esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Industry 4.0 Manufacturing Process Security Risks
## 3 Identifying the Problem
### Case Studies of Cyber-Physical Attacks
#### 3.1.4 BlackEnergy3 and the Ukraine Power Grid  
The cyber-attack on the Ukrainian power grid in December 2015 was the first documented case of a power grid being targeted by means of a cyber-physical attack. Presumably developed by the Russian cyber espionage team Sandworm, the BlackEnergy3 worm acted as a Trojan horse that contained distributed denial of service (DDoS) capabilities. The worm made its first appearance in 2007 (known as BlackEnergy), and underwent upgrades with more advanced features in 2010 (known as BlackEnergy2). Finally, in the spring of 2015, after the BlackEnergy3 version of the worm was released, attacks on power distribution companies by means of malicious e-mails commenced. When the attackers gained entry into the control systems network of the Prykarpattyaoblenergo power distribution center in December 2015, they disabled the entire plant by commanding all the circuit breakers to open, leaving more than 200,000 people without electricity. This incident was particularly catastrophic, since the people left without electricity could not operate heating systems in the middle of the winter. Although the electricity supply was cut off for only approximately six hours, plant technicians still struggled to restore the electricity supply fully to all of the affected regions [30].  
In the case of the Ukrainian power grid cyber-attack, the malicious entity was spread by means of malware through classic cyber-attack distribution techniques, such as infected e-mails and compromised networks. DDoS attacks prevented proper functioning and recovery of the infected industrial systems, while the worm prevented plant technicians from gaining access to the control system by logging them out of their user accounts and changing their passwords [3; 31].
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 4 Solution Approach
### Establishing Hub Links  
Once the spoke-and-hub facility nodes are determined, the model establishes the hub links by randomly selecting hub pairs and putting a link between those. The budget constraint is also updated after hub-and-spoke location determination, and the randomly selected hub links should satisfy the updated budget constraint. It is also checked to have a connected graph, while a not fully connected graph is necessary.
# Sustainable Approach of Generating Water and Energy:
## 3 Mathematical Model
### Validation  
Mathematical model for the used DCMD device was validated using experimental data reported in the literature [26]. Figure 4 shows the results of the developed model against the experimental data. Generally, the model showed a good agreement with the experimental results. The model-predicted under various operating conditions for the permeate mass flux, the hot water temperature outlet, and the cold-water temperature outlet were within \(\pm\)15, \(\pm\)5, and \(\pm\)7 division bands, respectively. The origin of deviations might be attributed to two reasons: (i) the variability of the experimental results, and (ii) the model's assumptions.  
The energy consumption of the physics model of a residential building that was created was compared to benchmark data from [35], which indicated energy intensities for Saudi Arabia's middle area. The average energy intensity for residential buildings was found to be 136 kWh/m\({}^{2}\)-year. The physics-based model developed in this study has an energy intensity of 148 kWh/m\({}^{2}\). In addition, the PVT model developed previously in prior research was utilized in this work [28].  
Figure 4: Comparison between experimental and model results of the DCMD module (**A**) Permeated mass flux, (**B**) Outlet of hot water temperatures, and (**C**) Outlet of cold-water temperatures.
## 1 Introduction  
Imaging spectrometers are widely used in the field of remote sensing because of their ability to obtain spatial and spectral information simultaneously. After the development in the 1980s and 1990s, a series of imaging spectrometers were applied on airborne platforms [1; 2; 3; 4; 5; 6]. In the 21 st century, this technology developed rapidly in the field of spaceborne hyperspectral remote sensing. Since the Earth observation satellite EO-1 carried the first spaceborne hyperspectral imager Hyperion [7; 8] in 2000, many satellites have carried or planned to carry imaging spectrometers to execute Earth observation missions. Hyperspectral remote sensing plays a role in ocean and coast monitoring [9; 10; 11; 12], environment monitoring [13], earth surface detection [14; 15], mineral exploration [16], and military activities [17]. Hyperspectral imaging methods [18; 19; 20; 21] and systems [22; 23; 24; 25] have also developed rapidly. With the increasing demand for acquiring spatial and spectral information more efficiently and accurately, imaging spectrometers are driven towards faster response, larger width, higher resolution, and higher signal-to-noise ratio. Since the satellite on geostationary orbit has the unique advantage of keeping relatively stationary with the ground, it has high timeliness and continuous observation capability, thus it can obtain information from ground scenes quickly and widely. However, most of the spectral-type remote sensing payloads serving in geostationary orbit are multispectral payloads [26; 27; 28].  
