esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Given the inherent characteristics of future WCNs that often rely on LoS channels, integrating UAVs with RISs is essential for achieving more robust and efficient wireless communication [34; 35]. UAV-enabled, RIS-assisted communication is generally formulated as a multi-objective optimization problem with the following joint objectives [34]:  
1. Power transmission for propagating communication signals and/or power allocation to multiple UEs, where the power transmit is implemented by the terrestrial BS or UAV as the airborne BS. 2. UAV energy-efficient path planning and trajectory optimization, taking into account communication performance and quality of service (QoS) requirements. 3. Design of BS beamforming (BS-BF) and RIS phase shift (RIS-PhSh) to enhance signal quality and communication reliability. In this context, with a focus on the integration of UAV-RIS in dense urban environments and wireless vehicular networks, our literature review identifies several research gaps, categorized into five main areas of study:  
1. A new paradigm is required for channel modeling in the context of UAV-enabled, RIS-assisted communication for IoT sensors and particularly for mobile IoVs in dense urban environments. Notably, the channel modeling is based on localization-aware communication, which is promising due to the intrinsic characteristics of future B5G and 6G WCNs with mmWave signals, particularly considering dynamic IoTs and IoVs networks for vehicular communications. Kinodynamic-aware, obstacle-free trajectory optimization for crash avoidance and navigation in dense urban areas has been largely overlooked in the wireless communication community and requires further exploration. * The emergence and applications of RIS-equipped UAVs (RISeUAVs). * A new perspective of optimization algorithms regarding the impact of trajectory optimization with LoS links. * Investigating integrated localization and communication within UAV-enabled, RIS-assisted communication systems for IoVs. In the following Section, we present a taxonomy of UAV-enabled, RIS-assisted communication studies, which structured around the five main areas above.
## 3 Materials and Methods
### Loss Function  
The discriminator network's job is to classify the real images from the fake images, which is considered a binary classification problem. Thus, we use binary cross-entropy (BCE) as the loss function, which is given by Equation (2).  
\[J_{\text{BCE}}(\theta)=-\frac{1}{M}\sum_{m=1}^{M}\left[y_{m}\times\log(h_{ \theta}(x_{m}))+(1-y_{m})\times\log(1-h_{\theta}(x_{m}))\right] \tag{2}\]  
where \(M\) is the number of training examples in a mini-batch, \(y_{m}\) is the target label for training example \(m\) (the real label is 1 and the fake label is 0), \(x_{m}\) is the input for training example \(m\), and \(h_{\theta}\) is the model with neural network weights \(\theta\).  
At the beginning of the equation, the summation sign indicates that we are summing over the variable \(M\), i.e., taking the average cost of all the examples in the entire batch. The first term, \(y_{m}\times\log(h_{\theta}(x_{m}))\), is the product of the true label \(y_{m}\) times the log of the prediction, which is \(h_{\theta}(x_{m})\) features parameterized by \(\theta\) for the model that penalizes the probabilistic false negatives. In the perfect case, if the training model outputs 1, then the loss is \(-\log(1)=0\) and the training example is a real image. The same logic applies for the second term, \((1-y_{m})\times\log(1-h_{\theta}(x_{m}))\), that penalizes the probabilistic false positives.
## 6 Conclusions  
This paper focuses on kinematic positioning over Antarctica with multi-GNSS observations. The conventional PPP and its ambiguity fixing in Antarctic stations are demonstrated to be negatively influenced by the poor orbit quality of the BDS satellites. A network-based approach, named POP, is implemented to overcome such effects. This approach's ambiguity fixing performance is investigated and compared with PPP.  
With data collected from a global network of 136 stations over 14 days, it is demonstrated that the PPP solutions involving BDS are not reliable; some positioning errors larger than 10 cm appear in the horizontal and vertical components with GC observations. However, the orbit errors can be mostly compensated by the satellite clocks when the clock offsets are estimated with a network of reference stations in POP processing. The kinematic positioning errors caused by the remaining orbit errors are below 5 cm. The POP also gives a better ambiguity fixing performance than PPP. A higher fixing rate can be achieved by POP than PPP because of the reduced effect of BDS orbit errors, an average improvement of 5.1 and 3.2%, respectively, can be obtained with GC and GREC observations. The ambiguity fixing can improve the kinematic positioning accuracy for both PPP and POP, and their fixed solutions are compared with their float solutions. Significant improvements of 16.9, 29.7 and 20.0% for PPP with GE, GC and GREC observations, respectively, are obtained. The POP represents relatively slight improvements of 11.2, 17.6 and 13.6% with the same types of observations.  
