esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Dissolved Helium Concentration  
The dissolved helium was extracted from the water sampling vessels by partial degassing by mechanical agitation, after which the extracted gaseous mixture was directly analyzed on site using  
Figure 11: Evolution of the tracer concentrations at the downstream piezometers: (**a**) PZ3; (**b**) PZ4; (**c**) PZ5. The curves correspond to the fit of the first peak for the dashed curve and of the second peak for the solid curve; the purple curve represents lithium and the green curve uranine.  
an ALCATEL ASM 122D mass spectrometer. The results obtained during baseline measurements at the two reference piezometers, located upstream (PZ1) and far downstream (PZ6), indicate that the groundwater does not contain a significant amount of helium. The measured values of dissolved gas are less than the equilibrium concentration with the surface-atmosphere (helium content about 5 ppm).  
During the experiment, the arrival of the helium plume at the PZ2 BIS piezometer, 5 m downstream of the injection well, occurred very quickly after injection (Figure 12): the maximum concentration of 1.47 mg\(\cdot\)L\({}^{-1}\) was measured 30 min after injection. After this, the helium concentration in water decreases. It has almost returned to its initial state at this piezometer 40 days after injection. In PZ3 and PZ4 piezometers, located respectively 10 m and 20 m downstream, the dissolved helium concentrations were significantly lower as at PZ2 BIS. The arrival of the helium was detected 3 h after the injection at PZ3 piezometer; and a little more than 5 h after at PZ4. At these two piezometers, the maximum helium concentrations were about 3 and 8 ug\(\cdot\)L\({}^{-1}\), respectively, and were recorded 9 days after injection. No significant trace of dissolved helium was measured at the other piezometers located further downstream (PZ5 at 30 m and PZ6 at 60 m).
## 3 Preliminaries and Motivations
### Problems Definition
#### 3.2.1 Long-Path Problem in The Non-Storing Mode  
The goal of the non-storing mode is to save memory space for the resource-constrained devices, whose memory limitation is heavy [18]. However, in this case, LBR takes much burden to generate source routing headers of all nodes in a RPL network. Moreover, the size of a source routing header is not only much longer, but also is variable (depending on the depth of a P2P path). These features bring communication overhead and increase the complexity of the processing logic.  
More severely, all the P2P communications in the non-storing mode must go through LBR. In this case, a P2P path is usually unnecessarily much long, which is defined as the long-path problem in this article. The long-path problem decreases the transmission performance among the whole network. Particularly, in a dense mesh network, this problem becomes more damageable because a mass of transmissions and retransmissions lead to network congestion.
# Accuracy Characterization of a MEMS Accelerometer for Vibration Monitoring in a Rotating Framework
## 2 Materials and Methods
### Wireless Vibration Monitoring System  
The wireless system for vibration monitoring consists of four main sub-systems (Figure 1):  
* **Electric motor**: the electric motor is driven by an inverter to adjust the blade angular speed.
* **Fan blade coupled with seven pairs of PZTs, shaft and interlocking system**: the fan blade is covered with 14 PZT actuators evenly distributed along the pressure side of the blade. An ad hoc interlocking was realized to couple the shaft with the blade. A counterweight was fixed to the interlocking to balance the weight of the blade.
* **Rotating system for electric voltage amplification and distribution**: the waveform generator _Yokogawa FG420_ provides \(V_{PZT}(t)=a\cos(2\pi ft)\) with \(-10\leq a\leq 10\) V and \(1\leq f\leq 100\) kHz. The PZT's driving signal from the waveform generator was magnified by in-house developed amplifiers, which are located within the rotating system for PZT actuation, by means of a slip ring (_SENRING SNG025-06S_). The amplifiers were supplied with batteries and DC-DC boost converters, that allows to obtain up to 300V peak-to-peak. One amplifier is set in inverting configuration, whereas the other is in non-inverting configuration. Thereby, two actual driving voltages \(V(t)\) and \(-V(t)\) can be used to drive the PZT actuators [43]: \[\pm V(t)=\pm\beta V_{PZT}(t)=\pm\beta a\cos(2\pi ft)\] (1) with \(\beta=20\) being the gain of the amplifiers. The PZT's driving voltage distribution was accomplished via mechanical relays handled by _Arduino Uno_. Each relay was electrically coupled to a pair of actuators which occupy the same radial gap on the blade's surface.