They have only a dozen spectral channels in full spectrum, which is not enough to obtain fingerprint information of ground scenes. Their ability to identify substances and components is far inferior to hyperspectral payloads. The research on hyperspectral payloads on geostationary orbit is also limited to some special spectral bands, focusing mainly on atmospheric monitoring [29; 30; 31; 32; 33; 34; 35].  
The collection of full-spectrum hyperspectral data can significantly improve recognition accuracy and provide more target descriptions. The development of full-spectrum wide-swath imaging spectrometers is a current trend in hyperspectral remote sensing from geostationary orbit. However, due to the great difficulty and high cost of this method, there is little related research at present. In this paper, we present an optical system of the Geostationary Full-spectrum Wide-swath High-fidelity Imaging Spectrometer (GeoFWHIS). The principle of GeoFWHIS is described, and the optical design is explained. We focus on the study of the spectrometers from each sub-band and analyze their difficulties and requirements in order to realize high-fidelity spectral imaging. Finally, prototypes of spectrometers are developed and some test results are presented.
# Cross-Correlation Wavelet-Domain-Based Particle Swarm Optimization for Lightning Mapping
## 4 Conclusions  
This study investigated the feasibility and performance of a PSO-based optimization algorithm for lightning mapping estimation. The proposed CCWD-PSO algorithm requires no prior information on the lightning location. The fitness function (objective function) was derived and used for searching for the optimum solution. Several sets of iterations were investigated and compared. The performance of estimating the lightning mapping was measured based on tuning window size and minimizing the error between the simulated and real lightning mapping over the time course of the analyzed ITF lightning signals. Simulations and real data from three NBEs were conducted to provide sufficient analysis with high performance for estimating lightning maps. In practical application, the CCWD-PSO algorithm can be used to search the lightning source location, with the size population of the swarm being less than 50 and the number of iterations being less than 500. Overall, PSO is an efficient global optimizer for continuous-variable problems, easily implemented, with very few parameters to fine-tune, and it can accommodate constraints by using penalty methods. Therefore, from all the results shown by the CCWD-PSO technique, it can be concluded that PSO is the superior method in solving high-dimensional numerical optimization problems of estimating lightning maps.  
Conceptualization, M.R.A., A.A.A. and A.A. ([PERSON]); methodology, A.A. ([PERSON]); software, A.A. ([PERSON]) and F.N.; validation, F.N., M.R.A. and Z.K.; formal analysis, A.A. ([PERSON]); investigation, F.N. and A.A. ([PERSON]); resources, A.A. ([PERSON]), A.A. ([PERSON]) and A.A.A.; data curation, A.A. ([PERSON]) and A.A. ([PERSON]); writing--original draft preparation, A.A. ([PERSON]); writing--review and editing, A.A.A., F.N., A.A. ([PERSON]) and M.R.A.; visualization, F.N.; supervision, M.R.A. and A.A.A.; project administration, A.A.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is available on request to any of the corresponding authors.  
Authors acknowledge the publication support through J510050002--BOLDEFRESH2025--CENTRE OF EXCELLENCE from the iRMC of Universiti Tenaga National (UNITEN).  
The authors declare no conflict of interest.
# Refined Study on Free Vibration of a Cable with an Inertial Mass Damper
## 1 Introduction
As for cable flexural rigidity, it has negative effects on the control performance for each mode of the cable [57; 58]. The presence of cable flexural rigidity results in the smaller maximum achievable damping ratio and the higher optimal damping coefficient than those predicted by the taut-cable model for the case of fixed-fixed boundary condition [59; 60]. The significance of boundary conditions on the dynamic behavior of cable-damper system was also addressed [61; 62]. Results have shown that the adverse effect of flexural rigidity becomes more significant when the boundary conditions of the stay cable change from pinned-pinned to fixed-fixed ends. A recent study by the authors has further examined the control performance of the IMD on a sagged cable [63]. However, the effects of the flexural rigidity and the boundary condition of a cable on the vibration mitigation performance of an IMD have not been evaluated. Therefore, this refined study extends the aforementioned work by the authors to further investigate the combined effects of the cable sag, the cable flexural rigidity and the boundary condition on the control performance of the cable with the IMD. In the current study, an analytical model of the cable-IMD system considering cable sag and cable flexural rigidity for different boundary conditions is developed. The equation of motion of the cable-IMD system is then transformed into a complex eigenvalue problem using the finite difference method. Subsequently, the data from a series of vibration control experiments on a scaled cable model with an IMD previously conducted by the authors are used to further verify the validity of theoretical results above. Finally, case studies on three typical cables in actual cable-stayed bridges are carried out to further explore combined effects of the cable sag, the cable flexural rigidity, together with boundary conditions on the control performance of the IMD.
