esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Underwater Targets Using Polarization Laser Assisted Echo Detection Technique
## 3 Underwater Target Polarization Detection System and Experimental Scheme
### Distance Test and Location Test  
The laser ranging module calculates the distance by using the time of the reflected light from the target. Due to the different transmission speeds of the light in air and water, the testing depth needs to be corrected.  
According to the refractive index of water and air, the transmission speed of light in water is about \(C_{\text{water}}=C_{\text{air}}/1.33\). If the actual ranging distance is \(d\), the distance between the ranging module and the water surface is \(h\), and the actual water depth is \(x\), then:  
\[\frac{d}{C_{\text{air}}}=\frac{h}{C_{air}}+\frac{x}{C_{water}} \tag{12}\]  
\[x=(d-h)/1.33 \tag{13}\]  
Figure 5: Experimental setup for measuring polarization characteristics under water.  
Figure 6: Structure diagram of simulated pool body.  
When the depth of water is corrected, only the distance between the ranging module and the water surface and the refractive index of the tested water body is needed. However, the ranging module has a certain angle of inclination when incident. The incident angle is \(i\), the refraction angle is \(r\), the refractive index of the water body is \(n\), then the actual water depth is  
\[x=\frac{d-\frac{h}{\cos i}}{n}\cos(\arcsin(\frac{\sin i}{n})) \tag{14}\]
## 6 Proposed Algorithms
### SDN-Based Interface Selection (IS) Algorithm  
In this algorithm, the SDN controller probes the achievable throughputs of each interface by sending some traffic via the OVS switch through this interface separately for each node. Indeed, every node has specific achievable throughput values for each interface based on its channel condition.  
The IS algorithm compares these achievable throughputs for each interface of each node. The interface that achieves significantly higher throughput than the other is selected to receive the traffic. If the channel conditions of both interfaces lead to slightly different throughput values (within 10%), both interfaces are selected.  
While sending the packets from the sender to the receivers, the algorithm checks which of the achievable throughputs of the receivers fall into which of the aforementioned  
Figure 6: Experimental setup II.  
categories. Consequently, the SDN controller alters the flow table accordingly. Thus, the SDN controller manages the data packet forwarding by executing the flow rules on the OVS switch, which forwards the packets to the selected interfaces of the receiving nodes.  
Since the achievable throughput controls the amount of time for which an interface is used to receive the transmitted data, it directly affects the energy consumption. MPTCP mainly uses RTT, packet loss, or both to estimate the amount of data to be pushed over each interface and hence the technology used by the wireless interface does not affect the operation of the IS algorithm. Thus, it can be applied to multiconnectivity networks using different technologies, such as 5G, LTE, and WiFi.  
In the following steps, we use the first interface and second interface to refer to the two different wireless interfaces used.  
* Step 1: Send the probe packets on the first interface of all the nodes and measure the achievable throughput \(R_{1}\) of each.
* Step 2: Send the probe packets on the second interface of all the nodes and measure the achievable throughput \(R_{2}\) of each.
* Step 3: If a slight difference between the first and second interface throughputs (within 10%) is detected, send the packets through both interfaces.
* Step 4: If the first interface throughput is greater than the second, send the packets through the first interface. Otherwise, send them via the second interface.  
Figure 7 summarizes the algorithm in a flowchart.
