esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data
### Satellite Imagery  
#### 2.1.1 Landsat  
The Landsat program operated by the National Aeronautics and Space Administration (NASA) is the longest-running mission for providing optical images of the Earth. Landsat-1 was the first satellite launched in 1972. Since then, seven further Landsat satellites have been launched in the frame of the Landsat Data Continuity Mission (LDCM) [22], from Landsat-2 in 1975 to Landsat-8 in 2013. Figure 1 gives an overview of time spans of the used Landsat missions. The technology of Landsat satellites has improved over the last few decades. Starting with Landsat-4, the spatial resolution has been increased from about 75 m to 30 m which improves the extraction of smaller features in images. In this study, we use the six bands (red, green, blue, near-infrared, shortwave infrared 1, shortwave infrared 2) with a resolution of 30 m from Landsat-4, Landsat-5, Landsat-7, and Landsat-8 which have comparable characteristics. The Landsat-6 mission failed to reach the orbit. All Landsat satellites have a Sun-synchronous orbit with an inclination of 98.2\({}^{\circ}\) and a height of 705 km. The Landsat missions have a repeat cycle of 16 days. Combining Landsat-7 and Landsat-8 increases the temporal resolution to 8 days. The Landsat satellites are equipped with different sensors. Landsat-4 and Landsat-5 have a Multispectral Scanner (MSS) and the Thematic Mapper (TM) on board. Landsat-7 carries an Enhanced Thematic Mapper (ETM+) which is similar to the MSS and TM of previous Landsat missions. Landsat-8 is equipped with an Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS).  
All Landsat data are freely available in the United States Geological Survey (USGS) archive. But the huge amount of data is the limiting factor in the processing of Landsat images. In order to decrease the amount of data, we use the Google Earth Engine [23], which allows us to extract the best cloud-free monthly composites of Landsat images. Google Earth Engine also provides tools for the cloud-processing of images. Furthermore, a quality band for cloud, shadow and snow coverage is already included in the Landsat data sets using the CFMASK (C Function of Mask) algorithm [24]. Since May 2003, a Scan Line Corrector Error has occurred on Landsat-7, leading to additional stripe-shaped data gaps which have to be considered in the data processing step. For this study, we use the Level-2 product \"Surface Reflectance\" for the Landsat satellites in the period between January 1984 and June 2018 for which the atmosphere has already been corrected.
## 3 Study Area and Data
### Zurich Summer Dataset  
Zurich Summer v1.0 dataset [25] contains 20 cropped QuickBird satellite images with pan-sharpened 0.61 m resolution. The images have four channels (NIR, R, G, and B) and include eight different urban and periurban classes. The classes are roads, buildings, trees, grass, bare soil, water, railways, and swimming pools. A sample image is shown in Figure 5. The dataset class distribution is unbalanced, similar to the real world. The images have approximately 23 million pixels. In the experiment, the ZH-16 and ZH-17 images were used as test images and the ZH-20 image was used as validation data; other images were used in the training.
## 2 Numerical Study: Physical Understanding of Wave Propagation Mechanism
### Numerical Parameters  
#### 2.1.1 Geometry  
The main duct had a diameter of \(D_{d}=11.45\) mm and was \(15D_{d}\) long; see Figure 3. The focus was set on a short region around the crack to allow observing the propagation and reflection mechanism of the wave around the leak location without reaching too long computation times. Experimentally speaking, measuring close to the leak would involve a few difficulties. The leak was located in the middle of the axial length of the duct. The expected crack size in fatigue-loaded metallic components corresponded to \(D_{crack}=50\) um. In order to respect the same diameter ratio (4) for both simulations and real-life applications (as explained in Section 1.5), the leak diameter was chosen as \(D_{leak}=0.4\) mm.
# Laser-Ultrasound-Based Grain Size Gauge for the Hot Strip Mill
## 2 Materials and Methods
### The Online Grain Size Gauge  
A robust grain-size-calculation method was developed in this work for measuring the grain size in a hot strip mill in an automated fashion. In addition to the necessary protections in the obviously harsh environment, the prerequisites for a grain size gauge suitable for a hot strip mill after the last stand include a large standoff distance (>0.5 m), measurable thickness from a few mm to \(\sim\)15 mm, measurable grain-size range from a few \(\upmu\)m to several tens of \(\upmu\)m and, preferably, a self-calibrating system.  
