esa-sceva/satcom-chunk-collection huggingface dataset:
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 5 Simulation Study
### Results and Analysis
#### 5.2.3 Network Performance Comparison in Scenario III  
Since there is no study in the existing literature which focuses on the mobility of sink nodes for evaluation of the security in RPL hence SMTrust performance is compared only with MRHOF. The results for Scenario III under Blackhole attacks, as depicted in Figure 8, indicate that SMTrust performs better than MRHOF for topology stability, packet loss rate, and throughput. The frequency of nodes rank change of SMTrust is quite stable with 95.6 and 71.3 under Blackhole and Rank attack, respectively, in Scenario III. The average packet loss rate of SMTrust for Scenario III under attacks is 26.6% and 22.9% shows significant improvement as compared to MRHOF (83.3% and 76.2%). This also shows that though the mobility of the sink node in Scenario III degrades the network, SMTrust still shows a lesser packet loss ratio. Similarly, the throughput of SMTrust (3.13 kbps and 4.4 kbps) is higher as compared to MRHOF (0.91 kbps and 1.29 kbps) under both attacks, indicating that the attack scenarios greatly affect the throughput for MRHOF, because of no defence mechanism. Moreover, in comparison with MRHOF, SMTrust shows a lesser average power consumption under Blackhole and Rank attacks in Scenario III, as compared to MRHOF.
## 2 Problem Statement and Mixed Integer Linear Programming (MILP) Model
Constraint (7) ensuresthat each job must be assigned to exactly one of the batches. Constraint (8) confirms that the total volume of jobs in batches must not be over the truck containing capacity. Constraint (9) guarantees that all jobs in the same batch should belong and be shipped to the same customer. Constraint (10) restricts that the shipping starting time of each batch must be the longest completion time of manufacturing jobs in the batch. Constraints (11)-(12) force a relation between the jobs in the batch and the customer. In Constraint (13), the shipping time of each batch can be calculated by determining the precedence relation of the batches delivered by the same truck. Constraint (14) confirms that a truck must deliver only one batch to an associated customer at a time. Constraints (15)-(17) have a relation that batches assigned to the same truck must appear exactly once in their sequence. Constraint (15) ensures that the beginning of the delivery sequence in each truck can assign, at most, one batch. So, one batch must be assigned to the first position if the rest of the batches are succeeded by the batch in each truck. Constraint (5) guarantees that one batch will be immediately preceded by one batch in a truck if it is shipped to the truck, and Constraint (6) also guarantees that one batch will be immediately succeeded by at most one batch, if the batch is shipped to one of the trucks. The above MILP formulation guarantees obtaining an optimal solution. However, the size of the formulation makes it hard to find an optimal solution within a limited time. This difficulty occurs from the number of integer variables and constraints. By the derived formulation, the numbers of integer variables and constraints depend on \(\left(JM+BJ+TB+CB+J^{2}M+B^{2}T\right)\) and \(\left\{4J+M+4B+T+J^{2}+B^{2}+3(JM+TB+BT)+JBC+BJ^{2}+J^{2}M+B^{2}T\right\}\), respectively. Thus, CPLEX failed to obtain an optimal solution before running out of memory in large-sized problems. If the problem is reduced to consider only a machine scheduling problem, it is equivalent to a total tardiness parallel-machine scheduling problem. If the problem is reduced to considering only a batching problem, it is equivalent to a well-known bin-packing problem. If the problem is reduced to considering only a truck scheduling problem, it is equivalent to a total tardiness parallel-machine scheduling problem. Each of those problems are known to be -NP-hard [19]. Thus, it is necessary to propose an efficient heuristic to solve the problem within a short amount of time.
# Feature Selection and Transfer Learning for Alzheimer's Disease Clinical Diagnosis
## 2 Materials  
In this paper, we collect and study two datasets to verify the performance of the proposed method. In this section, we first introduce the details of the two datasets. Then, we present the image processing, the feature extraction, and the feature selection processes.  
