esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results and Analysis
### Experimental Design  
In this paper, six groups of experiments are designed, from which the average of the results of one hundred experiments is taken as the final results for the model with added differential privacy due to the randomness of the added noise, and the six groups of experiments are as follows:  
1. In order to reduce the influence of data magnitude on the model impact, the data are subjected to Z-score standardization in this paper.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Hyperparameter** & **Meaning** & **Value** \\ \hline \multirow{2}{*}{RF} & n\_estimators & Number of weak classifiers & 200 \\  & oob\_score & Whether to use out-of-bag samples & TRUE \\ \hline \multirow{2}{*}{LR} & penalty & Penalty term & L2 \\  & solver & Optimization algorithm & liblinear \\  & C & The inverse of the regularized intensity & 1 \\ \hline \multirow{2}{*}{GDP-EBM} & min\_samples\_leaf & Minimum number of samples at leaf nodes & 2 \\  & learning\_rate & Learning rate & 0.03 \\ \hline \multirow{2}{*}{DP-NB} & var\_smoothing & Smoothing parameter & 0.00000001 \\ \cline{2-4}  & max\_depth & Maximum number of layers generated & 9 \\ \hline \multirow{2}{*}{DP-RF} & max\_depth & Maximum number of layers generated & 10 \\ \cline{2-4}  & n\_estimators & Number of weak classifiers & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal hyperparameters of the model.  
In order to eliminate weakly correlated variables and redundant features from breast cancer data, this paper includes hybrid feature selection on the data.
3. In order to test the optimization effect of the BGD model on the LR algorithm, the loss function graph of the BGD-LR is analyzed in this paper.
4. In order to testify to the impact of hybrid feature selection algorithms on model performance, we set up a control group experiment and analyze the results from the four main evaluation indicators: accuracy, precision, recall, and F1-score.
5. The experimental results of this paper are compared with those of other papers. The breast cancer classification model proposed in this paper is compared with existing research results without considering privacy protection.
6. The prediction results of the BDP-LR model in this paper are compared with other machine learning models based on differential privacy when privacy protection is considered.
###### Abstract  
We consider a dynamic lightpath provisioning problem in translucent spectrally-spatially flexible optical networks (SS-FONs) in which flexible signal regeneration is achieved with transponders operating in back-to-back (B2B) configurations. In the analyzed scenario, an important aspect that has a significant impact on the network performance is the decision on placement of transponders that can be used for two purposes: transmitting/receiving (add/drop) of optical signals at the source/destination nodes and regeneration of the signals at some intermediate nodes. We propose a new algorithm called scaled average used regenerators (SAUR). The key idea of the SAUR method is based on a data analytics approach, i.e., the algorithm exploits information on network traffic characteristics and the applied dynamic routing algorithm to obtain additional knowledge for the decision on transponder placement. The numerical results obtained for two representative topologies highlight that the proposed SAUR method outperforms reference algorithms in terms of the amount of traffic that can be accepted in the network. In other words, placement of transponders yielded by the SAUR method allows to increase the SS-FON throughput using only the existing resources, i.e., the network operator does not have to invest in new devices or fibers.  
dynamic routing; elastic optical network; lightpath regeneration; network optimization; space division multiplexing; transponder predeployment; translucent networks +
Footnote †: journal: Applied Physics Letters
## 5 Automatic Signal Recognition
### Validation  
Additional measurements are collected at three locations, in the early afternoon of two consecutive days. The target signals are identical as before, namely the Wi-Fi office network at 5540 MHz, the LTE downlink signal at 806 MHz and the DVB-T signal at 482 MHz. All measurements are conducted in an office building of 12 \(\times\) 80 m. To increase the diversity of signal strength, the measurement locations are placed on the north, east and west side of the building, respectively. Each day, 10 traces are collected per location and per technology. A trace contains \(1\times 10^{6}\) IQ samples, obtained by USRP for a duration of 1 s, at the ADC sample rate of 1 MHz. Note that 1 MHz falls within the bandwidth capability of common small-scale wireless devices, such as ZigBee (2 MHz) and Bluetooth (1 MHz). In total, 180 traces are collected, consisting of 60 traces per technology.  
