esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.6 Model Validation  
The model was validated with an independent data set corresponding to 20% of the data. The validation data set was used to generate GPP predictions (GPP\({}_{\mathrm{predicted}}\)). The predictions were compared with the flux-derived GPP (GPP\({}_{\mathrm{observation}}\)). The performances of the model were estimated in terms of Mean Absolute Error (MAE) (Equation (7)), Root Mean Squared Error (RMSE) (Equation (8)) and relative standard error of the estimates (Sest) (Equation (9)). The coefficient of determination (R\({}^{2}\)) between predicted and measured GPP was also calculated to evaluate to which extent the model explains the variance of the GPP.  
\[\mathrm{MAE}=\frac{\sum\abs{\mathrm{GPP}_{\mathrm{predicted}}-\mathrm{GPP}_{ \mathrm{observation}}}}{\mathrm{n}} \tag{7}\]  
\[\mathrm{RMSE}=\sqrt{\frac{\sum\left(\mathrm{GPP}_{\mathrm{predicted}}- \mathrm{GPP}_{\mathrm{observation}}\right)^{2}}{\mathrm{n}}} \tag{8}\]  
\[\mathrm{S}_{\mathrm{est}}=\frac{\sqrt{\frac{\sum\left(\mathrm{GPP}_{ \mathrm{predicted}}-\mathrm{GPP}_{\mathrm{observation}}\right)^{2}}{\mathrm{n }-2}}}{\frac{\sum\mathrm{GPP}_{\mathrm{observation}}}{\mathrm{n}}} \tag{9}\]  
where GPP\({}_{\mathrm{observation}}\) is the in situ GPP retrieved from the eddy covariance measurements, GPP\({}_{\mathrm{predicted}}\) is the GPP derived with the empirical formulation, and n is the number of observations in the validation data set.
## 1 Introduction
### Design Methods against Obsolescence
#### 1.3.3 Hal  
Modern test benches use the approach of the hardware abstraction layer (HAL) to mitigate obsolescence. HAL is a software layer what presents a common interface to test bench hardware. The applications access the test bench drivers only through HAL. The actual driver is hidden. When an I/O card becomes obsolete, only the HAL requires a programmatical adaptation, instead of all I/O accessing applications. That minimizes the costs drastically, but the core problem remains unsolved [4]. A proprietary HAL brings additional obsolescence risks when its software interface becomes updated.  
Other approaches to mitigate the I/O obsolescence, include HAL relying on open standards ARINC 653 [6] (36-1) and AUTOSAR specification of the I/O hardware abstraction [14]. They are technically open HAL. So far, they have only been implemented and specified for the control computers themselves. They are insufficient for the implementation of interfaces inside of the test benches. ASAM XIL [15] addresses test bench interfaces directly and is, at present, the only known HAL specification that is open. The above-mentioned problems remain.
## 3 Results and Discussion
### Efficiency Potentials through Lowered Process Temperatures
The extension of existing wastewater treatment plants with AD units for co-digestion of OFMSW and DSS could be an approach worth investigating. With or without prior AD, OFMSW typically provides compost products, mainly for application in agriculture. A major challenge in the utilization of OFMSW remains the removal of impurities, which was conducted manually in this study. Especially, effective removal of plastics is challenging, but a separated plastic fraction could be utilized in alternative treatment plants, e.g., for the production of fuels via pyrolysis [77; 78]. DSS is also used in agriculture, but its utilization as fertilizer is declining or even forbidden due to problematic ingredients such as heavy metals. Incineration plants currently utilize the major portion of the DSS. Therefore, the utilization possibilities or treatment options for digestates consisting of a mixture of OFMSW and DSS is a field of further research. Creating larger amounts of potentially problematic wastes with limited application possibilities should be avoided. The potentials for decreasing operating temperatures in AD are limited due to a decreasing diversity of microorganisms, as described by [PERSON] et al. [55]. The reduction of process temperatures could be an approach to improve the profitability of AD plants. This could also be relevant for energy crops-based AD plants. However, in a real-life and continuous AD, a constant temperature is also necessary. The temperature fluctuations should be lower for reactors at thermophilic temperature (\(\pm\)1 \({}^{\circ}\)C) than in AD at mesophilic conditions (\(\pm\)3 \({}^{\circ}\)C). A comparison of a heated German biogas plant and an unheated American biogas plant can be found in [PERSON] et al. [79].
