esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of Directional Antennas in ZigBee Networks under NLOS Propagation Conditions
## 2 Materials and Methods
### Experimental Setup  
We used XBee S2C modules from Digi [34] in the experimental setup of the work. These ZigBee sensor nodes operate in the Industrial, Scientific, and Medical (ISM) band of 2.4 GHz. The maximum EIRP allowed in Europe for this band is 10 dBm. The transmit power levels of the XBee S2C are between \(-5\) dBm and \(+5\) dBm. A boost mode allows a maximum power of 8 dBm. The receiver sensitivity is \(-100\) dBm with boost mode disabled, and \(-102\) dBm with boost mode enabled. The datasheet shows a transmit current of 33 mA @ 3.3 V with boost mode disabled (45 mA--boost mode enabled), receive and idle currents of 28 mA (31 mA--boost mode enabled), and a sleep mode current below 1 mA. The data rate was 250 kbit/s.  
Figure 3: Location 3—vegetation zone: (**a**) image of the location; (**b**) site plan.  
Figure 2: Location 2—street around the building: (**a**) image of the location; (**b**) plan with the transmitter and receiver positions.  
The transmitter was implemented with an Arduino Pro mini microcontroller and an XBee module programmed as a router. An 1800 mAh @ 3.7 V lithium battery was used as a power supply. The system transmitted packets of 40 bytes at a rate of one per second. We numbered the packets to estimate the PDR parameter. The receiver was implemented with an XBee module, programmed as a coordinator, and connected to a personal computer. We utilized the XCTU software from Digi [34] to record the packets and extract the RSSI value of the last received packet. The antennas employed were 2 dBi monopoles [35] and 9.5 dBi suspended patches as omnidirectional and directional antennas, respectively. We chose the patch antenna because of its compact size (10 cm \(\times\) 10 cm) and high gain. The transmitter and receiver used the same type of antenna because a sensor node must operate in both modes in practical situations. These antennas were mounted on 2.5 m high poles. The poles allowed rotation to determine the maximum received power of the directional antennas. The antennas operated in vertical polarization.
# Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines
## 2 Materials and Methods
### Selection of the Modelling Technique  
The particularities of the data, mainly summarised by the presence of inconsistent labelling and by the extreme imbalance in the representation of the classes, suggests the use of a normality model associated with a regression technique. Applying ELM to the problem is based on the following properties: (i) The model of the subsystem to be developed must be valid for all operating regimes of all wind turbines throughout the wind farm. Because a lot of data is available to build the model, records from several years can be used, ensuring that all operating regimes of all WTs are well represented; (ii) In addition, due tot he nature of the data, there is an over-representation of the normal state versus the state with warnings/alarms (state of malfunction). The ELM technique used in its original formulation is characterised by providing the solution that approximates all the points of the training by performing the minimum mean square error. This property is extremely interesting when we have an over-representation of the normal state, as the ELM model picks it up very well. This makes ELM very robust even with the inclusion of a small proportion of mislabelled data (i.e., if a few points of data corresponding to the fault state are included in the training of the normal state); (iii) The facility of interpretation and the speed of training makes the ELM paradigm the best technique for this solution.  
Subsequent experiments using Support vector machines, Partial least square and Deep artificial neural networks, all of them in their regression forms, will confirm the suitability of this choice, which is presented in detail in the next subsection.
## 3 Results and Discussion
### Efficiency of the Washing Tests and the Ion Cleaning  
An organic solution was used for painting, previously optimized according to former studies [25; 26; 27; 28; 29], using the organic mixture based on turpentine, absolute ethyl alcohol, ethylene glycol and cyclohexanol (1:1:1:1), and did not require washing tests. Instead, a wet cleaning system was applied to the old dirty gilding, with bronze based on fine brass powder, which was intended to optimize the wash test. The work area chosen was represented by the upper rod of the frame, which presented a layer of embedded dirt with uniform distribution and structure/morphology. The ten wet cleaning solutions were applied here. Moreover, it is known that the gold gilded ornaments are resistant to a large number of cleaning stages by wiping, which allowed the extension of this process until optimal cleaning, without damaging the old patina and undamaged varnish. Figure 12 shows the areas selected for the application of the ten wash tests.  
Using direct observation (Figure 12) and CIE L*a*b* colorimetric analysis (Figure 13), the wet cleaning efficiency was established for all the studied systems, based on the wash test, in the following descending order: **E2 > E7 > E8 > E10 > E6 > E1 > E3 > E5 > E4 > E9**.  
