esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Evaluation Methods of Black Soil Farmland Productivity Based on Field Block Scale
## 2 Materials and Methods
### Data Sources and Data Preprocessing
#### 2.1.3 Data Preprocessing  
In this study, data pre-processing was applied to ensure that the soil productivity evaluation model achieved excellent performance in terms of accuracy and stability. Specific methods employed include the 3\(\sigma\) principle, box-and-line plot analysis, Z-score normalization, outlier removal, and L2 feature selection. Outliers in the dataset were identified and removed by the 3\(\sigma\) principle and box-and-line plot analysis to eliminate anomalous data points that might be caused by measurement errors, data entry errors, or other reasons.  
Figure 1: Schematic diagram of the distribution of 21 sampling points in Plot A.  
In order to address the differences in different feature scales, a Z-score normalization method was used to transform the data for each feature into a standard normal distribution with mean 0 and standard deviation 1, ensuring that all features have equal weights in the model.  
In order to improve model performance, it is necessary to obtain important features from a large number of input features. The benefit of reducing the feature dimension is that it reduces the risk of overfitting while improving the model performance [24]. Among multiple feature selection methods, we compare the advantages of different methods and combine them with the characteristics of the data in this study; L1 feature selection, L2 feature selection, and PCA dimensionality reduction are more effective. L1 feature selection and L2 feature selection are regularization methods in machine learning; L1 feature selection tends to make the weight of some features zero through L1 regularization (Lasso), achieving sparsity in feature selection. L2 feature selection, on the other hand, uses L2 regularization (Ridge), which tends to smooth out the weights by penalizing the sum of the squares of the model parameters, but does not make them zero. PCA dimensionality reduction method maps the original features to a new low-dimensional space by finding the principal components in the data. PCA differs from L1 and L2 in that it does not focus exclusively on the target variable, but rather on selecting the principal components by maximizing the data variance to select principal components. In the experimental results L2 feature selection works best with the small dataset of this study. The advantage of L2 feature selection is that it is more robust to covariate data, helps to deal with the presence of highly correlated features without being easily over-influenced by specific features, and improves the stability and generalization ability of the model.
## 1 Introduction
We propose in this research work to design, and implement on an FPGA board, three chaos-based stream ciphers and evaluate their performance, in terms of security against statistical attacks, and cryptographic attacks, as well as in terms of hardware metrics, including throughput and efficiency. The proposed solutions cope with the trade-offs between security, performance, and cost, and have a better level of security compared to standard methods (thanks to the high non-linearity of chaotic systems). The remainder of this article is organized as follows: In Section 2, we introduce, implement, and evaluate the statistical and hardware performance of some discrete chaotic maps used here, specifically Logistic, Skew-Tent (STM), PWLCM, 3D-Chebyshev (3D-Ch), and a Linear Feedback Shift Register (LFSR) as the basis for the chaos-based stream ciphers design proposed in this work. Based on the performance results of the studied chaotic maps, we designed, implemented, and analyzed in Section 3 three-stream encryption methods that utilize three secure pseudo-chaotic number generators (PRNGs-CS), known as LSP-PRNG, LST-PRNG, and LSPT-PRNG, by combining different chaotic maps such as the Logistic map, STM, PWLCM, and 3D-Ch map. Then, we present the results of the experiments in terms of security evaluation and hardware metrics of the proposed PRNGs-CS and their corresponding stream ciphers. Finally, Section 4 summarizes the whole article. Notice that, for the proposed chaotic systems, in all statistical experiments, 100 random secret keys are utilized to produce 100 different sequences of 3,125,100 32-bit samples. However, just 3,125,000 samples were utilized per sequence (i.e., \(10^{8}\) bits). While the system internally generates the first 100 samples per sequence for each key, they are not employed to deviate from the transitional phase.
# Characterization of Carrier Phase-Based Positioning in Real-World Jamming Conditions
## 3 Cases for Investigation
### Jamming Detection
If an incident is detected, the remaining frequency bands and constellations are also run through the detector to determine the scope of the incident. Figure 2 shows the output of the SoS algorithm from the TAPAS 03 reference station on 18 January 2019. The red line marks the detection threshold. Using the described implementation of the algorithm, it is seen that a very low noise floor is achieved, and that three incidents clearly stand out; a small incident around 08:00, a medium incident at 16:30, and a large incident in the evening around 19:30. Figure 3 shows the C/N\({}_{0}\) values of the 19:30 incident to provide some insight into how the actual C/N\({}_{0}\) measurements translate into SoS values. This figure shows a typical short L1 incident in the TAPAS network: a short (approximately 60 s) incident with low jamming power, as all GPS satellites see a correlated, but small drop in the C/N\({}_{0}\) values. A challenge of the SoS algorithm is that longer incidents due to \(\mathrm{C/N_{0}}\) (and thus SoS) fluctuations will often be detected several times. In this implementation, a numb period of six minutes is introduced following a detection. Any further detections made within this period will be registered as a continuation of the original incident, and add to its duration. Should two jamming incidents occur in very close succession, it will thus only be registered as one incident. Based on the investigation in [8], it is unlikely that this would occur. Using the SoS detector, data from the entire operating period of TAPAS (2018-2020) were evaluated, and two of the most severe cases of jamming were selected, while we also chose to revisit the case described in [8].
