esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
## 3 Methodology
### NearMiss  
The last undersampling technique proposed in this study and introduced here is NearMiss [5]. This method is based on the K-nearest neighbors algorithm and categorized as NearMiss-1, NearMiss-2, and NearMiss-3. The main idea behind NearMiss is to consider the mean distances of samples from the majority class to the samples from the minority class.  
Contrary to randomly removing samples from the majority class, these methods eliminate these samples intelligibly. NearMiss-1 removes the majority class samples whose mean  
Figure 6: Illustration of Tomek Links. The distance between the two classes widens as the samples of the majority class of the pairs forming TLs are eliminated, resulting in a more evenly distributed dataset. (**a**) Original Distribution of Data, (**b**) Providing the Tomek Links, (**c**) Removing the links.  
Figure 7: Illustration of One-Sided Selection. Through a modification of the condensed closest neighbor rule, this method preserves the entirety of the minority class samples while eliminating the redundant majority class samples.  
distances to the three nearest samples of the minority class are minimal. On the other hand, NearMiss-2 deletes the samples from the majority class with minimal average distances to the three farthest minority samples. Finally, NearMiss-3 selects a certain number of the closest samples of the majority class regarding every minority class sample [57].  
As claimed in [5], the results of experiments showed that NearMiss-2 has a better performance than NearMiss-1 and NearMiss-3. Furthermore, it outperforms the RUS technique [23].  
It is worth noting that NearMiss can be fine-tuned in two aspects: The variant that can be chosen from 1, 2, and 3. In addition, the number of neighbors to consider for calculating the mean distances is three as the default. An outline of the NearMiss algorithms is shown in Figure 8.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps from Other Data Sources  
#### 3.5.1 Northern Hemisphere Forest Carbon Density Map  
[PERSON] et al. [101] presented a forest growing stock volume (GSV) map of boreal temperate forest at a spatial resolution of 0.01 degree for 2010. The GSV data was estimated from Envisat Advanced Synthetic Aperture Radar (ASAR) using the BIOMASAR algorithm, which was independent of forest field inventory measurements. Uncertainty of the estimated GSV was lowest in boreal and temperate forest and highest in subtropical forest, while at the administrative unit level, aggregated GSV estimates were mostly in agreement with corresponding values from NFI. Based on GSV, as well as wood density and biomass compartment data, [PERSON] et al. [102] provided forest carbon density and a corresponding uncertainty map at 0.01\({}^{\circ}\) resolution in Northern Hemisphere boreal and temperate regions (30\({}^{\circ}\)-80\({}^{\circ}\)N) for 2010. The accompanying uncertainty map could overestimate dependent variable errors and thus should represent an upper bound of the uncertainty of the carbon density estimates. Additionally, validation of the biomass map with independent datasets (Russian forest enterprise data, the NBCD2000 and European national statistics) showed that R\({}^{2}\) ranged from 0.7 to 0.9 at the regional scale.
## 3 Spectral Radiance Inter-Comparison
### Spectral Radiance Inter-Comparison Result  
Figure 4 shows the match-up points for GOSAT and OCO-2 from September 2014 to March 2017, after screening with CAM images. The number of match-up points are 240 for high gain and 146 for medium gain. Pre-launch cross-calibration was performed using single transfer radiometers, with an optical band pass filter for each band. The radiometric response within each band was not compared.  