# Asymptotic Performance Analysis of the MUSIC Algorithm for Direction-of-Arrival Estimation
## 1 Introduction
For the localization scheme to be successful, accurate estimation of RSS and AOA is very important. Performance of localization algorithm is dependent on the estimation accuracy of the RSS and AOA. The scheme presented in this paper can be used for determining the accuracy of the estimates in the MUSIC DOA algorithm, which is one of the most popular DOA algorithms. In [23], to improve ultrasonic image, Rician beamforming is applied. Sparse sampling in spatial domain is adopted. Basically, beamforming algorithm can be considered as spatial filtering. Beamforming and DOA estimation including the MUSIC algorithm are parts of array signal processing research. Rician beamforming-based array design can be formulated as a convex optimization problem. In this paper, the estimate is obtained from the property that the partial derivative of MUSIC cost function with respect to the azimuth angle at the estimate should be zero: therefore, the estimate of the convex optimization problem can be obtained from the observation that the partial derivative of the cost function with respect to each variable should be zero at an optimal point. Given the estimation error, the MSE can be obtained by exploiting the statistical description of noise or interference. In [24], a novel beamforming scheme is proposed to improve the previously used beamforming algorithm. Since beamforming technique is a spatial filtering, it can be employed in interference suppression. More specifically, TDMA-beamforming is proposed, and it is shown that the proposed scheme improves the signal to interference plus noise ratio (SINR) by approximately 18 dB. In [25], a hybrid beamforming employing variable phase shifters (VPSs) and constant phase shifters is proposed. To optimally combine VPSs and CPSs, greedy algorithm is adopted to get a near-optimal solution, since getting exact solution is intractable. In [26], for improvement of audio enhancement, a new improved distributed minimum variance distortionless response (MVDR) beamforming algorithm is proposed. The algorithm can be applied to wireless acoustic sensor networks. The proposed scheme outperforms two classical methods in the view point of the segmental signal to noise ratio, mean square error, and perceptual evaluation of speech quality. In Table 1, algorithms, applications, main ideas and contributions of the recently published works are tabulated. In Table 2, it is tabulated how the schemes presented in [17; 18; 19; 20; 21; 22; 23; 24; 25; 26] are validated. In Table 2, it is also described how the explicit expressions of the azimuth estimate and the MSE of the azimuth estimation error derived in this paper can be applied to the problems in [17; 18; 19; 20; 21; 22; 23; 24; 25; 26].
# First Assessment of GF3-02 SAR Ocean Wind Retrieval
## 3 Results  
First, the GF3-02 SAR image is averaged in the azimuth of 1 km and the range of 1 km in the center of the scene, and matched with the ERA5 data in space and time, where matched ERA5 wind speed range is 0 m/s to 12 m/s. Second, the ERA5 wind direction data is used as the input of the GMFs model to obtain the wind speed retrieved by the GF3-02 SAR. Finally, it is verified by comparing the NDBC, ERA5, and HY-2B SCA wind speeds.
## 6 Model Result
### Results and Discussions
Figure 5 shows the energy consumption for both single- and multi-hop scenarios. It is observed that devices located farther away from the GW (devices 44-50 are the farthest devices from the GW) consume more energy in a single-hop scenario. That is because high SF is used to achieve a long communication range. Sensor 45, for example, consumes 55.5% more energy in the single-hop scenario than in the multi-hop scenario. The results in Figure 6 show the total energy consumption per node for 1 day. Since devices in single-hop use larger SF values (9, 10, 11) to increase the transmission range, the energy consumption is higher than that in the multi-hop scenario. Figure 4: Generated bytes per day for 50 devices (10 km)—single-hop and multi-hop scenarios and payload = 20 bytes. Figure 5: Daily energy consumption per device for the 50 devices—single-hop and multi-hop scenarios—(10 km) where the payload is 20 bytes. The multi-hop scenario has advantages as we go farther than the GW, as we observe in Figures 5 and 6. That is because the sensors that are far away in the single-hop scenario have higher transmission ranges than in the multi-hop scenario. However, the result proves the benefits of setting SF to 7 in terms of energy consumption. Based on the pipeline application requirements and our results, multi-hop topology offers less energy and extends the network coverage. However, single-hop shows better results in the network traffic.
## 5 Results
### Placement Stability (Jittering)  
In addition to placement accuracy, the stability of the augmented model is an important qualitative aspect in choosing which placement method to use in a given context. Table 3 reports the obtained results for the studied methods.  
Figure 17: Placement result of the 3D model by (**a**) the GAMMA AR solution in indoor; (**b**) SiteVision solution in outdoor with automatic placement; and (**c**) SiteVision in outdoor in manual placement.  
By analyzing the results of Table 3, we notice that the marker-based method using the Vuforia SDK is the most unstable among the other remaining methods; this effect is due to the poor performance of the algorithm in detecting and tracking markers in the real scene. Moreover, the alignment performance in the case of Vuforia depends on the lighting and brightness conditions, the quality of the markers and the camera-to-marker distance (explained in Section 5.1.1). However, the ARcore method, which is based on the anchor method, allows a greater stability than Vuforia. We can so conclude that the SDK used affects the model's stability. Thus, to develop a stable marker-based AR application, the user needs to choose the best SDK [61].  
With the automatic placement mode of SiteVision, an unstable increase in the model is obtained with a standard deviation value of 130 cm. This degree of instability is explained by the low coverage of the RTX network at the study site. This is justified by the fact that the stability of marker-less methods depends on the quality of the positioning method [61]. Figure 19 below summarizes the different results of both marker-based and marker-less methods.
