esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of PID-Based Algorithms for UGVs
## 3 Methodology  
### System Description  
The UGV used for the development of this work is an internal combustion tractor called Green Climber LV600, manufactured by MDB in Abruzzo, Italy, as illustrated in Figure 1a. This platform is equipped with a sensory box (referred to as Sentry), as shown in Figure 1b, which integrates a variety of sensors and hardware elements. This setup is connected to the tractor, thus providing a set of features for autonomous navigation in unstructured environments.  
Thus, the goal is to enable the above robotic platform to perform autonomous trajectory navigation. The trajectories are defined by a set of Cartesian points, and the objective is for the robot to execute these trajectories using a PID algorithm, where the control variables are derived from a navigation system.  
The adopted navigation system, which will collaborate with the PID algorithms, is based on GNSS-RTK/INS, where the PID control algorithms will receive data on the position and orientation/heading of the UGV. The position and heading values are provided by a sensor located on top of the sensory box, called Duro Inertial. The Duro Inertial system is ideal for navigation systems as it consists of sensors such as GPS, an accelerometer, a gyroscope, and a magnetometer, and through the combination/fusion of these, it is possible to obtain the position and orientation. This sensor, when connected to the RTK system, can provide a position accuracy of between 1 cm and 1.5 cm [27].  
The system that inputs data into the PID controller can be seen in the diagram presented in Figure 2. The robot's position, whose origin/acquisition is based on work developed in [28], is essentially obtained by combining data from the GNSS + RTK system, which provides the latitude and longitude, along with an IMU that gives information on the robot's acceleration and velocity in three dimensions. These data are then aggregated in a Kalman filter, which allows, with some confidence, to provide the robot's x and y position. The robot's heading, which is derived from work developed in [28; 29], comes  
Figure 1: Robotic platform (forest tractor) equipped with a sensory box: (**a**) UGV; (**b**) sensory box (Sentry) to control the UGV.  
from a three-dimensional magnetometer, and through the magnetic fields, it is possible to obtain the heading in Cartesian space (which, in this case, refers to the yaw of the UGV). However, before processing the magnetometer data, it undergoes a filtering process, where a Raised Cosine Moving Average (RCMA) filter is applied to minimize the noise present in these types of sensors. After filtering, these data are integrated with IMU data to compensate for inclinations in the terrain.
## 4 Materials and Methods
### Introduction to RNN Algorithm  
A Recurrent Neural Network (RNN) is a class of neural networks specifically designed to process sequential data. By introducing a \"recurrent\" structure, the model can capture temporal dependencies in sequences. Its core characteristics are as follows:  
Parameter Sharing: The same weight matrices are used across different time steps, reducing model complexity.  
Memory Mechanism: Historical information is transmitted through hidden states to model the context of sequences.  
The update formula for the hidden state of an RNN is  
\[h_{t}=\sigma(W_{h}h_{t-1}+W_{x}x_{t}+b)\]  
where \(x_{t}\) is the input at time step t; \(h_{t-1}\) is the hidden state from the previous time step (with an initial value \(h_{0}\) as a zero vector); \(W_{h}\) and \(W_{x}\) are shared weight matrices; \(\sigma\) denotes an activation function.
# Random Access for Underwater Acoustic Cellular Systems
###### Abstract  
In this paper, a random access preamble (RAP) design technique for underwater acoustic cellular systems is proposed. After showing that the conventional RAP used in long term evolution (LTE) systems is not appropriate for underwater acoustic cellular systems, two different types of RAPs (RAP 1 and RAP 2) are proposed to detect the identity of underwater equipment/nodes (UEs) and estimate the time delay between a UE and an underwater base station (UBS) at the physical layer. RAP 1 is generated using a Zadoff-Chu (ZC) sequence where the identity of the UE is mapped to its root index, whereas RAP 2 is generated using a linear frequency modulation (LFM) waveform where the identity of the UE is mapped to its frequency sweeping parameter and frequency shifting parameter. Ambiguity functions (AFs) and cross-ambiguity functions (CAFs) of RAP 1 and RAP 2 are derived to investigate their correlation properties under the effect of time delay and Doppler shift. The performance of RAP detection is investigated by analyzing the detection probabilities and false alarm probabilities of RAP 1 and RAP 2 in a Doppler environment. By evaluating the performances of RAP 1 and RAP 2 in various situations, it is concluded that RAP 2 is more suitable for underwater acoustic cellular systems. The AF and CAF analytically obtained in this paper are shown to be similar to those obtained using experimental data.  
