esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment Setup and Results  
### Experiment Setup  
In order to verify the effect of the proposed equalizer, an experimental transmission system of 112 Gbit/s and 128 Gbit/s based on an EML laser was built. The system construction is shown in Figure 4. The DSP of transmitter and receiver are completed offline.  
First, a pseudo-random binary sequence (PRBS) is generated and encoded into a PAM-4 signal. The signal is then pulse-shaped by a root-rise cosine filter. The generated signal is resampled and loaded into the arbitrary waveform generator (AWG, Keysight M8196A) with a sampling rate of 92 GSa/s and a 3 dB bandwidth of 32 GHz. The generated electrical signal then modulates an EML module, which has a 3-dB bandwidth of 25 GHz. The EML operates at \(\sim\)1310 nm and has a chirp factor of 1. The maximum output power is 10 dBm and the linewidth is 100 kHz.  
After transmitting through an SSMF, the receiving power of the signal is adjusted by a variable optical attenuator (VOA). After the PAM-4 signal is detected and received by a PIN-PD, it is amplified using an RF amplifier and then sampled using an oscilloscope with a 3 dB bandwidth of 36 GHz at 160 GSa/s. Finally, the captured waveforms are processed offline. The digital signal process (DSP) after detection includes resampling, matched filtering, equalization and PAM-4 decoding. Bit error rate (BER) measurements are performed by direct error counting against symbols.
# Probability Density Estimation through Nonparametric Adaptive Partitioning and Stitching
## 3 Results and Discussions
### SQR Comparison between NAPS and NMEM  
An additional method for determining the quality of an estimate is the scaled quantile residual (SQR) for measuring uncertainty, defined as,  
\[SQR_{k}=\sqrt{N+2}(r_{k}-u_{k}), \tag{8}\]  
Figure 9: Localizing percent error within quantiles: (**a**) Displays the CDF, 2% quantiles, and PDF for the stable distribution evaluated at the quantiles. The mean percent error (MPE) is plotted per quantile for samples sizes: (**b**) \(N=2^{10}\) (**c**) \(N=2^{17}\) (**d**) \(N=2^{22}\).  
where \(k\) refers to the position index ranging from 1 to the sample size \(N\), \(u_{k}=\frac{k}{N+1}\), and \(r_{k}=CDF(x_{k})\) for each data sample point \(x_{k}\). The square root term scales the residuals by the sample size \(N\), which leads to a sample size invariant measure. When plotting the SQR as a function of position \(k\), it has been demonstrated [24; 25; 31; 26] that the expected values will fall within an oval shaped region as defined by the dotted lines shown in Figure 10. The inner dashed lines represent the area where 50% of residuals are expected to fall, and the outer lines represent the 98 th percentile. Therefore, when calculating the SQR for a CDF estimated from a sample \(x_{k}\), these plots are an effective visualization tool for diagnosing where estimates may fall outside of expected ranges.  
In Figure 10, all SQR values that fall outside of the 98 th percentile are colored in red. Statistically, we would expect approximately 2% of these points to appear outside of the large oval. However, significant deviations from this expectation, particularly if the errors are periodically spaced, is an indication that the estimate is poorly fitted to the data in this region. All the SQR plots show in Figure 10 are for estimates of the Beta (2, 0.5) distribution, which has very low density on the left and a divergence in density on the right. Plots in the (first, second) columns are for (NAPS, NMEM) shown for samples sizes of (\(2^{11}\), \(2^{18}\)) for the (top, bottom) rows. As with other examples shown thus far, the divergence is difficult to fit properly for NMEM with increasing sample size, as indicated by the outliers colored in red. The oscillations, such as those in Figure 10d, tend to increase in both amplitude and frequency, which often corresponds to unnatural wiggles, whereas the lower amplitude oscillations in the NAPS plots for Figure 10a,c indicate a smoother PDF.
# Key Technologies in the Context of Future Networks: Operational and Management Requirements
## 2 Key-Enabled Technologies for Future Networks  
Future Networks envisage a fully connected society where the user can enjoy enhanced services and the operators obtain enough revenues from the deployment and provision of their services. In this context, Future Networks could benefit from the evolution of novel technologies such as SDN, NFV, SON and Cloud Computing, as is detailed below.
