esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Changes in Hydrothermal and Radiation Factors  
The climatic factors in the study area exhibited significant seasonal variations. Most of the annual precipitation occurred between May and October--particularly during the summer months (June, July, and August) (Figure 3a). It was also observed from the figure (Figure 3a) that summer precipitation during 2001-2018 showed a decreasing trend, mainly in June and July. During the study period, the monthly average temperature ranged approximately from \(-15\ ^{\circ}\)C in January to \(15\ ^{\circ}\)C in July (Figure 3b), with no significant long-term trend observed over the years. The three climate factors--SM, ST, and VPD--also exhibited no clear interannual trends. All three increased gradually from spring (March to May), peaked during summer (June to August), and then gradually declined (Figure 3c-e). The long-term trend of PAR showed no significant changes in spring (mainly March and April), autumn, and winter. However, during the summer months (June to August), a clear increasing trend was observed, with the most pronounced increase occurring in July, showing a linear slope of 0.55 (Figure 3f).  
Figure 2: Schematic diagram of the SEM between hydrothermal—radiative factors and NPP.  
Figure 3: Interannual monthly variation in key climate factors: (**a**) precipitation, (**b**) temperature, (**c**) soil moisture, (**d**) soil temperature, (**e**) saturation vapor pressure deficit, (**f**) photosynthetically active radiation.
# Speech Inpainting Based on Multi-Layer Long Short-Term Memory Networks
## 1 Introduction
The primary contribution of this paper lies in addressing speech inpainting in real-world communication scenarios, utilising only the voice information preceding the gap. In two-way real-time conversational communication, supplementary information, such as visual data, transcripts, and speech signals immediately following the gap, is not always available or feasible to use in speech inpainting. Using this supplementary information requires buffering a large amount of data and significant processing capability, resulting in lengthy jitter and degraded quality of service and user experience. The proposed speech-inpainting technique provides effective speech restoration without the need for supplementary information and achieves remarkable restoration effects and superior speech quality across gaps of varying lengths, significantly outperforming existing deep learning- and generative-based methods [22; 23; 24; 25; 26; 27; 28; 29; 30; 31] that use supplementary information. This study not only contributes to the advancement of speech inpainting technology but also carries practical implications for applications such as speech enhancement and audio restoration. The remainder of this paper is structured as follows. Section 2 presents the related works corresponding to our study. Section 3 illustrates the proposed methodology, model architecture, evaluation methods, and fine-tuning of the hyperparameters. Section 4 presents the experimental and training setup. Section 5 illustrates the results and analysis of the study. Section 6 presents the conclusions and suggests some future research directions.
## 5 LPWAN Application Requirements
### Capacity and Densification  
One of the essential requirements for LPWAN is to support a massive number of simultaneously connected devices with low data rates. Many applications require support for 100,000+ devices in a scalable manner. Scalability refers to the ability to seamlessly grow from a network of a small number of heterogeneous devices to massive numbers of devices, new devices, applications, and functions without compromising the quality and provisioning of existing services [8]. As LPWAN end devices have low computational and power capabilities, network devices such as gateways and access stations can also play a vital role in enhancing scalability. Employing multi-channel and multi-antenna based on different diversity techniques can also significantly improve the scalability of LPWAN networks. However, it is to be ensured that such features do not compromise other performance metrics. A large number of devices also results in high densification [6]. The network needs to mitigate the issues of bottlenecks in media access and interference, and avoid degradation of performance of the network
# Joint Iterative Decoding Design of Cooperative Downlink SCMA Systems
## 6 Simulation Results
### BER Evolution for Rayleigh Channel  
In this subsection, we verify the effectiveness of the proposed downlink cooperative SCMA system for the Rayleigh channel. We compared the BERs and MPA detector for the proposed cooperative SCMA system under a Rayleigh channel with \(K=4\) and \(K=5\) in Figure 9. All the cases verify the validity of our analysis results in (27). We assume an ideal cooperative case in the simulation. Actually, this ideal cooperative system can be achieved when the SNR of the S-R link is above a certain value.  
