esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of High-Performance Computing Job Scheduling Based on Offline Reinforcement Learning
## 4 Experimental Results and Analysis
### Results and Analysis  
To evaluate the effectiveness of the proposed algorithm, we conducted comparative experiments using trajectories from various historical datasets. These datasets included the following: a random trajectory dataset from random scheduling, an expert-guided optimal trajectory dataset, a replay dataset strictly following trajectories generated by RLScheduler [22] during training, and a hybrid dataset composed of 80% expert-guided optimal trajectories and 20% early-stage trial trajectories from the RLScheduler.  
We tested two representative optimization metrics: average waiting time (AVGwt) and average bounded slowdown (AVGbsld). AVGwt measures the quality of service (QoS) by calculating the average waiting time for all jobs, denoted as M, in the system. In HPC systems, shorter average waiting times indicate better scheduling efficiency. As an important QoS indicator, AVGwt is calculated as follows:  
\[AVGwt=\frac{1}{M}\sum_{v}t_{wait}^{v} \tag{7}\]  
Since both the average bounded slowdown and average waiting time can only be computed after the entire job sequence has been scheduled, we set intermediate rewards for individual operations to zero during the reward calculation phase. The final reward is only computed upon the completion of the last job in the sequence. Once the entire sequence is scheduled, we collect the trajectory that includes the final reward for analysis.
# An Adaptive Multitask Network for Detecting the Region of Water Leakage in Tunnels
## 5 Results
### Stability Results  
To prove the stability of the algorithm proposed in this paper, we selected images with additive noise, images with chaotic backgrounds, flipped images, photographs with uneven illumination, and images with object occlusion as the test set to test the stability of the model. Part of the visualization results are shown in Figure 11.  
As can be seen from Figure 11, for the image with noise added in Figure 11a, the model in this paper can suppress noise and other interference factors and extract details of the edge of the leakage area. Figure 11b is an image with chaotic background and multiple bolt holes in the image, and the model in this paper can also effectively segment the leakage area. For flipped photographs, uneven light images, and blocked images, the proposed model can still segment the target region, which proves the stability of the proposed model. The algorithm's stability can be proved by the changes in indexes during the test using images of different environments. The changes in _MIOU_, _Dice_, _PA_, and \(F1\) in different environments are shown in Table 4.  
As can be seen from Table 4, when the image with complex background is tested, _Dice_ is 95.6%, _MIOU_ is 90.7%, _PA_ is 98.5%, and \(F1\) is 95.1%, which is because the data set also contains images with complex environments. When the image with noise is tested, _Dice_ is 94.8%, _MIOU_ is 90.3%, _PA_ is 98.1%, \(F1\) is 94.6%, and the index value is relatively high. In other environments, the model's performance indicators are still good. The stability of the model is proved quantitatively by the changes in the indexes in different environments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Environments** & **Dice** & **MIOU** & **PA** & **F1** \\ \hline Additive noise & 0.948 & 0.903 & 0.981 & 0.946 \\ Chaotic backgrounds & 0.956 & 0.907 & 0.985 & 0.951 \\ Geometric modifications & 0.946 & 0.898 & 0.972 & 0.946 \\ Uneven illumination & 0.949 & 0.896 & 0.965 & 0.939 \\ Object occlusion & 0.958 & 0.909 & 0.961 & 0.938 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The changes of MIOU, Dice, PA and F1 in different environments.  
Figure 11: Visualization results in different environments. (**a**) The image with additive noise; (**b**) The image with chaotic backgrounds; (**c**) The flipped image; (**d**) The image with uneven illumination; (**e**) The image with object occlusion; (**f–j**) Ground truth; (**k–o**) Visualization results of water leakage area segmentation.
###### Abstract  
Soil moisture (SM) is evidenced to dominate the interannual variability and trend of regional gross primary production (GPP) in the context of increasing drought and heat extremes, yet only a few light-use efficiency (LUE)-based GPP models consider SM stresses in modeling practice. This study utilized high-resolution GPP observational data collected from 16 flux tower sites in the US and Europe, integrating soil moisture and vapor pressure deficit (VPD) data to optimize the parameters of two typical LUE models (TL-LUE and VPM) and perform sensitivity analyses to assess the impact of SM and other moisture indicators on model performance. Our findings reveal that incorporating soil moisture (SM) significantly enhances GPP simulations, particularly in grassland ecosystems, where SM greatly improves model performance. However, in water-stressed forests, alternative indicators like VPD proved more effective, highlighting the challenges of modeling GPP in these ecosystems and the need for refined approaches. The results underscore the importance of adopting ecosystem-specific strategies when enhancing LUE models to better capture the impacts of water stress. This study provides valuable insights into improving GPP simulations under increasing droughts and climate change, emphasizing the necessity of tailored approaches for different ecosystem types.  
