esa-sceva/satcom-chunk-collection huggingface dataset:
# A Brief Review of Multipath TCP for Vehicular Networks
## 6 Multipath Routing in Vehicular Networks  
A vehicular ad hoc network has the well-known problems of frequent link disruptions and a high packet loss ratio. Besides MPTCP, multipath routing is considered to be another promising approach that can improve vehicular network performance by establishing multiple routes between vehicles to enhance the connectivity in the network layer. As shown in Figure 5, there are three paths between the two red cars.  
There are also studies that combine MPTCP and multipath routing [10; 11] to enhance network performance. In [111], [PERSON] et al. combined the connectivity aware routing (CAR) and MPTCP for file transfer between RSUs and vehicles in multi-hop communications. They also compared the performance of CAR and MPTCP. However, in sparse vehicular network environments, traditional routing protocols cannot perform effectively due to the lack of relay nodes to maintain the connections, which would result in excessive retransmissions. A delay tolerant network (DTN) is considered to be a promising way to solve this problem. [PERSON] et al. [112] conducted a systematic technical survey about the DTN. The VANET/DTN (VDTN) is also included. In the following section, we only give a brief review of traditional multipath routing research efforts. We classify the related studies into five categories, respectively, extensions of ad hoc routing protocols, fault-tolerance and security, cross-layer multipath routing, optimization model, and stochastic model.
# Low-Complexity GSM Detection Based on Maximum Ratio Combining
## 3 MRC-RS Detection Algorithm  
For signal detection in GSM, the computational complexity of the ML algorithm grows exponentially with the number of transmit antennas. To reduce the computational complexity while keeping the near-ML BER performance, we propose a new detection algorithm based on maximum ratio combining and repetitive sorting strategy, termed as MRC-RS detection algorithm.  
In the MRC-RS algorithm, the TAC and modulation symbol can be estimated through three steps. In the first step, half of the most likely TACs are reversed for detection by repetitive sorting, which can greatly decrease the computational complexity. In the second step, quantitate the transmit symbol using the HLML and LCML detector for M-QAM and M-PSK constellations, respectively. At last, the TAC and modulation symbol are estimated jointly using the ML detector. Due to the reduction of the number of TACs, the proposed algorithm is a suboptimal detection algorithm.
# The Impact of Tritium in the Environment
## 4 Biological Effects
The main component is the body's water compartment, which contains 97% of the total amount of tritium consumed with water. The second source is tritium in organic compounds in the body (3%). Studies conducted on animals have shown that 1-5% of tritium water entering the bloodstream is converted into an organically bound form [58]. Studies of people irradiated due to oral incorporation of the aqueous form of tritium have shown that tritium metabolism is subject to a three-component exponential dependence [106]. These three components, i.e., (1) metabolism of the aqueous phase of the body, (2) hydrocarbon forms, and (3) tritium incorporated into organic molecules, have biological half-lives of (1) 6-12 days, with an average of 9 days, (2) 10-34 (22) days, and (3) 130-550 (340) days, respectively. According to the four-component metabolism model [107], the main component corresponds to the retention of tritium in the aqueous phase of the body, and the other three are in different types of organic molecules. Available theoretically and experimentally substantiated studies and large number of publications show that the radiological impact of OBT on different age groups and tissues at (3.9-20) \(\times\) 10\({}^{-11}\) Sv/Bq is more than two times higher than that of HTO at (1.8-6.6) \(\times\) 10\({}^{-11}\) Sv/Bq (see Table 5).
## 4 Data Processing and Experimental Results: Sounder Mode
### Data Processing
It is also stressed that, similarly to the SAR case, accounting for the actual flight track leads to an increase in the computational complexity with respect to the focusing procedures that are based upon the assumption of straight-line flight trajectories [70; 71].
