esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Validation
Together, these comparisons underscore that convolution-augmented attention (ConvTransformer) best aligns predictions to observations, while pure attention (Transformer and Informer) also outperforms recurrent variants by reducing scatter and extreme errors. Figure 10 shows a Taylor diagram that the ConvTransformer (blue diamond) achieves the highest pattern correlation (r \(\approx\) 0.91) against observed groundwater depths, with a model spread of \(\sigma\approx\) 15 m and a centered RMS error of roughly 13 m. The pure Transformer (line circle) comes next, registering r \(\approx\) 0.83, \(\sigma\approx\) 12 m, and CRMSE \(\approx\) 12 m. The Informer (teal triangle) follows closely behind with r \(\approx\) 0.80, \(\sigma\approx\) 11 m, and CRMSE near 10 m. In contrast, the remaining architectures--CNN-LSTM, LSTM, and BiLSTM--fall farther from the target arc, reflecting their comparatively lower fidelity. Altogether, this ordering  
Figure 9: Scatter plots of observed vs. model-predicted groundwater level elevations. highlights the superior skill of the ConvTransformer's convolution-augmented attention for regional hydrological forecasting. A side-by-side comparison of each model's spatial performance across the network of in situ wells is shown in Figure 11. In the left column (panels a-f), the LSTM row shows moderate correlations (r \(\approx\) 0.6-0.8) but RMSEs that spike to \(\pm\)40 m around basin margins, while BiLSTM (second row) tightens correlation slightly but still exhibits pockets of 30-50 m error. In this context, pockets refer to specific clusters of data points where the BiLSTM model's error (standard deviation) is notably higher. The CNN-LSTM hybrid (third row) improves overall correlation to 0.5-0.9. Moving into pure attention, the Transformer (fourth row) yields correlations above 0.8 and reduces RMSE to 15 to \(-\)15 m, with only faint spatial bias around higher-elevation clusters. ConvTransformer (fifth row) further concentrates RMSE under \(\pm\)5 m almost everywhere, matching or exceeding the Transformer's correlation. Finally, the Pearson values are higher than 0.9 in almost all stations; the Informer (bottom row) uniformly achieves r \(>\) 0.80 and RMSE consistently within \(\pm\)15 m, with virtually no geographic \"hotspots\" of error. The right-hand Hovmoller panels reveal how each model's residuals vary over time and latitude. LSTM, CNN-LSTM, and BiLSTM exhibit pronounced seasonal stripes; high value residuals are located in northern areas. The best residual values correspond to the ConvTransformer method.
## 2 Materials and Methods
### Determination of Cholesterol and Oxysteros  
Extracted fat was diluted in 10 mL of hexane and 0.1 mL of 5\(\alpha\)-cholestane solution (40 mg/100 mL of trichloromethane--internal standard for sterols quantification) and 0.1 mL of 19-hydroxycholesterol solution (10 mg/100 mL of ethanol--internal standard for oxysterol quantification) were added to samples. Next, they were saponified with 0.5 mL of 2N potassium methoxide for 1 h at room temperature. Afterward, the solvents were removed under a stream of nitrogen, and the samples were derivatized due to the addition of 100 \(\upmu\)L of anhydrous pyridine (Sigma-Aldrich, St. Louis, MO, USA) and 100 \(\upmu\)L of silylation agents (Trimethylsilyl 2,2,2-trifluoro-N-trimethylsilylethanimide with the addition of 1% of trimethylchlorosilane from Sigma-Aldrich, St. Louis, MO, USA) and left for 24 h at room temperature. Cholesterol and oxystero's were analyzed on a gas chromatograph coupled with the mass spectrometer Shimadzu GCMS-QP2010 SE (Shimadzu, Kyoto, Japan). The separation of cholesterol and oxystero's was carried out with the usage of the RTX-5 (30 m \(\times\) 0.25 mm \(\times\) 0.1 \(\upmu\)m) capillary