esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Experimental Research  
Proposed algorithm (9) was experimentally tested in real conditions of a channel with noise and fading and non-ideal phase and timing synchronization. The purpose of these studies was to evaluate the level of additional energy losses during packet transmission of FTN signals at high bit rates. We used the NI USRP 2920 (Table 6) SDR platform as the experimental setup in our research. The parameters of the FTN signals corresponded to the parameters of the signals in the simulation (described in Section 5). Packets consisted of a preamble in the form of a 64-bit clock synchronization sequence and an information part of the packet (2048 bits). These values are typical and provide an estimation of BER performance in real conditions.  
The experimental setup (Figure 9) consisted of a transmitting (Tx) and receiving part (Rx). The carrier frequency was equal to 429 MHz. The area of the room was equal to 40 square meters. A spectrum analyzer (Agilent Technologies N9342C, manufacturer--Keysight Technologies (Agilent Technologies), Santa Clara, CA, USA) was located between the transmitter and receiver to study the energy spectrum of FTN signals.  
An example of the signal sequence at the output of the transmitter is shown in Figure 10a, \(E_{b}/N_{0}=10\) dB. The spectral characteristics are shown in Figure 10b.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline Frequency range & 50 MHz to 2.2 GHz \\ Frequency step & \textless{}1 kHz \\ Frequency accuracy & 2.5 ppm \\ Digital-to-analog converter (DAC) & 2 channels, 16 bit \\ Analog-to-digital converter (ADC) & 2 channels, 14 bit \\ \hline \hline \end{tabular}
\end{table}
Table 6: NI USRP 2920 specifications.  
Figure 9: Experimental setup.
## 1 Introduction
Different from other disturbance observer-based controllers, this paper also solves mismatched disturbances in addition to solving matched disturbances, which improves the control accuracy. At the same time, it improves the backstepping sliding mode controller, which accelerates the speed of systems reaching the sliding mode surface in limited time. The matched uncertainty and mismatched uncertainty are estimated by constructing two GSTDOs. The disturbance is compensated for by the GSTDOs in real time. Considering the parameter uncertainty and nonlinear factors in electro-hydraulic servo systems, a backstepping sliding mode controller is designed to precisely control electro-hydraulic servo systems according to the obtained disturbance values and state quantities. A control algorithm combining backstepping and the generalized super-twisting sliding mode is proposed to improve the robustness of systems and achieve the effect of asymptotic tracking. The remainder of this paper is formatted as follows. The nonlinear model of electro-hydraulic servo systems is introduced in Section 2. The design process and theoretical results of GSTDOs and the backstepping generalized super-twisting controller are presented in Section 3. The experimental outcomes are contrasted in Section 4. Finally, some concluding remarks are delineated in Section 5.
## 3 Physical Layer Improvement Proposals for PRIME and G3-PLC
### G3-PLC: Tone Map  
G3-PLC technology includes in its specification [13; 14] an adaptive mechanism named tone map. Based on channel estimation, the Tone Map identifies a list of subcarriers that can be affected by channel disturbances and, therefore, they will not be used to exchange data in the communication between two PLC modems.  
The previous work published by the authors [10] did not implement the tone map option but highlighted the interest of analyzing its technical possibilities. This third improvement combines the study of this technique as defined in G3-PLC, with an evolution proposal that consists of redistributing the power of the unused subcarriers.  
The study included in [20] evaluates the implementation limit of static subcarrier suppression or _notching_ for G3-PLC manufacturers. However, the state of art of subcarriers suppression mechanisms does not analyze the unitary impact of the dynamic suppression tone map in G3-PLC technology.  
Figure 4: PRIME physical layer reception diagram introducing Reed–Solomon.  
Figure 3: PRIME physical layer transmission diagram introducing Reed–Solomon.