underwater acoustic cellular system; random access; ambiguity function; Doppler environment +
Footnote †: journal: Sensors
## 3 Experiments and Results
### Experiments
#### 3.2.2 Normalization Types  
Once the orientation representation has been set, the question of which normalization technique to use, if any, is tackled. Each model was trained and validated with the five possibilities described in Section 2.3, and the results were mostly similar for all models. For illustration purposes, only the validation results of the SimpleMlp model are presented.  
As can be seen in Table 3 and Figure 11, the best results are obtained when using max-absolute normalization or robust scaling normalization. The MSE loss fares better for the former; however, the position and orientation error seem to favor the latter. It is, nevertheless, relevant to note that the position and orientation error are not influenced by the normalization in that they are calculated over de-normalized inputs and outputs. The MSE loss, on the other hand, is computed over a normalized dataset. Therefore, as the robust scaling technique yields the lowest position and orientation error, it is preferred over the other options.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Orientation** & **MSE** & **Position Error** & **Orientation Error** \\ \hline Quaternion & **0.5964** & 0.4847 & **0.6157** \\ Rotation matrix & 0.7476 & 0.4855 & 0.7356 \\ None & 0.8269 & **0.4433** & 0.8954 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of the different metrics obtained on testing for the SimpleMlp model after training with different orientation representations on a dataset with 10,000 non-normalized samples.  
Figure 10: Test run on sets of 10,000 non-normalized samples, using different representations for the orientation. The first plot depicts the validation loss given by the mean squared error.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Normalization** & **MSE** & **Position Error** & **Orientation Error** \\ \hline None & 0.6163 & 0.4770 & 0.6295 \\ Standardization & 0.7297 & 0.3351 & 0.7630 \\ Min–max & 0.3111 & 0.4740 & 0.9439 \\ Max–absolute & **0.1074** & 0.1750 & 0.3352 \\ Robust scaling & 0.1292 & **0.1470** & **0.3081** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the different metrics obtained on testing for the SimpleMlp model after training with different normalization techniques on the same dataset with 10,000 samples.
## 3 International Regulations on Ship Noise  
### IMO Provisions on Fatigue  
The International Convention on Standards of Training, Certification, and Watchkeeping for Seafarers (STCW), as amended in 1978, along with the International Safety Management (ISM) Code, the Maritime Labour Convention (MLC) 2006, and several resolutions, focus on ensuring the safety, health, and welfare of seafarers by establishing clear standards and guidelines.  
As far as rest and Fitness for Duty is concerned, the Regulation VIII/1 of STCW mandates specific rest periods for personnel to prevent fatigue, emphasizing the importance of rest for those involved in watchkeeping and safety-critical roles. It ensures that watch systems are designed to maintain personnel efficiency without fatigue, allowing for adequate rest before and during voyages [54].  
As per Watchkeeping Standards, it requires continuous monitoring to ensure safety at sea, guided by the principles outlined in the Regulation VIII/2 of STCW A Code, which all seafaring ships must adhere to under various conditions.  
Access to Information in accordance with the STCW Code also specifies that rest schedules should be visibly posted and easily accessible, ensuring that all crew members are aware of their rest entitlements and duties.  
Regulation 5. Part A of the ISM Code mandates ship companies to assess and mitigate risks to safety and the environment. It involves developing a comprehensive safety management system, ensuring the crew is qualified and medically fit, providing necessary support for the master's responsibilities, and offering training and familiarization to all shipboard personnel [55].  
According to Minimum Safe Manning and Fatigue Management (resolution A.1047(27)) Guidelines for determining the minimum number of crew required for safe manning are provided to ensure that the crew can manage the ship's safe operation, security, and environmental protection effectively. Additionally, there is an emphasis on understanding and mitigating the factors contributing to fatigue in ship operations by resolution A.772(18) [56].  
