esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Wireless Sensor Networks
### Energy Harvesting and SPWS Perpetual Operation
#### 3.4.3 Battery Charging in Research Room  
In a subsequent test, the Solems PV panel was connected to the lithium polymer battery. The current supplied to the battery was measured with an ammeter and the room illuminance by an Avago Technologies APDS-9007 sensor [33]. The charging circuit is this way a closed loop containing the PV panel, a diode, the ammeter and the battery, all connected in series in this order. The relation between current and illuminance is given by:  
\[I_{OUT}=10\upmu\text{A}\times\log(L), \tag{11}\]  
where \(I_{OUT}\) is the current measured at the sensor output and \(L\) is the corresponding illuminance. A ground floor working room, with 6 by 3 by 3 m was used to measure the panel output current, shown in Figure 13, and the illuminance, shown in Figure 14, for a 24-h summer period. The room has a window facing north that receives shading by a number of pine trees, and very small windows facing south near the ceiling.  
The test was executed on the 2 June 2015, and started at 14:46:32, in broad daylight. Analysing both figures it can be noticed that the highest peak occurs at around 15:36:00, with 110 \(\upmu\)A for 900 Lux. From 20:40 until 6:30, due to the absence of light, the panel output is zero. Due to the opening or closing of the window shutter, four periods where a sudden large increase or decrease occurs can be identified. A pair of these occurrences occurs at lunchtime, when the current is generated using the small illuminance entering the small windows facing south.  
The average current presented in Figure 13 was 27.4 \(\upmu\)A, corresponding to an average illuminance value of 198.8 Lux. Even with this very low and not recommended illuminance level, the majority of the sensors are able to operate perpetually. Only the air temperature and relative humidity with a sampling time of 34 s and the activity sensor, without the energy-saving policy, would not achieve that goal.  
Figure 13: Current charged to the battery, over the 24-h period.
## 3 Methods
### Bowen Ratio Method  
The turbulent pulsation values of the latent and sensible heat fluxes of the subsurface may be directly monitored and estimated to produce the ET of the subsurface based on the eddy covariance approach. However, there are still a lot of technical issues with the eddy covariance approach that need to be fixed, which cause inaccuracies in the observed turbulent fluxes. Eddy covariance is challenging to detect, especially at night when turbulence is weak, and the turbulence intensity directly influences energy closure, which can result in the phenomenon of energy non-closure (i.e., the sum of the sensible and latent heat fluxes is lower than the difference between the net radiation and soil heat fluxes), which understates ET. The Bowen ratio, i.e., the ratio of sensible to latent heat fluxes, was first proposed by [PERSON] ([PERSON], 1966) and is denoted by the following formula:  
\[\beta=\frac{H}{LE} \tag{14}\]  
Using the Bowen ratio approach and the energy balance equation, assuming that the observed values of net radiation and soil heat flux are accurate but that the measured values of \(H\) and \(LE\) are both lower than the actual values, the \(LE\) can be retrieved.  
\[LE=\frac{Rn-G}{1+\beta} \tag{15}\]  
The EC observations at four ground sites (Arou, Daman, Huazhaizi, and Sidaoqiao) from 2014 to 2020 were revised through the Bowen ratio method and then were upscaled to daily ET to examine the accuracy of the model.
# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 5 Experimental Results
### Experiments for Add PPL Loss to TDNN  
PPL loss is a supplement to classification loss. In order to test the performance of our proposed PPL loss method, we conducted experiments on the loss function commonly used in speaker recognition. We tested the impact of adding PPL loss to softmax loss, AM-Softmax loss and AAM-Softmax loss, with PPL loss set to a C number of 32 and \(\lambda\) value of 0.1. The results are shown in Table 4, and Figure 4 illustrates the DET curves for these conditions.  
