esa-sceva/satcom-chunk-collection huggingface dataset:
# A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks
## 3 The DRL Algorithm
### The Double Rate Scheme
#### 3.1.1 The Relationship between Code-Width and Transmission Range  
The sonar equation is shown in Equation (1).  
\[SL-TL-NL=SNR \tag{1}\]  
\(SL\) is the Source Level which is connected with the nodes' property;  
\(TL\) is the Transmission Loss;  
Figure 1: The Network Architecture.  
\(NL\) is the Noise Level;  
\(TL\) and \(NL\) should be selected according to the environment;  
\(SNR\) is the Signal to Noise Ratio;  
All of the parameters above are measured by \(dB\).  
\[SNR=10\lg\frac{P_{s}}{P_{n}}=10\lg\frac{P_{s}}{N_{0}B} \tag{2}\]  
\(P_{s}\) is the signal power;  
\(P_{n}\) is the noise power;  
\(N_{0}\) is the unilateral power spectral density of Gaussian white noise;  
\(B\) is the bandwidth of signal; for binary data,  
\[B=\frac{2}{T_{s}} \tag{3}\]  
\(T_{s}\) is the bit duration of signal. \(T_{s}\) means the transmission time duration of one bit data.  
From Equations (1)-(3), we can see that if \(T_{s}\) of transmitted signal is increased \(N\) times, the bandwidth \(B\) will reduce to \(\frac{1}{N}B\) and \(SNR\) will be increased by \(10\lg NdB\). While, if we keep \(SL\), \(N_{0}\) and \(SNR\) be unchangeable, the \(TL\) will be ultimately increased by \(10\lg NdB\).  
\(TL=20\lg r+\alpha r\)  
\(\alpha\) is the is the coefficient of sound absorption. \(\alpha=0.036f^{\frac{3}{2}}dB/km\). \(f\) is the frequency of transmission signal. \(r\) is the transmission distance.  
\[\begin{split} TL^{\prime}&=TL+10\lg N\\ &=20\lg r+\alpha r+10\lg N\\ &=20\lg r^{\prime}+\alpha r^{\prime}\end{split} \tag{4}\]  
From Equation (4) we can see that the increase of bit duration \(T_{s}\) will finally enlarge the transmission distance \(r\) which will make the ranging signal cover a larger scope and improve the distance measurement accuracy in multi-hop networks.
## 3 Spatial Phase Coding
### Constructive Spatial Phase Coding
As such, the proposed method uses the SPC scheme with simple implementation and high performance by using limited feedback information. In addition, the implementation complexity and performance of the constructive 2-bit SPC scheme are higher than those of the 1-bit SPC scheme and lower than those of the MRT scheme. Therefore, this paper uses the constructive 2-bit SPC scheme considering both implementation complexity and performance.
## 6 Instrument description
### Instrument characteristics
#### In terms of the acquisition mode:  
The acquisition mode aims to detect the useful signal and initialize the position of the analysis window whenever the instrument is powered up or loses track. The first phase consists of exploring all the possible altitudes and locking the tracking loops. Two options may be used for the Ka band altimeter. In the classical acquisition mode, a \(\pm\) 25 km range window is run through until a waveform is detected. In an experimental acquisition mode, a very narrow range window could be used around the altitude provided by DIODE.
