esa-sceva/satcom-chunk-collection huggingface dataset:
# IRONEDGE: Stream Processing Architecture for Edge Applications
## 2 Background and Related Work
### Asynchronous Messaging Middleware  
The term Asynchronous Messaging Middleware (AMM) is here used to describe a set of closely related technologies that share the characteristic of handling the delivery of some data element, a message, in an asynchronous fashion. These include solutions more typically described as messages queues (e.g., RabbitMQ [21]), as Publish/Subscribe solutions (e.g., Apache Pulsar [22]), and as Event stream platforms (Apache Kafka [23; 24; 25; 26]), albeit that these categories overlap depending on the details of each solution.  
Besides supporting Stream Processing solutions, the usage of an AMM also allows communication between other components in the system. These components may also be the IoT devices themselves or be located in other processing nodes. This type of communication offers a number of advantages [27], such as:  
1. Not all components need to be available at the same time. If a component is unavailable, it may consume the message at a later date;
2. They offer a queuing service to handle temporary bursts of messages or may drop messages above a set threshold;
3. Differing delivery guaranties, depending on the implementation details.  
Of particular note is the fact that some AMMs, such as Apache Kafka, can be configured to provide persistent message storage, allowing for their easy integration as the persistence layer of a system. This is publishing a message to transmit it to other components, which is the same operation as storing the message for future consumption. If queries akin to those offered by actual databases are required, systems such as ksqIDB [28], for Apache Kafka, may be used. ksqIDB, in particular, offers an interface equivalent to the Structure Query Language (SQL).
# Underestimation of Dry Matter of Anaerobic Media with High Bcarbonate Concentration
## 4 Conclusions  
The decomposition of bicarbonate is a uniquely thermo-dependent process that manifests itself measurably in dry mass measurements as early as 70 \({}^{\circ}\)C, let alone 105 \({}^{\circ}\)C. The resulting loss of mass during measurement is of the order of 35%, thus leading to an underestimation of the DM and indirect overestimation of the OM measurement in methanogenic reactors. For an anaerobic sample highly loaded with bicarbonate content, the drying at 105 \({}^{\circ}\)C, applying standard protocol, could give rise to a significantly wrongful results of DM and OM contents. This constitutes a major factor that should be considered when realizing mass balance in anaerobic reactors or reporting the specific methane potential of the sample concerned. It is also confirmed that no mass loss due to bicarbonate decomposition takes place at thermophilic conditions of anaerobic digestion.  
These tests show that it makes sense to correct the experimentally obtained DM values, obtained with the standard method, by considering the bicarbonate content, since the other ions do not interfere with the measurement.  
Conceptualization, X.L., A.P., L.A. and T.R.; methodology, X.L., A.P., L.A., J.F. and T.R.; validation, A.P. and T.R.; resources, M.M.-H. and J.-M.G.; writing--original draft preparation, A.P., X.L. and T.R.; writing--review and editing, A.P., L.A., X.L. and T.R.; visualization, T.R. and A.P.; supervision, A.P. and T.R.; project administration, J.-M.G., A.P. and T.R.; funding acquisition, J.-M.G., A.P. and T.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by ANRT (Association Nationale de la Recherche et de la Technologie, France, CIFRE n\({}^{\circ}\) 2017/1298) and Culture France Producteurs Associes for the Ph.D. thesis of [PERSON].  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors thank [PERSON] (UTC) for the TGA/DTA measurements.  
The authors declare no conflict of interest.  
Figure 3: Temperature (blue line) and mass loss (orange line) of 117 mg of solid sodium bicarbonate in the TGA/DTA device.
## 3 Materials and Methods
### Analysis of the Forecasting Methods
#### 3.1.4 Exponential Smoothing Methods  
Exponential smoothing methods are the Simple Exponential Smoothing Method (records only level), Holt's Exponential Smoothing Method (records level and trend), and Holt-Winters Exponential Smoothing Method (records level, trend, and seasonality).  
Figure 4: Example of forecasting using the Simple Moving Average Method.  