# Algorithms in Tomography and Related Inverse Problems--A Review
## 6 Global Seismic Tomography: The Inverse Problem and Beyond
A simplified form of seismic tomography relies on the arrival times of body waves. Ray Theory and Travel-Time Tomography: Travel-time tomography is based on the integration of slowness to determine travel time. [PERSON]'s principle and [PERSON]'s principle are utilized in the context of ray theory. C. Parametrization of the Model  
Model Parameters \(\delta v(r)\): Model aims to deduce from the data and exhibit continuity across positions. The model is expanded using fundamental functions \(B_{j}(r)\), and the problem is transformed into a linear inverse problem \(v(r)=\sum_{j=1}^{\infty}m_{j}B_{j}(r)\). D. Cost Function  
Cost Function \(C_{\lambda}=\Delta_{D}(\delta d,Am)+\lambda\Delta_{M}(m,m_{0})\): A cost function is defined to balance the fit to observed data and the size of the model. Regularization plays a role in determining the trade-off parameter \(\lambda\). E. Regularization  
Implicit and Explicit Regularization: Regularization is applied implicitly through choices such as upper limit selection \(L\) and basic function types. Explicit regularization involves parameters like \(\lambda\) and the use of a reference model \(m_{0}\). F. Inverse Operator  
Bayesian Perspective: Global tomography investigations often adopt a Bayesian perspective, seeking the most likely solution at the minimum of the cost function. Newton Approximation: The Newton approximation is a widely recognized algorithm \(\mathbf{m}_{n+1}=\mathbf{m}_{n}+\left(A_{n}^{\dagger}C_{d}^{-1}\mathbf{A}_{n}+\lambda C_{m }^{-1}\right)^{-1}\left(A_{n}^{\dagger}C_{d}^{-1}\delta d-\lambda C_{m}^{-1} \mathbf{m}_{n}\right)\) for minimizing the cost function, involving the Hessian matrix \(\
abla\
abla\mathbf{C}_{\lambda}^{n}\) where \(\mathbf{C}_{\lambda}(\mathbf{m}_{n})=\mathbf{C}_{\lambda}^{n}\) and gradient vector. In conclusion, global seismic tomography over the past two decades has made significant advancements in mapping three-dimensional elastic wave velocity fields, providing valuable insights for interdisciplinary discussions in Earth sciences.
# Cyberattacks and Security of Cloud Computing: A Complete Guideline
## 6 Possible Solutions
### Data Transmission  
Data are transmitted through the internet and stored in a cloud computing environment. There is always the risk of attack and malicious activities when the cloud provides its services. The attacker tries to access user data without their permission. Sometimes original data are replaced with fake data. For instance, the authors of [85] introduced a data encryption technology for cloud computing, i.e., DNA-based data security (DNABDS). The data owners encrypt the data in the proposed scheme, unlike traditional methods. The authors proposed the deduplication process in another article [86]. The deduplication process is an effective technique that has gained widespread attention in the storage system. Deduplication improves storage utilization, eliminates unnecessary data, and reduces storage costs. Another recent approach is proposed by [87]. In this technique, the authors proposed a safe and adequate access control model for cloud computing environments to share resources and knowledge by using attribute-based encryption (ABE), distributed hash table (DHT) networks, and identity-based timed-release encryption (IDTRE). Initially, the data and resources are encrypted using user attributes, and encrypted data are separated into enclosed ciphertext.
# A Survey of Multi-Focus Image Fusion Methods
## 2 Fusion Methods and Analysis
### Multi-Focus Fusion Methods Based on Transform Domain
#### 2.1.2 Sparse Representation (SR)-Based Methods  
Sparse representation is a new image fusion method. By processing the natural sparsity of image signals, a signal is approximately represented as a linear combination of several atoms in a redundant dictionary. Atomic libraries of sparse representations are provided by over-complete dictionaries. By selecting some atoms in the over-complete dictionary and using a linear combination to reconstruct the image, the dependence between data dimensions and feature vectors can be reduced. Figure 6 shows a multi-focus image fusion framework based on sparse representation.  
In 2010, [PERSON] et al. [39] introduced SR into multi-focus image fusion for the first time. In this method, sliding window technology was used to segment each source image into multiple overlapping small blocks, and orthogonal matching pursuit (OMP) algorithm was used to perform sparse decomposition for each small block. The sparse coefficient vectors after fusion were obtained by using the maximum selection fusion rule. Subsequently, a variety of improved algorithms based on sparse representation follow. [PERSON] et al. [40] obtained an adaptive dictionary based on rough k-means singular value decomposition. Then the fixed dictionary was combined with the adaptive dictionary to obtain the joint dictionary. The final joint dictionary was used to sparsely encode the source image to separate the complementary and redundant components. In addition, there are also cross sparse representation [16], group sparse representation [41], K-SVD [42], and other methods.  
