esa-sceva/satcom-chunk-collection huggingface dataset:
# Adjustment Algorithm for Free Station Control Network of Ultra-Large Deepwater Jacket
###### Abstract  
The offshore oil engineering jacket is a giant super-heavy steel frame structure with dimensions in the hundreds of meters. A high-precision free station control network is usually arranged around it to ensure construction accuracy. However, as the jacket is gradually assembled, its extreme weight will cause the widespread deformation of the surrounding ground surface, and each control point may be affected to varying degrees, resulting in the non-uniform deformation of the entire network. For adjustments of control networks in the subsequent phases, if the same starting point as the first phase is chosen without careful analysis, the starting points' non-uniform deformation will degrade the whole network's accuracy. Considering the particularities of the free station control network, this paper proposes an adjustment algorithm consisting of a three-step analytical method. Firstly, the initial coordinates of the points of the current phase are obtained through classical free network adjustment; second, stable and unstable points are identified via coordinate similarity transformation between the current and the first phase; and finally, quasi-stable adjustment is conducted. The experimental data analysis of a jacket control network shows that this method can effectively identify stable and unstable points, thereby ensuring construction accuracy and jacket stability.  
jacket; free station; classical adjustment of free network; coordinate similarity transformation; quasi-stable adjustment
# Efficient Enhancement of Second Harmonic Generation via Noninvasive Modulation
## 3 Results and Discussion  
### Enhancement of SHG without Scattering Effect  
It was proved experimentally that the optimization algorithm can indeed enhance the intensity of SHG without scattering, which can be shown in Figure 2. To ensure the credibility and repeatability of the experiment, we acquired multiple sets of experimental data of each experiment and used the statistical average as the comparison data. Figure 2a presents the calculated average values for each group of experimental data. In the experiment, the number of iterations of algorithm we uniformly used was 150 to strike a balance between calculation time and optimization effect, which can ensure the same time of each experiment for a contrast. Moreover, the spectrograms of optimized SHG without scattering were measured to visually show the optimization effect (Figure 2b), and the ten-fold intensity enhancement of 410 nm light was obtained.  
It was demonstrated that the optimized effect is related to the resolution of the phase mask loaded on the SLM [28]. In theory, the segments of SLM are positively correlated with the precision of modulation, which makes a high enhancement. However, we observed different results when applying different resolutions of phase in our experiment. It is not the case that the higher the resolution, the better the enhancement effect. On the one hand, we considered that it was limited by the number of iterations. Under the condition of a certain number of iterations, selecting an appropriate resolution of phase can allow it to converge quickly and achieve the best effect. Even increasing the resolution in this case, the optimization effect will not change. On the other hand, the optimization result is coordinated by the first-order phase gradient and the second-order phase gradient, which are affected by resolution. The best optimization effect can be achieved with a suitable phase gradient, but just increasing the resolution may lead to the destruction of the appropriate phase gradient. Therefore, enlarging the resolution only increases the calculation, but cannot make the optimization effect better.  
Figure 1: Experimental setup. The focal lengths of lenses L1, L2, L3, and L4 are 50 mm, 100 mm, 50 mm, and 20 mm, respectively. Numerical apertures of the objective lenses (O1 and O2) are 0.25 and 0.4, respectively. BBO: type I, 10 \(\times\) 10 \(\times\) 0.2 mm, \(\theta\) = 29.2\({}^{\circ}\), \(\varphi\) = 0\({}^{\circ}\).
## 2 Distributed Control and IoT Architectures in Smart Environments
### The Role of IoT in Smart Environments
The Application layer includes all the software applications which are specifically designed for the considered task, such as building automation or smart energy management. Evaluating the current solutions proposed by the literature, it is clear that the IoT middleware plays an indispensable role. It is responsible for the most part of the IoT architecture functionalities, which include: device management, security, application logic, storage, rules logic, and, along with graphical user interface, the intermediation between users and devices. Besides, a cloud infrastructure serves as the IoT middleware environment, acting as a Platform as a Service (PaaS) paradigm and providing advantages as: horizontal scalability for processing power and storage, data backup, redundancy, and high availability. An example of an IoT architecture than could be applied in complex smart environments, such as smart campuses, is the SaIoT (Smart Automation using Internet of Things) architecture [37; 38; 39], which was first developed for smart home and building applications. This architecture has been successfully in many measurement applications, such as water and electric energy consumption [40; 41], by also including the computation of proper KPIs.