This new LUS-based grain size gauge consists of a nanosecond-pulsed generation laser and a microsecond-pulsed detection laser. The generation laser is a Q-switched, flashlamp pumped laser delivering optical pulses at 20 Hz with a pulse energy up to 200 mJ and with a pulse length of 6 ns at a wavelength of 532 nm. The detection laser system comprises a single longitudinal-mode laser emitting at a 1064 nm wavelength that is amplified by a flashlamp-pumped Nd:YAG crystal delivering pulses of \(\sim\)100 \(\upmu\)s length and a peak power of 600 W. The reflected detection laser light is sent to a GaAs two-wave interferometer [26, 27] to convert the frequency-modulated light into amplitude-modulated light that can be detected by photodiodes. The GaAs crystal is optically pumped by the same amplified single longitudinal-mode laser.  
Figure 4a shows the layout of the hot strip mill and the placement of the LUS system. The hot strip mill process in Borlange starts with a reheating furnace that heats the slabs to \(\sim\)1250 \({}^{\circ}\)C, while the slabs are shifted through the furnace by walking-beams from one side to the other. After the slabs are heated, they are descaled before entering the roughing mill, where the slabs are reduced to \(\sim\)3 cm thickness. After the reversible roughing mill, the material is coiled in a coil box, which evens out the temperature along the strip. The material is then passed on to the finishing mill, where the strip is reduced by the six consecutive stands to the final thickness between 2 and 15 mm. Figure 4b shows a photograph of the installed EMG iLUS grain size gauge, developed by the Swedish metal research institute Swerim and manufactured by the German company EMG Automation GmbH, just before the runout table.  
The measurement head is the yellow box in the center of the image; in Figure 4b, it is mounted on a translation stage and moves up toward the hot strip between two strip guiding rollers during the grain-size measurement. The head contains the generation laser, whereas the detection laser is positioned in a cabinet a few meters away, and the laser light from it is guided through fibers to the optics inside the measurement head. The light from both lasers is re-focused onto the same spot on the strip, and the working distance of the measurement head is just over half a meter. On top of the head, a black funnel is visible, which has an integrated air-knife to protect the optics from dirt and water.
## 3 Description of the Solution
### DDS, DCPS and RTPS
Finally, it is assumed that DDS has been conceived as a scalable architecture for cloud-based deployments. A DDS distributed system is able to contain real-time, QoS and context aware communications. A hierarchy can be established from a networked system point of view, where DDS would be installed in administrative, central, control and machine domains. According to [33], there are several aspects that must be taken into account to fully understand how DDS works:  
1. _Publish/Subscribe messaging_: it is used for the discovery of new services and the management of data flows among the entities involved in a communication. 2. _Lifecycle awareness_: DDS provides support in information lifecycle awareness for its applications. Features like first and last appearances of data updates in topic instances are, for example, included in this characteristic. 3. _Relational data modelling_: data are handled as if they were relational databases. DDS relies on structure-related topics and enabling requests tailored in terms of time and content according to the settings used by filters. 4. _Reliable multicast_: UDP sockets can be used for reliable multicast. While the usefulness of this feature in an underwater scenario is limited, it has been taken into account for testing activities that have been done in order to prove the usefulness of the tested middleware solution. The main specification of the DDS introduced in its latest version (v1.4, [34]) is a Data-Centric Publish/Subscribe model referred to as DCPS for communication and integration of distributed applications. This solution puts the stress in real-time communications and is described as a model that has become popular in many real-time applications, since it is able to define the concepts of publishers and subscribers according to the context of DDS. The concept of data model, as well as data structures defined by topics and types, are introduced as major ideas in the description of DCPS. However, another aspect of DDS that is of major importance is the existence of ancillary software standards used as a way to offer interoperability for different DDS solutions. The latter refers to Real-Time Publish/Subscribe protocol (RTPS). It is explicitly mentioned in [35] that \"This specification defines an interoperability wire protocol for DDS. Its purpose and scope is to ensure that applications based on different vendors' implementations of DDS can interoperate\", so its objective is guaranteeing interoperability among DDS developments made by different vendors. RTPS can also be described as a wire protocol, in the sense that it establishes mechanisms for communications among remote entities that generate information at one point and send it to another part of a distributed system. The usage of RTPS and DCPS is implicit in the middleware solution that is described in the following section, since they are provided by a DDS development.
