esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
By performing spectrum analysis on the R-wave time domain signals received by the two transducers, the R-waves of the respective frequencies f are separated, and the R-wave velocity \(V_{R}\) of each frequency \(f\) is calculated, thereby the dispersion curve \(V_{R}\)-\(\lambda_{R}\) is obtained [21; 22; 23]. SASW is rarely used to evaluate the concrete-bedrock  
Figure 1: Schematic elastic wave propagation generated by impact excitation [12]. interface condition for hydraulic tunnel linings. Moreover, when the post-processing software of the equipment is used to analyze the phase unwrapping, the unreasonable data cannot be eliminated, and the obvious misjudgment usually results in unsatisfactory test results. In this paper, a numerical study on evaluating the concrete-bedrock interface condition for hydraulic tunnel linings using the SASW method is introduced. An SASW implementation program based on FFT is developed, and all data analyses in this study are done using this program. For signals collected by the sensor, it can be automatically interpreted by the program to obtain the dispersion curve and the interference can also be artificially eliminated to make the result more reasonable. The finite element simulation is carried out to verify the feasibility of SASW to evaluate the concrete-bedrock interface condition. For the first time, the study is able to find out the influences of different receiver spacings and impact duration on the efficiency of detecting the existence of a weak layer underneath the concrete. On this basis, the effects of different contact times and different track spacing values on the detection accuracy are studied. The accuracy of this method is verified by a field test.
## 3 Results and Discussion
### Effect of the Culture Conditions on the Pollutant Elimination
#### 3.2.3 Effect of CBZ Initial Concentration on Its Elimination  
The effect of the initial concentration of CBZ was analyzed using experimental data at two initial CBZ concentrations of 2.5 and 10 mg/L, in both media, starting with both initial inoculum concentrations. Experimental results in Figure 4 showed that the increase of the initial CBZ concentration led to a decrease in the percentage of its elimination in both media. For instance, for the same _A. braunii_ inoculum concentration 10\({}^{5}\) cells/mL, increasing the initial CBZ concentration from 2.5 to 10 mg/L in PP medium induced a decrease in the percentage of elimination from 79.2% to 66.0% after 60 days.  
This increase also forwarded the onset of the elimination of CBZ, and it is worth noting that this corresponded to the relative growth of _A. braunii_ at the time. This resulted in a more rapid elimination but with a lower final yield when starting with 10 mg/L of CBZ than when starting with 2.5 mg/L.  
Figure 3: Effect of inoculum concentration on the removal of CBZ in both media at: (a) 10 mg/L, and (b) 2.5 mg/L of CBZ.  
In fact, this is due, on the one side, to the more rapid consumption of the higher concentration of CBZ (10 mg/L), that turns out to be more toxic on _A. braunii_, leading to forwarded death, and on the other side, to the smaller amount of CBZ needed to be harvested by the same inoculum concentration when the initial CBZ content was 2.5 mg/L, leading to a higher elimination yield and reduced toxicity. Indeed, it must be pointed out that even though the yield decreased, the amount of CBZ removed strongly increased when its initial content was increased by a factor of four. This highlights the dual role of CBZ as a substrate and a toxic compound for microalgae.  
In conclusion, the elimination yield of CBZ is enhanced when the initial CBZ concentration decreases, as this causes a less toxic effect on the algae and hence, and enhanced capability for approaching a \"complete\" CBZ elimination. This is an advantage, as CBZ is usually present as a micropollutant in water, but the drawback is that a lower CBZ content induces slower elimination kinetics. Moreover, the obtained data indicate that _A. braunii_ is able to utilize the target compound either as carbon or nitrogen source, whatever these are made available by simpler substrates in the culture media.
