esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Test Fuels and Properties
### Test Fuel and Additives
Except for the pure diesel, the blended fuels' viscosities are more linear to their fractions. As a result, the viscosity reduction caused by the addition of small molecular hydrocarbons to diesel should be taken into account. The boiling temperatures of hydrocarbons are constant and lower; therefore, the addition of hydrocarbons will lead to a change in the distillation temperatures. The distillation tests were done with a distillation gauge. For the test, 100 mL sample fuel was added into the test tube after it was warmed up to 50 \({}^{\circ}\)C. The average speed of condensation was kept at 4-5 mL/min by adjusting the heating power. Table 2 lists the temperatures of the distillation ranges of 10%, 50% and 90%. T10 represents the volatility of the light components in the fuel, T50 indicates the average volatility of the fuel, and T90 represents the volatility of the heavy components in the fuel. The addition of hydrocarbons brings down the distillation characteristic temperatures of the blended fuels. More distillation information can be seen in Figure 3. The boiling point of heptane is 98.5 \({}^{\circ}\)C, so it has a significant effect on the distillation process. The addition of low-boiling-point n-heptane increases volatility, which causes a great reduction of T10. The boiling points of the remaining hydrocarbons are similar to the T10 of diesel (205 \({}^{\circ}\)C), so their effects are shown in higher-fraction blending cases. The LHV of diesel was measured by the calorimeter, and the four components were known. Through measurement, the volume of the blended components and diesel after mixing is consistent with the sum of the respective volumes. Therefore, the LHV of the blended fuels can be calculated according to the following formula [18]:  
\[H_{u}=\frac{(1-\varphi){\cdot}H_{D}{\cdot}\rho_{D}+\varphi{\cdot}H_{B}{\cdot} \rho_{B}}{(1-\varphi){\cdot}\rho_{D}+\varphi{\cdot}\rho_{B}} \tag{1}\]  
where \(H_{u}\), \(H_{D}\) and \(H_{B}\) denote the LHV of blended fuels (MJ/kg), diesel, and hydrocarbon components, respectively; \(\rho_{D}\) and \(\rho_{B}\) are the density of diesel and hydrocarbon components (kg/m\({}^{3}\)), respectively, and \(\varphi\) denotes the blended volume fraction (%). The calculated LHVs in mass are shown in Table 2, as well as the volumetric lower heating value. Though heptane has the highest LHV, the LHV of the heptane blended fuels is lower for its density. On the contrary, tetralin has the lowest LHV, but its blended fuels  
Figure 3: The distillation range curves of test fuels. have the highest volumetric LHV. Nevertheless, the difference is within 5% compared to diesel fuel.
###### Abstract  
Single-phase AC-AC converters with a direct power conversion approach are receiving rapid development as they have the ability to produce the regulated non-inverting and inverting form of the input voltage at the output. This feature enables them to correct the line voltage profile once they are used as dynamic voltage restorers if there is an issue of voltage sag or swell in the power distribution system. The regulated non-inverting and inverting form of the output voltage may also be used to obtain the step change in the output frequency, as it is required in many industrial drive systems. However, the realization of such existing circuits needs a large number of components and semiconductor devices, especially switching transistors. The operating control (on and off) of the transistors is directly associated with the use of gate control circuits. The count of such circuits is critical as their volume and cost are much greater than the operating transistors. The number of conducting semiconductor devices in the existing converters is also a big source of high conversion losses, thus leading to lower efficiency. This article introduces a new circuit topology realized only with the use of one full bridge of four IGETs and a full bridge of four diodes. The use of four switching transistors only requires four gate control circuits that drastically reduce the overall volume and size. All the operating modes of the proposed topology require the conduction of fewer semiconductor devices, which helps to lower the conduction losses. Detailed analysis and description were carried out to validate the attractive features of the developed circuit once compared with the existing circuit topologies. For validation purposes, the computer simulation was carried out on Simulink software. The results obtained from this environment were compared with the real results gained from a practically developed laboratory test bench. The voltage regulation characteristics of the output voltage by employing pulse width modulation (PWM) were confirmed for two values of the non-inverting and inverting outputs.  
