esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Today's digital world affects everyday life so extensively that there are hardly any sectors of the economy where there are no IT systems, whether it is to control, schedule and regulate production and production processes, or even to store operational data in near-human areas. This huge development that characterized the last 2-3 decades has brought not only positive contributions. This development has also been accompanied by its negative effects and by the vulnerability of digital data. These can be results of accidental misuse, malfunction, or intentional 'digital' damage. Simultaneously, with the development of technology, a layer of cybercriminals with a high level of expertise has emerged and is evolving, taking advantage of digital vulnerabilities to exploit the opportunities arising from victims' ignorance, fear and low IT skills. Cybercrime has become a particular mode of treatment for unsuspecting or irresponsible users. The goal is different, as it can simply be a form of self-expression that can be called very specific, which involves the attacker breaking into the victim's system because they are able to do it. Often, the user does not even notice this, because there are no clear signs of the attack to be interpreted. In recent years, however, there has been an increase in attacks in which victims find a clear message on their computers and servers. The message usually informs the user about the fact that their computer or network has been attacked or compromised and that their files have been encrypted, while the original files stolen. In these cases, hackers ask for a ransom with a short deadline of 24-48 h in a virtual digital currency, usually Bitcoin.  
The current research has been indicated by an increasing number of ransomware attacks, a significant increase in the extent of the damage caused. The fundamental question of the research was focused on small and medium-sized enterprises: how often these attacks occur in this entrepreneurial class, how successful they are, and what connections can be revealed between the elements of corporate IT culture and IT security, and whether these economic operators in the current situation are willing to adopt a new, complex, autonomous system of protection designed and modeled on the basis of emerging needs.  
This manuscript contains the research questions that were formulated with the help of the answers obtained during interviews. Based on the evaluation of the survey, a definable goal has been to design a cost-effective protection model that can work with currently available network and virus protection solutions, enable the early detection of ransomware attacks, protect the data undertaken, and in all cases, allow recovery in the case of damage or incident.  
The following subsection gives a brief overview of cyberattacks from the last 6 months that have affected different sectors and demanded ever higher ransoms from users.
## 2 Materials and Methods
### Volatile Analysis by HS-SPME-GC-MS  
To simulate the process of cooking rice that is normally performed for consumption, 5 g of the brown rice (BR) and the germinated brown rice (GBR) from each of the rice cultivars, and 7.5 g of distilled water for every 5 g of rice, were combined in 20 mL headspace glass vials. The glass vials were loosely sealed with 18 mm magnetic screw craps fitted with a polytetrafluoroethylene (PTFE) silicone septum and were kept boiling for 30 min in a rice cooker until no white spots were leaching out. After the addition of 50 \(\mu\)L of the internal standard (1-octanol, 0.05 mg/mL in water) to the cooked rice samples, the automatic SPME sampler (AOC-6000 Plus autosampler, Shimadzu) equipped with a divinylbenzene (DVB)/carboxon (CAR)/polydimethylsiloxane (PDMS) 50/30 \(\mu\)m fiber (10 mm, Supelco, PA, USA) was employed to extract the headspace volatiles of cooked rice. After the equilibration of cooked rice samples at 60 \({}^{\circ}\)C for 15 min, the pre-conditioned (200 \({}^{\circ}\)C for 10 min) SPME fiber was inserted into the glass vial. The samples were agitatedat 300 rpm and the extraction lasted for 30 min. Subsequently, the fiber was thermally desorbed in the GC injector port at 250 \({}^{\circ}\)C for 5 min. Instrumental analyses were carried out by a GCMS-TQ8040 (Shimadzu, Kyoto, Japan) coupled with a DB-WAX fused silica capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m; Agilent Technologies, Santa Clara, CA, USA) under conditions previously described [14]. The volatile compounds were tentatively identified by comparing the mass spectra with the NIST 17 mass spectral library and the Kovats' retention index (RI) (calculated from C7 to C40 alkanes) with the NIST Chemistry WebBook database ([[https://webbook.nist.gov/chemistry/name-ser/](https://webbook.nist.gov/chemistry/name-ser/)]([https://webbook.nist.gov/chemistry/name-ser/](https://webbook.nist.gov/chemistry/name-ser/)), accessed on 20 May 2021). The concentrations of the volatile compounds were expressed as 1-octanol (internal standard) equivalents, and all the response factors were regarded as 1.0.
