esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Bacterial Community  
According to the sequencing results, the values of the \(\alpha\)-diversity indices (Sobs and Shannon indices) are presented in Figure 1a,b. Both of the indices decreased sharply on day 4 and then increased slightly with the extension of storage time, indicating that the richness and diversity of the bacterial community in vacuum-packaged peeled potatoes decreased rapidly first and then increased gradually.  
The distribution of the bacterial community at the genus level (Figure 2) showed that the bacterial community in peeled potatoes was mainly composed of _Ralstonia_ (43.08%), _Pseudomonas_ (13.66%), _Pantoea_ (8.48%), _Comomonas_ (5.37%), _Enterobacteriaceae_ (3.24%), _Brevundimonas_ (2.63%), _Lysobacter_ (2.59%), _Delftia_ (2.31%), _Limnobacter_ (2.1%), _Serratia_ (1.43%), _Bacillus_f_Bacillusae_ (1.2%) and others (13.77%) on day 0. The composition and abundance of the bacterial community in vacuum-packaged potatoes changed significantly during storage. A higher relative abundance of _Serratia_ (52.27%), _Pantoea_ (11.19%), _Erwinia_ (0.4%) and _Leuconostoc_ (0.03%) was observed in the samples on the 4 th day, while the abundance of _Ralstonia_, _Pseudomonas_, _Comomonas_ and other genera decreased (Supplementary Table S1). [PERSON] et al. [24] also found that the relative abundance of _Leuconostoc_ spp. Increased.  
Figure 1: Sobs (**a**) and Shannon (**b**) indices of the genus level of vacuum-packaged peeled potatoes during storage. The significant difference between the storage days, \(p\leq 0.05\), is marked as *, and \(p\leq 0.01\) is marked as **.  
whereas _Pseudomonas_ spp. decreased in fresh-cut lettuce during storage, which may have been due to the reduction of oxygen inside the package. On the 8 th day, the abundance of _Serratia_ (4.86%) decreased significantly, whereas the abundance of _Enterobacteriaceae_ (70.95%) and _Pantoea_ (22.51%) increased. On the 12 th day, the presence of anaerobic bacteria such as _Clostridia_ (6.81%), _Clostridium_ (19.64%) and _Lacrimispora_ (0.38%) (Supplementary Table S1) increased, which could have been due to the development of near-zero oxygen conditions [25]. [PERSON] et al. [26] reported that _Clostridium_ strains have the ability to produce ethanol through the fermentation of carbohydrate-rich sources, such as potatoes. _Lactococcus_ and _Leuconostoc_ were also detected on the 12 th day (Supplementary Table S1), which had previously been observed in vacuum-packaged peeled potatoes by [PERSON] et al. [27] and may result in sensorial quality loss by producing organic acids in fresh-cut produce [24; 28].
## 3 Implementation of RUL Prediction Based on TCNMABG
### Ablation Experiments
#### 3.5.2 Results of Ablation Experiments  
The RUL prediction results of Bearing1_5 based on four methods are shown in Figure 13. As can be seen from the figure, the RUL prediction curve obtained by the TCNMABG model is closer to the real RUL value than the other three ablation models, and the error curve is concentrated around 0 value, with smaller fluctuation amplitude compared with other models.  
Figure 13: RUL prediction curve and error curve of ablation experiment on test set bearings1_5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Experimental Scheme** & **BiGRU** & **MA** & **TCN** \\ \hline
1 & TCN & \(\times\) & \(\times\) & \(\surd\) \\
2 & TCN-MA & \(\times\) & \(\surd\) & \(\surd\) \\
3 & TCN-BiGRU & \(\surd\) & \(\times\) & \(\surd\) \\
4 & TCNMABG & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Usage of different modules in ablation experiment.  
To better illustrate the errors between the proposed method and the other three methods, the results are listed in Table 6. The results show that the prediction of the TCNMABG model on the test set is better than the other three ablation methods, with higher prediction accuracy and smaller prediction error.  
As can be seen from Table 6, the MSE predicted by RUL based on TCNMABG in the test set is 81.11%, 77.70%, and 22.11% lower than the other three methods, respectively, compared with TCN, TCN-MA, and TCN-BIGRU. Compared to the TCN benchmark model, the MSE metrics of the TCN-MA and TCN-BiGRU models are reduced by 15.29% and 75.75% for the test set, respectively. The results of the ablation experiment results show that the multi-head attention module and the bidirectional gated recurrent neural network module can improve the RUL prediction effectiveness of TCN. Therefore, the added MA and BG are essential to improve the performance of TCN.