# Research Progress on Machine Learning Prediction of Compressive Strength of Nano-Modified Concrete
## 3 Common Algorithms for Machine Learning
### K-Nearest Neighbor (KNN)  
The K-nearest neighbor (Figure 6) is a basic classification and regression method. It is one of the simplest algorithms in data mining technology, and its core function is to solve the supervised classification problem. The selection of the k value is also very important, in the application, if the k value is selected too large, it can easily cause underfitting, while too small can easily cause overfitting. Cross-validation results further reveal the prediction characteristics of the KNN model on the nano-concrete dataset (Data volume = 1030). When k = 3, the model shows sensitivity to outliers, particularly at a nano-SiO\({}_{2}\) dosage of 0.5%, resulting in a relatively high MAE of 3.2 MPa. Conversely, using k = 10 leads to over-smoothing, which increases prediction deviation in the high dosage range (1-2%), with an MAE of 4.5 MPa. The best performance is achieved at k = 5, where the model reaches an optimal MAE of 2.8 MPa [42]. We generally select a smaller value, usually using cross-validation to select the optimal k value. The advantages of the algorithm are as follows: 1. simple, good implementation, no need to estimate the parameters, no need to train; 2. suitable for multi-classification problems (objects with multiple class labels), KNN performs better than SVMs in these cases. In small-scale nanomaterial datasets (e.g., n < 500), KNN demonstrates strong performance but is sensitive to feature scaling. It is recommended to apply Min-Max normalization to parameters with significant differences in magnitudes, such as nanoparticle size (nm) and specific surface area (m\({}^{2}\)/g). However, the KNN algorithm has the following drawbacks: (1) When the samples are unbalanced, such as a large sample capacity of one class and a small sample capacity of other classes, it is possible to lead to a majority of samples of large-capacity classes among the K neighbors of the sample when a new sample is input. (2) A large amount of computational effort, and for each sample to be classified, the distance from it to the full set of known samples has to be computed in order to find its K nearest neighbors [43; 44]. Although the current application of KNN in nano-modified concrete is limited, its performance on small datasets and multi-label tasks shows potential. As more nanomaterial datasets become available, KNN may gain broader application in this field. Therefore, it is included here for completeness and future research reference.  
Figure 5: ANN schematic diagram.
## 2 Background Literature
### Groundwater Modelling with MODFLOW  
According to the United States Geological Survey (USGS), \"(MODFLOW) is a finite-difference groundwater flow modeling application that allows you to create a arithmetic representation (i.e., a groundwater model) of the hydrogeologic environment\" [60]. MODFLOW can be used to estimate groundwater resources and gain a better knowledge of the system from hydrological, geological, chemical, and hydrological perspectives. A three-dimensional transient groundwater flow model [61] was used to simulate three climate time periods from 1960-1999, 2010-2039, and 2040-2069 to assess the impacts of climate change on groundwater levels. Similarly, MODFLOW-2000 was utilized by [62; 63] to better understand regional groundwater flow and Murzuq aquifer systems in Libya, respectively. In addition, ref. [64] used the widely used MODFLOW-2005 model to examine variations in groundwater levels. In addition, ref. [65] used a combined hydrological-hydrogeological model, i.e., employed MODFLOW, under climate change scenarios, defining the spatio-temporal dynamics of water balance and groundwater-surface water (GW-SW) interactions for the upper stream basin of Del Azul. Groundwater numerical models with a traditional rectilinear grid geometry, such as MODFLOW [66], have rarely been used to simulate aquifer test results at a pumping well, in contrast to analytical models, because they are not designed or expected to accurately reproduce the head gradient near the well. MODFLOW is a groundwater modeling approach based on mathematical models. At the time of the present study, the most recent version of MODFLOW 6 was version 6.3.0, which was released on March 4, 2022. Finally, for estimating two-dimensional groundwater flow, ref. [67] used numerical models. According to our research, finding a systematic approach to this problem is difficult.