From the results, we conclude that adding PPL loss to the classification loss can improve the system performance, and PPL loss is applicable to softmax, AM-softmax and AAM-softmax loss. Therefore, it can be determined that adding PPL loss can guide the network training to refine frame-level features and obtain speaker features containing speech information. Moreover, PPL loss can be implemented with only simple clustering pseudo-labels and does not need to prepare any other data labels to train the ASR network.  
\begin{table}
\begin{tabular}{c c c} \hline
**Loss Function** & **EER (\%)** & **minDCF (0.01)** \\ \hline Softmax Loss & 3.932 & 0.4103 \\ Softmax Loss + \(\lambda\)PPL Loss & 3.825 & 0.3643 \\ AM-Softmax Loss & 3.473 & 0.3469 \\ AM-Softmax Loss + \(\lambda\)PPL Loss & **3.321** & **0.3234** \\ AAM-Softmax Loss & 3.754 & 0.3657 \\ AAM-Softmax Loss + \(\lambda\)PPL Loss & 3.412 & 0.3445 \\ \hline \end{tabular}
\end{table}
Table 4: Effect of adding PPL loss on TI-SV task on VoxCeleb. (\(\lambda\) = 0.1, C = 32).  
\begin{table}
\begin{tabular}{c c c} \hline
**Implementation Method** & **EER (\%)** & **minDCF (0.01)** \\ \hline baseline(1&3) & 3.473 & 0.3469 \\ baseline(2) & 4.364 & 0.3797 \\ Version 1 & 3.321 & **0.3234** \\ Version 2 & 4.168 & 0.3887 \\ Version 3 & **3.244** & 0.3459 \\ \hline \end{tabular}
\end{table}
Table 3: Results of different PPL loss implementation methods on VoxCeleb. (AM-Softmax + \(\lambda\)PPL \(\lambda\) = 0.1, C = 32).
## 6 Conclusions  
The variation in the antenna baseline has an impact on the accuracy of the LoS angle and subsequently in the 3D positioning, with respect to both range and phase difference measurement errors. In the case of the range measurement errors, the effect on LoS angle accuracy is noticeable especially at small distances under 1 m and remains practically constant (in percentage) once the distances increase over 10 m. A smaller baseline has a positive effect on the LoS angle determination for small distances. In the case of phase measurement errors, the effect on LoS angle accuracy is noticeable especially at large distances over 100 m and remains practically constant (in percentage) once the distances are less than 10 m. A smaller baseline has a negative effect on the LoS angle determination for large distances. As further work, we plan to develop an experimental setup on a software-defined radio platform that may host the proposed network protocol stack to validate our theoretical findings. The results should be corroborated through modelling and tests.  
Figure 8: Relative error of the LoS angle with respect to a baseline range of d = 0.95 m and 1 m, respectively, and phase difference measurement errors of 0.01 rad and 0.02 rad, respectively.  
**Author Contributions:** Conceptualization, O.R. and B.S.K.; formal analysis and investigations, V.R. and B.S.K.; resources, O.R.; writing--original draft preparation, B.S.K.; writing--review and editing, V.R. and O.R.; project administration and funding acquisition, O.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** This is a theoretical study, no data was generated or used.  
**Conflicts of Interest:** Author [PERSON] was employed by the Control Data Systems SRL company. Author [PERSON] was double affiliated; he was employed by the Control Data Systems SRL company, while conducting his Ph.D. studies at the Technical University of Cluj-Napoca. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Case Study
### Effect of the Train System on the Prediction Accuracy  
#### 4.3.1 The Riding Speed of the Train  
This section aims to investigate the impact of train riding speed on the prediction accuracy of bridge dynamic responses. The study utilizes the German ICE EMU, comprising four carriages. The fixed wheelbase \(l_{1}\) is 2.3 m, the vehicle spacing \(l_{2}\) is 17.375 m, and the vehicle body length \(l_{3}\) is 24.775 m. The bridge response sampling frequency is set at 150 Hz, and the number of bridge units is 200. For the LSTM network model is trained using dataset C with 100 samples. The neural network model structure comprises 2 layers of LSTM with 128 neurons.  