If we consider the previous observations and the data in Table 3, the E2 system was used to clean the gildings, as after only three washing steps it effectively cleaned the dirt deposit, without affecting the old patina and the undegraded varnish.  
Figure 12: Washing tests for the gold gilded wood carving and embroidery with mono or mixed organic solvent solutions (E1–E10).  
Figure 13: Cleaning efficiency by CIE L*a*b* colorimetry with E1–E10 systems (\(\Delta\)E* is the chromatic deviation).
# Blind Deconvolution with Scale Ambiguity
## 1 Introduction
This paper focus on imposing the \(L^{p}\) normalization constraint on the blur kernel (i.e., \(\|k\|_{p}=(\sum_{i}k_{i}^{p})^{1/p}\), where \(i\) denotes the index of kernel elements) associated with a non-convex hyper-Laplacian prior, or rather, \(\|\
abla u\|_{0.5}\) ( \(\|\
abla u\|_{0.5}=\sum_{x}\big{(}|u_{h}(x)|^{0.5}+|u_{p}(x)|^{0.5}\big{)}\), where \(x\) denotes the pixel index ). With the constraint \(\|k\|_{p}=1\), the employed sparsity prior \(\|\
abla u\|_{0.5}\) can be converted into a joint prior. We should point out that this joint prior is formulated by equivalent mathematical derivations and we do not propose a fully novel image or kernel prior in this work. Our main contributions are as follows:  
* We introduce \(L^{p}\) normalization constraint on the blur kernel associated with a hyper-Laplacian image prior. This prior can be converted to a joint prior, which ensures the physical validity of the blur kernel. * We provide statistical analysis on the joint prior and quantitatively verify that it favors ground-truth solutions over trivial solutions for some proper selections of \(p\). This property contributes to the success of our methods for blind image deblurring. * An efficient algorithm is developed to solve the deconvolution formulation alternatively, which converges well in practice. * Quantitative and qualitative experiments on both synthesized datasets and real images demonstrate the proposed method with \(p=2\) performs favorably against the state-of-the-art deblurring methods.
## 3 Experimental Setup
### Optical and THz Transmitter Modules  
Figure 3 provides the experimental setup of the optical and THz transmitter modules. An arbitrary waveform generator (AWG) of 92-Gsa/s sampling rate is used to generate the I and Q components of the baseband electrical signals. A parallel electrical amplifier (EAs) is used to amplify the I/Q electrical signals. Then, a 193.5-THz linewidth external cavity laser-1 (ECL-1) is used to produce the optical carrier, which is modulated via an I/Q modulator with 30-GHz bandwidth and approximately 7-dB insertion loss. The modulated optical signal is divided to the polarization multiplexing channels by an optical coupler (OC) to simulate signal delay and attenuation. One is transmitted through a 1-m fiber direct link (DL) and the other passes through a variable optical attenuator (VOA). Then, after a polarization beam coupler (PBC), the 193.5-THz optical baseband signal is sent to SSMF.  
Erbium-doped fiber amplifiers (EDFA) are used to compensate for the optical-fiber transmission loss after a 20-km SSMF transmission link. In order to suppress the out-of-band amplified spontaneous emission (ASE) noise, a passband tunable optical filter (TOF) is used. A free-running tunable external cavity laser (ECL-2) is operated as an optical local oscillator (LO), which has a linewidth of less than 100 kHz. An optical baseband signal with 10.5 dBm optical power and an optical LO with 13.5 dBm optical power are coupled by an OC. The optical spectra of the optical signal with the tunable optical LO after OC (0.03 nm resolution) are shown in Figure 3b. Note that the optical power of the X- and Y-polarization components after PBS should be as equal as possible. The AIPMs used in our setup are polarization-sensitive, with a maximum of 4.5 dB polarization-dependent responsivity (PDR). The X- and Y-polarization imbalance will result in 2 \(\times\) 2 MIMO THz-wave imbalance and deteriorate the system performance. Therefore, two polarization controllers (PCs) are required before the OC. In the test, the optical signal and optical LO separately adjust the incident X- and Y-polarization direction to maximize the optical power in the antenna-integrated photomix module (AIPM, NTT Electronics Corp. IOD-PMAN-13001).  