## 4 Results and Analysis
### Analysis of Vegetation Area Changes
Figure 6: Area and percentage of five vegetation types in the Yellow River Delta, 2020–2022. In terms of distribution patterns, the _Spartina alterniflora_ community continues to maintain a significant presence on both sides of the estuary. However, since 2020, Dongying City has implemented various management measures for _Spartina alterniflora_, such as \"mowing\", \"mowing + flooding\", \"mowing + tiling\", and so on, depending on different areas. After adopting the above management measures, the trend of the invasion of _Spartina alterniflora_ communities into the _Phragmites_ communities on both sides of the river channel has slowed down, and it has gradually formed a pattern of staggered distribution with the _Phragmites_ communities, presenting a trend of extension to the sea. The _Phragmites_ communities are mainly distributed on both sides of the Yellow River channel. Overall, their distribution tends to be interconnected and clustered. However, due to the impacts of other vegetation types, such as _Spartina alterniflora_, and human activities, some _Phragmites_ communities exhibit fragmentation. The _Suadea glauca_ community is mainly distributed in the southern saline mudflats and some mudflats in the north. The community has a wider distribution area but is characterized by sparse growth. The spectral reflectance of remote sensing images is easily affected by the mudflat substrate, so it shows a larger fragmentation feature in the classification map, particularly in the northern region. The _Tamarisk_ community and _Typha orientalis Presl_ community are niche vegetation communities in the study area, with a relatively small area of vegetation, usually mixed with reed communities growing on both sides of the river, with a stable community distribution trend. \begin{table}
\begin{tabular}{c c c c} \hline
**2020 Raw Images** & **2022 Raw Images** & **Classification Results for 2020** & **Classification Results for 2022** & **Major Changes in Vegetation** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Main areas of vegetation change. Hyperspectral remote sensing images offer the advantage of a high spectral resolution, which is a great advantage in discriminating different vegetation types. However, hyperspectral remote sensing images have the common problem of a low spatial resolution, causing errors in the classification results. Simultaneously, the random forest classification algorithm exhibits better accuracy and versatility compared with other traditional classification algorithms, but the classification algorithms employed in this study primarily rely on implementation within the ENVI software. Moreover, there are still more advanced algorithms for machine learning and deep learning that can be used in this field of study, and we will continue to explore and test the content of the two areas in the future research.
# Experimental Study of the Effect of Ultrasound on the Freezing Process of Bo Chinh Ginseng
## 3 Results and Discussions
### Effect of Ultrasound Irradiation on the Color of Materials after Freezing  
Color is an important indicator to assess the sensory quality of materials. The indices of L*, a* and b* are used to evaluate the color change of product samples.  
In Table 2, it can be seen that the values of L*, which were obtained using the WI of frozen material samples corresponding to different modes, are higher than that of fresh material samples. This is because when freezing, water moves from the inside of the material to the surface and crystallizes. As a result, frozen samples will have a lighter color than fresh samples. The frozen samples do not have much of a difference between the L* and WI values, which shows that using ultrasound irradiation to support the freezing process does not affect the color of materials as compared to non-ultrasound freezing. A similar phenomenon was reported by [PERSON] et al. [25], as their work showed that freezing lotus root by using ultrasound did not cause a significant change in its brightness in comparison to freezing without ultrasound, but both samples were brighter compared to the raw material. However, the values of the a* and b* color parameters of the frozen samples changed under different freezing conditions. The values of the a* of the control sample increased significantly, whereas those of the ultrasound-treated sample slightly went up. Ultrasonic treatment samples decreased in yellowness compared with the raw and control samples.
# Multi-Objective Optimization for EE-SE Tradeoff in Space-Air-Ground Internet of Things Networks
## 2 System Model and Problem Formulation  
In this paper, we consider a three-tier space-air-ground network as shown in Figure 1. We assume there are \(I\) IoT devices on the ground, denoted by \(\mathcal{I}\) = {1, 2,..., \(I\)}, and a satellite with an orbital altitude of \(h_{s}\) provides the full coverage to all these devices. In our setup, \(U\) UAVs, denoted by \(\mathcal{U}\) = {1, 2,..., \(U\)}, are deployed to collect data from IoT devices and transmit them to the satellite. The Cartesian coordinate system is introduced, so the coordinate of IoT device \(i\) is denoted by \((x_{i}^{1},y_{i}^{1},0)\). The coordinates of UAV \(u\) are denoted by \((x_{u}^{U},y_{u}^{U},h_{\rm U})\), where \(h_{\rm U}\) is the fixed flight altitude. In order to support communication with a large number of IoT devices at the same time, OFDMA is adopted, which has the advantages of flexible bandwidth and power allocation over users [21]. We assume that the total bandwidth available to IoT devices is equally divided into \(I\) resource blocks, and each IoT device is allocated a resource block. Meanwhile, we assume that each UAV can only communicate with \(K\) IoT devices simultaneously. This means each channel between UAVs and IoT devices is divided into \(K\) sub-channels and there is no interference between different sub-channels. Assume the sub-channel bandwidth between each UAV and IoT devices is \(B\) and there is no interference between different channels. For convenience of discussion, let \(I=UK\). Similarly, it is assumed that the channel bandwidth between each UAV and the satellite is \(W\), and there is no interference between different channels.