We also analyzed the BERs of the proposed ideal cooperative SCMA system under a Rayleigh channel with different numbers of iterations for the MPA detector under \(K=4\) and \(K=5\) in Figure 10 and Figure 11, respectively. The advantage of the cooperative SCMA system over the non-cooperative one was highlighted, demonstrating the validity of the designed cooperative system. By using the sparse-based channel estimator in [51], we also  
Figure 8: The BER performance of the proposed non-ideal cooperative SCMA system under an AWGN channel and different SNRs of the S-R link for \(K=5\).  
Figure 9: Comparison of the BERs and MPA detector for the proposed cooperative SCMA system under a Rayleigh channel with \(K=4\) and \(K=5\).  
investigate the BER performance of the proposed ideal cooperative SCMA system under a Rayleigh channel with imperfect channel state information (CSI) for \(K=4\) and \(K=5\) in Figure 12. It is obvious that BER degradation is negligible, which further clarifies the robustness of the proposed joint detection structure.  
In the last set of simulations, the BER performance of the proposed non-ideal cooperative SCMA system over a Rayleigh channel and different SNRs of the S-R link for \(K=4\) and \(K=5\) were considered, as shown in Figure 13 and Figure 14, respectively. It can be observed from Figures 13 and 14 that the non-ideal cooperative SCMA system's performance decreases significantly when the SNR of the S-R link is only 5 dB. This is because the detected bits in the relay consist of a large amount of errors, propagating into the destination users, and a large SNR is more suitable for practical implementation.  
Figure 11: The BER performance of the proposed ideal cooperative SCMA system under a Rayleigh channel and different iterations of the MPA detector for \(K=5\).  
Figure 10: The BER performance of the proposed ideal cooperative SCMA system under a Rayleigh channel and different iterations of the MPA detector for \(K=4\).  
Figure 14: The BER performance of the proposed non-ideal cooperative SCMA system under a Rayleigh channel and different SNRs of the S-R link for \(K=5\).  
Figure 12: The BER performance of the proposed ideal cooperative SCMA system under a Rayleigh channel with imperfect channel state information for \(K=4\) and \(K=5\).  
Figure 13: The BER performance of the proposed non-ideal cooperative SCMA system under a Rayleigh channel and different SNR of the S-R link for \(K=4\).
## 3 Preliminaries
### Validity Indices
#### 3.2.2 Davies-Bouldin Index (DBI)  
This index evaluates the average similarity ratio between each cluster and its most similar cluster. A lower Davies-Bouldin index indicates better clustering performance, as it reflects greater separation between clusters. Computationally, the DBI requires several computations.  
1. For each cluster C\({}_{i}\), calculate the average distance between each point in the cluster and the cluster centroid: \[S_{i}=\frac{1}{\left|c_{i}\right|}\sum_{x\in C_{i}}\ \left\|x-c_{i}\right\|\] (10) where \(\left|C_{i}\right|\) is the number of points in cluster C\({}_{i}\), \(x\) is a point in the cluster, and \(c_{j}\) is the centroid of C\({}_{j}\).
2. For each pair of clusters C\({}_{i}\) and C\({}_{j}\), calculate the distance between their centroids: \[M_{ij}=\left\|c_{i}-c_{j}\right\|\] (11)where \(c_{i}\) and \(c_{j}\) are the centroids of clusters \(C_{i}\) and \(C_{j}\), respectively.
