esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
## 4 ML-Based Designs
### Design of a Mobile Cloud/Cloudlet  
ML can be used to analyze the data that can be stored and processed in the cloud (see Figure 3). A number of clouds can be placed into a certain number of UAVs, local clouds, and terrestrial BSs, so called mobile clouds or cloudlets. Collecting, storing, and processing big data, to balance centralized (e.g., mobile cloud) and distributed (e.g., mobile edge computing) paradigms in a U-RAN are the main challenges. The cloudlet can have different cloud computing characteristics. Thus, for instance, in order to support the processing requirements of different users in the area, the cloudlet should have an optimized speed or location.  
UAV-based cloudlets have been established in [73] as an efficient approach to reduce the latency and effectively enlarge the computational abilities of IoT devices. In particular, the problem of jointly optimizing the number and positions of deployed UAV cloudlets in 3D space in order to support IoT services with stringent latency requirements is discussed. The method of [73] exhibits good performance with low complexity, under some restrictive assumptions. For instance, it is assumed in that the uplink bandwidth is equally distributed among the nodes and, moreover, the average time services and QoS of the different users are equal. These assumptions may not be fulfilled in many scenarios of practical interest and, in this case, model-based solutions are difficult to be found.  
A very comprehensive survey on using U-RANs in this area has been provided in [70], where the main challenges of managing an UAV-based cloud computing in heterogeneous environments are discussed, without, however, providing possible solutions to the reported issue.  
Let us consider a scenario where multiple UAVs are employed to offer cloud-based services to ground users with different communication requirements. The goal is to find the best speed of the mobile cloud and the number of UAVs that are equipped with cloudlets. The number of UAVs can be determined by means of SL, where computing/processing time of a certain task and the percentage of satisfied users are used as dataset. Indeed, consider a scenario with users having different communication requirements. If we have a cloudlet that serves the users, the number of the UAVs as processors in a cloudlet should be increased as the number of the users rises. The users' density, their communication requirements, and UAVs features (e.g., processing/hovering time) can be considered as an input dataset for an SL box, whose output can be the number of the UAVs in the cloudlet.  
Deep RL can also be used to dynamically change the speed of the UAV cloudlet(s) in order to maximize user performance. It can happen that such a dataset is not available, and the users are not delay sensitive. In this case, agents are still UAV, action is the increasing speed of the cloudlet and the state is the current speed of the cloudlet. The reward function is the percentage of the satisfied users that is achieved, as stated in previous sections.
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 6 Results
### Frequency Selective Channel  
Physical layer tests for a frequency selective channel scenario are described in Section 5.1 applying AWGN noise and the channel described in Section 4.2.2.  
This test is considered for modulation schemes with convolutional encoding only, as frequency selective channels remove some of the subcarriers making every modulation scheme without error correction unsuccessful.  
Figure 12 represents the relation between FER and SNR values for each modulation schemes with convolutional encoding.  
This graph shows how for frequency selective channel scenarios, using DBPSK with convolutional encoder, a SNR of 6.5 dB is required for \(FER\)\(10^{-2}\). For DQPSK with convolutional encoder, a SNR of 10 dB is required (3.5 dB more than DBPSK), and for D8 PSK with convolutional encoder, a SNR of 14.8 dB (8.3 dB more than DBPSK).  
Comparing the results in Figure 12 and the results obtained with a flat channel in Figure 10, the sensitivity loss due to the selective channel can be measured. The sensitivity loss compared to a flat channel is of 2.5 dB for DBPSK, 3 dB for DQPSK and 3.5 dB for D8 PSK.
# CasOmniMVS: Cascade Omnidirectional Depth Estimation with Dynamic Spherical Sweeping
## 2 Related Works  
### End-to-End Deep Learning Methods for Stereo Depth Estimation  
Stereo matching algorithms using deep learning have achieved surprising results. MC-CNN [12] uses a convolutional neural network(CNN) to extract features, then regularizes the cost volume with a traditional SGM [13] algorithm. GC-Net [14] constructs the cost volume by simply concatenating left and right feature maps, and uses 3D convolutions to regularize it in an end-to-end manner without post-processing, which has had a profound influence on subsequent research. PSMNet [15] based on [14] uses feature pyramids to extract multi-scale features, further improving accuracy.  