## 3 Materials and Methods
### Modified Model  
Statistical analysis models can deduce the distribution regularity, correlation, etc. of landslide data via statistical analysis of related datasets [24]. The information value model, which is a type of statistical analysis model, can comprehensively utilize environmental data from potential landslides to calculate the susceptibility of potential regional landslides. First, a parameter system was established, and the relevant data were collected, in which the factors leading to landslide hazard occurrence were identified. Then, the information value of each factor was calculated, and the seismic landslide hazard zone was calculated comprehensively according to the formula. The equation is as follows:  
\[I\ =\ \sum_{i\ =\ 1}^{n}\ I(Y,X_{i})\ =\ \sum_{i\ =\ 1}^{n}\ln\left(\frac{S_{i}/ S}{A_{i}/A}\right) \tag{6}\]  
where \(I\) represents the information value provided by the environmental factors; \(n\) represents the total number of factors; \(X_{i}\) represents the environmental factors; \(Y\) represents the seismic landslide events; \(S\) represents the gross area of landslides in the research region; \(S_{i}\) represents the area of landslides of different grades for \(X_{i}\); \(A\) represents the area of the research region; and \(A_{i}\) represents the area of different grades for \(X_{i}\). When \(I<0\), \(X_{i}\) represents information that is beneficial to the occurrence of landslides; and when \(I>0\), \(X_{i}\) represents information that is not beneficial to the occurrence of landslides.  
In this work, Formula (2) is obtained by simplifying the empirical Formula (1) according to the rainfall conditions in the study area, which makes the results more realistic. In addition, Equations (4) and (5) were cited to calculate the Newmark displacement (\(D\)) under different peak ground acceleration (\(a\)) conditions. The information value model was introduced to supplement the Newmark displacement model, for which only the internal physical parameters were considered, by calculating the landslide hazard with external environmental factors (relative height difference, slope, lithological group, distance to the fault). In order to not excessively affect the results of the Newmark displacement model, the weight of 0.1 is assigned to the information value model result to modify the results of the Newmark displacement model by adding together.
## 2 VDES Overview and Related Work
### VDES Overview
Each horizontal slot line within a TDMA frame represents a TDMA channel. Figure 1: VDES functions and frequency usage. Furthermore, in VDE-TER, one slot function is allocated to each slot in a TDMA frame. The same slot function can be allocated to several consecutive slots. Six slot functions are defined in VDE-TER: bulletin board signalling channel (BBSC), random access channel (RAC), announcement signalling channel (ASC), data signalling channel (DSCH), data channel (DC), and ranging channel (RC). These slot functions can be repeated within a TDMA channel. Note that the VDES standard provides a default slotmap for VDE-TER, depicted as Figure 3.
## 4 Results
### Predictions  
The last subsection of the result section evaluates the best LSTM candidate from a qualitative point of view. The LSTM was chosen because it is the model with the lowest \(RMSE\) on average. In order to get concrete forecasts for all three test scenarios, a single model has to be taken. It is worth mentioning that the training process was executed thrice with identical hyperparameters and model initialization. The only difference between the test scenarios is the data set used for training and testing. The best hyperparameter set is considered the best choice for all scenarios as they perform best on average.  
Figures 10-12 show the forecasts of the best LSTM model. The cyclical pattern is predicted fairly well and is aligned with the actual day-ahead prices. Neither overshooting nor undershooting happened over the forecast period. However, a price spike on September 14 th is not detected, and a drop at the end of the forecast horizon is not predicted.  
Figure 8: Model comparison over time for test scenario 2.  
Figure 10: LSTM prediction for test scenario 1.  
Figure 9: Model comparison over time for test scenario 3.  
Test scenario 2 is more challenging to predict as the cyclical pattern is interrupted by a price drop. Nonetheless, the LSTM predicted the price fall early, characterized by a declining trend. The recovery of the price, starting about two days after the price drop, is correctly identified as well. Overshooting is not an issue, but undershooting can be observed in tAhe forecasts for the first three days.  
The last scenario adds complexity as the cyclical pattern is not steadily continuing, there is a price offset, and the overall volatility is higher. This pattern is also seen in the predictions. They are substantially more volatile and less smooth than the other two test scenarios. Despite the high fluctuation, both overshooting and undershooting are observed.