Sparse representation can better solve the problem of fused image noise. However, the processing effect of image details (edge, texture, etc.) is not ideal and easy to blur. In addition, the method has high complexity, low computational efficiency, and poor real-time performance.
# Multi-Objective Collaborative Control Method for Multi-Axle Distributed Vehicle Assisted Driving
## 3 Lower-Level Torque Distribution Strategy
When \(\varsigma\leq 5\), the control logic belongs to the priority allocation mode, and when \(\varsigma>5\), the control logic switches to the average allocation mode. Taking \(2<\varsigma<3\) as an example, in order to achieve higher energy efficiency, the priority control mode is to select two motors from the five motors for torque allocation, while making them work in the high-efficiency region as much as possible. Then, select one motor to distribute the remaining torque, and the remaining motors try not to participate in torque allocation as much as possible. Considering the low utilization rate of lateral force on the tires of the middle axle of multi-axle vehicles, priority should be given to torque  
Figure 10: Motor system efficiency MAP. distribution. The specific order of torque distribution is: third axle > fourth axle > fifth axle > first axle > second axle. The relevant variables for defining the weight matrix and reference control matrix are:  
\[c_{1I}=1,c_{12}=1,c_{3I}=1,c_{4I}=1,c_{5I}=0.1 \tag{34}\]  
\[T_{1I,ref}=0,T_{2I,ref}=0,T_{3I,ref}=T_{3I,me},T_{4I,ref}=T_{4I,me},T_{5I,ref}=T_{5I,me} \tag{35}\]  
In priority allocation mode, when the allocation coefficient is other values, the weight matrix \(\mathbf{P}_{3}\) and reference control matrix \(\mathbf{u}_{ref}\) can be updated through this control logic. When energy efficiency optimization is in the average allocation mode, the relevant variables are defined as:  
\[\mathbf{B}_{CA}\cdot\mathbf{u}_{T}=\mathbf{v}\]0  
\[\mathbf{B}_{CA}\cdot\mathbf{u}_{T}=\mathbf{v}\]1  
Among the three torque optimization objectives, the virtual control objective has the highest priority, followed by tire load rate and energy efficiency optimization objectives that should be dynamically adjusted based on the real-time state of the vehicle. When the stability weight adjustment coefficient \(\lambda_{\beta}\) in the upper layer multi-objective MPC controller is larger, the optimization of the tire load rate should be dominant. When \(\lambda_{\beta}\) is smaller, energy efficiency optimization is considered before the tire load rate. Based on this principle, the optimization objectives of redefining torque distribution control are:  
\[\mathbf{B}_{CA}\cdot\mathbf{u}_{T}=\mathbf{v}\]2  
At the same time, the torque output of the motor should consider the maximum torque limit and the limitation of the attachment circle.
## 5 Results and Discussion
### Performance Units
#### 5.2.2 Throughput  
It is determined as the amount of successful distribution of knowledge per unit period in a given network and systematically explained as defined in Equation (4).  
\[Throughput=\frac{\sum_{i=1}^{n}y_{i}}{T_{n}} \tag{4}\]  
From the Equation (4), '\(y_{i}\)' represents the number of packets accepted by hops '\(i\)', '\(T_{n}\)' represents the time taken for simulation in minutes, and '\(n\)' represents the overall nodes within the network. A throughput comparison of our proposed scheme with other protocols is shown in Figure 7. The MAMC-MAC scheme performs well in throughput maximization when compared with other four protocols. Our proposed work shows an increase in throughput up to 35 nodes, whereas other protocols' throughput is increased up to 25 nodes only. This shows that the MAMC-MAC protocol has the maximum message delivery rate.  
Figure 6: PDR comparison.
