esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Discussion  
Unfortunately, it was hard to be equipped with pseudolites which operates as a GPS simulator for this system. Also we could not make a hardware GPS simulator by ourselves. Therefore, the only way to verify the system was the test using simulated data. By the results, we tried to show the feasibility of the proposed new concept of the pseudolite based navigation system.  
Due to the reasons above, the simulated data and SDR (Software Defined Radio) are used to verify the proposed system. The SDR used in the verification is a platform to test various receiver algorithms. The operation of legacy GPS receivers also can be implemented in the SDR. In this paper, the SDR which is designed to operate just as the legacy GPS receivers is used for the test. Therefore, the use of the SDR instead of the legacy GPS receiver seems reasonable.  
The rest of this section shows some simulation results to verify the proposed system. First, a simulation is performed to show the feasibility of the algorithm. Second, the same test is carried out under poor geometries. Third, a test using live GPS signal is performed. Some manual delays due to the pseudolites are added to the live GPS measurements in this test.
###### Abstract  
The aim of this study was to analyze the joint characteristics of the foot and ankle in competitive swimmers aged 16-18 and 19-24 years and their relationship with the presence of muscle pain during swimming. A total of 74 swimmers were evaluated: 38 'junior' (16-18 years) and 36'senior' (19-24 years). The following parameters were recorded: ankle dorsiflexion, rearfoot mobility, first metatarsophalangeal dorsiflexion, presence of hallux valgus, foot posture, first ray mobility, arch height, and plantar pressure. Additionally, the frequency and location of muscle pain in the triceps surae were analyzed. A cluster analysis was performed to identify variables that differentiated both groups. Ankle dorsiflexion was limited in both groups, with a greater restriction observed in adults (\(p<0.001\) with an extended knee; \(p<0.014\) with a flexed knee). The predominant foot type was the cavus foot. The most common pain was localized in the triceps surae, followed by the plantar musculature, with no significant differences between groups. Swimmers exhibited gastrocnemius shortening, which could limit ankle dorsiflexion and contribute to the onset of muscle pain in the leg and foot. These findings suggest the importance of incorporating lower limb flexibility strategies into the training of competitive swimmers.  
s +
Footnote †: journal: Applied sciences  
1 Faculty of Nursing, Physotherapy and Podiarity. University of Seville, 41009 Seville, Spain; [EMAIL_ADDRESS] 1
## 3 Results and Discussion
### Creatinine Removal  
Initially, the model was validated against experimental data for the same membrane, published in the literature [30], in a diffusion-adsorption experiment.  
Figure 4 shows that the model behavior using the Langmuir adsorption constants of [PERSON] et al. [22], which where extracted from a previous generation mixed-matrix membrane (M2) filled with the same AC particles, is reasonably accurate at the early stages of the separation process, whereas at later stages of the removal process, the model overestimates the creatinine removal (red curve). This is attributed to the fact that the adsorption capacity for creatinine in [22] is high (3.07 g/g AC), in contrast to other publications that report lower values of adsorption constants [31; 32], rendering adsorption the dominant mechanism for solute removal in the beginning of the process.  
An improved prediction of the creatinine removal over time can be obtained (grey line) if the unit step function is employed to act on the aforementioned adsorption constants of the creatinine, in order to nullify their effect after saturation [3600 s of operation]. This is based on the fact that the sorbents are saturated at this time, and the separation process beyond that point is carried out by diffusion only [30].  
In the absence of any information on the adsorption isotherm or the sorbent saturation time, a non-linear optimization routine is employed to evaluate the adsorption constants that are treated as free variables. Specifically, the Nelder-Mead method [33] is utilized to minimize the relative error between the experimental curve and the curve obtained by solving the set of partial differential equations, generating a best fit curve with the optimum adsorption constants [34]. The best fit is obtained for maximum adsorption capacity \(q_{\textit{creatinine,di}}=0.804\,\mathrm{mol/kg}\) and Langmuir constant \(Kl_{\textit{creatinine,i}}=6.9\,\mathrm{m^{3}/mol}\) and is shown in Figure 4 (black dashed curve).
