esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Fluid Film Lubrication Ratio in a Whole Period  
\(\psi\) is defined as the ratio of the gate-rotor rotating range in which the meshing pair is lubricated by fluid film to the whole period \(\gamma\). Based on the calculation results above, the relations between \(\psi\) and other lubrication parameters are shown in Figure 20. The influences of discharge pressure and relative velocity \(v_{\rm{r}}\) (machine size) are analyzed, respectively.  
In Figure 20a, the 6 m\({}^{3}\cdot\)min\({}^{-1}\) machine is chosen to analyze the impact of the discharge pressure on \(\psi\). For SSLT, \(\psi\) equal to 12.2%, 10.3% and 0 under 0.8 MPa, 1.2 MPa and 1.6 MPa. For SCT, \(\psi\) is always a constant of 0. For MCT, \(\psi\) equal to 100%, 100% and 54.11% under the three discharge pressure levels. It can be easily observed that along with the discharge pressure increases, \(\psi\) decreases for the SSLT and MCT. This indicates that the fluid film lubrication range can be shortened by the increasing pressure and machines with excessive high pressure ratio may not achieve a satisfied life.  
In Figure 20b, the discharge pressure is set to 0.8 MPa to compare the influence of the relative velocity \(v_{\rm{r}}\) on \(\psi\). For SSLT, \(\psi\) equal to 0, 12.22% and 73.51% under 26.3 m\(\cdot\)s\({}^{-1}\), 31.4 m\(\cdot\)s\({}^{-1}\) and 40.6 m\(\cdot\)s\({}^{-1}\). For SCT, \(\psi\) varies from 0 to 83.77%. For MCT, \(\psi\) increases from 84.81% to 100%. In the studied range, \(\psi\) increases with the relative velocity \(v_{\rm{r}}\). This implies that the fluid film lubrication range can be extended by increasing \(v_{\rm{r}}\), which can be achieved by the larger machine size or frequency conversion.
###### Abstract  
Hash-based data structures have been widely used in many applications. An intrinsic problem of hashing is collision, in which two or more elements are hashed to the same value. If a hash table is heavily loaded, more collisions would occur. Elements that could not be stored in a hash table because of the collision cause search failures. Many variant structures have been studied to reduce the number of collisions, but none of the structures completely solves the collision problem. In this paper, we claim that a functional Bloom filter (FBF) provides a lower search failure rate than hash tables, when a hash table is heavily loaded. In other words, a hash table can be replaced with an FBF because the FBF is more effective than hash tables in the search failure rate in storing a large amount of data to a limited size of memory. While hash tables require to store each input key in addition to its return value, a functional Bloom filter stores return values without input keys, because different index combinations according to each input key can be used to identify the input key. In search failure rates, we theoretically compare the FBF with hash-based data structures, such as multi-hash table, cuckoo hash table, and \(d\)-left hash table. We also provide simulation results to prove the validity of our theoretical results. The simulation results show that the search failure rates of hash tables are larger than that of the functional Bloom filter when the load factor is larger than 0.6.  
Bloom filter; hash table; functional Bloom filter; key-value data structure; search failure; load factor +
Footnote †: journal: Applied Sciences
# Edge AI for Real-Time Anomaly Detection in Smart Homes
## 3 Proposed System Architecture
### Overview of the Edge AI-Based Framework  
At the core of this architecture lies a four-layered system, each playing a distinct role in ensuring seamless anomaly detection and response (Figure 1).  
The first layer, IoT Sensor Layer, is responsible for data collection. Various smart home sensors continuously monitor environmental conditions and activities. These include temperature sensors (DHT22 and DS18B20), motion detectors (like PIR sensors and mmWave radar), and energy meters tracking power consumption. The raw data collected from these sensors form the foundation for subsequent anomaly detection.  