## 4 Experimental Results and Model Interpretability Analysis  
### Susceptibility Evaluation  
After 150 repeated trainings, the results of the best-performing models in terms of their accuracy metrics were selected for landslide susceptibility evaluation. The accuracy and sensitivity of the chosen models are shown in Table 5.
# New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System
## 3 Bridge Experiment
### Introduction
#### Test-Bed  
The SeoHae cable-stayed bridge Figure 7), which is selected by the test-bed of this research, is part of the total length of the 7.31-km SeoHae Bridge. It is a continuous bridge that is composed of a steel box girder with five spans totaling 990 m (60 + 200 + 470 + 200 + 60). From the point of the main span, it is the second longest cable-stayed bridge in service in Korea following the Incheon cable-stayed bridge.  
Figure 6: Antenna of the reference station and rover.  
Figure 7: SeoHae cable-stayed bridge [8]. (**a**) Photographs of the SeoHae cable-stayed bridge; (**b**) Location of the GNSS receiver and name of station.
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 6 Computing the Minimum Frame with a Genetic Algorithm
### Genetic Algorithms
#### 6.1.3 Generational Substitution  
After performing the selection-crossover-mutation process, the next step is to evaluate each chromosome and to determine which individuals will constitute the new population. Although there are different strategies to complete this step, in the simple GA that is used in this work, generational substitution of half of the worst-conditioned population is performed; that is, a temporary population equivalent to the sum of the current generation and the descendant are sorted according to the condition of each, and then half of the set is discarded. The chromosome population surviving the next generation results from competition between parents and descendants of the current generation.
# Volumetric Modulated Arc Therapy for Radiosurgery of Brain Metastases: A Single-Center Study
## 2 Materials and Methods
### Study Cohort  
Sex, age, primary tumor, presence of extracranial metastatic disease, number of total BMs at the time of radiosurgery treatment, prevalence of epilepsy related to brain metastases, local treatments before and/or after SRS treatment, seizures, Karnofsky Performance Status (KPS), and the median survival time (MST) (calculated from the date of radiosurgery to the date of death or the last clinical follow-up) were collected for each patient. The updated Graded Prognostic Assessment (GPA) was used for breast, lung (NSCLC ade nocarcinoma and NSCLC non-adenocarcinoma), renal cell carcinoma, melanoma, and gastrointestinal tumors [18]. For all other primary tumors, the GPA (2008) [19] was used. Finally, the main molecular biomarkers of the different types of primary tumors found in our study cohort are listed. Regarding breast cancer, we analyze, on the one hand, the hormone receptor (HR) status, which refers to the presence or absence of estrogen or progesterone receptor positivity, and, on the other hand, the HER2 (human epidermal growth factor receptor 2) status. For lung cancer, patients were grouped into small cell lung carcinoma, non-small cell lung carcinoma (NSCLC) adenocarcinoma, and NSCLC non-adenocarcinoma. Finally, in the case of melanoma patients, the presence of a BRAF mutation was analyzed.  
For each of the BMs, the anatomical locations in the CNS were collected, being classified into frontal, parietal, temporal, occipital lobes, basal ganglia, brainstem, and cerebellum. The gross tumor volume (GTV), planned target volume (PTV), planned treatment dose, and dose fractionation scheme were also collected. The assessment of local control to treatment is performed by applying the mRECIST 1.1 [20] criteria individually for each BM, which is classified into one of the following categories: progressive disease, stable disease, partial response, or complete response. Finally, we analyzed the biological effective dose (BED) of the different treatment schemes for all treated patients. Equation (1) [21].  
\[\text{BED}=nd\bigg{[}1+\frac{d}{\alpha/\beta}\bigg{]}, \tag{1}\]  
Equation (1): the biological effective dose (BED), \(d=\) dose administered in each treatment session, \(n=\) the number of treatment sessions, \(\alpha\) and \(\beta\) are the radiosensitivity coefficients.