###### Abstract  
The escalation of urbanization in Sichuan Province has resulted in irrational land use and excessive resource exploitation. These developments have consequently diminished the functionality of ecosystem services and exacerbated landscape fragmentation in the region. These challenges pose significant threats to the ecological security of the area. In this study, we computed the ecosystem service value and the landscape ecological risk index of Sichuan Province from 2005 to 2018. We analyzed the spatial autocorrelation between the ecosystem service value and the landscape ecological risk index, constructing a framework for landscape ecological risk assessment and zoning control based on ecosystem service value. The results show the following: (1) Between 2005 and 2018, the total value of ecosystem services in Sichuan Province increased from CNY 10,261.17 \(\times\) 10\({}^{8}\) to CNY 10,310.43 \(\times\) 10\({}^{8}\), with forest land and grassland being the primary contributors to the ESV. (2) High- and higher-risk areas within the landscape ecology of Sichuan Province are expanding, forming a pattern of high-level risk concentration from west to east. (3) There exists a negative correlation between the ecosystem service value and the landscape ecological risk index in Sichuan Province. (4) In the ecological conservation zone, the principle of low-impact development must be upheld. In the ecological cultivation zone, adjusting the proportion of land use types is necessary to enhance the rationality of the land use structure. The ecological agricultural zone should fully utilize the advantages of agriculture, while the ecological improvement zone requires focused attention to ecological restoration and land remediation.  
Keywords:landscape ecological risks; ecosystem service value; spatiotemporal change; zoning control; Sichuan Province +
Footnote †: journal:  
Article  
Landscape Ecological Risk Assessment and Zoning Control Based on Ecosystem as an Example
## 3 Results
### Regression  
A plot of \(V_{\rm S30}\) versus \(f_{\rm p}\) is shown in Figure 4a. The figure shows very little difference between the acceleration and velocity datasets, although the velocity dataset has one less data point. Although both datasets are relatively small, the data are apparently concentrated in the range of 4 to 20 Hz with very little data outside that range.  
Figure 4a also shows the resultant regression. The regressed equation using acceleration HVSR is a power law:  
\[\log V_{\rm S30}=0.47\log f_{p}+2.33 \tag{4}\]  
with a standard deviation, \(\sigma_{\rm log}=0.12\), actually 0.120. A plot of the residuals in log units is shown in Figure 4b. The regressed equation using velocity HVSR is similarly  
\[\log V_{\rm S30}=0.53\log f_{p}+2.28 \tag{5}\]  
with a standard deviation, \(\sigma_{\rm log}=0.12\), actually 0.121. Again, a plot of the residuals in log units is shown in Figure 4b. The difference in regression coefficients is due to having 1 fewer data point as well as one common station showing a different \(f_{\rm p}\) for acceleration and velocity. Interestingly, if the regression equation structure is in linear form (i.e., \(V_{\rm S30}\) and \(f_{\rm p}\)) or logarithmic form (i.e., \(V_{\rm S30}\) and \(\log f_{\rm p}\)), then the RMSE values do not differ by much. Moreover, the power law fit allows for a more natural comparison with other HVSR models.
###### Abstract  
Adaptive optics--optical coherence tomography (AO-OCT) is a non-invasive technique for imaging retinal vascular and structural features at cellular-level resolution. Whereas retinal blood vessel density is an important biomarker for ocular diseases, particularly glaucoma, automated blood vessel segmentation tools in AO-OCT have not yet been explored. One reason for this is that AO-OCT allows for variable input axial dimensions, which are not well accommodated by 2D-2D or 3D-3D segmentation tools. We propose a novel bidirectional long short-term memory (LSTM)-based network for 3D-2D segmentation of blood vessels within AO-OCT volumes. This technique incorporates inter-slice connectivity and allows for variable input slice numbers. We compare this proposed model to a standard 2D UNet segmentation network considering only volume projections. Furthermore, we expanded the proposed LSTM-based network with an additional UNet to evaluate how it refines network performance. We trained, validated, and tested these architectures in 177 AO-OCT volumes collected from 18 control and glaucoma subjects. The LSTM-UNet has statistically significant improvement (\(p<0.05\)) in AUC (0.88) and recall (0.80) compared to UNet alone (0.83 and 0.70, respectively). LSTM-based approaches had longer evaluation times than the UNet alone. This study shows that a bidirectional convolutional LSTM module improves standard automated vessel segmentation in AO-OCT volumes, although with higher time cost.  
adaptive optics; optical coherence tomography; deep learning; vessel segmentation; long short-term memory; recurrent neural network; glaucoma +
Footnote †: journal: Applied Sciences
## 4 Experimental Results
### The EMR-SF Construction Results by LRBF  
#### 4.3.1 Experimental Results  
The EMR-SF constructed using LRBF is shown (Figure 13). The EMR-SF constructed by LRBF is similar to a sector, which is one of the regions of the EMR-SF constructed based on the electromagnetic radiation propagation model with a strong radiation source as the center of the circle.  