column Restek (Bellefonte, PA, USA), and the volume of the injected sample was 1 \(\upmu\)L (splitless mode). A helium flow rate of 1.18 mL/min was used. The injector temperature was 275 \({}^{\circ}\)C, and the column temperature was programmed as follows: 65 \({}^{\circ}\)C for 2 min, subsequently increased to 250 \({}^{\circ}\)C at the rate of 15 \({}^{\circ}\)C/min, and then to 310 \({}^{\circ}\)C at the rate of 5 \({}^{\circ}\)C/min for 6 min. The interface temperature for GC-MS was 255 \({}^{\circ}\)C. The temperature of the ion source was 250 \({}^{\circ}\)C, and the ionization energy was 70 V. Total ion monitoring (TIC) was used to detect sterols and oxysterols (\(m/z\) ranged from 100 to 600). The identification of sterol and oxysterols was made based on mass spectral libraries (NIST 47, NIST 147, and Wiley 175) as well as data from the literature and by a comparison of their retention times with the authentic standards, including cholesterol, 7\(\beta\)-OH, 7\(\alpha\)-OH, triol, and 7-ketocholesterol (Sigma-Aldrich, St. Louis, MO, USA) [23]. The limit of detection for sterols was between 0. and 0.5 ppm. The limits of detection for particular COPs were between 0.2 and 5 ppm. All samples were analyzed in triplicate.
# High-Throughput Cross-Technology Communication via Chip-Level Side Channel
## 3 Framework Design
### On-OFF Keying-Based Modulation/Demodulation  
#### 3.2.1 Modulation  
Intuitively, On-Off keying modulates CTC bit '1'/'0' by modifying chips to be the extended sinusoidal waveform or not. In other words, if we want to modulate \"0\", we do not need to modify the chips. In this case, however, we cannot distinguish CTC bit \"0\" with the case where there are no CTC data. If we employ On-Off keying to modulate CTC bits directly and there are no CTC data, we would treat this ZigBee signal as CTC bit \"0\". Therefore, for each CTC bit, we need a reference point to inform the CTC receiver that the next ZigBee symbol contains CTC bit.  
Our modulation scheme can be illustrated in Figure 10. In the first symbol, there is an extended sinusoidal waveform in the first ZigBee symbol indicating that this symbol is a reference symbol and next symbol contains CTC bit. Since there is no extended sinusoidal waveform detected in the second ZigBee symbol, the second ZigBee symbol is determined to be CTC bit '0'. On the third chip of second symbol, there is an extended sinusoidal waveform indicating the reference. Since there is an extended sinusoidal waveform in the fourth ZigBee symbol, the fourth ZigBee symbol represents the CTC bit \"1\".
## 2 Dataset and Methods
### GNSS-R Data Processing
These footprints were then tested for their enclosure within imaginary polygons (allowing for an additional 20 m buffer, i.e., \(\sim\)1.5 ra) delimiting the plots. In the present study, to avoid the potential influence of strong noise resulting from cross-polarization isolation degradation for elevation angles lower than 50\({}^{\circ}\)[30], as well as multi-scattering effects with the direct and reflected signals (as described in [32]), elevations ranging between 50\({}^{\circ}\) and 90\({}^{\circ}\) only were considered.
## 5 Conclusions  
The research results presented in this paper indicate that aging processes occur with different intensities depending on the soldering alloy used. Diodes soldered with the classic SACX0307 alloy were damaged most quickly. Most diodes soldered with this alloy failed before testing was completed, and some were damaged after 100 h of the test.  
For diodes soldered using an alloy with a TiO\({}_{2}\) addition, no damage to the semiconductor chip occurred during the test. Damage to the diodes soldered using alloys with a TiO\({}_{2}\) addition involved damaging the lens, which reduced the value of the luminous flux of the emitted light.  