## 4 Join Berth and Time Coordination Optimization Model
### Constraints  
Below, the problem constraints are enumerated together with their descriptions.  
\[a\Big{(}first_{s(c)}\Big{)}\ \geq\ a(c);\ \ \forall\ c,\forall\ s\ \in\ S(c) \tag{2}\]  
Constraint (2) reflects that the first stay of the call cannot start before the arrival of the ship to the port.  
\[a(s)\ \geq\ a(prev(s))+\ d(prev(s)),\ \forall\ c\ \in\ C,\ \forall\ s\ \in\ S(c) different\ to\ the\ first\ stay \tag{3}\]Constraint (3) states that the order of the stays in a call has to be respected so that the starting of a stay cannot be before the operations of the previous one end. In fact, _prev(s)_ denotes the previous stay in the same call.  
\[\sum_{s\in\text{Q}(s)}y\;(q,s)\;=\;1;\;\forall\;c\;\in\;C,\;s\;\in\;S(c) \tag{4}\]  
Constraint (4) indicates that each stay is assigned one and only one quay, among those eligible.  
\[M\;(x1(p)\;+\;o(p)\;-\;2)\;\leq\;a(s2)-(a(s1)\;+\;d(s1,q))\;\leq\;M\;(x1(p)+1- \;o(p))),\;\;\forall\;p\;\in\;P \tag{5}\]  
\[M\;(x2(p)\;+\;o(p)\;-\;2)\;\leq\;a(s1)-(a(s2)\;+\;d(s2,q))\;\leq\;M\;(x2(p)+1- \;o(p))),\;\;\forall\;p\;\in\;P \tag{6}\]  
\[X1(p)+x2(p)=\;1,\forall\;p\;\in\;P \tag{7}\]  
Constraint (5) sets the value of \(x1\) for each potential conflict \(p=(s1\),\(s2\),\(p)\) when both \(s1\) and \(s2\) are assigned the same quay \(q\) (and thus \(o(q)=1\)): if \(s1\) is before and \(s2\) and does not overlap it (and thus \(t(s2)-(t(s1+d(s1,q)))\geq 0\)), then \(x1\) is forced to have the value of one and is forced to take the value of \(0\) otherwise. For this to happen, parameter \(M\) should be a sufficiently large constant, greater than any difference between time events in the system. Constraint (6) repeats the same behavior for the case of \(x2\), setting its value to \(1\) if and only if \(s2\) occurs before \(s1\) and does not overlap it timewise. Constraint (7) means that \(s1\) is earlier and does not overlap \(s2\), or else \(s2\) is earlier and does not overlap \(s1\), but one of the two options must occur.  
Assuming that \(s1\) and \(s2\) are not assigned the same quay, the variable \(o(p)=0\) and Constraint (6) and Constraint (7) do not restrict the values of \(x1\) and \(x2\), which are free to take values of \(0\) or \(1\). Then, the variables would take either the value of \(x1(p)=0\), \(x2(p)=1\), or the opposite, to satisfy (7).  
\[O(p)\leq\;y(q,s1),\;\forall\;p=(q,\;s1,\;s2)\in P \tag{8}\]  
\[O(p)\leq\;y(q,s2),\;\forall\;p=(q,\;s1,\;s2)\in P \tag{9}\]  
\[O(p)\leq\;y(q,s1)+y(q,s2)-1,\;\forall\;p=(q,\;s1,\;s2)\in P \tag{10}\]  
Constraints (8)-(10) make \(o(p)\) take the value of \(1\) when both \(s1\) and \(s2\) in the conflict are assigned quay \(q\) (\(y(q\),\(s1)=1\), \(y(q\),\(s2)=1\)) and \(0\) otherwise.