## 5 Experimental Setup  
### Preparation of the Training Data Set  
The initial and crucial step in this study involved the assembly of a representative image database, mirroring real-world scenarios. This step was imperative for the effective training of the neural network, ensuring its competence in real traffic conditions. Given the system's intended operation amidst actual traffic, we sourced images from authentic environments. Despite the abundance of datasets related to traffic signs and pedestrians, only relevant ones were utilized--specifically, those focusing on traffic signs indicative of priority, given the potentially severe consequences for both drivers and pedestrians.  
To bolster the system's reliability in real-time use, a proprietary dataset was meticulously crafted, aligning the positions and orientations of traffic signs and pedestrians in images with their live camera stream counterparts. We sourced images directly from the same cameras implemented into the system providing a more authentic representation of the system's anticipated operating environment. The main goal was to enhance the system's dependability across diverse conditions encountered in daily life. Such conditions encompass variations in weather, including cloudy/rainy weather or instances during sunrise/sunset, as well as scenarios with partially obscured objects, such as tree branches (see Figure 4). The dataset comprised over 740 images captured by the two cameras, encompassing 400 daytime images and 340 night images, spanning diverse weather and sunlight conditions (see Figure 5).  
We undertook the standardization of the collected images to a consistent size of 1280 \(\times\) 720 and annotated the objects in them using the CVAT (Computer Vision Annotation Tool). The resulting annotations encompassed categories and coordinates of the objects, meticulously stored in XML files. The dataset underwent a systematic partitioning into three distinct subsets: 70% of the images designated for training, 15% for validation, and another 15% for testing purposes. The validation subset served as a crucial gauge for evaluating the model's performance post each epoch, while the testing subset encompassed images that had not been encountered by the learning algorithm during training.  
Figure 4: Partially rotated/unfolded traffic signs.  
Figure 5: Distribution of objects by time of day.
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 2 Main WSN Applications
### Urban Applications
#### 2.6.4 Structural Health Monitoring  
The aim of Structural Health Monitoring (SHM) in buildings and other types of civil engineering substructures, is to monitor their integrity and to detect the existence and the extent of damages in the materials or/and the structure of their bodies. The use of wireless sensors facilitates the accomplishment of tasks of this kind both in a periodic basis and after critical events (e.g., earthquakes).  
Figure 21: A depiction of the various sensors deployed on a car for experimenting [57].  
Figure 20: An illustration of the WSN deployed for an Intelligent Transport system [56].  
A WSN application of this kind is presented in [58]. Specifically, a WSN incorporating nodes with appropriate sensors, such as accelerometers and strain gauges, was developed to monitor the restoration works carried out in an old church building located in Italy, which was damaged after an earthquake. The system monitored the on the fly response of the structure to vibrations and enabled the transmission of alert signals when needed.  
In [59] the design, development, and deployment of a, WSN based, structural health monitoring platform for a stadium located in the USA is described. By using vibration sensing, the system collects real time, data during athletic and other major events to verify the structural behavior of the stadium, in correlation with the behavior of the audience.  
Another structural health monitoring system which makes use of a WSN is presented in [60]. This system is designed, implemented, deployed, and tested to monitor the structural performance of a bridge in China by sensing vibration signals that are produced under various conditions in specific points of interest located at the body of the bridge.
## 3 Numerical Model
### Energy Conservation and Phase Change Modelling  
Energy conservation has been successfully modelled with COMSOL Multiphysics in previous studies by modifying the specific heat capacity of the PCM to simulate the latent heat of fusion (\(L\)) for phase change once the melting temperature (\(T_{m}\)) is reached [35; 36]. The phase change effect is based around a piecewise melt fraction function:  
\[\phi(T)=\left\{\begin{array}{rl}0,&T<T_{m}-\Delta T/2\\ \frac{T-(T_{m}-\Delta T/2)}{\Delta T},&T_{m}-\Delta T/2<T<T_{m}+\Delta T/2\\ 1,&T>T_{m}+\Delta T/2\end{array}\right. \tag{1}\]  
where this piecewise function models the state of the PCM as a solid with a value of 0 and liquid with a value of 1 and varies linearly between 0 and 1 over the mushy zone, defined over an interval \(\Delta T\) around the melting temperature \(T_{m}\). This function was used to define the change in PCM properties from solid to liquid as can be seen in Equation (3). The latent heat of fusion component of the modified specific heat capacity was created by applying a Gaussian function centered about \(T_{m}\)[37].  