During the aging tests lasting 1000 h and conducted at an elevated temperature (equal to 80 \({}^{\circ}\)C), no significant deterioration of the thermal and optical parameters of the tested diodes was observed. This means that according to the classical relationships describing the lifetime of electronic devices [25], the activation energy of the damage process has a minimal value. The lifetime of the tested diodes exceeds tens of thousands of hours. Using a soldering alloy with a TiO\({}_{2}\) addition of 1% ensures a much longer lifetime than that obtained for a classical soldering alloy. The obtained results prove that the use of soldering alloy with TiO\({}_{2}\) reinforcements is profitable for the users of power LEDs and can be used in the electronic industry.  
The presented research results may be useful for technologists who assemble solid-state light sources. They may also provide guidance for researchers developing new soldering composite alloys. In further investigations the authors will examine the influence of the aging process on the spectral power distribution of the tested devices and luminance distribution on the lighting surface. Another scientific problem for future consideration concerns research on the optimal value of the concentration of TiO\({}_{2}\) reinforcements in the soldering alloy.  
Conceptualization, K.G., P.P. and A.S.; methodology, K.G. and P.P.; validation, K.G., P.P. and A.S.; investigation P.P. and A.S.; writing--original draft preparation, K.G. and P.P.; writing--review and editing, K.G., P.P. and A.S.; visualization, P.P.; supervision, K.G. and A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data available for request.  
The authors declare no conflict of interest.
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 5 Environmental Cues Affecting Biofilm Formation
### Arginine and Urea  
Proteins involved in colonization, such as urease and ferritin, are upregulated in biofilm cells [31]. Urease and ferritin are important for acid environment conditioning before colonization. _H. pylori_ utilize arginine and urea as key environmental cues in biofilm formation. The bacterium possesses multiple chemoreceptors, including TlpA, TlpB, TlpC, and TlpD, which are crucial in sensing these cues. Studies have shown that deleting specific chemoreceptors can impact biofilm formation differently. For instance, the strain lacking TlpA exhibited enhanced initial biofilm formation, while the strain lacking TlpB showed reduced biofilm formation [33].  
It has been observed that arginine promotes the growth of biofilms, while urea hinders their development in _H. pylori_. This is significant as _H. pylori_ possesses the rocf gene encoding arginase, which is involved in the pathogenesis of H. pylori infection [70]. Additionally, the urease enzyme in _H. pylori_ catalyses the hydrolysis of urea to ammo-nia and carbon dioxide, which can influence biofilm development.
## 2 Materials and Methods
### Data Collection and Analysis  
Throughout the experiments, every 24 h or for longer time intervals, fungal colonies were visually inspected and photographed; moreover, for each sample, two perpendicular diameters were measured, by means of a caliper, and averaged. Fungal colony growth rate was calculated according to (Equation (1)), where \(\text{d}_{1}\) and \(\text{d}_{2}\) are the averaged diameters, measured at two consecutive incubation times, that is \(\text{t}_{1}\) and \(\text{t}_{2}\):  
\[\text{Growth rate}=(\text{d}_{2}-\text{d}_{1})/(\text{t}_{2}-\text{t}_{1}) \tag{1}\]  
Known the average diameter of the control, dC, and that of the treated samples, dS, at a certain treatment time, the Minimum Growth Inhibition (MGI%) was calculated according to Equation (2):  
