of hybrid strategies have been introduced with the purpose of obtaining the parameters of PV models. Ref. [22] integrated reinforcement learning with DE in the study. The evaluation of the fitness function value occurs primarily during the reduplication process, wherein it serves to calculate the reward for adjusting parameters. Using reinforcement learning techniques, the parameter value is then modified in order to obtain the optimal algorithmic parameters for the provided environmental model. Study [23] proposed the utilization of a gaining-sharing knowledge-based approach (GSK) for the purpose of determining the parameters of a solar PV model. The program demonstrates competitiveness in effectively tackling optimization difficulties by simulating the process of gathering and exchanging information across the entirety of the human life cycle. There are two crucial stages that are necessary. The junior level is characterized by a beginner to intermediate skill level, whereas the senior level is characterized by an intermediate to expert skill level.
## 6 Simulation Environment
### Performance Metrics  
This subsection highlights the performance metrics of our proposed routing protocol as follows.  
Packet Delivery Ratio (PDR): The packet delivery ratio is a way to measure the reliability of the network and can be defined as the ratio of total number of data packets positively received by the surface sink nodes to the total number of data packets transmitted by the source node. A higher PDR indicates better network performance in terms of reliability. Mathematically, PDR can be calculated as  
\[\text{PDR}=\left(\frac{\text{Packets received by sink}}{\text{Packet transmitted by source}}\right)\times 100 \tag{26}\]  
Average Energy Consumption (AEC): Average energy consumption (AEC) represents the amount of energy utilized by the sensor nodes during the network operation. It can be defined by taking the difference between the initial energy (Einitial) and the remaining energy (Eresidual) of all the sensor nodes and dividing it by the total number of sensor nodes (N). Mathematically, it can be calculated as\[\text{AEC}=\frac{\sum_{i=1}^{\text{N}}(\text{E}_{\text{initial,i}}-\text{E}_{ \text{residual,i}})}{\text{N}} \tag{27}\]  
Average End-to-End Delay (AE2 ED): Average end-to-end delay involves measuring the time it takes a data packet (P) to successfully travel from the source node \((\text{T}_{\text{send}})\) to the sink node (Tarrival). It includes transmission delays, propagation delays, processing delays, and queuing delays. Mathematically, it can be calculated as  
\[\text{AE2 ED}=\frac{\sum_{i=1}^{\text{P}}(\text{T}_{\text{arrival,i}}-\text{T}_{ \text{send,i}})}{\text{P}} \tag{28}\]  
Dead Nodes (DN): Dead nodes (DNs) refer to the number of nodes that have exhausted their energy and can no longer participate in network operations. Dead nodes are crucial for understanding the longevity and sustainability of the network. Mathematically, they can be calculated as the difference of the deployed nodes (N) to the operational nodes (ON):  
\[\text{DN}=\text{N}-\text{ON} \tag{29}\]  
Total Energy Consumption (TEC): Total energy consumption is the absolute energy used by all the sensor nodes in the network. It is based on the transmission energy, reception energy, and the energy consumed by all the nodes remaining in the idle state. This work only considered the energy consumed during activities such as transmission and reception.  
\[\text{TEC}=\sum_{i=1}^{\text{N}}\left(\text{E}_{\text{tx}}^{i}+\text{E}_{ \text{rx}}^{i}\right) \tag{30}\]  
Network Throughput (NT): Network throughput is defined as the total amount of data successfully delivered to the sink nodes per unit of time. It is measured in kilobits per second. Mathematically, it can be calculated as  
\[\text{NT}=\left(\frac{\text{Packets received by sink}}{\text{Total Time}} \right)\times 1000 \tag{31}\]
## 4 Empirical Study
### Exploring the Causal Relationships Using DEMATEL  
The causal relationships between key barrier factors were analyzed using grey DEMATEL. Following standard procedures, we constructed the initial influence matrix (Table 6) and applied Equations (7)-(9) to generate the total influence relationship matrix (Table 7). Using the CFCS method through Equations (12)-(16), we converted the grey matrix into a crisp matrix (Table 8). We then calculated the total influence (\(h+l\)) and net influence (\(h-l\)) for each factor using Equations (10) and (11), as shown in Tables 9 and 10. The results indicate that the most important factors by total influence are \"C\({}_{11}\) (OEM outsourcing policies and regulations)\", \"C\({}_{12}\) (aviation certification regulations)\", \"C\({}_{9}\) (degree of internationalization)\", and \"C\({}_{8}\) (management strategy and enterprise learning capability)\".  