# Beam-Hopping-Based Resource Allocation in Integrated Satellite-Terrestrial Networks
## 5 Resource Allocation Algorithms for MMCDR  
In this section, the optimization problem P2 is reformulated as an MILP problem in a way similar to Section 4.1, and then, a greedy suboptimal algorithm is proposed to solve P2 with low computational complexity.
## 3 Theoretical Foundation
### Relevant Theories Supporting the Theoretical Foundation
#### 3.6.6 The Systems Theory (TST)  
TST is implemented in the 6 GCIoHE systems for evaluation applications. One of the essential mechanisms of systems analysis is systems thinking, enabling the contouring of systems from a broad perspective as opposed to precise activities within the system [37].
# Development of Energy Storage Systems for High Penetration of Renewable Energy Grids
## 4 Frequency Regulation Technology Simulation
### Analysis of Frequency Regulation Technologies
According to the proposed analysis results, the dynamic regulation strategy has good capability in regard to frequency nadir and frequency recovery, but its capability regarding RoCoF limitation is weak when the parameter is set to 0.5. The static regulation strategy has good capability regarding frequency nadir, and its capability regarding RoCoF limitation is great when the power system has a peak load. However, when the battery energy storage system with a large capacity operates according to the static regulation strategy, the grid frequency will oscillate during the frequency recovery period. Additionally, the static regulation strategy has weak capability in regard to system oscillation regulation because the strategy of the frequency deadband is larger than that of other strategies. The virtual inertia strategy has good capability regarding RoCoF limitation, and the capability of RoCoF limitation can be maintained under light load and peak load. However, its frequency recovery capability is weak because the battery energy storage is in the charging state during this period. Therefore, the improvements that need to be made to the strategy are as follows:  
1. Static regulation strategy: When the power system has a peak load and larger battery energy storage system capacity, the recovery time of compensation needs to be limited under the static regulation strategy. The problem of frequency oscillation can be avoided during the frequency recovery period. 2. Virtual inertia control: The virtual inertia strategy can be directly replaced by the dynamic regulation strategy when the battery energy storage system is operating in the charging state. The weak capability of frequency recovery can be strengthened by the combination of strategies. Finally, according to the analysis results, the capability evaluation and improvement points of the strategy lay the foundation for large-capacity battery energy storage systems. It can help power operators design the battery energy storage operation strategy, when the system is peak load or light load, and it can help them avoid designing the wrong strategy to support the power system. At the same time, correct strategies can be implemented for the weak capabilities of the power system to achieve the best benefits of the strategy. In addition, the proposed strategy improvement suggestions can help improve the capability of the battery energy storage system operating strategy.