We present typical spectral radiance comparisons in the Appendix A: Figure 11 shows the comparison (a) over the Pacific Ocean with glint high gain, (b) over a farm area in North-East Hungary with high gain, and (c) over the Sahara Desert with medium gain. Figure 5a shows the spectral radiances differences between GOSAT and OCO-2 for all five points within each band shown in Figure 2. They agree well within 5% for all input radiance levels, showing no significant evidence of nonlinearity, except for slightly larger deviations in the ABO2 band. Low illumination input data show larger scattering due to the lower SNR of the observed spectra. Figure 5b shows the radiance bias derived from the average of the difference. All five points of the WCO2 band fall within \(\sim\)3%. The larger bias at shorter wavelengths in the ABO2 band result from shortcomings in the correction for O2 absorption in the prelaunch GOSAT calibration. [PERSON] et al. [14] presented initial larger and faster degradation in the sensors compared to the prelaunch values. Strong H2O absorption in the SCO2 band caused large errors in the GOSAT pre-launch in ambient conditions and caused a bias in cross-calibration. The GOSAT integrating sphere was used in a clean room for satellite integration but was not nitrogen-purged. Alternately, these differences may indicate spectrally-dependent errors in the post-launch GOSAT or OCO-2 degradation corrections. Overall the agreement for all three bands is well within 5% when the five spectral points per band are averaged.  
Figure 4: The match-up points between GOSAT and OCO-2 used in the spectral radiance comparison. Blue and red dots represent the GOSAT high and medium gain observations, respectively. Numbers of match-up points are 240 for high gain and 146 for medium gain.
# Review of Magnetic Properties and Texture Evolution in Non-Oriented Electrical Steels
## 2 Magnetic Properties
### Magnetic Properties under AC Conditions
Other than the effect shown in Equation (4), a high frequency AC condition makes the number of effects apparent. Those effects include but are not limited to skin effect, proximity effect, and geometric effects. [PERSON] et al. [8] reported a numerical model for calculation of eddy current losses under high frequency conditions. It computes the leakage field, taking into consideration the effective magnetic permeability of the multiturn winding as a heterogeneous medium. Magnetic induction \((B_{H})\) is another important magnetic property for application under AC conditions. Magnetic induction of electrical steels at a given applied field critically depends on the microstructure and the present crystallographic texture. [PERSON] et al. [9] reported a general quantitative model for the dependence of the magnetic induction at a given applied field as a function of the mean grain size, a texture-related parameter, and the Si content of the material. This relation is expressed as follows [9]:  
\[B_{H}\big{(}d,A,Si_{eq}\big{)}=p_{0}+p_{1}\times A+\frac{p_{2}}{D}+p_{3}\times Si _{eq}, \tag{6}\]  
where \(A\) is the texture parameter, \(p_{0}\), \(p_{1}\), \(p_{2}\), and \(p_{3}\) are material parameters, \(d\) is the grain size, \(Si_{eq}\) is defined as \((Si+2\times AI)\) in wt.%.
# A Traceable High-Accuracy Velocity Measurement by Electro-Optic Dual-Comb Interferometry
## 3 Experiment Setup  
Figure 1 shows the experimental system. The output of the seed laser (RIO ORION Laser Module, 1543.5 nm, 5.1 kHz line width, 18 mW) is spill into two parts. One part is modulated by an electro-optic phase modulator (EOM, EOSpace, \(<\)5 V half-wave voltage V\({}_{\pi}\), 2 W RF power) with 10 GHz repetition frequency, which works as the seed of the signal source. The output of the EOM is amplified to 500 mW by an Er-doped fiber amplifier. After the optical circular, the beam size of the signal source is expanded to 20 mm by a large collimator (micro laser system, FC40), which can guarantee that the received reflected light is strong enough. Then, the laser is incident on a moving corner prism, which is mounted on an electrical rail (Zolix, LMA-TR-200, travel 200 mm, resolution 1 \(\upmu\)m/s) whose velocity can be precisely controlled. The other part serves as the seed of the local oscillator. An acousto-optic modulator (AOM; AA opto-electronic MT110, 110 MHz frequency shift) is used to avoid the frequency ambiguity. Then, the output of the AOM is modulated by another EOM (EOSpace, \(<\)5 V V\({}_{\pi}\), 2W RF power) with 10.001 GHz repetition frequency. The RF driver1 (Agilent N5173B) and RF driver2 (Agilent E8267D) are locked to a rubidium atomic clock (Microsemi 8040, \(2\times 10^{-11}\) stability with 1 s averaging  
time). Finally, the two parts are combined by a beam splitter and detected by a fast photodetector (Menlosystems FD310). The stored waveforms by an oscilloscope (LeCroy Waverunner 610 Zi) can be processed to obtain the Doppler shift information.
