esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This experiment investigated the effects of different additives on the fermentation quality, in vitro digestibility, and aerobic stability of amaranth and wheat bran (AWB) mixed silage. In this experiment, a two-factor (moisture content \(\times\) additive) completely randomized experimental design was used, with amaranth as the raw silage material, and the moisture content was adjusted to 60%, 65%, and 70% using wheat bran. At each moisture content condition, the silage treatments included groups without any additives (control), with lactic acid bacteria (L), with cellulase (E), and with lactic acid bacteria and cellulase (M). Six replicates of each treatment were analyzed for fermentation quality, chemical composition, and in vitro digestibility of AWB mixed silage after 60 days of ensiling. The results showed that in the same L group, the pH and the lactic acid (LA) in the 60% moisture content (MC) group were lower and higher, respectively, than in the 70% MC group (\(p<0.05\)). In the same E and M groups, the ammonia nitrogen to total nitrogen (AN/TN) in the 60% MC group was lower than that in the 70% MC group, and the in vitro crude protein digestibility (IVCPD) was higher than that in the 70% MC group (\(p<0.05\)). At the same time, the aerobic stability of AWB mixed silage gradually decreased as the MC of the raw material increased (\(p<0.05\)). Under 60% MC, the IVCPD and in vitro neutral detergent fiber digestibility (IVNDFD) in AWB mixed silage from the E and M groups were higher than those in the control group (\(p<0.05\)). Under 60% MC, the pH and AN/TN in AWB mixed silage from the M group were lower than those in the control group (\(p<0.05\)). Compared with the control under 60% MC, the aerobic stability of AWB mixed silage inoculated with L, E, and M increased by 33 h, 42 h, and 57 h, respectively. It was shown that the addition of M resulted in the best fermentation quality, in vitro digestibility, and aerobic stability of AWB mixed silage when the amaranth MC was 60%.  
amaranth; additives; aerobic stability; fermentation quality; in vitro digestibility; wheat bran +
Footnote †: journal: Information Systems
## 2 Principle
### Amplitude Mismatch in Differential Driven Mode  
Similarly, as shown in Figure 1c, the impact of amplitidue mismatch in the differential-driven mode can be taken into consideration. Equation (9) gives the modulated optical field \(E_{3}(t)\).  
\[E_{3}(t)=e^{j(t)+i\varphi}+e^{-j\varphi(t)-j\varphi}\approx e^{i\varphi}[1+j \varsigma(t)]+e^{-j\varphi}[1-j\varsigma(t)]=C+\Big{(}je^{i\varphi}-jke^{-j \varphi}\Big{)}s(t) \tag{9}\]  
were \(k\) is defined as the amplitude ratio between the differential outputs. The corresponding photocurrent \(I_{3}(t)\) after PD detection is derived in Equation (10).  
\[\begin{array}{l}I_{3}(t)=\left|E_{3}(t)\otimes h(t)\right|^{2}\\ =\left|C+\big{(}je^{i\varphi}-jke^{-i\varphi}\big{)}s(t)\otimes h(t)\right|^{ 2}\\ =C^{2}+\big{(}je^{i\varphi}-jke^{-i\varphi}\big{)}Cs(t)\otimes h(t)+\big{(}-je^ {-j\varphi}+jke^{i\varphi}\big{)}Cs^{*}(t)\otimes h^{*}(t)+\ldots\\ \approx C^{2}+Cs(t)\otimes\big{[}\big{(}je^{i\varphi}-jke^{-j\varphi}\big{)} h(t)+\big{(}-je^{-j\varphi}+jke^{i\varphi}\big{)}h^{*}(t)\big{]}\end{array} \tag{10}\]  
In this case, the overall transfer function \(H_{3}(f)\) can be acquired as  
\[\begin{array}{l}H_{3}(f)=\big{(}je^{i\varphi}-jke^{-i\varphi}\big{)}h(f)+ \big{(}-je^{-j\varphi}+jke^{i\varphi}\big{)}h^{*}(f)\\ =2\cos\big{(}\pi\lambda^{2}DLf^{2}/c+\varphi+\pi/2\big{)}+2k\cos\big{(}\pi \lambda^{2}DLf^{2}/c-\varphi-\pi/2\big{)}\end{array} \tag{11}\]  
One can find that the overall transfer function \(H_{3}(f)\) is the combination of two terms. If \(k=1\), Equation (11) would return to Equation (4) as the differential driven mode, while Equation (11) becomes Equation (7) as the upper-arm single driven mode if \(k=0\) is satisfied. In addition, when \(k\in[-1,0)\), intensity modulation is gradually transformed towards phase modulation, which in fact changes the hardward configuration rather than using the negative output, as in Figure 1c. Nevertheless, we still considered it to be in the class of amplitude mismatch due to similar mathematical form.
