esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Optimisation Modelling of Gate Assignments  
### Description of the Gate Assignment Problem  
Airport gate assignments can be subject to a variety of conditions. To achieve fast, safe, and effective flight docking, reasonable gate assignments play a vital role. The following is a detailed analysis of the gate and flight characteristics. For gates, there are three important features as follows.  
1. Matching characteristics of gates  
Gates can be divided into three types according to size: large, medium, and small. Large gates can park large and small aircraft, medium gates can park small and medium aircraft, and small gates can only park small aircraft.
2. Parking uniqueness  
Only one aircraft can be assigned to the same gate at any one time during a time slot.
3. Auxiliary facilities  
Due to geographical and land constraints, an airport has a limited number of boarding bridges. In addition to the bridges, passengers can board from the apron by transfer bus.  
For the landing characteristics of flights, the following types of characteristics exist.  
1. Flight No.  
To facilitate the organisation of transport production and differentiate management, each flight is coded with different numbers according to certain rules.
2. Flight types  
This refers to the types of aircraft owned by the airlines. The aircraft type is determined by the number of seats and the layout of the cabin. Different performance variables (such as range, fuel consumption, etc.) mean the aircraft will vary in fuselage size and operating costs. Common flight models are Airbus 320, Boeing 737, Boeing 767, etc.  
3. Dynamic characteristics  
Each flight has an arrival time and a departure time, with prearranged runways for take-off and landing.
## 3 Result and Discussion  
### The Effect of the Presence or Absence of PTI  
In a tunnel with auxiliary ventilation, the airflow in the vicinity of the WF is particularly complicated. The low-temperature airflow blown out of the ventilation duct flows to the tunnel outlet after passing over the WF. Due to the relatively high airflow velocity on both sides of the tunnel, there is a significant heat convection between the wall rock and the wind flow [25], and the temperature of the newly pressed airflow increases sharply in this part. However, the PTI layer can avoid the high ambient temperature caused by this phenomenon. In the test, the ventilation and cooling conditions of the working area with or without PTI layers were tested. The inlet air temperature was 16.5 \({}^{\circ}\)C, the air velocity at VDO was 7.5 m/s, the air duct was suspended at a height of 0.025 m, and the average temperature of the working area (ATWA) was obtained from the average temperature of sensors 101 to 108 and 111 to 118. As shown in Figure 4, the ATWA varied as the ventilation time increased.  
After a period of ventilation, the temperature of the working space, with or without a TIL, approached a stable value. It is obvious that the tunnel with the PTI layer had a lower ultimate stable temperature. From the temperature difference represented by the striped area at the bottom of the picture, the temperature difference in the tunnel with and without insulation increased gradually at the beginning of auxiliary ventilation and finally reached a plateau at 1.5 \({}^{\circ}\)C. The low-temperature airflow was ejected from the ventilation ducts to cool the internal air of tunnel. With continuous cooling, the temperature difference gradually decreased, so the cooling rate decreased and eventually converged to a thermal equilibrium state. In partially insulated tunnels, the insulation layer isolates the airflow from the high-temperature rock in the area with the highest airflow velocity. Since there is a phenomenon where the surrounding rock is unable to transfer heat directly to the airflow, which prompts the airflow to have enough cooling capacity to exchange heat with the air inside the tunnel, the internal environment can stabilize at a much lower temperature.