\[D(T)=e^{-\frac{(T-T_{m})^{2}}{(\Delta T/4)^{2}}}/\sqrt{\pi(\Delta T/4)^{2}} \tag{2}\]  
The Gaussian function has a value of zero except within the interval of \(T_{m}-\Delta T/2\) to \(T_{m}+\Delta T/2\). The function integrated over the range of all temperatures is equal to 1, which allows \(D(T)\) to be multiplied by \(L\) to produce an energy balance through the phase change, centered about \(T_{m}\). Using \(D(T)\) combined with the melt fraction function \(\phi(T)\) creates the modified specific heat capacity function:  
\[C_{p}(T)=c_{p,s}+\phi(T)\Big{(}c_{p,l}-c_{p,s}\Big{)}+L\cdot D(T) \tag{3}\]  
A similar function (excluding the latent heat of fusion) was created for the thermal conductivity to simulate the gradual change in \(k\) as the PCM transitions between the solid and liquid phase in the mushy zone:  
\[k(T)=k_{s}+\phi(T)(k_{l}-k_{s}) \tag{4}\]  
The density of the PCM was considered constant in the simulation to ensure the conservation of mass and energy, since the volume changes of the PCM were not simulated. The selected density value was the average between the solid and liquid density values: \(\rho\) = 907.5 kg/m\({}^{3}\).
# The Impact Assessment of Water Supply DMA Formation on the Monitoring System Sensitivity
## 4 Conclusions  
The problem of detecting and locating leaks in water supply networks is a very complex and still valid research task. In order to solve it, it is necessary to combine monitoring sensors and specialised software into one integrated diagnostic system. The model tests carried out as part of the paper, using such a system based on the indications of a pressure gauge and a flow meter located at the entrance to the analysed network structures and commercial software for searching for leak locations, showed no clear relationship between the effectiveness of leak detection and the network complexity. This result contributes to the existing research gap in the field of monitoring systems sensitivity and its dependence on the network complexity and accuracy of measurement devices. As in the case of other researchers, these studies allowed us to find a significant improvement in the correctness of indications for this location if the monitoring system was supplemented with an additional network pressure gauge. The further addition of successive network manometers did not result in the expected significant improvement of location indications.  
A major difficulty in the process of locating leaks is the uncertainty of the manometers used. This problem is exacerbated by the constant oscillations of pressure present during normal operation of the real water supply network. The model tests presented in the article showed that, when taking it into account, the detection area of the pressure drop caused by the opening of a hydrant may be very small, which suggests the need to use a large number of pressure sensors or acoustic or vibration sensors to locate leaks. The experiment carried out in real network conditions confirmed the existence of the still unsatisfactory level of leak detection using a flow meter and pressure sensor at the supplying border of the zone. In addition, the increase in the number of these sensors in our study improved the quality of leak location indications. Interestingly, the application of two additional network flowmeters did not improve this quality.  
A promising direction to increase the accuracy of leak indications is to limit the size of the DMAs and the number of supplied consumers in the implemented DMA zones. Another direction is the integration of various types of sensors, including manometers, flow meters, and acoustic and vibration sensors into an integrated leak detection and search system.  
