esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Platform Testing and Validation
### Platform Validation  
The validation methodology focuses on the end-user experience (UX), gained during demo activities on both an individual tool level (e.g., the use of a single tool) and a platform level (e.g., the experience obtained from the use of CLAP). The methodology is based on identifying key parameters (named tool and platform traits) that characterize tool and platform performance, as seen by the end user, and thus allows him/her to evaluate performance based on qualitative questions linked to these traits.  
Seven main trait categories are identified: (i) ease of installation; (ii) facilitation of user learning; (iii) data requirements; (iv) support; (v) integrity; (vi) usability; and(vii) usefulness. All traits are applicable to both tool and platform level, which formulate seven different chapters with questions for the questionnaires. With questionnaires at tool and platform level, the end users can provide validation scores (in a rank from 1.0 to 5.0) for every trait category. These scores are then aggregated and used to assess the quality of an individual tool or of the platform as a whole. The scores can be further used for relevant reflection activities and to provide feedback to the developers to improve the design of specific tools or platform functions. The validation process is summarized as follows:
## 5 RegTech Applications in Non-Financial Sectors
### International Trade and Business  
RegTech is crucial in facilitating compliance and streamlining international trade and business processes. It can benefit international trade and business in the following aspects:  
* _Trade Compliance Automation:_ The complex international political environment brings significant regulatory challenges to cross-border trades [87] that need to comply with regulations imposed by governments and international bodies. Providing real-time monitoring and screening of parties involved in trade transactions against sanctioned lists or restricted entities can ensure compliance with export control requirements, mitigate reputational risks, and help businesses avoid penalties. The trade compliance processes can be automated by digitizing documentation, automating data collection, and providing real-time updates on regulatory changes to achieve a simplified compliance process and smoother cross-border transactions with reduced error.
* _Cross-Border Payments:_ The settlement of payments for international trade carries a high risk. In international trade, the consignee often refuses to pay the final payment for various reasons. This problem can be avoided to a certain extent by using smart contracts [88].
* _Efficient Documentation Management:_ International trade involves extensive documentation, including contracts, invoices, bills of lading, and certificates of origin. Proper digitalization reduces reliance on physical paperwork [89], improves accessibility, and facilitates efficient sharing of documents with trading partners and regulatory authorities. It also ensures proper record-keeping, easy retrieval, and compliance with document retention requirements. For instance, RegTech can assist in classifying products more accurately in terms of their harmonized system (HS) codes and automatically calculating customs duties to minimize errors, improve efficiency, and ensure compliance with tariff regulations.  
Implementing RegTech in international trade and business helps businesses navigate complex regulatory landscapes, streamline compliance processes, mitigate risks, and enhance efficiency, facilitating smoother international trade transactions and overall business success in the global marketplace.
# An Improved PSO Algorithm and Its Application in GNSS Ambiguity Resolution
## 4 Conclusions  
In this study, we firstly proposed an improved PSO algorithm, introduced the population maturity to adjust the inertia weight adaptively, and adopted the strategy of population classification, for the optimal population, we made a Gauss mutation in the process of particle evolution, which can effectively avoid the defect of premature and falling into local optimum existing inPSO algorithm. Then the proposed IPSO algorithm was applied in the GNSS AR, abbreviated as IPSO-AR. Finally, we conducted a series of experiments with measured data to analyze the performance of IPSO-AR. We obtained the following valuable conclusions:  
1. The correct rate of IPSO-AR is superior to that of SPSO-AR. The superior correct rate of IPSO-AR, however, is obtained at the expense of computational efficiency. The improvement in the correct rate of IPSO-AR is particularly pronounced under high-dimensional ambiguity.
2. The performance of IPSO-AR is closely related to the property of estimated ambiguity float resolution. Under reasonable parameter settings, the efficiency of IPSO-AR mostly depends on the dimension of the estimated ambiguity float resolution. Meanwhile, the correct rate of IPSO-AR mainly depends on the precision of the estimated ambiguity float resolution. Given this principle, we can adaptively adjust the parameters of IPSO-AR on the basis of the estimated ambiguity float resolution, to achieve high efficiency and high correct rate, simultaneously.