* **Wi-fi data acquisition system.** This system is connected directly to the rotating shaft (on board). A battery-powered real-time data monitoring and acquisition system was developed to measure the acceleration exerted by PZT actuators. A MEMS accelerometer (_ADXL356_) was glued on the PS blade's tip, and its signal was acquired by means of a commer  
Figure 1: Functional scheme of the wireless vibration monitoring system. The shaft rotating speed is set with an inverter that drives the electric motor. The PZT’s driving voltage \(V_{PZT}(t)\) is generated by a waveform generator and, by means of a slip ring, is amplified within the rotating system for PZT actuation and data acquisition. The amplification stage provides both \(V(t)\) and \(-V(t)\), which are distributed on each PZT actuator’s pair in order to excite the resonance of the blade. The MEMS accelerometer data were acquired with an on-board data acquisition and storage system that sends data to a remote PC via wi-fi communication. The MEMS accelerometer is battery-powered at 3.6 V.  
cial DAQ hat (_MCC118_). The DAQ is capable of acquiring analog signals (\(\pm\)10 V) with 12-bit resolution, up to 100 kS/s, and it is powered and handled by a Raspberry PI 4. A remote PC was used to control the Raspberry PI 4 and visualize the accelerometer signal in real time, exploiting wi-fi communication.
# Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data
## 4 Results
### Model Evaluation

Table 7: Cent. If we evaluate the accuracy of predictions using the correlation between predicted and actual vehicle numbers, we can say that the created neural network models generate very accurate predictions. The lowest correlation is in the individual models, with 0.73 for trucks_in, and in the comprehensive model, with 0.69 for trucks_in. This lower predictive accuracy of the model is due to the low variability of truck numbers of this type, which range from 0 to 26 vehicles every 15 min. The best-performing models are for predicting cars (cars_in and cars_out) and vans (vans_in and vans_out), where the correlation ranges from 0.92 to 0.93 in individual models and from 0.89 to 0.91 in the comprehensive model. Therefore, predictions generated by these neural network models are considered very accurate, as the dependency between estimated and actual numbers of vehicles of a given type is very strong.
## 3 Test Results and Analysis  
### Test Phenomena and Results  
The six test beams all showed flexural failure of the normal section, and the failure mode is shown in Figure 4.  
Figure 4: _Cont._  
Figure 3: Test setup.  
We take L-1-30 as an example to describe the test phenomenon during the loading process. When loaded to 30 kN, tiny vertical cracks appeared in the tension zone of the test beam in the middle of the span. The width measured by the crack width gauge was about 0.05 mm, and the height was about 41 mm, reaching the position of the BFRP bars. When it was loaded to 75 kN, a through crack appeared at the bottom of the beam. With the continuous increase of the load, the cracks continued to extend upwards, and the width continued to increase. At the same time, the number of through cracks at the bottom was increasing, and similar cracks also appeared in areas far from the middle of the span. When the load reached 340 kN, the local area of the concrete compression zone was crushed, the bearing capacity could not continue to increase, and the specimen was destroyed. The maximum crack width of the beam was about 2.8 mm. The height of the compression failure area was 92 mm, and the mid-span deflection was 43 mm. After unloading, the deformation of the beam loading point and the corresponding position in the middle of the span rebounded by 28 mm, 32 mm, and 24 mm from left to right. The crack development and specimen failure of beam L-1-30 are shown in Figure 5.  
Figure 4: Failure mode of the test beams. (**a**) L-0-0; (**b**) L-0-30; (**c**) L-1/2-30; (**d**) L-1-0; (**e**) L-1-30; (**f**) L-1-45.  
Figure 5: _Cont._  
The concrete mechanical properties (\(f_{cu}\) is compressive strength, and \(f_{t}\) is split tensile strength), cracking load, and ultimate load of each beam are shown in Table 5.