Keywords:gross primary productivity (GPP); light-use efficiency models; drought impact; soil moisture; ecosystem modeling optimization
## 2 Proposed Method
### Tone Mapping Operator (TMO)  
The previous sub-section has introduced how to extract the salient regions. Now this sub-section will discuss the proposed TMO in detail. The proposed TMO is given the images in CIE XYZ tristimulus values, likely linear and absolute values. The absolute luminance Y channel is in the unit of cd/m\({}^{2}\). The input luminance value \(L_{in,\ i}\) is the product of combining the key value of the scene and the visual gamma value, as  
\[L_{in,\ i}(x,\ y)=\alpha_{i}L_{i}(x,\ y)^{\gamma_{y,\ i}};i\in\{X,\ Y,\ Z\} \tag{20}\]  
where \(L_{i}\) is the luminance value and \(\alpha_{i}\) refers to the key value of the scene, which is used to enhance the contrast (or brightness) in the given HDRI as suggested in ref. [16]. The parameter \(\gamma_{y,\ i}\) is defined  
Figure 2: Attentional gaze and detail layer extraction: (**a**) original images, (**b**) attentional gaze extraction, (**c**) detail layer extraction.  
as a visual gamma [17]. Now the conventional tone mapping methods commonly use the gamma correction to scale the dynamic range, but they show several color distortion problems. To overcome such significant drawbacks, the proposed method adopts the visual gamma as a means to control the dynamic range according to the input HDRIs, with the key value of scene and visual gamma correction set automatically as a result. Figure 3 shows the characteristic curve resulting from using the visual gamma correction based on [17]. Therefore, the visual gamma correction will bring better performance in scaling dynamic range than conventional gamma correction methods.  
Based on Equation (20), the proposed TMO (\(L_{TMO,\,i}\)) is described as  
\[L_{TMO,\,i}(x,\,y)=\frac{L_{i}(x,\,y)}{1-L_{in,\,i}(x,\,y)};i\in\{X,\,Y,Z\} \tag{21}\]  
where \(L_{i}(x,\,\,y);i\in\{X,\,\,Y,\,Z\}\) is the CIE XYZ tristimulus values in the given HDRIs. \(L_{TMO}\) is the final outcome of the tone-mapping.
# Long-Term Dynamics of Persistent Organic Pollutants in Water Bodies of the Aral Sea-Syrdarya Basin
## 3 Results and Discussion
### Small Aral Sea
A higher content of the toxicants was registered in the water of the near-river zone and bays (Figure 9). PCBs found in the Small Aral Sea water were characterized by a wide range of congener compositions (Figure 9). Twenty-one individual congeners were registered in 2021 and 14 congeners in 2022. Among these were \"marker\" PCB congeners 52, 138, and 153 and dioxin-like PCB congeners 105, 114, and 118. \"Marker\" congeners comprised 7 to 37% of the individual samples in 2021 and 9 to 19% in 2022; dioxin-like congeners comprised 12 to 15% in 2021 and 9 to 16% in 2022. The protection of the natural environment and the population from the POPs' impact is one of the most acute problems for the whole territory of Kazakhstan. In terms of POP waste stockpiles, Kazakhstan ranks second among Central, Eastern Europe, and CIS countries after Russia. At the present time, a large amount of PCB-containing equipment \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Sampling Sites**} & \multicolumn{1}{c}{**DDT**} & \multirow{2}{*}{**Hexachlorobenzene**} & \multirow{2}{*}{**Heptachlor**} & \multirow{2}{*}{**Aldrine**} & \multirow{2}{*}{**HCCH (\(\alpha\), \(\beta\), \(\gamma\)–Isomers)**} \\ \cline{1-1} \multicolumn{1}{c}{} & **(Metabolites)** & & & & \\ \hline East coast & 0.006 & n/d & n/d & n/d & 0.002 \\ Central zone & 0.007 & n/d & n/d & 0.001 & 0.001 \\ Northeast zone & 0.012 & n/d & n/d & 0.001 & 0.001 \\ Shevchenko Bay & 0.002 & n/d & n/d & 0.002 & 0.003 \\ Butakov Bay & 0.016 & n/d & n/d & 0.001 & 0.003 \\ Near the Kokaral dam & 0.006 & 0.001 & 0.001 & n/d & 0.002 \\ Lower embankment of the & 0.001 & n/d & n/d & n/d & 0.002 \\ Kokaral dam & & & & & \\ \hline \hline \end{tabular} Note: n/d—no data (no toxicant).  