Conceptualisation, D.K. and P.S.; methodology, D.K.; software, P.S.; validation, D.K. and P.S.; formal analysis, D.K.; investigation, P.S.; resources, P.S.; data curation, P.S.; writing--original draft preparation, D.K.; writing--review and editing, D.K. and P.S.; visualisation, D.K. and P.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network
## 1 Introduction
Convolutional neural networks (CNNs) are the most widely used network structures in image processing and long short-term memory (LSTM) networks [41; 42] are widely used in natural language processing (NLP); both are gradually receiving attention from experts in the field of automatic modulation classification. [PERSON] et al. [43] first introduced CNN into the field of automatic modulation classification and proposed an AMC method based on CNN. [PERSON] et al. [44] introduced a network structure combining CNN and LSTM, such as CLDNN [45], with an accuracy of more than 80% for the classification of 11 modulations when the signal-to-noise ratio (SNR) of \(\geq\)0 dB. [PERSON] et al. [46] transformed the two-dimensional convolution in VGG [47] and ResNet [48]; [PERSON] et al. [49] used one-dimensional convolution for extracting time-domain features with an accuracy of over 90% for the classification of 24 modulations when SNR \(\geq\) 10 dB. [PERSON] et al. [50] fully considered the characteristics of electromagnetic signals, treated the in-phase quadrature (IQ) signals as complex signals, and designed a convolutional neural network in the complex form; its accuracy exceeded 80% when SNR \(\geq\) 10 dB in the classification of 11 schemes. Unfortunately, however, the above methods only compare the classification accuracy and do not investigate the parameters, computational efforts, and complexity. Due to the limitations of size and power consumption, the storage space and computational power of lightweight equipment are significantly restricted. Furthermore, deep neural networks have a large number of parameters and high computational complexity, limiting their application in communication systems. To address this challenge, reduce the size of the network model, decrease its complexity, and increase the model inference speed, researchers in the field of image processing [51] have proposed lightweight network structures, such as SqueezeNet [52], MobileNet [53], and ShuffleNet [54] by optimizing the structure of neural networks. In this paper, we propose a lightweight complex-valued residual network (CVResNet)-based AMC method. A hybrid data augmentation method was designed to compensate for the decrease in classification performance resulting from the lightweight design. The main contributions of this paper are summarized as follows. * A lightweight residual network based on complex-valued operations is proposed, named CVResNet, aiming to address the high computational effort and complexity of traditional non-lightweight networks. * A hybrid data augmentation method is designed to compensate for the potential performance degradation caused by the lightweight network. * Comparative experiments are conducted in the same scenario, and the results demonstrate that CVResNet can significantly reduce computational complexity and effort while achieving better classification performance.
# Validating Precise Orbit Determination from Satellite-Borne GPS Data of Haiyang-2D
## 3 Results and Discussion
### Orbit Determination Strategies and Analysis of Accuracy
#### 3.2.3 SLR Range Validation  
SLR range validation is the method of calculating the difference between laser ranging and geometric distance using the coordinates of the GPS satellites and the coordinates of the stations [40]. The ranging accuracy reaches 1 cm, making it one of the most widely used external methods [41]. When POD accuracy is assessed using SLR range validation, ocean tide, solid-earth tide, and polar tide models are used to eliminate the effect of tidal correction, and station velocities provided by ITRF2014 are used to eliminate the effect of plate motion. The tropospheric delay correction, center of mass correction, general relativity correction, and station eccentricity correction are performed on the SLR observations. This article used the coordinates and speeds of stations provided by ILRS in SLRF2014 as a priori values. During the 7 days from DOY 262 to DOY 268 in 2021, a total of 19 stations participated in tracking HY-2D.  
During the experiment, a total of 1407 normal point (NP) data from 19 stations were used to validate the orbits of HY-2D. Due to missing observations from HY-2D at DOY 266, the orbit in this period was calculated by Bernese 5.2 internal interpolation, and the results of POD are not accurate. Therefore, the NP data observed during the missing period  
\begin{table}
\begin{tabular}{c c c c} \hline
**DOY** & **Max (m)** & **Min (m)** & **RMS (m)** \\ \hline
262 & 0.0581 & \(-\)0.0579 & 0.0076 \\
263 & 0.0656 & \(-\)0.0588 & 0.0078 \\
264 & 0.0623 & \(-\)0.0590 & 0.0079 \\
265 & 0.0608 & \(-\)0.0551 & 0.0079 \\
266 & 0.0584 & \(-\)0.0601 & 0.0070 \\
267 & 0.0551 & \(-\)0.0572 & 0.0080 \\
268 & 0.0691 & \(-\)0.0517 & 0.0081 \\ \hline \end{tabular}
\end{table}
Table 4: Statistics of carrier-phase residuals.  