# IoT-Based Intelligent Modeling of Smart Home Environment for Fire Prevention and Safety
## 4 IoT-Based Intelligent Modeling of Smart Home Environment for Fire Prevention and Safety
### Processing Unit  
The information collected from the sensors is then sent to central processing hub or main home sink wirelessly. Nowadays raspberry pi, abbreviated as (RPI), is being used as the primary processing unit. The main reasons for choosing it are its popularity and versatile functionalities. This device works as a processing unit and processes the data that comes from sensors to decide whether to activate the alarm. To handle the information which comes from the sensor nodes, RPI starts initializing the modules that are required for that specific processing. For example, in our proposed method, we used SMS alert system via GSM technology and then GPIO general purpose input-output. If the measurements that come from the sensor nodes consist of a fire alert, the GSM module is activated and send an alert SMS automatically to the specific user. The general purpose of the input-output GPIO module is to check the user response to the fire. This response will always be in the form of \"yes\" or \"no\".  
If the response is \"yes\", this means a fire is present, but if the response is \"no\", that means no fire. For each of the deployed sensors in homes, the GSM module gets activated automatically. The final decision of alarm depends on both the user's response and the sensor's readings. The flow diagram of this system is shown in Figure 8. If a single sensor's reading contains a fire alert message, then the system does not make any decision on behalf of this single reading. The system waits for the response to a pre-warning SMS. After receiving a response to the pre-warning SMS, that can be either \"yes\" or \"no\", the alarm will be activated if the response is \"yes\" or if the system receives the event notification from two or more sensors.  
Figure 7: (a) Beacon-enabled in ZigBee (b) Non-Beacon-enabled in ZigBee.
## 2 Materials and Methods  
### Materials  
Zinc acetate dihydrate (ZAD, Zn(CH\({}_{3}\)COO)\({}_{2}\cdot\)2H\({}_{2}\)O, >99.9%) was obtained from Sigma-Aldrich (Sofia, Bulgaria). Monoethanolamine (MEA, HO-CH\({}_{2}\)CH\({}_{2}\)NH\({}_{2}\), >99.9%) and 2-methoxyethanol (2-ME, C\({}_{3}\)H\({}_{8}\)O\({}_{2}\), >99.9%) were sourced from Fluka (Buchs, Switzerland). Palladium nitrate (Pd(NO\({}_{3}\))\({}_{2}\cdot\)(H\({}_{2}\)O)\({}_{\times}\), 99.8% metal basis, 39% Pd) was acquired from Thermo Scientific (Waltham, MA, USA).  
All reagents were of analytical grade and used without further purification. Standard microscope soda-lime glass slides (76 mm \(\times\) 26 mm, ISOLAB GmbH, Eshau, Bayern, Germany) were used as substrates. Doxycycline (DC, C\({}_{22}\)H\({}_{24}\)N\({}_{2}\)O\({}_{8}\), \(\lambda_{\max}\) = 275 nm, Teva, Sofia, Bulgaria) was chosen as a model pollutant due to its extensive usage and potential environmental impact.
# Quantum Recurrent Neural Networks: Predicting the Dynamics of Oscillatory and Chaotic Systems
## 2 Fundamental Quantum Computing Concepts
### Quantum Entanglement  
Quantum entanglement is a phenomenon in which multiple qubits become interconnected in such a way that the state of one qubit cannot be described independently of the state of the other qubits, even when the qubits are separated by large distances.  
To explain the significance of entanglement in this study, consider an analogy involving the conventional method of information exchange between two individuals. The most traditional method of information exchange involves one individual physically moving within audible distance to relay news to another person. This method, while effective, is indeed time-consuming and limits the speed of information transfer. However, by incorporating quantum entanglement into our framework to enable instantaneous information exchange between two entangled parties we significantly enhance the efficiency and speed of learning. This quantum advantage mirrors the leap from having to physically move towards someone to share information, to an immediate and direct exchange of knowledge.  