Figure 3: Example of forecasting using the Simple Average Method.  
The Simple Exponential Smoothing Method uses historical averages of a variable over a period to attempt to predict its future behavior. The forecast is based on a weighted average of the previous forecast and the latest demand point, but there is no seasonality. This method is very similar to the previous one, except that it is a weighted average of all past points, where more recent points are given more weight (Figure 5). The evaluation criterion RMSE for this model is equal to 32,123.  
[PERSON]'s Exponential Smoothing Method is an extended form of the simple exponential smoothing model. In addition to the level influence, this model also takes into account the influence of trend when forecasting future demand and captures both level and trend. It is possible to capture the trend of the data, but it is not possible to capture seasonality (Figure 6). The evaluation criteria RMSE for this model is equal to 30,289.  
Holt-Winters Exponential Smoothing Method, or Winters model, also known as the Holt-Winters model or the \"trend and seasonally adjusted\" exponential smoothing model, is an extension of [PERSON]'s model that is used in situations where it is desired to apply the influence of seasonality when calculating demand forecasts. It records the level, trend, and seasonality, as per Figure 7. The evaluation criterion RMSE for this model is equal to 19,027, which shows the best fitness of the model (compared to the previously shown models).  
Figure 5: Example of forecasting using the Simple Exponential Smoothing Method.  
Figure 6: Example of forecasting using [PERSON]’s Exponential Smoothing Method.
# Advanced Smartphone-Based Identification of Transport Modes: Resilience under GNSS-Based Attacks
## 3 Deceiving the Ticketing System
### Only-GNSS-Based Transport Mode Identification
#### 3.2.2 Map Matching  
The only-GNSS-based identification of transport modes will be calculated only in the case that the system is not under attack. If the system is not considered under attack, the identification of transport modes is carried out using the out-of-the-box GNSS position and GNSS velocity with the map-matching algorithm explained in Section 3.1.2. The figures of the transport modes identified correspond to Figures 14 and 15.  
the system-under-attack check B is simply based on the long-term incongruities between the outputs of the convolutional neural network and the map matching. By default, the system decides that the GNSS signal has been attacked because this is more likely to happen than that the accelerometer, magnetometer and barometer are all malfunctioning simultaneously. Third, the transport mode identification can also be realized in scenarios where the satellite reception is not possible, such as stations or underground, as shown in Figure 15, using only the convolutional neural network.  
The only-GNSS-based identification of transport modes has the advantage of being computationally cheap, since the information required for the identification of transport modes is only the out-of-the-box GNSS position and GNSS velocity at 1 Hz. This reduces considerably the amount of data that has to be either processed in the smartphone or transmitted to the cloud, compared with the multisensor method. Both the GNSS position and GNSS velocity are always available provided the smartphone has satellite reception. The system-under-attack check B only requires checking for outliers between the GPS time and the GNSS receiver clock of the smartphone, as Figures 17 and 18 show. Likewise, if there is no satellite reception, the attack might not be detected, since outliers between the GPS time and the GNSS receiver clock of the smartphone can also be due to losing the satellite reception, e.g., entering a roofed station or using the subway.  
The system-under-attack check B, based on the GNSS receiver clock, can also be applied to the multisensor-based identification of transport modes as a redundancy to confirm whether the GNSS is under attack.
## 6 Conclusions  
The present study investigated the role of reverberation and magnitude spectra of direct parts in contralateral and ipsilateral ear signals on perceived externalization of virtual sound sources at different azimuth angles in a listening room. Two modifications were performed on the measured BRIRs of each ear, (a) smoothing the magnitude spectra of the direct parts and (b) removing the reverberant part. Such modified BRIRs were convolved with a 1.3 s-long speech signal, and the resulting binaural sounds were used for the listening experiment. The result of the listening experiment revealed the following:  
1. Spectral details in direct parts of the ipsilateral ear signals were more important for perceived externalization than those of the contralateral ear signals.