## 3 Simulation Studies
### Multiple Targets  
The second simulation is conducted to test the dynamic cooperation when two targets need to be caught. It is assumed that there are two targets and six AUVs. Figure 5 shows this process of six AUVs hunting two targets clearly. Figure 5a shows the distribution of AUVs, targets, and obstacles. As well as hunting one target, six AUVs began searching the work area in different directions. Figure 5b shows AUVs' search trajectory for the first target. Because the target has the same intelligence of AUV except the cooperation, target \(T1\) will escape. The AUV \(R1\) will track the target \(T1\) and send the location information of the target to other AUVs. According to the location of the target \(T1\). The multi-AUV system selects the four AUVs closest to the \(T1\). \(R1\), \(R2\), \(R3\), and \(R4\) are assigned to the target \(T1\). Since \(R5\) and \(R6\) fail in the competition, they will not join in the pursuing task but keep search target. After the completion of the task assignment, the proposed algorithm automatically plans a collision-free pursuing path for each hunter. Figure 5c shows \(R1\), \(R2\), \(R3\), and \(R4\) hunting trajectory for the first target \(T1\). Same principle, the second target \(T2\) is found by the AUV \(R5\), \(R6\). The target \(T2\) is hunted by the AUV \(R2\), \(R4\), \(R5\), and \(R6\). Figure 5d shows final trajectories of the AUVs hunting targets. Obviously, the simulation result shows that the proposed algorithm realizes multi-AUV cooperative hunting for two dynamic targets. The results show in the Figure 5, it validates that it is available to apply the proposed algorithm to the multi-AUV hunting task, and it can effectively avoid AUV coordination conflict problem in path planning.  
Figure 4: Simulation of hunting process with dynamic obstacle. (**a**) The initial state; (**b**) AUVs’ search trajectory for the target; (**c**) Final trajectories of the AUVs.  
Figure 5: \(Cont.\)
# Calibrated Phase-Shifting Digital Holographic Microscope Using a Sampling Moire Technique
## 1 Introduction  
It is arduous to measure three-dimensional (3-D) objects via conventional optical microscopy due to the finite focal depth of an imaging lens. Although confocal laser scanning microscopy (CLSM), which can achieve high-precision 3-D measurement, has been developed, it cannot be used to measure a fast and dynamic object owing to its long scanning time. Digital holographic microscopes [1; 2; 3; 4; 5; 6] can capture the 3-D information of an object without depth scanning and can focus at arbitrary depths. They can also obtain both intensity and phase information from holograms and thus can measure materials that are mostly transparent, such as biological cells and glass. Thanks to these advantages, the digital holographic microscope has been widely used.  
The recording method of digital holographic microscopes is the same as that of digital holography. It is grossly divided into two groups: in-line [7; 8] and off-axis [9] recording methods, which depend on the angle between the object wave and the reference wave received by the image sensor. In the off-axis recording method, the spatial-frequency spectra of an object and the 0 th-order diffracted wave are not easily separated when large objects are recorded and reconstructed [10]. Therefore, the in-line digital holography using phase-shifting calculation is much more efficacious than the off-axis method. In general, a phase-shifting device such as a mirror mounted on a piezoelectric transducer (PZT) is applied to shift the phase of the reference wave to record multiple phase-shifted holograms for the phase-shifting calculation [11]. However, phase-shifting errors occur due to environmental disturbances, the non-linearity of the PZT, frame loss in the camera, and the wavelength fluctuation of lasers. Numerous methods have been proposed to solve this problem, such as a closed loop phase control system using a single photodiode [12], an algorithm using a random phase-shifting method [13], and self-calibrating algorithms utilizing a statistical method [14]. However, these techniques suffer from certain disadvantages. For example, the output power fluctuation of a laser reduces the detectingprecision of phase-shifting errors in the closed loop phase control system. It also dramatically influences the quality of the reconstructed image in the random phase-shifting method. The self-calibrating algorithms are powerless for measuring objects that are mostly transparent because the object must be assumed as sufficiently random in the diffraction field.  
Hence, we propose a calibrated phase-shifting digital holography (CPSDH) system that is able to improve the quality of reconstructed images by detecting phase-shifting errors using a sampling Moire method. The effectiveness of this technique was demonstrated with respect to a reflective object in a preliminary experiment [15]. We are the first to present such a technique. In comparison with conventional phase-shifting digital holographic microscopes, the proposed method markedly improves the quality of the reconstructed images.