# A Novel Visual System for Conducting Safety Evaluations of Operational Tunnel Linings
## 5 Conclusions  
The visualized structure safety evaluation system for operational tunnels, based on self-developed Fuzzy-AHP programmed model, was developed by using MATLAB, and the rationality of the system was verified by the BP-RBF combined neural network.  
1. The user-friendly interface of the visualized system simplified operations and integrated functions such as data input, management, analysis, and application. This system solved the problem of large amounts of monitoring data that were difficult to calculate and analyze, and promoted the development of structure safety evaluations from qualitative to quantitative results for operational tunnels.
2. The system can conveniently edit and modify core calculation functions, such as membership functions, based on different practical engineering projects, which improves the applicability of the system. In addition, this system provides an important construction method for the programming and visualization of other kinds of evaluation models.
3. Based on the monitoring data, the system was applied to the structure safety evaluation of a mountain tunnel during its operational period. The system provided the evaluation results of each section of the tunnel and key disease indexes to focus on, which was conducive to the sustainable operation of the tunnel.  
Conceptualization, Y.J., S.Y., H.G., L.H., S.S., H.S. and J.Z.; Methodology, Y.J., G.L., H.S., S.S. and J.Z.; Software, Y.J., S.Y., H.G., L.H., S.S., H.S. and J.Z.; Validation, Y.J., S.Y., H.G., L.H., S.S., H.S. and J.Z.; Formal analysis, Y.J., S.Y., H.G., L.H., S.S., H.S. and J.Z.; Investigation, Y.J., S.Y., H.G., L.H., S.S. and J.Z.; Resources, Y.J., S.Y., H.G., S.S. and J.Z.; Data curation, Y.J., S.Y., H.G., J.Z. and G.W.; Writing--original draft, Y.J., S.Y., H.G., J.Z. and G.W.; Writing--review & editing, Y.J. and S.Y.; Visualization, Y.J.; Supervision, Y.J. and G.W.; Project administration, Y.J. Funding acquisition, Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the Natural Science Foundation of Jiangsu Province (BK2020234), General Funded Project of China Postdoctoral Science Foundation (2023M733760), Natural Science Foundation of the Jiangsu Higher Education Institutions of China (21 KBJ580004), and Construction System Technology Project of Jiangsu Province (2023 ZD033).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Ocean Chlorophyll (OCH)  
Phytoplankton is the main photosynthesizer in oceans providing the ocean's food chain and primary production ([PERSON] et al., 2014; [PERSON] et al., 2014). Therefore, investigating the impact of principal phytoplankton groups on marine ecosystems and global biogeochemical cycles has long been a hot research topic. In this regard, Chl concentration as a biological property along with phytoplankton absorption coefficient as an optical property can be considered as the key information about phytoplankton biomasses. Since Chlorophyll a (Chl-a) is the main pigment involved in photosynthesis, it has been mainly studied to monitor and analyze phytoplankton concentrations in many ocean studies. Ocean Chl-a studies help to understand the reaction of the marine ecosystem to human activities and facilitate detecting and monitoring eutrophication [199]. Moreover, estimating Chl-a concentration at the ocean surface can help in identifying potential fishing zones [200]. Finally, observing the spatiotemporal distribution of Chl-a concentration can reveal the ocean's role in climate change [182].  
The global distribution of Chl-a has been reported to be rich in areas located along the coasts and continental shelves, especially in the north of the northern hemisphere [191]. Temperate Chl-a concentrations have also been observed in the south of the 45 th parallel south [191]. Although coastal waters account for a small portion of the Earth's ocean water, they contain almost a quarter of the global marine primary production and represent the effect of coastal detrimental phytoplankton blooms on human activities [199]. Therefore, coastal waters have been the major focus of most studies investigating the variability and concentration of Chl-a [185,199].  
Although various RS systems, such as optical, TIR radiometer [201], microwave radiometer [202,203], and SAR [204,205] have been used for Chl-a mapping, optical OC systems have been most frequently used for this application. Therefore, Chl-a mapping using only optical RS systems is discussed in this section.
# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
###### Abstract  
Leaving the current 4 th generation of mobile communications behind, 5G will represent a disruptive paradigm shift integrating 5G Radio Access Networks (RANs), ultra-high-capacity access/metro/core optical networks, and intra-datacentre (DC) network and computational resources into a single converged 5G network infrastructure. The present paper overviews the main achievements obtained in the ALLIANCE project. This project ambitiously aims at architectecting a converged 5G-enabled network infrastructure satisfying those needs to effectively realise the envisioned upcoming Digital Society. In particular, we present two networking solutions for 5G and beyond 5G (B5G), such as Software Defined Networking/Network Function Virtualisation (SDN/NFV) on top of an ultra-high-capacity spatially and spectrally flexible all-optical network infrastructure, and the clean-slate Recursive Inter-Network Architecture (RINA) over packet networks, including access, metro, core and DC segments. The common umbrella of all these solutions is the Knowledge-Defined Networking (KDN)-based orchestration layer which, by implementing Artificial Intelligence (AI) techniques, enables an optimal end-to-end service provisioning. Finally, the cross-layer manager of the ALLIANCE architecture includes two novel elements, namely the monitoring element providing network and user data in real time to the KDN, and the blockchain-based trust element in charge of exchanging reliable and confident information with external domains.  
SDN/NFV; RINA; KDN; monitoring; blockchain +
Footnote †: journal: 30 September 2021
## 4 Results
### 643 GHz  
The HCl retrieval errors induced by various instrument uncertainties are shown in Figure 17. NEDT is the major error source which leads to an error of less than 20% below 60 km and 20%-60% above. The local oscillator offset (LO) causes an error of 1%-10% above 50 km, which is similar to other radiometers. The effect of sideband weight is around 0.5%, which is relatively small. The effect of pointing offset (LOS) can be neglected above 20 km, but becomes large suddenly in the troposphere, since the measurement response in this region is small.  
The brightness temperature difference \(T_{A}-T_{B}\) at various tangent heights measured by 643 GHz radiometer (625 GHz spectrometer) are shown in Figure 18. Since the FOV of 643 GHz radiometer  
Figure 16: The O\({}_{3}\) retrieval error induced by external error source: (**a**) absolute and (**b**) relative retrieval errors. The uncertainties assumed are summarized in Table 5.  
Figure 17: The HCl retrieval errors induced by different instrument uncertainty sources: (**a**) absolute and (**b**) relative retrieval errors. The uncertainties assumed are summarized in Table 3.  
is 0.9 km, the antenna pattern only has a little impact on the observation. The largest brightness temperature offset is 0.6 K.  
Figure 19 shows the HCI retrieval errors induced by different calibration uncertainties. The retrieval error induced by the R-J approximation is very large because the brightness temperature calculated by the Planck function and R-J approximation are quite different at this frequency region. The retrieval error can be as large as 100% in the troposphere and mesosphere. The effect of the antenna pattern has an error of less than 1% above 20 km, but becomes very large in the troposphere. The residual nonlinearity of calibration leads to an error of less than 2%. The hot target offset causes an error of less than 1% above 20 km, but with increasing rapidly in the troposphere. This sudden increase in the troposphere is due to the small measurement response at these altitudes.  
Figure 19: The HCI retrieval errors induced by different calibration error sources: (**a**) absolute and (**b**) relative retrieval errors. The uncertainties assumed are summarized in Table 4.  
Figure 18: The brightness temperature difference \(-T_{B}\) at various tangent height measured by 643 GHz radiometer (625 GHz spectrometer): (**a**) 10 km; (**b**) 20 km; (**c**) 30 km; (**d**) 50 km; (**e**) 70 km; (**f**) 90 km.  
The HCl retrieval error induced by a priori error is shown in Figure 20. The retrieval error induced by a priori profile can be neglected at 20-80 km. However, the error increases in the troposphere and mesosphere due to the measurement information which has lacked.
# Research of Enzyme-Induced Carbonate Precipitation on Strength Behavior of Reinforced Sand
## 3 Results and Discussion
### Micro-Mechanism of EICP-Reinforced Sand  
#### 3.2.1 Crystal-Soil Cementation Patterns  
The crystal-soil cementation patterns refer to three distinct microstructural mechanisms (shown in Figure 10) through which enzymatically precipitated calcium carbonate (CaCO\({}_{3}\)) interacts with sand particles to enhance mechanical strength. The spatial distribution characteristics and bonding morphology of crystals observed in SEM images can be systematically categorized as follows:  
1. Bonding effect (red arrows): Calcium carbonate precipitates around points of contact between soil particles, effectively binding them together and enhancing their interaction. This effect is most noticeable in particles with plane-plane contacts, where the extended contact surface facilitates greater crystal aggregation, resulting in stronger cementation.