2. The contribution of the spectral details in direct sounds of the ipsilateral ear signals to perceived externalization increased as the source moved laterally. However, maintaining the correct magnitude spectra in direct sounds only at the ipsilateral ear was not sufficient to externalize a sound image well.
3. The reverberation at the contralateral ear had more influence on perceived externalization than that at the ipsilateral ear.
4. The contribution of the reverberation at the contralateral ear to perceived externalization increased as the source moved laterally. For azimuth angles larger than 30\({}^{\circ}\), the reverberation at the ipsilateral ear did not have noticeable contributions to externalize sound images.  
Further work in our framework is to relate perceived externalization to physical binaural signals and to set up an externalization model to objectively predict the degree of externalization using the ILD-based [17], dynamic binaural cues-based (ILD and IC fluctuation) [14], and the monaural cues-based [40] concepts.  
Conceptualization, S.L.; methodology, S.L.; software, S.L.; validation, S.L.; formal analysis, S.L.; investigation, S.L.; resources, S.L.; data curation, S.L.; writing, original draft preparation, S.L.; writing, review and editing, S.L., R.S., and J.P.; visualization, S.L.; supervision, J.P.; project administration, S.L., R.S., and J.P.; funding acquisition, J.P.  
This work is supported by the Huawei Innovation Research Program FLAGSHIP (HIRP FLAGSHIP) project.  
The authors would like to thank all the subjects who participated in the listening experiment.  
The authors declare no conflict of interest.
# Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review
## 2 Method of PD Detection Based on Different Types of Signals Emitted during PD Activities
### Chemical Method  
Chemical testing procedures, which depend on the analysis of gas and oil specimens extracted from the PD activities, are essential for identifying PD in high-voltage applications. High-performance liquid chromatography (HPLC) and dissolved gas analysis (DGA) are two frequently used methods for measuring chemicals. HPLC is employed to study the by-products of PD, such as glucose in its degenerated forms resulting from insulation breakdown [74; 75]. On the other hand, DGA utilizes differential gas chromatography to analyze the entire volume of gas generated by PD activities [74]. These approaches provide valuable insights into the chemical composition of PD-related substances, aiding in the detection and assessment of PD in high-voltage systems.  
A variety of online testing methods have been developed to detect partial discharge (PD) in power systems. These approaches include oil-immersed sensors with fiber Bragg gratings [76], photoacoustic spectroscopy [77], and oil-immersed sensors [78]. Other than that, photoacoustic spectroscopy with membrane-based methods [79], and hydrogen detection [76]. Figure 4 [80,81] shows a typical gas chromatography setup. In this configuration, the oil specimen undergoes vaporization near the injection port, and the resultant gases--which include some of the lighter gases including argon, the element helium nitrogen, and hydrogen--are then fed into the column. The column separates the gases based on their retention times before they reach the heat detectors [80]. The data collection system records and plots the identified PD signals, generating chromatograms.  
The gas concentration and retention time are then used to determine the identity of the gases. Hydrogen gas detection has become the preferred method due to its high accuracy and has largely replaced other detection methods. If the hydrogen gas level exceeds the safety threshold during overheating and discharges, it indicates a need to diagnose the internal insulations [82]. FBG sensors have been extensively used in various studies to measure the concentration of hydrogen gas within power transformer tanks operating at typical temperatures ranging from 60 \({}^{\circ}\)C to 90 \({}^{\circ}\)C. The remarkable sensitivity of hydrogen gas detection at 80 \({}^{\circ}\)C, combined with its minimal interference from other gases, points towards a promising outlook for this technology [83]. Furthermore, there have been advancements in the development of Pd-capped Mg-Ti thin-film-based hydrogen sensors  
Figure 3: (**a**) IEC 60,270 based on test circuit for measuring self-excited test objects; (**b**) test circuit for measurement at a tapping of a bushing reprinted by [71].  
(improved FBG), which exhibit enhanced sensitivity over a broader temperature range from 10 \({}^{\circ}\)C to 80 \({}^{\circ}\)C and significantly surpass the sensitivity of traditional FBG sensors [84].