Using the data in Table 8, we plotted the INRM. For clearer visualization, we organized the twelve factors into four categories: corporate governance (\(C_{1}\), \(C_{2}\), and \(C_{5}\)), R&D and manufacturing (\(C_{3}\), \(C_{4}\), and \(C_{6}\)), operations and marketing (\(C_{7}\), \(C_{8}\), and \(C_{9}\)), and regulatory and quality systems (\(C_{10}\), \(C_{11}\), and \(C_{12}\)).  
Using Equations (19)-(21), we derived the first-level reachability set (\(R\)), the antecedent set (\(A\)), and their intersection (\(C\)) from the reachability matrix, **M** (Table 13). Factors \(C_{3}\) and \(C_{7}\) were identified as first-level factors, as their reachability sets (\(R\)) matched their intersection sets (\(C\)). After eliminating these factors' corresponding rows and columns, we iterated the process to construct the complete hierarchical structure, as shown in Figure 3.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Factor** & **Reachability Set** & **Antecedent Set** & **Intersection** & **Level** \\ \hline Iteration 1 & & & & & \\ \hline \(C_{1}\) & 1,2,3,4,5,6,7,8,9,10,11,12 & 1 & 1 & 1 \\ \(C_{2}\) & 2,3,4,5,6,7,8,9,10,11,12 & 1,2
## 2 Methods  
### MODIS NPP--\"Space Based\" Approach  
We use the algorithms for the MODIS products MOD17A2 and MOD17A3, which provide global Gross and Net Primary Production (GPP, NPP) estimates for a 1 \(\times\) 1 km pixel on an eight-day time-step [7,8]. The algorithm calculates _GPP_ as  
\[GPP=LUE_{\text{max}}\times 0.45\times SWrad\times FPAR\times f_{\text{{}_{TPD}}} \times f_{\text{{}_{Tmin}}} \tag{1}\]  
where \(LUE_{\text{max}}\) is the maximum light use efficiency, \(SWrad\) is the short wave solar radiation load at the surface of which 45% is photosynthetically active, _FPAR_ the fraction of absorbed PAR (Photosynthetic Active Radiation) from the MOD15 product, and \(f_{\text{{}_{TPD}}}\) and \(f_{\text{{}_{Tmin}}}\), which are multipliers between 0 and 1 addressing water stress due to vapor pressure deficit (_VPD_) and low temperature limits (_Tmin,_ daily minimum temperature). Values that determine the \(LUE_{\text{max}}\) and the \(T_{\text{{}_{Tmin}}}\) and _VPD_ multipliers limits are stored in the Biome Property Type Look Up Tables (BPLUT) and cover five forest biome types: (i) ENF--Evergreen Needleleaf Forest, EBF--Evergreen Broadleaf Forest, DNF--Deciduous Needleleaf forest, DBF--Deciduous Broadleaf Forest, and MF--Mixed Forests ([3] for the model coefficients).  
Annual Net Primary Production (_NPP_) is calculated from Gross Primary Production (_GPPP_) by subtracting the autotrophic respiration components (i) maintenance respiration \(R_{m}\) and (ii) growth respiration \(R_{g}\) and summing up over a year to get annual values:  
\[NPP=\sum_{i=1}^{365}GPP-R_{m}-R_{g} \tag{2}\]With 366 days for leap years, \(R_{m}\) and \(R_{g}\) are calculated using leaf area index (_LAI_) from the MOD15 product, climate data, and parameters from the BPLUTs. For further details, please refer to the original literature [7, 8].