The first dataset of 507 subjects was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) public database ([[http://www.loni.ucla.edu/ADNI](http://www.loni.ucla.edu/ADNI)]([http://www.loni.ucla.edu/ADNI](http://www.loni.ucla.edu/ADNI))). The ADNI was initiated in 2003 by the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and DrugAdministration (FDA), pharmaceutical companies, and nonprofit organizations for the development of diverse biomarkers for the early detection of AD [25].  
All data obtained from ADNI's MRI examinations of brains were performed on a 1.5T MRI scanner. We acquired a high-resolution T1-weighted Magnetization Prepared Rapidly Acquired Gradient echo (MP-RAGE) three-dimensional (3D)-sequence for analyzing the classification of 507 subjects, including 177 AD patients, 245 MCI and 85 NC. Table 1 lists the demographics of all these subjects.  
The second dataset was sourced from clinical diagnostic results in a local hospital; the procedure just follows ADNI's. The MRI examinations of brains were also performed on a 1.5T MRI scanner and then high-resolution T1-weighted MP-RAGE 3D-sequence were obtained, as detailed in Table 2.  
We must point out that ADNI database provided results of AD patients with MMSE mean of 23.07, CDR mean of 0.78, classified as AD at early stage. The data from the local hospital showed a MMSE (Mini-mental State Examination, _MMSE_) reading average of 10.7, CDR average at 1.3, classified as moderate AD. The figures suggest that the AD patients at local hospital suffered more severe symptoms when compared with the patients in ADNI database.
# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
## 2 Literature Review and Related Work
### Ways of Network Traffic Measurement  
Although measuring network traffic flow within the specific application is the same, different types of data and channel conditions, etc., vary the data rate and other aspects of performance such as delay.  
According to [14], managing communication resources with minimum latency requires a new technique. Although 5G requirements support system resource management, achieving better latency is difficult because of growing communication capacity. The authors proposed a novel architecture with the cognitive engine and data engine, which influences resource cognitive intelligence. In this architecture, latency can be improved through computing, caching, and communication strategies.  
Mobile data traffic is increasing with mobile users, but managing dynamic data traffic and network planning requires an intelligent solution [15]. Designing a cell association and traffic-offloading algorithm in real-time traffic measurement may be the ultimate solution for improving traffic problems.  
Cognitive radio network architecture enhances the network traffic facilities and the quality of service (QoS) when service providers deliver the variable data traffic and channel conditions [16]. Artificial Fish Swarm Algorithm (AFSA) is a new intelligent optimization algorithm suitable for solving large-scale complex optimization problems when we consider the data traffic among the billions of IoT devices in 5G environments. Using AFSA, the traffic and link capacity of crowdsourcing tasks such as the services of IoT devices can be optimized intelligently [17].
### Fuzzy Cognitive Maps for Decision Support  
Fuzzy Cognitive Maps for Decision Support Systems (FCM-DSS) have been applied across various domains, particularly in health-related systems, where they serve as powerful tools for decision-making. These maps are a type of soft computing technique that uses concepts and causal relationships to simulate reasoning processes based on expert knowledge and experience. Essentially, an FCM is visually represented as a directed graph, with nodes representing concepts and arcs indicating weighted causal connections between them. In FCM-DSS, the decision-making process involves input concepts influencing an output concept, which represents the final decision. FCMs have been widely applied in medical decision support, where they help model complex relationships among symptoms, risk factors, and potential diagnoses or treatments [74].  
Competitive Fuzzy Cognitive Maps (CFCMs) [75] were developed to enhance FCMs for decision-making support. In CFCMs, two types of nodes are utilized: factor nodes, which interact with other factors influencing decisions, and decision nodes, which receive inputs from these factors and compete with one another through inhibitory feedback (i.e., negative feedback) to arrive at a single decision. This structure is particularly useful when decisions are mutually exclusive, making CFCMs highly applicable in medical decision-making, where selecting a single diagnosis or intervention plan is critical. Figure 1 presents a diagram of a CFCM, where factors C1-C4 represent input factors and C5-C7 denote output decisions.  