## 4 Results and Analysis
### Comparative Testing of Transmission Mechanisms and Results Analysis
#### 4.2.1 Synchronization Control Delay  
For the sliding window transmission mechanism, only the time from when the receiver successfully polled to the start of the next poll was calculated, excluding the time for the sender to process the ACK. This is because the sender handles ACKs in real time using a dedicated ACK polling thread, which does not interrupt the sending thread. Since the measurement steps for synchronization control latency and the amount of transmitted data remain constant regardless of the number of memory blocks, the communication control latency for the sliding window transmission mechanism was measured only once. In contrast, the status-byte-assisted RDMA transmission mechanism blocks the sending thread during communication control operations. Therefore, the time for the receiver to modify the status byte is negligible, while the amount of communication control transmission data varies with the number of memory blocks. As a result, the communication control latencyfor the status-byte-assisted RDMA transmission mechanism was remeasured each time the number of memory blocks changed.  
Figure 10 compares the synchronization control latency of various transmission mechanisms with different numbers of memory blocks. As shown in Figure 10, the synchronization control latency of the proposed status-byte-assisted RDMA transmission mechanism is significantly lower than that of the sliding window transmission mechanism for different number of memory blocks. For instance, with three memory blocks, the control latency for the status-byte-assisted RDMA transmission mechanism is 5.1 us, compared to 70 us for the sliding window transmission mechanism. This discrepancy arises because the sliding window transmission mechanism employs RDMA Send to transmit synchronization data, a bidirectional operation that requires both sending and receiving ends to generate Completion Queue Entries (CQEs), resulting in increased latency. Moreover, the control latency of the sliding window transmission mechanism does not vary with window size as it immediately returns an ACK after processing a memory block. In contrast, the control latency of the status-byte-assisted RDMA transmission mechanism increases with the number of memory blocks because it retrieves the status byte for all memory blocks simultaneously.
## 5 Experimental Verification
### Single- and Continuous-Exposure Method Experiments  
Under identical experimental conditions, single- and continuous-exposure methods were compared with the double-exposure approach.  
Single-Exposure Protocol:  
(1) The initial interference field is recorded with a CCD under static conditions.  
(2) Deformation is applied and interference fringes between the deformed object beam and reference beam are dynamically captured.  
(3) The phase distribution is extracted via Fourier transform, unwrapping phases, and computing deformation.  
Continuous-Exposure Protocol:  
(1) Time-varying interference patterns during deformation were continuously recorded using a high-speed CCD.  
(2) Temporal Fourier transform was performed on the sequence to extract phase evolution.  
(3) Phase data were fitted to quantify deformation.  
Regarding the interference fringe results, Figure 10 compares the fringe patterns:  
Figure 10a: Single-exposure fringes.  
Figure 10b: Continuous-exposure fringes.  
According to Table 1, the following can be seen:  
(1) The single-exposure method has the lowest fringe density (0.75) and highest noise (0.18) due to single-frame limitations. It also has the fastest computation (0.157 s) so it suits real-time applications.  
(2) The double-exposure method has improved fringe density (0.83) and noise suppression (0.06) via cosine modeling. It has moderate detail retention (0.78) with practical computation time (0.172 s).  
(3) The continuous-exposure method has the highest fringe density (0.92) and detail retention (0.89), but also the highest noise (0.23) and computational load (0.214 s).  
The double-exposure method achieves an optimal balance between fringe clarity, noise control, and computational efficiency, making it ideal for engineering applications requiring high precision and real-time capability.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Double-Exposure** & **Single-Exposure** & **Continuous-Exposure** \\ \hline Fringe density & 0.83 & 0.75 & 0.92 \\ \hline Noise level & 0.06 & 0.18 & 0.23 \\ \hline Detail retention & 0.78 & 0.68 & 0.89 \\ \hline Computational time (s) & 0.172 & 0.157 & 0.214 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of the three methods.  