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 4 Proposed Solution  
### Sender-Based Solution  
Normally the receiver application calculates the playout time for multimedia data streams. The playout time calculation mostly results in different values at different receivers since it is calculated locally on each receiver. To get a common playout time at all receivers, we decided to determine the playout time for all the receivers at a single place in spite of being calculated at each receiver. Thus  
Figure 5: Timeline diagrams showing transmitted and playout time. (**a**) Time line of base and actual playout time of two receivers (R\({}_{1}\), R\({}_{2}\)); (**b**) Difference of packet reception at R\({}_{1}\) and R\({}_{2}\) due to receivers locations; (**c**) Timeline showing Synchronizing base playout time of R\({}_{1}\) and R\({}_{2}\).  
in our solution, the normal behavior of the sender and receiver is bypassed and the playout time is calculated at sender side nor at the receiver side.  
To select the sender for calculating the playout time of the participating receivers, these steps are followed:  
1. The sender is accessible by all the receivers and can communicate the playout time to all the intended receivers. Also, the receivers can easily exchange the control information with the sender.
2. The sender remains available throughout the session while receivers can join and leave the session at any time during the proceedings. If the sender leaves then there will be nothing to playout and thus no playout time needed.  
In our proposed solution, to handle playout time, following constraints must be addressed:  
1. Determine the playout time at the source of the media stream.
2. Enable the receivers to meet the suggested playout time.
3. Provide a reference clock.  
Figure 6 shows the timeline view of the solution. As it is observable that both the receivers receive packets at different time instances R\({}_{1}\)Tr and R\({}_{2}\)Tr. However, their playout time is same. This playout time is suggested by the sender. In this case receiver, R\({}_{2}\) received packet earlier than receiver R\({}_{1}\). To play the packet at the same playout time, the receiver R\({}_{2}\) had to buffer the packet for a longer time than the receiver R\({}_{1}\).
# Technological Processes for Steel Applications in Nuclear Fusion
## 10 Summary and Conclusions
### Machining and Solid-State Bonding  
The aim of our studies was to determine elementary diffusion bond parameters for EUROFER97 steel with respect to industrial production processes, which may be applied to future breeding blanket and/or TBM fabrication. The effects that have been studied are surface fabrication and surface contamination combined with cleaning. Moreover, possible fault-tolerant diffusion bonding processes were elaborated.  
From the presented results the following conclusion may be drawn:  
* An appropriate choice of parameters allows very robust solid-state bonding processes. Only low pressure, low temperature bonding (e.g., \(p\) = 20-50 MPa and T! 1050 \({}^{\circ}\)C) makes the process sensitive to surface quality.
* Whether or not a surface is suited for diffusion bonding cannot be determined by surface roughness measurement. It is better for surfaces to be additionally inspected and visually qualified.
* In principle, all common milling cutters can be used for surface fabrication. Special attention, however, has to be paid to chipping. If the chips break off inappropriately, the surface may be littered with pits, which could still remain after the welding procedure. Such cavities deteriorate the weld properties (upper shelf energy and DBTT).
* Unfavourable chipping and, therefore, surface quality can be mitigated by increasing the hardness with a pre-fabrication heat treatment.
* Tensile tests are unsuitable for qualifying diffusion bonds. Charpy tests are much more sensitive to weld faults.
* Weld defects are clearly mirrored in the fracture surface of Charpy specimens.
* Remaining cavities (as a result of low-quality surfaces, for example) can be removed by a second high-pressure HIP step. Weld defects in the order of about 20 um in diameter vanish after a HIP cycle of 100 MPa/1050 \({}^{\circ}\)C/2 h.
* After surface fabrication, diffusion bonding should be performed within 24 h. Thereafter, formation of oxide layers leads to a significant reduction of the weld properties.
* Further reduction of the weld properties does not take place if surfaces are already passivated (this is the case after about 48 h in air with 70% relative humidity).
* Oxidation may be reduced, but not suppressed completely, by spraying the surfaces with oil.
* Surface cleaning with alcohol (isopropanol) leads to better welds compared to cleaning with water and soap.  
* The use of coolant during surface machining is acceptable, but the surfaces have to be cleaned immediately after fabrication with isopropanol. Without cleaning (that is just drying) the diffusion bond properties are reduced clearly.
* Increasing the HIP temperature to 1150 \({}^{\circ}\)C for diffusion bonding, reduces the negative effect of oxidation and cleaning to an acceptable amount.