## 4 Discussion
### Influence of Feature Extraction Methods on Sweetness Prediction  
The effective features were important for the development of sweetness estimation models. The removal of non-informative features will produce better prediction results with simpler and more stable models. Both SPA and CARS algorithms were implemented for selection of key wavelengths and reduction of the redundant information generated through hyperspectral data. After feature selection, the count of features processed by the SPA and CARS algorithm was 8-20, which was much lower than the full bands. The general distribution trend was similar for two algorithms. The overlapping variables could be observed in the spectral range of 400-756 nm, 900-1250 nm, and 1900-2500 nm, which were corresponding to the absorbance range of the O-H and C-H bonds present in total sugar. Notably, both SPA and CARS algorithms extracted wavelengths in the red and blue regions, demonstrating that kiwifruit exhibits significant absorption of red and blue light during its growth period for photosynthesis to produce sugar. This phenomenon corresponds with the findings of [PERSON] et al. (2022) and [PERSON] et al. (2017) [49; 50]. Furthermore, characteristic bands near 760, 970 [51], 1200 [52], and 1400 nm were identified and significantly contributed to the regression model. The best model for sweetness estimating was suggested by the SPA method. It was because SPA iteratively selected characteristic wavelengths in geometric space considering the representativeness and difference of the samples contributing to more accurate sweetness forecasts.
## 4 Discussion  
### Model Applicability  
AI technologies are widely applied in the field of geotechnical engineering and solve many practical problems. For SMA prediction of EPB shield machine, we chose eight basic AI algorithms for comprehensive analysis and comparison. As shown in Figure 11, the average prediction effect of DL algorithms was better than that of ML algorithms.  
Furthermore, KNN has few parameters to be optimized [13], and the calculation results mainly depend on the calculation distance, which determines that KNN may produce poor prediction results for large-scale data features (Figure 8a). Based on the decision tree algorithm, the RF and AdaBoost algorithms have the same regressors, and their prediction results are obtained by these regressors [33; 38]. Thus, when solving the prediction problem of SMA, the effects of RF and AdaBoost algorithms were better than KNN algorithm and SVR algorithm [29]. As the simplest DL algorithm, BPNN is composed of a multilayer stack of simple modules, and the model with 5-20 hidden neurons can achieve extremely complex functions [40]. However, BPNN structure cannot store historical information. With a depth of network structure increase, gradient explosion or disappearance will occur, which means that it is not suitable for predicting time sequence data. Similarly, CNN predictors based on convolution dimension reduction have good performance in solving image recognition problems [45; 64] but are slightly inadequate in processing time sequence data. The LSTM and GRU, which are good at processing time sequence, had the best prediction result.  
To sum up, various AI algorithms have their own range of adaptation. There is no algorithm that is suitable for all problems [20]. For the SMA prediction problem, the algorithms that are good at capturing time sequence in DL technologies have the best prediction effect.
## 6 Performance Evaluation
### Results by Epidemic, GSaR, TCCB, TC, and PRoPHETv2
#### 6.3.3 Case (c)  
Let \(|M_{dr}|\) be the total amount of messages dropped by nodes due to buffer overflows and TTL expirations, \(|M_{ab}|\) be the total amount of messages aborted in the network, and \(|M_{bt}|\) be the average time duration that messages take in buffers. The higher the value of \(|M_{f}|\) and \(|M_{dr}|\), the more buffer space and resources are used to forward messages toward target nodes. Therefore, higher values of \(|M_{f}|\) and \(|M_{dr}|\) can be considered additional overhead in a network. Table 2 presents the performance comparison between the Epidemic, PRoPHETv2, TCCB, GSaR, MPMF, and TC policies in terms of \(|M_{f}|\), \(|M_{dr}|\), \(|M_{ab}|\), and \(|M_{bt}|\). Note that the \(|M_{bt}|\) denotes the average time duration in seconds. As Table 2 shows, a longer TTL results in higher values in all metrics for all policies. It is apparent that the MPMF results in lower \(|M_{f}|\) and \(|M_{dr}|\) regardless of the TTL as compared with other policies. With the increase in the TTL and node population in TVCM, Epidemic, PRoPHETv2, TC, and GSaR result in higher values of \(|M_{f}|\) and \(|M_{dr}|\). In NCCU, all the policies result in higher values of \(|M_{f}|\) and \(|M_{dr}|\) for longer TTL values. Messages in the MPMF are relayed at a limited number of times. Messages will likely remain in buffers for a longer time; thus, buffer overflow occurs infrequently. Thus, \(|M_{bt}|\) is higher in the MPMF as compared with the other policies. According to the results in Table 2, it is clear that the MPMF policy can result in lower additional overhead in comparison with all other policies.