RSSI is derived according to Equation (1). First, the performance for \(N=20\), during the observation time of one second, is analyzed. Then, we extend the evaluation for \(N\in[20,320]\), incremented at the step of 20. In our solution, large \(N\) corresponds to a longer average interval, hence lower update frequency of the RSSI measurements. The analysis for variable \(N\) is important, as developers on constrained devices usually do not have direct access to the raw IQ samples, so RSSI is provided by accessing a register or other types of interface towards hardware modules, which have a limited access rate. For instance, the TelosB sensor platform allows the RSSI register to be read at the rate of 32.15 kHz after customization [29], while the solution in [30] only refreshes RSSI at 11.3 kHz. Note that this is the rate at which the RSSI register can be accessed, it is not the sample rate used by the ADC on the RF front-end. Therefore, how often RSSI needs to be updated has a strong impact on the feasibility of implementing the solution on constrained devices. Additionally, we also evaluate the impact of the total observation time--the time interval during which the RSSI series are derived, denoted as \(T\)--on the detection accuracy. This is helpful to explore the minimum waiting time needed for the algorithm to achieve a given recognition accuracy.  
We use three-fold cross-validation to evaluate the performance for all of the chosen \(N\) and \(T\) settings. In each round of the validation, the RSSI traces of a given technology are divided into two parts: 70% of the data are used as training data to derive thresholds used in the algorithm, while the remaining 30% are used to validate if the signal is correctly classified. This process is repeated three times; each time, different portions of the traces are used for training and validation, respectively. For each technology, the collected traces are numbered chronologically from 1 to 60, and the ranges of testing data on the three validation rounds are [1; 18; 36; 19; 37; 54].
# Design and Testing of an Intelligent Control System for Maize Picking Harvest
## 1 Introduction
Most control models have been developed empirically to accommodate the nonlinear characteristics of agricultural machinery and lengthy time delays of complex systems. [PERSON] et al. established non-linear prediction models for both the material other than grain(MOG)content in the grain bin and sieve losses on a combine harvester, and used these two models to develop an intelligent controller for the cleaning part to reduce the cleaning loss [1; 18; 19]. [PERSON], et al. analyzed the influencing factors of the feeding amount and harvest loss based on experiment data, and found that a quadratic function model can be used as an effective tool to predict the loss during the harvesting process [20]. In the present study, an intelligent control system was designed for the maize picking process to deal with the above issues and lack of solutions in the literature. The experience of experts and experimental data were used to obtain a data-based prediction model. The model was used to predict the maize picking loss rate, and the working parameters were optimized to minimize the maize picking losses. The proposed system has two modes: manual control and automatic control. The double closed-loop control strategy was adopted for automatic control of the maize picking loss to realize the online and real-time adjustment of the working parts. In the faster loop, the fuzzy PID control algorithm is used to adjust the rotational speed of the pulling rollers and the operating speed to the target values. In the slower loop, the PID method is used to adjust the header height to the target value until the feedback (i.e., maize picking loss rate) meets harvest standards. The proposed system is a significant contribution to realizing automatic control for maize picking harvest.
# Intelligent Fusion Structure for Wi-Fi/BLE/QR/MEMS Sensor-Based Indoor Localization
###### Abstract  
Due to the complexity of urban environments, localizing pedestrians indoors using mobile terminals is an urgent task in many emerging areas. Multi-source fusion-based localization is considered to be an effective way to provide location-based services in large-scale indoor areas. This paper presents an intelligent 3D indoor localization framework that uses the integration of Wi-Fi, Bluetooth Low Energy (BLE), quick response (QR) code, and micro-electro-mechanical system sensors (the 3D-WBQM framework). An enhanced inertial odometry was developed for accurate pedestrian localization and trajectory optimization in indoor spaces containing magnetic interference and external acceleration, and Wi-Fi fine time Measurement stations, BLE nodes, and QR codes were applied for landmark detection to provide an absolute reference for trajectory optimization and crowdsourced navigation database construction. In addition, the robust unscented Kalman filter (RUKF) was applied as a generic integrated model to combine the estimated location results from inertial odometry, BLE, QR, Wi-Fi FTM, and the crowdsourced Wi-Fi fingerprinting for large-scale indoor positioning. The experimental results indicated that the proposed 3D-WBQM framework was verified to realize autonomous and accurate positioning performance in large-scale indoor areas using different location sources, and meter-level positioning accuracy can be acquired in Wi-Fi FTM supported areas.  
indoor localization; inertial odometry; Wi-Fi fine time measurement; MEMS sensors; robust unscented Kalman filter
## 3 Analysis and Results
### Measurement Precision  
The accuracy of the automatic machine is considered as given by the technical specifications of the manufacturer's axis [33; 34; 35]. These data specifications are summarized in Table 8.  
Regarding the manual measurement, the average precision depends on the characteristics of the operator, namely the qualification, experience, and skill. Then, a typical number is hard to identify, but on average, a highly qualified operator can reach a precision of about 5 mm (common to all directions--axis).