Figure 10: (**a**) Single-exposure interference fringes. (**b**) Continuous-exposure interference fringes.
# Flexible UHF RFID Tag for Blood Tubes Monitoring
## 4 Fabrication and Experimental Characterization of the Proposed Tag Antenna  
The proposed UHF RFID tag antenna (Figure 2) was fabricated and experimentally characterized. In addition to the impedance measurement, other useful parameters for the characterization of the proposed tag antenna such as the realized gain, reading range, tag power sensitivity, differential radar cross section, and radiation patterns were measured wirelessly in an anechoic chamber by using the Voyantic Tagformance measurement system in two different scenarios, namely, when the clinic tube is filled of blood and when the tube is empty. Finally, the proposed tag was compared with other tag designs for blood and liquid monitoring. In this way, the operation of the designed tag in blood traceability is characterized. All measurements presented here were carried out with the proposed tag antenna attached to the smallest clinic tube. Very similar results were also obtained using different tag prototypes mounted in several blood tubes standards manufactured by the Becton Dickinson (BD) company. However, the number of tests performed is still small to be considered relevant for a statistical analysis and, therefore, this will be carried out in future work.
## 2 Measurement Principles and Device
### The Structure of a Cryogenic Vacuum Apparatus
#### 2.2.1 The Design of a Light-Weight Blackbody  
The blackbody is a crucial component in measuring the emissivity of samples, and its emissivity and temperature uniformity precision are directly related to the overall accuracy of the device's emissivity measurements. Standard blackbodies are typically used in air environments. However, in this paper, the blackbody operates within a vacuum chamber, which is significantly different from an air environment, necessitating a distinctly different design approach.  
Since the cooling primarily relies on thermal radiation in a vacuum, the temperature of the blackbody decreases very slowly. To enhance the cooling efficiency, a combined method of thermal conduction and thermal radiation was employed. In selecting the material for the blackbody shell, aluminum was preferred over copper, despite its lower thermal conductivity. Specifically, a TEC1-12706K10 thermoelectric cooler (TEC) (Hebei I.T. (Shanghai) Co., Ltd., Shanghai, China) was selected and attached to the rear surface of the blackbody, with its cooling side facing the blackbody and the heating side connected to a metal braided strap that transfers heat to the liquid nitrogen device. The braided strap is made of copper due to its high thermal conductivity and relatively low cost. The length of the copper braided strap is 0.8 m, which is slightly longer than the distance from the blackbody to the liquid nitrogen device. The cooling curves before and after the improvement are shown in Figure 3a, and the revised design drawing of the blackbody is shown in Figure 3b.  
As can be seen from Figure 3a, compared to the ordinary blackbody, the temperature drop of the improved blackbody significantly accelerates.
# Biology and Regulation of Staphylococcusococcal Biofilm
## 4 Activity of the _ica_ Operon Encoded Enzymes
### 5 Regulation of icaADBC Expression by Repressors  
The _icaR_ gene, localized upstream of _icaA_ in an inverse orientation, belongs to the _tetR_ family of transcriptional regulators. IcaR acts as a repressor of _icaADBC_ expression [66]. The target site of IcaR is the 164 nt-long intergenic region (IGR). Within this region, IcaR binds to 42 nt long sequences upstream of the _icaA_ start codon. Binding to this region most likely prevents the binding of the RNA polymerase, thus preventing the transcription of _icaADBC_[93]. TcaR, a MarR family of transcriptional regulators of the teicoplanin-associated locus, appears to weakly downregulate the transcription of the _ica_ operon, whereas IcaR is a strong negative regulator. Thus, in the absence of _tcaR_ and _icaR_, PIA/dPNAG production and biofilm formation was significantly enhanced [74]. Rob, a regulator of biofilm formation, is the third described negative regulator of the _ica_ operon [94]. Deletion of _rob_ increased the production of PIA/dPNAG. Like IcaR, _Rob_ appears to also act as a repressor that binds to the operator site within the _icaR-caA_ intergenic region.