The evaluation metrics of predicted bridge displacement and acceleration with the varying train speed are depicted in Figures 17 and 18. The results indicate that the Pearson correlation coefficient and \(\text{R}_{\text{W}}^{2}\) for displacement prediction remain consistently high, with values exceeding 0.998. For acceleration prediction, the Pearson correlation coefficient and \(\text{R}_{\text{W}}^{2}\) range between 0.7 and 0.95. The APVE values for displacement prediction range from 0.2% to 2.5%, while those for acceleration prediction range from 5% to 20%. In terms of dimensional indices, the displacement ranges from 0.003 mm to 0.01 mm, while the acceleration ranges from 2 mm/s\({}^{2}\) to 10 mm/s\({}^{2}\). Additionally, one can observe that all the evaluation metrics show mutation and obvious deterioration when the train speed reaches 70 km/h. The cause of this phenomenon may be the resonance effect in the vehicle-bridge coupling vibration [31].  
Overall, the accuracy of predicted train-induced bridge responses tends to decrease with the increased train speed. The prediction of acceleration is notably sensitive to changes in train speed, potentially resulting in larger prediction errors as speed increases. In contrast, displacement prediction maintains consistent performance across varying train speeds, demonstrating good robustness with train speed.  
Figure 16: The influence of the number of bridge structure elements on acceleration prediction accuracy.
## 2 RS Systems
### Active
#### 2.2.8 Marine Radar  
Marine radars mainly include X-band and S-band radar systems. These systems record the radar backscattering intensity from the ocean surface as a grayscale image [72; 73]. X-/S-band radars are usually installed on offshore platforms, marine vessels, and harbors (nearshore buildings and structures) [74]. Regarding the wavelength, X-band marine radars have smaller antennas, are assembled on boats, and provide better target resolution. In addition to analyzing wave characteristics (e.g., height, length, and period), X-band radar can scan real-time ocean surface at high temporal and spatial resolutions. On the other hand, S-band radar is more useful in harsh weather conditions. S-band radar also provides acceptable accuracy for tracking and identification [75]. Recently, marine radars have also been developed to monitor OOS, support offshore platforms, and study air-sea interaction.
# Green Hydrogen Storage in an Underground Cavern: A Case Study in Salt Diapir of Spain
## 3 Methodology and Results
### Operation  
The well must be completed before the cavities begin to function as hydrogen stores. The production tubing should be placed and the remaining brine removed, then the wells prepared for safe gas injection and production.  
Figure 6: Typical scheme of the process of generation, injection, and extraction of green hydrogen.  
The first hydrogen filling is carried out when the leaching is finished and the caverns have design dimensions that will favor the removal of the residual brine. For this purpose, the production pipe is placed, which replaces the larger diameter leaching pipe, while the smaller diameter pipe remains in the well for brine extraction.  
The ratio of injection and extraction of hydrogen from the cavity is limited by the maximum and minimum pressure, by the maximum pressure change per unit time that allows its stability, and by the maximum flow velocity inside the production tubing, which depends on its diameter [11,26].  
The maximum pressure is reached at the end of hydrogen injection and the minimum pressure occurs when the working hydrogen is extracted, with a portion remaining as a cushion gas to ensure stability. In addition to this function, the base gas provides the pressure necessary to produce the working hydrogen.  
The mass of hydrogen injected will depend on its density, which is a function of the operating pressure and temperature. The injection ratio is conditioned by the pressure ranges to be maintained in the salt cavern, and in no case should the maximum pressure of 158 bar be exceeded. Assuming a constant injection rate during the months of October to June of 0.34 kg/s (30 ton/day) [29], obtained by simulation studies, the amount of hydrogen stored in the cavern can be calculated (Equation (14)).  
In the unloading of the storage during the months of July to September, a lower extraction rate of 0.24 kg/s (21 ton/day) is considered, due to the decrease in the pressure in the cavern, and consequently, to the decrease in the hydrogen density (Equation (15)).  
\[\mathrm{H_{2}injected}=30\frac{\mathrm{ton}}{\mathrm{day}}\times 30\frac{ \mathrm{day}}{\mathrm{month}}\times 8\;\mathrm{month}=7200\;\mathrm{ton} \tag{14}\]  
\[\mathrm{H_{2}produced}=21\frac{\mathrm{ton}}{\mathrm{day}}\times 30\frac{ \mathrm{day}}{\mathrm{month}}\times 4\;\mathrm{month}=2520\;\mathrm{ton} \tag{15}\]  
The percentage of hydrogen recovered, that is, the ratio of hydrogen produced to hydrogen injected, is 35%, providing 84 GWh of energy.