The side-mode suppression ratio (SMSR) of the optical signal and optical LO is >50 dB. In our proposed system, THz-wave wireless signals with a tunable carrier frequency range from 340 GHz to 530 GHz are generated by photonic heterodyning using AIPMs. The AIPM consists of an ultra-fast uni-traveling-carrier photodiode (UTC-PD) and a bow-tie or log-periodic antenna. Two parallel AIPMs are used, each with a typical \(-\)28 dBm output power and operating wavelength range from 1540 nm to 1560 nm. The typical photodiode responsivity is 0.15 A/W, and the maximum optical input power is 15 dBm. PBS is used to separate the X- and Y-polarization components by APIMs to generate two parallel THz-wave wireless signals, respectively. To drive the AIPMs, another EDFA is used to boost the optical power of combined lightwaves before PBS.
## 4 Numerical Examples
### Nonseparable Spectrum
#### 4.1.1 Parametric Analysis
This may be because these elements changed drastically more with frequency, and the corresponding interpolation accuracy was relatively worse under the same interpolation points. However, its impact on the simulation results was minimal. As can be seen, the interpolation accuracy for \(N_{\overline{\omega}}=60\) met the requirement of \(E_{2}<1\%\). Further, the influence of frequency interpolation points on the average relative error \(E_{2}\) is displayed in Figure 7. It can be seen that the average relative error decreased gradually with the increase in frequency interpolation points and the accuracy met the requirement of \(E_{2}<1\%\) when \(N_{\overline{\omega}}\geq 40\). Figure 4: Effect of time-frequency interpolation point number on decoupling accuracy. Figure 5: Cont. Figure 3: Effect of interpolation point number and POD mode number on decoupling accuracy. Based on the above parameter analysis, the simulation parameters selected in this example are presented in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Considered POD mode number & \(N_{\phi}=11\) \\ Frequency interpolation point number & \(N_{\phi}=40\) \\ Time-frequency interpolation point number & \(N_{\bar{\omega}}\times N_{\bar{t}}=80\times 60\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 5: Comparison of target and approximated \(\sqrt{S(\omega,t)}\): (**a**) exact \(\sqrt{S(\omega,t)}\); (**b**) approximated \(\sqrt{S(\omega,t)}\); (**c**) frequency slice; (**d**) time slice. Figure 6: Relative error \(\varepsilon_{jk}\) under three typical interpolation points. Figure 7: Effect of the interpolation point number on the accuracy of Cholesky decomposition.
###### Abstract  
Weather conditions and chemical composition of the atmosphere are the most uncontrollable challenges for free-space optical (FSO) communications. Environmental parameters contribute directly in the characterization of atmospheric turbulence. Thus, it is very interesting to prepare a test setup to study the effect of such parameters on FSO transmissions especially in a region like Qatar, which is arid and has harsh climate. Unlike that of Europe and USA, Qatar's climate is characterized by high temperatures throughout the year, exceeding 14 \({}^{\circ}\)C even in winter. The experiment was carried at Qatar University (QU) using a system that comprises of two Field Programmable Gate Array (FPGA) boards and two FSO terminals installed at 600 m apart from each other. Each terminal operates at 1550 nm and had a capacity of 1 Gb/s. On the other hand, the environment parameters were recorded using a weather station installed near the transmitter. To have a wider set of data for analysis, parameters from four different seasons were recorded and analyzed. We present a comparison between the FSO performances for the different seasons in terms of Packet Delivery Ratio (PDR). We notice a significant difference in the behavior of FSO during summer and winter seasons. Furthermore, using statistics on the data collected from the weather station and based on Blaunstein model (BKB), we correlate the FSO behavior to the variation of refractive index (\(C_{n}\)\({}^{2}\)) in winter. Also a closed form expression, estimated from the statistical data, has been derived to relate the PDR with environmental parameters for the summer season.  
