esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Unified Earthquake Catalog for North East India
## 1 Introduction
General orthogonal regression (GOR) methodology is better suited for homogenizing seismicity catalogs to create regression relationships between distinct magnitude classes [11; 12; 13; 14; 15; 16; 17; 18; 19]. Various scaling relationships have been developed to homogenize an earthquake database for Northeast India [5; 11; 13; 14; 15; 16; 17; 18; 19]. Ref. [11] used GOR to convert body and surface wave magnitude scales into M\({}_{\rm w}\), and substantial dispersion was found in the translation of m\({}_{\rm b,BSC}\) into M\({}_{\rm w}\). Ref. [21] made the conversion of m\({}_{\rm b}\) and M\({}_{\rm s}\) into M\({}_{\rm w}\) using the least-square regression (SLR) approach. Refs. [13; 14] performed regression analysis for m\({}_{\rm b}\) to M\({}_{\rm w}\) and M\({}_{\rm s}\) to M\({}_{\rm w}\) using SLR, GOR, and inverted standard regression (ISR). Ref. [5] recently developed empirical relationships between different magnitudes into M\({}_{\rm w}\) using an improved version of the GOR technique (GOR1). Still date, the developed empirical relationships are in terms of M\({}_{\rm w}\) for the Northeast India region. This is the first study that gives a homogenous earthquake database for the Northeast India region using an improved magnitude scale. An enhanced GOR approach (GOR1) is applied in this investigation, as detailed in Ref. [20], for establishing regression relationships from different magnitudes to M\({}_{\rm wg}\) on a regional basis. For the period 1737-2012, a dataset of 9968 earthquake occurrences in the magnitude range 1.6-8.7 corresponding to the study area (lat. 20-30\({}^{\circ}\) and long. 87-98\({}^{\circ}\)) is employed. GOR relationships for conversion of m\({}_{\rm b}\) and M\({}_{\rm s}\) into M\({}_{\rm wg}\) are developed using regional datasets. The prepared homogenous catalog will be useful not only for seismic risk assessment but also for other seismological applications. Following the unification of the earthquake catalog towards seismic moment magnitude scale M\({}_{\rm wg}\), declustering has been performed, and its completeness is assessed in the sections that follow.
## 2 Operation and Specifications of the Optoelectronic FDS System
### Specifications  
In THz spectroscopy, two of the most important instrument specifications are the operational range of frequencies and the frequency-dependent dynamic range (DR). The two parameters are related, because the operational frequency range is defined as the region where DR > 2.  
Dynamic range is defined as the ratio or maximum measurable signal (\(S_{max}\)) and the noise floor (NF): DR = \(S_{max}\)/NF [24]. Dynamic range is important because it represents the range of signal amplitude values that can be measured by the system. As a consequence, it limits the maximum material loss that can be accurately quantified [25]. For TDS and FDS systems, DR is frequency-dependent, because it is proportional to \(S_{max}\). DR can be increased by averaging, since it is inversely proportional to NF. For random (Gaussian) noise, averaging lowers the noise floor by a factor of \(N^{1/2}\), where \(N\) is the number of traces [26]. Due to the spectral profile of the instrument, reducing the noise floor extends the frequency range.  
Figure 2 presents the DR of the optoelectronic FMCW system using from 1 to 100,000 averages in steps of a factor of 10. The spectral profile is roughly exponential, as expected in this type of system, falling by 27 dB per terahertz. However, there are some deviations from pure exponential behaviour, which are attributed to emitter and/or receiver geometry. In particular, the sharp dip at 0.88 THz is a system artifact whose origin is being investigated. Notably, the DR saturates above 20,000 averages due to slow variations and drifts in the environment. In the configuration used in this work, the spectral range is 0.1-2 THz for a single shot and 0.1-3 THz for 10,000 averages, requiring 24 ms and 235 s (around 4 min) scanning time, respectively.  