# Antijamming Improvement for Frequency Hopping Using Noise-Jammer Power Estimator
## 5 Conclusions  
We proposed a NJPE and jammer detector based on gamma distribution for detecting jammed slots and combining nonjammed slots in FHSS systems. We theoretically derived the NCRB of the proposed estimator, and developed the jammer detector based on gamma distribution. By computer simulations, the derived NCRB was tested using NMSE, and BER performance for the proposed and the conventional methods was evaluated. The detection performance of the proposed method had a 15, 13, and 5 dB \(E_{b}/J_{0}\) gain compared to the conventional jammer detectors. The proposed scheme showed the best detection and BER performance for all scenarios.  
Figure 6: Bit-error-rate (BER) performance: (**a**) \(\rho=0.0005\), (**b**) \(\rho=0.005\), (**c**) \(\rho=0.05\).  
**Author Contributions:** Conceptualization, H.L. and J.C.; methodology, H.L.; software, H.L.; validation, J.A., Y.K., and J.C.; investigation, H.L., Y.K., and J.A.; writing--original-draft preparation, H.L.; writing--review and editing, J.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by Inha university research program.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Methodology
### Add Small-Target Detection Head  
After feature fusion, the detection layers of YOLOv8 output three feature maps with sizes of 80 \(\times\) 80, 40 \(\times\) 40, and 20 \(\times\) 20, which are used to detect objects larger than 8 \(\times\) 8, 16 \(\times\) 16, and 32 \(\times\) 32 pixels, respectively. Shallow feature maps with larger dimensions and smaller receptive fields contain more local details and positional information, making them suitable for detecting small objects. Conversely, deeper feature maps with smaller dimensions and larger receptive fields encode more complex semantic information, but lack distinct local details, making them more appropriate for detecting larger targets.  
However, real-world dense pedestrian detection scenarios often contain numerous small-scale pedestrians with pixel sizes below 8 \(\times\) 8 that are densely distributed. If the original algorithm continues to be used for detection, significant loss of small-target feature information will occur, leading to false positives and missed detections. To address these issues, this paper improves the original network architecture by adding a small-target detection layer with a 160 \(\times\) 160 feature map in the prediction head. This small-target detection layer can effectively extract features of pedestrians smaller than 8 \(\times\) 8 pixels, thereby improving the model's detection capability for small-scale pedestrians.
###### Abstract  
Assessment and verification of constancy of performance (AVCP) is a complex process. Without it, the manufacturer cannot mark the product with the Conformite Europeenne (CE) marking and place it on the EU market. The verification of the correctness of the AVCP is carried out by market surveillance. In Poland, supervision authorities regularly check manufacturers by collecting construction products that exist on the market for inspection. Among the dry-mix mortars, adhesives for ceramic tiles (CTA) constitute an essential group. The requirements for CTAs are specified in EN 12004. According to the standard, the basic characteristics of cementitious CTAs are tensile adhesion strength and open time. The adhesion measurements are performed after various laboratory conditions tests. The study analyzes the results obtained for 129 samples of cementitious CTAs, collected between the years 2016 and 2019 from the market, and tested in notified laboratories at the request of Polish construction supervision authorities. Many tested products did not meet the threshold values, which resulted in removing them from the market. The paper discusses the complexity of the tensile adhesion strength measurement. The obtained test results are considered in various dimensions, including using the simple acceptance rule method when evaluating the results, which does not consider the measurement uncertainty.  
assessment and verification of constancy of performance (AVCP); market surveillance; ceramic tiles adhesive (CTA); cementitious ceramic tile adhesives; tensile adhesion strength Article Assessment of the Constancy of Performance of Cementitious Adhesives for Ceramic Tiles: Analysis of the Test Results Commissioned by Polish Market Surveillance Authorities [PERSON], [PERSON] and [PERSON]  
Research and Development Center, Atlas sp. z o.o., 2, Kilinskiego St., 91-421 Lodz, Poland; [EMAIL_ADDRESS] (M.L.); [EMAIL_ADDRESS] (B.M.)  