dynamic voltage restorer; sag and swell; industrial drives; gate control circuits; conversion losses; output frequency; pulse width modulation +
Footnote †: journal: Applied Sciences
## 3 Experimental Results and Analysis
### Experimental Steps and Results Analysis
The flow Equation of the proportional valve, the flow continuity Equation of the hydraulic cylinder control chamber, the hydraulic cylinder and the force of the load are established respectively. The balance Equation ensures the accuracy of the mathematical model of the double-cylinder forging hydraulic press system and improves the control accuracy. As shown in Table 1, the synchronization control algorithm based on a fuzzy neural network has a shorter control completion time, which means that the algorithm can control the double-cylinder forging hydraulic press in time to ensure the normal operation. In the control process based on the fuzzy neural network synchronization control algorithm, the fuzzy neural network model is built to reduce the control of the calculation steps and improve the control completion speed. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**O/Times** & **X/ms** & **Y/ms** & **Z/ms** \\ \hline
1 & 129 & 335 & 342 \\
2 & 137 & 346 & 351 \\
3 & 141 & 352 & 355 \\
4 & 134 & 340 & 346 \\
5 & 145 & 358 & 361 \\
6 & 140 & 349 & 353 \\
7 & 148 & 364 & 365 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of control completion times for different algorithms. Figure 5: Comparison of control results of different algorithms. (**A**) Synchronous control algorithm based on fuzzy neural network. (**B**) Synchronization control algorithm based on multi-factor self-tuning fuzzy control. (**C**) Synchronization control algorithm based on improved fractional non-growth adaptive law.
# Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter +
## 3 Adaptive Techniques in DSKF  
This section describes the three adaptive tracking techniques under evaluation: the \(C/N_{0}\)-based DSKF, the LBCA-based DSKF, and the LBCA-based LUT-DSKF.
# An Algorithm to Find the Shortest Path through Obstacles of Arbitrary Shapes and Positions in 2D
## 3 Materials and Methods
### [PERSON] and [PERSON] Graphs  
[PERSON] and [PERSON] have tackled the situations in which the obstacles have arbitrary shapes and positions [24; 25]. They have pointed out the important fact that the shortest path consists of a sequence of convex segments of obstacle boundaries and the free rectilinear bitangents that connect these segments. They defined a graph (the L-A graph) with edges of these two types, and for which the nodes are the points of contact of the rectilinear tangents and the obstacle boundaries. They then proposed to use the A* algorithm to obtain the shortest path but did not explain how it should be implemented. Figure 3 shows the edges and nodes of this graph for two obstacles. There are 18 nodes, represented by the dots, and 21 edges in this graph that are the rectilinear segments and convex arcs that link the nodes. One can see that the number of nodes and edges becomes enormous in situations in which there are many obstacles and in which the obstacles are very jagged, as is the case in most realistic situations such as those shown in the figure of Section 6. Thus, the points on which our work improves on that of [PERSON] and [PERSON] are that needless nodes and edges are discarded; for example, for the obstacles shown in Figure 2(a), our graph will only have the 8 nodes shown as dots in Figure 2(b). It will also have only 8 edges; their exact definition is given in Section 3.4. Furthermore, we provide a detailed description of how to represent the data and how to implement the A* algorithm to deal with our defined graphs.
# Computationally Efficient Soft Detection Schemes for Coded Massive MIMO Systems +
## 3 Proposed Method  
In this section, we propose to approximate the Gram matrix with a derived constant diagonal matrix to reduce the computational complexity of the MMSE-PIC-based JIDD scheme. With the proposed scheme, the computations of estimating the Gram-matrix, filtering matrix and PE-SINR are highly reduced. In addition, we derive compact equations to estimate soft-symbol values, and then expand the results for well-known \(M\)-ary QAM with highly reduced complexity, compared to (2) and (3). Finally, the symbol mapping technique in (11) is tailored to the proposed scheme for further complexity reduction.