Once collected, the sensor data flow into the Edge Processing Layer, where real-time analysis occurs. Instead of sending data to the cloud for processing, this layer runs machine learning-based anomaly detection algorithms directly on the edge device. A key advantage  
Figure 1: System architecture of the Edge AI-based anomaly detection framework. The architecture consists of four layers: an IoT Sensor Layer for data collection, an Edge Processing Layer for real-time anomaly detection, an optional cloud layer for advanced processing, and a user and alerting system for notifications and responses.  
of this approach is the significant reduction in latency, allowing anomalies to be identified instantaneously rather than waiting for cloud-based analysis. More importantly, keeping data local enhances privacy, as no sensitive information is transmitted over the internet.  
While core anomaly detection happens at the edge, the cloud layer is optional. In cases where additional model training or updates are required, the cloud can provide a more computationally intensive environment for refining machine learning models. If an anomaly is detected with low confidence, metadata (rather than raw data) are transmitted to the cloud layer for further validation, reducing bandwidth usage while improving accuracy through cloud-based models. If cloud-based verification is required, the system ensures that only minimal and necessary data are sent to mitigate privacy risks and computational overhead.  
Cloud-based retraining is particularly beneficial when the system detects ambiguous anomalies that edge models cannot accurately classify. By aggregating data patterns from multiple smart homes, the cloud-based models can learn more sophisticated anomaly detection patterns and periodically update edge devices with improved detection capabilities.  
Table 1 summarizes the key functionalities and components of each system layer to provide a structured overview of the proposed Edge AI-based anomaly detection framework. This breakdown highlights how different layers interact to enable efficient, real-time anomaly detection in smart home environments.
## 1 Introduction
Moreover, the traditional fixed-weight approach struggles to fully capture the complex relationships between audio and visual data. To overcome this limitation, we introduce an adaptive attention mechanism, which dynamically adjusts the importance of audio and visual features based on the specific input data. This adaptation enhances the model's ability to selectively process multimodal information, enabling more flexible and intelligent integration of audio-visual data in complex recognition tasks. This paper mainly proposes the following contributions:  
1. We propose an adaptive fusion Transformer network based on a sparse attention mechanism. This network introduces sparse attention during the feature encoding process, effectively reducing excessive focus on unimportant regions. Additionally, the attention weights are dynamically adjusted through adaptive fusion, enabling more-efficient capture and integration of multimodal information while minimizing the negative impact of redundant data on model performance. 2. We introduce an adaptive fusion-based multimodal information integration method. This method allows for dynamic adjustment of the fusion weights between audio and visual modalities. By continuously evaluating the effectiveness of each modality, the model can automatically adjust its attention focus based on the current context, thereby improving recognition accuracy in diverse environmental settings. 3. We conducted experiments on the LRS2 dataset and compared our method with several state-of-the-art approaches. The experimental results demonstrate that our proposed method significantly reduces word error rates across audio-only, visual-only, and audio-visual bimodal recognition tasks.
## 2 Instrument Design
### Additional Sensors
#### 2.2.1 GNSS Reflectometer  
GNSS-R can be used for the retrieval of many geophysical variables such as soil moisture [27] and ocean wind speed [28]. Moreover, GNSS-R may be used combined with L-band radiometry in order to correct errors in temperature brightness measurements induced by the characteristics of the measured surface [10]. For this reason, a GNSS-R device using Global Positioning System (GPS) L1 C/A code has been installed in MERITXELL.  
The antenna of the GNSS reflectometer consists of an array of 5 Left-Hand Circularly Polarized (LHCP) GPS L1-band (1575.42 MHz) ceramic patches (see Figure 2), with a power combiner that assigns different weights to every patch depending on its position in the array. As in L-, S-, and C-band antennas, Barlett tapering has been applied to reduce the side lobes and improve the main beam efficiency.  
The output of the antenna is boosted by means of a tuned amplifier whose output is connected to a SIGE GN3S GPS sampler module [29]. The sampler module is connected by a USB cable to the on-board computer which can be accessed via Ethernet. The on-board computer acts just as a gateway between the internal devices and the host computer. GNSS-R sampled data is finally sent to the host computer for further processing.
## 3 Proposed Fault Diagnosis Method
### CEEMD-Based Signal Processing  
The signal processing is the first step of our proposed method.  
Typically, the vibration signal is composed of multiple narrowband time series. In order to extract the multi-scale intrinsic feature of signals, CEEMD is utilized to adaptively decompose the non-stationary raw signal into several sub-signals.  
