esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Low-Frequency Passive Acoustic Source Localization
## 5 Conclusions  
We developed three benchmark cases for low- and very-low-frequency passive acoustic source localization using deep learning. The limitations of conventional beamforming necessitated the use of a deep learning-based approach. The cases used a shallow CNN with limited training data and sought to optimize the performance at low source frequencies under the training constraints. We obtained promising qualitative and quantitative results, and it can be concluded that the benchmark cases collectively prove the viability of deep learning-based approaches for low- and very-low-frequency ASL. At the same time, the results provide insight into the network and training parameters that can be changed to further expand the scope of these cases and obtain better results. The size of the input feature is also an aspect to be considered. Smaller input sizes would be reduced by the network to the point that learning from them would become very difficult as the problem becomes more and more complex. We would like to mention that, while obtaining good results with shallow networks and limited data is feasible, with the appropriate resources, deeper networks like ResNets and DenseNets can be trained to give better results; however, the dataset must be commensurate with the size of the network otherwise the network will tend to overfit the data and generalize poorly. Studies employing ResNet-50 used between 200-800 million training samples to prevent overfitting.  
Conceptualization, A.J. and J.-P.H.; methodology, A.J.; software, A.J.; validation, A.J.; formal analysis, A.J. and J.-P.H.; investigation, A.J.; data curation, A.J.; writing--original draft preparation, A.J.; writing--review and editing, A.J. and J.-P.H.; visualization, A.J.; supervision, J.-P.H.; project administration, J.-P.H.; funding acquisition, J.-P.H. All authors have read and agreed to the published version of the manuscript.  
A portion of the computational resources were supported by Scinet and the Digital Research Alliance of Canada through the RAC program.  
Not applicable.  
Data and code have been uploaded to a repository: [[https://github.com/aljoshi/deep-learning-based-passive-ASL](https://github.com/aljoshi/deep-learning-based-passive-ASL)]([https://github.com/aljoshi/deep-learning-based-passive-ASL](https://github.com/aljoshi/deep-learning-based-passive-ASL)), accessed on 25 September 2024.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Model Testing  
The model at this stage with the most optimal cross-validation performance that included all spectral data gathered during the training stage, hereon referred as \"Model 1\", was selected and tested against a new set of samples (i.e., 'testing set').  
In a similar way to the data collection stage (Section 2.4), two sets of five samples of each corrosive, concentration and container were prepared in a new batch of plastic bottles. In addition to corrosive substances, two batches of negative samples (i.e., water, Lucozade and Fruit Shoot) were included into the training set to assess the performance of the created model(s) against safe substances. Empty bottles were also scanned as negative samples to evaluate the contribution plastic materials could have to the overall spectral signal. All the negative samples were prepared following the same protocol used for the positive samples, as described in Section 2.4.  
Each sample in the testing set was scanned 10 times, making a total of 300 scans for each sample set, and the outcome, positive or negative, was noted to estimate the performance of a qualitative model. The output of each single scan was classified as True Positives (TP) if the corrosive sample was correctly classified, True Negatives (TN) if a'safe' sample was not identified as a corrosive, False Positives (FP) if a'safe' sample was identified as any of the corrosives and False Negatives (FN) when a corrosive sample was not classified as any of the defined classes, i.e., a 'null' result. In addition to this, some outputs were labelled as Misclassification (M) in situations where a corrosive sample was classified as a different corrosive. As the purpose of this study was to test the capability of NIR to discriminate between safe and corrosive substances, the relevance of these outputs  
Figure 3: Examples of raw spectra of HCl 37%, H2 SO4 30%, NaOCI 4%, NaOH 50% and NH4 OH 30% scanned through bottles of Transparent PET (Trans PET), Coloured PET (Col PET) and HDPE. Raw spectra were downloaded using the SciOM34 Lab application and then plotted using GraphPad Prism 8.0.1.  
is lower but nonetheless they are accounted for here. The substances that were not included in the library, such as water and soft drinks samples, were expected to provide 'null result', since no data was present within the training set to match their spectra with. For the purpose of these calculations, a null result is considered a negative result.  
To further study and compare the performance of each of the final models tested, their 'Sensitivity' or 'True Positive Rate', often referred as a model's ability to correctly identify a true positive sample, were calculated following the equation below:  
\[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}}\]
## 5 Efficiency Analysis
### Efficiency of the Cryptographic Primitives
#### 5.1.1 Blind Digital Signature Scheme  
For our tests, we assumed messages of fixed length (50 characters). For the Partially Blind Digital Signature Scheme we used an implementation of the scheme presented in [57]. Blinding requires a computation of a random integer and a hash function (SHA-256 was used). Signing of a message requires 1 random integer generation, 1 exponentiation, 1 multiplication, 1 modular addition and 1 hash function execution. To verify the signature, 2 hash functions, 2 exponentiations and 1 modular multiplication is required. According to [58] with the help of precomputed values exponentiation can be approximated by 120 modular multiplications.