## 2 Methodology and Materials
### ML and DNN Algorithms
#### 2.4.2 Proposed DNN Architecture  
In this section, information was given about how the DNN was designed and which parameters were used. DNN algorithm was employed to figure out how various features such as steel alloy compositions, different temperatures, different times, boriding media and boriding methods affect the boride thickness on steel surfaces and to predict the thickness. It is essential to identify the most effective data preprocessing methods, optimization algorithms, and loss function to enhance the performance of model after determining the optimal DNN structure and learning rate. Due to the nonlinear relationship of the problem, the choice of hyperparameters such as learning rate, number of layers and number of neurons per layer is critical in achieving high prediction accuracy. To prevent overfitting during the training process, an early stopping strategy was employed. Data preprocessing plays a critical role in ML, contributing significantly to the predictive accuracy of models. Four commonly used data preprocessing methods, which are Z-score normalization, Min-Max normalization, robust normalization, and Quantile Transformer normalization, were utilized to determine the most suitable approach to minimize improper convergence and some challenges in this study. Several optimization algorithms were tested, such as Stochastic Gradient Descent (SGD) [74], Adam [75], Adamax [75], RMSprop [76], and Nadam [77]. In this study, the learning rate was chosen as 0.00728. The learning rate was selected based on preliminary experiments to balance optimization stability and convergence speed. A range of values was tested, and 0.00728 was identified as the most suitable, providing a stable training process and optimal performance. Higher values led to instability in the training process, while lower values significantly slowed convergence. All the optimization algorithms were evaluated with a learning rate of 0.00728. This learning rate value was chosen due to the relatively small dataset (375 samples) and the complexity of the regression problem. A small learning rate, e.g., 0.00728, allows finer adjustments during training, helping the model to converge smoothly without exceeding the optimal minima. A DNN architecture was designed and proposed regarding the above-mentioned parameters shown in Figure 2. The proposed DNN architecture consists of a series of hidden layers with the following neuron configuration: 128 \(\times\) 64 \(\times\) 32 \(\times\) 16 \(\times\) 1. This design was chosen to balance model complexity and generalization capability. The designed DNN is made of one input layer with 56 neurons, four hidden layers with 128, 64, 32, and 16 neurons, and an output layer with 1 neuron. This architecture in Figure 2 is a classical fully connected DNN model. ReLU activation functions are used in the hidden layers and linear activation functions are used in the output layer to process the data between the layers and produce output.
## 3 Lds-f-Ofdm System Model
### Filter Design  
The main reason behind the filtering is to eliminate the side lobes for an efficient transmission, it is why filters based on cardinal sin are taken into consideration thanks to their sharp filter response. However, the filter characteristic should be truncated due to its infinite impulse response. As a result, we obtain a finite sinc filter and a window pulse w[n] is applied to smooth its transitions. Once the filter is computed, it should be shifted around the central frequency of the subband.  
\[p[n]=sinc(\frac{W+2\delta_{W}}{N}n) \tag{17}\]  
Figure 3: LDS-F-OFDM block diagram.  
\[p_{B}[n]=\frac{p[n]w[n]}{\sum_{n}|p[n]w[n]|} \tag{18}\] \[f_{TX}[n]=p_{B}[n]e\frac{j2\pi nf_{c}}{N\Delta_{f}} \tag{19}\]  
where \(W\) is the number of active subcarriers, \(\delta_{W}\) is the tone offset, \(N\) is the FFT size, \(f_{c}\) is the frequency of the centre subcarrier in the baseband and \(\Delta_{f}\) is the subcarrier spacing. The window function of the chosen windows are defined in Table 1.  
At the receiver side, a matching filter \(f_{RX}[n]\) to the transmission filter \(f_{TX}[n]\) is performed at the received signal, after that the CP is removed and the signal is transmitted to an OFDM receiver. After the grid demapping and pilot extraction, channel estimation is performed based on the pilots. The output data are then considered as the input of LDS detector explained in Section 2.
## 2 Materials and Methods
### Test Procedure  
The schematic view of the experimental setup is shown in Figure 3. A lamp load unit was built that had 500, 750, 1000, 1250 and 1500 W lamps in order to determine engine performance and emissions under different load states at constant 3000 rpm.  