Without considering the EMR bypassing and reflection, the EMR intensity value is almost the same when the distance between the measurement position and the radiation source is the same. Usually, the EMR value at a position closer to the transmitting antenna is changed little and is enhanced after a certain distance. The EMR value begins to decrease after reaching the peak [27; 28; 29] (Figure 14). Therefore, the EMR value is weak at the place close to the mobile base station location, and the EMR value is enhanced at a place far from the mobile base station location.  
Figure 12: Grid point layering results. (**a**) The _L(d)_ value of the EMR sampling data. (**b**) The result of layered grid points.  
Figure 13: EMR-SF constructed by LRBF.  
Although there are mobile base station transmitting antennas on Teaching Building 1 and Teaching Building 2, the EMR value around them is not affected by them and is affected by the transmitting antenna of Teaching Building 4 (Figure 15). The transmitting antennas are on Dormitory Building 1, Dormitory Building 2, and Dormitory Building 3 (Figure 15); although they exist, they face the dormitory building area instead of the teaching building area. Generally, there exists a main transmitting direction for the transmitting antennas, and the radiation obtained in other directions is much smaller than the main transmitting direction. In this study area, the EMR mainly originates from the transmitting antennas in the northeast direction of Teaching Building 4 and Teaching Building 3, which causes the established EMR-SF to appear in the form of concentric sectors.  
Figure 14: The EMR intensity changing with the horizontal distance of the measuring point from the antenna.  
Figure 15: Mobile base station location and antenna pattern.
# Comprehensive Evaluations of NLOS and Linearization Errors on UWB Positioning
## 3 Tests and Evaluation  
In order to investigate the impacts of NLOS and truncation errors on UWB positioning, the practical UWB data collected by LinkTrack P (Table 2) and the corresponding simulated UWB data were analyzed. These UWB data, ranging the distance between the tag and different base stations, were synchronized by using the NAssistant software ([[https://www.nooploop.com/en/download/](https://www.nooploop.com/en/download/)]([https://www.nooploop.com/en/download/](https://www.nooploop.com/en/download/)), accessed on 9 May 2023) provided by Nooploop Company. Since the Nooploop Company increases UWB's transmission power, it makes the maximum UWB ranging distances up to 500 m. Meanwhile, a GNSS/INS-integrated navigation system and a Leica electronic total station were also adopted to provide the reference position solutions and the coordinates of UWB base stations. Here, the reference positions were calculated by the tightly coupled integration of real-time kinematic (RTK) and INS. Then, the position differences between the reference positions and these positions calculated by Bancroft, LS, EKF, UKF, CKF, PF, and RPF will be projected into the local leveling frame (North-East-Down), and the corresponding statics in terms of maximum values, averages, and standard deviation will be used to present the positioning accuracy of UWB. Here, the position errors in the north and east directions were to present the horizontal positioning accuracy, and the position errors in the down component were to illustrate the vertical positioning accuracy. Figure 2a,b show, respectively, the experimental environment and the detailed trajectory of the experiments. Figure 2c shows the distribution of eight UWB base stations. During the practical data collection, NLOS errors were generated by arranging people passing different base stations at different times. In the simulation experiment, the desired tag's position was set as the center of these eight base stations. Then, the UWB distances between the tag and eight base stations simulated by only adding the Gaussian distribution white noises with a mean value of 0.0 m and standard deviation of 0.3 m, and no NLOS errors were added.  
Figure 1: Algorithm structure of UWB positioning.  
Figure 2: (**a**) Part of the used equipment; (**b**) the trajectory of the field test; (**c**) UWB base station distribution; (**d**) time-series of observed distances.
## 1 Introduction
Although conventional techniques are projection-based such as Principal Component Analysis (PCA) (unsupervised method) or Linear Discriminant Analysis (LDA) (supervised method), feature selection techniques are considered as promising alternatives. Feature selection methods are designed to deal with the combinatorial search problem. These methods require a search strategy to select candidate subsets which are evaluated by an objective function. A search strategy is therefore needed to direct the feature subset selection process as it explores the space of all possible combination of features. The object function evaluates candidate subsets and returns a measure of their goodness, a feedback signal used by the search strategy to select new candidates. These new candidates are fewer features mean fewer parameters for pattern recognition. They enable improving generalization capabilities, computation complexity, and execution time. Thus, this work proposes a feature selection model to reduce high dimensional data. By this way, an IDS framework based on deep neural networks is applied to the result of the proposed feature selection model to improve accuracy performance as well as reduce FAR. Besides, this propose can detect attack types well, especially U2R and R2L attacks. The proposed algorithm is evaluated on two IDS datasets including NSL-KDD and ISCX datasets. The rest of this paper is organized as follows. Section 2 presents related works. Section 3 presents the proposed IDS framework. Section 4 shows the experiment results and discussion. The conclusions are presented in Section 5.