Table 1 summarises the essential measurement parameters of the individual devices. These are the results of the selected settings used for the following measurements in Sections 3 and 4.
## 2 Sensing Method
### Piezolectric Sensing
#### 2.2.1 Applied Technologies
This advancement offers a practical solution for endowing the aforementioned biodegradable piezoelectric sensors with wireless functionality, thereby augmenting their overall practicality and applicability in biomedical domains.
## 2 Materials and Methods
### Research Methods  
#### 2.3.1 Resource Balance Measure  
Measurement of Spatial Balance  
Spatial aggregation and dispersion of public facilities can express the equilibrium of spatial distribution at a certain level. In this paper, kernel density estimation is used to assess the spatial equilibrium of public restrooms. Kernel density estimation is often used to reflect the degree of concentration of point elements in spatial distribution, and the degree of aggregation of spatial point elements is estimated with the help of a regular moving sample square [41; 42]. The calculation formula is as follows.  
\[f(x)=\frac{1}{m\hbar}\sum\limits_{l=1}^{n}k\left(\frac{x-c_{l}}{\hbar}\right) \tag{1}\]  
where \(f(x)\) is the kernel density estimation function at spatial location \(x\), \(h\) is the distance decay threshold, \(k\) is the spatial weight function, and \(n\) is the number of points whose distance from location \(x\) is less than or equal to \(h\).  
Measurement of Population Balance  
The visual representation of the parity of public facility services per resident between regions can reveal the distribution pattern of parity at a certain level. In this study, the accessibility of public toilets was assessed by the number of toilets per 10,000 people (NTP). The calculation formula is as follows.  
\[NTP_{i}=\frac{T_{i}}{T_{i}} \tag{2}\]  
Figure 1: Schematic diagram of the study area: (**a**) the urbanization rate of China’s provinces; (**b**) the urbanization rate of cities in Liaoning Province; (**c**) the urbanization rate of districts of Shenyang.  
where \(NTP_{i}\) denotes the number of public toilets per ten thousand people in the \(i\)th study unit, \(T_{i}\) denotes the number of public toilets within the \(i\)th study unit, \(P_{i}\) denotes the number of people within the \(i\)th study unit. \(i=1,2,3,\ldots,n\), and \(n\) is the number of study units.  
Measurement of Spatial-Population Balance  
The balance of the spatial distribution of public toilets is mainly measured by the density index. The sanitation resource density index is used as the basis for constructing the public facility resource density index to reflect the balanced allocation of public resources in terms of population and area [43]. The spatial allocation of public facilities needs to balance the spatial and population. The public resource density index is constructed to measure the public toilet density index (TDI). The calculation formula is as follows.  
\[TDI_{i}=\frac{N_{i}}{\sqrt{TP_{i}\cdot A_{i}}} \tag{3}\]  
where \(TDI_{i}\) is the density index of public toilets in the \(i\)th study unit, \(N_{i}\) is the number of public toilets within the \(i\)th study unit, \(TP_{i}\) is the population of the \(i\)th study unit (unit: thousand people), and \(A_{i}\) is the area of study unit \(i\) (unit: kilometer).
## 2 Problem Description--Energy Monitoring in Industries
At M-ACE, implementing this IIoT-based energy and air consumption monitoring system presented several challenges, particularly due to the factory's continuous 24/7 production cycle. Unlike conventional industrial environments where scheduled downtimes allow for maintenance and retrofits, the high operational demand at M-ACE left minimal opportunities for sensor installation and system integration. To mitigate disruptions, installation and configuration efforts had to be strategically planned and executed during short maintenance windows, ensuring that production efficiency remained unaffected. Additionally, retrofitting legacy machines required overcoming technical limitations, including limited built-in monitoring capabilities and lack of modern communication protocols.
## 4 CKC-Based Taxonomy on Security Threats to SF and PA
### Ckc  
As suggested by CKC model, a typical complex attack consists of the following stages.  