## 2 Detailed Model of MMC-Controlled Wind Turbine Generator
### Wind Turbine Model  
#### 2.1.1 Power Conversion and Characteristics of Wind Turbine  
In this paper, a wind turbine direct driven generator with variable speed concept is considered. The actual mechanical power output of a wind turbine can be written as follows [18]:  
\[P_{w}=0.5\;\rho\pi R^{2}\;V_{w}^{3}\;C_{p}(\lambda,\beta) \tag{1}\]  
where \(P_{w}\) is the converted power from wind energy (W), \(R\) is rotor blade radius (m), \(V_{w}\) is wind velocity (m/s), \(\rho\) is air density (Kg/m\({}^{3}\)), and \(C_{p}\) is power coefficient. The \(C_{p}\) depends on characteristic wind turbine coefficients (\(c_{1}\) to \(c_{6}\)), pitch angle (\(\beta\)), and tip speed ratio (\(\lambda\)), which can be calculated as follows [16]:  
\[C_{p}(\lambda,\beta)=c_{1}\left(\frac{c_{2}}{\lambda_{i}}-c_{3}\;\beta-c_{4} \right)e^{\frac{-c_{6}}{\lambda_{i}}}+c_{6}\lambda \tag{2}\]  
\[\frac{1}{\lambda_{i}}=\frac{1}{\lambda-0.08\;\beta}-\frac{0.035}{\beta^{3}+1} \tag{3}\]  
\[\lambda=\frac{\omega_{r}R}{V_{w}} \tag{4}\]  
The values of the characteristic coefficients of the wind turbine, \(c_{1}\) to \(c_{6}\), are 0.5176, 116, 0.4, 5, 21, and 0.0068, respectively [18], and \(\omega_{r}\) is the rotor speed of the wind turbine (rad/s).  
From Equations (1)-(4) the characteristics of the wind turbine are shown in Figures 2 and 3 can be depicted. The relation of power output and rotor speed is depicted in Figure 2, and the relation of power coefficient and tip speed ratio characteristic is depicted in Figure 3. The optimum tip speed ratio (\(\lambda_{opt}\)) of 8.1 and the optimum power coefficient (\(C_{opt}\)) of 0.48  
Figure 1: Configuration of the MMC-controlled wind turbine generator in the detailed model.  
are obtained when wind velocity is at a rated speed of 12 m/s. The wind turbine power output through Maximum Power Point Tracking (MPPT) is calculated as follows [19]:  
\[P_{mpt}=0.5\rho\ \pi\ R^{2}\bigg{(}\frac{\omega_{r}R}{\lambda_{opt}}\bigg{)}^{3}C_{ popt} \tag{5}\]  
where the wind turbine reference power (\(P_{ref}\)) is limited to the rated power, 1.0 pu, when the rotor speed is equivalent to or over the rated speed, 1.0 pu.
# Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV
## 5 Test Process and Result Analysis
### Field Experiments Compared to Those of Other Ultra-Low-Cost Drones
#### 5.2.2 Presentation and Analysis of Test Results  
We conducted several test experiments, and the process of each experiment is as follows.  
The UAV carries a simulated parcel and automatically enters autonomous flight mode after launching, following a predetermined flight path. During the flight, a computer at the ground station is used to monitor and record real-time data, such as the UAV's position, velocity, attitude, control inputs, and flight path images. Autonomous flight algorithms and control laws enabled the UAV to fly stably along the preset route, meeting specific performance indicators and constraints and demonstrating the effectiveness and superiority of the autonomous flight and control systems.  
When approaching the target, the UAV automatically switched to reconnaissance mode, using an image recognition system to search for and locate the target. Despite the complex background and lighting conditions, the image recognition system was able to identify the target quickly and accurately, demonstrating its accuracy and reliability.  
After successfully identifying the target, the UAV begins to drop, utilizing the control system to accurately drop the simulated package. The control system dynamically adjusts the control inputs according to the position and motion of the target, enabling the UAV to release the simulated parcel at the optimal time and position, thus highlighting the flexibility and robustness of the control system.  