Through a real flight experiment over Antarctica, it is shown that the DD solutions are not always robust under a baseline length up to 750 km. When comparing the baseline results among three antennas installed in an aircraft, it is found that some outliers in the PPP float and fixed baseline results disappear in the POP solutions. The ambiguity fixing can accelerate the convergence and overcome the fluctuations and jumps in the positional estimates. After convergence, the POP fixed solutions generate the best baseline estimates, with a STD of 1-3 cm.  
[PERSON] conceived the study and wrote the original manuscript; [PERSON] and [PERSON] analyzed the experimental results and provided the funding information; [PERSON], [PERSON] and [PERSON] made the data acquisition; all authors had read and contributed to subsequent draft. This study is supported by the Chinese Scholarship Council (No. 201506560002) and the National Natural Science Foundation of China (41574013, 41,874,032 and 41731069). We are very grateful to [PERSON] for his valuable comments and suggestions and our colleagues in GFZ for their kind help, cooperation, and discussion. We are also thankful to [PERSON] from the British Antarctic Survey and [PERSON] from the Danish Technical University/National Space Institute for providing GNSS data from ESA's PolarGAP campaign. The authors declare no conflict of interest.
# Blending Sea Surface Winds from the HY-2 Satellite Scatterometers Based on a 2D-Var Method
###### Abstract  
The launch of the Haiyang-2 (HY-2) satellite constellation fosters the quick acquisition of global sea surface vector winds from the perspective of remote sensing. This study intends to develop a six-hourly mesoscale analysis of sea surface winds based on the microwave scatterometers onboard the HY-2 satellite series, with the objective of meeting the considerable demand for accurate and gap-free ocean wind forcing products. First, the accuracy of HY-2 scatterometers (HSCATs) in measuring wind is evaluated. In particular, the standard deviation (SD) errors of HSCATs data are assessed using the collocated buoy measurements with different temporal windows in order to account for the temporal representativeness errors in the blending analysis. Afterwards, a two-dimensional variational (2D-Var) method is implemented to blend the HSCATs measured winds and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis winds over the global ocean surface. This approach is different from existing methods in that it takes both wind error and background error correlation into account. The results show that the blended wind product is of a promising quality compared with independent wind references. Interestingly, the blended winds derived from 2D-Var in combination with an empirical background error correlation show smaller SD errors than those using a Gaussian error correlation function. Overall, the blended wind product should be valuable for forcing global ocean models or describing air-sea interaction processes on a scale close to the scatterometer measurements.  
HY-2 scatterometers; sea surface wind; 2D-Var method; error correlation +
Footnote †: journal: Remote Sensing  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Blending Sea Surface Winds from the HY-2 Satellite  
Searteoremeters Based on a 2D-Var  
Method. _Remote Sens._**2023**, _15_, 193. [[https://doi.org/10.3390/s15010193](https://doi.org/10.3390/s15010193)]([https://doi.org/10.3390/s15010193](https://doi.org/10.3390/s15010193))
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 4 Experiment
### Quantitative Comparison with Different Methods
#### 4.4.2 Compared Methods on Other Datasets
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Methods** & **DICM** & **LIME** & **MEF** & **NPE** & **VV** \\ \hline Zero-DCE [50] & 4.58 & 5.82 & 4.93 & 4.53 & 4.81 \\ EnlightenGAN [67] & 4.06 & 4.59 & 4.70 & 3.99 & 4.04 \\ RetinexNet [58] & 4.33 & 5.75 & 4.93 & 4.95 & 4.32 \\ KinD [68] & 3.95 & 4.42 & 4.45 & 3.92 & 3.72 \\ KinD++ [69] & 3.89 & 4.90 & 4.55 & 3.91 & 3.82 \\ SNR-Aware [73] & 6.12 & 5.93 & 6.44 & 6.44 & 11.50 \\ DCC-Net [74] & 3.70 & 4.42 & 4.59 & 3.70 & 3.28 \\ GSAD [28] & 3.28 & 4.32 & 3.40 & 3.55 & **2.60** \\ \hline Ours & 3.12 & 4.20 & 3.67 & 3.63 & 3.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative comparisons of different methods on DICM, LIME, MEF, NPE, and VV datasets in terms of NIQE, where smaller values lead to better performance.