## 5 Discussion and Future Research Directions
### Signal Processing and Data Preparation  
Signal processing and data preparation are critical steps in predictive maintenance, ensuring that raw data are transformed into meaningful insights. Frequency and time-frequency transformations, such as FFT, STFT, DWT, CWT, WPT, and EMD, enable the extraction of spectral and temporal features essential for fault detection and diagnosis. Feature extraction techniques include basic statistics, envelope methods, and advanced dimensionality reduction, while feature fusion combines multiple data sources for robust analysis. Data cleaning processes, such as interpolation, normalization, resampling, and temporal alignment, enhance data quality and consistency. Expert knowledge integration through knowledge-based rules or synthetic signal generation (data augmentation) also enriches datasets for improved model performance (see Figure 11).
## 2 Materials and Methods
### Synthesis of ligands
#### 2.2.5 2-(2,3,4-Tri-O-benzyl-6-acetyl-\(\alpha\)-D-glucopyrananoyll)-1-azidoethane **11**  
Alcohol **10** (2598 mg, 5 mmol) was dissolved in dry THF under argon. Ph\({}_{3}\)P (15 mmol) was added and the solution cooled to 0 \({}^{\circ}\)C. Disopropyl azodicarboxylate (DIAD, 15 mmol) and, after 10 min, diphenylphosphorl azide ((PhO)\({}_{2}\)PON\({}_{3}\), 16 mmol) were added dropwise; the reaction was then raised and stirred at room temperature. The disappearance of the starting compound was followed by TLC (Hexane/EtOAc 7:3); after 2 h, the crude was concentrated and purified by flash chromatography (silica gel, eluent: Hexane/EtOAc 9:1). A total of 2446 mg (4,48 mmol, 90% yield) of azide **11** was obtained.  
\({}^{1}\)H NMR (400 MHz, CDCl\({}_{3}\)) \(\delta\) 7.38-7.26 (m, 15H, CH Ar), 4.94 (d, J = 10.9 Hz, 1H, CH\({}_{2}\)Ph), 4.86 (d, J = 10.8 Hz, 1H, CH\({}_{2}\)Ph), 4.80 (d, J = 10.8 Hz, 1H, CH\({}_{2}\)Ph), 4.71 (d, J = 11.6 Hz, 1H, CH\({}_{2}\)Ph), 4.62 (d, J = 11.6 Hz, 1H, CH\({}_{2}\)Ph), 4.56 (d, J = 10.9 Hz, 1H, CH\({}_{2}\)Ph), 4.23 (d, J = 3.4 Hz, 2H, H6a,b), 4.16-4.07 (m, 1H, H1), 3.82-3.70 (m, 2H, H2, H3), 3.68-3.60 (m, 1H, H5), 3.50-3.27 (m, 3H, H4, H2\({}^{\prime}\)a,b), 2.05 (s, 3H, CH\({}_{3}\)CO) and 1.96 (dd, J = 13.7, 6.5 Hz, 2H, H1\({}^{\prime}\)a,b).  
\({}^{13}\)C NMR (101 MHz, CDCl\({}_{3}\)) \(\delta\) 170.90 (CH\({}_{3}\)CO), 138.45, 137.95 and 137.76 (Cq Ar), 128.66, 128.61, 128.23, 128.13, 128.06 and 127.92 (Cr), 82.07 (C3), 79.48 (C2), 77.71 (C4), 75.60 (CH\({}_{2}\)Ph), 75.17 (CH\({}_{2}\)Ph), 73.38 (CH\({}_{2}\)Ph), 71.34 (C1), 70.19 (C5), 63.64 (C6), 47.96 (C2\({}^{\prime}\)), 24.64 (C1\({}^{\prime}\)) and 21.93 (CH\({}_{3}\)CO).  
MS: \(m/z\) 546.5 = [M+H]\({}^{+}\).
## 3 Results and Discussion
### Dynamic Vapour Sorption Analysis  
Dynamic vapour sorption (DVS) analysis was conducted to compare the moisture sorption behaviour of the PBAT composites with varying aluminum sludge contents to that of neat PBAT. All samples were initially de-moisturised at 0% RH before being exposed to RH levels of 65%, 75%, and 85% at room temperature. The % mass change as a function of time is shown in Figure 7.  