# Wireless Power Transfer via Subterahertz-Wave
## 3 Research Report on Millimeter-Wave High-Power Wireless Power Transmission
### 94 GHz Rectenna
On the output side, the notch filter is inserted to reapply the fundamental wave to the diode. The inline structure is shown in Figure 16. Measurement of the rectifier is done using inline to convert the transmission mode of the MSL-to-waveguide by sandwiching the inline in the Al fixture [26]. The measurement setup is shown in Figure 17. Results are shown in Figures 18 and 19. The DC voltage was measured by connecting the oscilloscope and by calculating DC output power and RF-DC conversion efficiency from the load resistance value and input power. To increase the power received by the antenna, the beam is focused by a condensing lens. Results show that the maximum RF-DC conversion efficiency was 38%. The DC output power was 39 mW. Figure 14: Circuit fabrication method by semiconductor microfabrication 2. Figure 16: Process of MSL—to—waveguide transmission mode conversion by finline. Figure 17: Measurement setup of 94 GHz wireless power transmission. Figure 15: 94 GHz rectenna: (**a**) rectifier and (**b**) rectenna.
# A Novel Test Rig for the Basic Nonlinear Characterization of Bolted Joints
## 1 Introduction
[17], represents a common framework for several studies, since the identified hysteresis parameters can be used to define the model of a joint under the action of tangential loads. However, the out-of-plane vibration of the bolted joint is ignored in these rigs, and the influence of the normal relative displacement at the interface still needs to be investigated. Vibration modes of real engineering structures are characterized by a complex kinematics that unavoidably affects the relative displacements at the contact interface between two jointed components. In this regard, the beam with shear lap joints may be tested, assuming different boundary conditions (e.g., fixed-free [18], free-free [19]) to excite different vibration modes. In the last few years, the Brake-Reu8 beam has become a reference academic-standard test rig for the investigation of the damping, contact kinematics, and contact properties in bolted joints. Based on the Brake-Reu8 beam, all the aspects related to the effects of the applied boundary conditions [20], excitation and measurement techniques [21], different control strategies [22], as well as variability and repeatability [23] on the measurements of the system's stiffness and damping properties, have been investigated. Some other test rigs showing different bolts layouts and excited vibration modes can also be found in the technical literature. Among these, it is worth mentioning the beam assembly with regularly spaced bolts along the beam midline [24] and the \"S4 Beam\" [25]. Other test rigs were purposely developed for specific applications to characterize the dynamical effect of bolted joints in real structures. For example, [PERSON] and [PERSON] [26] confirmed the nonlinear behavior induced by bolted joints in an aero-engine casing, which was also tested by [PERSON] using a specially designed slip table [27]. In this paper, a novel test rig for the identification of the stiffness and damping characteristics of the bolted joints employed in flanges subjected to out of plane vibrations, is presented. The test case consists of two beams clamped by one single bolt, which makes the experimental setup simple and useful for a deep investigation of different experimental parameters. The dynamic response of the bolted joint around the first bending mode of the beams is detected, and the modal damping ratio is identified to characterize the nonlinearity introduced by the bolt, for under different levels of harmonic excitation and bolt tension. Furthermore, a similar test rig including one equivalent monolithic beam is also used to separately investigate the influence of the bolt head/nut contact interface on the forced response.
# A Survey of Convolutional Neural Networks on Edge with Reconfigurable Computing
## 4 Reconfigurable Computing Architectures for CNN
The cost of increasing flexibility of ASICs and high energy requirements of programmable devices moved designers to a balanced solution offered by reconfigurable computing, in particular FPGAs and SoC FPGAs. With the advent of SoC FPGAs, where a high speed processor is tightly integrated with reconfigurable logic, edge computing has a new solution for their implementation with a balanced design between performance and flexibility. FPGAs and SoC FPGAs can implement deep learning models efficiently in hardware and software/hardware. Like any other hardware platform, designs can become parallel, pipelined, and optimized for each model using data quantization and reduction techniques. The same platform can be later upgraded with a new deep learning model without any board modifications, only by reconfiguring the device. In the following we describe reconfigurable platforms for execution of convolutional neural networks with an emphasis on edge computing, including coarse-grained, fine-grained, and configurable devices.
# Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System
## 4 Lunar Orbital Filter
### Kalman Filter Application
The combination of the separate filter terms to the master filter \(m\) is given by  
\[P_{m} =(P_{1}^{-1}+\cdots+P_{N}^{-1})^{-1}, \tag{37}\] \[\mathbf{x}_{m} =P_{m}(P_{1}^{-1}\mathbf{x}_{1}+\cdots+P_{N}^{-1}\mathbf{x}_{N}), \tag{38}\]  
where state and covariance information is given for a set of \(i\subset[1,N]\) sensors. The importance of one sensor over another is given by a weight \(\gamma_{i}\), where the covariance matrix is multiplied by this weight term on each time step \(k\), i.e. \(P_{i}=\gamma_{i}P_{m}\). The covariance propagation update Equation (30) is also modified as  
\[P_{k} =\Phi P_{k-1}\Phi^{\intercal}+Q, \tag{30}\]0  
The weights \(\gamma_{i}\) should reflect the mean inverse average \(1/N\), so,  
\[P_{k} =\Phi P_{k-1}\Phi^{\intercal}+Q, \tag{30}\]1  
and should be within \(1\leq\gamma_{i}\leq\infty\). When handling inter-satellite links, the noise of the measured range must be transferred between satellites. The approach, known as a Decentralised Schmidt EKF [18; 57],combines the covariance from the current and neighbouring satellite within the measurement update. Equations (31)-(33) are rewritten as  
\[P_{k} =\Phi P_{k-1}\Phi^{\intercal}+Q, \tag{30}\]2 \[P_{k} =\Phi P_{k-1}\Phi^{\intercal}+Q, \tag{30}\]3 (42) \[P_{k} =\Phi P_{k-1}\Phi^{\intercal}+Q, \tag{30}\]4 (43) \[P_{k} =\Phi P_{k-1}\Phi^{\intercal}+Q, \tag{30}\]5 (44) \[P_{k} =\Phi P_{k-1}\Phi^{\intercal}+Q, \tag{30}\]6  
where \(H_{i}\) is the derivative of satellite \(i\)'s measurement model in terms of its own state, \(H_{j}\) is the derivative of satellite \(i\)'s measurement model in terms of satellite \(j\)'s state, i.e. \(H_{j}=\frac{\partial\mathbf{h}_{i}}{\partial\mathbf{x}_{j}}\), and \(P_{ij}\) is a new covariance matrix of the two satellite combined states, with \(P_{ij}=P_{ji}^{\mathrm{r}}\). The combination of sensor models and the two proposed Kalman Filters are illustrated in \[P_{k} =\Phi P_{k-1}\Phi^{\intercal}+Q, \tag{30}\]7 The orbit propagation element is included within each EKF box and follows the procedure described in this subsection. Celestial sensors are fused by the Federated EKF for some satellite \(i\), followed by any ISL measurements from a satellite \(j\) integrated using the Decentralised Schmidt EKF.
## 2 Relevant Methodology
### Gru  
The Gated Recurrent Unit (GRU), a variant of the Recurrent Neural Network (RNN), was introduced by [PERSON] et al. in 2014 to address the long-term dependency challenges in RNNs [30; 31]. GRUs employ a gating mechanism to retain information effectively. Ina GRU, data are initialized using a weight matrix and bias vector, which are fine-tuned during training. Critical components of GRUs include the update gate, which determines the extent of retaining old hidden state information for new state constructions, and the reset gate, which similarly influences the retention of old hidden state information for constructing candidate hidden states. Furthermore, GRU considers the candidate's hidden state, incorporating new information for updating the hidden state. The final new hidden state is a blend of the old hidden state and candidate hidden state information, modulated by the update gate.  
In a practical application, [PERSON] et al. [32] demonstrated the efficacy of an end-to-end bearing diagnostic classification model based on a 1D Convolutional Neural Network (CNN) and a GRU. This model leverages the synergy of GRUs and 1 DCNNs to extract spatial features from vibration signals and learn time series characteristics, enhancing data representation. This approach also reduces the complexity of the diagnostic process by diminishing the reliance on specialized knowledge typically required in traditional feature extraction methods.  
The GRU model structure is shown in Figure 1.  
The Bidirectional Gated Neural Unit (BiGRU) is derived from GRU, a variant of GRU. However, in BiGRU, two separate GRU units perform the forward and backward computations, respectively. Each unit has independent parameters, such as forward and backward update gates, reset gates, and weights and biases of candidate hidden states. Therefore, there are more than two sets of parameters in a BiGRU. At the end, the forward and backward hidden states are concatenated to obtain bidirectional hidden states, which can better capture the information in the sequence.  