However, using 3D convolutions to process the cost volume leads to enormous resource consumption. Some methods attempt to avoid using 3D convolutions or to optimize them. GA-Net [16] proposes a more efficient guided aggregation (GA) strategy to replace costly 3D convolutions, while achieving higher accuracy. RAFT-Stereo [17] takes optical flow as the control variable to tackle the depth estimation problem from input images and effectively propagates information in images using multi-scale GRUs, thus enabling real-time inference with high accuracy and robustness. CFNet [18] introduces a fused cost volume to alleviate domain shifts for initial disparity estimation and constructs cascade cost volumes using variance-based uncertainty estimation to balance disparity distributions, which adaptively narrows down the pixel-level disparity search space in later stages.  
Three-dimensional reconstruction from multiple views has also advanced rapidly in recent years. MVSNet [19] introduces a differentiable homography-based cost volume for  
Figure 2: Network architecture overview. Our network is roughly divided into four parts: **feature extraction**, **spherical cost volume construction**, **cost regularization**, and **cost regression**.  
multi-view depth estimation. RMVSNet [20] replaces 3D convolutions with GRU recurrent networks, to reduce the model size. PointMVSVNet [21] predicts per-point depth, which is then combined with images to form 3D point clouds, which are further optimized using point cloud algorithms. MVSCRF [22] introduces conditional random fields (CRF) into multi-view stereo matching. In the field of multi-view stereo matching, several methods are based on cascade architectures and coarse-to-fine concepts, to optimize the processing of cost volumes. CasMVS [11] proposes a cascade architecture, utilizing the depth prediction from the previous stage as input to the next stage, which reduces the plane sweeping range. UCS-Net [10] presents an approach using an adaptive resolution cost volume, where the dynamic depth hypothesis space is derived from the uncertainty of the predictions in the previous stage.
## 3 Quality of Service Reliability in Cyber Physical Sensor Network
Deployment of CPSN architectural parts require placement of sensing and actuator devices at strategically critical points with intelligent algorithms for node localization and geo-location detection. The Medium Access Control (MAC) at the sensing side should consider that the negotiation between neighboring data collection devices and sensors must conserve resources like bandwidth, number of channels, buffer storage and transmission energy [23]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Implementation Layers** & **Protocol** & **Scope** \\ \hline Application & HTTP, COAP & End to End \\ Transport & UDP, TCP & End to End \\ Network & IP & End to End \\ Routing & RPL & Per Hop \\ PAN & 6 LowPAN & None \\ Data Link & IEEE 802.15.4 & Per Hop \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Example implementation layers and protocols for a cyber-physical sensor network environment. Figure 6: A division of cyber-physical sensor network requirements. Software, hardware, middleware and operating systems are required that provide reliability beyond existing technologies. The software and hardware must be highly dependable, certifiable, configurable and, where required, be able to fully integrate with complex systems. A complex CPSN system must possess reliability that is currently lacking in many deployments. Though overdesign is currently the safest path for system deployment, this approach becomes intractable for complex designs and systems where interoperability is required. Evidence based methods are needed for reasoning inference about system reliability. New methods, algorithms, models and tools are needed that can incorporate verification and validation of software and systems at the control stage. A major focus must be on systems which are verifiably robust in order to keep operating under situations and environments with uncertainty combined with potentially rapidly changing objectives. CPSNs built for optimization, control and scheduling will have to interoperate efficiently in real time. Hence, further study is required to explore the organizing principles of such interactions and appropriate abstractions that can support services with significantly shortened design cycles. Before CPSN implementation, it is important to develop representative models of all agents including electrical, mechanical and computational components as well as important environmental factors which influence the dynamics of the system. With the availability of a high level of computational power, several tools have also emerged for direct modeling of dynamic systems. Issues that dominate the reliability and prediction design requirements include: (i) verification of system wide safety/reliability properties; (ii) predictable interaction between cyber (communication/computation) and electromechanical (physical) components; (iii) computational tractability including algorithms that can scale; (iv) health, state and information management _via_ communication over wireless and wired links.