1. **Reconnaissance**, where the attacker starts to identify and profile the victim via gathering as much information as possible. Any relevant information, such as email addresses, can be of interest in the reconnaissance stage. The primary goal of this stage is to discover the vulnerabilities of the victim. If properly accomplished, this stage can facilitate and accelerate a cyber-attack and make it difficult to detect via identifying the weak and strong points of the victim system. Moreover, the reconnaissance itself should not be suspicious to security mechanisms in the victim's system.  
Figure 2: Classification of attacks on SF and PA.  
We can consider a passive and active approach for Reconnaissance. In the context of a cyberattack, passive Reconnaissance is known as footprinting. Active Reconnaissance is commonly referred to as scanning. An easy scan would be to ping every IP address owned by the destination network to see which ones went to real hosts. More sophisticated exploitation methods connect to every port number of the IP address to determine what services run on that host and which ports are open. In contrast to footprinting, scanning provides more specific information but is more intrusive. Additionally, the target may be alerted to a potential attack since scanning can trigger more abnormal connections, which must be avoided when scanning.
2. **Weaponization**, wherein the attacker designs and implements the _remote access malware (the weapon)_ e.g., the backdoor, virus or worm tailored to the vulnerabilities of the victim (discovered in the reconnaissance phase).
3. **Delivery**, in which the attacker launches the remote access malware onto the victim (e.g., via a USB device, an e-mail attachment or a website).
4. **Exploitation**, which triggers the remote access malware. In this stage, the attacker utilizes the remote access malware to action on the victim and the related network in order to exploit vulnerability.
5. **Installation**, where the attacker tries to get permanent access to the victim via installing proper Command and Control (C2) servers.
6. **C2**, wherein the attacker communicates with the C2 server in order to control the victim.
7. **Action on Target**, where the attacker completes the attack scenario and achieves the final goal by compromising the victim.  
The aforementioned stages are demonstrated in Figure 3. A point to note here is that not every attack goes through all of the above stages strictly and clearly. Moreover, some stages may not be observed, tracked, or reported. For example, the Reconnaissance stage is usually a secret stage, although the vulnerabilities identified in this stage may be reported later.  
Figure 3: The stages of a typical cyber-attack according to CKC ([PERSON] [85; 86]).
## 1 Introduction  
Remote sensing has played a pivotal role in advancing the fields of archaeology, geography, and earth sciences, offering new perspectives and unparalleled opportunities for research, analysis, and interpretation within these disciplines. With the continuous development and refinement of sensors, these sophisticated tools can now be deployed on various aerial platforms like satellites, aircrafts, and drones. This enables researchers to acquire invaluable observational data that enhances our understanding of both the Earth's natural and cultural landscapes. These data sources encompass a wide array of information, such as multispectral images, thermal data, LiDAR, radar data, and more. By harnessing these diverse types of observational data, researchers can delve into the study of environmental shifts, land use patterns, and archaeological sites with accuracy and efficiency. This Special Issue comprises 11 papers that center around the pioneering applications and advancements of remote sensing technology in areas such as monuments, traditional architectural art, and environmental monitoring. Moreover, it explores the integration of cutting-edge deep learning techniques to aid researchers in the detection and identification of archaeological remnants. The papers presented in this collection showcase the innovative utilization and extraction of insights from remote sensing data, contributing to the expansion of knowledge and the refinement of methodologies in these domains.
## 2 Data and Methods  
### Study Area  
The study area is located in the Grassland Natural Region of southern Alberta, Canada (area = 44,536 km\({}^{2}\)), within the larger PPR of Canada and the United States (Figure 1). The Grassland region, often called prairie, is characterized by extensive grasses with shrubs in moist environments, and trees growing only along rivers [33]. The climate is continental semi-arid, with warm, dry summers and cool, dry winters [33]. The mean annual warmest and coldest month temperatures are 17.8 \({}^{\circ}\)C and \(-\)11.7 \({}^{\circ}\)C, respectively, whereas mean annual precipitation is 374 mm, with maximums typically occurring in June [33]. During our period of study, 2017-2020, the area experienced considerable drought conditions in summer 2017 and a higher-than-average snowpack in March and April of 2018 [34].  