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## Overall Architecture of the Proposed ME System
### The ME System Block Diagram  
The block diagram in Figure 4 summarizes the overall architecture of the proposed ME system. At the management level, it includes elements from a standard NFV framework, complemented with three blocks that provide specific MEC functionalities, i.e., the CFS portal, the OSS, and the MEAO.  
Figure 4: Overall architecture of the proposed ME system.  
The NFVO is provided by Open Baton, an available and well-established open source NFV framework [26]. The Open Baton NFVO includes the VNF Managers (VNFMs). However, no restriction applies to this aspect, and external VNFMs could also be adopted.  
The SGWLBO VNF shown in Figure 4 implements both the ME platform and the ME data plane. In this study, the SGWLBO VNF and its Element Management System are provided by Athonet. They make it possible to integrate the ME system into a 4G LTE network, in accordance with the \"Distributed SGW with Local Break Out\" scheme defined in Reference [27]. A detailed description of the SGWLBO VNF block is given in Section 3.4.  
The MEAO invokes operations towards the NFVO adopting a master/slave approach via the MEC Mv1 reference point [8]. To use an ETSI-standard NFVO, we will assume Mv1 equivalent to the ETSI NFV Os-Ma-nfvo reference point [29]; thus, Mv1 inherits the interfaces and operations defined for Os-Ma-nfvo [10]. For the same reason, Mv2 and Mv3 correspond to the NFV reference points Ve-Vnfm-em and Ve-Vnfm-vnf, respectively ([10], issues #4 and #5).  
The MEPM-V block is given by the combination of the Element Management Systems of the SGWLBO VNF and the i-EVS VNF; it interacts with the MEAO through the modified Mm3* reference point [10]. Additionally, Mm3* inherits the configuration requirements, originally defined for Mm2 in [7; 10].  
The NFVI is a standard virtualized infrastructure in accordance with ETSI NFV. The NFVI used includes computing Hardware (HW) accelerators, in particular GPUs by NVIDIA. The hypervisor is KVM, and GPUs are assigned to the corresponding VNFs using the PCI passthrough mode [25]. The VIM is Openstack [38].  
To better clarify the functionalities offered by the proposed framework, in the following paragraph we describe a possible realistic scenario of its use.
## 2 Literature Review
### Discussion  
Many methods have been proposed for wildfire detection in the past. The first organized fire alert networks in the US came in the form of fire lookout towers built in the early 1900s, after the Great Fire of 1910. These towers were built on top of high mountains and staffed by \"fire spotters\". The spotters would inspect the surroundings for signs of fire and communicated using heliographs. As better technologies were made available, fire towers were retired from service by the end of the 20 th century. In this paper, we focus on the advanced mechanisms currently utilized for early wildfire detection, which can be broadly classified into the following main groups: Section 2.2.1. Sensor Nodes; Section 2.2.2. Unmanned Aerial Vehicles (UAV); Section 2.2.3. Camera Networks; Section 2.2.4. Satellite Surveillance.
## 4 Case Study
### Results from Different Construction Scenarios
#### 4.4.3 Case 3: Scaffolding Tasks  
A construction site with the presence of the worker on a scaffold was studied. The presence of scaffolds is very common in construction tasks (facades, upper levels, and  
Figure 8: Results provided by the IOT-JFML system for Scenario 1 (ground level to lower level).  
Figure 7: Visual example of fall from a building floor.  
roofing). Scaffolds are one of the major sources of fatal falls in construction [72]. The majority of accidents related with scaffolding are probably very serious or fatal due to the hazardous working conditions [73]. Then, other authors assessed the safety-risk-related scaffolding tasks, based on the accidents research, the identification habitual scaffolding accidents situations, and the evaluation of the probability of accidents [68; 74]. Based on that, a scenario related with scaffolding was modeled and tested (Figure 9).  