## 4 Experimental Results
### Classification Results
#### 4.4.2 Performance Evaluation Using Different Datasets
The results obtained for four datasets are depicted in Tables 3-7. Table 3 shows that higher results are obtained on the INbreast dataset in the single-dataset evaluation (INbreast was used in training and testing) whilst other datasets achieve higher results in the double-dataset evaluation. Table 4 presents that DDSM and BCDR datasets obtain high results when Mini-MIAS is used as the training dataset. Table 6 shows that the Mini-MIAS obtains a sensitivity of 100% when the INbreast dataset is used for training. The proposed M-FD obtained optimal accuracy, sensitivity, and F-measure of 99.68%, 100%, and 99.75%, respectively, for Mini-MIAS when INbreast was used as the training dataset. The best specificity, 100%, was achieved by INbreast in single-dataset evaluation and DDSM when using BCDR in training. Table 7 demonstrates that the DDSM dataset obtained a specificity of 100% when the BCDR dataset was used for training. Table 7 also indicates that the Mini-MIAS dataset achieves optimal results when the BCDR dataset is used in training by obtaining a performance classification of 100% for accuracy, sensitivity, specificity, and F-measure. We used the ROC plot to evaluate the power of the texture feature (M-FD) and performance when different texture features are used. The ROC curve that visualizes the performance of the classifier is commonly used in medical image decision-making and is increasingly used in malignant identification research. Figure 10 shows the performance of traditional fractal dimension features for the Mini-MIAS dataset. Figure 11 presents the performance of the proposed fractal dimension for the Mini-MIAS dataset. The area under the curve (AUC) and the performance of ANN increased in training, validation, and testing. Figure 10: ROC for training, validation, and testing of the proposed model using traditional FD features.
# Assimilation of Earth Observation Data Over Cropland and Grassland Sites into a Simple GPP Model
## 3 Methods
### Definition of \(\varepsilon_{\text{max}}\)  
Values in the range from 0.86 to 2.5 (gC m\({}^{-2}\) d\({}^{-1}\) MJ\({}^{-1}\)) have been reported for grassland and crop \(\varepsilon_{\text{max}}\) in the literature [45; 46; 47]). Various definitions also exists for it in different communities and in different modelling setups (e.g., depending on weather non-photosynthetic tissues are included in the definition of FAPAR) [48]. Here we used an empirical parametrization that was derived using EC GPP data of two crop and two grass sites randomly extracted from the EC database (BE-Lon and FR-Aur for a total of 17 site-year crop samples; CZ-BK2 and IT-MBo for a total of 17 site-year grassland samples). A nested inversion of the Sim model was run on the two calibration sites. The inversion set-up was configured in standard mode (Section 3.5) and run iteratively for the calibration sites changing the value of \(\varepsilon_{\text{max}}\) in steps of 0.1 between 0.8 and 2.5 gC m\({}^{-2}\) d\({}^{-1}\) MJ\({}^{-1}\). The value minimizing the RMSE between the modelled and observed GPP was then selected. As a result, crop and grass \(\varepsilon_{\text{max}}\) was set to 1.7 and 1.1 gC m\({}^{-2}\) d\({}^{-1}\) MJ\({}^{-1}\), respectively. It is noted that \(\varepsilon_{\text{max}}\) of crops was estimated using sites where C3 crops were numerically more represented (only one year of maize, a C4 crop, is present for the site BE-Lon) and may thus underestimate the \(\varepsilon_{\text{max}}\) of C4 crops (maize in our data sample) ([PERSON] et al., 2015). However, the vast majority of the crop samples is indeed represented by C3 crops.
# Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs
## 4 Results and Analysis
### Effect of Land Cover Type  
Figure 9 shows the temporally-averaged MAD (<MAD>) for five land covers. The <MAD> trends from the three forest classes are similar, where <MAD> for evergreen forest is the lowest, followed by deciduous forest, and then the mixed forest. The <MAD> for forest classes are all lower than the crop classes since the forest classes in this study have less temporal NDVI variation and lower peak NDVI than the crop classes. For crop fields, the prediction accuracy is better for corn than for soybeans, especially during the growing peak season. One of the reasons may be the different field sizes and planting areas for these two crops at study site 1. In fact, corn fields are typically larger than soybean fields in site 1. A larger field size means it is easier to find MODIS pure pixels and, thus, better predictions are obtained. Comparing with the forest land cover, crops have relatively larger MAD in this study. One of the reasons may be that cropped landscapes are more heterogeneous (see the discussion in Section 5.2). Additionally, crops change faster than forests due to the human management. One typical example for soybean and corn is shown in Figure 10. It can be found that the discrepancy between fused and original NDVI for corn is slightly smaller than that for soybean. One possible reason is that soybean during the growing season in the study area is shorter than corn so that its reflectance/NDVI change is faster than corn. According to the USDA crop progress statistics, soybeans at site 1 are generally planted 2-3 weeks later, and harvested about two weeks earlier than corn. This makes the seasonal evolution in reflectance harder to predict given a single input pair. Crop planting and harvesting are a kind of land cover change in the data fusion process. The spatial information from Landsat images during the non-growing season may not be applicable to the growing season, and vice versa.