* Correspondence: [EMAIL_ADDRESS]
## Nomenclature  
\begin{tabular}{l l} C & Speed [m/s] \\ \(C_{I}\) & Propagation speed [m/s] \\ \(C_{s}\) & Sound speed [m/s] \\ D & Dimension (LBM) \\ \(dx_{j}\) & The tangential to the wall \\ \(e_{i}\) & particle velocity [m/s] \\ F & External forces [N] \\ \(F_{v}\) & Body force [N] \\ \(f\) & Distribution function \\ \(f_{i}\) & Distribution function for a set of particles \\ \(f_{i}^{eq}\) & Equilibrium distribution function \\ \(g\) & Gravity acceleration [m/s\({}^{2}\)] \\ \(g_{i}\) & Energy distribution function \\ \(g_{i}^{eq}\) & Equilibrium energy distribution function \\ H & Dimensionless height of the cavity \\ \(H_{i}\) & Dimensionless height of the wall \\ \(K_{mn}\) & Central moment of order (m, n) \\ \(k_{m,n}^{eq}\) & Equilibrium central moment of order (m, n) \\ \(IT\) & Iteration Number \\ \(L\) & Dimensionless length of distance between heat sink \\ \(L^{\prime}\) & Dimensionless length of heat sink \\ \(L^{\prime\prime}\) & Dimensionless length of extraction opening \\ \(l\) & Dimensionless width of opening for supply air \\ \(M_{r},M_{T}\) & Transformation Matrix \\ \(n\) & Mesh \\ \(n_{i}\) & Normal on the wall \\ N\({}_{l}\),N\({}_{T}\) & Shift Matrix \\ \(Nu\) & Local Nusselt number \\ \(Nu_{g}\) & Global Nusselt number \\ \(N_{g}^{m}\) & Average global Nusselt number \\ \(Nu_{R-R}\) & Number of Nusselt on the right-hand side heat sink right \\ \(Nu_{R-l}\) & Number of Nusselt on the right-hand side heat sink left \\ \(Nu_{l-R}\) & Number of Nusselt on the left-hand side heat sink right \\ \(Nu_{l-l}\) & Number of Nusselt on the left-hand side heat sink left \\ \(Nu_{h-R}\) & Number of Nusselt of the source horizontal heat sink right \\ \(Nu_{h-l}\) & Number of Nusselt of the source horizontal heat sink left \\ p & Dimensionless static pressure \\ \(P_{r}\) & Prandtl number \\ Q & Direction (LBM) \\ \(R_{a}\) & Rayleigh number (\(Ra=\frac{gg\Delta TH^{p}}{\mu W}\)) \\ \(R_{e}\) & Reynolds number (\(Re=\frac{V_{0}H}{\
u}\)) \\ \(S\) & Source term \\ \(T\) & Fluid temperature [\({}^{\circ}\)C] \\ \(T_{0}\) & Reference temperature [\({}^{\circ}\)C] \\ \(T_{h}\) & Hot temperature [\({}^{\circ}\)C] \\ \(T_{c}\) & Cold temperature [\({}^{\circ}\)C] \\ \(t_{ref}\) & reference time \(t_{ref}=\frac{H^{2}}{a}\Delta x^{2}\) \\ \(time^{*}\) & dimensionless time \(=\frac{tT}{t_{ref}}\Delta t\) \\ \(U\) & Dimensionless speed on ox \\ \(V\) & Dimensionless speed on oy \\ \(\Gamma\) & Moment [N-m] \\ \end{tabular}
# Efficient Multipath Clutter Cancellation for UAV Monitoring Using DAB Satellite-Based PBR
## 4 Performance Analysis  
The computational cost and the clutter cancellation capability of ECA-C/LF are analyzed and contrasted in this section. Due to their advantages in convergence and intuitive parameters, the comparison is only among different ECA versions.