# Aerodynamic Characteristics of Supersonic Rocket-Sled Involving Waverider Geometry
## 1 Introduction
Most waverider studies have investigated supersonic intake and several have studied aircraft lift increase and drag decrease optimization. To the author's knowledge, there has been no study considering control of the ground effects of waverider. Therefore, the main objective of this study was to investigate how to use waverider theory to control ground effects and reduce shockwave oscillation of a rocket-sled. As with other wave phenomena, a shockwave will reflect when it meets a solid wall. As the distance between an aircraft and the ground shrinks, the reflected shock may impinge again on the aircraft. When the distance is small enough, this phenomenon may happen more than once [45], causing significant pressure differentials. As shown in Figure 1a, additional disturbances occur when there are obstacles on the ground, as shown in Figure 1b shockwave oscillation in the flow field will cause the rocket-sled body to vibrate, which may reduce the service life of rocket-sled equipment. However, according to waverider theory, we know that its special configuration can control the shockwave effectively. The shockwave of waverider is attached to the leading edge, referred to as \"attached shock\", as shown in Figure 1c. In this way, aerodynamic oscillation caused by head shock can be reduced, or even eliminated, directly at the source. In this paper, a scale-adaptive simulation (SAS) method and dynamic grid method are used to simulate the conventional axisymmetric slender rocket-sled and the waverider rocket-sled. The shock generation and evolution pattern are analyzed. The waverider configuration is introduced in an innovative way into the rocket sled test platform. The ability of the waverider rocket-sled to control ground effects is illustrated by comparing the results of two configurations. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pattern** & **Methods** & **Advantages** & **Disadvantages** \\ \hline Active & Jet [22; 23; 24; 25; 26] & Easy to maintain, cost-effective & Affects performance and operating life \\ Active & Structural deformation [27; 28] & Adaptable, effective & Complex, big weight \\ Active & Periodic excitation [29; 30] & Compact construction and energy saving & Limited control scope \\ Active & Heating [31] & Simple structure & Weak ability \\ Passive & Changing surface & Effective & Difficult to adjust and narrow \\  & condition [32; 33; 34] & & applicability \\ Passive & Adding extra objects [35; 36; 37; 38] & Simple in theory & Difficult to apply \\ Passive & Shape optimization [39; 40] & Adaptable, effective & Complex process and limited \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of several common active and passive flow control methods.
## 3 Leica Pegasus Backpack Survey  
The Pegasus backpack is a terrestrial mobile mapping system recently developed by Leica: it features two laser profilers, five cameras, a GNSS receiver and a 200-Hz INS. The rationale of this system is that of allowing accurate and fast surveys of areas accessible by a human carrying a backpack. This is of clear interest especially in the case of places where it is difficult to carry other instruments: the weight of the Pegasus backpack, namely 11.9 kg, is surely reasonable for a backpack carried by an average person.  
The SLAM technique using the two laser profilers, which acquire 600k points per second at a maximum range of 50 m, and with a high precision IMU, nominally ensures position accuracy in indoor environments from 5 cm to 50 cm walking for 10 min. However, as stated in the backpack specifications [14; 15], several factors can affect this value.  
Each camera acquires 4 Mpixel images at 2 Hz. Battery life nominally ensures 4 h of operating time, which should be enough for most use cases.  
The Leica Pegasus backpack survey was done on 21 December 2016 and lasted less than a hour: approximately half an hour was spent for system setting up and calibration, whereas data acquisition required only a few minutes.
## 2 Basic Principle, Experimental Setup and Results
### Experimental Description
#### 2.2.2 Experimental Digital Signals Transmission
The bit-error-rate (BER) was numerically evaluated by the relationship of the Q-factor to the BER, defined as [13]:  
\[BER=\frac{1}{\sqrt{2\pi}}\frac{e^{-\frac{Q^{2}}{2}}}{Q} \tag{4}\]  
For most practical optical networks, the BER is \(10^{-10}\), which is convenient to sustain a robust communication system. Thus, from Table 3, it can be noticed that the BER for the filtered microwave band-pass windows centered at 4.2 GHz was less than the threshold established by ITU (International Telecommunications Union) [14]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency (MHz)** & 50 & 100 & 155 \\
**Q-factor** & 44.48 & 45.87 & 42.76 \\
**Eye Height (mV)** & 871 & 875 & 881 \\
**Eye Width (ns)** & 19.36 & 9.81 & 6.49 \\
**Eye Jitter RMS (ps)** & 113.13 & 42.76 & 40.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Features of the digital signals to be transmitted. Figure 5: Proposed experimental setup to carry out the digital signals transmission.