## 1 Introduction
This is currently an important area because of the development of network distribution systems and the need to support them with logistics services. Therefore, we have extended the range of flow mechanisms analysed, while recognising that for omni-channel distribution, it is necessary to take into account, in addition to market, hierarchical, and social forms, a form of logistical network coordination. Considering the adopted assumptions and gaps related to the previous research presented in the literature, we sought answers to three posed questions:  
RQ.1: What role can a logistics operator play in the network coordination of omni-channels? RQ.2: What forms of network coordination can a logistics operator use to ensure reliable omni-channel flows? RQ.3: Which network coordination mechanisms are most powerful in building customer satisfaction in omni-channel systems? Using the AHP method, we investigated how individual network coordination mechanisms, specific to a particular form of coordination, contribute to the reliable realisation of omni-channel flows. At this stage, we drew on the knowledge and experience of experts. The key criteria for selecting experts for the study were knowledge and experience of omni-channel distribution network coordination. Since in the research we were considering the role of logistics operators in the coordination of omni-channel distribution, the experts were the senior managers of such organisations. The layout of the paper follows these objectives: The research started from the theoretical background, in which we identified the concept of network coordination and, against the background of previous research on it and the challenges posed by complex distribution systems such as omni-channels, we presented the construct of logistical coordination. We discussed the issue of omni-channels from the perspective of the logistical service provided by logistics operators. In the Methods section, we presented the research procedure and also justified the selection of experts for the study and the use of the AHP method. In the Results section, the discussion concerning the results of the conducted research is pro vided, which fills a gap in the existing research on both network coordination and logistics service of omni-channels. Central to this stage of the research was the identification of the overarching importance of logistics coordination, which enhances the effectiveness of the logistics operator in ensuring reliable flows in omni-channels, while, among its mechanisms, the emergence of resources management and demand forecasting methods are the main mechanisms that allow the logistics operator to coordinate flows. In the Discussion section, the discussion concerned the results obtained against the background of the existing body of the literature and it also pointed out the limitations of the study and prospects for further research.
# Remote Technologies as Common Practice in Industrial Maintenance: What Do Experts Say?
## 4 Results
### Category System
#### 4.1.4 Category 4--IT Infrastructure  
This category includes all statements from the interviews regarding the IT infrastructure required for remote maintenance. This includes aspects such as the availability of WiFi, bandwidth and stability of the network, equipment and selection of suitable technologies.  
From the core statements of the category, the following can be summarised: In order to be able to carry out remote maintenance successfully, technical framework conditions such as sufficient WiFi coverage throughout the company, a high data bandwidth and network stability are essential. It is also often not possible to use the technologies properly, as the software used is often developed quickly and can contain many errors at the beginning (Interview F, I). The technologies needed for remote maintenance would also have to be available on both communication sides. For emerging technologies, such as data glasses,there is not yet a large market and the acquisition costs for these can be very high. This could create a financial barrier for companies that cannot afford the necessary investments and running costs, such as licences for AR software. In addition, the lack of a standard for software and hardware poses a major challenge for the efficient use of remote solutions. By standardising the software and hardware used, processes would be better monitored and adjustments could be made remotely (Interview I).
## 2 Materials and Methods
### Hybrid Adaptive PID Controller Design
#### 2.3.3 Performance Optimization  
The control system implements comprehensive performance optimization based on the Integral Time Absolute Error (ITAE) index:  
\[\text{ITAE}=\int_{0}^{\infty}t|e|(t)dt \tag{16}\]  
where  
\(t\) is the time variable  
\(e(t)\) represents the control error  
\(\int_{0}^{\infty}\) denotes infinite time integration  
The optimization process considers multiple performance criteria in Table 2:The weighted performance index is expressed as follows:  
\[\text{J}=\alpha_{1}\text{ITAE}+\alpha_{2}\int\text{e}^{2}(\text{t})\text{dt}+ \alpha_{3}\int\text{u}^{2}(\text{t})\text{dt}+\alpha_{4}\int\text{d}^{2}(\text {t})\text{dt} \tag{17}\]  
where  
* u(t) is the control signal
* d(t) represents external disturbances
* \(\alpha_{\text{i}}\) are weighting factors  
This comprehensive optimization approach ensures the following:  
* Minimal settling time.