# Contact Fatigue State Identification of Specim Based on Heterogeneous Data and Evidence Theory
## 4 Conclusions  
Most of the state recognition of contact fatigue is based on a single information source of vibration signal or image signal. However, it is well known that vibration detection has shortcomings such as poor robustness and difficulty in identifying critical features, while visual detection has shortcomings such as unstable image quality and being affected by the test environment. In this paper, a fusion recognition model is proposed, which innovatively combines the essential information of two different structural types of vibration and image signals, and realizes the information complementation between vibration and image signals, so as to improve the accuracy of contact fatigue pattern recognition. Relying on the monitoring data of the self-developed rolling contact fatigue test equipment, and comprehensively using multi-source information fusion, pattern recognition, deep learning as well as other theories and methods, a state recognition network model based on vibration and image spatiotemporal heterogeneous data is constructed. The contact fatigue state identification of the specimen is realized. The results show that fusion of vibration and image data can achieve information complementarity and may identify the contact fatigue state of specimen more accurately. The accuracy of state identification after fusion is 98.67%, which is at least 3% higher than that of a single information source. This research is of great significance for the accurate acquisition of material contact fatigue properties and has certain reference value for the fusion of heterogeneous data from different sources.  
[PERSON] conceived this research; [PERSON] and [PERSON] built various neural network model and made various charts; [PERSON] and [PERSON] did experiments and data processing; [PERSON] wrote the original draft preparation; [PERSON] and [PERSON] completed the editing and review of this article; [PERSON] was responsible for overseeing the progress and visualization of the article. All authors have read and agreed to the published version of the manuscript.  
This research was founded by the National Natural Science Foundation of China (No. 52075062), Chongqing Municipal Education Commission Major Project of China (No. KJZD-M202001101), and Supported by Graduate Student Innovation Program of Chongqing University of Technology (Grant No. gzlcx20223183).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 12: State identification ROC curves for validation set data.
## 3 Results and Discussion  
### Synthesis and Evaluation of the Anti-staining Coating Material  
1.1 Synthesis of a PFPE which Possesses -(CF\({}_{2}\)CF\({}_{2}\)O)- as a Repeating Unit (Scheme 3)  
It has so far been quite limited to synthesize a perfluorinated poly(ethylene glycol) structure. It has only been achieved by liquid-phase direct fluorination. [PERSON] _et al._ reported this synthesis by utilizing liquid-phase direct fluorination with elemental fluorine [12]. However, the method cannot be applied directly to the synthesis of PFPE derivative **3**, because **3** has a non-fluorinated functionalized group in one end. The synthesis of PFPEs starting from diols and tetrafluoroethylene was also effective [13]. However, it seemed to be difficult to apply it to the mono-functionalized PFPE derivative **3**.  
**Scheme 3.** Synthesis of PFPE derivative **3**.  
On the other hand, the PERFECT method enables it by employing a partially-fluorinated compound as the substrate, synthesized from a non-fluorinated alcohol [9]. According to the typical PERFECT procedure [8], firstly, poly(ethylene glycol) monomethyl ether (**5**) was reacted with a perfluoroacyl fluoride **6** to obtain partially-fluorinated ester **7**. Next, perfluorination was achieved by liquid-phase direct fluorination with elemental fluorine to give the perfluorinated ester **8**. Injection of a diluted solution of benzene after substrate addition was effective for complete perfluorination, because benzene reacts with elemental fluorine to generate many fresh fluorine radicals. Instead of the thermal elimination in the typical PERFECT procedure, methanol was added to perfluorinated ester **8** to provide the desired methyl ester **9** of the PFPE and the methyl ester of the starting perfluoroacyl fluoride **6**, which was removed from the mixture by distillation. Finally, **9** was treated with **10** to afford the desired **3**. The overall yield from the starting material **5** was 83%.  