At all RH levels, the composites exhibit distinct sorption kinetics compared to neat PBAT. During the initial uptake stage, the composites demonstrate significantly faster sorption rates, as evidenced by the steeper slopes of the sorption curves. These increased rates are attributed to the hydrophilic nature of alum sludge, which enhances moisture diffusion pathways within the composite. The hydroxyl-rich surfaces and porous structure of alum  
Figure 6: XRD patterns of neat PBAT, alum sludge, and alum sludge/PBAT composites containing 5 wt.%, 10 wt.%, and 20 wt.% alum sludge.  
Figure 7: Dynamic vapour sorption curves of alum sludge PBAT composites with 0 wt.%, 5 wt.%, 10 wt.%, and 20 wt.% weight percent at (**a**) 65% RH, (**b**) 75% RH, and (**c**) 85% RH at room temperature.  
sludge readily interact with water molecules, facilitating rapid sorption, as highlighted by [PERSON] et al. and [PERSON] et al. [39,40].  
However, no clear correlation was observed between alum sludge concentration and sorption rate across the RH levels. At 65% RH, the slopes for the 5 wt.%, 10 wt.%, and 20 wt.% composites are comparable, with the 10 wt.% sample exhibiting slightly higher sorption after approximately 1 min. At 75% and 85% RH, the 5 wt.% composite demonstrates the fastest sorption rate, suggesting that higher alum sludge concentrations may lead to particle aggregation, obstructing effective moisture diffusion pathways. All samples approach saturation within 6 min at 65% RH and within 3 min at 75% and 85% RH.  
In terms of maximum moisture gain (MMG), all composite samples absorb more moisture than the neat PBAT, indicating a reduction in hydrophobicity due to the addition of alum sludge. Alum sludge is assumed to enhance water adsorption in composites through its hydroxyl-rich surface and porous structure, suggesting a proportional relationship between alum sludge content and MMG in alum sludge composite materials. However, the results revealed that the 5 wt.% composite achieved the highest MMG, followed by the 20 wt.% and 10 wt.% composites. This disparity implies that 5 wt.% alum sludge enables optimal particle dispersion within the polymer matrix, whereas higher concentrations promote aggregation, reducing the effective surface area for water interaction.
# A Comparison of Households' Energy Balance in Residential Smart Grid Pilots in the Netherlands
## 3 Results
### Monthly Electricity Consumption  
Figure 1 shows that PV electricity generation is high during summer months for all pilots. For the month of July, the PV generation even matches the total consumption in the JEM-Meulenspie pilot. PV generation is the least for Zonnedael, as only a few households were equipped with PV. Next to the PV system size, the location and the year play a significant role in the electricity produced by PV systems. PMC PV output indicates that the month of May 2012 was relatively sunny. The JEM-Meulenspie PV output shows that, for 2013, the highest solar energy production occurred in July. This result is in line with the weather station data. May 2012 was the month with the highest solar irradiation measured in the weather station that is closest to PMC, i.e., Eelde, with 15.5% of the total irradiation of that year (on a horizontal plane) [51]. An additional effect is the somewhat lower ambient temperatures in May, which resulted in fewer losses compared to higher temperatures. In 2013, July was by far the month with the highest solar irradiation in the weather station closest to Meulenspie, i.e., Gilze-Rijen, with 16.6% of the total irradiation in that year [51].  
The consumption patterns are relatively constant for Zonnedael as well as JEM-Easy Street. As mostly students volunteered to participate in the pilot, the lowest consumption occurred at JEM Easy Street. This is clearly related to household size.  
Seasonal variations are observed in both JEM-Meulenspie and PMC. In those two pilots, large electricity consumption differences are observed between the winter and summer months due to the heat pumps, which demanded higher amounts of imported electricity during cold periods. The electrical heating resulted in evident variation of consumption patterns. Therefore, for the predefined KPIs, we  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Connection**} & **Household** & **Appliance** & \multirow{2}{*}{**Availability**} & \multirow{2}{*}{**Application**} \\  & **Limit (kW)** & **Limit (kW)** & & \\ \hline
**1 × 25 A** & 5.75 & 3.68 & No longer applied & \\ \hline
**1 × 35 A** & 8.05 & 7.36 & No longer applied & \\ \hline
**1 × 40 A** & 9.2 & 7.36 & Only for small houses & Standard household appliances \\ \hline
**3 × 25 A** & 17.25 & 11 & \
\begin{tabular}{c} Standard house connection \\ for new connections \\ \end{tabular} &
\begin{tabular}{c} Plus PV panels and \\ EVs \\ \end{tabular} \\ \hline
**3 × 35 A** & 24 & 11 & Large connections &
\begin{tabular}{c} Plus PV panels, EVs, \\ and heat pumps \\ \end{tabular} \\ \hline \hline \end{tabular}  
* Considering security plugs of 16 A, which is the standard for the Netherlands [50].  