\[\text{MGI}\%=(\text{dC}-\text{dS})/\text{dC}\times 100 \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Section** & **Diffusion** & **Media** & **Petri \(\boldsymbol{\varTheta}\) (cm)** & **Temperature** (\({}^{\circ}\)C) & **EO Amount** & **Disk Removal Time Time** & **Duration (Days)** & **Inoculum Type/Age (h)** \\ \hline
**Section 3.1.** & MAM * & MEA & 9 & 30 & \
\begin{tabular}{c} 5, 7.5, 10 \(\mu\)L \\ 50, 75\({}^{\circ}\)\(\
u\)/\(\
u\) \\ in DMSO \\ \end{tabular} & 7 days & 42 &
\begin{tabular}{c} M \\ 72 \\ \end{tabular} \\ \hline
**Section 3.2.** & MAM * & MEA & 9 & 30 & 10 \(\mu\)L & \
\begin{tabular}{c} 6 h \\ 1, 2, 7 \\ days \\ \end{tabular} & 42 &
\begin{tabular}{c} M \\ 72 \\ \end{tabular} \\ \hline
**Section 3.3.** & \
\begin{tabular}{c} GAC, \\ Beaker ** \\ \end{tabular} & MEA & 9 & 25 & 420, 210 \(\mu\)L & - & 42 &
\begin{tabular}{c} M \\ 72 \\ \end{tabular} \\ \hline
**Section 3.5.** & \
\begin{tabular}{c} GAC, \\ paper disk * \\ \end{tabular} & CZ\({}_{\text{mod}}\) & 5.5 & 25 & 210 \(\mu\)L & - & 21 &
\begin{tabular}{c} M \\ 72 \\ \end{tabular} \\ \hline
**Section 3.5.** & \
\begin{tabular}{c} GAC, \\ Beaker ** \\ \end{tabular} & CZ\({}_{\text{mod}}\) & 5.5 & 25 & 10 \(\mu\)L & - & 8 &
\begin{tabular}{c} S \\ 24, 72, 96 \\ \end{tabular} \\ \hline
**Section 3.5.** & \
\begin{tabular}{c} GAC, \\ Device ** \\ \end{tabular} & CZ\({}_{\text{mod}}\) & 5.5 & 25 & 10 \(\mu\)L & - & 24 &
\begin{tabular}{c} S \\ 72 \\ \end{tabular} \\ \hline \hline \end{tabular}  
* Vapor contact; ** Vapor contact; ** Vaporisation. GAC = glass airtight container; M = mycelial plug; S = spore suspension.  
\end{table}
Table 1: Details of the experiments in the presence of thyme EO.
# Current Status of Optical Systems for Measuring Lycopene Content in Fruits: Review
## 4 Current Systems for Estimating Lycopene  
It is necessary to highlight that these optical systems have elements, such as multiple lenses, mirrors, prisms, and windows. In this sense, the measurement of lycopene is determined with the interaction of light and the optical properties of the samples [49]. This section deals with the optical systems for the identification, estimation, and measurement of lycopene. These can be grouped in high-performance liquid chromatography (HPLC), colorimetry, UV-Vis spectroscopy, IR spectroscopy, Raman spectroscopy, and multispectral and hyperspectral imaging systems. The analysis of these was focused on the mode of acquisition of lycopene information, the region of the electromagnetic spectrum, and the processing of the study sample.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multicolumn{2}{c}{**Exhibition**} & \multicolumn{4}{c}{**Lycopene Concentration mg/Kg**} \\  & **Time** & & & & & \\ \hline  & & **0 Days** & **5 Days** & **10 Days** & **15 Days** & **20 Days** \\ \hline Dark (Control) & 24 h & 42.07 & 44.22 & 131.92 & 961.37 & 871.16 \\ \hline \multirow{3}{*}{Dark + UV} & Only two UV & & & & & \\  & radiation of & - & 54.61 & 49.6 0 & 855.14 & 850.84 \\  & 15 min daily & & & & & \\ \hline Red light & 24 h & - & 40.92 & 1049.08 & 1176.50 & 1507.17 \\ \hline \multirow{3}{*}{Red light + UV} & 24 h of red light & & & & & \\  & with two UV & - & 54.65 & 1280.18 & 1324.43 & 1413.91 \\ \cline{1-1}  & radiation of & 15 min daily & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Lycopene content, per kg tomato irradiated by red light + UV [34].
## 6 Simulation Study
### Simulation Configurations  
We use the ns-2 network simulator to evaluate the hop-by-hop worm propagation strategy in a mobile sensor network. In our simulation, 100 mobile sensor nodes are placed within a square area of 500 m \(\times\) 500 m. Furthermore, In order to configure node movement patterns, we use the Random Waypoint Mobility (RWM) model with the steady-state distribution provided by the Random Trip Mobility (RTM) model [17]. In particular, we generate a steady-state version of RWM model by using the program codes of [18].  