\end{table} 
Table 3: Organochlorine pesticides in the water of the Small Aral Sea (\(\mathrm{\SIUnitSymbolMicro g}\)/L). Figure 9: PCB contents and their congeners in the water of the Small Aral Sea and the Kokaral dam downstream. at 8 PCB-contaminated sites (areas) of about 2500 ha was identified in the territory of the Kazakh Republic. In other natural objects and ecosystems of water reservoirs of Kazakhstan, POPs are practically not studied. These xenobiotics are not monitored by the Kazakh Hydrometeorological Survey and other authorities of the Republic of Kazakhstan. However, the results of studies conducted in some years show the stable pollution of PCBs and OCPs in the water resources of the main transboundary river basins of Kazakhstan [2,66,67].
# Grating-Assisted Fiber to Chip Coupling for SOI Photonic Circuits
## 9 Conclusions  
In summary, we review the recent advances on SOI based surface grating couplers. To date, grating couplers have proven to be the most versatile way of ensuring on-chip fiber coupling; they are compatible with both CMOS fabrication and large scale assembly and testing. Although still lagging in coupling performance compared to in-plane edge couplers, newer non-uniform designs based on both shallow-etched as well as subwavelength gratings, promise to bridge this divide at least in terms of efficiency, if not bandwidth. Moreover, emerging concepts like compact tapers paves the way for a significantly reduced footprint with much more relaxed fabrication constraints. A grating based approach to fiber-chip coupling offers numerous benefits such as compatibility with planar processing and facilitating wafer-scale testing and assembly. In addition, the relaxed fabrication and alignment tolerances of gratings couplers could offer low-cost packaging solutions.  
[PERSON] conceived, devised and supervised the manuscript preparation; [PERSON] designed, fabricated, performed experiments and analyzed data on subwavelength and focusing gratings; [PERSON] designed, fabricated, performed experiments and analyzed data on linear grating based compact tapers; [PERSON] and [PERSON] performed numerical modelling and simulations; [PERSON] and [PERSON] wrote the manuscript in consultation with S.K.S.  
This research was in part funded by DRDO, MeitY and DST Nano Mission. [PERSON] would like to acknowledge N-PDF fellowship from DST-SERB.  
Use of the following facilities at CeNSE; National nanofabrication center (NNFc) and Micro and nano characterization facility (MNCF) is acknowledged. The authors would like to acknowledge assistance of [PERSON] and [PERSON] in carrying out some of the device fabrication and characterization.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SOI & Silicon-on-Insulator \\ DBR & distributed Bragg reflector \\ PSGC & polarization splitting grating coupler \\ PDL & polarization dependent loss \\ TE & transverse electric \\ TM & transverse magnetic \\ SWG & subwavelength grating \\ \end{tabular}  
Figure 18: Two prominent fiber-chip packaging schemes. (**a**) Vertically mounted rigid fiber array coupling from g-pack, a packaging service offered by ePIXpack ( image reprinted with permission from [65]). (**b**) Multiarrayed fiber packaging with quasi-planar coupler at input and output ends of a photonic integrated circuit (Image courtesy of Tyndall National Institute).
# Multiband Frequency Tuneable Antennas for Selection Combining Strategy in White Space Applications
## 2 Antenna Design  
### Antenna Structure  
The proposed antenna design is based on the folded monopole structure with dimensions of 27 \(\times\) 8 \(\times\) 2 mm\({}^{3}\), which is suitable for a tablet device. The radiating element was placed in a compact area of 30 \(\times\) 8 mm\({}^{2}\) at the corner of the printed circuit board. Furthermore, an open stub was added to improve the antenna impedance matching, as shown in Figure 1. The digitally tuneable capacitor was soldered onto the monopole. To bias and control this component, five strip lines were designed to connect the DTC to the control unit. RF block inductors were used to reduce the influence of DC and I2C signals on antenna performance.  
The equivalent circuit of the antenna is shown in Figure 2. The proposed antenna generally consists of three main parts: the matching circuit, the radiating element, and the DTC model. First, the matching circuit was designed as an open stub at the antenna input. Changing the length or width of this element allows for the antenna impedance to be matched for different values of the DTC capacitor. The open stub dimensions were chosen so that the antenna impedance was optimal at the intermediate values of the DTC, and less good for the higher and lower capacitance values. Due to this concept, antenna coverage could be ensured for all frequencies when the DTC was reconfigured. Second, the radiating element is described by a group of RLC components. These components'  
Figure 1: Antenna geometry.  
values are characterized on the basis of the antenna shape and dimensions. Third, the DTC element was modeled as a capacitor in series with a parasitic resistor. Capacitance varied from 0.38 to 4.32 pF on the basis of the characteristics of the DTC component announced in the data sheet. According to the equivalent circuit structure, the resonance frequency of the antenna decreases if the capacitance value of DTC increases and vice versa.  