## 2 Materials and Methods
### Experimental Data and Remote Sensing Imagery  
Several satellite-synchronous remote sensing experiments in Hedi Reservoir were conducted between August and October 2015, and water surface spectra and water samples were collected. A total of 35 water samples were collected in this reservoir in August (N = 12) and October ((N = 23), 2015, including 29 valid samples (Figure 1b). Further, we referred to the measured TSS data from Poyang Lake, China's largest freshwater lake, to increase the applicability of the model. Poyang Lake's TSS data used in this paper were obtained by [PERSON] et al. (2018) and [PERSON] (2019) in June 2017. The data include a total of 36 samples and 19 among them with valid spectral data. Table 1 summarizes the variability of all the collected TSS concentrations from different locations. In total, there are 49 measured TSS data that range from 4 to 66 mg/L. All the data are divided into two parts, which are used for model calibration (34 sites) and validation (15 sites).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Date**} & \multicolumn{4}{c}{**Samples**} & \multicolumn{4}{c}{**TSS Concentration (mg/L)**} \\ \cline{3-10}  & & **Total** & **Calibration** & **Validation** & **Max** & **Min** & **Mean** & **Standard Deviation** \\ \hline \multirow{4}{*}{Hedi Reservoir} & August & \multirow{2}{*}{10} & \multirow{4}{*}{21} & \multirow{4}{*}{8} & \multirow{4}{*}{50} & \multirow{4}{*}{5} & \multirow{4}{*}{11.65} & \multirow{4}{*}{1.52} \\  & 2015 & & & & & & & & \\ \cline{1-1}  & October & \multirow{4}{*}{19} & \multirow{4}{*}{2015} & \multirow{4}{*}{13} & \multirow{4}{*}{7} & \multirow{4}{*}{66} & \multirow{4}{*}{4} & \multirow{4}{*}{28.48} & \multirow{4}{*}{3.64} \\  & 2015 & & & & & & & & \\ \hline Poyang Lake & June 2017 & 20 & 13 & 7 & 66 & 4 & 28.48 & 3.64 \\ \hline All & - & 49 & 34 & 15 & 66 & 4 & 18.52 & 2.09 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of total suspended solids (TSS) concentration (N = 49) from the synchronous in situ spectra collected at different sites during several in situ campaigns.  
Figure 1: (**a**) Location of the study area (Hedi Reservoir) and the location of the Hedi Reservoir Basin across Zhanjiang, Guangdong Province, and Yulin, Guangxi Province, in China, and (**b**) location of in situ data labeled by black stars and the distribution of sampling stations where different color lines represent different boundaries of towns.
## 5 Conclusions  
The colour gradient two-phase LBM was used to study dynamic displacement of immiscible fluids in a 2D micromodel of Berea sandstone. The effects of the viscosity ratio, interfacial tension, and contact angle on the fluid distributions at breakthrough and the evolution of displacing fluid saturation were systematically investigated. When the viscosity ratio is no more than 20, a higher viscosity ratio results in a lower displacing fluid saturation which is less favourable for the recovery of the displaced fluid. However, when the viscosity ratio is larger than 20, the saturation is almost constant for both imbibition and drainage, regardless of the interfacial tension. At the viscosity ratio of unity, the displacing fluid saturation has the highest value, and it linearly increases with time.  
The interfacial tension affects the flow pattern. When the interfacial tension is as low as 0.0005 mu\(\cdot\)ts\({}^{-2}\), thin viscous fingers appear. A number of small drops of displaced fluid are left behind. As the interfacial tension increases from 0.0005 mu\(\cdot\)ts\({}^{-2}\) to 0.015 mu\(\cdot\)ts\({}^{-2}\), the size of the trapped blobs increases, and the number decreases. The displacing fluid flows into large pores more easily due to the increased capillary pressure. When the interfacial tension continues to increase, smaller amount of the displaced phase is left behind as the invading phase continues to move forward. In addition, increasing interfacial tension results in a higher saturation of wetting fluid.  
The contact angle generally does not change preferential flow paths except for a few branches in the imbibition, suggesting that the contact angle has limited effect on the efficiency of displacement within 2D Berea sandstone.  
[PERSON] performed the simulations, analysed the results and wrote the paper, [PERSON], [PERSON] helped to analyse the results and write the paper.  
UK Engineering and Physical Sciences Research Council (EPSRC) under grant number EP/L00030X/1 and EP/K000586/17.  
The authors would like to thank the \"UK Consortium on Mesoscale Engineering Sciences (UKCOMES)\" (www.ukcomes.org) funded by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant number EP/L00030X/1 to provide computing time on ARCHER. The simulations were also performed on ARCHIE-WeSt funded by EPSRC (EP/K000586/17).  
The authors declare no conflict of interest.  
Figure 10: Evolution of the displacing fluid saturation for contact angles of \(162^{\circ}\), \(150^{\circ}\), \(135^{\circ}\), and \(120^{\circ}\).