## 7 Future Research Directions  
We extend our efforts to explore the impact of mobile devices with high velocity in a fully dynamic environment to improve offloading decisions as a future research direction. Aside from that, in the case of a server overload or breakdown, we make no provision for task or virtual machine relocation. Task and mobility-aware virtual machine migration will be the focus of future research efforts. Also, when numerous users have similar destinations, the mobility issue gets more intriguing. For example, mobile crowd sensing (MCS) works better when users' whereabouts are predicted. Similarly, in vehicular edge networks, many cars may share a destination. As a future study topic, users with comparable mobility circumstances can be aggregated into homogenous groups and their activities integrated. Moreover, very few studies on device-to-device task offloading. The moving users while executing the applications such as MCS might need to offload some resource hungry task to a neighbor edge user's device in a collaborative fashion. A lightweight machine learning model to find the best local resource is an interesting area to work in future. The existing works do not explore the relationship between the offloaded task and its content. Since each subtask has its own application and content, it has its own delay requirement, which is defined by the subtask's content characteristics. Due to the limited CPU capabilities of mobile devices, each subtask has a variable delay. The creation of a content-aware framework can improve offloading decisions in the future. Securing MEC deployments is a critical challenge. The adoption of edge cloud servers presents substantial security risks due to mobile device exploitation. The existing security solutions cannot keep up with the rate of rising security threats. Many present security protocols need complete connectivity, which is impossible in mobile edge networks as many links are by default intermittent. MEC, on the other hand, sends user data to an edge server that controls access for other mobile users. This causes issues with data integrity and authorization. Further, the data owners and data servers have different identities. As a result, a complete research investigation is required to uncover MEC security vulnerabilities.  
Conceptualization, S.K.u.Z.; Methodology, S.K.u.Z.; Software Implementation, S.K.u.Z.; Supervision, A.I.J.; Writing and Editing, S.K.u.Z. and T.M.; Review, M.A.K.; Validation, A.I.U.; Funding, N.Z.J., M.S. and M.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Taif University Researchers Supporting Project number (TURSP-2020/79), Taif University, Taif, Saudi Arabia.  
Not applicable.  
The authors thank all reviewers for their helpful comments and suggestions.  
The authors declared no conflict of interest.
## 3 Background
### Ascon  
The ASCON algorithm is a lightweight cryptographic algorithm designed for constrained environments such as those of IoT devices, blockchain networks, and decentralized applications. Originally developed for the CAESAR competition, ASCON was later standardized by NIST as the lightweight cryptography standard [33; 34]. It is designed to provide strong security while maintaining minimal computational overhead.  
**Features of ASCON**  
ASCON employs an Authenticated Encryption with Associated Data (AEAD) scheme [35], which ensures both confidentiality and integrity in a single step. It utilizes a sponge construction, a permutation-based processing technique that enhances efficiency in encryption and hashing operations. ASCON is optimized for fast execution with low resource consumption, making it suitable for environments with limited processing power. Additionally, it is resistant to side-channel attacks [36], such as timing- and power-based attacks, which commonly target cryptographic implementations.  
**Security Mechanisms of ASCON**  
ASCON integrates encryption and authentication into one streamlined operation, reducing the computational burden compared to traditional AES-based schemes that require separate authentication mechanisms [37]. It is designed with nonce misuse resistance, preventing security vulnerabilities that arise from nonce repetition. Its low memory footprint ensures efficient performance on resource-constrained devices, such as embedded systems and decentralized applications [34; 38].  
By integrating blockchain, IPFS, and ASCON, the proposed protocol ensures robust security, decentralization, and high efficiency. Blockchain provides an immutable and transparent record-keeping system, IPFS offers distributed data storage with verifiable integrity, and ASCON guarantees lightweight cryptographic protection. This combination makes the protocol particularly well suited for decentralized authentication and secure data management in resource-constrained environments.
# GNSS Performance Modelling and Augmentation for Urban Air Mobility +
## 3 GNSS Performance Characterization
### Error Modelling  
The primary observable from a GNSS signal is what is termed the _pseudorange_, which is essentially the difference in time between when a signal is transmitted from the satellite, and the time at which it is received at the receiver antenna, multiplied by the speed of light. The reception of at least four such pseudoranges allows the receiver position to be determined, typically through a least-squares solution. The pseudorange essentially comprises the true range between the satellite and receiver, along with a number of errors and biases that contaminate the measurement and cause the estimated position to deviate from the true position. The model of the pseudorange observable from a single satellite is [10]:  
\[\mathrm{P}=\rho+\mathrm{d}_{\rho}+\mathrm{c}(\mathrm{dt}-\mathrm{dT})+\mathrm{ d}_{\mathrm{iono}}+\mathrm{d}_{\mathrm{prop}}+\varepsilon_{\mathrm{mp}}+ \varepsilon_{\mathrm{noise}} \tag{1}\]  
where c is the speed of light. \(\mathrm{d}_{\rho}\) jointly denotes the ephemeris and satellite clock errors. dt and dT are the satellite and receiver clock offsets, respectively. \(\mathrm{d}_{\mathrm{iono}}\) and \(\mathrm{d}_{\mathrm{prop}}\) are the ionospheric and tropospheric delays, respectively. \(\varepsilon_{\mathrm{mp}}\) is the code multipath error, which is a function of the relative geometrical configuration of the receiver and satellite antennas, and reflective surfaces in the local  
Figure 4: Proposed system architecture for GNSS-based UAM operations.  