3. For each cluster \(C_{i}\), calculate the similarity ratio with another cluster \(C_{j}\). This ratio considers the intra-cluster distances and the inter-cluster distance: \[R_{ij}=\frac{S_{i}+S_{j}}{M_{ij}}\] (12)
4. For each cluster \(C_{i}\), find the maximum similarity ratio \(R_{ij}\) with respect to all other clusters \(C_{j}\). The DBI is then the average of these maximum values over all clusters: \[DBI=\frac{1}{K}\max_{j\
eq i}R_{ij}.\] (13)  
The optimal value \(C\) for the number of clusters is given by the value \(c\) for which \(XBI(c)\) is minimum; it is given by the following:  
\[C=\underset{c}{argmin}DBI(c) \tag{14}\]
## 3 Data and Methods
### Net Primary Production (NPP) Trend Analysis  
To detect the changing trends in NPP across the study region, the seasonal Kendall test was applied. It is a non-parametric test which can address the time series with seasonal variation, having missing values, tied values, or outliers, and does not require normality of the time series [34]. It is widely used in the field of hydrology to evaluate the significance of trends in time series such as water quality, stream flow, and precipitation [34, 35, 36]. This method is developed from the Mann-Kendall test which is based on the correlation between the ranks of a time series and their time order, but is improved to be able to use seasonal data sets to address variability [34]. The seasonal Kendall test was conducted on the per-pixel NPP time series to obtain the spatial pattern of changing trends. The significant and positive Kendall correlation coefficient (\(\tau\)) value indicates an increasing trend, while the significant and negative \(\tau\) value suggests a decreasing trend.
# Protein Kinases in Mediating Phage-Bacteria Interactions
## 5 Bacteria HKs in Phage-Bacteria Interactions
### The Cpx Envelope Stress Response System
#### 5.3.3 Cpx in Plasmid Mobilization and Phage Interaction  
The Cpx system, named for its role in \"conjugative pilus expression\", traces its origins to early studies in _E. coli_ where it was first identified as a regulator of F-plasmid conjugation and related to phage interaction [94; 95]. While subsequent work [73] expanded Cpx's functions to include global envelope stress management, the original nomenclature endured, reflecting its foundational association with plasmid biology. Recent study [44] in pathogens like _Klebisella pneumoniae_ further cement this link (Figure 2C), demonstrating that CpxR plays a dual role in enhancing carbapenem resistance and plasmid transfer. Through genetic and biochemical analyses, researchers found that CpxR directly binds to the promoter regions of both the carbapenemase gene _blaKPC_ and the conjugative _tra_ operon on IncFII plasmids, upregulating their transcription. Deletion of _cpxR_ increased carbapenem sensitivity and impaired plasmid transfer, while complementation restored resistance. Notably, CpxR's regulation of _blaKPC_ expression and plasmid conjugation was demonstrated in both classical (cKP) and hypervirulent (hvKP) _K. pneumoniae_, suggesting a conserved mechanism. These findings provide direct evidence that a host-encoded TCS (CpxAR) controls plasmid-borne resistance genes and facilitates their spread, offering new insights into how _K. pneumoniae_ adapts to antibiotic pressure and acquires resistance. This regulatory link between chromosomal signalling and mobile genetic elements could inform strategies to combat carbapenem-resistant _K. pneumoniae_ infections.  
To echo the link with phage interaction, recently, a lot of novel, underexplored phages were identified that exploit the plasmid-encoded conjugative pili for host cell infection [45]. This raises several critical questions: (1) How does the Cpx system modulate conjugative pili biogenesis upon phage infection? (2) Does phage infection interfere with or even exploit horizontal plasmid transfer? (3) How do environmental factors--particularly clinical conditions--shape these dynamics to influence bacterial evasion of antibiotics, phage therapy, or their combination? Addressing these questions could be useful in offering new strategies to combat multidrug resistant superbugs.
# ANTSpin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna
## 7 Conclusions  
Nowadays, research regarding indoor positioning is in full swing. Our proposed scheme ANTspin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna, rotate antennas to collect dynamic data, get more information for localization. The scheme can be applied in 2D and 3D localization scenes. We implement ANTspin while using COTS RFID devices and the experimental results show that it achieves a mean accuracy of 9.34 cm in 2D and mean accuracy of 13.01 cm in 3D. It has a good promotion prospects, with high localization accuracy, low deployment cost, and strong practical application significance.  