# Fault ride-Through Control Strategy for Variable Speed Pumped Storage Unit with Full-Size Converter
## 4 Fault Ride-Through Control Strategies
### LVRT Strategy in Generation Condition
The criteria for injecting capacitive reactive current during voltage dips are specified below:  
\[i_{gqref}\geq K_{1}(0.9-e)i_{N},\ \ \ 0.15\leq e\leq 0.9 \tag{15}\]  
where \(i_{gqref}\) is the reference value of reactive current during the fault period; \(e\) is the system effective voltage during the fault period; \(i_{\text{N}}\) is the rated current of the GSC; \(K_{1}\) is the proportional coefficient of the dynamic reactive current. The dynamic reactive current control strategy of GSC is shown in Figure 10. Essentially, it uses variable operating factors to quantitatively compensate for reactive power, ensuring normal and stable power output. Therefore, the active support strategy for reactive current on the grid side can also suppress the increase in DC bus voltage. Considering that the use of a crowbar circuit can lead to energy waste in the FSC-VSPSU and that long-term operation of the resistor may cause overheating or even explosion. However, from the perspective of engineering reliability, the crowbar circuit is also necessary. When using the aforementioned single strategy, it is difficult to achieve satisfactory results. Therefore, a combination of strategies is proposed. Prioritizing the rotor energy storage control strategy allows the system's excess energy to be stored as rotor kinetic energy. If the fault is severe and the DC voltage exceeds the allowable value of 1.1 pu, the crowbar circuit should be activated to avoid excessive DC bus voltage. If the DC voltage remains above the tolerable value even after applying the crowbar circuit, the power anti-regulation strategy should be triggered. Meanwhile, reactive current injection control should be activated during the LVRT control.
# Design and Application of a Smart Lighting System Based on Distributed Wireless Sensor Networks
## 3 Materials and Methods
### Occupancy Sensing and Daylight Sensing
Light sensors are placed in the sensing modules equipped with the LEDs that are closest to windows. The illuminance value of daylight at time \(t\) detected by a light sensor \(m\) attached to LED \(n\) can be expressed as:  
\[d(t)=\left\{ \begin{array}{l}y_{m}(t),\text{ if LED }n\text{ is switched off }and\text{ }t\text{ is in daytime,}\\ y_{m}(t)-u_{n}^{L},\text{ if LED }n\text{ is switched on }and\text{ }t\text{ is in daytime,}\end{array} \right. \tag{1}\]  
where \(y_{m}(t)\) denotes the output of a light sensor \(m\) at time \(t\), and \(u_{n}^{L}\) denotes the illuminance value of LED \(n\) with a dimming level \(L\). The illumination contribution of luminaires to a light sensor is simplified as \(u_{n}^{L}\), which indicates that only the contribution of LED \(n\) colocated with the light sensor \(m\) is considered. The reason is that during the day, the amount of natural light coming in from a window will be relatively larger than the amount of light emanating from the LEDs. Besides, since most sensing modules are ceiling mounted, the coupling between LEDs can be neglected. \(u_{n}^{L}\) is obtained in the absence of daylight and stored by the sensing module. At right, the sensing module containing light sensor \(m\) dims LED \(n\) at level \(L\), with other luminaires off, and collects the measured illuminance value at light sensor \(m\). As illumination contributions due to the luminaires and daylight are additive [15], Equation (1) can be obtained. Figure 4: Flowchart of the data fusion method with passive infrared (PIR) and microwave sensors.
# Data-Driven Modeling of Low Frequency Noise Using Capture-Emission Energy Maps
## 3 Results and Discussion
It is observed in Figure 4a that the recoverable component of CEE map gives a \(1/f\) spectrum form with a slope of \(-1\). For asymmetric RTSs (\(\tau_{c,\mu n}/\tau_{c,\mu n}\)), cyclo-stationary condition may lead to an increase in noise power [28]. Figure 4b shows a linear relationship between \(S_{V_{g}}(\omega)\) and \(\Delta V_{th}\) after a stress time \(t_{s}=1000\) s. The same strong correlation is also observed for the device with SiON/poly-Si gate [20]. Figure 2: CEE map with fitted Gaussian mixture (GMM) using soft clustering. (**a**) Scatter plot of GMM data. (**b**) Comparison of GMM data with numerical data of CEE map.
## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.5.4 Justification and Interpretability  
We conducted a weight analysis on the question \"Before 7 October 2007, with whom did the Prime Minister of Bangladesh declare his intention to cooperate?\" First, we performed a normalization operation on the weights, converting the original weight values into weight coefficients ranging from 0 to 1. The variations in weight coefficients are illustrated in Figures 8 and 9. Before using the convolutional network, some words that are less relevant to the question, such as \"the\", \"of\", and \"to\", had relatively high weight coefficients. After employing the convolutional network, the weight coefficients of these words decreased, indicating that the model became more focused on information closely related to the question, such as \"before\", \"7\", \"declare\", and \"intention\". This demonstrates that the use of convolutional networks can better capture key information in the question, thereby enhancing the effectiveness of question answering.