# Novel Fault Injection Attack without Artificial Trigger
## 4 Experiment
### Experimental Setup  
#### 4.1.1 Specifying the Time of a Fault Injection Attack  
As emphasized, the experimental setup constructs Figure 3(b) system to execute a fault injection attack with relaxing an attacker's assumption. We acquired the electromagnetic traces while AES encryption was performed on the Arduino UNO board, as shown in Figure 5. The collected electromagnetic trace is represented in Figure 7. Figure 6(a) depicts the electromagnetic trace of AES. Figure 6(b) shows the magnified electromagnetic trace corresponding to 8 th, 9 th, and 10 th rounds in AES considered to identify the time of a target operation. As mentioned in Section 3.2, the location of the specific operation corresponds to the part of the 9 th round in SubBytes and ShiftRows, performing the electromagnetic fault injection attack in the range of 655\(\sim\)675 \(\upmu\)s.
## 4 Experimental Study
### Dataset Description  
As illustrated in Figure 7, the evaluation dataset consists of panoramic images collected from three indoor construction sites in South Korea. The dataset includes two newly constructed apartment buildings (Site B and Site J) and one university renovation project (Site K), representing different architectural types, namely residential and educational facilities. The images were captured using an Insta360 ONE X camera, with 12 images per site, totaling 36 panoramic images. All images were taken during the construction phase when the structural framework was completed but while the finishing work was in progress. The ground truth labels for the collected data were manually annotated using Roboflow.  
This dataset incorporates various environmental factors that influence segmentation performance, including lighting conditions that combine natural and artificial illumination, structural complexity with both open and enclosed spaces, and obstacles with varying densities, such as workers, materials, and equipment. For instance, in Site B, large windows obstruct portions of the walls, allowing natural light to enter through openings. Site J contains scaffolding and workers, with numerous scattered construction materials on the floor. In Site K, an open space with abundant natural light creates distinct shadow patterns on the floor. By incorporating these realistic environmental conditions, the dataset enables a comprehensive and practical evaluation of the proposed method's segmentation performance.
# A Near-Ultraviolet Photodetector Based on the TaC: Cu/4 H Silicon Carbide Heterostructure
## 2 Materials and Methods
### Characterization  
The deposition rate and film thickness of the TaC: Cu alloy on the glass substrate were measured using a Dektak (Bruker Corporation, Hillsboro, OR, USA) surface profiler, ensuring a precise assessment of material growth. Surface morphology analysis was conducted with a scanning electron microscope (SEM, FEI Verios) at a working distance of 5 mm, with operating voltages ranging from 5 kV to 15 kV. The elemental composition was evaluated through energy-dispersive X-ray spectroscopy (EDX), utilizing an Oxford INCA X-act detector integrated within the SEM system. To investigate the cross-sectional architecture of the fabricated photodetector (TaC: Cu/4H-SiC), a focused ion beam (FIB) system (FEI Helios 600 NanoLab (FEI, Hillsboro, OR, USA)) was employed. This advanced tool, featuring high-resolution SEM imaging (0.9 nm) and ion beam capabilities, enabled detailed observation of the film's cross-sectional morphology.  
The dielectric properties and refractive index of the TaC: Cu alloy were determined using a JA Woollam M2000D (J.A. Woollam, Lincoln, NE, USA) ellipsometer, which operates over a broad spectral range spanning from 190 nm to 1690 nm. Optical characteristics, such as absorption and transmission, were simulated with the Lumerical FDTD software (2024R2.1) packages. These simulations leveraged the thickness and refractive index data obtained through ellipsometry. The electrical properties of the TaC: Cu alloy deposited on a glass substrate were assessed using a four-point probe system from Ossila.  