Keywords:free space optical communication; humidity; refractive index; solar irradiance; temperature; \(t\)-test; \(p\)-value approach; multiple regressions +
Footnote †: journal: Applied Sciences
## 3 Results
### Seasonal-Scale Contrast of GPP Estimations  
Seasonal variation of the field GPP measurement (GPP_EC) and five MODIS-based GPP estimations (GPP_MOD, GPP_MODR1, GPP_MODR2, GPP_MODR3, and GPP_MODR4) showed similar tendency but with different quantities in all observational years, especially in growing season (Figure 3a-e). GPP_EC was generally greater than any other GPP estimation from the MOD17A2 algorithm, which indicated that all MODIS-based GPP estimations in this site were underestimated in predicting GPP. GPP_MODR3 got the closest agreement with GPP_EC estimation, GPP_MODR4 took the second place, and the rest MODIS-based GPP estimations (GPP_MOD, GPP_MODR1, and GPP_MODR2) strongly underestimated GPP in this alpine wetland with corresponding values less than half of GPP_EC estimation (Figure 3). In growing season, statistical results showed that the mean GPP estimation from the same method, either the EC-based or the MODIS-based, had no significant differences (\(p\) > 0.1) amid different observational years (Table 2). However, diverse estimation methods significantly affected the mean GPP values (\(p\) < 0.01) in all observational years (Table 2). In contrast, the GPP_MOD and GPP_MODR1 showed the worst underestimation (less than 35% of GPP_EC), and the GPP_MODR2 came the second  
Figure 2: The maximum light use efficiency estimation using the light response curve derived from the Michaelis-Menten equation based on daytime EC measurement data from 2009 to 2012 (**a–d**). The _Pmax_ (\(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) is the maximum ecosystem photosynthesis rate. \(\alpha\) (\(\mu\)mol CO\({}_{2}\)\(\mu\)mol photons\({}^{-1}\)) is the maximum light use efficiency. (about 40% of GPP_EC). There also existed a significant underestimation (about 64% of GPP_EC) from the GPP_MODR4 despite of significant promotion compared to the former MODIS-based GPP estimation. However, GPP_MODR3 did not show significant difference with GPP_EC (Table 2) thus provided an instrumental approach in daily GPP estimation for this alpine wetland. The significant linear regression between EC-based GPP estimation (GPP_EC) and MODIS-based GPP estimation indicated that temporal variation of GPP could be well explained by MODIS-based GPP estimation (\(R^{2}>0.83\), \(p<0.0001\), \(n=190\)) (Figure 4). However, the absolute magnitudes of GPP were undervalued with a attenuation coefficient (slope in the Figure 4) range from 13.0% (GPP_MODR3) even to 75.0% (GPP_MODR1) depending on the methodology we used (Table 1). In contrast with the GPP_MOD estimation, GPP_MODR1 reduced the capacity in the GPP estimation about 10.0%
# Mechanical Structure Design and Experimental Study of Gamma-ray Monitor for Small Satellite Payload
## 3 Modeling  
### Structural Finite Element Static Analysis Equations  
Structural static analysis mainly studies the mechanical behavior of satellite structures under static or quasi-static load conditions to solve the static strength, stiffness, and stability problems of the structure. Based on the assumption of small deformation, the fundamental equations of the finite element displacement method for static analysis are established [24].  
\[[K]\{u\}=\{F\} \tag{1}\]  
where \([K]\) is the stiffness matrix.  
By solving the above equations, the displacement vector \(\{u\}\) is obtained at each point. According to the displacement interpolation function, the relationship between strain and displacement and the relationship between strain and stress are given in elastic mechanics. The strain and stress of the unit nodes are expressed as:  
\[\left\{\varepsilon^{d}\right\}=[B]\{u\}-\left\{\varepsilon^{th}\right\} \tag{2}\]  
Figure 2: GRM payload: (**a**) Assembly of bracket for radiant cooling plate. (**b**) Assembly of GRM payload.  
\[\{\sigma\}=[D]\Big{\{}\varepsilon^{d}\Big{\}} \tag{3}\]  
where \(\Big{\{}\varepsilon^{d}\Big{\}}\) is the structural strain vector, \(\Big{\{}\varepsilon^{th}\Big{\}}\) is the thermal strain vector, \([B]\) is the strain-displacement conversion matrix of the nodes, and \([D]\) is the elasticity coefficient matrix.
# A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
## 6 Experimental Results of Decoding by Proposed Multi-class NN-Based Decoder
### Experimental Results
#### 6.2.3 Results for QR (23, 12) Code  
The code length of QR (23, 12) code is \(n=23\), the message length is \(k=12\), the error correcting capability is \(t=3\), and the code rate is \(12/23=0.52\). In Figure 13a,b, the performance of the OSD method is slightly better than that of the proposed NN-based approach. The proposed method still outperforms other decoding algorithms. The DNN-based decoder [14] is inferior to HDD both in CER and BER performance. The DNN-based denoiser [13] combined with the HDD obtains no decoding benefits as compared to the HDD itself. When the code length increases, the advantage of the proposed method becomes insignificant.