2. Bridging effect (blue arrows): When soil particles are not directly in contact, calcium carbonate crystals form on the particle surfaces and grow to bridge the intergranular pores. This \"bridging effect\" strengthens the overall soil structure by connecting the particles and improving its integrity.
3. Encapsulating effect (yellow arrows): Calcium carbonate crystals that form on isolated soil particles gradually accumulate and encapsulate the particles. While this does not directly enhance soil cohesion, it increases intergranular friction, especially in cases where particles are prone to rotation or sliding.  
These three cementation modes collectively form a hierarchical reinforcement system: The bonding effect establishes the primary framework (first level), the bridging effect forms  
Figure 10: Different cementation patterns of EICP-reinforced sand.  
Figure 9: Relationships between \(C\) and conversion rate of calcium ions.  
the secondary support (second level), and the encapsulating effect optimizes stress transfer efficiency through interfacial modification (third level).
## 1 Introduction
### Motivation
#### 1.1.1 Google Maps Driving Directions  
As one of the applications of our balanced-partitioning algorithm, we study a balanced-partitioning problem in the Google Maps Driving Directions. This system computes the optimal driving route for any given _source-destination query_ (pairs of locations on Google Maps). A plausible approach to answer such pairwise queries in this large-scale graph application is to obtain a suitable partitioning of the graph beforehand so that each server handles its own section of the graph, called a _shard_. In this application, ideally we seek to minimize the number of cross-shard queries, for which the source and destination are not in the same shard, that is, the total cross-shard traffic is as low as possible, since handling cross-shard queries is much more costly and entails extra communication and coordination across multiple servers. In order to reduce the cross-shard query traffic we want to minimize the cut size between the partitions, with the expectation that less directions will be produced where the source and the destination are in different partitions. Reducing the cut size in practice results in denser regions in the same partition, therefore most queries will be contained within one shard. We confirm this observation with extensive studies using historical query data and live experiments (see Section 7.1 for more details).  
Conveniently, a linear embedding provides a very simple way of specifying partitions by merely providing two numbers for each shard's boundaries. Therefore it is straightforward and fast to identify the shard a given query point belongs to. As we will discuss later, our study of linear embedding of graphs while minimizing the cut size is partly motivated by these reasons.
## 5 MPDD Algorithm  
The MP detector described in this paper works in parallel with the tracking stage, because it uses the raw data after demodulation and the correction terms calculated by the DLL. The detector does not influence the normal operations of the receiver, but raises an alert when MP or, more in general, distortions affect the signal.  
In principle the structure of the multicorrelator used in the detector can be different from the one used in the DLL. However MPDD will have an impact on the computational complexity of blocks working at the tracking layer, since it requires a number of additional correlation points with respect to a single DLL. The applicability of this scheme to specific GNSS receivers will be considered in future work, since this paper is only focused on a feasibility study. In particular we are planning to work on the possibility to distribute the evaluation of the correlation points in different epochs, so as to make the method feasible also for consumer grade GNSS receivers (of course reduced performance is expected).  
The detector is composed of four elements:  
1. _Multicorrelator_, which computes \(N_{points}\) of the correlation between the received GNSS code, and a local code
2. _Moving average filter_, which smoothes the noise effect on the points generated by the multicorrelator
3. _Linear adaptive filter_, which estimates the MP contribution in the measured correlation
4. _Decision metric_, which decides if the measured correlation is either distorted or clean.  
The number of points \(N_{points}\) computed by the multicorrelator impacts on the computational load and on the resolution achieved in the estimation of the delay created by a MP. Therefore the choice of \(N_{point}\) will be a result of a trade-off between complexity and resolution. Another critical point is the effect of noise, which degrades the quality of the measured correlation. To mitigate the effect of noise a moving average filter is used to smooth the input of the LAF block. Two possible implementations of this filter are continuous moving average or block moving average. The first one is a moving average filter with overlap between slices of the signal, while the second one evaluates the average of independent slices, without overlapping. To reduce the computational load we decided to adopt the block moving average [12]. The LAF module decomposes the input correlation in a weighted sum of triangles (ideal correlation of GPS L1 C/A) and estimates the vector \(\hat{\mathbf{w}}\) of the filter taps. The last step analyses \(\hat{\mathbf{w}}\) and declares if some distortion is present. The results presented in [12], based on the _covariance method_ exploit the information provided only in the right side of the peak of the correlation. Therefore, the technique handles positive delays with respect to the LOS and limits the number of used taps being \(\mathbf{U}\) in Equation (4) dependent on \(M\). The LAF-based method presented here uses the data windowing in Equation (6), containing additional points to avoid loosing data points.