Thus, the target PFPE derivative **3** for an anti-staining coating material was successfully synthesized by employing the PERFECT process as a key step.
## 2 Analysis of OFDM-Induced IM2 Distortion Effects
Let us take an example of 16K FFT mode of ATSC 3.0 [21], which has \(N_{sub}\) = 13,825 and \(f_{sub}\) = 421.875 Hz. When the IF band of a low-IF MedRadio receiver resides in 150-450 kHz, the IM2 tones appearing within the band are from \(k_{i}\) = 356 th to \(k_{f}\) = 1066 th components. Then, \(P_{offset}\) is computed to +6.56 dB by (8). It implies that if an ATSC blocker of \(-\)10 dBm is injected to a receiver and induces IM2 distortion at the baseband, the same amount of IM2 distortion power is also induced by a simple two-tone blocker of \(-\)16.56 dBm. Moreover, assuming the input signal power of the receiver is \(-\)50 dBm, and the receiver \(IIP_{2}\) is +30 dBm, \(P_{IIM2.total}\) will be \(-\)63.1 dBm (=\(2\times(-10-6.56)-30\)) according to Equation (7), and the resulting SNR will be +13.1 dB as the input signal power is \(-\)50 dBm and the input-referred distortion power \(P_{IIM2.total}\) is \(-\)63.1 dBm
## 4 Results and Discussion
### Assessment of Daily ET, GPP, and WUE at Footprint Scale
Considering the high coupling between photosynthesis and transpiration, the remote sensing-based models were mostly consistent in their estimates of GPP and ET biases, which helps to reduce the bias in WUE estimates. However, there were also larger inconsistencies between GPP and ET, as shown in Figure 5. These large inconsistent biases were amplified in the WUE estimation, resulting in poorer estimation results. Although the biases are sometimes small, there will still be a large WUE bias due to the lower ET value and low magnitude at this time. At this point, it is due to the limitations of the WUE model itself. The anomaly of the carbon-water coupling process in the oasis ecosystem found in this study was consistent with previous reports [96]. Overall, the estimation models captured changes in ET, GPP, and WUE following weather conditions (precipitation) and anthropogenic activities (irrigation or fertilizer application), which can be fully caught in the changes in crop growth and phenology using multi-source remote sensing data. Figure 5: Scatterplots of daily GPP and ET biases with daily WUE.
## 2 Materials and Methods
### Trial Location, Experimental Design and Growing Conditions  
The field study was conducted at the Research Station of the Seed and Plant Improvement Division in Fars Agricultural and Natural Resources Research and Education Center (AREEO, Darab, Iran). The research field was located near the city of Eqlid, in the province of Fars in Iran (30\({}^{\circ}\)53\({}^{\prime}\)43.8\({}^{\prime\prime}\) N 52\({}^{\circ}\)24\({}^{\prime}\)07.5\({}^{\prime\prime}\) E). The field trial was carried out from October 2018 to August 2019.  
The soil of the field is a loamy soil consisting of 22% clay, 40% loam and 38% sand. Before sowing, 200 kg ha\({}^{-1}\) superphosphate (48% P2O5) was applied. An N fertilization (urea--46% N) of 300 kg ha\({}^{-1}\) was split into two applications with 150 kg ha\({}^{-1}\) each at BBCH 24 (tillering) and BBCH 32 (stem elongation) [19]. To control weeds, the herbicide 2,4 dichlorophenoxyoxyacetic acid was applied at the end of the tillering phase. Genotypes were sown with a seed rate of 400 seeds per m\({}^{2}\) at a depth of 4 cm.  