The MLC 2006 covers a range of welfare aspects, including regulated work and rest hours (regulation 2.3), entitlement to leave (regulation 2.4), sufficient crew for safe operations (regulation 2.7), decent living conditions (regulation 3.1), access to quality food and water (regulation 3.2), and a safe and healthy work environment (regulation 4.3) [57].  
These frameworks collectively contribute to the overall safety, efficiency, and well-being of seafarers by establishing comprehensive standards for rest, work conditions, crew qualifications, and ship management practices.
###### Abstract  
The primary goal of steganographic methods is to develop statically undetectable methods with high steganographic capacity. The embedding efficiency is one kind of measure for undetectability. Block-based steganography methods have been proposed for achieving higher embedding efficiency under limited embedding capacity. However, in these methods, some blocks with larger embedding distortions are skipped, and a location map is usually incorporated into these methods to record the embedding status of each block. This reduces the embedding capacity for secret messages. In this study, we proposed a block-based steganography method without a location map for palette images. In this method, multiple secret bits can be embedded in a block by modifying at most one pixel with minimal embedding distortion; this enables each block to be used for data embedding; thus, our method provides higher embedding capacity. Furthermore, under the same capacity, the estimated and experimental embedding efficiencies of the proposed method are compared with those of [PERSON] et al. and [PERSON] et al.'s methods; the comparisons indicate that the proposed method has higher embedding efficiency than [PERSON] et al. and [PERSON] et al.'s methods.  
Keywords:block embedding; embedding capacity; embedding efficiency; optimal selection; parity assignment +
Footnote †: journal: Applied Sciences
## 5 Experiments
### Model Recognition Accuracy Evaluation
Rows indicate the true class labels, and columns indicate the predicted class labels. These experiments are conducted under the complex channel model in Section 3.2. Figure 10: _Cont._  
In order to deeply explore the performance of different modulation types under various signal-to-noise ratio conditions, Figure 10 plots the variation in classification accuracy with signal-to-noise ratio for multiple deep learning models for signal modulation types. When dealing with BPSK, GPSK and B-FM signals, the performance of the classification models with different network structures is relatively close to each other. As shown in Figure 10, the recognition accuracy of DSB-AM and SSB-AM signals is significantly affected by the combined effect of the harsh electromagnetic environment and noise interference, resulting in confusing recognition results. However, compared with other image classification models, the CDRNN shows significant performance improvement in the classification task for QPSK, 8 PSK, 16 QAM, PAM4, and CPFSK signals, which becomes the key contribution of this network in terms of classification accuracy improvement. In particular, it is worth pointing out that the CDRNN model is able to effectively distinguish QPSK from 8 PSK and 16 QAM from 64 QAM signals, highlighting its unique advantage in solving the classification challenges of similar but different order modulated signals.
## 5 Experimental Results and Analysis
### Simulation Setup  
To support the performance analysis and comparison of small-sample jamming signal data augmentation in this paper, four types of jamming signals were provided for sample generation by a GAN: Multi-Tone Jamming (MTJ), Frequency-Hopping Jamming (FH), Comb-shaped Sweeping Jamming (CSJ), and Linear Sweeping Jamming (LSJ). The specific parameters are shown in Table 1. In the dataset, the jamming signals are transmitted through a random noise channel. The JNR can be selected within the range of \(-4\) dB to \(16\) dB (with a step size of \(2\)). The shape is set as \(12\times 100\), where the number of channels is \(12\) and the jamming time slots number \(100\).  
* **Multi-Tone Jamming**: The energy of this jamming is evenly concentrated on multiple fixed channels. The interval between the jamming channels is \(4\) channels, and the initial jamming position is channel \(2\). It continuously acts on multiple fixed channels within \(100\) time slots.
* **Frequency-Hopping Jamming**: This type of jamming adopts a pseudo-random hopping mode, performing time-varying frequency point switching within \(12\) channels. Only \(1\) channel is activated in each time slot, and the activation position is determined cyclically by the frequency-hopping pattern map.
* **Comb-shaped Sweeping Jamming**: This type of jamming presents a dynamically changing comb-tooth shape in the time-frequency domain. The interference channels are spaced \(4\) channels apart, starting from channel \(2\), and the sweep is at a rate of \(1\) channel per time slot.