# Intersatellite and Downlink Agile Attitude Maneuvers
###### Abstract  
This research is focused on the problem of agile attitude maneuvering, aimed at the precise pointing of a satellite forming a typical constellation in low Earth orbit. We consider two different operational scenarios: (a) pointing toward a specific ground station, located on the Earth surface (for downlink data routing), and (b) pointing toward a companion satellite (for establishing an intersatellite connection). The two preceding operational requirements can both be formulated as attitude tracking problems. In this study, we use an inertia-free nonlinear attitude control algorithm based on rotation matrices and possessing remarkable stability properties, in conjunction with a pyramidal array of single-gimbal control momentum gyroscopes. Numerical simulations, in both nominal and nonnominal flight conditions, demonstrate that the attitude control architecture proposed in this work is effective for the purpose of performing agile attitude maneuvering, aimed at precise pointing during downlink and intersat data routing.  
interast data routing: agile attitude maneuvering; nonlinear attitude control; momentum exchange devices; satellite constellations 2023
## 2 Materials and Methods
### Satellite-Based ET Modeling Framework
#### 2.3.3 Alexi/Disalexi  
The ALEXI model is currently run operationally over the continental United States (CONUS), producing daily ET estimates at 4 km spatial resolution. ALEXI applies the TSEB model twice during the morning hours (approximately 1.5 h after local sunrise and 1.0 h before local noon) using sub-hourly land-surface temperature (LST) observations from the Geostationary Operational Environmental Satellites (GOES). A simple slab model of atmospheric boundary layer (ABL) growth [68] is then used to relate the rise in air temperature (\(\mathrm{T_{a}}\)) in the mixed layer to the time-integrated influx of sensible heat from the surface, allowing the vertical temperature gradient and sensible heat flux of the surface layer to be estimated. Due to the time-differential approach, ALEXI flux estimates are less sensitive to biases in LST and non-representative \(\mathrm{T_{a}}\) fields [54]. However, due to its coupled nature, ALEXI is constrained to the coarse spatial scales of geostationary platforms such as GOES (4 to 10 km or greater).  
For finer-scale applications, an ALEXI disaggregation procedure (DisALEXI) was introduced [55; 56]. DisALEXI operates by running TSEB over each ALEXI pixel (Equations (1)-(9)) using higher spatial resolution vegetation cover (LAI, NDVI), albedo, and LST information from polar-orbiting satellites--in this case, from Landsat. To ensure consistency between the ALEXI and DisALEXI ET estimates, an initial \(\mathrm{T_{a}}\) map (set at a nominal blending height of 50 m) is iteratively adjusted at the ALEXI pixel scale until the DisALEXI ETd fluxes, spatially averaged over the ALEXI pixel, converge to the ALEXI daily value. This procedure also ensures consistency when applying DisALEXI to other sensors with thermal imaging capabilities, a key advantage when wanting to fuse multiple sources of data [58; 59; 60]. However, under initial DisALEXI assumptions and differences in PT or PM approaches, values of \(\mathrm{T_{a}}\) (in combination with LST) will produce varying \(\mathrm{T_{c}}\) and \(\mathrm{T_{s}}\) and by association, \(\mathrm{AE_{s}}\) (E) and \(\mathrm{\lambda E_{c}}\) (T). Some choices in \(\mathrm{T_{a}}\) may result in unrealistic estimates of T and E partitioning under some climatic and/or surface conditions, even though the components may sum to reasonable estimates of ET. One such case may be under conditions of strong advection over irrigated crops, where deviations between DisALEXI and ALEXI ET may be impacted more by horizontal advection of energy rather than air temperature boundary conditions at the blending height.