For each concept, the activation model is:  
\[A_{i}^{(t+1)}=f\left(\sum_{\begin{subarray}{c}j=1\\ j\
eq i\end{subarray}}^{n}W_{ji}A_{j}^{(t)}\right), \tag{1}\]  
where the nonlinearity function f is represented by the sigmoid function.  
\[f(x)=\frac{1}{1+e^{-x}} \tag{2}\]
## 1 Introduction
The SRN broadcasts the information packets toward the DSN and RLN, the former checks the received packet BER. If the BER exceeds the calculated threshold value, then the DSN sends out a request towards the RLN for sending the same data packet.The RLN amplifies the packet and sends it back towards the DSN, the RLN also sends an acknowledgement (ACK) to the DSN, which combines the two copies of packets through the maximal ratio combining (MRC) mechanism and forwards optimal information directly towards the sink node. However, If the sink node does not lie inside the range, then the DSN selects another DSN and RLN to forward the information. This process is continued unless the information packets reach the sink.
## 2 Background
### Feature Extraction Methods used for Object Detection in AVs
#### 2.1.1 Camera-Based  
Most standard commercial cameras can capture rich color and texture information from images used to locate objects and obstacles along the course. An image typically has a representation of three channels. On the one hand, attempting only to use these components' values is defined as a pixel feature approach; this means that the machine learning algorithm will obtain and learn the raw data features by itself. On the other hand, crafted feature representation consisting of image processing techniques to extract the features before the learning process, such as edge detection, corner detection, transformations, smoothing, or blurring (among others), can be extracted before the learning process. This process is known as an _extracted feature approach_. The latter follows a series of traditional image pre-processing methods to extract a series of key points and their respective feature descriptors. For instance, Scale Invariant Feature Transform (SIFT) [7] is one of the most widely used methods for such purpose because of its outstanding capabilities of extracting pixel colors, intensities, position, and Gaussian properties such as the Difference of Gaussian (DoG) pyramid and edge key points. All of this information is extracted while preserving scale invariance, as its name suggests. Although this is a suitable feature extractor for a camera's raw images, it is very slow in implementation and unusable in a real-time scenario.  
In contrast, the Speeded-Up Robust Features (SURF) [8] method is one of the fastest and most robust feature extractors. It behaves similar to SIFT, but its outstanding accuracy in extracting important key points between matching images, its real-time performance capability, and the possibility for parallelization with GPU architectures using CUDA (a parallel computing platform developed by NVIDIA using C and C++ libraries) puts this feature extractor at the forefront [9]. SURF uses Hessian matrices to detect blobs and to find critical key points. Then, these are used to measure the focal length with each neighboring point in the image. Afterward, the sum of Haar cascades is used to obtain rotational and translational invariance. Finally, its descriptors are compared to those on another image picturing the same object but from a different angle.
## 2 Related Work
### On-Body Communication
#### 2.1.1 Simple Circuit Type  
This type utilizes the human body as an electrical conductor in which electrical currents flow between the devices through the body. The operating principle is also used in bioelectrical impedance analysis. Although the electrical circuit including the body part is simple and straightforward to implement, this type requires systems to have an external wire that acts as a return path; therefore, it is difficult to implement this type for real-world applications, as pointed out in [6]. In addition, it requires electrodes to be robust against external factors, such as sweat/humidity and debris.