Prairie pothole wetlands within our study area occur in small (<1 ha), isolated, shallow depressions often <1 m [10], as they are throughout the PPR. Since they generally overlay glacial till with low water permeability and occur in a semiarid climate, their hydrology is largely dependent on precipitation, snowmelt, and evapotranspiration, which renders them highly dynamic [35]. These wetlands vary from temporary, where surface water is present only for a short time after heavy rain or spring snowmelt, to seasonal, where surface water lasts most but not all of a growing season, to semi-permanent or permanent, where surface water remains most or all of the year [8]. Temporary and seasonal wetlands are most abundant in our study area, and are particularly sensitive to climatic variability, climate change, and human modifications of the landscape [10]--e.g., ditches, drainage. A high annual variability in precipitation and evaporation rates leads to considerable variability in the number of water-filled basins within the PPR in any given season or year, changing with the occurrence of common drought and deluge climatic cycles [36].  
Cultivation occurs throughout most of the area but is most prevalent in the northwest [33]. Other anthropogenic activities in this area include oil and gas developments, mines explorations, and feedlot operations [37]. For optimizing satellite data availability, the study area was processed as three sections (west, centre, and east) based on S2 orbital paths.
# Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification
## 2 Materials and Methods
### Differential Evolution  
DE is a population-based stochastic search method for global optimization problems, which mimics the concept of natural evolution [37]. In particular, a population of candidate solutions evolves iteratively to improve their fitness according to a chosen objective function. The current work relied on such an approach to optimize the hyperparameters for SVC, MLP, and XGB ML classifiers. The DE algorithm operates in six steps:  
1. Population initialization: An initial population of \(NP\) candidate solutions is created, which is denoted by \(\{\theta_{i}^{G}\mid i=1,2,\ldots,NP\}\) for the generation \(G\). Each solution is a vector representing a point in the search space.
2. Evaluation: Each candidate solution fitness is evaluated based on the objective function (e.g., the F1-Score).
3. Selection: Three mutually distinct solutions, \(r_{1},r_{2}\), and \(r_{3}\), are randomly selected from the population as parents to generate a new descendant solution.
4. Mutation: A mutation operation is applied to each parameter within the descendant vector. Such mutation introduces a small perturbation by adding or subtracting a user-defined scaling factor \(F\), multiplied by the difference between two parent solutions, namely \(\theta_{r_{2}}^{G}\) and \(\theta_{r_{3}}^{G}\), finally generating a mutated vector \(\mathbf{v}_{i}\) as follows: \[\mathbf{v}_{i}^{G+1}=\theta_{r_{1}}^{G}+F(\theta_{r_{2}}^{G}-\theta_{r_{3}}^{ G})\] (7)
5. Crossover: A crossover operation combines information from the parents and the mutated vector to create a trial solution \(\boldsymbol{\mu}_{ji}\). Each parameter in the latter trial solution is inherited from either the parent vector or the mutated vector with a probability determined by a user-defined crossover rate \(CR\). The trial solution \(\boldsymbol{\mu}_{ji}\) is determined for the next generation \(G+1\) as follows: \[\boldsymbol{\mu}_{ji}^{G+1}=\begin{cases}\mathbf{v}_{ji}^{G+1},&\text{if }rand_{b}(j)\leq CR\;\;\text{or}\;\;j=rnbr(i),\\ \boldsymbol{\theta}_{ji}^{G},&\text{if }rand_{b}(j)>CR\;\;\text{or}\;\;j \
eq rbr(i),\end{cases}\] (8) where \(j=1,2,\ldots,D\) represents the number of dimensions, \(rand_{b}(j)\) is a uniform random number within the interval \([0,1]\), \(D\) is the dimensionality of the search space, and \(rnbr(i)\) is a randomly selected index which ensures at least one parameter comes from the mutated vector.