# Congestion Control and Traffic Differentiation for Heterogeneous 6 TiSCH Networks in IIoT
## 2 Problem Statement and Related Work  
In this section, we first highlight the congestion and traffic priority issues in RPL networks, then we discuss related works in this context.
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 4 Cloud Security Challenges and Proposed Solutions
### Technical Security Threat
#### 4.2.4 Account, Service, and Traffic High-Jacking  
Threats to accounts, services, traffic, and hijacking are major security problems in cloud computing settings. Cloud account hijacking is the process by which an attacker steals or hijacks a person's or organization's cloud account. Cloud account hijacking is a typical identity theft strategy in which the attacker utilizes stolen account details to undertake harmful or unauthorized behavior. When a cloud account is hijacked, an attacker often impersonates the account owner by using a hacked email account or other credentials [45, 46]. Here is a rundown of each danger category, as well as potential mitigating strategies: [47].  
_Account:_ Account dangers include unauthorized access to cloud user accounts and the compromising of user credentials. Mitigation strategies include: To improve the security of user accounts, implement robust authentication systems such as multi-factor authentication (MFA).  
Enforce safe password rules, such as utilizing complicated passwords and updating them on a frequent basis.  
_Service Threats:_ Service threats are vulnerabilities or assaults that target cloud services. Mitigation strategies include Patch and upgrading cloud services on a regular basis to address known vulnerabilities.  
Implement strong network and application-level firewalls to prevent unauthorized access to services. Monitor and detect any unusual activity using intrusion detection and prevention systems (IDPS) [48].  
_Traffic:_ Interception, manipulation, or interruption of network traffic inside cloud systems are examples of traffic risks. Mitigation strategies include:  
Encrypt network communications using secure communication protocols such as HTTPS or Transport Layer Security (TLS).  
DNSSEC (DNS Security Extensions) should be implemented to secure the integrity and authenticity of DNS answers and to prevent DNS hijacking [49].  
Use network segmentation and access restrictions to limit traffic flow and mitigate the effect of any possible breach [50]. What are the risk of the discussed cloud security problems on infrastructure has been discussed in Table 10.
# Benchmarking Deep Learning Models for Instance Segmentation
## 3 Background
### Non-Maximum Suppression (NMS)  
Most object detection algorithms generate bounding boxes (bboxes) with high scores around the object's location. NMS is a technique that removes all bboxes around the same object except for the bbox with the highest score. NMS is an essential component of object detection and instance segmentation, and research is being conducted to improve the speed of traditional NMS. YOLACT's Fast NMS [8] removes more anchor boxes than NMS, and hence it causes some performance degradation, but unlike NMS, it performs matrix operations, and hence it can be calculated more quickly by GPUs. SOLOv2's matrix NMS [6] can handle 500 masks with a simple Python implementation in less than 1 ms by simultaneously solving the problems of hard removal and sequential operations.
# Detection of Underwater Targets Using Polarization Laser Assisted Echo Detection Technique
## 4 Results and Analysis
### The Influence of the Same Object on the Degree of Polarization in Different Environments  
In order to more clearly show the polarization DOP comparison of three objects, iron, tile and plastic, in air and water, the data in Tables 2 and 3 were made into the line graph in Figure 10. It can be seen that in air, the average polarization of iron is 0.56, tile is 1.00, and plastic is 0.48; in water, the average polarization of iron at 7.5 m is 0.3625, tile at 7.5 m is 0.359, and plastic at 7.5 m is 0.3805. The polarization intensity of the tile in air is the largest, but it is not as strong as that of the iron in water. However, in water is not as good as iron, taking into account the absorption of light and water fluctuations and other miscellaneous wave effects, and the polarization of various types of targets in air is significantly greater than the polarization of targets in water, the material of the target material and the smoothness of the surface has a greater impact on the polarization.  