We divide the entire time domain into 25 time slots and one time slot is 20 simulation seconds. Accordingly, all simulations are performed for 500 simulation seconds. We fix a pause time of 10 simulation seconds and a minimum moving speed of 1 m/s of each node. Each node uses IEEE 802.11 as the medium access control protocol in which the transmission range is 50 m. To investigate how node mobility affects hop-by-hop worm propagation strategy with the carryover epidemic model, we vary each node's maximum speed (Vmax) in the range of 20, 40, 80 m/s. In the discrete time version of carryover epidemic model, we configure the pairwise infection rate \(\theta=\) 0.005, 0.01.  
Moreover, we assume that a susceptible node is infected by a single worm packet whose size is 46 bytes. This is reasonable because a single worm packet infection indicates the best case for the attacker and a data packet consists of 11 byte header, 28 byte data, and 7 byte meta information in Tiny OS 2.x.
## 5 Security Analysis and Comparison  
An important part of an image encryption algorithm is security analysis. This section assesses its effectiveness from four angles in order to define the level of security: histogram analysis, key sensitivity, robustness, and as well as correlation.  
Figure 11: Lyapunov exponent distribution for 3D Gauss map.  
Figure 10: Horizontal, vertical, and diagonal correlation coefficients of the original (**left**) and encrypted (**right**) Lena image.
## 4 Maximization of Weighted Sum Rate Based on ZF Scheme
### Problem Statement  
In this paper, we take \(\widetilde{R}_{all}=\sum_{k=1}^{K}\mu_{k}\widetilde{R}_{k}\) as our optimization objective, aiming to maximize the system's weighted sum rate based on the ZF scheme by designing the precoding matrix \(\mathbf{W}\). Specifically, the optimization problem can be described as  
\[\mathbf{1}: \max_{\mathbf{W}} \sum_{k=1}^{K}\mu_{k}\Big{[}1-\frac{1}{2\ln 2}-\frac{\mathcal{F}( \frac{n_{\text{s}}\mathbf{g}_{k}^{T}\mathbf{w}_{k}}{\sqrt{\lambda_{k,1}}})+\mathcal{F}( \frac{n_{\text{s}}\mathbf{g}_{k}^{T}\mathbf{w}_{k}}{\sqrt{\lambda_{k,2}}})}{4\ln 2} \Big{]}\] s.t. C1: \[n_{\text{s}}\sum_{k=1}^{K}\mathbf{w}_{k}S_{k}+\mathbf{d}\succcurlyeq\mathbf{0},\] \[\text{C2:}\quad n_{\text{s}}\sum_{k=1}^{K}\mathbf{w}_{k}S_{k}+\mathbf{d} \preccurlyeq\frac{P_{\max}\tau}{\hbar v}\mathbf{1},\] (16) C3: \[\|\mathbf{W}\|_{\infty}\leq 1,\] C4: \[\mathbf{g}_{k}^{T}\mathbf{w}_{j}=0,\forall j\
eq k,\quad\forall j,k=1, \cdots,K,\] C5: \[\lambda_{k,1}=-\zeta n_{\text{s}}\mathbf{g}_{k}^{T}\mathbf{w}_{k}+\zeta g _{k}^{T}\mathbf{d}+n_{\text{b}},\quad\forall k=1,\cdots,K,\] C6: \[\lambda_{k,2}=\zeta n_{\text{s}}\mathbf{g}_{k}^{T}\mathbf{w}_{k}+\zeta g _{k}^{T}\mathbf{d}+n_{\text{b}},\quad\forall k=1,\cdots,K,\]  
where \"\(\succcurlyeq\)\" and \"\(\preccurlyeq\)\" denote element-wise inequalities, and \(\mathbf{0}\) and \(\mathbf{1}\) represent \(N\)-dimensional column vectors with elements all being \(0\) and \(1\), respectively. To sustain a linear conversion from current to light, \(\mathbf{P}\) in (2) must be limited to the range \([0,P_{\max}]\), where \(P_{\max}\) represents the maximum transmit power that the LED array can withstand. Correspondingly, C1 signifies the non-negativity requirement, and C2 represents the upper limit on the transmit power. C3 is derived from the requirement for normalized precoding design. C4 is obtained from the content in Section 3.2, which aims to ensure the utilization of ZF in our approach, without excluding the utilization of a pseudo-inverse approach. C5 and C6 represent the mean values of the Poisson distribution when \(S_{K}\) is \(1\) or \(-1\), respectively.