Entanglement can be achieved using certain quantum gates. For instance, the combination of a Hadamard gate followed by a Controlled-NOT (CNOT) gate is commonly used to entangle two qubits.  
First, the Hadamard gate is applied to one of the qubits to create a superposition, as previously discussed. Then, the CNOT gate, which flips the state of the second qubit if the first qubit is in the \(|1\rangle\) state, is applied. The operation of the CNOT gate can be described by the following matrix:  
\[\text{CNOT}=\begin{pmatrix}1&0&0&0\\ 0&1&0&0\\ 0&0&0&1\\ 0&0&1&0\end{pmatrix}. \tag{7}\]  
This operation creates entanglement between the two qubits.  
Consider two qubits, initially in the state \(|00\rangle\). Applying a Hadamard gate to the first qubit creates the superposition \(\frac{|0\rangle+|1\rangle}{\sqrt{2}}\) for the first qubit. Now, the combined state of the two qubits is \(\frac{|00\rangle+|10\rangle}{\sqrt{2}}\). Applying a CNOT gate afterward results in the entangled state \(\frac{|00\rangle+|11\rangle}{\sqrt{2}}\), where the state of each qubit cannot be described independently of the other.
# Architecting and Deploying IoT Smart Applications: A Performance-Oriented Approach
## 4 Performance Analysis: Design and Methods
### Design Decisions and Deployment Configurations
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multirow{2}{*}{**Layer**} & \multirow{2}{*}{**Implementation**} & \multirow{2}{*}{**Description**} \\ \hline \multirow{5}{*}{\begin{tabular}{c} SenSE Sensor \\ Simulator \\ \end{tabular} } & \multirow{5}{*}{L1-Device} & \multirow{5}{*}{SenSE Tool [56]} & The Sensor Simulating Environment (SenSE) is an \\  & & & open-source large-scale IoT sensor data generator able to \\  & & & abstract real devices and to model different complex \\ \cline{1-1} \cline{3-4}  & & & scenarios, such as smart cities [58] and smart agriculture [48]. \\ \cline{1-1} \cline{3-4}  & & & Sense is a traffic workload generator that emulates \\ \cline{1-1} \cline{3-4}  & & & heterogeneous sensors representing tens of thousands of IoT \\ \cline{1-1} \cline{3-4}  & & & devices, sending data simultaneously via MQTT. Although \\ \cline{1-1} \cline{3-4}  & & & the sensors are synthetic, the traffic is real. \\ \hline \multirow{3}{*}{\begin{tabular}{c} ChirpStack \\ \end{tabular} } & \multirow{3}{*}{L2-Transport} & \multirow{3}{*}{ChirpStack [34]} & Implementation of LoRaWAN that can be installed in a \\  & & & private deployment. Composed by ChirpStack Network \\ \cline{1-1} \cline{3-4}  & & & Server and ChirpStack Application Server, Redis and \\ \cline{1-1} \cline{3-4}  & & & PostgreSQL databases, and Mosquitto MQTT Broker [59]. \\ \hline \multirow{3}{*}{\begin{tabular}{c} IoT Agent \\ \end{tabular} } & \multirow{3}{*}{L2-Transport} & \multirow{3}{*}{FIWARE} & Translates specific data formats carried by IoT Protocols \\  & & & (such as Ultralight 2.0 over MQTT in this case) into standard \\ \cline{1-1} \cline{3-4}  & & & FIWARE JSON NGSI model. IoT Agent stores its data in \\ \cline{1-1} \cline{3-4}  & & & MongoDB. We considered two implementations of the IoT \\ \cline{1-1} \cline{3-4}  & & & Agent: the FIWARE Ultralight 2.0 IoT Agent and a \\ \cline{1-1} \cline{3-4}  & & & custom-made LoRaWAN IoT Agent. We developed the \\ \cline{1-1} \cline{3-4}  & & & latter one since the existing one in the FIWARE repository is \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Orion \\ \end{tabular} } & \multirow{3}{*}{L3-Data} & \multirow{3}{*}{FIWARE} & Orion is a publish/subscribe context broker with a central \\ \cline{1-1} \cline{3-4}  & & & role in data distribution for the FIWARE platform. Orion \\ \cline{1-1} \cline{3-4}  & & & works with entities defined in JSON NGSI and stores them \\ \cline{1-1} \cline{3-4}  & & & directly in MongoDB. \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Consumer \\ \end{tabular} } & \multirow{2}{*}{L4-Model} & \multirow{2}{*}{Specific purpose} & A simple consumer of IoT data playing the role of a generic smart application model. \\ \cline{1-1} \cline{3-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Software components for the performance analysis study.