## 3 Space Segment  
### Instrument  
#### 3.1.1 Dual Spectrometer Instrument Concept  
The push-broom type instrument features two prism-based spectrometers for the VNIR and the SWIR spectral ranges coupled to a field splitter slit assembly, which carries two separate slits for in-field separation and a micro-mirror to redirect the SWIR field into the SWIR spectrometer. A schematic view of the instrument design is presented in Figure 2. The slit is located at the image plane of a near diffraction-limited three mirror anastigmatic telescope with an across-track field of view of \(\pm\)1.3\({}^{\circ}\). Radiation enters the system through a calibration device, which allows switching between Earth view, full-aperture Sun diffuser calibration and launch protection modes. A baffling system ensures good out-of-field stray light performance. The spectrometer optics with unit magnification are derived from an Offner relay imaging concept and employ curved Fery-type prisms in dual pass configuration as dispersion elements. This configuration allows for high throughput, low polarization sensitivity and low smile and keystone distortions. Two 2D focal planes acquire images at a frame rate of 230 Hz with 14-bit resolution. The SWIR system features a mercury-cadmium-telluride (MCT)-type detector coupled to an integrated read-out circuit, which is operated at 150 K by the use of a pulse tube cryocooler. The VNIR system consists of a highly functionally-integrated back-illuminated complementary metal-oxide-semiconductor (CMOS) imager with on-chip analog-to-digital converters (ADCs). The optical system, a mixed material system with aluminum mirrors and two types of glass, is housed in an aluminum structure with stringent heater control to achieve a high stability of critical performance parameters, such as spectral stability and co-registration. The instrument line-of-sight is coupled to the attitude control system by a high stability sensor assembly structure to achieve good system pointing knowledge. Further details on the instrument concept can be found in [40,48].
# A Computational Study on the Aeroacoustics of a Multi-Rotor Unmanned Aerial System
## 3 Results and Discussion
### Sound Pressure Level
#### 3.1.3 Effect of Velocity and RPM  
Directivity plots of SPL and OASPL are provided in this section to evaluate the inflow velocity and RPM effects on the CRC-3 emitted noise. The left side of Figure 10 compares three cases at fixed \(\text{RPM}=6000\) and \(\theta=15^{\circ}\), but variable velocities of \(U_{0}=4,~{}7,~{}13\) m/s and the right side compares two cases with \(\theta=15^{\circ}\) and \(U_{0}=7\) m/s, but variable propeller rotating velocities of \(\text{RPM}=6000,~{}12,000\). The results show that the OASPL does not change regularly with increase in \(U_{0}\) or the advance ratio. It should also be mentioned  
Figure 8: SPL spectra at BPF, downstream: (**a**) isolated propeller; (**b**) CRC-3.  
that the trend at BPF is dependent on the microphone location on the circular array (\(\Phi\)), similar to that reported by [PERSON] et al. (2018). To understand the predicted trend, one should notice that one of the major sources of CRC-3 noise is the loading noise, while the thickness noise becomes important only if the blade-tip speed's Mach number (\(Ma\)) exceeds 0.7. In all cases studied in the present work, \(Ma<0.37\), which means the thickness noise is negligible for CRC-3. In addition, the quadrupole noise source is also important for transonic speed range, where \(0.8<Ma<1.2\)(Kal  
Regarding the RPM effect on the right side of the figure, as expected, doubling the tip speed of the propeller results in significant increase in the generated noise, which agrees with the similar studies reported before [29]. Increase in RPM in fact results in growth of both steady and unsteady loading noise sources at all frequencies, but SPL at BPF experiences a smaller increase comparing to OASPL (14% versus 18%), which indicates that having a larger RPM results in slightly further growth in broadband noise compared to tonal noise.
## 1 Introduction  
Polypoidal choroidal vasculopathy (PCV) is a distinct phenotype of exudative age-related macular degeneration (AMD) and is characterized by polyp-like nodules and branching vascular networks, which are hyperfluorescent on indocyanine-green angiography (ICGA) [1,2]. A previous study reported better visual prognosis in patients with PCV than in patients with other forms of exudative AMD [3]. However, other studies demonstrated that the best-corrected visual acuity (BCVA) deteriorated significantly in the natural course and half of the patients who experienced repeated bleeding and leakage, resulting in chorio-retinal atrophy and vision loss [4,5].  
There are two main treatment modalities for PCV, including photodynamic therapy (PDT) and intravitreal injection (IVI) of anti-vascular endothelial growth factor (VEGF) agents. VEGF plays an important role in exudative AMD and PCV, and VEGF levels in aqueous solution elevated in patients with PCV [6]. Anti-VEGF agents, such as ranibizumab and aflipercept, have a favorable visual outcome for treating PCV, but a large number of injections are required to maintain the vision. In three initial monthly injections followed by an as-needed reinjection regimen, the mean number of injections ranged from 4.2 to 5.0 during a one-year follow-up [7, 8, 9, 10]. PDT is effective in occlusion of the polypoidal vascular lesions, and the complete polyp regression rate of PDT alone is greater than that of anti-VEGF monotherapy [11]. However, PDT may induce choroidal ischemia and retinal pigment epithelium damage, which may contribute to VEGF production [12]. PDT combined with anti-VEGF is thought to have synergistic effects on polyp regression and a good visual outcome.  