# A Novel SINS/SRS/CNS Multi-Information Fusion Global Autonomous Navigation Method
## 3 The Multi-Information Fusion Autonomous Navigation System Based on SINS/SRS/CNS
### The Principle of Spectral Redshift Navigation
#### 3.3.2 Measurement Equations  
(1) SINS/SRS velocity measurement  
In the high latitudes, it is required to convert the velocity in the \(i\) frame calculated by SRS to the \(G\) frame:  
\[\mathbf{v}_{SRS}^{G}=\mathbf{C}_{i}^{G}\mathbf{v}_{p}=\mathbf{C}_{e}^{G}\mathbf{C}_{i}^{c}\mathbf{v}_ {p} \tag{21}\]  
where \(\mathbf{C}_{i}^{G}\) is the direction cosine matrix from the \(i\) frame to the \(G\) frame; \(\mathbf{C}_{e}^{G}\) is the direction cosine matrix from the \(e\) frame to the \(G\) frame; and \(\mathbf{v}_{SRS}^{G}\) is the velocity in the \(G\) frame calculated by SRS.  
The velocity differences between SINS and SRS were chosen as one measurement to construct the velocity measurement equation:  
\[\mathbf{Z}_{v}^{G}(t)=\mathbf{v}_{SINS}^{G}-\mathbf{v}_{SRS}^{G}=\mathbf{H}_{v}^{G}(t)\mathbf{X}^{ G}(t)+\mathbf{V}_{v}^{G}(t) \tag{22}\]  
where \(\mathbf{v}_{SINS}^{G}\) is the velocity in the \(G\) frame calculated by SINS; \(\mathbf{H}_{v}^{G}(t)=\begin{bmatrix}0_{3\times 3}&\text{diag}\begin{bmatrix}1&1&1 \end{bmatrix}&0_{3\times 9}\end{bmatrix}\) is the measurement matrix of the velocity; and \(\mathbf{V}_{v}^{G}(t)\) is the velocity noise.  
2. [label=(2)]
3. SINS/CNS position measurement  
In the state equations of the high latitudes, the UAV position is represented by the coordinates in the \(e\) frame. Therefore, it is necessary to convert the latitude and longitude from CNS and the altitude from BA to the \(e\) frame:  
\[\begin{cases}x_{CNS}=(R_{N}+h_{BA})\cos{L_{CNS}}\cos{\lambda_{CNS}}\\ y_{CNS}=(R_{N}+h_{BA})\cos{L_{CNS}}\sin{\lambda_{CNS}}\\ z_{CNS}=\left[R_{N}\big{(}1-f^{2}\big{)}+h_{BA}\right]\sin{L_{CNS}}\end{cases} \tag{23}\]  
where \(x_{CNS},~{}y_{CNS},~{}z_{CNS}\) are the position coordinates in the \(e\) frame obtained by CNS and BA.  
The position differences between SINS, CNS, and BA in the \(e\) frame were selected as the other measurement:  
\[\begin{split}\mathbf{Z}_{p}^{G}(t)=\begin{bmatrix}x_{SINS}-x_{CNS}\\ y_{SINS}-y_{CNS}\\ z_{SINS}-z_{CNS}\end{bmatrix}=\mathbf{H}_{p}^{G}(t)\mathbf{X}^{G}(t)+\mathbf{V}_{p}^{G}(t) \end{split} \tag{24}\]  
where \(x_{SINS},~{}y_{SINS},~{}z_{SINS}\) are the position coordinates in the \(e\) frame obtained by SINS, \(\mathbf{H}_{p}^{G}(t)=\begin{bmatrix}\mathbf{0}_{3\times 6}&\text{diag}\big{[}1&1&1\big{]}&\mathbf{0}_{3\times 6}\end{bmatrix}\) is the measurement matrix of the position; and \(\mathbf{V}_{p}^{G}(t)\) is the position noise.
# GNSS-R Observations of Marine Plastic Litter in a Water Flume: An Experimental Study
## 4 Discussion
Results presented in [18] show the DDM peak change due to wind speed for clean water, and its increase due to the presence of oil, with respect to the DDM computed for the same 10-m height wind speed (U\({}_{10}\)), sea surface salinity (SSS), and sea surface temperature (SST) in clean water. It is worth noting the very significant increase of the DDM peak due to the damping of the capillary waves by oil spills, which produce a stronger forward scattering.