Two similar antennas were simulated using ANSYS EM high-frequency structure synthesizer software. The structure was designed on a 0.8 mm thick FR4 substrate with \(\epsilon_{r}=4.4\), tan\(\delta=0.02\), and a 35 \(\upmu\)m-copper thickness. Both antennas had the same dimensions, as shown in Figure 3. To obtain tuneability at the resonance frequency, a DTC was positioned at the top of the monopole.
# Mapping Radar Glacier Zones and Dry Snow Line in the Antarctic Peninsula Using Sentinel-1 Images
## 4 Results  
### Melting Days in the AP  
The winter mean SSM/I 19.35 GHz \(T_{b}\) in horizontal polarization varied greatly over the AP (Figure 6a). A melting day was counted when either \(T_{b}\) of the ascending and descending orbits pass beyond the thresholds. Heavy snowmelt was found on the Larsen Ice Shelf and Alexander Island (melting for more than two months), whereas the mountainous area of Palmer Land was frozen (Figure 6b). We also noticed that the places with long melting seasons show lower winter \(T_{b}\) compared with the frozen area. During refreezing, equitemperature metamorphosis results in coarse snow grains in places with a long melting season [60], leading to the stronger scattering and lower \(T_{b}\) compared with the places frozen all year around. As discussed in Section 3, meltwater refrozen in snowpacks also alters the morphological features, resulting in different backscattering characteristics in cold seasons.  
We calculated the mean melting days at 100 m elevation intervals. As shown in Figure 7a, mean melting days decreased with the increase in elevations. The correlation coefficient is \(-\)0.85. No melting signal was detected over 1700 m. The frequency of melting days showed two peaks, which represented short (0-5 days) and intense (60-65 days) melt seasons, respectively. Using the calibrated Sentinel-1 images listed in the left column of Table 1, we produced a winter mosaic of the AP from May to July in 2015 (Figure 6c). Figure 7b shows a comparison between the melting days derived from SSM/I data and \(\sigma^{0}\) derived from the winter Sentinel-1 mosaic. The Sentinel mosaic was resampled to the same size as the SSM/I data. We calculated the mean winter \(\sigma^{0}\) for every five melting days. The places with more melting days were much brighter on SAR images. The correlation coefficient is 0.78. This trend was significant for the areas where snowmelt lasts for less than 50 days, but not significant for the heavy melting places. Surface snowmelt led to the formation of ice layers and coarse grains in cold seasons, resulting in increased backscatter energy. The result supports the simulations of the frozen percolation radar zone in Section 3.2.  
Figure 6: (**a**) Winter mean ascending SSM/I 19.35 GHz \(T_{b}\) in horizontal polarization. (**b**) Melting days over the AP derived from SSM/I data during 2014–2015. (**c**) Normalized radar backscatter of Sentinel-1 mosaic with the reference angle of \(30^{\circ}\). The yellow star shows the location of the Larsen F Ice Shelf, and the upstream Swan Glacier.
## 2 System Model  
Let us define \(f_{c}\) and \(W\) separately as the carrier frequency and bandwidth of the observed radio signal. Generally, SS is regarded as a binary hypothesis issue,  
\[H_{1}:\ y(n)=h(n)s(n)+u(n), \tag{1}\]  
\[H_{0}:\ y(n)=u(n), \tag{2}\]  
where \(H_{1}\) denotes the presence of PU while \(H_{0}\) denotes the absence of PU; \(y(n)\), \(s(n)\) and \(u(n)\), respectively, represent received mixed signal, clean signal and noise at the receiver of CR; \(h(n)\) signifies the corresponding channel gain. In addition, (1) and (2) are based on three assumptions as follows [32]:  
(1) \(s(n)\) and \(u(n)\) are assumed as independent and identically distributed stationary random process with mean zero and \(E\Big{[}|s(n)|^{2}\Big{]}=\sigma_{s}^{2}\), \(E\Big{[}|u(n)|^{2}\Big{]}=\sigma_{u}^{2}\),  
(2) \(s(n)\) and \(u(n)\) are independent mutually;  
(3) \(h(n)\) is a complex Gaussian random variable with mean zero.  