The total precipitation during the growing season was 515 mm (Figure 1), and occurred mainly in November 2018 and between January and April 2019. Zero precipitation occurred during the late season from June to August 2019. The field trial was a split-split plot design consisting of three irrigation treatments in the main plot and six wheat cultivars in the sub-plot in four replications. Three irrigation treatments were established: well watered (WW) plants were irrigated 10 times between October 2018 and July 2019. Before the anthesis all treatments were well watered. During the anthesis, two times irrigation was applied in well-watered treatment. In moderate stress (MD), plants were irrigated only once, while in severe stress (SD) there was no irrigation. When anthesis was fully completed, all treatments were well watered. Six genotypes \(\times\) three stress levels \(\times\) 4 biological replicates (\(n=4\)) resulted in a total of 72 treatment plots. The individual plots were laid out with a size of 0.6 \(\times\) 2.5 m\({}^{2}\).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Genotype** & **Quality Level** & **Company** & **Protein Concent.** & **Drought Resistance** \\ \hline \multicolumn{4}{c}{Iranian genotype} \\ Alvand & class 1 & National Seed and & high & sensitive \\ Mihan & class 2 & Plant Improvement Institute (SPII) & low & tolerant \\ \multicolumn{4}{c}{German genotype} \\ Impression & A & I.G. Pflanzenzucht & high & sensitive \\ Discus & A & DSV & high & sensitive \\ Rumor & B & Saaten-Union & low & tolerant \\ Hybery & B & Saaten-Union & low & tolerant \\ \hline \end{tabular}
\end{table}
Table 1: Quality classes of the genotypes used and corresponding protein concentration and drought response.
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 4 Protection Techniques
### Resilience against Jamming
Another jammer detector was introduced in [23], which is intended to reveal nonce-manipulation attempts via jamming. As the network server drops join attempts with an already used nonce value, such a procedure is an efficient way to craft DoS attacks in LoRaWAN. With a statistical method employing Kullback-Leibler divergence to compare a legitimate set of nonces and attacked nonces, the authors were able to achieve up to 98% detection accuracy. Lastly, one of the most effective ways to improve data availability under jamming attacks is to introduce novel decoding and signaling strategies as countermeasures. Techniques regarding the former category were discussed in [24]. Interestingly, as noted by the authors, the collision recovery algorithms for decoding colliding LoRa packets [3; 79] are less well suited to separating legitimate payloads from jamming waveforms, given that the jammer is able to synchronize in both the time and frequency domains. Subsequently, by assuming such an advanced attacker model, the authors presented a LoRa decoder that is able to overcome synchronized jamming attempts. The essential idea to enable recovery is to monitor the signal amplitude components during the FFT-based demodulation phase of LoRa payloads. Since a successful jammer needs to adjust signal power such that the jammer waveform power exceeds the legitimate signal power by at least 6 dB, selection of the correct chirp signal components becomes feasible. It should be noted that such a method works well given that the RSSI of the device does not fluctuate heavily, e.g., due to movement or fading. Alternatively, improved medium-access techniques for LoRaWAN have been given [71; 72], which employ cryptographic frequency hopping to improve data availability and scalability simultaneously. As noted in [71], cryptographic-frequency hopping can prevent selective jamming attacks to some extent, as the jammer is not able to predict randomly selected channel center frequencies. In order to avoid more powerful jammers listening to several channels in parallel, the authors proposed a continuous center frequency selection over, e.g., the EU868 ISM band, instead of the regular discrete-channel utilization.
# Risk Assessment and Source Apportionment of Heavy Metals in Soils from Handan City
## 3 Results and Discussion
### Source Apportionment of Heavy Metals  
#### 3.4.1 Correlation Analysis  
The correlation coefficient between elements can indicate similarities in their sources [48]. The results of the Spearman correlation analysis (SCA) of seven metals are listed in Table 3. Based on the SCA, there was a markedly positively correlation between Cr and Ni (_p_ < 0.01, \(R\) = 0.634). Thus, they possibly had the same sources. Cr and Ni might be come from the parent materials of soil [12]. There was a significant positive correlation between Zn, Cu, Cd and Pb (_p_ < 0.01). Zn, Cu, Cd and Pb were also correlated (_p_ < 0.05), which means that these metals might come from the same source.