The photoresponse properties of the proposed device were thoroughly assessed using state-of-the-art instrumentation. An optical light source from Thorlabs S1 FC405 (Thorlabs, Newton, NJ, USA) laser, emitting light at a wavelength of 405 nm, was used as the light input for the device. Electrical measurements, including current-voltage (I-V) profiling, were carried out using a Keithley 2450 source meter. To evaluate the time response, a Q-switched laser with a pulse duration of 10 ns was employed, ensuring precise analysis of the device's dynamic behavior.
## 1 Introduction
However, the stability bound result is shown in the transfer function model domain, which reflects, indirectly, the effect of the physical domain parameters such as delay length of an acoustic system, ANC filter length, and center frequency of target noise. In [3], the effects of each error in the parameters of secondary path model are treated as a single transfer function model. The upper bound of secondary path model error is also obtained as a transfer function bound that defines the magnitude bounds for each frequency. In [7], though the effects of the errors in magnitude response and pure delay components are treated separately in the analysis, the effect of the delay error of the estimated secondary path model is not treated in the analysis. Another appropriate frequency response approach for stability analysis of the feedback system is the Nyquist stability determining technique [12; 13]. In the Nyquist criterion, a feedback system's stability is guaranteed when the polar plot of the open loop frequency response does not enclose the Nyquist point (\(-1\), \(0\)) for any frequency. The Nyquist technique is basically considered as a trial-and-error based approach, in which the stability bound can be obtained through direct drawing of polar plots by changing the values of the parameters. In this paper, we derive a stability bound equation in terms of the delay error of the secondary path model, the ANC filter length, and the target noise's frequency. This paper is considered as a major extension of the authors' previous work [8], which can be considered as an incomplete work to reflect only the magnitude response of the system's open loop response in deriving the system's stability bound. This paper provides a more realistic stability bound by exploiting both the magnitude and phase of the FBANC system's open loop response in deriving the stability bound equation. The magnitude and phase response equations of the open loop function of the feedback ANC, which guarantee the Nyquist stability criterion, are approximated as linear equations and combined. In the derivation process, we use several assumptions in which the primary noise is a single-tone sinusoid and the secondary path is modeled as pure delay. The exclusion of the magnitude mismatch effect of secondary path model is considered acceptable in narrowband ANC applications, while it would not be acceptable in cases of wideband ANC applications. One of the significant advantages compared to the Nyquist technique is that the closed-form equation can provide the fast computation of parameter bounds. The real-time computation of the stability bounds is expected to be useful for the design and operation of the feedback ANC system. The obtained stability bounds are verified by comparison with both the original Nyquist stability condition and the simulation results. Simulations with a practical secondary path measured from commercial headphones are also added to verify the analysis based on the assumption of the pure delay model.
## 7 Antenna Qualification and Acceptance Test
### Initial and Final RF Functional Test  
Prior to the initiation of vibration, thermal vacuum, and shock tests (Initial RF Functional Test), and upon completion of these environmental tests (Final RF Functional Test), full RF tests are conducted. The Initial RF Functional measurements (see Figures 15-18) act as a reference point for comparison with measurements taken after the completion of these tests, thereby ensuring the continued functionality of the AUT. The RF functional measurements (initial and final) are conducted by means of the QM S-Band antenna positioned on an RF dummy (see Figure 15a). The primary distinction between the isolated QM measurements depicted in Figure 9 and the QM integrated within the RF-dummy configuration illustrated in Figure 15a pertains to the presence of a UHF antenna affixed to the test antenna, based on metal rods, which directly interferes with the radiated field lines of the QM antenna. Additionally, a magnetometer, a metallic component that the QM antenna also perceives, is employed. The combined effect of these elements on the radioelectric characteristics of the QM antenna is a consequence of these interferences, and; therefore, it is important to characterize this effect (Section 4).
# Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection
## 3 Material and Characterization
### Contextual Characteristics  
Most monitoring technologies are developed for specified causes and work independently with interconnected data. In this study, due to data restriction, available nonwaveform data include time stamps, meteorological data, geographical data, protection data and query information. These informative values are preprocessed and integrated into the pool  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Pool Type** & **Feature** & **Total Number** \\ \hline \hline \multirow{8}{*}{Waveform} & Maximum sequence voltage/current & 5 \\  & Maximum change of three-phase signals and sequence components & 6 \\  & Sequence component values & 24 \\  & Custom time constant of sequence current & 30 \\  & DC and harmonic content & 6 \\  & Wavelet energy and energy entropy & 6 \\  & Maximum DC current & 1 \\  & Form factor, crest factor, skewness and kurtosis & 4 \\  & Approximation constants & 2 \\  & FIPA & 1 \\ \hline \multirow{8}{*}{Contextual} & Time stamp: season, day/night, mouth, hour & 4 \\  & Location: landform, zone & 2 \\ \cline{1-1}  & Meteorological data: weather, temperature, humidity, rainfall, cloud & 7 \\ \cline{1-1}  & cover, maximum wind speed, wind scale & \\ \cline{1-1}  & Protection data: reclosing, fault phase, fault duration, tripping time, & 7 \\ \cline{1-1}  & breaker quenching time, reclosing time, number of triggering & 2 \\ \cline{1-1}  & Others: voltage level, number of faults & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Feature pools.  
of candidate contextual features, as shown in Table 2. Considering that there is no accurate discretization standard, we only discretize text data roughly if necessary. The time stamp information is discretized twice based on season and day/night as a contrast of months and daytime. As for dynamic records such as meteorological value, the records closest to the fault time are retained. Protection data are feedback information of protection devices after fault, usually obtained from the production management system. Although these collected data are related to fault events, they are not suitable for fault cause identification. These irrelevant features pose a great challenge in feature selection.
## 3 Results and Discussion
### Optical Characterization
Figure 9: Reflectance spectra obtained for samples subjected to various treatments: untreated sample (blue curve), sample after heat treatment (red curve), sample after laser modification with a wavelength of 1064 nm in darkening mode (pink curve), and a sample after laser modification with a wavelength of 1064 nm in the bleaching mode (violet curve). Below are the Fourier reflection and transmission spectra of the films for various processing methods (Figure 10). As a result of heat treatment in the oven, an increase in reflection was observed (Figure 10a) relative to the initial PbSe film in the wavelength range from 3 \(\upmu\)m to 4.5 and from 5 \(\upmu\)m to 6.2 \(\upmu\)m, which may be due to a change in the sample structure, including  
Figure 10: Fourier spectra of (**a**) reflection (excluding the film substrate) and (**b**) transmission (taking into account the film substrate) obtained for samples subjected to various treatments: initial sample (blue curve), sample after heat treatment (red curve). Sample after laser modification in the darkening mode modified by continuous radiation (dark green curve), a sample after laser modification in the bleaching mode modified by continuous radiation (light green curve), a sample after laser modification in the darkening mode modified by pulsed radiation (violet curve), a sample after laser modification in the bleaching mode modified by pulsed radiation (light violet curve). Absorption spectra of various gases (CH\({}_{4}\), CO\({}_{2}\), CO, NO, NO\({}_{2}\), NH\({}_{3}\)). the oxide surface formation. Laser-treated films have the least reflection, especially films treated with continuous radiation in the bleaching mode, in the wavelength range from 3 \(\upmu\)m to 3.7 \(\upmu\)m and from 4.6 \(\upmu\)m to 4.8 \(\upmu\)m, in which there are peaks of toxic gaze absorption, CH\({}_{4}\) and CO, respectively [24]. The Fourier transmission spectra (Figure 10b) show that laser treatment at a wavelength of 405 nm in the darkening mode caused a decrease in transmission in the wavelength range from 4.2 \(\upmu\)m to 5.2 \(\upmu\)m. In the bleaching mode with modification by continuous radiation, in the range from 5.2 \(\upmu\)m to 7 \(\upmu\)m, the transmission of the sample also decreased relative to the initial film, and the optical characteristics of such a film approached an absolutely dark body. The film's transmission value after modification with continuous radiation was predominantly lower than the film's transmission value processed in the furnace, especially in the wavelength ranges corresponding to the absorption peaks of CH\({}_{4}\) and CO gases. During samples' laser processing using short pulses, it was observed that, in the bleaching regime, the films have a high transmission both in the bleaching and darkening regimes.