Figure 10: The degree of polarization of the same object in air and water.  
Figure 9: Comparison of the degree of polarization of different objects at different depths in water.
## 2 Problem Formulation  
The dynamics of a platoon with \(N\) vehicles are given by:  
\[\dot{p_{i}}= v_{i}\] \[m_{i}\;\dot{v_{i}}= f_{i}+u_{i} \tag{1}\]  
for \(i=1,\ldots,N\). The system is approached with second-order nonlinear dynamics where \(p_{i}\), \(v_{i}\) indicate the position and the velocity of each vehicle, respectively. Additionally, \(m_{i}\) is the mass of each vehicle, \(f_{i}\) resembles the aerodynamic drag resistance force, and \(u_{i}\) is the vehicle acceleration force. In this study, other forces such as rolling resistance, gradient resistance, and so on are considered much smaller, and thus they are neglected. According to the aerodynamics of platooning when considering fuel consumption [2], drag can be modeled by:  
\[F_{d_{i}}=\frac{1}{2}\;C_{d_{i}}(d_{i})\,\rho\,A_{i}\,v_{r_{i}}^{2} \tag{2}\]  
where \(C_{d_{i}}\) is the drag coefficient related to the inter-vehicular distance \(d_{i}\), \(\rho\) is the air density, \(A_{i}\) is the frontal area of the vehicle, and \(v_{r_{i}}=v_{i}-v_{\text{air}}\) is the relative velocity of the vehicle with respect to the airflow \(v_{\text{air}}\). Combining Equations (1) and (2) and considering that \(d_{i}=p_{i-1}-p_{i}\), it can be deduced that  
\[m_{i}\;\dot{v_{i}}=-\frac{1}{2}\;C_{d_{i}}(d_{i})\,\rho\,A_{i}\,|v_{r_{i}}|v_{ r_{i}}+u_{i} \tag{3}\]  
For the system, the force \(u_{i}\) is the control input, whereas \(m_{i}\), \(C_{d}(d_{i})\) are considered unknown, thus rendering \(f_{i}\) uncertain.  
The aim of the proposed algorithm is to seek the optimal rigid formation with the extremum seeking technique by minimizing the estimation of the aerodynamic drag coefficient, thus approximating the unknown \(f_{i}\), and to ensure that this formation will be settled with the prescribed performance control protocol. For the drag coefficient estimation, an extended state observer is proposed. Additionally, rigid formation is represented geometrically by the gaps \(\Delta_{i-1,i}\), for \(i=1,\ldots,N\) between consecutive vehicles, where \(\Delta_{i-1,i}^{s}>0\) is the optimal distance, which extremum seeking calculates, between the \((i-1)\)-th and the \(i\)-th vehicle. Eventually, the algorithm exports the optimal force that should be applied to each vehicle in order to achieve the minimum drag. The closed-loop system diagram given in Figure 1 provides a better understanding of the above.  
Therefore, the proposed scheme requires:  
* Tracking the desired optimal distance with the prescribed performance control protocol.
* Seeking the optimal desired distance with extremum seeking control.
* Estimating the air drag coefficient.  
In the following section, a detailed description of the aforementioned modules is provided.