\end{table}
Table 2: Residential connections and their power limits [49].  
have analyzed the demand patterns by separating the groups with conventional heating systems from those with hybrid/electric heating systems. Pilots that contain heat pumps are treated separately from those that did not because of their distinct results in power demand.
## 3 Analysis
### Spherical Mode Decomposition
The interelement spacing of the proposed antenna is adjusted in the way of increasing the distance between two ports, as shown in Figure 13. Then, we calculate ECCs by S-parameters and radiation efficiencies applying Equation (9) introduced in [44] as follows:  
\[\rho_{ECC}\ =\ -\frac{S_{11}S_{12}^{*}+S_{21}S_{22}^{*}}{\sqrt{\left(1-|S_{11} |^{2}-|S_{21}|^{2}\right)\left(1-|S_{22}|^{2}-|S_{12}|^{2}\right)\eta_{1}\eta_ {2}}}, \tag{9}\]  
where \(S_{ij}\) (\(i\) = 1, 2 and \(j\) = 1, 2) is the complex S-parameter for each array port, and \(\eta_{k}\) (\(k\) = 1, 2) is the radiation efficiency of the observing antenna. Figure 14 illustrates the ECCs depending on the interelement spacing in wavelength for the three types of the two-element arrays. The blue, red, and green lines with circular markers designate the proposed, monopole, and patch arrays, respectively. We obtain the ECC values by simply increasing the interelement spacing of \(d\) between the two ports of the proposed array. In particular, when the distance becomes larger than half the size of the patch element, the two ports are separated by some distance and share a common ground. The proposed array can maintain the low ECC levels below 16% despite decreasing the port spacing between the elements. However, the patch and monopole array antennas require minimum separation distances  
Figure 12: The even mode coefficient \(a_{n0}\) according to the mode \(n\) for monopole and patch element radiation patterns. due to the physical antenna size, and the ECC levels increase drastically as the distance between the array antennas narrows.
## 2 System Overview
### Power and Data Transfer Principle  
In some WPT applications such as implanted biomedical devices or robot charging, the information such as battery status, output voltage and output current should be transmitted from the secondary side to the primary side. The proposed data transfer circuit is shown in Figure 3, where \(S_{d}\) is an AC switch composed of two semiconductor switches (e.g., IGBTs or MOSFETs). \(C_{sd}\) is used to compensate \(L_{s}\) at the data carrier frequency. \(V_{d}\) is the injected data carrier while \(V_{o}\) is the received data carrier. \(L_{d}\) and \(C_{d}\) comprise an \(LC\) tuning circuit to maximize the output carrier \(V_{o}\), satisfying \(\omega_{d}=1/\sqrt{L_{d}C_{d}}\), where \(\omega_{d}\) is the angular frequency of the data carrier. \(R_{d}\) is the input resistance of the data processing circuit. The data transfer topology is shown in Figure 4, where the data transmitter side consists of a modulation module, while the data receiver side consists of a bandpass filter, an operational amplifier and a demodulation module. An amplitude shift keying (ASK) modulation method is used to generate the data carrier. The generation function of the data carrier can be given as:  
\[d_{c}(t)=\begin{cases}A_{c}\cos(2\pi f_{d}t),&\text{ \ \ \ \ \ }^{\prime\prime}1^{ \prime\prime}\\ 0,&\text{ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \  
Figure 3 shows that data is transferred during the off state of the decoupling switch \(S\) (\(S_{d}\) is turned on when \(S\) is off). The proposed system has two working modes: (1) when \(S\) is on and \(S_{d}\) is off, power is transferred to the load while data transmission is blocked; (2) when \(S\) is off and \(S_{d}\) is on, data is transferred from the secondary side to the primary side while the output capacitor \(C_{L}\) is free running. The circuits of these two working modes are shown in Figure 5.  
To simplify the analysis, the following assumptions are made: (1) the data carrier frequency \(f_{d}\) is selected higher than the power carrier frequency \(f_{p}\). This is because the extra data transfer channel would not significantly affect the power transfer and the interference of power transfer on data transfer is easy to suppress; (2) the resonant frequencies of the primary and secondary resonant circuits are identical:  
\[\omega_{p}=2\pi f_{p}=\frac{1}{\sqrt{L_{p}C_{p}}}=\frac{1}{\sqrt{L_{s}C_{s}}}. \tag{2}\]