## 4 Results and Discussion
### Parametric Analysis
#### 4.3.1 Effect of Collector Slope  
The inclination angle of the collector has a significant impact on the overall SACS performance. Figure 9 shows the variation in solar coverage with the collector field inclination in Baghdad. The evaluation based on a change in the tilt angle from 5\({}^{\circ}\) to 50\({}^{\circ}\) by a step of 5\({}^{\circ}\) was carried out in order to compute the optimum angle of the solar field that provides the highest solar fraction. The change in this variable shows that the tilt angles (15\({}^{\circ}\), 20\({}^{\circ}\), 25\({}^{\circ}\), 30\({}^{\circ}\)) give a higher solar fraction, contrarily to the last three angles where solar fraction decreases. The reason for this difference is solar radiation perpendicularity that provides optimal results during summer with low tilt angle values, which help capture more solar radiation, as reported by [PERSON] and [PERSON] [(38; 39)]. The optimum tilt values giving higher solar fraction are between 15\({}^{\circ}\) and 25\({}^{\circ}\); therefore, operating at optimum value for tilt angles can readily expand the amount of solar energy incident and, thus, enhance both the thermal and economic efficiency of the SACS.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Number of Collectors**} & \multicolumn{3}{c}{**Solar Tank Volume L**} \\ \cline{2-7}  & **10 (25 m\({}^{2}\))** & **12 (30 m\({}^{2}\))** & **14(35 m\({}^{2}\))** & **1000** & **1500** & **2000** \\ \hline \multicolumn{7}{c}{Solar Fraction\%} & 53.1 & 62.3 & 70.2 & 61.5 & 62.3 & 63.9 \\ \hline \multicolumn{7}{c}{\(\mathrm{Tele}_{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{ \mathrm{ \mathrm{ }}}}}}}}{}}{}}}}}}\)totaltotaltotaltotaltotal\} & 11.2 & 11.5 & 11.9 & 11.6 & 11.9 & 12.1 \\ \hline \multirow{7}{*}{
\begin{tabular}{c} PE \\ save \\ \end{tabular} } & PESave kWh\({}_{\mathrm{PE}}\) & 1361 & 3759 & 5661 & 4669 & 5761 & 6342 \\ \cline{2-7}  & PEref (Primary & & & & \\ Energy References) & 15,469 & 15,545 & 15,666 & 15,628 & 15,666 & 15,306 \\ \cline{1-1}  & kWh\({}_{\mathrm{PE}}\) & & & & & \\ \cline{1-1} \cline{2-7}  & Relative \% & 8.8 & 24.8 & 36.8 & 29.9 & 36.8 & 40.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Primary energy performance for various collector areas and solar tank volume.
## 3 Optimization Method  
The idea of genetic algorithms is based on the evolution processes in the world of living nature [33]. It was first described by [PERSON] [34]. Due to their advantages, genetic algorithms have quickly become an efficient tool in science and technology [35]. The optimization process is based on the following assumptions:  
* Individuals representing different versions of solutions compete with each other;
* The structure of each individual is coded by an entity called a genotype;* A genotype is randomly varied by mutations;
* Across-over operation causes random pairs of individuals to partially exchange their genotypes;
* The quality of each individual can be evaluated as a single value called fit function;
* Fit function determines the probability of transition to the next generation, which corresponds to the selective pressure;
* The combination of random mutation and cross-over with aimed selective pressure leads the whole population towards the optimal solution.  
The main advantage of the genetic algorithms is they are able to find a solution close to the optimal one investigating only a scant part of the solution's space. Additionally, they are able to avoid getting stuck in the local optimum. A specific cross-over operation gives the generally faster convergence in comparison with other methods based on the solutions space exploration.  
The selective pressure means that individuals with a higher value of the fit function are preferred to pass to the next generation. In the presented work, the method of the roulette wheel was used, in which the probability of passing to the next generation is directly proportional to the value of the fit function. Additionally, a few of the best individuals pass unconditionally (elite selection). The size of the elite selection should be relevant to avoid wasting from the population individuals that are poor now but could be improved.  
Additionally, the current leader of each generation is compared with the stored general leader. The better of both becomes a new general leader. This stored solution does not take part in the optimization process, but it contains the result.  
The described issue involved two optimization problems--optimization of stations locations and optimization of lines layout.