# Design of SBS-Modified Bitumen Stabilizer Powder Based on the Vulcanization Mechanism
## 1 Introduction
[16] considered that the storage stability of the polymer-modified bitumen can be improved by using various types of modifiers that form the bonds between the components of these binders. Some experimental studies have been done by [PERSON] et al. [17], and the results show that using nano-composites can improve the storage stability at high temperature. [PERSON] et al. [18] studied the influence of carbon nanotubes on the storage stability of SBS-modified bitumen binders, and the results showed that adding carbon nanotubes to the SBS-modified binder can improve the stability. Storage stability of bitumen binder modified with recycled plastic was studied by [PERSON] et al. [19]. The results showed that for a recycled high-density polyethylene content below 4% and a recycled low-density polyethylene content below 6%, either SBS or Polybilt (PB) will bring good storage stability. However, the development of the aforementioned stabilizers was prohibitive due to the over complex production process, the strict preparation environment requirement and the high cost of raw materials. The main objective of this paper was to propose a novel design of a stabilizer, which has a simple production process and lower economic cost. Based on the mechanism of the vulcanization reaction, the selection of the raw materials and corresponding content can be properly determined. The laboratory experiments were also conducted in this paper to analyze the performance of the proposed SBS-modified bitumen. \"Technical Specification of Construction of Highway Asphalt Pavement (JTG F40-2004, China)\" [20] was adopted in this paper as a reference. The Mechanism of Stabilizer Components to Improve the Thermal Storage Stability of SBS-Modified Bitumen
# Optimal Design of a New Rotating Magnetic Beacon Structure Based on Halbach Array
## 3 The Effect of Changing the Direction of Magnetization on the Distribution of the Magnetic Field  
A Halbach magnetic array is designed to achieve the enhancement of a unilateral magnetic field by adjusting the magnetization direction of each magnet. Therefore, in order to further strengthen the unilateral magnetic field cohesion of the array, the influence law of the magnetization direction of the permanent magnets on the magnetic field distribution of the array is first analyzed. Here, the ternary linear array is chosen, as shown in Figure 7.  
Figure 6: Schematic of the traditional Halbach array.  
The angle between the magnetization direction of the three permanent magnets and the positive direction of the Y-axis is \(\alpha_{1}\), \(\alpha_{2}\), \(\alpha_{3}\), and the magnetization direction is adjusted respectively for simulation. Firstly, the included angle is adjusted to \(\alpha_{1}=45^{\circ},\alpha_{2}=180^{\circ},\alpha_{3}=-45^{\circ}\). The magnetic flux distribution of the three-element linear array is shown in Figure 8.  
Then, the included angle is adjusted to \(\alpha_{1}=90^{\circ},\alpha_{2}=180^{\circ},\alpha_{3}=-90^{\circ}\), and the magnetic flux distribution of the ternary linear array is shown in Figure 9.  
Finally, the included angle is adjusted to \(\alpha_{1}=135^{\circ},\alpha_{2}=180^{\circ},\alpha_{3}=-135^{\circ}\), and the magnetic flux distribution of the ternary linear array is shown in Figure 10.  
It can be seen from the magnetic flux distribution under different conditions that the magnetic accumulation effect is obvious on one side of the array under the three included angles. In order to further compare the effect, two measurement paths are set on both sides of the positive and negative Y-axis. That is, from P\({}_{1}\) (\(-4\), \(-4\), \(0\)) to P\({}_{2}\) (\(4\), \(-4\), \(0\)) and from P\({}_{3}\) (\(-4\), \(4\), \(0\)) to P\({}_{4}\) (\(4\), \(4\), \(0\)), the magnetic field intensity on both sides was collected and plotted, as shown in Figure 11.  
Figure 8: Magnetic flux distribution chart (\(\alpha_{1}=45^{\circ},\alpha_{2}=180^{\circ},\alpha_{3}=-45^{\circ}\)).  
Figure 7: Schematic diagram of ternary linear array.  
It can be seen from the figure that when \(\Delta\alpha=45^{\circ}\), the magnetic field of the negative half-axis of the Y-axis has the best aggregation, and the magnetic field of the positive half-axis of the Y-axis is weakened a lot, so the magnetic field has the best characteristics. On this basis, the length of the permanent magnet is adjusted so that the array can produce a magnetic field with better characteristics. In this paper, particle swarm optimization is chosen to optimize the structure.