**Author Contributions:** L.S. and Q.Z. conceived and designed the experiments, and wrote the paper; J.P. performed the experiments and analyzed the data; H.X. contributed RFID devices and guided their use. P.L. and D.X. helped with language modification. All authors contributed to the final version.  
Figure 21: Express post stations. (**a**) Large express parcels situation; (**b**) Small express parcels situation.  
Funding: This work was supported in part by the National Key R&D Program of China under Grant 2018 YFB1003201, in part by the CERNET Innovation Project under Grant NGH20180605, in part by the National Natural Science Foundation of China under Grant 61572260, Grant 61672296, Grant 61602261, Grant 61872196 and Grant 61872194, in part by the Jiangsu Natural Science Foundation for Excellent Young Scholars under Grant BK20160089, in part by the Scientific and Technological Support Project of Jiangsu Province under Grant BE2016185, and Grant BE2017166, in part by the STITP of the Nanjing University of Posts and Telecommunications(NUPT) under Grant SZDG2018014, in part by the Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant SJKY19_0825, and in part by the 1311 Talent Plan of the Nanjing University of Posts and Telecommunications (NUPT).
# Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems
## 2 Related Work
Similarly, in [34], the authors used different LR algorithms to design the IF receiver and studied its performance in terms of ergodic capacities and orthogonal deficiencies. The main focus of this paper is to develop the lattice reduction enabled IF receivers. In this study, we demonstrated the effectiveness of ZF and MMSE algorithms when they were combined with the LLR and KZ algorithms and proved they are better than IF, as presented in [34]. In [39], the authors put emphasis more on the LLL algorithm-based receiver for large scale MIMO systems and proposed sophisticated QR decomposition-based LLL algorithms. In this paper, the authors demonstrated the performance of the proposed LLL algorithms concerning BER performances. Nevertheless, its effect on the channel capacity is not analyzed. The main objective of this paper is to show the benefits of KZ decomposition-based receivers in terms of the capacity improvements along with its comparison with the LLR-aided receivers. Overall, the works mentioned above deal with the LR-based MIMO detection techniques and their performance analysis is in regard to the system BER performance and the complexity of the system. Therefore, this paper focuses on the impact of the channel correlation over the capacity of a massive MIMO system. The proposed method aims to improve the capacity by using an LR-aided receiver for the massive MIMO system. Moreover, this paper also highlights the utility of KZ-aided receivers over LLL-aided receivers in terms of the improvement in the capacity of the MIMO system.
## 4 Proposed Migration Approach and Its Evaluation
### Migration Approach
#### 4.1.5 Data Validation  
The purpose of the fifth step is to create automatic data validation. Transformed data have to be validated before releasing it into production. In the first sub-step, test cases have to be created based on the functional requirements and data in the mainframe monolith database. The second sub-step is to create a test engine that has to be able to execute created test cases in the previous sub-step. The purpose of the last three sub-steps is to execute the test cases and make amendments if needed (Figure 9). The step is finished only when all of the test cases are passed.  
For example, the functional requirements for the data records of _PolyglotModel_ defined in Figure 7 are: _read_, _create_, _update_, and _delete_. Four test cases have to be created in order to validate the data integrity and persistence, one test case for one functional requirement. The first functional requirement is the possibility to read the data. In this example, it is the possibility of reading data records of _PolyglotModel_. Two main criteria have to be verified. First, in each data record of _PolyglotModel_, all fields have to be mapped correctly and the data have to be consistent. Second, the multi-model polyglot persistence has to contain the same number of records as the data table _ModelTable_ in the monolith database. Figure 10 contains an example of the possible records. The _monolithModel1 Record_ represents a record of the data table _Model1 Table_ in the monolith database, the _monolithModel2 Record_ represents a record of the data table _ModelTable_ in the monolith database, and the _polyglotModelRecord_ represents a record of the _PolyglotModel_ in multi-model polyglot persistence. The test case  
Figure 8: An example of the simplified record creation class.  
for functional requirement _read_ has to verify that all fields that exist in the _MonolithModel1_ and _MonolithModel2_ models also exist in _PolyglotModel_ and the values are the same. For example, the _PropertyB_ value in _monolithModel2 Record_ should be the same as the PropertyB value in _polyglotModelRecord_. To verify that all records were transformed to multi-model polyglot persistence during step 4, the test case has to be executed as many times as the _ModelTable_ table has records.  