## 3 Seasonal Global Maps of Far-Red SiF Derived from GOME-2A  
Figure 2 shows several monthly mean maps that reveal the expected spatial patterns of vegetation activity (four months in 2013). For example, in the temperate and subtropical regions of the Northern Hemisphere, such as the eastern US, western Europe or Southeast China, SiF values start increasing from March onwards, peak in June and decrease again to their minimum towards November. In the more continental climate zones of eastern Europe, Russia and northeastern US, the SiF seasonal cycle is somewhat shorter: SiF values increase from May onward, have a clear peak in June and reach background levels again in October. In the subarctic climate zones of the northern parts of Eurasia and North America, the SiF cycle shortens even further. In the lower latitudes, significant SiF values occur in all months, but there is a latitudinal shift in peak values during the year. In the Southern Hemisphere, SiF values in southern Australia are high in September, October and November.  
Figure 2: Global maps of monthly mean SiF values at 737 nm for March, June, September and December 2013.  
Apart from these large-scale biome patterns, very detailed spatially and temporally heterogeneous SiF patterns can also be observed from GOME-2A when evaluating over specific regions. Especially in arid regions with narrow strips of agricultural land such as in North Africa and the Fertile Crescent (Figure 3) the possibility of satellite monitoring of vegetation activity is demonstrated. The agricultural land use type shows substantially higher SiF values compared to its arid background with the highest values in March, April and May. From Fall on, retrieved values over the Fertile Crescent fade to background levels of the desert. Note that the surface albedo of desert areas is comparable to the surface albedo of vegetated land.  
Finally, we calculated monthly mean SiF values for the PC reference area and for open ocean grid boxes. In both cases, no significant SiF is expected and these values can serve as an error estimate for the SiF time series. The overall mean SiF bias and its standard deviation for the PC reference area are 0.03 and 0.06 mW/sr/m\({}^{2}\)/nm, respectively. The overall mean SiF bias and its standard deviation for open ocean are 0.17 and 0.05 mW/sr/m\({}^{2}\)/nm, respectively. Small but systematically negative values over shallow or murky waters (e.g., lakes, river deltas) are observed, which are perhaps related to the interaction of visible light with suspended matter in the water.
## 3 Results and Discussion
### Consumption Shares, Recuperation  
Other than an ICE, an electric motor is able to convert the kinetic energy of the vehicle into electricity and thus recharge the battery while driving because it can work not only as a motor but also as a generator. Recuperation reduces fuel consumption and extends the vehicle's range, but it is only generated by the two physical forces of mass inertia and gravity. While air and rolling resistance can only act against the direction of driving, both mass inertia and gravity can also develop effective pushing forces in the direction of driving. The motor/generator partially converts these forward-acting forces into battery charge. The ratio of the battery charge generated to the kinetic energy available in the direction of driving is the recuperation factor. Its magnitude must of course be less than 1. The recuperated energy has a negative sign as it helps to reduce consumption (energy consumption is usually described with a positive sign, as in our work). Recuperation in an FCET is lower than in a BET because of the additional efficiency loss of the fuel cell (Table 11).  
Figure 12 shows that at about 45%, the dominant share of consumption comes from air resistance. The reasons are the less than aerodynamic shape of trucks and the high proportion of motorway driving. On the long tour, the truck drives more than half of the total time at speeds between 80 and 90 km/h. This also explains the low consumption share of mass inertia: the truck hardly accelerates throughout the journey.  
In the case of the FCET, the consumption shares of gravity and mass inertia are lower than in the case of the ICET-CNG, although the mass of the FCET is greater. It is recuperation that reduces these consumption shares. This important aspect can solely be studied by simulation as road experiments will only provide integral consumption values. Similar remarks apply to the BET, except that the heavy battery of the BET almost completely compensates for the advantage of recuperation compared to the ICET-CNG.  
Details of simulation results can be found in [41].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **BET** & **FCET** & **Dimension** \\ \hline Recuperation factor & 66.8 & 64.9 & \% \\ \hline Total recuperated energy & \(-\)49.70 & \(-\)38.49 & kWh \\ \hline Recuperated energy by gravity & \(-\)38.61 & \(-\)29.88 & kWh \\ \hline Recuperated energy by mass inertia & \(-\)11.09 & \(-\)8.61 & kWh \\ \hline \hline \end{tabular}
\end{table}
Table 11: Recuperation in electric 20-ton trucks (long reference tour, 2024).