## 2 Background  
### An Overview of Internet of Vehicles  
Transportation has become a significant challenge in many countries due to population growth. Often, the transportation system itself is outdated, making upgrading a costly and daunting task. By 2035, the number of vehicles around the globe is estimated to reach two billion. This substantial number will strain existing transportation systems and most likely result in more accidents and traffic jams. Therefore, changes must be made in the transportation system's framework to adjust to emerging prerequisites of new vehicles, travelers, and drivers [2]. Technological advancements have motivated the enhancement of a wide array of gadgets to be used in various fields, including IoT. Additionally, the Internet is helping societies develop much faster, and people in developed societies, in turn, are seeking a better way of life [3]. A few of these technologies have resulted in the further advancement of IoV, a field commonly considered an extension of IoT. IoT is a universal network of interconnected smart devices equipped with embedded hardware and software for environmental sensing and data exchange, with the capability to act on that information. Therefore, including vehicles as devices makes IoV a field with applications in intelligent transportation, crash prevention, and smart cities [4]. IoV networks require software applications to monitor vehicle movements and provide security against malicious attacks. These systems function through interactions with various components,including vehicle communication with roads, roadside units, and sensors [5]. IoV brings together two cutting-edge dreams--the network and intelligent vehicles--while centering around the objects (e.g., humans, vehicles, systems) to create a perceptive system that relies on information technology and communication features to assist authorities in huge urban territories and entire countries [3]. IoV enables extensive communication between vehicles in various forms, including vehicle-to-vehicle, vehicle-to-road, vehicle-to-human, vehicle-to-infrastructure, and vehicle-to-sensor connections through wireless communication technologies [6]. Additionally, human-to-human interaction occurs in IoV. Generally, though, the human component is gaining importance as the services develop. In their research, [PERSON] et al. [7] view IoV as a worldwide network with three integrated subnets: the intravehicle network, the intervehicle network, and the vehicular mobile internet. By contrast, [PERSON] et al. [3] define IoV from the angle of integration of on-board sensors and communication technologies. These researchers view IoV as intelligent vehicles with advanced devices that utilize modern communication and networking technology to provide vehicles with complex environment sensors, intelligent decision making, and control functions.
## 6 Verification and Results
### Episode 3: Security Integrity (req: ii, viii)  
For this episode, we simulated that the intermediate ISP (ISP-B) is compromised. The objective of the demonstration is to show that the integrity of the architecture is maintained for ISP-A and ISP-C even if ISP-B is compromised. We demonstrate this by showing that the architecture supports flow-based encryption (req: ii) and by showing that it is resistant to manipulation of the packet headers or flow injections (req: viii).
## 4 IETF 6 LoWPAN Working Group (IPv6)
### Leveraging upon 6 LoWPAN to Realize the IoT
#### 4.3.1 Improvements to Core Specifications  
In addition to the core specifications, other sub-protocols are being discussed in the working group. As was already mentioned, the improved compression mechanism specified in RFC 6282 introduces a next header identifier to enable any arbitrary next header compression. The _Generic Compression of Headers and Header-like payloads_ (draft-bormann-6 lowpan-ghc-05) [26] is one of the internet drafts that utilizes the identifier to perform compression of arbitrary headers.  
Adaptation Layer Fragmentation Indication is another area that is under discussion. The draft, (draft-bormann-intarea-alf), proposes a mechanism to indicate the presence of fragmentation at the adaptation layer and to indicate preferred MTU for the communication. This draft aims at improving application performance by limiting packet sizes to the smallest possible size to avoid fragmentation on link layers with small MTU values.
# HSM4 SSL: Leveraging HSMs for Enhanced Intra-Domain Security +
## 3 HSM4 SSL Architecture
### HSM4 SSL: High-Level Architecture
Moreover, HSM4 SSL is a modular architecture designed to provide uniform integration across various network environments and configurations. This adaptability is essential for addressing legacy and modern systems' different security requirements and designs. This is achieved by the usage of uniform CLI commands that provide a consistent interface to eliminate overhead when using HSM4 SSL's services in pre-existing infrastructures. Plus, the ADCP parameters can be dynamically adjusted to align with the exact requirements of both advanced and legacy systems, and the HAL can also be effortlessly modified to include HSMs' drivers when more recent HSMs are introduced in the physical layer to fulfill the organization's security requirements.
## 4 Materials and Methods
### Molecular Docking  
The hNudt16 crystal structure in complex with IMP and magnesium ions (PDB ID:2 XSQ [17]) was used as a template for docking. The ligands were docked to hNudt16 using MOE software (version 2018) (Chemical Computing Group ULC, Montreal, QC, Canada). The hydrogen atoms were assigned using the MOE Protonate 3D function. The ligand models (IDP, GppG, and AppA) were manually prepared using the MOE molecule editing plugin. Dockings were conducted using a rigid receptor protocol. With the Triangle Hatcher placement method, 100 ligand poses were identified, which were then refined and scored based on the binding free energies estimated using the GBVI/WSA dG algorithm [53]. For each ligand, docking experiments were conducted at least twice,each generating five ligand poses. VMD [54] and MOE Ligand Interaction plugins were used to generate the figures. The docking protocol was verified on the structure of the hNudt16-IMP complex (PDB ID: 2 XSQ [17]) showing that the root-mean-square deviation (RMSD) between the conformation of the docked and crystallographic IMP (heavy atoms) was only 0.22 A (Figure S5).  