environment. \(\epsilon_{\rm noise}\) is the receiver thermal noise. \(\rho\) is the true geometrical satellite-receiver range at a given epoch given by:  
\[\rho=\sqrt{\left(w^{p}-u_{r}\right)^{2}+\left(v^{p}-v_{r}\right)^{2}+\left(w^{p }-w_{r}\right)^{2}} \tag{2}\]  
where at any given epoch, \((u_{r},v_{r},w_{r})\) are the Cartesian coordinates of the receiver and \((u^{P},v^{P},w^{P})\) are the Cartesian coordinates of the satellite. Given a system of equations of the form of (1), the receiver coordinates (and GNSS time) can be derived from the simultaneous observation of four (or more) satellites. If more than four satellites are visible, a least-squares routine can be implemented to solve the over-determined set of equations. Each pseudorange error term in Equation 1 is described briefly.
# A Capacity Enhancement Method for Frequency-Hopping Anti-Jamming Communication Systems
## 3 Results
### Design of Demodulation Algorithm for FH Capacity Boost Signal
Additionally, the carrier recovery  
Figure 12: Costas ring structure diagram. Figure 13: Quality of carrier recovery under different conditions. quality is not sensitive to the value of J or the number of hopping cycles occupied by the Information B code element, and the carrier quality, \(\sigma_{c}\), varies within 0.01 between different values of J at the same signal-to-noise ratio, which suggests that carrier recovery can be carried out normally on the basis of the reconstructed carrier.
# Nighttime Image Dehazing Based on Point Light Sources
## 3 Proposed Method
### Overview of Algorithm  
In order to solve the problems of serious texture loss and poor dehazing effect in the nighttime dehazing algorithm, this paper proposes a nighttime dehazing method based on the light source influence matrix. The algorithm flow is shown in Algorithm 1. As illustrated in Figure 1, the whole algorithm consists of a pre-processing section (Color Correction), a section of calculating light source influence matrix, and a dehazing section.  
Color Correction: The color of the artificial light source in the night scene has a certain influence on the reflected light of the object. It is necessary to perform color correction on the image at first to prevent color distortion of the image after dehazing. Therefore, the first step is to correct the color shift of the image through the maximum reflection prior algorithm, so as to maintain the color balance of the image and enhance the details of the image.  
Figure 1: An overview of proposed nighttime dehazing method.  
Light Source Influence Matrix: The low-frequency component is extracted from the image through a mid-pass filter, and the initial atmospheric light value is calculated with the low-frequency component. Then the image is transferred to the HSI space to extract the photometric map. According to the photometric map, the artificial light source pixels are filtered out and the corresponding influence matrix is calculated. After superposition, the final matrix is obtained.  
Nighttime Dehaze: Using the light source influence matrix and introducing the adaptive atmospheric light deviation coefficient, the atmospheric light value near the light source area is corrected. The initial transmittance of the two regions is estimated separately from the dark channel method and then refined to fuse the boundary parts of the two regions. Among them, the transmittance of the near-source region is modified and adjusted using the edge-preserving filtering method. Finally, the dehazing image is obtained by optimizing the atmospheric light value, transmittance and the light source influence matrix.  