# An Attention-Based Method for Remaining Useful Life Prediction of Rotating Machinery
## 2 Attention-Based RUL Prediction of Rotating Machinery  
The attention mechanism was proposed by a team from Google in 2017 [17], inspired by the observation that humans tend to selectively focus on some salient regions in complex scenes instead of paying attention to all regions indiscriminately. Attention-based RUL prediction for rotating machinery involves two processes: qualitative analysis and quantitative analysis. The model consists of a performance evolution state classification module, a selective feature extraction module, and an RUL prediction module, as shown in Figure 1. The purpose of qualitative analysis is to classify the performance evolution states through clustering; quantitative analysis reconstructs the network channel weights assigned by the attention mechanism based on the classification result.
## 4 Discussion  
With the installation of motion sensors for lighting, the expected reduction of monthly electricity energy consumption is between 30% and 40% lower compared to the one without sensors installation, with 30% reduction being for peak usage in the beginning of the first semester, and 40% for semester breaks. Base measurement yields power consumption of 55.576 kWh, with base electricity fare of 1.467,28 Rp/kWh; this costs Rp 327.356,04 per month or Rp 23.6 Million annually for just six restrooms only. This figure is constant for each month without modification to the electricity installation. From Table 1, the expected reduction of 40% is firmly achieved after 14 hours of operation, and after 28 hours, it only consumes half than without sensors for the same duration. The first phase of implementation on restrooms yields a cost saving of Rp 15 Million.  
Annual CO2 emission equivalent for normal operation without sensor can be calculated as a product of electrical power consumption (664 Wh) and carbon factor coefficient (0.00785 kg/W [5]) which yield 1902.57 kg/year). With 10 restrooms, total CO2 emission amounts to 19.03 tons/year. For annual CO2 reduction, ERP must be factored by 60.83 [5], and the resulting annual reduction is 1.812 \(\text{ICO}_{2}\)/MWh/year when implemented for all restrooms; projected contribution in reducing CO2 emission is calculated at 10.95 tons/year. With carbon credit pricing at USD 11.5 per tons of CO2 equivalent for energy efficiency [5], this project could potentially save another Rp 1.7 Million.  
A further phase of implementation would include sensors installation on corridors, replacement of fluorescent lamps with LEDs, and rooftop Photo-voltaic Panels installation on all buildings, expected to reach the target of 150 tons of CO2 equivalent emission reduction. When the implementation is completed, this installation could be optimized by Building Information Modelling [14] and evaluated in the future by the use of Building Design Index and Building Operation Index [15].  
This project is initiated by [PERSON] and [PERSON], cost analysis is performed by [PERSON] and environmental aspect is by [PERSON], with method and guidance provided by [PERSON].  
This project is funded by Universitas Budi Luhur and supported by Yayasan Penditikan Budi Luhur Cakti.  
The authors declare no conflict of interest.  
Figure 4: Power measurement comparison graph.
# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
## 3 Data and Methods
### Product Intercomparison  
In order to further evaluate the extent to which this new mapping approach contributed new information, we compared GRAS-SWD results with two global surface water products JRC-GSWE and GLAD-GSWD. The JRC-GSWE product is readily available as a monthly product; however, the GLAD-GSWD product is available as monthly frequency and therefore, a threshold of 50% was used to convert it to a binary product. For each product, the total area of permanent and seasonal water was calculated and evaluated across the time series, using the definition described in Section 3.3. Furthermore, various locations characterized by specific hydrological features (i.e., Poyang Lake, Miyun reservoir, Hunan province and an aquaculture area located near Haizhou bay) were identified and visually compared to demonstrate the improved details of a 10 m surface water product, relative to the 30 m products.