## 3 Results
### Experimental Setup  
#### 3.1.1 Experimental Configurations  
In this study, the experimental platform uses an Intel(R) Xeon(R) Gold 6248R CPU (Intel Corporation, Beijing, China) with 72 GB of memory. The GPU is NVIDIA RTX4090 (NVIDIA Corporation, Beijing, China). The operating system is Ubuntu 20.04, PyTorch 2.3.0, Python 3.9, and CUDA version is 11.8. For network training, the learning rate is set to \(1\times 10^{-3}\), batch size is 16, epochs are 500, the optimizer is Adam, and L1 regularization is applied.
# Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning
## 3 Methodology
### Deep Learning Models for Classification
#### 3.3.2 Recurrent Neural Network  
The recurrent neural network is designed to work with sequence prediction problems. Long short-term memory (LSTM) network is a special kind of RNN, which is skilled in learning long-term dependencies to help RNN in remembering long-term information lost during training.  
Figure 7: AlexNet architecture.  
RNNs and LSTMs have received a high success rate when working with sequences of words and paragraphs. This includes both sequences of text and spoken words represented as time series. RNNs are mostly used for text data, speech data, classification prediction problems, regression prediction problems, and generative models. LSTM is a unidirectional network that learns only forward sequence as it can only see past. Whereas BLSTM is used to predict backward and forward sequence: one from past to future and the other from future to past [47]. We have used BLSTM for the training of our dataset and the architecture is shown in Figure 8. The most commonly used in audio sequences for spectral feature extraction for input in RNN networks. We have used the following Algorithm 3.
# Domain Adaptation Speech-to-Text for Low-Resource European Portuguese Using Deep Learning
## 5 Experiments and Results
### Performance Metric  
Word error rate (WER) is a metric for evaluating speech-to-text models and is defined as shown in Equation (1). This metric determines the distance between transcripts by evaluating substitutions, insertions, and deletions made to a single word or word sequence in order for the transcripts to match.  
\[\text{WER}=\frac{\text{Substitutions}+\text{Insertions}+\text{Deletions}}{ \text{Number of Spoken Words}} \tag{1}\]  
WER results range from 0 to 1 (or 0% to 100%), but these results can be higher than the upper bound if the number of additional insertions, substitutions, or deletions required for the transcripts to match is very large. WER was the selected metric for the experiments of the current work.
# Opto-Electronic Oscillators for Micro- and Millimeter Wave Signal Generation
## 3 Recent Development of the OEO's Application  
In this section we present and detail some recent advances in the application of OEOs in different fields as well as some new developments such as integrated OEOs and the wideband tunable OEO.
## 4 Experimental Results
### Comparison of Model Performance  
To thoroughly evaluate the performance of the LSTM forecasting model developed in this study, comparisons were conducted with other commonly used deep learning and artificial intelligence-based models, namely RNN, GRU, and NARX. The impact of weather variables--particularly temperature and wind--on passenger flow prediction was previously examined in detail within the LSTM framework. All comparison models are widely accepted AI algorithms with proven success in various time series forecasting tasks. To ensure a fair comparison, all models were trained using identical input variables and data samples under equivalent conditions. Due to the greater RMSE improvements observed for the Kadikov-Pendik route compared to the broader Asian side, temperature and wind data from one hour prior were included in all models as supplementary temporal features. LSTM, GRU RNN, and NARX models were subsequently analyzed comparatively using this enhanced input structure.  
The results presented in Tables 10 and 11 reveal that the NARX model consistently achieved the lowest MAPE and RMSE values across both datasets, thereby delivering the most accurate forecasting performance. These findings highlight the NARX model as a particularly strong candidate for short-term passenger flow forecasting.
# Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5
## 6 Conclusions and Future Work  
To realize real-time detection of grassland animals using aerial drones, this paper proposes a real-time detection method for grassland animals based on the YOLOv5 network model. In the improved YOLOv5s model, in order to improve the accuracy of object detection, a SENet structure is added. To achieve a lightweight model, the BottleneckCSP module in the Neck layer was replaced with the BottleneckCSPS_X module. To realize the detection of small and medium grassland animal text, the SPP module is optimized and a 3\(\times\)3 maximum pooling layer is added to improve the receptive field of the model. The experimental results show that compared with YOLOv3, EfficientDet-D0, YOLOv4, and YOLOv5s, the improved YOLOv5s network model demonstrated an increase of 0.186, 0.03, 0.007, and 0.011 in the value of mAP_0.5, an increase of 0.216, 0.066, 0.034 and 0.051 in the value of mAP_0.5:0.95, and an average detection speed of 26 fps. At the same time, the scale of the improved model is also small and meets the needs of aerial drones for the real-time detection of grassland animals.  