# Development of Automatic Berthing Support Program for Autonomous Ships
## 1 Introduction
Although neural network-based automatic berthing systems show good performance, they require a large amount of training data to achieve this level of accuracy. Berthing data for diverse situations, such as various ship types, berthing processes, and external environmental conditions, are generally difficult to obtain. Additionally, while reinforcement learning-based automatic berthing systems can produce results solely based on data obtained through interactions with the environment--without requiring a ship's dynamic mode--they need numerous attempts and a large volume of data, which presents a risk of failure during the learning process. Furthermore, models developed using neural networks and reinforcement learning are often validated in limited experimental environments, which may not fully reflect real-world berthing conditions. Ultimately, to achieve automatic berthing, it is necessary to calculate thruster output values by considering external forces and berthing speed. Between 2018 and 2019, Mitsui E&S Shipbuilding Co., Ltd. (MES-S) (Tokyo, Japan), Mitsui O.S.K. Lines, Ltd. (MOL) (Tokyo, Japan), Tokyo University of Marine Science and Technology (TUMST), Akishima Laboratories (Mitsui Zosen) (Tokyo, Japan), and MOLFerry conducted automatic berthing operations using a virtual pier in open water, with the training ship \"Shioli Maru\" [17]. In 2022, the project team announced the success of an actual demonstration test of their jointly developed auto berthing and unberthing system, which was equipped on the car ferry \"Sunflower Shiretoko\" [18]. Also, in 2022, MOL completed the world's first containership sea trial for unmanned docking and undocking, with the 1870 DWT containership \"Mikage\" [19]. In 2018, Rolls-Royce and Wartsilia announced significant achievements with automatic berthing control systems. Rolls-Royce successfully demonstrated automatic berthing with its developed autonomous navigation system, with no crew intervention, on the fully autonomous 53.8 m car ferry \"Falco\" [20]. Wartsilia successfully conducted the world's first autodocking test on the 83 m car ferry \"Folgefonn\" [21]. In 2019, MacGregor, part of Cargotec, and Kongsberg Maritime have entered into an agreement where MacGregor will deliver an automated mooring system for Yara Birkeland. This will enable mooring operations to be undertaken without human intervention and supports effective operation of the fully electric, zero-emission vessel [22]. In the same year, Volvo Penta released their assisted docking system for boat docking, designed to mitigate the effects of wind and current, and to improve maneuvering control in tight spaces [23]. This study, therefore, proposes an automatic berthing support program that calculates the required thruster output values by taking into account external forces and turning moments acting during the berthing process, as well as the ship's relative position, heading, and speed, to ensure safe berthing. The program's performance was verified through full-scale experiments.
## 4 Hardware Implementation
### State Control Level
#### 4.1.1 Manual Operation  
In manual mode, the set value for the drive speed is first calculated from the parameters transmitted by the mini-computer. Then, this manipulated variable and the steering value that is also transmitted are transferred to the hardware control level.
# EE-UWSNs: A Joint Energy-Efficient MAC and Routing Protocol for Underwater Sensor Networks
## 3 The Proposed EE-UWSNs Protocol
### The Mechanism of the EE-UWSNs Protocol
Therefore, the half of the responding nodes (i.e., two nodes) that have the highest score value will be in the inactivation list (S3 and S2 in the example). Note that the number of active nodes is three (S4, S5, and S6), which does not exceed the active nodes threshold. Figure 3: Flow chart of the proposed protocol from sender side. When a node receives the INACT packet, it checks if its name is on the inactivate list or not. If so, it enters the inactivation mode for a specific period, only if the outgoing packet queue is empty. There is an assumption that the inactivation period is known already for every node in a network. During that time, the node is powered off. In other words, it does not send, receive or generate packets. After transmitting the INACT packet, the sender transmits data to the selected relay node and awaits acknowledgment from it. After receiving the data packet successfully, a relay node (S6) responds with an ACK packet and starts the same process discussed previously, but as a forwarder. It will search for the next relay node in the direction of the sink. If it cannot find nodes inside the transmission cone that can be reached at the lowest power level P1, it will increase the transmission power level to P2, and send a new RTS. When the next relay has been selected, the process continues until the destination is reached. Figure 4: Flow chart of the proposed protocol from receiver side. Generally, the power level will be increased until a node reaches the next relay, or until all power levels have been tried. If the node is unable to reach any relay node at the maximum power level, PN, it will shift its cone and begin searching for relay nodes across the primary cone. The proposed protocol uses a special packet called the silence (SIL) packet. A very small SIL packet is used in order to inform about other communication engagements. After sending a multicast RTS, the neighbor nodes may currently operate in another communication as relay nodes. To prevent the situation where relay nodes are placed in inactive mode, a node that overhears the RTS packet will transmit an SIL packet to the requesting node. When a silence packet is received by the requesting node, it will defer its transmission. In fact, an SIL packet has another benefit in terms of saving energy, which is that it prevents an increase in the transmission power level when there is a node but it is already engaged in another communication. The pseudocode for the proposed EE-UWSNs protocol is shown in Algorithm 1. Figure 5: Representation of the routing process based on the proposed EE-UWSNs protocol.