# Satellite On-Board Change Detection via Auto-Associative Neural Networks
## 2 Materials and Methods
### Dataset  
Sentinel-2 is a Copernicus Programme mission developed by the European Space Agency (ESA) with an open data distribution policy. It comprises a constellation of two polar-orbiting satellites with wide swath width and a combined revisit time of five days at the equator, which monitor variability in land surface conditions. Data are acquired in 13 spectral bands spanning from the visible and the near infrared to the short-wave infrared. The spatial resolution varies from 10 m to 60 m, depending on the spectral band [27].  
To have a dataset of sufficient size to train the AANN, 36 multispectral images with the processing level 1C (top-of-atmosphere reflectance) of urban and suburban areas worldwide  
Figure 1: Main steps of the testing phase.  
were collected. Images were obtained through the Copernicus Open Access Hub [30]. The considered scenes are approximately 700 \(\times\) 700 pixels in size and do not contain clouds or haze. Figure 2 shows two sample images in RGB composition. This dataset was integrated with the Onera Satellite Change Detection (OSCD) dataset, which is similarly composed of 24 Sentinel-2 multispectral images acquired in different locations around the world [31]. For each location, registered pairs of images at a temporal distance of 2-3 years are provided, so the image for the same area is available for the time t\({}_{1}\) and for the time t\({}_{2}\). The pixel-level change ground truth is given for 14 images, and the annotated changes focus on urban changes, such as buildings or roads, while natural changes, such as vegetation, sun exposure and brightness condition, are ignored. A sample image pair and its corresponding ground truth are shown in Figure 3.  
Thus, the final dataset consists of 60 multispectral images. Among these, 5 labelled OSCD images were used to test the final performance of the algorithm.
## 4 Application of the SIW LWA for Radio-Over-Fiber-Based 5G Hot-Spots  
### Radio-Over-Fiber Photonic Beam Steering via SIW Leaky-Wave Antennas  
In this section, we seek to outline the application of the presented SIW LWA for providing beam steering functionality for 5G hot-spots. Therefore, the architecture of the targeted communication system including fiber-based and wireless links is illustrated in Figure 10. This system is comprised of three parts: the central baseband unit (CBU), the radio access unit (RAU), and the mobile unit(s). At the CBU, an optical carrier from a signal laser is modulated with data. After amplification and several km SMF, the signal arrives at the RAU, where a local oscillator laser (LD LO) is added and a high frequency photodiode (HF PD) generates the RF signal as the beat frequency of the two lightwaves via heterodyne detection. After amplification, the RF signal is radiated to the mobile unit(s). There it is received, amplified again, and downconverted to baseband via a Schottky barrier diode detector (SBD). Since the SBD acts as an envelope detector, the mobile unit does not require a LO, and a large operational bandwidth is obtained without tuning any components at the receiver. The fronthaul link between CBU and RAU is essentially a radio-over-fiber scheme with optical upconversion, which allows the radio frequency to be changed by tuning the wavelength of either the local oscillator  
Figure 9: E-plane radiation pattern of the presented SIW LWA at 60 GHz radio frequency as the normalized gain in dB over beam angle in degrees for the co-polarization (diamonds, blue), and cross-polarization (circles, red).  
laser or the signal laser. Thus, by employing the SIW LWA, the beam angle of the transmitting antenna can be steered by tuning the signal or local lasers and thus changing the RF. This means that centralized beam steering is possible, enabling user mobility, and no additional control signals are necessary. Furthermore, by generating multiple densely spaced optical carrier at the CBU, multiple mobile users can be served by this DWDM scheme. The data for every user can thus be modulated onto a separate optical carrier, which can be tuned independently to provide beam steering of the individual beams. To multiplex the individual channels for each user onto one fiber, only simple passive combiners are required. At the RAU, no demultiplexing is required as the individual optical channels are inherently radiated in different directions towards the mobile users. This way, wavelength multiplexing is inherently converted to spatial multiplexing.