Figure 2: Carrier-phase residuals sequence.  
needed to be deleted, and a total of 105 NP data were deleted from six stations: 7090, 7810, 7840, 7941, 8834, and 7839. The RMS value of the check and the number of NP data for each station were calculated and are tabulated in Figure 3.  
As shown in Figure 3, the 7403 station has the best accuracy of observation with the RMS value of 0.0145 m, but it also has the least number of NP data, with only 7. The worst accurate result of validation is 7941 station, with the RMS value of 0.0578 m and 99 NP data. The 7825 station had the most NP data, with 189, and the RMS value was 0.0521 m. The results of SLR range validation from DOY 262 to DOY 268 in 2021 are summarized in Table 5.  
When summarizing the SLR range validation residuals of 7 days of RD orbits of HY-2D, the RMS value was 0.0495 m. The results showed that the overall accuracy of the RD orbit of HY-2D obtained by orbit determination was better than 0.05 m.
# Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging
## 3 Experimental Results and Discussions
### Hyperparameter Optimization and Model Visualization
#### 3.5.2 Model Visualization  
Figure 11 presents a comparison of model performance across three different configurations, with accuracy visualized by the three 'Model Comps' and mean absolute error (MAE) represented by two 'MAE Comps'.  
Model Comp 1 corresponds to the accuracy achieved after applying regularization techniques, without applying Principal Component Analysis (PCA) to any models. The highest accuracy in this configuration was observed for the Random Forest model (0.9874), suggesting potential overfitting due to its high accuracy on the training data.  
In Model Comp 2, where three hyperparameter optimization techniques (grid search, random search, and Bayesian search) were applied, the accuracy was more balanced across models. CatBoost achieved the highest accuracy at 0.9104, indicating that these optimization methods improved performance while mitigating overfitting.  
Model Comp 3 shows the accuracy obtained using the ensemble voting classifier, which combines the best models from each optimization method. This configuration achieved the highest accuracy, particularly for CatBoost and LightGBM, both of which reached 0.9416, demonstrating strong generalization capabilities.  
The MAE Comp 1 represents the error in the regularization scenario (Model Comp 1), where the Random Forest model, despite its high accuracy, had a relatively high MAE, suggesting poorer generalization. In contrast, the MAE Comp 2 shows the error when using the ensemble voting classifier (Model Comp 3). The lower MAE values, especially for CatBoost and LightGBM (with the lowest MAE at 0.0584), indicate that these models not only achieved higher accuracy but also significantly reduced prediction error.  
In summary, the MAE comparison reveals that while regularization can improve accuracy (Model Comp 1), it may not be sufficient to reduce prediction error. The ensemble voting approach in Model Comp 3, however, not only achieved superior accuracy but also minimized average error, reflecting improved generalization and robustness across models.
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 5 Results and Discussion
### Power and Energy  
To demonstrate the efficiency of our implementation, we measure power and energy for the different DOPs. The power is obtained with the external power meter VOLTCRAFT VC 870. It is measured when the PL is idle as well as during processing of the samples. We report the dynamic power consumption as the difference between idle and processing power. To obtain the energy consumption, we multiply the dynamic power by the average runtime for processing one sample. Figure 9b,c illustrates how the DOP influences the power and energy consumption. In general, the power rises with higher resource utilization, while the energy has fewer deviations because the increasing power consumption is compensated by the lower runtime for high DOPs.  
The results illustrated in this section provide the basis for our parallelism exploration framework. Especially the trade-off between low-latency and low-power can be exploited to satisfy the requirements of the application by separately adjusting DOPs for inference and training module. For instance, minimal DOP corresponds to the low-power objective while maximal DOP corresponds to low-latency.  
All in all, the presented analysis demonstrates the high flexibility and efficiency of our design. This flexibility is shown in terms of resource requirements and regarding the latency, power and energy trade-off. This is highly important for our application as it provides a solution to adapt to the different requirements introduced by the variety of baseband processing systems and it shows that our solution is not only optimized for one specific use-case.