## Appendix B Statistical Validation  
To ensure reproducibility and support statistical evaluation, all experiments were repeated five times using different random seeds for client selection, data shuffling, and FL initialization. Paired two-tailed t-tests were applied to compare the detection performance (F1-score and AUC-ROC) between local-only and federated training paradigms. A confidence threshold of 95% (\(p<0.05\)) was used to assess significance (Table A1).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Training** & **F1-Score (\(\pm\sigma\))** & **AUC-ROC (\(\pm\sigma\))** \\ \hline Autoencoder & Local-only & 0.79 \(\pm\) 0.014 & 0.85 \(\pm\) 0.012 \\ Autoencoder & Federated FL & 0.89 \(\pm\) 0.011 & 0.94 \(\pm\) 0.010 \\ LSTM & Local-only & 0.84 \(\pm\) 0.013 & 0.89 \(\pm\) 0.011 \\ LSTM & Federated FL & 0.91 \(\pm\) 0.009 & 0.96 \(\pm\) 0.008 \\ \hline \hline \end{tabular}
\end{table}
Table A1: Mean and standard deviation of F1-score and AUC-ROC across five independent experimental runs for autoencoder and LSTM models under local-only and federated learning (FL) training regimes. Results confirm consistent performance gains under federated training.
# 3R Composites: Knockdown Effect Assessment and Repair Efficiency via Mechanical and NDE Testing
## 3 Results and Discussion
### ILSS Geometry
#### 3.1.2 Non-Destructive Evaluation via Impedance Spectroscopy  
Impedance spectroscopy (IS) was utilized to assess the manufacturing and the damage of the ILSS specimens. Figure 5 (left) shows representative IS results of an RTM6 ILSS specimen before and after the mechanical test. The magnitude of the impedance of the virgin specimen (black curve with filled squares) was ca. \(10^{3}\) ohms and exhibited an ohmic behavior (independent of the frequency), as indicated by the fact that the phase remained near \(0^{\circ}\) (black curve with unfilled squares). This behavior is indicative of good resin impregnation which prevents or better limits direct contact of the conductive carbon fiber reinforcement which would lead to an electric shortcut and much lower impedance values. The subsequent introduction of damage caused an increase in the magnitude of the impedance by four orders of magnitude (red curve with filled squares). A transition from ohmic to non-ohmic behavior also appeared in the plot at 1 kHz and was depicted by a decrease of the magnitude of the impedance at 1 kHz frequency and onwards (red curve with filled squares). This was also confirmed by a shift in the phase from almost \(-10^{\circ}\) to \(-90^{\circ}\) (red curve with unfilled squares), which reveals that capacitive phenomena occurred in this frequency range. This is a typical behavior that accompanies the introduction of damage, i.e., delaminations, microcracks, etc., in composite materials, as the trapped air disrupts the pre-existing conductive path [7].  
On the other hand, the 3R ILSS specimen exhibited distinctly different behavior. In Figure 5 (right), the magnitude of the impedance of the virgin specimen (black curve with filled squares) fluctuated at a few ohms, and the phase curve (black curve with unfilled squares) fluctuated around \(0^{\circ}\), accompanied by significant noise as the recorded values were beyond the lower detection limit of the experimental equipment. This indicates that in this case, the conductive carbon fibers presented a significant number of interfiber contacts which in turn implies poor wetting/impregnation by the 3R resin. Ultrasonic inspection and IR-thermography results presented in Section 3.2.2 confirmed the existence of poor wetting areas (dry spots).  
After the mechanical test, the magnitude of the impedance increased by two orders of magnitude (red curve with filled squares) as a result of the introduction of air and the reduction of the contact area between the plies of the laminate (fewer spots of contact  
Figure 4: Representative plots of ILSS specimens (**left**) and knockdown effect for maximum strength (**right**).  
between CFs). The phase remained around \(0^{\circ}\), revealing a purely ohmic behavior (red curve with unfilled squares).