* **Linear Sweeping Jamming**: This jamming exhibits continuous frequency sweeping characteristics. The jamming starts from a random channel number and scans at a constant rate of \(1\) channel per time slot. It reflects at the spectrum boundary, forming a saw-toothed jamming path.  
In the experiment, firstly, the jamming signals are input into the LACGAN for data augmentation. Then, the generated jamming samples are subjected to time-stitching data expansion, input into the DQN model for pre-training, and stored in the experience pool, so as to improve the anti-jamming capability of the DQN.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Ref** & **Application** & **Method** & **Key Feature** \\ \hline
[22] & Dynamic spectrum access & SWGAN & Simulates the spectrum environment for offline training. \\ \hline
[23] & Network intrusion detection & CTGAN & Generates datasets via a GAN to pre-train DRL. \\ \hline
[24] & Traffic signal control & GAN & Reconstructs neighboring traffic data, using traffic statistics. \\ \hline
[25] & Dormitory environment design & GAN & Rapidly predicts indoor performance with a GAN. \\ \hline
[26] & Network routing optimization & GAN & Produces rare traffic features; optimizes the discriminator boundary. \\ \hline
[27] & Power system scheduling & KEG-GAN & Synthesizes additional critical scenario samples. \\ \hline \end{tabular}
\end{table}
Table 1: GAN-enhanced DRL models.
## 4 Simulation and Experimental Results
### Simulation Results
#### 4.1.1 Speed Trajectory Tracking  
For the first evaluation, we consider speed trajectory tracking. In Figure 2(a), the speed tracking is shown, and the desired trajectory is traced with a dotted line while the solid line corresponds to the angular speed of the rotor. The tracking error is presented in Figure 2(b).  
The trajectory tracking of the currents \(i_{d}\) and \(i_{q}\) are presented in Figures 5 and 5.  
According to [27], the technique used to obtain the temporal derivative has a natural phase lag due to the presence of low-pass filters in its structure, which generates an error in the speed tracking. As observed, the error increases with time due to the difference between the reference and the answer of the PMSM. Despite this, when the motor reaches the nominal speed, the tracking error is less than 1% of the reference maximum value.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Nominal Voltage & 24 V \\ \hline Nominal speed & 4000 rpm \\ \hline Stator resistance (\(R_{s}\)) & 0.7 \(\Omega\) \\ \hline Stator inductance (\(L_{s}\)) & 0.6 mH \\ \hline Magnetic flux linkage (\(K_{m}\)) & 0.0355 V/(rad/s) \\ \hline Rotor inertia (\(J\)) & 4.8035 \(\times\) 10\({}^{-6}\) Nms\({}^{2}\) \\ \hline Pole pairs (\(n_{p}\)) & 4 \\ \hline \end{tabular}
\end{table}
Table 1: PMSM parameters.
###### Abstract  
In recent years the use of UAVs (Unmanned aerial vehicles) have proliferated in the civil sector for purposes such as search and rescue, remote sensing or real-time monitoring of road traffic, among others. In the architecture, engineering and construction fields (AEC) UAVs have demonstrated to be an ideal technology due to their optimal performance in terms of time, precision, safety and cost. Given the rapid growth of interest in this technology, this research presents a critical review of the literature on the use of UAVs in architecture and urbanism to define the most widely used techniques and delimit the fields of application based on the experimentation published by the scientific community. A scientific mapping was carried out in two stages using the VOSiewewerTM software: a scientometric and a bibliometric analysis. This technique allowed us to analyse a large body of literature and bibliographic data to obtain trends, patterns and directions of this domain of knowledge. Then, a literature review was presented, highlighting the relevant information identified in the previous analysis. The fields of application of UAVs were delimited and the most commonly used payload types and the most appropriate post-processing techniques were specified, depending on the aerial mission objective. The fields of application identified included different techniques related to the generation of 3D models, land mapping, construction site monitoring, building surveying to detect structural damage and energy losses and urban remote sensing. The literature review showed that UAVs provide a useful multi-tasking tool at any stage of an architectural project. These techniques can be applied to buildings or public spaces from the design and construction processes when the project is initiated to the later stages of maintenance and inspection of the building during its life cycle.  