Three more test cases have to be created to validate _create_, _update_ and _delete_ functional requirements. The test case for _create_ should try to create a new record of _PolyglotModel_ and verify that the record is actually created and that all of the fields are filled correctly. The test case for _update_ should try to update all of the value fields in a record of _PolyglotModel_ and verify that all of them are updated correctly. Finally, the test case for _delete_ should try to delete a record of _PolyglotModel_ and verify that it was actually deleted.
## 4 Experiments
### Implementation Details  
All experiments in this study were conducted on an RTX 3090 Ti GPU (Manufacturer: Intel Corporation, City: Santa Clara, State: CA, Country: USA). Dynamic object detection was performed using the deep learning-based QD-3 DT [30] method. The distance parameter \(r\) for dynamic region removal was set to 0.3 m, and the variance parameter \(\sigma^{2}\) was configured to 50 to model the distribution of Gaussian point clouds. For dynamic region removal, the dynamic point removal weight \(\lambda\) was set to 50 to control the influence of dynamic artifacts. Additionally, for ground points within dynamic regions, the local average height was used to fill in missing ground surfaces, ensuring the completeness of the reconstruction. For 3 DGS rendering, the initial opacity value \(\Lambda\) was set to 0.8 and the photometric loss weight \(\alpha\) was configured to 0.5. The weight attenuation for dynamic regions was calculated using a dynamic mask. To improve the efficiency and density of scene modeling, a sparse control strategy based on geometric prior information was applied to the Gaussian points, removing redundant points that were far from object surfaces. The hyperparameters for the sliding window method were set as follows: depth gradient threshold \(\tau_{d}=0.1\), color gradient threshold \(\tau_{c}=30\), temporal decay factor \(\alpha=0.2\), spatial decay factor \(\beta=0.1\), and window size \(w=20\). These settings demonstrated excellent performance on both public and custom datasets, showcasing robustness in dynamic environments and complex scenes.
# A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection
## 3 Materials and Methods
### Genetic Algorithm  
The genetic algorithm, inspired by genetics in biological systems, can perform well in high-dimensional feature-selection problems because of its robustness, making it suitable for credit card fraud detection. Given a set of candidate features (called the population), the GA finds the optimal solution via a series of iterative genetic operations. It is superior to most traditional search methods in three main areas: firstly, the GA conducts a parallel search all over the population of solutions; instead of optimizing its parameters, the GA uses chromosomes, an encoded form of a possible solution, to achieve faster convergence. Lastly, the GA employs a fitness value to identify a potential solution. The flowchart of the GA is shown in Figure 1.  
The genetic operators include crossover, mutation, and selection [50]. The crossover operator determines how the chromosomes are combined to obtain a new chromosome for the next generation [51]. In the GA, a population of candidate solutions (also known as individuals) to the optimization problem is evolved to obtain the optimal solution. The various candidate solutions have certain attributes called chromosomes that can be mutated; meanwhile, these solutions are represented in 0 and 1 binary strings, indicating whether the corresponding attribute has been selected or not. The mutation operator ensures some bits of the chromosomes are flipped randomly based on probability. The selection step involves choosing chromosomes based on their fitness score for further processing.  
A common termination criterion is running the algorithm for a specified number of times [52]. Therefore, the algorithm ends after the specified number of iterations, outputting the optimal solution identified after going through all the generations.