# Optimization of Optical Trapping and Laser Interferometry in Biological Cells
## 3 Materials and Methods
### Control of Intracellular Concentrations  
The refractive index of the cytoplasm in living cultured cells was varied by changing the concentration of intracellular macromolecules. In order to investigate the dependence of LI sensitivities on refractive indices in cells, cells were osmotically compressed by adding sucrose to the culture media. The relation between the cell volume and the osmotic pressure is known to follow Ponder's relationship [33; 34; 35], \(\Pi[v-(1-R)v_{\text{iso}}]=\Pi_{\text{iso}}Rv_{\text{iso}}\). Here,\(\Pi,\Pi_{\text{iso}}\), \(v\), and \(R\) are osmotic pressure, isotonic osmotic pressure, volume fraction of a cell, and the Ponder's value (volume fraction of intracellular liquid in isotonic condition, \(R=0.7\)), respectively [35]. By using this relation, we controlled the cell volumes while the amount of bio-macromolecules was fixed. The refractive index of inside of cells \(n\) was then estimated by using the Lorenz-Lorenz equation described as  
\[n^{2}-1/n^{2}+2=(1-v_{\text{iso}}/v)(n_{\text{water}}^{2}-1/n_{\text{water}}^{2 }+2)+(v_{\text{iso}}/v)(n_{\text{iso}}^{2}-1/n_{\text{iso}}^{2}+2), \tag{8}\]  
where \(n_{\text{water}}\) and \(n_{\text{iso}}\) represent the refractive index of water and isotonic cell, respectively [36].
# A Benchmark for Water Surface Jet Segmentation with MobileHDC Method
## 1 Introduction
The main contributions of this paper are as follows:  
* We introduce a benchmark for water jet trajectory extraction accompanied by two publicly available datasets, Libary and SegQinhu, providing a challenging set of real-world data to facilitate the development of intelligent jet systems;
* In light of the extreme pixel imbalance characteristic of the jet datasets, we propose a new evaluation protocol that comprehensively assesses the model's segmentation results from the aspects of accuracy, continuity, and efficiency;
* We propose a pipeline solution with a novel backbone named MobileHDC, and extensive comparative experiments demonstrate that our method achieves optimal performance on the proposed benchmark.
# KDM Security IBE Based on LWE beyond Affine Functions
## 1 Introduction  
The emergence of IBE solves the problem of frequent public key authentication methods in the traditional public key encryption scheme (PKE) and greatly improves its efficiency. In traditional IBE security reduction schemes, a plaintext and private key are required to be independent of each other [1]. However, in IBE, a secret key or secret key-related message is commonly used to encrypt the plaintext, which violates and undermines the security of the scheme, possibly leading to theoretical difficulties when proving its security [2].  
This vulnerability can be mitigated by the key-dependent message (KDM) security method, which ensures that even if the plaintext is generated by calculating the secret key or a related message, the scheme still preserves the required secrecy. The concept of KDM security was originally proposed by [PERSON] and [PERSON] for symmetric encryption in the year 1984 [1]. Later, [PERSON], [PERSON], and [PERSON] used this concept in public-key encryption services in 2002 [3]. Since then, KDM security has been widely applied in various fields, such as computational security, fully homomorphic encryption, homomorphic secret sharing, obtaining chosen ciphertext attack (CCA)-safe public-key encryption schemes, and non-interactive zero-knowledge proofs [4; 5; 6; 7; 8; 9]. It has proved to be an important security notion in a modern cryptography setting.  
In KDM-IBE schemes, the challenge function ensemble plays a critical role in determining the level of security achieved. A challenge function ensemble \(F\) specifies the set of functions that an adversary may use to generate related messages for a given secret key. Generally, if the adversary cannot distinguish the ciphertext of \(f(sk_{1},\cdots,sk_{l})\) under the \(i\)-th public key from the ciphertext of the constant message (such as all 0), where function \(f\in F\), \(i\in\{1,\cdots,l\}\), we consider this scheme as KDM-chosen plaintext attack (CPA) security with respect to \(F\). Additionally, this means that when the adversary uses a function \(f\
otin F\), the scheme cannot guarantee the security of the ciphertext of \(f(sk_{1},\cdots,sk_{l})\).  
Therefore, the security of the scheme improves with the richness of the challenge function ensemble \(F\), which can be typically classified into several categories, includingselection, affine, polynomial, and arbitrary functions, with the corresponding security levels increasing in a hierarchical manner.