## 4 Tactile Internet Application Characteristics  
Various Tactile Internet applications have different characteristics. The parameters of latency, reliability, and data rates are the key drivers for Tactile Internet applications. Table 3 presents the relevance of the major characteristics of the important Tactile Internet applications, such as virtual reality/augmented reality, remote surgery, gaming, smart cities,autonomous vehicles, industrial automation, brain-machine interfaces, and telepresence/holography. In addition to the primary core parameters, such as latency, reliability, and others, some additional characteristics are also included to have a holistic view of future requirements. The relative scales for the applicability of the characteristics to applications are indicated as high (H), medium (M), and low (L) [4]. For a given application, the table provides enough directions for architectural and design decisions.
###### Abstract  
A multilayer bandpass filter with high selectivity is proposed in this letter. Discriminating coupling formed by slot-coupled quarter-wavelength and half-wavelength resonators introduces a zero at 3f\({}_{0}\) (f\({}_{0}\) is the center frequency) and the second harmonic is also suppressed due to the quarter-wavelength resonators. Owing to multilayer structure, source-load coupling is introduced to improve selectivity. Then an extra coupled line path is added with the same amplitude as the discriminating coupling path while they are out of phase. Thus signal cancellation produces three extra transmission zeros, with the selectivity and suppression performance further improved. To validate the design, a prototype bandpass filter centered at 2.49 GHz with 3 dB fractional bandwidth of 8.1% is fabricated. Both simulated and measured results are in good agreement and show good performance of the proposed bandpass filter.  
bandpass filter; discriminating coupling; high selectivity; source-load coupling +
Footnote †: journal: Applied Physics Letters
# Numerical Modeling, Analysis, and Optimization of RFID Tags Functioning at Low Frequencies
###### Abstract  
RFID (radio frequency identification) tags play a crucial role in a wide range of applications, from wireless communications to personal tracking and smart city infrastructure. These tags come in various shapes and sizes, prompting the authors to review the specialized literature and focus on optimizing planar designs with different geometries. This study prioritizes reducing the size of the most commonly used tags while enhancing their reliability. The primary objective of this article is to understand and improve the performance of planar RFID tags operating at 13.56 MHz through numerical simulations based on structures generated by algorithms developed in MATLAB. Building on previous research, the methodology is validated, followed by a detailed description of the algorithm designed and implemented by the authors in MATLAB to identify all possible structures that meet the design criteria. The authors compared various analyzed structures, considering different inductor shapes, dielectric materials, and thicknesses while examining their effects on gain and resonant frequency. The study also provides thermal analysis of the structures, and experimental validation of the studied designs. Finally, the researchers conclude with recommendations on the optimal structure for RFID tags.  
capacitance; inductance; resonant frequency; coil shape; MATLAB program +
Footnote †: journal:
# Analysis of Vulnerabilities That Can Occur When Generating One-Time Password
## 2 Background
### Random Number
#### 2.1.2 Unpredictability  
The (pseudo) random numbers used in cryptography should be unpredictable. In the case of Cryptographically secure Pseudo-Random Number Generators (CPRNG), if the attacker does not know the seed, the output must be unpredictable, and he must not know the input value only by looking at the generated random number sequence (providing forward/backward unpredictability) [9; 10]. In general, algorithms for random number generators are public, so the seed must be kept secret to provide unpredictability of the output random number.