The technical specifications of SPIN ITALO PLUS exhaust emission device are given in Table 3. The exhaust emission analyzer can measure CO (% vol) with 0.001 sensibility, HC (ppm), NO\({}_{\text{x}}\) (ppm) and opacity (%) values. The exhaust gas analyzer device measurement method is based on laser absorption spectroscopy technology, which determines the gas concentration and temperature from the optical absorption at a specific wavelength. The instantaneous exhaust gas concentration and temperature are obtained by the analysis of laser light falling on the measurement sensor after passing through the exhaust gas. A K-type thermocouple with a temperature indicator was mounted to the exhaust pipe for measurement of the Exhaust Gas Temperature (EGT).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measured Parameter** & **Measuring Method** & **Measuring Range** & **Accuracy** \\ \hline CO (\%, \(v/v\)) & NDIR & 0–9.99 & 0.01 \\ HC (ppm) & NDIR & 0–2500 & 1 \\ NO\({}_{\text{x}}\) (ppm) & CLD & 0–2000 & 1 \\ Opacity (\%) & NDIR & 0–99 & \(\pm\)2 \\ Operating temperature (\({}^{\circ}\)C) & & 5–40 & \\ Operating Voltage (Vdc) & & 12 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Exhaust gas analyzer and opacimeter technical specifications.  
Figure 2: Encoder output signals.  
The digital scales having 1 mg sensitive and a digital chronometer were used to measure both the diesel and LPG fuels flow by weight difference in a constant period. The specific fuel consumption was calculated. The calculated mass fuel consumption is compared to the engine power and the brake specific fuel consumption was found. The Brake Specific Fuel Consumption (BSFC) is estimated in g/(kW h) by using Equation (2).  
\[\text{BSFC}=\frac{\text{mf}_{\text{diesel}}+\text{mf}_{\text{LPG}}}{\text{P}_{ \text{e}}} \tag{2}\]  
The effective efficiency was calculated with Equation (3) by considering the lower heating value and mass flow rate of both fuels diesel and LPG.  
\[\eta=\frac{\text{P}_{\text{e}}}{\left(\text{mf}*\text{LHV}\right)_{\text{ diesel}}+\left(\text{mf}*\text{LHV}\right)_{\text{LPG}}} \tag{3}\]  
Before the data collection, the test engine was run until it reached engine operating temperature of 90 \({}^{\circ}\)C with experimental fuels. Experiments were conducted on stable operation modes by loading with lamp load unit. For each fuel blends (D-100, LPG-30, LPG-50, and LPG-70) used in experiments, engine performance and emissions were measured and recorded according to entire load conditions. Experimental measurements were repeated for three times for each operation point and obtained results were averaged. In the experiments, effective efficiency, EGT, fuel consumption, BSFC, and exhaust emissions (NO\({}_{\text{v}}\), Smoke, HC, CO) were measured.
# Challenges of PBFT-Inspired Consensus for Blockchain and Enhancements over Neo dBFT
## 4 dBFT 2.0
### Effects of a Third Phase  
Figures 7 and 8 shows good operation cases for dBFT 2.0, while Figure 9 demonstrates a commit signature exposure.  
An interesting feature of dBFT 2.0 is its capability to quickly achieve consensus, with lightweight payloads and independent messages. On the other hand, some challenges arise due to the delivery of messages out of order (P.4) and especially adversarial conditions such as large/variable delays (A.2). One may fear that _pending_ consensus messages may cause unexpected effects after a replica is already committed, and for this reason, change view phase enforces that previous payloads are not received anymore (focus is on the new consensus round). Figure 10 presents how NEO is currently handling this using a synchrony condition.  
Figure 8: Good case (faulty non-primary replica 2). Request sent and received by Primary (replica 0). After \(2f+1\) responses/proposal, replica is _prepared_ for block \(\#H\). All except 2 relays block \(\#H\) after commit phase (in blue).  
Figure 9: Request sent by replica 1 and 2/PrepareResponse to replica 3 that becomes _Prepared for \(\#H\)_. Replica 3 exposes its commit signature, so it will deny any view change message. Others may change view (due to message delays) and replica 2 becomes new primary, generating valid (and unique) block \(\#H^{\prime}\) (no spark happens).  