3. IPSO-AR exhibits high efficiency and high correct rate when the dimension of estimated ambiguity float resolution is low (\(N<6\)). The correct rate of IPSO-AR can be validated when baseline length is known, such as in GNSS attitude determination. Thus, IPSO-AR may have considerable engineering application value.  
[PERSON] devised the research. [PERSON] and [PERSON] designed the methods and experiments. [PERSON] wrote the study. All authors have read and approved the final manuscript.  
This work was supported by the Fundamental Research Funds for the Central Universities, Chang'an University, 2018 (Grant No. 300102268102), the China Postdoctoral Science Foundation, No. 2018M633441, and the Programs of the National Natural Science Foundation of China (41790445, 41774025, and 41731066).  
The authors declare no conflicts of interests.  
Figure 13: Baseline length errors after single-epoch real-time kinematic (RTK) process with IPSO–AR method.
# Ultra-Reliable Link Adaptation for Downlink MISO Transmission in 5G Cellular Networks
## 1 Introduction
### Related Work  
General aspects of reliability and availability for communication networks have been studied in a number of works. The problem formulation, proposed methods and algorithms are often network specific. In particular, reliable routing and data delivery protocols have been studied quite extensively for mobile _ad hoc_ networks [9], and wireless sensor networks [10].  
In the context of 5G networking, the concept of ultra-reliability is, however, different in terms of scope, motivation, and requirements. From a 5G perspective, the area is in its infancy, and existing works mostly discuss ideas at a conceptual level, and propose different use cases which may benefit from ultra-reliability [1]. A detailed exposition is given in [2], which proposes a reliable service composition framework for guaranteeing a high level of reliability--the probability that a certain amount of data is transmitted in the given time frame. Identified sources of unreliability include resource depletion and interference. In [11], an availability indicator parameter is introduced as a key enabler for ultra-reliability in 5G, where availability is defined in terms of absence or presence of link reliability at the time of transmission.  
In cellular packet data systems, hybrid automatic repeat requests (HARQ) are utilized in conjunction with AMC to enable robustness [12; 13]. A certain probability of retransmissions is desirable, when maximizing the system throughput with a given reliability target, see e.g., [14; 15]. In order to achieve near-optimal performance, open-loop link adaptation is a viable option [16], where the AMC thresholds are tuned to keep a desirable retransmission probability. However, when targeting high reliability with strict latency required by URC, one cannot solely rely on HARQ. To make intelligent link adaptation decisions, it is beneficial to know the statistics of the channel, or the signal-to-interference-plus-noise ratio (SINR), of the realized transmission conditioned on the measured SINR. Next, by selecting a suitable backoff, a suitable modulation and coding scheme can be chosen, which support a target outage probability. An example of such SINR-statistics aware link adaptation related to interferer precoding variation can be found in [17].
## 5 Link Budget  
To gain a more comprehensive understanding of the antenna's data transmission capability, its link margin (LM) must be calculated. It is well known that the body is a multi-layered, lumped medium, and inside the human body is a complex electromagnetic environment. So, we simplified the external propagation link into a free-space model (unit: mm), and the formula of the path loss \(L_{f}\) is as follows [29]:  
\[L_{f}=20\log_{10}(4\pi d/\lambda) \tag{2}\]  
Here, \(d\) represents the distance between the Tx and Rx antennas, and \(\lambda\) represents a wavelength in free space with respect to the working frequency. The \(LM\) is used to measure the communication behavior of the implanted antenna. The \(LM\) can be calculated as follows [31]:  
\[LM=CNR_{link}-CNR_{required} \tag{3}\]  
\[CNR_{link}=P_{t}+G_{t}+G_{r}-L_{f}-N_{0} \tag{4}\]  
\[CNR_{required}=E_{b}/N_{0}+10\log_{10}B_{r}-G_{c}+G_{d} \tag{5}\]  
\[N_{0}=10\log_{10}k+10\log_{10}T_{i} \tag{6}\]  
\[T_{i}=T_{0}(NF-1) \tag{7}\]  
The parameters required in the calculation are listed in Table 5. Assuming the proportion of the signal power received by an outside antenna at a specified distance to the noise power density of an implanted antenna emitting at a specified power, the required CNR is  
Figure 13: Comparison of the measured and simulated radiation patterns. (**a**) 1.4 GHz; (**b**) 2.45 GHz.  