6. Selection: The offspring, i.e., the trial solution, is compared with its parent. If the latter offspring exhibits better fitness, it replaces the parent in the next generation. Otherwise, the parent is retained.
7. Termination: The algorithm iterates through the above-reported phases until a stopping criterion is satisfied, such as attaining a predetermined number of generations or fitness levels.
# Integration of Geoscience Information for Disaster Resilience in Kuala Lumpur, Malaysia
## 2 Materials and Methods
### Acquisition of Geoscience Information  
Borehole records from 1979 to 2015 were obtained from site investigation reports within the archives of the Department of Mineral and Geoscience (JMG) and supplemented with data from IKRAM, which is an engineering consultant company affiliated with the Malaysia Public Works Department. The data from 1650 boreholes in previous studies and site investigation reports were mainly in hard copy format. The information was digitised and reorganised into an inventory. The historical landslide inventory includes 700 points obtained from JMG, whereas the landslide inventory of December 2021 was recorded from field observation by the authors. The main dataset comprises 2014 Light Detection and Ranging (LiDAR) topographic data obtained from the Kuala Lumpur City Hall (DBKL) in the form of a Digital Terrain Model (DTM) with a pixel size of 1 m. The DTM was used for landslide and flood susceptibility modelling. The flood model required inputs of local datasets such as DBKL boundary, building footprints and land use vector layers, river gauge data from the Kuala Lumpur Drainage and Irrigation Department as well as the Malaysian Meteorological Department. Aerial photographs and topographic maps of various scales from the Department of Survey and Mapping Malaysia were used to assess temporal and spatial land use changes. This is critical to identify the areas affected by past tin-mining activities and reclamation works. Digital orthorectified aerial photographs were sourced from DBKL, while satellite images were downloaded freely from the United States Geological Survey (USGS) public domain accessed through the website ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)), accessed on 18 December 2017) for the same  
Figure 1: Geological map of the Kuala Lumpur area. The city of Kuala Lumpur is located within the Selangor state, in central–west Peninsula Malaysia (modified after [PERSON], 1964 [51] and [PERSON], 1986 [52] and [PERSON] et al., 2023 [18]).  
purpose. The input base maps were acquired from relevant government agencies in both hard copy and soft copy format. The geological data of the study area are primarily from field investigation and were used to update and supplement available information for the spatial analysis.
# An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing
## 2 Related Work
### Security Evaluation Methodologies  
Security protocol evaluation is a critical part of IoD research. [PERSON] et al. (2018) established a solid security evaluation framework for IoD systems, suggesting metrics for the evaluation of authentication protocols in resource-constrained systems, and their work guided our methodology towards performance evaluation. Simulation-based security analysis can be applied in IoD studies, as evidenced by [PERSON] et al. (2018), who utilized NS2 simulations to compare and contrast secure routing protocols for FANETs in various attack scenarios, illustrating that mobility patterns must be considered when assessing the performance of security protocols. Building on this approach, [PERSON] et al. (2018) suggested a hybrid simulation model combining network and hardware simulation to evaluate the impact of security measures on drone battery life ([PERSON] et al., 2018). Formal verification methods provide mathematical guarantees of security protocol correctness, as shown by [PERSON] et al. (2018), who used AVISPA formal verification to examine mutual authentication protocols for IoT devices, proving its usability in resource-constrained systems. Also, [PERSON] et al. (2018) employed BAN logic to certify the security properties of a lightweight authentication scheme for heterogeneous wireless sensor networks, establishing a basis for formal security analysis in IoD scenarios. Modern solutions include DroneSim for simulating energy consumption effects of security mechanisms [65] and an automated theorem proving for authenticating security protocols in time-critical IoD applications [66]. Our strategy integrates formal verification with AVISPA and systematic performance analysis in practical mobility patterns.