* Reduced overshoot.
* Improved steady-state accuracy.
* Optimal control effort distribution.
* Enhanced robustness to disturbances.  
The hybrid adaptive PID controller combines classical control theory with modern machine learning techniques to achieve superior performance while maintaining implementation simplicity and robustness.
## 2 Problem Description
### The Construction of the Identification Model  
Parameter identification relies on the development of an identification model that accurately reflects the input-output relationship of the USV. To simplify the analysis, the mathematical model is designed in a linearized form that represents the system's input-output relationship as expressed below:  
\[\mathbf{Y}=\mathbf{H}\mathbf{\theta} \tag{2}\]  
where \(\mathbf{Y}\) is the output matrix; \(\mathbf{H}\) is the input matrix; \(\mathbf{\theta}\) is the parameter matrix.  
Since Equation (1) is a continuous model, it needs to be discretized. The heading angle \(\psi\) is typically easier to measure in actual navigation compared to other parameters. Therefore, the physical quantities in Equation (1) can be differentiated with respect to the heading angle using the forward difference method. The differentiation process is then substituted into the second-order nonlinear Nomoto model to complete the model discretization. Assuming that \(z(t)=\psi(t+1)-\psi(t)\), the expressions could be obtained by rearranging the terms:  
\[\begin{array}{ll}z(t+2)-2z(t+1)+z(t)&=(K\delta(t)h^{3})/T_{1}T_{2}+K\delta_ {r}h^{3}/T_{1}T_{2}+KT_{3}[\delta(t+1)-\delta(t)]h^{2}/T_{1}T_{2}\\ &-z(t)h^{2}/T_{1}T_{2}-(1/T_{1}+1/T_{2})(z(t+1)-z(t))h-\alpha z(t)^{3}/T_{1}T_ {2}\end{array} \tag{3}\]  
where \(h\) is the time interval. To transform Equation (3) into Equation (2), the following matrix could be designed as follows:  
\[\left\{\begin{array}{l}\mathbf{Y}_{o}=[z(t+2)-2z(t+1)+z(t)]\\ \mathbf{H}_{o}=[h^{3}\delta(t),h^{3},h^{2}(\delta(t+1)-\delta(t)),\\ -h^{2}z(t),-h(z(t+1)-z(t)),-z(t)^{3}]\\ \mathbf{\theta}_{o}=[K/T_{1}T_{2},K\delta_{r}/T_{1}T_{2},KT_{3}/T_{1}T_{2},1/T_{1} T_{2},1/T_{1}+1/T_{2},\alpha/T_{1}T_{2}]^{\mathrm{T}}\end{array}\right. \tag{4}\]  
where \(\mathbf{Y}_{o}\) is the output matrix of the USV Nomoto model; \(\mathbf{H}_{o}\) is the input matrix; \(\mathbf{\theta}_{o}\) is the parameter matrix.  
Based on the experimental data, the input matrix and output matrix can be calculated, and then the parameter matrix can be identified. The specific parameter calculation formulas are as follows:  
\[\left\{\begin{array}{l}K=\mathbf{\theta}(1)/\mathbf{\theta}(4),\delta_{r}=\mathbf{ \theta}(2)/\mathbf{\theta}(1),T_{3}=\mathbf{\theta}(3)/\mathbf{\theta}(1),\\ \alpha=\mathbf{\theta}(6)/\mathbf{\theta}(4),T_{1}+T_{2}=\mathbf{\theta}(5)/\mathbf{\theta}(4),T_{1}T_{2}=1/\mathbf{\theta}(4)\end{array}\right. \tag{5}\]