## 3 Materials and Methods
### Sampling Techniques  
To address the imbalance problem, we utilize SMOTE [34] and ADASYN [35] techniques, which are the commonly used benchmark oversampling algorithms. The SMOTE regular technique was designed by [PERSON] et al. in 2002, where the minority class concerns synthetic minority class samples that are homogeneously distributed around the original positive instances that lie close together. The synthetic oversampling technique can produce artificial data instead of general random replacement of actual samples. Depending on the oversampling rate, a certain number of samples from the k nearest neighbors are randomly chosen. The synthetic samples are generated as follows: estimate the difference between an instance and its nearest neighbor, which occur in minor class. Later, a random number among 0 and 1 multiplies this difference. Finally, add it to the feature vector that corresponds to the new synthetic instances of the minority class.  
ADASYN is known as the pioneer of developing algorithms that are derived by synthetic techniques, including SMOTE. The overall process for ADASYN adaptively generates more synthetic data of minority class instances according to their weighted distributions using nearest neighbors. The main idea of this technique is that it can adaptively generate synthetic data for a minor class to decrease the bias, which occurred from the data imbalance problem. Therefore, it is using weighted distributions of the different minority class in terms of their level of difficulty in learning, thus it improves the learning performance.
## 4 Results analysis  
### SMAs DTC Stiffness Modulus Analysis  
For SMAs DTC stiffness modulus test, four 100 mm diameter SGC (superpave gyratory compactor) specimens (EN 12697-31, 75 gyrations) were prepared. The selected temperatures were 10, 20, 30 and 40 \({}^{\circ}\)C. Six frequencies were adopted for each temperature: 0.3, 0.6, 1, 3, 10 and 20 Hz. Before being tested, each sample was kept at the test temperature for 4 h. For a clearer interpretation of results, a volumetric analysis based on the evaluation of the air voids content (EN 12697-8) of the SMACR samples was carried out. Results are presented in Table 6.  
Results show an average air void content equal to 5.59% for SMACR, which is in line with the values suggested by the adopted Italian technical specifications for this type of AC.  
Figure 3 shows the average DTC stiffness moduli for the experimental SMA with CR.  
As a general result, the stiffness moduli comply with values traditionally suggested by the Italian technical specifications for this SMA (Stiffness Modulus \(>\) 3500 MPa at 20 \({}^{\circ}\)C). Extending the use of the time-temperature superposition (TTS) principle, which represents the theoretical basis for the rheological analysis of a bitumen in the linear field [21], a single DTC master curve can calculated to show the SMACR behavior in terms of stiffness. Using the calculated time-temperature shift factors (TTSf) shown in Table 7, it is possible to translate data at different temperatures along the time axis to create a unique master curve that refers to a specific temperature. In the case under study, the reference temperature was 20 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mixture** & **Va [\%]** & **AVG Va [\%]** & \(\rho\) **[g/cm\({}^{3}\)]** \\ \hline SMACR\_1 & 6.03 & & \\ \hline SMACR\_2 & 5.50 & 5.59 & 2.376 \\ \hline SMACR\_3 & 5.26 & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Air voids content and density for SMA–crumb rubber (CR).  
Figure 3: Direct tension-compression (DTC) Isothermal curves for SMACR.  
The SMACR DTC master curve at 20 \({}^{\circ}\)C, obtained with the aforementioned TTsf using the traditional Williams-Landel-Ferry (WLF) model, is presented in Figure 4.