In the EVEREST II study, patients receiving combination therapy of PDT with intravitreal ranibizumab (IVR) had a favorable visual outcome and complete polyp regression, and few injections were required [13]. Other studies also reported preferable visual outcomes of combination therapy of PDT with either IVR or intravitreal affibercept (IVA) [14, 15, 16, 17, 18]. However, the different efficacy of combination therapy with different anti-VEGF agents was not well established.  
The purpose of this study is to compare the two-year visual and anatomical outcomes of combination therapy of PDT with IVA or IVR for patients with PCV in order to investigate the clinical factors with a final visual outcome and retreatment.
# Effects of Chitosan Coatings on Controlling
## 3 Results  
The initial concentration of _S. aureus_ in beef and mutton pieces was \(\sim\)10\({}^{6}\) CFU/g. Over time, there was a slight decrease in _S. aureus_ counts in the control samples of beef and mutton pieces; on day 21, the _S. aureus_ count was approximately 6.00 log\({}_{10}\) CFU/g (Table 1). However, a different trend was observed in chitosan-treated pieces that were stored aerobically or under vacuum. After the application of chitosan emulsions, an abrupt decrease of 1.24 log\({}_{10}\) CFU/g (standard deviation = 0.10 log\({}_{10}\) CFU/g) was observed in the _S. aureus_ concentrations. Thereafter, the microbial concentrations were variable; after 21 days of storage they were further reduced, leading to a 1.95 log\({}_{10}\) CFU/g decrease in the _S. aureus_ count (standard deviation = 0.078 log\({}_{10}\) CFU/g) compared to the initial inoculum. Chitosan-treated pieces had significantly lower counts of _S. aureus_ than controls. No significant differences were observed between chitosan-coated pieces stored aerobically and those stored under vacuum. The staphylococcal counts (Table 1) were significantly higher in mutton pieces than in beef pieces, especially on days 14 and 21 of storage. Specifically, the chitosan-coated beef on days 14 and 21, as well as the vacuum-packed chitosan-coated beef on day 21, had significantly lower _Staphylococcus_ counts than the corresponding mutton pieces. The latter trend was not observed in the control groups, where the differences between beef and mutton pieces were not significant. The microbial counts of _S. aureus_ in the beef and mutton pieces are depicted in Figure 1.  
A similar trend was observed in the _L. monocytogenes_ counts. The beef and mutton pieces had an initial _L. monocytogenes_ concentration of approximately 10\({}^{6}\) CFU/g. The microbial counts of _L. monocytogenes_ in the control pieces were variable and approximately 5 \(\times\) 10\({}^{5}\) CFU/g throughout the experiment (Table 2). Conversely, a 1 log\({}_{10}\) decrease was observed in the microbial concentrations of chitosan-coated beef and mutton pieces on day 1 of storage. Over time, the _L. monocytogenes_ counts showed a declining trend in the chitosan-coated beef and mutton pieces stored either aerobically or under vacuum. On day 21 of the analysis, the _L. monocytogenes_ counts were further decreased, finally reaching a concentration of 10\({}^{4}\) CFU/g. Similarly to the _S. aureus_ counts, the chitosan membranes affected the concentrations of _L. monocytogenes_ in all meat pieces. No significant differences were observed between beef and mutton pieces or between aerobically stored and vacuum-packaged pieces. However, on day 21 of storage, the counts of _L. monocytogenes_ in chitosan-treated and aerobically stored meat pieces were 1 log\({}_{10}\) lower compared to thevacuum-packaged pieces. The microbial counts of \(L\). _monocytogenes_ in the three batches of beef and mutton pieces are depicted in Figure 2.