the carrier-to-noise ratio needed at the receiving end to satisfy a certain communication rate and BER requirements and is associated with the sensitivity of the receiver [31]. Here, we used BPSK modulation, which requires a BER of less than \(1\times 10^{-5}\) and a bit rate Br of 1 Mb/s [31]. Currently, the input power of the antenna when operating at 1.4 GHz and 2.4 GHz is 4.24 dBm and 5.02 dBm, respectively, and the outer receiving antenna adopts a circularly polarized antenna with a gain of 2.15 dBi. Transceiver ranges of 8.2 m and 9.7 m are achieved at 1.4 GHz and 2.4 GHz, respectively, when the communication link margin exceeds 20 dB (Table 5).
# Progressive Multi-Scale Fusion Network for Light Field Super-Resolution
## 4 Experiments
### Comparison to the State of the Art
#### 4.3.1 Quantitative Results  
As shown in Table 2, the quantitative results have a \(5\times 5\) LF views for 2\(\times\) SR and 4\(\times\) SR. These results show that our approach is comparable with the state-of-the-art SR methods. We also notice that our network achieves the best performance on EPFL, INRIA, and STFgantry datasets. The best results are shown in red and the second results are shown in blue. In Table 2, the value of PSNR (SSIM) of our PMFN far exceeds that of EDSR in STFgantry, which differs by 3.73 dB (0.011) in terms of PSNR (SSIM) for 2\(\times\) SR. That is because these SISR methods ignore the complementary information existing in other views, which limits the performance of SR. Compared with the traditional LFSR method (GB), our method has 3.97 dB (0.041) higher in terms of PSNR (SSIM) for 4\(\times\) SR. Our method is based on deep CNNs to achieve better performance compared with GB, which benefits from the representation learning capability of CNN. The average PSNR (SSIM) results of all testing scenes for LF-DFNet, MEG-Net, DPT, and our method are 31.32 dB (0.934), 31.72 dB (0.937), 31.56 dB (0.940), and 31.97 dB (0.940) for the 4\(\times\) task, respectively. Note that it can be observed that our PMFN has the best generalisation among all datasets. The performance of our network is mediocre in HCInew and HCIold. That is because our network implicitly addresses the influence of disparities by using multiple receptive fields to capture the corresponding pixels, which is not suitable for disparities in the middle. In summary, our method can incorporate complementary information among different views, especially in small and large disparity datasets (EPFL, INRIA, and STFgantry).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Training** & **Test** & **LF Disparity** & **Type** \\ \hline HCInew [26] & 20 & 4 & [\(-\)4,4] & Synthetic \\ HCIold [27] & 10 & 2 & [\(-\)3,3] & Synthetic \\ EPFL [28] & 70 & 10 & [\(-\)1,1] & Real-world \\ INRIA [29] & 35 & 5 & [\(-\)1,1] & Real-world \\ STFgantry [30] & 9 & 2 & [\(-\)7,7] & Real-world \\ \hline Total & 144 & 23 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Public LF datasets used in our experiments.
# Drought and Carbon Cycling of Grassland Ecosystems under Global Change: A Review
## 2 Drought and Grassland \(C\) Cycling under Global Change
### N Deposition  
CO\({}_{2}\) fertilization effects can depend on the amount of available nitrogen and water [73]. Therefore, nitrogen becomes a limiting factor when droughts occur due to strong interactions between water and \(N\)[17]. Although variances in droughts can have different effects on ecosystem productivity and \(C\) cycling [10], how these might interact with \(N\) deposition is not clear.  