# Design of Robust Sensing Matrix for UAV Images Encryption and Compression
## 1 Introduction
The SRE is decreased to LSRE so that the excessive error is effectively controlled when being projected into measurements. * By employing **LSRE** to minimize the projected Energy, a more robust Sensing Matrix named **LESM** is designed to build an optimal CS system. * The new CS framework with the robust sensing matrix **LESM** is established to compress and encrypt the UAV images to improve the security and the transmission speed, which can confront the SRE in the image and lead to more accuracy of image recovery. The rest of the paper is arranged as follows. Section 2 introduces the preliminary knowledge and related algorithms of sensing matrix design. In Section 3, the new structure to build lower SRE, and the proposed LESM algorithm for sensing matrix design is detailed. The flow chart and the procedure of the proposed CS framework are also concluded in this section. The experiments on UAV images are carried out to test the recovery performance of the proposed sensing matrix in Section 4. Section 5 makes the conclusion of this paper.
# A Novel Framework Leveraging Large Language Models to Enhance Cold-Start Advertising Systems
## 5 Results and Discussion
### Transformer-Based Feature Representation and Ensembler Model Evaluation Results
In DigiX (Table 4), the results also confirm that integrating the transformer-based feature representation module yields consistent improvements across various base models in terms of AUC ROC and accuracy (Table 2). The DIFM model shows the most notable gain, with a 3.45% increase in AUC ROC, rising from 0.6581 to 0.6808. Similarly, MMOE improves from 0.6777 to 0.6870 (1.36%), and FiBiNET from 0.6087 to 0.6178 (1.78%). Although DCN exhibits a smaller improvement (0.33%), it still benefits from transformer integration. These gains are reflected in accuracy metrics as well, where all the enhanced models either match or outperform their baseline versions. The ENSEMBLER model, combining multiple enhanced models, achieves the highest AUC ROC (0.6893) and maintains competitive accuracy, highlighting the additive strength of ensemble learning. Statistical significance was assessed using the DeLong test for AUC comparison, revealing significant improvements of the proposed ensemble over DIFM (\(p<0.05\)) and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **AUC ROC** & **Accuracy** & **PI (\%)** \\ \hline DIFM & 0.6581 & 0.9497 & \\ DIFM-TR & 0.6808 & 0.9626 & 3.45 \\ \hline DCN & 0.6598 & 0.9614 & \\ DCN-TR & 0.6619 & 0.9623 & 0.33 \\ \hline MMOE & 0.6777 & 0.9630 & \\ MMOE-TR & 0.6870 & 0.9630 & 1.36 \\ \hline FiBiNET & 0.6087 & 0.9267 & 1.78 \\ FiBiNET-TR & 0.6178 & 0.9462 & 1.78 \\ \hline ENSEMBLER & 0.6893 & 0.9684 & 0.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results using DigiX dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **AUC ROC** & **Accuracy** & **AUC—PI (\%)** \\ \hline DIFM & 0.7179 & 0.8255 & 0.69 \\ DIFM-TR & 0.7229 & 0.8256 & 0.45 \\ \hline DCN & 0.7192 & 0.8254 & 0.45 \\ DCN-TR & 0.7224 & 0.8273 & 0.19 \\ \hline MMOE & 0.7188 & 0.8254 & 0.45 \\ MMOE-TR & 0.7202 & 0.8283 & 0.19 \\ \hline FiBiNET & 0.7203 & 0.8271 & \\ FiBiNET-TR & 0.7214 & 0.8272 & 0.15 \\ \hline ENSEMBLER & 0.7251 & 0.8286 & 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Initial results using Avazu dataset. FiBiNET (\(p<0.05\)) on the DigiX dataset. The absence of statistical differences among most state-of-the-art models is common and reflects the maturity of the field and similar performance levels, making the ensembler's significant improvements over established models particularly noteworthy. On the other hand, even a small AUC improvement is critical at scale because it translates to millions of additional accurate recommendations in real-world systems, directly impacting revenue and user engagement. For advertising platforms, this marginal gain can significantly reduce wasted impressions and improve targeting efficiency across billions of served ads. Overall, the transformer-based feature representation and ensemble method consistently enhance model performance, supporting its integration as an effective mechanism across diverse model architectures.