All data are available online at [[https://www.mdpi.com/article/10.339](https://www.mdpi.com/article/10.339)]([https://www.mdpi.com/article/10.339](https://www.mdpi.com/article/10.339)) 0/ijms222010929/s1.  
Investigation, M.C., M.W.-S., N.O., E.B., J.S., L.M., M.L. and M.T.; methodology, M.C., N.O., E.B., J.S., M.K. and J.T.; data analysis, M.C., M.W.-S., E.B., N.O., J.T., M.K., E.D. and M.T.; writing--original draft, M.C. and R.G.; writing--review and editing, E.D., M.L., M.W.-S., N.O., J.T., M.T., M.K., M.C. and R.G.; conceptualization, M.C., J.T. and R.G.; supervision, R.G.; project administration, R.G. and M.L.; resources, E.D.; funding acquisition, E.D. All authors have read and agreed to the published version of the manuscript.  
Financial support for this work was provided by the National Science Centre, Poland, grant no. UMO-2019/33/B/NZ1/01322 to E.D. and UMO/2017/27/B/NZ1/01859 to E.D. M.K. and M.T. acknowledge also a support from a research grant no. 2017/27/B/ST4/00485 from the National Science Centre, Poland.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The equipment used was sponsored in part by the Centre for Preclinical Research and Technology (CePT), a project co-sponsored by European Regional Development Fund and Innovative Economy, The National Cohesion Strategy of Poland. Circular dichroism spectrophotometer CHIRASCAN Plus of Applied Photophysics, used in the present work, was co-financed by the European Union within the European Regional Development Fund Project [POIG.02.01.00-14-122/09].  
The authors declare no conflict of interest.
# Internet of Things (IoT) Cybersecurity: Literature Review and IoT Cyber Risk Management
## 2 Literature Review
### Cybersecurity in IoT Architecture
#### 2.1.1 Cybersecurity at the Perception Layer  
While many IoT devices are designed to be low energy and lightweight, they often collect enormous amounts of data from the environment in real time and therefore apply various energy-saving methods. Technologies such as machine learning are often used to make reliable inferences from the data generated [12]. However, due to the resource-constrained capacity of devices, embedding computation-intensive security and privacy measures into lightweight IoT devices has been challenging [13; 14; 15; 16].  
One of the major security issues at the perception layer is the cloning of device chips for cyberattacks. For example, clones of RFID tags may be used to launch distributed denial-of-service (DDoS) attacks. Physical unclonable functions (PUFs) have been used for authentication and identification as well as cryptographic key generation for a chip [17]. PUFs chips enhance security through tamper resistance, device identification, authentication, and the prevention of cloned devices [18]. Since the components of IoT devices are often implemented on resource-constrained ones, lightweight PUF designs are required [19]. While PUFs are not cloneable, it is possible to clone a PUF key once it is extracted. Hence, a number of authentication protocols are proposed based on PUFs [11; 20; 21; 22]. For example, [PERSON] et al.'s protocol is based on PUFs and the lightweight cryptography to carry out an efficient verification of a single tag [21].  
The enhancement of IoT cybersecurity requires doing tasks manually, expanding staff knowledge and tools, and addressing risks with manufacturers and other third parties [23]. IoT cybersecurity needs to take into account device security, data security, and individuals' privacy [23]. Certifying the security level of IoT devices is instrumental in achieving the acceptance of IoT devices, but the dynamic and heterogeneous nature of the IoT devices makes the development of a cybersecurity certification framework complicated from both technical and legal perspectives [24].