There are more rapid ecosystem alterations caused by the interactive effects of \(N\) deposition and droughts than when \(N\) deposition or droughts occur alone [12]. Drought and \(N\) additions increase enzymatic efficiency and induce faster decomposition of litter [103]. In the Mongolian steppe, elevated \(N\) deposition can enhance the recovery of grassland productivity after drought [104]. Drought can cause nutrient deficiencies, even in fertilized soil. The increased duration and intensity of drought are usually linked to decreasing \(N\) mineralization [105], mobility, and absorbance of inorganic nutrients [106; 107]. Moreover, plant biomass, \(N\) concentration levels, and the amount of \(N\) in the whole plant increases by adding water after a short-term or medium-term drought, while they decrease after a long-term drought [108]. \(N\) addition increases grassland productivity after a drought [104]. In contrast, \(N\) fertilization has been shown to reduce the ability of grassland ecosystems to sustain net CO\({}_{2}\) assimilation. \(N\) addition enhances within-plot variability in plant size structure at the species level, but did not change total aboveground biomass [109]. Indeed, droughts affect soil respiration by influencing the feedback from soil \(N\) pools, because the microbial processes that regulate soil \(N\) availability are sensitive to short-term variations in soil moisture [110]. Meanwhile, \(N\) and water supplies limit potential NEE. Conversely, soil \(N\) availability has little effect on the short-term stability of ecosystem processes to extreme drought [49]. Some results suggest that increases in ANPP associated with \(N\) deposition may be offset by precipitation-mediated shifts, with negative consequences for the strength of the ecosystem \(C\) sink [111; 112]. For grasslands, the frequency and intensity of the disturbances have a key role in \(C\) balance [113]. Thus, annual grasslands experiencing higher rates of \(N\) may provide lower-than-expected \(C\) storage. In short, how \(C\) storage responds to \(N\) depends on the longer term variations of droughts.
# Minimizing Maximum Feature Space Deviation for Visible-Infrared Person Re-Identification
## 4 Experiments and Analysis
### Datasets  
The SYSU-MM01 dataset [7] contains 491 pedestrian subjects captured by four visible cameras and two infrared cameras. The training set includes 22,258 visible images and 11,909 infrared images of 395 subjects. In the testing processing, there are two test modes, i.e., all-search mode and indoor search mode. In the all-search mode, the query set contains 3803 infrared images of 96 subjects to search from a gallery subset containing 301 visible images of the same subjects. In the indoor-search mode, the query set contains 3803 infrared images of 96 subjects which is same as all-search mode, while the gallery set includes 112 visible images of the same subjects. Following [7], the single-shot evaluation protocol is used, and the final results are based on an average of 10 tests with randomly selected gallery images.  
The RegDB dataset [8] includes 412 pedestrian classes, and each class contains ten visible images and ten infrared images captured by the overlapping visible and infrared cameras. Following [1; 12; 17], this dataset is randomly split into ten trials. In each trial, the training set contains 206 classes, while the testing set includes the non-overlapping 206 classes. The final results are based on an average of 10 tests with 10 trials. Both visible-to-infrared and infrared-to-visible retrieval are evaluated.
# Collaborative Unmanned Vehicles for Inspection, Maintenance, and Repairs of Offshore Wind Turbines
## 4 Redundancy for Collaborative UVs
### Potential Failure Modes of a Collaborative System  
The common failure modes for a single UV may be caused by a component failure (sensors, actuators, software, and hardware), human error (poor navigation, faulty setup, and poor maintenance), or environmental factors (bad weather, unsuitable ambient temperature) [130]. Other common failure modes may also be caused by positioning errors due to GNSS issues [135], electromagnetic interference [136], or security breaches from spoofing activity [137].  