# Shear Wave Velocity Estimation Based on Deep-Q Network
## 1 Introduction
designed a DRL-based spectrum allocation algorithm for the internet of vehicles discriminating services. It has been proven that the designed method allocates spectrum resources quickly and efficiently in a highly dynamic environment [20]. [PERSON] et al. utilized DQN for controlling the autonomous walking of an underground load-haul-dump machine and demonstrated the effectiveness of the proposed DQN-based method through experimental verification [21]. [PERSON] et al. proposed a hierarchical DQN-based path-planning method for controlling the long-term data collection of unmanned aerial vehicles in dynamic scenarios [22]. [PERSON] et al. exploited DRL to set optimal contention windows under different network conditions for wireless LAN performance enhancement [23]. Even though it has been illustrated that the DRL outperforms to control the agent's behavior for performing well in a specific environment, the application of DRL still needs a specific configuration of the environment, action space of the agent, and reward. For instance, [PERSON] et al. proposed a stochastic inversion of magnetotelluric data based on DRL [24], in which the environment state is defined as the layer information and the resistivity, and the agent space includes three linear operations (addition, subtraction, and keeping no variation). However, the problem of magnetotelluric inversion is quite different from the geoacoustic inversion. Moreover, the parameter space of the latter is a high-dimensional space, which means that the naive linear operations (e.g., addition or subtraction) are inefficient for the agent to explore in the space and determine the optimal solution. To the best of our knowledge, DRL has not been used for geoacoustic inversion. Therefore, our motivation is to investigate the potential of DRL and define a useful configuration of the environment and agent for the field. In this paper, we propose a geoacoustic inversion framework based on a popular method of DRL and the DQN for estimating shear wave velocity from the dispersion data of interface waves. In the framework, a carefully designed configuration for the environment and agent is also proposed. A comprehensive performance analysis is presented to compare the proposed framework with three popular optimization methods (i.e., GA, DE, and ASSA) widely used for geoacoustic inversion. The remainder of this paper is organized as follows. Section 2 states the considered problem. The theories of DRL and DQN are introduced in Section 3. Section 4 describes the proposed framework for geoacoustic inversion. A comprehensive performance analysis is presented in Section 5. Finally, the conclusions are given in Section 6.
## 1 Introduction  
The removal of acidic gases or liquids such as carbon dioxide (CO\({}_{2}\)), sulfur dioxide (SO\({}_{2}\)), hydrogen sulfide (H\({}_{2}\)S), carbonyl sulfide (COS) and carbon disulfide (CS\({}_{2}\)) from natural settings, refineites, synthesis gas streams and petrochemicals are of increasing importance as environmental protection becomes more and more serious [1]. It is a significant operation in gas processing to eliminate acid compounds by means of various processes, among which is gas absorption by chemical solutions such as alkanolamines (monoethanolamine, diethanolamine, diisopropanolamine, or methyldiethanolamine) [2]. The importance of basic physicochemical properties for the density and viscosity data is an indispensable requirement over a broad range of temperatures for the absorption and desorption processes of SO\({}_{2}\)[3].  
Dimethyl sulfoxide (DMSO) was used intensively in SO\({}_{2}\) absorption because of its low volatility and good affinity with SO\({}_{2}\)[4, 5]. The physicochemical properties of solutions of glycols are useful, since such solutions are used in several processes in the pharmaceutical, petroleum, cosmetic, oil and food industries [6]. Binary solution of DMSO with glycols may attract attention due to the possible intermolecular interplay of S=O group in DMSO with -OH group in glycol [7].  
In this work, the densities and viscosities of binary systems of dimethyl sulfoxide (DMSO) + diethylene glycol (DEG) or methyldiethanolamine (MDEA) were measured at temperatures between 293.15 and 313.15 K, over the entire composition range and at atmospheric pressure. Investigations into the literature have shown that these systems have been examined but not in the same conditions. [PERSON] et al. [6] reported the values of densities for diethylene glycol with dimethylsulfoxide at 298.15 K and [PERSON] et al. [8] investigated the densities and viscosities of diethylene glycol with dimethyl sulfoxide at 308.15 K. [PERSON] et al. [1] studied densities of binary mixtures of dimethyl sulfoxide with methyldiethanolamine at atmospheric pressure with temperatures ranging from 293.15 to 363.15 K. [PERSON] et al. [9] studied the densities and viscosities of diethylene glycol + dimethyl sulfoxide solutions in the temperature range 298.15-313.15 K.  
The present work was mainly focused on investigating density and viscosity data of binary solutions of DEG + DMSO and MDEA + DMSO at T = 293.15, 298.15, 303.15, 308.15, and 313.15 K for the whole composition range. From our experimental data, excess molar volumes and viscosity deviations were calculated and correlated with the polynomial equations. The thermodynamic functions of activation of viscous flow have been estimated from the experimental values. Five equations were tested to correlate viscosity of the binary mixtures.