```
Data:\(I_{0}\) Result:\(J\)
1 Color Correction:
2\(I\gets MRP(I_{0})\)
3 Light Source Influence Matrix:
4 calculate the matrix \(K\) according to Algorithm 2
5 Parameter Estimation:
6\(A\leftarrow\Phi(I)\);
7 classify \(A\) according to Equation (15);
8 calculate the initial transmittance map \(t_{near}\) and \(t_{far}\) according to Equation (16);
9\(t\leftarrow\mathit{Fuse}(t_{near},t_{far})\);
10 Nighttime Dehaze:
11 synthesize \(J\) according to Equation (21)
```  
**Algorithm 1**Nighttime dehazing algorithm
# Molecular Techniques and Target Selection for the Identification of _Candida_ spp. in Oral Samples
## 3 Molecular Identification of _Candida_ spp.
### Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS)  
MALDI-TOF MS is a molecular method broadly implemented in modern clinical microbiology laboratories [141]. This approach is a rapid and reliable alternative for yeast identification and consists of the generation of protein 'fingerprints' that are compared with reference spectra [142; 143; 144]. MALDI-TOF involves sample ionization with a laser striking a matrix of molecules to cause the analyte molecules to enter into a gas phase without fragmentation. It is coupled with the principle of Time-of-Flight analysis, in which ions of different mass/charge ratios are dispersed in time during their flight along a path of known length (the equipment analyzer) [145].  
MALDI-TOF MS has been used for the speedy identification of _C. albicans_ and non-_albicans_ species on blood [146; 147] and oral samples [148; 149; 150], with shorter turn-around times and higher accuracy compared to conventional biochemical methods [151]. MALDI-TOF MS was performed on a library composed of clinical and reference strains with an accuracy of 94% when compared with _ITS_ sequence analysis [142; 152]. This method provided results of genus-specific proteins within 24 h of _Candida_ causing bloodstream infections [143; 50; 153].  
MALDI-TOF is a promising technique, but the lack of spectra characterization for microorganisms still needs to be addressed. Without available reference spectra, results cannot be achieved. This availability of spectra seems to be changing through the emergence of new studies, building information about _Candida_ spp. [154; 155; 156; 147]. The equipment cost is one of the strongest disadvantages of a clinical and routine setting.  
Figure 1 provides a qualitative comparison of the molecular techniques used for the identification of _Candida_ spp. Table 2 presents advantages and disadvantages for each molecular technique.  
Figure 1: Qualitative analysis of the main molecular approaches for _Candida_ species detection. RT-PCR: Real-Time - Polymerase Chain Reaction; NGS: Next Generation Sequencing; HRMA: High-Resolution Melting Analysis; FISH/PNA-FISH: Fluorescent In Situ Hybridization/Peptide Nucleic Acids-FISH; PCR-RFLP/AFLP: Polymerase Chain Reaction - Restriction Fragment Length Amplification/Amplified Fragment Length Polymorphism; LAMP: Loop-Mediated isothermal amplification; PCR-ESI-MS: Electrospray Ionization Mass Spectrometry coupled with broad-spectrum PCR; MALDI-TOF MS: Matrix-Assisted Laser Desorption IonizationTime of Flight Mass Spectrometry; [95; 157]. Brown: Low; Yellow: Medium; Green: Good.
## 2 Materials and Methods
### Methodologies
#### 2.3.3 LUCC Scenario Prediction and the Response of Carbon Uptake  
In this study, the CLUE-S model was used to predict the change in vegetation land, and then to explore the response of the carbon budget under different LUCC scenarios. The CLUE-S model was developed by the research group \"Land Use Change and its Effects\" at Wageningen University, the Netherlands, on the basis of the earlier CLUE model [32; 33; 34], which can realize the dynamic simulation of land use change under multiple scenarios in a region based on the quantitative relationship between the spatial distribution of land use and the driving factors [35; 36].  
Considering the land use planning under different RCP scenarios [37] and the development trend of Dongting Lake Basin, three land use simulation scenarios were adopted in this study: (1) Natural development scenario: Based on the conversion probability of each land type in the basin from 2010 to 2020, the demand for each land use type in 2030 was calculated based on the current land use situation in 2020. (2) Ecological protection priority scenario: Priority was given to the protection of all vegetation land, the conversion probability of cultivated land and forestland to urban land was reduced by 30%, the transfer of grassland waters to urban land was reduced by 20%, and the conversion probability of urban land to forestland and grassland was increased by 10%. (3) Economic development priority scenario: This scenario advocates that regional economic development should be given priority, the probability of transferring cultivated land to urban land should be increased by 20%, the probability of transferring woodland and grassland to urban land should be increased by 10%, and the probability of transferring urban land to land other than cultivated land should be reduced by 30%.  