Within a collaborative system, these failure modes may be amplified due to the involvement of multiple UVs instead of just a single UV. When UVs are working together in close proximity, there is greater potential for collision between them that might be caused by the common failure modes. For collaborative UVs performing a mission or an IMR operation, a single UV malfunction may halt the entire operation, and eventually will cause a significant loss of valuable operating time. Poor coordination or coupling between UVs might also cause losses or damage to one or more of the UVs, for instance during launching, docking, or transportation of a UAV by a USV.
## 1 Introduction
The proposed methodology is based on the dynamic rail responses from rail acceleration measurements. To increase the sensitivity to this type of periodic damage, a Fast Fourier transform (FFT) is applied to the time series accelerations. The performance of different feature extraction techniques concerning their sensitivity to the classification of the polygonal damage is evaluated. The features based on principal components analysis (PCA) are suitable for a harmonic-based classification, while the continuous wavelet transforms (CWT) features present a very good efficiency for an amplitude-based classification. To reduce the volume of extracted data while retaining the most relevant information, as well as increasing sensitivity to damage, the data are merged using the Mahalanobis distance. As a final step, a cluster analysis is employed to automatically classify a polygonal wheel, in terms of harmonic order (harmonic-based classification) and defect amplitude (amplitude-based classification). This proposed unsupervised methodology presents several novel aspects related to existing studies:  
* A wayside monitoring system capable of classifying polygonal wheels, while previous work has only focused on detecting wheel defects [33; 43; 44];
* Proposed an automatic methodology capable of identifying different types of polygonal wheels according to harmonic number (harmonic-based) and according to defect amplitude (amplitude-based);
* High effectiveness in classifying polygonal wheels (with no misclassification) using a very reduced layout of accelerometers, contrasting with other studies [16; 38; 39].
## 3 Results
Vegetation indices: CWSI (crop water stress index), NDRE (normalized difference red edge), NDVI (normalized difference vegetation index), and GNDVI (green normalized difference vegetation index) at two different time points (1, June; 2, September). Agronomic parameters: fresh and dry fruit weight per tree (Yield_FW and Yield_DW), number of bunches (Bunch) per tree, and average bunch weight (Yield_raw). The commercial caliber, percentage of open husk, closed husk, and empty nuts (Caliber, Split, Non_Split, and Blank) and the weight of open husk, closed husk, and empty nuts per tree (DW_split, DW_non_split and DW_blank). Regarding quality, fiber, fat (Fat), carbohydrates (CBH), protein (PROT), calcium (Ca), magnesium (Mg), phosphorus (P), potassium (K), sodium (Na), iron (Fe), zinc (Zn), and several fatty acids (palmitic, palmitoyl, stearic, oleic, and linoleic) are included. correlations in September were found between CWSI and carbohydrates (r = 0.62), NDVI and iron (r = 0.54), CWSI and iron (r = 0.54), and GNDVI and iron (r = 0.52). Finally, non-thermal-based VIs were correlated with the thermal-based VI, aiming to show the potential to estimate the CWSI values in pistachio crops (Figure 9). In this sense, the highest correlation with CWSI in June was found for NDRE and the highest correlation in September was found for NDVI. In general, the non-thermal-based VIs were more correlated with CWSI in September than in June. Figure 8: Linear regression analysis shows the highest correlations between vegetation indices (VIs) and agronomic and quality traits of pistachio. VIs: CWSI (crop water stress index), NDRE (normalized difference red edge), NDVI (normalized difference vegetation index), and GNDVI (green normalized difference vegetation index) at two different time points (June and September). Parameters: number of bunches (Bunch) per tree, carbohydrates (CBH), iron (Fe), and oleic fatty acid. Figure 9: Linear regression analysis between thermal- and non-thermal-based vegetation indices (VIs). Thermal VI: CWSI (crop water stress index). Non-thermal VIs: NDRE (normalized difference red edge), NDVI (normalized difference vegetation index), and GNDVI (green normalized difference vegetation index) at two different time points (June and September).