In this scenario, a worker was working on the scaffold. He had harness unattached and he was close to the BLE fence. The altitude value was 12 m, while the wind value was 25 km/h. Although there is a safety rail guard on the scaffold, there are some areas in this scenario without collective protection. The risk score of the proposal was 7.424078 and Rule 10 and Rule 11 were fired with weight 75% and 25%, respectively (Figure 10). In this scenario, the risk score obtained is high due to high altitude and wind velocity. Similarly, the values estimated by OHS experts in these circumstances where aligned with scores from the IoT-JFML system. Expert values are based on the lack or the misuse of the harness at construction sites. Previous authors found that a high percentage of fall accident victims did not use fall protection [75]. In the same study, it was concluded that fall protection use and injury class were associated with statistical significance. These results are aligned with similar studies [8].  
In regard to scaffolding tasks, some authors proposed the prevention of FFH using computer vision and IoT-based monitoring [76]. Their experimental results showed high accuracy in the tested scenarios. The main disadvantage of the cited solution is that the necessary infrastructure for computer vision devices is complex and their configuration is expensive. Moreover, scenarios with confusing images as a consequence of insufficient light or more workers in the same picture cannot be suitable for this technology.  
Figure 9: Visual example of fall from scaffolding activities.
# Security Enhancement for Data Migration in the Cloud
## 5 Experimental And Result Evaluations
\begin{table}
\begin{tabular}{l c} \hline \hline
**Process** & **Duration** \\ \hline Encryption & 0.367 \\ Slicing-IDA & 1.125 \\ Hashing and Concatenation & 0.474 \\ Verification & 1.453 \\ Reconstruction-IDA & 1.157 \\ Decryption & 0.297 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average encoding/decoding time. Figure 4: Encoding results. Encryption is everywhere; most of the products gathered today support a cryptographic algorithm and particularly AES. Even those that support other algorithms tend to recommend using AES [27]. However, most of the cryptographic algorithms (AES, RSA, etc.) are not secure against quantum computing [28]. In the following lines, we are especially looking at products that encrypt files, not at whole-disk solutions like VeraCrypt. **Boxcryp**: is a file-encryption software designed and created specifically for cloud use, with support for all major cloud-storage providers. The user has the ability to encrypt, decrypt, share, add and remove files from cloud storage directly through his/her end device. Furthermore, there is a possibility to protect files by encrypting them locally before posting to a cloud-storage provider, ensuring file security and file-data privacy throughout the process. Boxcryptor uses AES-256 and RSA encryption algorithms. It ensures data accessibility, confidentiality and privacy protection. **SecureDoc CloudSync**: designed by WinMagic, is an enterprise endpoint encryption solution that encrypts data at rest on endpoint devices before they are stored in cloud, providing an added layer of security to that offered by the storage service provider. SecureDoc CloudSync uses Advanced Encryption Standard-New Instructions (AES-NI) 256-bit encryption. It provides data confidentiality and data privacy but does not prevent eavesdropping. In regard to these two product solutions and their deployement, it is quiet obvious that they provide faster execution time than the proposed methodology since both are relying upon encryption, which corresponds to the first layer of our algorithm (AES-256). However, their use ensures data confidentiality and privacy but does not guaranty data integrity and availability. However, our implementation enhances data CIA (Confidentiality-Integrity-Availability) [5]. Confidentiality is achieved by combining AES-256 and IDA, integrity by concatenating each particular slice file with its corresponding SHA-512 hashcode, and, finally, data availability (Section 2.3) is accomplished by dispersing (IDA) the resulting data slices, as we suggested earlier in Section 4.1.