## 2 Materials and Methods
### Data Sources and Preprocessing  
The NDVI data utilized in this study were sourced from the MOD13Q1 dataset provided by the National Aeronautics and Space Administration (NASA) of the United States ([[https://www.earthdata.nasa.gov/](https://www.earthdata.nasa.gov/)]([https://www.earthdata.nasa.gov/](https://www.earthdata.nasa.gov/)), accessed on 18 February 2023). This dataset has a spatial resolution of 250 m and a temporal resolution of 16 days. Using Google Earth Engine (GEE), a total of 460 NDVI images from 1 January 2001 to 2020 were stitched together, subjected to coordinate transformation, and then cropped. Afterward, the mean value method was used to obtain annual NDVI images for the study area. The boundary data for the mining areas were extracted from the \"Planning for the Construction of Large Coal Bases\" issued by the National Development and Reform Commission (NDRC) of China (Fa Gai Energy [2006] No. 352), which includes eight nationally planned coal mining areas within the Shanxi section of the Yellow River Basin. The study boundaries were adjusted based on the boundary data of the Shanxi section of the Yellow River Basin. Digital Elevation Model (DEM) data were obtained from the Geographic Spatial Data Cloud ([[https://www.gscloud.cn/search](https://www.gscloud.cn/search)]([https://www.gscloud.cn/search](https://www.gscloud.cn/search)), accessed on 18 February 2023). Meteorological data, including daily temperature and precipitation records from meteorological stations within the Shanxi section of the Yellow River Basin, were sourced from the China Meteorological Data Network ([[http://data.cma.cn](http://data.cma.cn)]([http://data.cma.cn](http://data.cma.cn)), accessed on 14 March 2023). Interpolation and resampling processes were performed using ArcGIS 10.2 software to match the resolution of the NDVI data.
## 5 Empirical Case Study: Courier Company in Bangkok
### Real Problems  
Two real-world scenarios, Scenarios A and B, in Bangkok road network of dimensions with 609 and 978 locations are considered. The number of vehicles are 10 and 15, respectively. Service time for each customer was taken to be 4 min. Working time limit for each vehicle was set to 480 min (8 h). Figure 2 gives the initial assignment according to the vehicle-zone map of the two scenarios.  
We define a _location_ or _point_ as a pair of longitude-latitude coordinates. A _trip_ or _arc_ refers to an ordered pair of points (origin and destination). A _trip instance_ is a trip taken at a specific _time instance_. Map API's route choice for time and hence distance are calculated based on the shortest travel time method.  
Figure 1: Didactic example: (**a**) pre-adjusted mapping; (**b**) post-adjusted mapping; (**c**) the adjusted working time violation of each vehicle as a function of the swap number.  
Travel time of one trip instance can be significantly different from the other trip instance due to the traffic. Therefore, for a problem with \(n\) locations, since the reverse trip time is not necessarily same as the forward trip time, we have to collect \(n\left(n-1\right)\) travel times for each time instance. Collectively, these \(n\left(n-1\right)\) queries take roughly 15 min for one time instance.  
In order to see the effect of the time instance on the routes built, and in particular on vehicle time violation, a travel time of each trip was collected on a weekday between 9 a.m. and 7 p.m. (hourly basis) with durations and distances reported in seconds and meters, leading to 11 time instances for each trip. Since the route which gives the shortest travel time may be different from one time instance to the other, we can see from Figure 3 that distributions of the pairwise distance at different time instance is slightly different. Figure 4 gives the mean speed of \(n\left(n-1\right)\) trip instances for both scenarios as a function of time instance at which the data was collected. The reason for the plots having a near perfect parallel relationship is the vehicle types. In fact, vehicles consist exclusively of motorcycles for Scenario A, and vans for Scenario B. Since the data of both scenarios was also collected on the same day, this explains why a strong relationship exists between mean speeds of the two scenarios. Table 1 summarizes relevant information for Scenarios A and B.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Characteristic** & **Scenario A** & **Scenario B** \\ \hline Vehicle type & Motorcycle & Van \\ \hline Number of vehicles & 10 & 15 \\ \hline Number of customers & 609 & 978 \\ \hline Median pairwise distance (km) & 7 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of problems in Scenarios A and B.  
Figure 2: Initial assignment according to the vehicle-zone map of the two real-world scenarios. Scenario A: 609 locations, 10 vehicles; Scenario B: 978 locations, 15 vehicles.