As presented in Figure 10, the synchrony condition \(c^{\prime}+f^{\prime}>f\) is helpful to handle extreme situations, especially when a node is permanently dead. In practice, it would be better to avoid this kind of condition, which is not present on classic PBFT, for example. But why is it needed here and not there? Although quite subtle, adversarial and network conditions that serve as the basis for dBFT are slightly more complex than those for PBFT. One must keep in mind that in PBFT, _requests are independent_, and this is also assumed by many other consensus mechanisms such as Spinning and Redundant BFT [9]. In fact, RBFT and others directly assume some _propagation phase_ that largely reduces these issues (by having requests synchronized between replicas). Unfortunately, this is unfeasible in practice for dBFT, since the _mempool_ is naturally non-deterministic, and replicas are not directly connected between them, but through a decentralized P2P network where things may not work the way you would prefer. Anyway, by fully understanding PBFT mechanisms we may also shed some light on elaborating recovery mechanisms, such as this aforementioned synchrony condition, for dBFT 2.0 and its next generations.
## 3 Experiments and Comparisons
### Performance Comparisons  
We compared the proposed vehicle detection framework using the selected feature set with the conventional vehicle detection frameworks using HOG, LBP, and HOG + LBP features and a CNN detector. We used AdaBoost to train the detectors for each model and included the second-best performing proposed method detector (as shown in Figure 15).  
Figure 16 compares the six models. The proposed vehicle detection framework exhibits the best performance with \(10^{-4}\) FPPW. As the selective multi-stage features extracted from the CNN model contained the characteristic vehicle image information, this reduced the false positive rate. The proposed method was more robust to noise than the handcrafted feature methods, even the CNN detector, and provided computational advantages because it had smaller dimensionality than conventional handcrafted methods, requiring 0.07 s to extract the selective multi-stage features, whereas the time taken to extract the LBP and HOG features was 0.0529 s and 0.0926 s, respectively. Thus, the proposed multi-stage feature set extracted from the CNN was the most suitable for vehicle detection.  
Figure 14: Feature map combinations.  
Figure 15: Feature map combination performances.
# Dynamic Relevance-Weighting-Based Width-Adaptive Auto-Encoder
## 3 Width-Adaptive Autoencoder
### Motivation
After training, a subset of the encoded features was selected and sorted based on their variance across the dataset. Heatmaps were generated to visualize these features, where each row corresponds to a data instance and each column represents a feature. The color intensity in the heatmap indicates the magnitude of the feature values. The aim was to assess the activity and relevance of the encoded features, particularly to observe whether all 5000 nodes were contributing meaningful information. This experiment provides visual evidence that supports the earlier claim regarding the importance of determining the optimal number of hidden nodes in an autoencoder. As shown in the heatmaps (Figure 2), a significant number of features exhibit zero or near-zero variance, indicating that these nodes are either inactive or contribute negligibly to the learned latent representation. This observation suggests that the autoencoder, when configured with 5000 hidden nodes, possesses excessive capacity, resulting in the learning of redundant or overlapping features. Specifically, Figure 2a,b illustrate the high-variance features that actively participate in encoding meaningful patterns, while Figure 2c,d highlight a large portion of nodes that remain unused or underutilized, as evidenced by their zero or near-zero variance. These results have several important implications. First, they empirically validate the hypothesis that overly wide autoencoders do not necessarily enhance performance and instead may waste computational resources. Second, they highlight the need for a principled approach to selecting or adapting the width of the hidden layer to avoid overparameterization. The saturation effect observed, where increasing the number of nodes fails to improve the representation, reinforces the motivation behind our proposed method. Therefore, this visual analysis of quantitative feature variance reinforces the idea that selecting an optimal width is crucial for both effective representation learning and computational efficiency.