Among all the adaptive decomposition algorithms, EMD is the one that is earliest proposed and obtains the most applications in fault diagnosis. However, the EMD has the problem called mode mixing [23]. Although, EEMD, a variant of EMD had been proposed to deal with this problem through the ensemble of original signal with Gaussian noise. Considering all the above issues, CEEMD, a more complementary variant of EMD, is applied in this paper to obtain the better separated modes, which combines the improved noise addition and ensemble strategies [22].  
As described in Section 2 given a raw vibration signal \(\mathbf{x}(t)\), it will be adaptively decomposed into \(S\) sub-signals \(IM_{s},s=1,2,\ldots,S\). Taking a vibration signal under the inner-race fault mode as the example, the 10 sub-signals decomposed by CEEMD is shown in Figure 2.  
As shown in Figure 2, 10 Imfs are sequentially decomposed in order of the frequency using CEEMD. We can see that the first 5 Imfs are approximately stationary, where their means and variances are approximately independent of time. However, as shown in the red dashed boxes in the figure, the last 5 Imfs present relatively more obvious non-stationary characteristics, namely, the means and variances of the series present large differences at different time instants.  
To sum up, multiple narrowband sub-signals are decomposed from the non-stationary vibration signal using CEEMD, which depict the intrinsic characteristic of raw signals from the different scale. However, it should be noted that these components are not strictly stationary, which means that both time and frequency dimensions need to be considered jointly, so as to fully extract effective fault-related information.  
Figure 1: The procedure of the proposed fault diagnosis method.
## 6 Experimental Results  
In order to verify the performance of the proposed strategy further, experimental tests on a 1 kW DFIG system were performed. Figure 12 schematically shows the tested rig. The DFIG is driven by a squirrel-cage induction machine as the wind turbine. Both the GSC and RSC were controlled by a TM320F28335 digital signal processor. Table 3 shows the system parameters. The Chroma 61,704 programmable three-phase voltage source is set up to simulate the unbalanced and harmonic distorted power grid. It was found during the tests that there exhibited the unbalanced, \(5-\) and \(7+\) harmonics in the three-phase voltage source detected being around 3.14%, 4.34% and 2.29%, respectively. During the experiment validation, the rotor speed was set to 800 r/min. In Figures 13-15, the waveforms from top to bottom are (a) three-phase grid voltage \(\mathbf{u}_{\text{gabc}}\) (225 V/div); (b) three-phase rotor current \(\mathbf{i}_{\text{rabc}}\) (4.7 A/div); (c) three-phase stator current \(\mathbf{i}_{\text{sabc}}\) (4.6 A/div); (d) three-phase GSC current \(\mathbf{i}_{\text{gabc}}\) (2.5 A/div); (e) three-phase total current \(\mathbf{i}_{\text{tabc}}\) (12 A/div); (f) system reactive power \(Q_{\text{t}}\) (624 Var/div); (g) DFIG's torque \(T_{\text{e}}\) (8.6 Nm/div) and DC-link voltage \(U_{\text{dc}}\) (667 V/div), respectively.  
Figure 11: Simulation results of the MRSMC with continuous variation of rotor speed.  
Figure 12: Configuration of test rig.  
Figures 13 and 14 show the experimental result of the DFIG wind power system with ISMC and MRSMC, respectively. The current harmonics and torque pulsations are shown in Table 4. As listed in the Table 4, with the control of ISMC, the severely distorted total current would be produced, i.e., 27.7% unbalanced component, 10.56% 5 th and 3.58% 7 th harmonics; the electromagnetic torque pulsations would be 0.27 N\(\cdot\)m. Therefore, it can be found out that the DFIG operation performance would be severely degraded with ISMC under distorted grid voltage condition. Whereas, with the control of MRSMC, the unbalanced, 5 th and 7 th harmonics components of system total current would be reduced to 3.81%, 2.42% and 0.24%, respectively; the electromagnetic torque pulsations would be reduced to 0.096 N\(\cdot\)m. Thus, it can be validated that, with the MRSMC, the suppression of total current harmonics and electromagnetic torque pulsations would be better than that with conventional ISMC, under distorted grid voltage conditions.  