To address the limitations of the model proposed in this paper, a searchlight could be hung on the drone to facilitate the collection of pictures of wild animals at night. Add the collected pictures to the training set to solve the problem of observing the habits of wild animals at night. At the same time, in practical applications, observing the living habits of wild animals requires tracking and observing the target. The model proposed in this paper can be fused with the model of object tracking. The fused model can get the position information of the target more stably, transmit this information to the UAS, and use coordinate conversion to get the 3D information of the target. According to this information, the target can be tracked easily by using UAV control technology. In order to cope with some dead ends in tracking, the camera angle can be controlled by using a servo, which can greatly improve the stability of tracking. Because the drone is too high, the target proportion is small, so that the target is difficult to detect is also a problem to be solved. In addition, it would also a good research direction to deploy the model proposed in this paper in other embedded devices for application in the field of robotics.  
Conceptualization, Z.L. and W.W.; methodology, Z.L.; software, Z.L.; validation, Z.L., W.W. and A.N.; formal analysis, Z.L.; investigation, Z.L.; resources, S.S.; data curation, Z.L.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L.; visualization, Z.L.; supervision, A.N.; project administration, T.Z. and Q.W.; funding acquisition, W.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.
# Multifrequency Impedance Method Based on Neural Network for Root Canal Length Measurement
## 2 Material and Methods
### Data Augmentation  
The impedance ratio was the software-processed output of the circuit system, as well as the critical data in root canal length measurement. Its expression is as follows:  
\[\textit{Impedance Ratio}=\frac{Z(f_{high})}{Z(f_{low})}, \tag{1}\]  
where \(Z(f_{high})\) and \(Z(f_{low})\) are the impedances with the signal at high and low frequency separately. In the circuit system, the ratio of impedance was calibrated with the impedance obtained by the detection of signals through the root canal. Figure 3 shows the time domain diagram of an input signal and the signal after it had passed through the root canal.  
The signal input to channel A then passed through the measured root canal and was detected in channel B. The frequency of the signal in Figure 3 exhibits a periodic variation with time (i.e., in the time domain). The energy of the signal in the frequency domain after carrying out a fast Fourier transform was uniformly distributed over this particular frequency. Other signals at different frequencies all exhibited the same time and frequency domain characteristics. It was verified that the circuit system could generate sine sweep signals meeting the measurement requirements. The output sine sweep signal was processed to deduce the impedance of the root canal with this signal at a particular frequency.  
The circuit system emitted the sine sweep signals at different frequencies, corresponding to different impedance values at the frequency. Two impedance values were combined together to calculate the impedance ratio, which was used as the deep learning training data.  
The goal of the multifrequency impedance method based on deep learning is to train an advanced neural network; therefore, the data had to be augmented before their substitution into the model. The impedance ratio related to the root canal length is the ratio of impedance with a high and a low frequency signal. The circuit can generate a current of frequencies from 100 Hz to 20 kHz, in which an arbitrary high frequency signal and an arbitrary low frequency signal were combined to obtain the impedance ratio, as shown in Figure 4.  
Figure 3: Time domain diagram of the acquisition signal.  
The multifrequency method utilizes the signals of more frequencies than the traditional dual-frequency method. The impedance ratio data set was augmented by using arbitrary combinations of high and low frequency signals. An example of data augmentation is shown in Figure 5.  
For the two high frequency signals at 20 kHz and 10 kHz and the two low frequency signals at 0.5 kHz and 1 kHz, the number of the impedance ratio can rise from two to four. It is obvious that the number of data can be considerably increased through data augmentation. Moreover, the combinations containing all the signals had an utmost use in the measurements in order to avoid exceptions. The accuracy of the proposed multifrequency impedance method based on deep learning could be improved significantly by training the neural network with the augmented data.