After the release of the simulated package, the UAV automatically resumed its autonomous flight mode and returned to the landing site along the predetermined route, thus completing the mission. After the mission was completed, the fixed-wing aircraft described in this paper was flown against a common aircraft available on the market under the same flight conditions. After exporting the flight logs for this paper, using the mission planner, the roll, pitch, and yaw data were presented from the logs in 40 mm units and plotted separately using the origin. The final results were compared with those of commercially available airplanes to obtain Figure 34.  
The test results of this study are shown in Figure 34.  
As illustrated, a comparison of the angles before and after the optimization reveals that the UAV optimized in this study exhibits a smaller deviation from the expected values while also meeting specific performance indices and constraints. This indicates the superior control performance and structural design of this UAV, highlighting the effectiveness and superiority of the autonomous flight algorithms and control laws that were implemented. Moreover, the UAV successfully conducts target search and location, demonstrating the accuracy and reliability of the image recognition system. Following the precise target strike and smooth return to the base, the comprehensive and practical nature of the UAV design and control scheme proposed in this study are further verified.
## 4 Discussion
### Empirical Models of GPP, Re, and NEP
It should be noted that the estimations of GPP, Re, and NEE obtained using continuous measurements of solar radiation and meteorological parameters at the Qianyanzhou station were more reliable than the observations, in which such data were removed by several methods in data processing [101]. Meanwhile, the better and consistent performances (overestimations) of GPP, Re, and NEP (especially NEP) from 2013-2016, using the observation data of [PERSON]'s group, provided reasonable simulations of GPP, Re, and NEP [40; 41]. Additionally, the solar radiation sensors were calibrated, and a HOBO weather station was newly bought before the measurement in the Qianyanzhou station from 2013-2016. It would be an advantage and enable much progress to develop a generalized empirical model of GPP, Re, and NEP in China, following the procedures such as EMBE development from fitting a typical forest (temperate forest, subtropical bamboo, and plantation) to a primary generalized EMBE for representative forests in China [70].
## 5 Reactive Protection Concept for Industrial Automation Systems
### Security Policies, Operational Policies and Reactive Actions in Real-Life Automation Systems
#### 5.3.3 Reactive Actions  
The following classes of reactive actions have been derived by identifying possible countermeasures to violations of the aforementioned security policies. These actions constitute the _Action_ element of the aforementioned security policies:  
* **Block User Account (A1):** A user account is blocked. This may result in failure during authentication.
* **Revoke User Privileges (A2):** Privileges belonging to a user account are revoked. This may result in failed authorization to carry out certain actions or access certain services.
* **Communication Session Termination (A3):** A communication session is either locked or terminated based on session-specific information (e.g., user participating on the session).
* **Communication Termination (A4):** An active communication is terminated based on communication-specific attributes (e.g., protocol, logical port, routing addresses, etc.).
* **On-demand Analysis (A5):** An analysis of specific system components is carried out on-demand. Examples of this analysis may be on-demand audits, scans or other types of check (e.g., antivirus check).
* **On-demand post-intrusion configuration and information collection (A6):** After an intrusion has been detected, a change is made in the current configuration and state of an asset. Additionally, information that may be used by third-party components for further analysis may be collected from certain system components (e.g., event logs are collected).  
Execution of each of these actions without consideration of the aforementioned operational policies may influence the operational requirements of IAS (e.g., real-time capabilities, high performance and availability).  
Furthermore, it is important to highlight that the reactive protection concept presented in this work does not necessarily require the full execution of the reactive actions to be carried out by the Reactive Protection System. This system can only prepare the information necessary to execute the action and later on forward it to an appropriate component that has the features to do so. Examples of this may be the following: blocking of a user account or revocation of user privileges may be carried out by a user management system. Communication may be terminated through one of the communication parties (e.g., Virtual Private Network session termination on either a server or client) or by a third-party component (e.g., adding a new firewall rule dynamically). A network scan may be done by a firewall. Finally, a log collection may be carried out by a log collection and management system.