Figure 15 shows the experimental results of the MRSMC strategy with active power command change suddenly. In this fig, the total active power command was changed from \(-0.75\) pu to \(0.0\) pu at t\({}_{1}\). It can be seen, the total currents were sinusoidal and the DFIG torque was stable, even at the moment of total active power command change, and the active power followed the command within \(0.05\) s. It can be validated the satisfactory dynamic of the control system.
# Characterization of the Aroma Profile of Commercial Prosecco Sparkling Wines
## 2 Materials and Methods
### Analysis of Major Volatile Compounds  
For quantification of alcohols, esters, fatty acids, and benzenoids were extracted using SPE and then analyzed by GC-MS as described by [PERSON] et al. (2020) [12]. Before extraction, 50 mL of wine sample was diluted with deionized water (50 mL), and added with 100 \(\upmu\)L of internal standard 2-octanol (4.2 mg/L in ethanol). SPE cartridge BOND ELUT-ENV, (Agilent Technologies, Santa Clara, CA, U.S.A.) was activated by eluting 20 mL of the following solvents, in the following order: dichloromethane, methanol and water. Then the entire sample was loaded by percolating it through the SPE cartridge and then washing with 15 mL of water. A volume of 10 mL of dichloromethane was used to elute volatile compounds. The organic phase was then concentrated to 200 \(\upmu\)L using a nitrogen stream. The sample was then ready for GC injection. The gas chromatograph used was an HP 7890A (Agilent Technologies), while the mass spectrometer was a single quadrupole 5977B analyzer. The capillary column used was a DB-WAX UI (30 m \(\times\) 0.25, with a film thickness of 0.25 \(\upmu\)m, Agilent Technologies), and using helium as carrier gas at a constant flow of 1.2 mL/min. The injection of 2 L of sample extract into the GC-MS system was conducted using an auto sampler Gerstel MPS3 (Mullheim/Ruhr, Germany). The inlet was configured in splitless mode at a temperature of 250 \({}^{\circ}\)C. The GC oven temperature schedule was set at 40 \({}^{\circ}\)C for 3 min, then increased at 4 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C. The final temperature was maintained for 20 min. The mass spectrometer operated with the following parameters: the ionization was in EI mode with a potential of 70 eV; the source and quadrupole temperature parameters were 250 \({}^{\circ}\)C and 150 \({}^{\circ}\)C, respectively; and the analysis was conducted in SIM mode. Samples were analyzed in random order.  
For quantification, a calibration curve was obtained for each analyzed compound. Seven different analyte concentrations were prepared in triplicate in a wine-like solution (12% \(v/v\) ethanol, 3.5 g/L tartaric acid, pH 3.5). Each point was analyzed using the same SPE extraction and GC-MS analysis procedure described above for the wine samples.
# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## 4 Our Proposed Architecture for Smart Solar Plant
### Perceptual Layer
#### 4.1.1 Microcontroller Board  
The choice of microcontroller board at the end is fundamental for sustainable design. The selection of the micro-controller board is based on parameters like computation power, embedded memory, power consumption, and communication capabilities. For power optimization, end devices mainly operate in sleep mode and wake up at user-defined intervals for data sensing and transmission [51]. In Tables 2 and 3, a comprehensive list of popular microcontroller boards for solar plant monitoring is presented, including Uno, Nano, Mega, and NodeMCU. In the majority of the proposed solutions, Raspberry Pi (RPi) has been employed as presented by [3; 4; 6; 7; 16; 20; 32; 37; 41; 43; 49; 50]. However, RPi is not an optimum choice for end devices considering its monetary cost and power requirements. Arduino Nano is the optimum choice in the end because of its low cost, power requirements, and wireless communication support.