UAV; UAS; RPAS; drone; architecture; urbanism; construction; review; state-of-the-art; science mapping; VOSiewer +
Footnote †: journal: Applied Sciences
## 5 Analysis
### Relationship between Plugging Condition and Soil Information  
The relationship between the plugging condition and soil properties was investigated next. Histograms for the voted plugging condition with respect to the predominant soil type along the pile length and the bearing soil layer are presented in Figure 5a,b, respectively. The predominant soil type was determined by taking the weighted average of the heights of the soil layers along the depth of the piles. Predominant soil types were classified into three arbitrary groups depicting sand (0-30% clay), mixed soils (30-70% clay) and clayey soils (70-100% clay). Once again, no clear inference could be drawn, except that (1) piles bearing in sand had a higher tendency to be unplugged, which could be attributed to the frictional resistance of clay, increasing the likelihood of the pile being plugged in clay; and (2) that the predominant soil type does not materially influence the analysis. This observation is somewhat surprising, and the occurrence of plugging in sand is likely related to its relative density, a factor that is not accounted for in Figure 5.  
Figure 5: Histograms of (**a**) the predominant soil type, and (**b**) the bearing soil layer for each plugging condition.
## 4 Conclusions  
In this work, the influence of the atomic composition and structural-phase state of Co\({}_{\rm x}\)(MgF\({}_{2}\))\({}_{100\text{-}\rm x}\) nanocomposites on their nonlinear transport and magneto/magneto-optical properties was studied.  
We demonstrated that in Co\({}_{\rm x}\)(MgF\({}_{2}\))\({}_{100\text{-}\rm x}\) nanocomposites of variable composition obtained by ion-plasma sputtering, nanocrystals of various phases are formed depending on the composition. Under a low metal content in the nanocomposite (x \(<\) 42 at.%), magnesium fluoride is in the nanocrystalline state. As the metal content increases, nanocrystalline cobalt is formed (x \(>\) 42 at.%). Our article showed that the value of the percolation threshold of nanocomposites (x\({}_{\rm per}\) = 37 at.%), determined from the concentration dependences of the electrical resistance of nanocomposites, coincides with the beginning of nucleation of hexagonal cobalt nanocrystals in the MgF\({}_{2}\) dielectric matrix.  
It was shown that the absolute value of the maximum negative magnetoresistive effect in the studied nanocomposites is 5% in a field of 5.5 kG at a cobalt concentration of x = 27 at. %, up to the percolation threshold. The threshold of magnetic percolation in the Co\({}_{\rm x}\)(MgF\({}_{2}\))\({}_{100\text{-}\rm x}\) NC system occurs at x\({}_{\rm fm}\) = 32 at.% along with the appearance of a hysteresis loop and the coercive force of H\({}_{\rm c}\approx\) 18 Oe, i.e., up to the resistive percolation threshold x\({}_{\rm per}\) = 37 at. %, as a result of the exchange interaction. At a low content of metallic cobalt, x \(<\) 27 at.%, nanocomposites are superparamagnetic (H\({}_{\rm c}\) = 0). With an increase in the cobalt content, the coercive force increases to H\({}_{\rm c}\approx\) 95 Oe.  
The results obtained in this article indicate that phase composition and structure are the decisive factors influencing the electromagnetic properties of nanocomposites.  
In our future work, we intend to investigate the effects of the annealing process and low temperature on the structural phase and electromagnetic properties of Co\({}_{\rm x}\)(MgF\({}_{2}\))\({}_{100\text{-}\rm x}\) nanocomposites.  
[PERSON] and [PERSON] conceived and designed the experiments and analyzed the data; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] contributed the materials. All authors have read and agreed to the published version of the manuscript.  
The work was partially supported by the Ministry of Science and Higher Education of the Russian Federation as part of the state task for Universities in the field of scientific activity for 2020-2022, project No. FZGU-2023-0006. The study was supported by the Ministry of Science and Higher Education of Russia under agreement N 075-15-2021-1351 in the field of materials characterization.  
Data obtained by request.  
The authors declare no conflict of interest.