## 4 Protein Quality Evaluation Methods
### In Vivo Models
#### 4.1.2 Digestibility Indispensable Amino Acid Score (DIAAS)  
In 2013, the Food and Agricultural Organization of the United Nations/World Health Organization (FAO/WHO) proposed the DIAAS as an alternative to the PDCAAS for evaluating protein quality in human foods. Unlike PDCAAS, which is based on the total tract digestibility of crude protein (CP) in rats and assumes equal digestibility for all amino acids (AAs), DIAAS is based on the true ileal digestibility of each indispensable AA. As a result, the DIAAS method provides a more accurate evaluation of protein quality [44]. Since its recommendation by the FAO/WHO, DIAAS has replaced the previous PDCAAS method in evaluating protein quality in human foods. To calculate the DIAAS for protein quality evaluation, it is necessary to determine the true ileal digestibility of individual AAs, which can be accomplished by surgically inserting a T-cannula into the end of the small intestine in pigs. The pig has been recognized as an appropriate model for estimating crude protein (CP) and AA digestibility in foods for humans, and this method has been widely adopted to determine the ileal digestibility of AAs associated with various foods and food ingredients [41; 42]. Thus, the surgical insertion of a T-cannula is an effective approach used globally to measure AA digestibility in pigs.  
The direct determination of the digestibility of each AA in the small intestine is feasible by collecting digest at its terminal end, which enables the application of specific digestibility to each AA. Conversely, faecal collections used in the PDCAAS method only permit the estimation of crude protein digestibility, which is then uniformly applied to all AAs under the assumption that the digestibility of all AAs is equal. However, this assumption is incorrect [42]. Therefore, the DIAAS method is preferred as it allows for determining the true ileal digestibility of each AA, providing a more accurate assessment of protein quality. Therefore, the DIAAS methodology has emerged as a superior alternative to the PDCAAS method for evaluating protein quality. It considers the individual ileal digestibility of AAs, with pigs being preferred models over rats. Unlike the PDCAAS method, which calculates the digestibility of CP and applies it to all AAs, DIAAS applies unique digestibility values to each AA. Furthermore, reactive digestible lysine, instead of total digestible lysine, is recommended for determining the DIAAS of processed and cooked foods [47]. The DIAAS value determines the quality of the protein, which is classified as <75 (no statement regarding quality), 75-99 (premium protein), and \(\geq\)100 (superior-quality protein) [41]. The FAO has revised the age-specific AA reference scoring patterns and recommends classifying the population into three distinct age groups: 0-6 months (infants), 0.5-3 years (children), and individuals older than 3 years (the remaining population) [47].  
Table 1 summarizes the protein digestibility of various plant protein sources obtained by in vivo methods.
# Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review
## 4 Remote Sensing Applications
### Health Monitoring
Integrating multiple indices can provide a more robust assessment. For example, combining NDVI with soil moisture and evapotranspiration indices offers a more detailed picture of water stress and its impact on vegetation health. The application of advanced machine learning techniques, as demonstrated by [23], can further enhance the predictive accuracy of vegetation health assessments. These methods allow for the integration of diverse data sources, such as drone and satellite imagery, to produce more reliable and actionable insights. Future research should focus on developing more sophisticated models that incorporate a wider range of environmental variables and leverage the latest advancements in remote sensing technology. Establishing standardized protocols for the application and interpretation of vegetation indices is also crucial to ensure consistency and comparability across different studies and regions. It is recommended that future studies explore the synergistic use of various vegetation indices and machine learning algorithms to improve the accuracy and applicability of vegetation health assessments in diverse ecological contexts.
# Retrieving Freeze/Thaw Cycles Using Sentinel-1 Data in Eastern Nunavik (Quebec, Canada)
## 3 Results  
This study aims not only to test whether the seasonal threshold approach can be used for retrieving the FT state in the Nunavik region but also to discuss the performance of Sentinel-1 SAR data in this method from different perspectives, including comparisons of the sensitivity of different polarizations, as well as the sensitivity at different surface soil depths. The analysis results are expected to pave the way for future works with more focus on FT state monitoring under diverse environments.
# Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling
## 4 Evaluation Setup
### Congestion Control Algorithms  
Our evaluation focused on the performance impact of employing different congestion control algorithms (CCA) within a multi-access multipath tunneling framework. We used NewReno or BBR as tunnel CC when TCP tunnels were deployed, and DCCP's CCID2 [47] or CCID5 [48] when using DCCP tunnels. This enabled the comparison of TCP and DCCP, as CCID2 and CCID5 are implementations of NewReno and BBR, respectively. We also used BBRv2 as tunnel CCA, which had not yet been implemented for DCCP. Furthermore, we used Cubic or BBR as server CC for TCP-Iperf. More details on all CCAs can be found in Section 5. Considering that the focus of this study was to analyze the challenges of nested congestion control, coupled congestion control in the multipath layer was not included in the evaluation. Rather, the tunnel CCs worked independently from each other. Results obtained using TCP E2E are applicable to other E2E protocols, like QUIC, that use similar CCAs as TCP. Figure 3 illustrates where each CC instance was located.
# Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes
## 2 Concepts and Technologies  
The industry is renewing itself, and telecommunications have more relevance and impact than ever in production processes. Intelligent devices have increased in number in factories, including sensors able to measure any physical parameter, such as temperature, acceleration, and humidity. All the aforementioned data can be used to improve processes and optimize the performance of machinery in industrial environments. However, in order to achieve this objective, multiple important points such as data processing, transmission and storage must be taken into account. The introduction of intelligence in industrial processes can only be beneficial if solutions are proposed for these challenges.  
This section presents and describes the most important concepts and technologies involved in the INC system proposed in this paper. First, IIoT communications are analyzed; then the architecture of industrial networks is explained, including different paradigms that can be used; and finally, the P4 programming language for networking devices is introduced.
## 4 Results and Discussion
### Model Validation and Experimental Results
#### 4.1.2 Validation of the FEM and Theoretical Stress Models  
Figure 11 shows the simulated and experimental diaphragm stress results for different types of diaphragm cavity profiles. As shown in Figure 11a, for the SEP-type cavity, a comparison between the diaphragm A and the diaphragm C in the FEM results show that the three-layer diaphragm assembly exhibited identical stress distribution characteristics within the cavity. The simulation results show consistent trends with experimental data from Reference [10], with a maximum stress deviation of 17.2% observed at the four radial measurement points. This result was consistent with the theoretical analysis results reported in the literature [10], which validated the correctness and accuracy of the simulation model. Moreover, the FEM results revealed that the surface stress distributions of different diaphragm layers (A and C) were consistent. Consequently, in the subsequent analysis, the stress distribution characteristics of the single diaphragm layer (A) were investigated and discussed. Furthermore, for the DAP-type cavity depicted in Figure 11b, the stress simulation results show abrupt changes at the junction point (\(\lambda_{N}\) = 0.85) due to the profile's composition of two circular arcs. Moreover, both FEM and theoretical stress analysis exhibited congruent trends, with a calculated maximum surface stress deviation of 9.2%. Therefore, these findings confirmed that both FEM results and theoretical stress predictions effectively characterized the stress distributions of the deformed diaphragms in diaphragm compressors.
# Nanomaterials for Healthcare Biosensing Applications
## 4 Organic Nanomaterial Applications for Healthcare Biosensing
### Molecularly Imprinted Polymeric Nanoparticles
Using this approach, Altitnas and co-workers reported a proof-of-concept study suggesting the establishment of epitope libraries to combat the challenges of protein imprinting by simulating the most stabile surface conformation of the neuron specific enolase (NSE) protein in the sensing media [294]. Altitnas et al. also reported on ultrasensitive detection of _E. coli_ endotoxins using ITA as a functional monomer [303]. The monomer was selected based on in silico monitoring of monomer-endotoxin interaction. The SPR sensor detected the endotoxins in a 15.6-500 ng mL\({}^{-1}\) range with a limit of \(0.44\pm 0.02\) ng mL\({}^{-1}\). Wang and co-workers used AuNP-graphene-MIP(Py) modified GCE to develop an electrochemical levofloxacin sensor [304]. Levofloxacin (LEV) is a common antibiotic used in the treatment of gastroenteritis,pneumonia, and urinary tract infections. The integration of AuNP-graphene in the sensor spurred the electrocatalytic oxidation of LEV [304]. A detection range of 1.0-100 uM was achieved and the LOD was found to be 0.53 uM. The sensor exhibited reproducibility in addition to good specificity and excellent sensitivity. High affinity optical sensing for waterborne viruses was achieved using optical nanoMIP sensors, wherein bacteriophage MS2 was chosen as a template and the functional monomers included \(N\)-Isopropylacrylamide (NIPAm), acrylic acid (AAc), \(N\)-tert-butylacrylamide (TBAm), and \(N\),\(N^{\prime}\)-methylenebis(acrylamide) (BIS) [305]. A state-of-the-art solid phase polymerisation strategy was employed. Virus detection was achieved using an SPR sensor to develop the assay. The high affinity nanosensor achieved an LOD of \(5\times 10^{6}\) pfu mL\({}^{-1}\) and a detection range of 0.33-27 pM.