# Robust Downlink Mechanism for Industrial Internet of Things Using LoRaWAN Networks
## 4 Results
### Effect of DROB Mechanism in Packet Delivery Ratio
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **DR5 24 h** & **DR3 24 h** & **DROB 24 h** & **DR5 1 h** & **DR3 1 h** & **DROB 1 h** \\ \hline AVG & 85.26\% & 91.07\% & 85.86\% & 69.14\% & 83.71\% & 85.72\% \\ MIN & 71.54\% & 76.85\% & 72.35\% & 36.84\% & 20.00\% & 54.17\% \\ Packets & & 43,806 & 19,387 & 30,913 & 2968 & 1483 & 2704 \\ Delivered & & & & & & \\ Nodes & 9 & 0 & 3 & 129 & 37 & 25 \\ PDR \(<\) 75\% & & & & & & \\ Nodes & & & & & & \\ PDR \(<\) 80\% & 28 & 1 & 25 & 162 & 70 & 48 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Overall network results for DROB and fixed DR configurations. Figure 12: Average PDR per room during the 1 h of high frequency interference (**a**), and during 24 h (**b**). Furthermore, the configuration changes in DR for each node are summarized in Figure 13, where the number of DR increases and decreases (both representing a change in DR) for each node during the 24 h simulation can be seen. Additionally, the figure of DR changes during an interval of 1 h in the simulation that introduced high frequency interference shows that the DROB mechanism reduced DR during that time to adapt to channel conditions, because it is during those periods that the mechanisms triggered DR down operations, while in other periods, if possible, it triggered DR up operations.
## 3 Results  
### Performance of the Univariate Forward Model  
Figure 2A illustrates the TRFs estimated using the detrended cross-correlation function (left panel) and ridge regression (right panel) for a typical Cz channel. The P1-N1-P2 peaks in the TRFs were observed at 75, 120, and 210 ms, respectively. The Pearson correlation coefficient between the TRFs of the two methods is 0.941, indicating a high level of similarity between the results obtained from the detrended cross-correlation function and ridge regression. Figure 2B shows an example of EEG prediction at Cz and the prediction accuracy of the 64 channels in the topology format for both approaches. Both the detrended cross-correlation model and the ridge regression model performed well in the central and temporal areas, with Pearson's r values of 0.308 and 0.300, respectively, for the Cz channel (Figure 2C).  
Figure 2: Performance of the univariate model computed using the detrended cross-correlation function and ridge regression. (**A**) The temporal response function (TRF) estimated using the detrended cross-correlation and ridge regression at the Cz channel. The solid line represents the grand-averaged TRF across subjects (n = 20). The shaded area represents the confidence band (95%) computed by the standard deviation of 20 subject models’ weights. Pearson correlation between the TRF of the detrended cross-correlation and ridge regression is denoted in the middle. (**B**) The example of predicted EEG at the Cz channel. The Pearson correlation between the model’s prediction and EEG signal is denoted at the top of each figure. (**C**) The EEG prediction performance on a test set across the 64 channels. a.u. denotes amplitude unit. D, R, P, and O in the subscript denote detrended cross-correlation function, ridge regression, predicted EEG, and original EEG, respectively.
# Flexibility Based Day-Ahead Generation-Reserve Bilevel Decision Model
## 1 Introduction
However, flexibility assessments in these studies are based on the given system operating reserve, and there are few researches about reserve decision based on flexibility requirements. This paper proposes a day-ahead generation-reserve bilevel decision model. Based on historical data and forecast information, the proposed model establishes the quantitative relationship between reserve and flexibility, and ensures system flexibility to meet the requirements while realizing conventional unit output scheduling. In the upper level, the unit commitment (UC) model is solved on the basis of predicted load and wind power output, and what is different is the relationship between reserve and flexibility established by the lower level is taken as a constraint. In the lower level, the ramping state of load and wind power is formulated based on historical data and forecast information. As well as, the conventional unit is modeled by forced outage rate and planned output. Then, the ramping probability distribution of the equivalent system is obtained by universal generating function algorithm. In this way, the relationship between reserve and flexibility is established. Then, the UC scheme is verified by the flexibility requirement, and if it could not that provide enough reserve capacity, the UC model of upper level is corrected and re-solved. The remainder of the paper is arranged as follows. Day-ahead unit commitment model considering flexibility constraint of the upper level is introduced in Section 2. The flexibility based reserve decision method of lower level is developed in Section 3. Section 4 presents case studies, followed by the conclusion in Section 5.