## 5 Antenna Farm Based on Two Dual-Band Offset Parabolic Reflectarrays
operating at slightly different frequencies would produce the second half of the required four-color coverage. Figure 19 shows the horizontal cut in the plane v = 0 of the radiation patterns for the beams generated at 19.7 and 29.5 GHz. The main cuts of the radiation pattern have shown similar values of C/I to those of the previous configurations, with a XPD larger than 20 dB. Due to the large electrical size of the antenna at 29.5 GHz, the beams in Rx show a higher maximum gain than the beams in Tx; however, the shaping of the beams in Tx and Rx can be matched by the optimization procedure applied in [26]. In conclusion, the simulated results of the dual-band parabolic reflectarray have demonstrated a very satisfactory performance. The simulations show a suitable distribution of the beams with appropriate values of gain and XPD. As in the previous configurations, the C/I can be improved by a small increase of the antenna size to reduce the edge illumination levels on the reflectarray. Moreover, the reflectarray cell considered in the design makes it possible to implement additional optimization to shape the beams in Rx and to maintain the cross-polar levels in band, as in [26], by a proper adjustment of the dimensions and rotation angle of the elements [27]. Figure 19: Cut of the simulated radiation patterns in v = 0 for the beams generated at (a) 19.7 GHz and (b) 29.5 GHz in same CP. Figure 18: Contours of 32 beams generated simultaneously at 19.7 GHz and 29.5 GHz by the 1.8-m dual-band parabolic reflectarray illuminated by 16 feeds.
# Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter +
## 2 Tracking Scheme of Direct-State Kalman Filter  
The DSKF considers the standard STL as part of the KF [15]. This section describes the relation between standard STL and DSKF and analyzes the DSKF's convergence in the steady-state. First, an overview of the STL's open-loop state space model (SSM) and transfer function will facilitate the comparison of STL with the DSKF. Second, the DSKF is explained, and the equivalence to a standard STL is proven. Third, the process noise covariance matrix \(\mathbf{Q}\) and measurement noise covariance matrix \(\mathbf{R}\) are described. Fourth, the Kalman filter's discrete algebraic Riccati equation (DARE) solution leads to the relation between the bandwidth \(B\), the Kalman gains \(\mathbf{K}\), \(\mathbf{R}\), and \(\mathbf{Q}\). Finally, from the DARE's solution, the LUT-DSKF is presented.
# Feature Mining and Sensitivity Analysis with Adaptive Sparse Attention for Bearing Fault Diagnosis
## 4 Model Analysis and Comparative Validation  
### Introduction to Dataset  
This section validates the diagnostic effectiveness of the proposed method with the dataset provided by the Case Western Reserve University (CWRU) Bearing Data Center [39], and it is compared with other methods and related studies. In order to reduce the influence of randomness, the reported experimental results were averaged by 10 experiments. All experimental results were obtained on an Intel (R) Core (TM) i5-8400 CPU with 2.80 GHz and 16.0 GB RAM via TensorFlow and Keras. Python is used to complete the entire experiment. A photograph of the test stand is shown in Figure 5. The features of the measurement system are shown in Table 2.  
This study uses the bearing data from the drive end of the test stand motor. The experiments were conducted on the bearing arrangement 3 o'clock, 6 o'clock, and 12 o'clock directions of failure, and vibration signals under four different health states were used: (1) Health state (Health); (2) Outer ring failure (OF); (3) Ball failure (BF); (4) Inner ring failure (IF). The fault sizes were 7, 14, and 21 mils (1 mil = 0.001 in). The specific settings of the test faults and classification labels are shown in Table 3. The one-hot encoding labels are used in the program. The operating loads were 0 and 3 hp in order to explore the cross-condition diagnostic performance of the model for working conditions. Therefore, the dataset contains 10 bearing health conditions under 2 loads.  
Referring to [20], this section uses the envelope spectrum as the input signal, takes 8192 data points as a sample, normalizes the sample data, and divides the training set, verification set, and test set into 60%, 20%, and 20%, respectively. The test load and bearing fault characteristic frequency are shown in Table 4, and the calculation method of the bearing fault characteristic frequency is referred to in [40].  
\begin{table}
\begin{tabular}{c c} \hline
**Description** & **Functional Parameters** \\ \hline Electric motor & 2 hp (1 hp = 746 W) \\ Torque transducer \& encoder & Measuring torque and speed \\ Electronic controller & Adjusting torque \\ Signal type & Vibration acceleration signal \\ Data logger & 16 channels \\ Sampling frequency & 12,000 Hz \\ Measuring bearing position & drive end, fan end, base \\ \hline \end{tabular}
\end{table}
Table 2: The features of the measurement system.  
Figure 5: CWRU bearing test bench.