# Experimental Research on the Effects of Suction Ports on Twin Screw Expander Performance
###### Abstract  
An organic Rankine cycle (ORC) system is an efficient technology for generating power from low-grade thermal sources. Twin screw expanders are widely used in ORC systems. Pressure loss caused by a suction port influences the suction process and the system performance. In this paper, the effects of suction ports on twin screw expander performance are experimentally investigated. The pressure loss value of R245\(a\) is measured under different working conditions, and its effects on the suction coefficient are discussed. Results show that a lower rotation speed results in a lower pressure loss value and a higher suction coefficient under certain working conditions. Increasing the suction pressure leads to a higher pressure loss value but a lower pressure loss ratio. Discharge pressure has little effect on the suction process.  
organic Rankine cycle (ORC); twin screw expander; suction port; performance
# Deterministic Propagation Modeling for Intelligent Vehicle Communication in Smart Cities
## 3 Simulation Results
### Multipath Metrics  
This subsection presents the analysis of multipath metrics such as Power Delay Profile (PDP), Mean Excess Delay, Root Mean Square Delay Spread (RMS delay spread) and Coherence Bandwidth (CB). These are required parameters to develop a proper scheme for communications and determined for different environments to design some general guidelines for WSN deployment.
## 2 Materials and Methods
### Cytotoxicity Assay
#### 2.4.2 MTT (Cell Proliferation) Assay  
Preparation of Cell Lines for MTT Assay and Culture Condition  
Vero cell line (ATCC CCL-81) and human fibroblast (CO135C) were grown in Dulbecco's Modified Eagle Medium (DMEM) (Sigma, Hamburg, Germany) with high glucose contents, supplemented with 10% Fetal bovine serum (FBS) (Sigma, Hamburg, Germany), glutamine (2 mM), penicillin (100 units/mL) (Euroclone, Figino, Pero, Italy) and streptomycin (100 \(\mu\)g/mL) (Euroclone, Figino, Pero, Italy) and were cultured at 37 \({}^{\circ}\)C in a humidified 5% CO\({}_{2}\) and incubated for a week. The cells were diluted to a density of \(1.5\times 10^{4}\) cells/well for Vero cell and \(1\times 10^{4}\) cells/well for human fibroblast. The cells were treated with _Camellia sinensis_, _Aloysia citrodora_, _Hibiscus sabdarifa_, _Glycyrrhizza glabra_, and _Thymus vulgaris_ according to manufacturer's instructions CellTiter-Glo. Eight concentrations (500, 400, 300, 200, 100, 50, 25, and 12.5 \(\mu\)g/mL) were prepared from each selected plant extract. Exponentially growing cells were seeded (in 100 \(\mu\)L of growth medium) in 96 well microtiter plates (Sigma, Hamburg, Germany) and incubated for 24-h incubation to form a partial monolayer. After that, the media was removed, added 100 \(\mu\)L of the DMEM medium containing the plant extract (initially dissolved in DMSO), and reincubated for 24 h. Then 15 \(\mu\)L of the MTT solution (Promega, Madison, WI, USA) was added to each well and incubated for 1 to 4 h. Blue crystals were formed after 4 h of cell contact with the MTT solution. Solubilization/stop solution (100 \(\mu\)L) was added and incubated for another 1 h. The absorbance can be recorded in 1 h or up to several days later. Untreated cells were used as negative control; doxorubicin (Indiamart, Vadodara, India) was used as a positive control and prepared in the same manner. Each treatment was repeated in triplicate. The optical density (OD) was measured at 590 nm using a microplate reader (BioTek, Winooski,VT, Uruguay) [19]. The percentage of the cell viability was calculated according to the following equation:  
\[\%\text{ of the cell viability}=\frac{1-\text{ OD of treated cells}}{\text{ OD of untreated cells}}\times 100\%\]
## 3 Discussion
Collectively, our results demonstrate that OrtT is a new toxin despite being a homolog of GhoT. Our findings indicate that cells utilize these two similar toxins for different physiological roles; _i.e._, they utilize the two toxins for different stress conditions. The reduced growth via OrtT under TMP antimicrobial stress could be an outcome of energy depletion from membrane damage by OrtT just like GhoT [42]; however, closer scrutiny is required to evaluate the molecular mechanism of how these toxins are activated in response to specific environmental cues (_i.e._, reduction in THF level or nutrient limiting condition). TMP treatment causes immediate accumulation of ppGpp [49] as it reduces THF and induces the stringent response. This nutritional stress response is triggered by the initial rapid depletion of glycine [5]. If glycine is supplied exogenously, the second metabolite pool to be exhausted is ATP [5]. _ortT_ is predicted to be under RpoE (\(\sigma^{\text{E}}\)) and RpoH (\(\sigma^{\text{H}}\)) regulation, and our results confirm this prediction. RpoE is the stress response sigma factor for extracytoplasmic stress [53]. However, RpoE is also induced by the stringent response alarmone ppGpp [53]. Furthermore, the heat shock sigma factor RpoH is induced by RpoE [54]. This suggests that accumulation of ppGpp due to glycine depletion induces _ortT_ expression via activation of RpoH and RpoE. OrtT in turn damages cellular membrane and contributes to reduce the intracellular ATP due to PMF disruption or via direct leakage of intracellular ATP. In this manner, when THF is depleted, the stringent response is activated to shut down growth via accumulation of ppGpp and OrtT (Figure 6). Therefore, OrtT appears to be part of the stringent response, and since it lacks an antitoxin, perhaps OrtT is employed as a \"fail-safe\" regulator, the activation of which ensures attainment of a non-growing state even faster through exhaustion of the ATP pool.