## 2 Materials and Methods
### Simulation Setup  
The simulation model is shown in Figure 2, where four LEDs and multiple PDs (representing multiple users) were horizontally placed on the roof and ground in a room with a size of 5 m \(\times\) 5 m \(\times\) 3.25 m, respectively. The X-Y coordinate of these four LEDs were \(LED_{1}\) (\(-1.25\) m, \(1.25\) m), \(LED_{2}\) (\(1.25\) m, \(1.25\) m), \(LED_{3}\) (\(-1.25\) m, \(-1.25\) m), and \(LED_{4}\) (\(1.25\) m, \(-1.25\) m), respectively. We set the LED light source as Lambertian radiation and ignored the reflected light. Then, \(51\times 51\) testing points were evenly placed in the receiving plane with a size of 5 m \(\times\) 5 m, with each testing point able receive the signals from all LEDs. We evaluated the system performance under different numbers of users and various lengths of Walsh-Hadamard code. The other simulation parameters are shown in Table 1.  
Figure 2: Diagram of the simulation model for the multi-user VLCP system.
# Optimizing Aircraft Routes in Dynamic Conditions Utilizing Multi-Criteria Parameters
## 5 Discussion  
The analysis of our results shows both common features and differences compared to the above sources. In [14], classical algorithms, such as A* and Dijkstra, are used to optimize aircraft routes for aircraft. Our proposed model for improving the A* algorithm shows a 3% reduction in fuel consumption compared to traditional methods, which provides significant benefits in real flights. At the same time, in study [9], a similar approach also shows effectiveness, but specific figures for fuel consumption optimization are not provided, which limits the possibility of comparison with our results.  
In [19], weather conditions are taken into account to optimize aircraft routes. For example, the influence of wind in our model reduced the flight time for an Airbus A320 neo by 5% (from 7 to 6.65 h), which significantly increased efficiency. Paper [22] also mentions the impact of weather factors, but our study uses more accurate time and fuel consumption indicators, which allows us to assess the efficiency of flights in different weather conditions in more detail. For comparison, ref. [15] also studies route optimization using weather factors, but their approach does not take into account changes in fuel consumption, which is a significant distinguishing feature of our work.  
In addition, papers [21; 23] discuss the peculiarities of using adaptive algorithms to reduce fuel consumption, but our approach, which is based on classical algorithms, shows greater efficiency, which makes it more applicable in real-world conditions. This is also confirmed by [24], where it is shown that the use of traditional algorithms in optimizing flight routes, taking into account weather conditions, has higher stability and accuracy compared to adaptive methods when weather conditions change during the flight.  
The study demonstrates the significant benefits of using classical algorithms to optimize aircraft routes in combination with accurate weather conditions, which can achieve significant reductions in fuel consumption and increase flight efficiency. Compared to other studies, our approach is more specific and can be effectively applied to real-world air transportation.  
Based on the results of our work, we can formulate the following scientific novelty and practical significance of the research results.  
The developed and improved algorithms for routing aircraft flights (A*, B*, D*, and their variations) can be used to optimize aircraft routes of airline companies in order to reduce flight duration and fuel consumption, which will help reduce operating costs and increase the environmental friendliness of aircraft flights. The results obtained can also be used in logistics planning systems to improve the efficiency of computing in variable weather conditions, which will ensure the accuracy of route construction and adaptability to external factors.