## 4 Methodology  
To determine the optimal set-point planning for all the rooms (for a given day) the use of GAs is proposed. One of the main advantages of GAs is that they search for a global optimal solution (even if in a stochastic sense) to complex optimization problems. Furthermore, they can find the optimal solution without a specific initialization, while other methods need reasonable initial guesses that have a great effect on the convergence speed and on the final result in most cases.  
The optimal parameters to be found are the time instants \(\tilde{t}_{ij}\) (in case of step reference signals) or \(\tilde{t}_{ij}^{-},\tilde{t}_{ij}^{+}\) (in case of ramp reference signals). A simple choice for the definition of the search space for each parameter \(\tilde{t}_{ij}\) is therefore from the beginning of the day until \(\tilde{t}_{ij}\). Regarding \(\tilde{t}_{ij}^{-}\) and \(\tilde{t}_{ij}^{+}\), in the first case the search space can be defined from the beginning of the day until \(\tilde{t}_{ij}\), while in the second case it can be defined from \(\tilde{t}_{ij}^{-}\) until the end of the day.  
The rationale of a genetic algorithm is to improve the population of individual solutions, by means of an evolutionary process, until the optimal solution is found. To determine the fitness function (4) for each individual during the optimization process, a simulation of the building must be performed. In this context, it must be noted that using a dynamic model of the building that takes into account all the thermodynamic issues is rather impractical, as the simulation time in this case is very long and it is worth using a big population size and many generations to converge to the global optimum. Thus, we propose to use the thermal model developed in [28], which is a seventh-order autoregressive-moving-average with exogenous inputs (ARMAX) model where the couplings between the rooms are also taken into account, to predict the temperature behavior of each room. In case of buildings, the time constants of all the internal spaces is actually quite long, usually some hours, therefore the sampling period that characterize the black-box model is chosen equal to 4 min. It has to be stressed that the model requires, in addition to the measurements of the temperature of each room, also the solar radiation and the external temperature as inputs at each sampling time. Hence, in case of reliable weather forecast, these can be effectively employed as inputs. However, if these data are not available, the weather conditions of the previous day can be in any case used, by assuming a slow variation of them.  
Finally, it is worth highlighting that the constraint (3), which is used to preserve in any case the user comfort, can be included in the fitness function of the genetic algorithm by simply multiplying the difference between the obtained room temperature and the required one by a constant that is set to zero is the constraint is satisfied and it is set to a high value if the constraint is not satisfied.
## 6 Discussions on Future Research Directions and Challenges
### The Impacts of ML on Channel Estimation  
Channel state information (CSI) has a significant impact on the design of secure transmission strategies. However, the availability of CSI at the communication entities differs from full knowledge or partial knowledge to zero information. The acquisition of the CSI of an eavesdropper for the transmitter is not a simple task in actual security design. Due to the hidden nature of eavesdroppers and CSI feedback, it is difficult to obtain the CSI of wiretap channels at the transmitter. On the other hand, the CSI of legitimate channels can be obtained through CSI feedback from the legitimate receiver in FDD systems. To obtain the optimal performance of secure communication, correct channel estimation is indispensable for guaranteeing the quality of service (QoS). Nevertheless, it is not easy to obtain perfect CSI due to the limited feedback and estimation errors. In many situations, CSI can be imperfect or even unknown. Therefore, the challenges that are raised by channel estimation aspects are due to the difficulties in the perfect channel estimation of eavesdroppers and the considerations of CSI feedback delay, node mobility and channel correlations.  
Recently, there have been plenty of efforts to use ML and DL techniques in channel estimation [189; 190; 191; 192]. Based on these investigations, issues regarding the estimation of the proper channel of an eavesdropper and the tackling of imperfect CSI in system design still remain. It has been proven that the existing channel estimation schemes that are based on channel modeling are insufficient for obtaining perfect and timely CSI. By introducing ML techniques into channel estimations, the performances of existing channel estimation techniques can be improved, with low complexity in the practical applications [193]. Furthermore, DL has been employed for channel estimation in mmWave massive MIMO systems [194]. By applying intelligent denoising-based message passing techniques, the system can learn channel structures from training datasets and estimate accurate channels. In [195], an off-grid channel estimation algorithm was proposed for orthogonal time frequency space (OTFS) systems. This algorithm can adopt the sparse Bayesian learning model to accurately exploit channel sparsity. From the above discussions, the development of generalized ML- and DL-based channel estimation schemes for different scenarios remains a major challenge for future research. In order to design these generalized schemes, a large number of training datasets need to be used by the DL and ML algorithms to learn channel features.