# Blockchain-Embedded Strategic Options for Satellite Operators: Sell, SataS, or Dual?
## 3 Models excluding Blockchain Technology  
In this section, we model the optimal strategy for the SO based on business features. The notations and assumptions of the model are described in Table 2 and Table 3. We assume satellite data users are heterogeneous in different channels. We denote customers' valuation by \(v\), which is uniformly distributed in \([0,1]\). If the user chooses to own a satellite  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Reference** & **Satellite-as-a-Service** & **Dual-Channel Supply Chain Management** & **Blockchain Technology** \\ \hline [PERSON] and [PERSON] [1] & \(\surd\) & \(\surd\) & \\ [PERSON] et al. [3] & \(\surd\) & \(\surd\) & \\ [PERSON] et al.[4] & \(\surd\) & & \\ [PERSON] et al. [6] & \(\surd\) & & \\ [PERSON] and [PERSON] [20] & & \(\surd\) & \\ [PERSON] and [PERSON] [19] & & \(\surd\) & \\ [PERSON] et al. [20] & & \(\surd\) & \\ [PERSON] [27] & & & \(\surd\) \\ [PERSON] et al. [37] & & \(\surd\) & \(\surd\) \\ [PERSON] et al. [38] & & \(\surd\) & \(\surd\) \\ [PERSON] et al. [39] & & & \(\surd\) \\ [PERSON] et al. [41] & & & \(\surd\) \\ This paper & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the related literature.  
to acquire data, they need to pay the SO \(p_{r}\) to purchase the satellite. Plus, they need to take the risk of a failed launch. We assume that the probability of a successful launch is \(e\). Thus, the utility of customers under the sell channel can be measured as  
\[U_{r}=ev-p_{r} \tag{1}\]  
If the user chooses the SataaS channel to acquire data, they need to pay \(p_{s}\) as the subscription price to the SO. It is worth noting that users have to face a security risk in acquiring data through the SataaS channel; in order to portray the utility loss of customers, we introduce \(s\). To ensure that the demand is non-negative, we assume that \(0\leq s\leq 1\).  
\[U_{s}=v-p_{s}-s \tag{2}\]  
We next explore the SO's payoff, consumer surplus, and social welfare in the NR model, NS model, and ND model when BCT is absent. By analyzing the performance of different channels, we obtain the optimal strategy for the SO.
# Interaction of Machining Parameters on MRR of Sintered NdFeB Processed by EDM-Milling
## 4 Interaction of Machining Parameters on the MRR
### Interaction of Pulse off Time and Voltage on the MRR
#### 4.4.2 Significance of Pulse off Time on the MRR  
According to Figure 9, it can also be found that the significance of the influence of pulse off time on MRR increases with the increase in voltage. When the value of voltage is 70 V, \(\triangle_{U}(T_{\text{off}})\) reaches a maximum of 28.84 mg/min. When the value of voltage is 30 V, \(\triangle_{U}(T_{\text{off}})\) is at its minimum of 5.51 mg/min. The \(\triangle_{U}\)\(T_{\text{off}}\) is 23.33 mg/min, as listed in Table 2.  
When the voltage is 30 V, the distance between the electrode and the workpiece is small. The dissipation of the energy is less. The pulse off time affects the deionisation and the discharge frequency. An improvement in deionisation time and a decrease in frequency will be induced along with an increase in the pulse off time, and vice verse. The improvement of the deionisation favours the increase of MRR. However, the decrease in discharge frequency is not conducive to the increase of MRR. Therefore, the effect of pulse off time on MRR is less significant when the voltage is low.  
When the voltage is 70 V, the machine tool increases the breakdown delay time to increase the average voltage. Deionisation is improved, and the densities of residual corrosion products and heat are reduced. The effect of pulse off time on deionisation is weakened. However, pulse off time affects the utilization of the discharge energy effectively. As shown in Figure 10, the utilization of the discharge energy under higher voltages (Figure 10b and Figure 10d) is obviously less than that under the corresponding lower voltages (Figure 10a and Figure 10c), respectively. Moreover, when the voltage is 70 V, the MRR under a long pulse off time (Figure 10d) will be significantly lower than that under a short pulse off time (Figure 10b) due to the insufficient utilization of discharge energy. Thus, the significance of the effect of pulse off time on MRR increases with the increase in voltage.