Suppose \(f_{s}\) is the sampling frequency and \(\tau\) is the sampling time at the receiver of CR. The corresponding sampling point is \(N=\tau f_{s}\). As a result, the false-alarm probability is defined as  
\[P_{f}(\varepsilon,\tau)=Q\Bigg{(}\Bigg{(}\frac{\varepsilon}{\sigma_{u}^{2}}- 1\Bigg{)}\sqrt{\tau f_{s}}\Bigg{)}, \tag{3}\]  
and the corresponding detection probability could be written as  
\[P_{d}(\varepsilon,\tau)=Q\Bigg{(}\Bigg{(}\frac{\varepsilon}{\sigma_{u}^{2}}- \gamma-1\Bigg{)}\sqrt{\frac{\tau f_{s}}{2\gamma+1}}\Bigg{)}, \tag{4}\]  
\[Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}exp\Bigg{(}-\frac{t^{2}}{2}\Bigg{)} dt, \tag{5}\]  
where \(\gamma=\frac{\sigma_{u}^{2}}{\sigma_{u}^{2}}\) is the environment SNR in the location of CR; \(\varepsilon\) is the presupposed threshold; \(Q(x)\) is the complementary distribution function of standard Gaussian.  
Denote \(C_{0}\) as the throughput of the secondary network in the \(H_{0}\) case and \(C_{1}\) as the throughput in the \(H_{1}\) case. Since a length of period \(\tau\), out of the total frame time \(T\) is used for sensing [32], the achievable throughputs of the SUs under these scenarios are, respectively, given as  
\[R_{0}(\varepsilon,\tau)=\frac{T-\tau}{T}C_{0}\Big{(}1-P_{f}(\varepsilon,\tau) \Big{)}P(H_{0}), \tag{6}\]  
\[R_{1}(\varepsilon,\tau)=\frac{T-\tau}{T}C_{1}(1-P_{d}(\varepsilon,\tau))P(H_{ 1}), \tag{7}\]  
where \(P(H_{0})\) denotes the absence probability of PU in the observed bands and \(P(H_{1})\) denotes the presence probability of PU in the observed bands. We assume, in this paper, that the secondary user (SU) can stop communicating with its users at once when PU resumes the observed band (\(C_{1}\approx 0\)). Therefore, the average throughput for the secondary network is given by  
\[R(\tau)=R_{0}(\varepsilon,\tau)=\frac{T-\tau}{T}C_{0}\Big{(}1-P_{f}( \varepsilon,\tau)\Big{)}P(H_{0}). \tag{8}\]
## 4 Results
### Variable Importance  
Permutation variable importance was used to compute variable importance using the two estimators (SVM and Xgboost). The permutation algorithm can be defined to be the decrease in a model score when a single feature value is randomly shuffled [65]. Variable importance for each VV, VH, and VV/VH PCA 1, 2, and 3 polarizations are depicted in Figure 5. The VH and VV PCA polarizations formed the top six most important variables and the least important variables were the VV/VH PCA ratios. Specifically, the VH PCA 3 received the highest score, followed by the VV PCA 1 and VH PCA 2. The dominance of VH and VV polarizations was expected. Figure 6 depicts the VV, VH, and VV/VH PCA polarization composites. Smallholder maize farms are clearly enhanced by the VV and VH PCA polarization composites compared to the VV/VH polarizations.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Overall Accuracy** & \begin{tabular}{c} **Cross-** \\ **Validation** \\ \end{tabular} & \multicolumn{2}{c}{**Confusion Matrix**} \\ \hline SVM & 0.971 & 0.89 +/\(-\)0,05 & \begin{tabular}{c} Planted Maize \\ 20,139 \\ 628 \\ \end{tabular} & \begin{tabular}{c} Non-Planted \\ 1457 \\ 50,790 \\ \end{tabular} \\ \hline Xgboost & 0.968 & 0.96 +/\(-\)0.02 & \
\begin{tabular}{c} Planted Maize \\ 20,115 \\ 825 \\ \end{tabular} &
\begin{tabular}{c} Non-Planted \\ 1481 \\ 50,593 \\ \end{tabular} \\ \hline \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**Xgboost**} \\ \hline Classes & Planted Maize & Non-Planted & Planted Maize & Non-Planted \\ \hline Precision & 0.97 & 0.972 & 0.961 & 0.972 \\ Recall & 0.933 & 0.988 & 0.931 & 0.984 \\ F1-Score & 0.951 & 0.98 & 0.946 & 0.978 \\ \hline \end{tabular}
\end{table}
Table 2: Accuracy assessment produced for the Sentinel-1 multi-temporal classification using the Support Vector Machine (SVM) and Extreme Gradient Boosting (Xgboost) algorithms.