Combined with the historical law of LUCC response characteristics of terrestrial ecosystem carbon uptake, the overall characteristics of carbon uptake under different scenarios are predicted to provide comparable options for future regional territorial spatial planning and optimization.
## 2 Materials and Methods
### 0.34 THz Tripler Development  
With the geometrically optimized diode chip, a 0.34 THz triplet using GaAs monolithic technology was proposed. The detailed circuit configuration is shown in Figure 4. It can be seen from the figure  
Figure 3: Simulated optimum conversion efficiency with (**a**) different surface channel widths and (**b**) different anode junction areas.  
Figure 2: Schematic diagram of (**a**) wave port configuration of diode anode junction and (**b**) setup for high-frequency 3D EM simulations.  
that the whole circuit was functionally divided into three parts. The 4-die diode chip generated the fundamental wave and the harmonics. The local oscillator (LO) matching and filtering part consisted of a LO transition probe for input, a filter based on a compact microstrip resonant cell (CMRC) for harmonics energy recovery, a CMRC filter for DC bias, and an intermediate matching circuit to make the input fundamental energy fed into the diode chip as much as possible. Similarly, the radio frequency (RF) matching and filtering part was optimized to maximize the output of the third harmonic. All linear circuit structures, including filters and matching networks, were simulated and designed in HFSS. S-parameter files with seven ports were then imported into ADS for harmonic balance simulation. After several iterations of co-simulation and optimization, the tripler circuit was finalized to suffice requirements, as shown in Figure 4.  
A photograph of the fabricated THz GaAs monolithic integrated tripler circuit within a split block is shown in Figure 5. As can be seen, the 4-die SBDs were integrated with the matching circuits, and the whole tripler circuit was fixed on the block through the beam lead. Due to the large relative dielectric constant of gallium arsenide, it was impractical to connect the tiny DC feeder circuit directly to the external subminiature version A (SMA) connector. Therefore, the transition circuit on Rogers RT/duroid 5880 was fabricated and connected to the GaAs circuit through silver glue. The zoomed view of the diode chip was given as proof that the monolithic integrated diode technology eliminated the manual assembly errors that may have been introduced by the flip-chip mounting of discrete diode chips. Simulation results show that the output power of the tripler was more than 3.6 mW in the frequency range of 324-352 GHz and reached the peak efficiency of 10.7% at 340 GHz under the condition that input power was 100 mW and bias voltage for each die was \(-1.95\) V.  
Figure 4: Circuit configuration of the proposed 0.34 THz tripler in HFSS.  
Figure 5: Photograph of the assembled tripler block connected with the SMA-connector for DC bias (**left**). Picture of the fabricated THz GaAs monolithic integrated tripler circuit installed in a split block with the enlarged view of the diode chip (**right**).
# A Novel Anomaly Behavior Detection Scheme for Mobile Ad Hoc Networks
## 2 Background
### Zone-Based Routing Protocol (ZRP)  
The ZRP is a hybrid protocol that offers the merits of both reactive and proactive schemes. It distributes the network into flexible size zones. The zone size is dependent on the radius of length \(\rho\), where \(\rho\) is the number of hops to the perimeter of the zone and not the physical distance. The communication between the nodes commonly takes place in close proximity, i.e., within the zone. Hence, finding out the routing information is easy with ZRP, showing the benefits of proactive protocols termed interzone routing protocols (IERPs). To find the route between the zones, an intrazone routing protocol (IARP) is employed, i.e., a reactive protocol [9]. At the time of communication, when one node requests to send a packet to another node, it checks for the availability of a destination within the zone; if it is present, then it casts the route request to border nodes that, in turn, check the availability in their zones. This continues until the search for a destination ends. On the other hand, as the routing zones greatly intersect, a node may belong to more than one routing zone. The problems to be resolved arise when a node receives the same query multiple times. [PERSON] et al. [10] describe how to resolve some of these problems and to overcome the traffic.