# Accuracy of Code GNSS Receivers under Various Conditions
## 4 Results
### Point  
Figure 6 shows the deviations in the NE components for each of the five receivers. The handheld receiver G_1 (GPS) did not exceed 13 m in the code solution for the E component, while the values for the N component were within 10 m. The measurement points were very unevenly distributed. For the G_2 (GPS+GLONASS) receiver, the accuracies presented themselves more consistently, the points were in close proximity to the center of the coordinate system, and both for the NE components, the values did not exceed 3 m. Differences in the G_1 and G_2 might be the effect of the number of visible satellites during measurement. G_2 also registered GLONASS signals; thus, this receiver registered almost twice as many satellites as G_1, which was clearly visible in the obtained accuracy. The solutions obtained from the smartphones were significantly more accurate. The X_1 receiver started recording data with poorer accuracy (from 4 to 6 m), but after a minute, the measurement stabilized to 2.5 m for the N component and to 1 m for the E component. This receiver had quite large single deviations, exceeding even 15 m by the end of the measurement. Smartphone X_2 obtained deviation values close to 1 m for both components, but this value increased to 3 m for the E component in the middle of the measurement. Receiver X_3 recorded one measurement point located at a distance of 2 m for the N component and 1.4 m for E from the reference point. In the case of the point measurement, the worst accuracy was obtained by the handheld receiver G_1 based only on the GPS signal. A significant part of the obtained deviations of the NE components were values in the range of 6 to 13 m. Compared to the G_2 receiver, which used the GPS+GLONASS mode, one can notice the lack of consistency of the data with respect to each other. The point measurement using cell phones proved to be more accurate compared to the Garmin receivers. The results obtained were very similar for all the smartphones tested. The dominant values did not exceed 3 m. The worst results were obtained by the X_1 phone due to the occurrence of single errors with values greater than 15 m.  
In the determination of the elevation component using the G_2 (GPS+GLONASS) handheld receiver, it received slightly better accuracies than G_1 (GPS), but the difference was small (Figure 7).  
For the G_1 receiver, the deviation was in the range of 7 to 11 m, while for G_2, the range was from 7 to 9 m. For both measuring instruments, the error was constant. When measuring on a point, the receiver operating in GPS+GLONASS mode obtained better accuracies for both NE and U component deviations compared to G_1 based on GPS mode.
## 4 Review: Slant Links Rain Fade Models
### Fade Slope Model
#### 4.4.2 Das Fade Model  
The Das Fade model [133] was implemented with a modification of the Van de Kamp (VDK) model [127]. This work also blended the fade slope concept to predict the time series attenuation series instead of customarily using velocity [64; 97; 135; 134] or the synthesizer-based technique [136]. Algorithm 20 shows the procedure for this model.  
Figure 5: Illustration of the fade slope and fade duration in the received signal level (RSL) time series [98].  
\[\xi(i) = \left(A(i)-A(i-1)\right)/T_{s}\ \ (\mathrm{dB/s}) \tag{55}\] \[P\bigg{(}\frac{\xi}{A}\bigg{)} = 2/\left(\pi\sigma_{\xi}\Big{(}1+\left(\xi/\sigma_{\xi}\right)^{2} \Big{)}^{2}\ \left((\mathrm{dB/s})^{-1}\right)\right)\] (56) \[\sigma_{\xi}(A) = -0.00031\mathrm{A}^{3}+0.0038\mathrm{A}^{2}-0.0047\mathrm{A}+0.04\] (57) \[A(t+T_{s}) = A(t)+\xi(t)xT_{s} \tag{58}\]  
```
1 Apply LPF and MAF
2 Find the fade slope
3 Calculate PDF /* PDF: probability density function */
4 Extract statistical coefficient (\(\sigma_{\zeta}^{*}\))/* \(\sigma_{\zeta}\): standard deviation of \(\zeta\) */
5 Fit polynomial to fit attenuation with \(\sigma_{\zeta}\)
6 Use \(\zeta\) to calculate rain attenuation for time series data prediction (\(A_{p}\)) Return \(A_{p}\)
```  
**Algorithm 20** Das Fade slope model [133]  
Advantages  
The enhanced fade-slope model updates the slope from the modified VDK model and can predict the time series of rain attenuation of the Ku-band signal during rain events for tropical locations.  
Limitations  
According to the results, there exists a 10% error compared to the measured value for predicting attenuation by this model.