# Fall Recognition Based on Time-Level Decision Fusion Classification
## 2 Related Work
### Radar-Based Methods  
Research is also underway to detect falls by installing radars in the surrounding environment and analyzing the obtained signals [10; 11; 12; 13]. Studies on fall detection using radar sensors were presented in [10]. A custom-made radar sensor and support vector machine (SVM) were employed in [11] to detect falls. In [12; 13], a frequency-modulated continuous-wave (FMCW) radar was installed on the ceiling to capture signals for fall detection. In [12], signal preprocessing was applied, followed by the utilization of an SVM as a classifier to detect falls. In contrast, [13] employed a random forest algorithm as a classifier. Radar-based fall detection offers the advantage of detecting falls even in low-light conditions without performance degradation. However, it faces drawbacks related to the installation cost of radar sensors and the potential misclassification of non-human falls, such as objects dropping, as falls.
_Applied_  
_sciences_  
_Article_  
**Robust Visual-Aided Autonomous Takeoff, Tracking, and Landing of a Small UAV on a Moving Landing Platform for Life-Long Operation**  
**[PERSON] 1,*\(\copyright\)**, [PERSON] 2\(\copyright\)**, [PERSON] 3\(\copyright\)** and [PERSON] 4\(\copyright\)  
**and [PERSON] 4\(\copyright\)**  
Received: 31 May 2019; Accepted: 25 June 2019; Published: 29 June 2019  
**Featured Application: Autonomous takeoff, tracking, and landing maneuvers on a moving target with application to a fleet of robots with aerial and ground vehicles that need to operate for extended periods of time, as in Search and Rescue tasks.**  
**Abstract: Robot cooperation is key in Search and Rescue (SaR) tasks. Frequently, these tasks take place in complex scenarios affected by different types of disasters, so an aerial viewpoint is useful for autonomous navigation or human tele-operation. In such cases, an Unmanned Aerial Vehicle (UAV) in cooperation with an Unmanned Ground Vehicle (UGV) can provide valuable insight into the area. To carry out its work successfully, such as multi-robot system requires the autonomous takeoff, tracking, and landing of the UAV on the moving UGV. Furthermore, it needs to be robust and capable of life-long operation. In this paper, we present an autonomous system that enables a UAV to take off autonomously from a moving landing platform, locate it using visual cues, follow it, and robustly land on it. The system relies on a finite state machine, which together with a novel re-localization module allows the system to operate robustly for extended periods of time and to recover from potential failed landing maneuvers. Two approaches for tracking and landing are developed, implemented, and tested. The first variant is based on a novel height-adaptive PID controller that uses the current position of the landing platform as the target. The second one combines this height-adaptive PID controller with a Kalman filter in order to predict the future positions of the platform and provide them as input to the PID controller. This facilitates tracking and, mainly, landing. Both the system as a whole and the re-localization module in particular have been tested extensively in a simulated environment (Gazebo). We also present a qualitative evaluation of the system on the real robotic platforms, demonstrating that our system can also be deployed on real robotic platforms. For the benefit of the community, we make our software open source.**  
**Keywords: robust autonomous landing; unmanned aerial vehicle; unmanned ground vehicle; multi-robot systems; Kalman filter; PID controller; re-localization module**