## 4 Simulation and Results
On the other hand, the round trip time between Mars-to-Earth is about 6-40 min, which is leading to the one-threshold policy being optimal with \(\tau=1\), and _betting opportunistically_ is completely unfeasible as shown in Figure 7b, there is no data bit that can be transmitted if the transmitter perform channel sensing. And the two-thresholds policy transmission scheme in this scenario is degenerated to the one-threshold policy transmission scheme, where both of the transmission schemes have exactly the same expected total number of transmitted bits.
## 2 Materials and Methods
### Stochastic Shape Model  
The first result of the whole procedure is related to the definition of the number of PCs which are required to explain at least 90% of the total variation among subjects. Given the size of database used here, it is obvious that 40 PCs would cover 100% of the whole variability in the case of the mandible and 98 PCs would do so in the case of the proximal femur. However, the procedure actually comes to be useful if the number of PCs needed to cover 90% variability is significantly smaller than the number of available subjects. Indeed, PCs represent the main possible shape variations present in the analyzed cohort and they are sorted starting from those explaining the largest variability. The goodness of fit resulting from using a limited set of PCs can be checked employing Equation (6) to fit the shapes of the training set, and calculating average distance between the actual node positions and the fitted node positions. In addition, the frequency distribution of the shapes scale factor \(S_{i}\) (Equation (9)) as well as the frequency distribution of the weight \(W_{i}\) assigned to each \(i\)th component (Equation (6)), can be estimated. Both these factors were evaluated here considering all bones belonging to a training set and the respective nodes.  
One application of these stochastic distributions is setting up a procedure to generate new mandible and femur morphologies based on the respective distributions. This could be useful in order to test innovative surgical techniques and new prostheses on large samples (virtual population of 3D bone models).  
The stochastic bone model can be developed recurring to the so-called \"inverse transform sampling method\", as explained in the following:  
1. A random number ranging between 0 and 1 is generated for each component and for the scale factor;The random number is considered as a value of the cumulative density distribution of scale factors or weights and the respective inverse function is calculated. In this way, random weights (\(W_{\text{iR}}\)) and a random scale factor (\(S_{\text{R}}\)), are obtained, distributed according to the empirical cumulative density distribution function, recorded from each bone training set;
3. A new bone is generated according to the following equation: \[G\prime_{\text{R,CNR}}=\left(G_{\text{CNR,avg}}+\sum_{i=1}^{M}W_{\text{iR}} \cdot P\mathcal{C}_{i}\right)\cdot S_{\text{R}}\] (17)
# Energy-Efficient Clustering Multi-Hop Routing Protocol in a UWSN
## 2 Related Works
In [21], the authors reviewed and compared the routing protocols to select the forwarding node in a UWSN. Most of the routing protocols are multi-hop localization-free protocols which are energy-efficient and reliable. Therefore, the routing protocols for UWSNs should consider the depth of the node, energy consumption, and multi-hop routes in order to develop an efficient, reliable routing protocol. In addition, the clustering protocol for UWSNs shows the benefits in terms of high network coverage, high connectivity, and adaptivity to the dynamic environment. It should be noted that the existing routing protocols have some limitations. However, it is necessary to consider the role of the cluster relay and cluster head duration in minimizing energy consumption. The longer the nodes are at the cluster head, the more energy the nodes consume. In the paper, we propose a clustering protocol which takes into account the residual energy and the depth of nodes. The proposed protocol is described in the following section. A qualitative comparison of the clustering protocols is presented and discussed in Section 3.