The basic structure of a BiGRU is shown in Figure 2.  
Figure 1: The GRU model.
## 2 Experimental Test
### Experimental Results
#### 2.5.3 Sand Deformation
Compared with the influence of \(H\) on \(\theta_{\rm 2a}\) and \(\theta_{\rm 2b}\), as shown in Figure 10, it can be found that \(\theta_{\rm 2a}\) decreases by \(4^{\circ}\) and \(\theta_{\rm 2b}\) decreases by \(1^{\circ}\) when \(H\) increases from 31.5 cm to 63 cm under push \(u_{\rm t}\). \(\theta_{\rm 2a}\) decreases by \(13^{\circ}\), and \(\theta_{\rm 2b}\) decreases by \(1^{\circ}\) when \(H\) increases from 31.5 cm to 63 cm under pull \(u_{\rm t}\). Therefore, it can be concluded that with an increase in \(H\), \(\theta_{\rm 2a}\) gradually decreases, while \(\theta_{\rm 2b}\) remains nearly the same. \(\theta_{\rm 2a}\) is relatively larger than \(\theta_{\rm 2b}\) because, when \(H\) is the same, compared with the slip surface AB, the shear deformation of the sand at the slip surface CD is restricted by the frictional effect between the upper surface of the FAS specimen and the sand above it. When \(H\) is small, the sand above the FAS specimen is thin, and the friction on the FAS specimen is large, resulting in \(\theta_{\text{2a}}\) being relatively larger than \(\theta_{\text{2b}}\). With an increase in \(H\), the influence of friction on the FAS specimen gradually decreases, resulting in a smaller \(\theta_{\text{2a}}\). The variations in \(\theta_{\text{2b}}\) and \(\theta_{\text{1b}}\) with \(H\) are both less than \(3^{\circ}\), as shown in Figure 10, because they are not affected by the friction between the FAS specimen and the sand. The measured \(\theta_{\text{2b}}\) and \(\theta_{\text{1b}}\) were compared with [PERSON]'s theoretical earth pressure. It can be found that \(\theta_{\text{2b}}\) in the passive state of approximately \(37.6^{\circ}\) under push \(u_{\text{t}}\) and \(\theta_{\text{1b}}\) in the passive state of approximately \(33.5^{\circ}\) under pull \(u_{\text{t}}\) are \(10.1^{\circ}\) and \(6^{\circ}\) larger than the horizontal angle \(\theta_{\text{rp}}=45^{\circ}-\varphi_{\text{s}}/2=27.5^{\circ}\) of [PERSON]'s passive earth pressure, respectively. \(\theta_{\text{2b}}\) in the active state of approximately \(75.6^{\circ}\) under pull \(u_{\text{t}}\) and \(\theta_{\text{1b}}\) in the active state of approximately \(60.5^{\circ}\) under push \(u_{\text{t}}\) are \(13.1^{\circ}\) and \(8^{\circ}\) larger than the horizontal angle \(\theta_{\text{ra}}=45^{\circ}+\varphi_{\text{s}}/2=62.5^{\circ}\) of [PERSON]'s active earth pressure, respectively. The differences may be related to the existence of matric suction in the sand. Matric suction enhances the binding force between soil particles, which is equivalent to increasing \(\varphi_{\text{s}}\)[48; 49]. In addition, factors such as the particle gradation and compactness of the sand may also affect the horizontal angle of the slip surface.
## 6 Performance Analysis
### Energy Consumption of Nodes
The case '4' is represented as energy consumption of nodes '4' and is presented in Table 7. The 16 nodes are represented as node '1', node '2', node '3', node '4', node '5', node '6', node '7', node '8', node '9', node '10', node '11', node '12', node '13', node '14', node '15', and node '16'. The high energy consumption of nodes is recorded at Tx of 11 dBm for the Node '13', i.e., 3.10 mW, and low energy consumption of nodes is recorded at Tx power of 14 dBm for the Node '10', i.e., 1.73 mW. Here, the energy consumption for Node '5' is increased, and, for Node '6', from the Tx power of 2 dBm to 14 dBm. The energy consumption for Node 1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 14, and 16 are increased from Tx power of 2 dBm to 8 dBm and for node 2, 8 the energy consumption have increased from 2 dBm to 11 dBm. The graphical representation of the energy consumption is represented in Figure 7d.