## 4 Experimental Results and Discussion
### Estimating Results  
Figure 7 shows the estimation results of PM\({}_{2.5}\) concentration for the test sets of monitoring stations 1001, 1002, 1003, and 1023 using GRU, LR-TCN, and GRU-LR-TCN. The subplots display the zoomed-in estimation results from the 20 th to the 30 th hour for each monitoring station. In Figure 7, the red solid line represents the actual measurements, the yellow dashed line represents the GRU estimation, the black dashed line represents the LR-TCN estimation, and the blue dashed line represents the GRU-LR-TCN estimation. From Figure 7, it can be observed that the GRU-LR-TCN estimation is closer to the actual measurements, indicating better estimation performance. However, GRU, LR-TCN, and GRU-LR-TCN all show inaccuracies in estimating both high and low local PM\({}_{2.5}\) concentrations, which may be due to excessive missing values in the dataset. This suggests that the data preprocessing methods need to be further improved. In the future, machine learning-based missing value imputation techniques can be considered to further enhance data quality. It is also necessary to consider the spatial relationships between monitoring stations and the spatiotemporal characteristics of the dataset to improve the
## 3 Experiments and Results
### Experimental Results  
Table 4 shows the statistics of the matched tie points \(N\), running time \(T\), model error \(RMSE_{M}\), and verification error \(RMSE_{T}\) of the three datasets using the three algorithms. Both the quadratic (\(N_{\text{d}}=2\)) and cubic (\(N_{\text{d}}=3\)) polynomial models were considered in the experiments.
# A Binaural MFCC-CNN Sound Quality Model of High-Speed Train
## 3 Methods
### Mel-Scale Frequency Cepstral Coefficients (MFCC)  
The MFCC is a feature extracted from sound signals and widely used in automatic speech recognition (ASR). The human auditory system works like a set of filters. It responds differently at different frequencies. Humans are much more sensitive to small changes in low frequencies than they are at high frequencies. The Mel scale filter banks simulate the human auditory system by being more discriminative at lower frequencies and less discriminative at higher frequencies. The Mel (\(m\)) scale can be converted from Hertz (\(f\)) using the following equation:  
\[m=2595 log_{10}\bigg{(}1+\frac{f}{700}\bigg{)}. \tag{5}\]  
Each filter in the filter banks is a triangular filter. It has a response of 1 at the center frequency and decreases linearly to 0 when it reaches the center frequencies of the two adjacent filters. The filter banks in this paper's sound quality model contain 26 filter banks. It starts at 0 Hz and ends at 20,000 Hz, as shown in Figure 9.  
The filter bank coefficients are obtained after applying filter banks to the power spectrum of the sound signal. Then, take the log of each of the 26 coefficients. The discrete cosine transform (DCT) is applied to decorrelate the 26 log coefficients and to give 26 Mel frequency cepstral coefficients. Figure 10 shows the steps involved in MFCC feature extraction.
# Smart Meter Gateways: Options for a BSI-Compliant Integration of Energy Management Systems
## 3 Key Aspects of the BSI Technical Guideline with Relevance for Energy Management
### Consumer Interface  
The _Consumer Interface_ is accessible inside the HAN. Its purpose is to give consumers insights into their personal consumption. To access the interface and the data the respective consumer's credentials are required. Aside from the data specific to the tariff use case, the _Consumer Interface_ must allow access to additional information. A complete list can be found in [7], whereby the most relevant items for energy management are:  
* Current meter readings of meters relevant to the consumer
* Measurements of the past 24 h in the same granularity the SMGW registers them from the meters  
In this context, it is not exactly clear to which extent \"current\" readings need to be up-to-date or whether it is sufficient to provide the last readings that have been registered according to the SMGW's configuration. Theoretically, in the case of a bidirectional communication, the SMGW could request the latest meter readings from the respective meters whenever they are requested via the _Consumer Interface_.  
It is important to note that there is no standardized protocol for the communication via the _Consumer Interface_. Hence, for every SMGW a device-specific interface needs to be implemented. A possible solution to this issue is provided by the project \"TRuDI--transparency and display software\" (\"Transparenz- und Display-Software\") [31]. It aims to provide a manufacturer-independent and standardized displaying solution. The source code is available at [31] and includes proprietary modules for communicating with the SMGWs of different manufacturers.