###### Abstract
The exact methods for linear programming models provide an accurate solution to the problem, which is significant for validating new mathematical models. Since the current problem is new in the literature and our study proposes a mathematical model for it, the present study uses CPLEX to solve the problems used for its validation. The CPLEX solver can be used to code and solve integer programming, mixed integer programming, multi-objective optimization, and quadratic programming problems. However, for large-sized problems, it requires more computational time, data handling from various data sources, and a greater number of parameters to define the problem. The objective of the current study is to develop and solve a mathematical model using the theory of constraints for operating room planning and scheduling considering all the decision levels, such as the strategic, tactical, and operational levels, and integrating them with downstream wards. The current study contributes to the planning and scheduling literature in the following ways. 1. The current research is new in integrating all the planning levels of the hospital, considering the higher-level, medium-level, and lower-level planning considering constraints of the interlinked resources, including the operation theatre, ICU, and wards;
2. The current research is new to applying the theory of constraints for multi-level planning in hospitals;
3. The current research proposes a new mixed integer linear programming model for multi-level planning and scheduling in hospitals considering the theory of constraints concept;
4. The current research develops a new mixed integer linear programming model considering the capacity constraints of the operating room, ICU, and wards. The rest of the paper is organized as follows: Section 2 presents the problem description and mathematical model. Sections 3 and 4 present the solution approach, computational experiments, and results. Finally, Section 5 shows the conclusion of the research with important findings and highlights the limitations and future extensions of the work.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 4 Applications of LLMs in Maritime Operations
### Predictive Maintenance and Diagnostics  
Maintenance of maritime vessels is a complex task that significantly impacts safety and operational efficiency. LLMs can contribute to predictive maintenance and diagnostics by analyzing maintenance data and assisting engineers [132].
## 3 ML Model Development
### K-Nearest Neighbors (KNN)  
As a non-parametric method, K-nearest neighbors (KNN) methods classify the input features into two or more classes by assigning the input features to the class that is most common among its K nearest neighbors. The main advantage of using KNN is that, sinceit considers the K nearest testing data, the classes do not have to be linearly separable. However, KNN could be very sensitive to imbalanced data and outliers. KNN has been used to estimate aboveground carbon from satellite imagery [45]. In this study, KNN with 10 nearest neighbors, with Euclidean distance weights selected as hyper-parameters, which are selected by grid search algorithm.
# Fast-Convergence Reinforcement Learning for Routing in LEO Satellite Networks
## 2 System Model
### Problem Statement  
The LEO satellite network can be represented as graph G=(V,E), where V represents the set of satellite nodes and E represents the set of satellite links. Consider an Iridium-like system with M number of orbits and N satellites per orbit; we use \((i,j)\) to represent the position of a satellite, where i represents the satellite's orbit number and \(j\) represents the satellite's number in orbit (\(1\leq i\leq M,1\leq j\leq N\)). There are intra-satellite links between satellites in the same orbit and inter-satellite links between satellites in different orbits, which means that each satellite can communicate directly with up to four satellites. For clarity, we list the notations and definitions in Table 2.  
In this article, we only consider the process of packet transmission between satellites. The packet starts from the initial node \(N_{i}\); the node first finds the next hop node \(N_{j}\) from the set of neighbor nodes \(N_{gi}\), and then sends the packet out. The transmission delay of the packet is \(D_{ij}\), and then the next hop node repeats the previous action, sending the packet to its neighbor node, and then updating the transmission delay of the packet D according to the delay accumulation rule. The above steps are repeated until the data packet is forwarded to the destination node. The problem is planning a route which minimizes D. In a real-world scenario, thousands of packets are passed through the network, so the algorithm needs to consider the congestion of the link. Firstly, the algorithm must be able to plan a feasible path from the source node to the destination node, and secondly, the algorithm should minimize the delay of this path, including propagation delay and queuing delay. Therefore, the ultimate goal of the algorithm is to minimize the transmission delay of packets while ensuring the packet arrival rate.
Droop control is adopted to obtain power distribution between n battery converters, and a DC bus voltage compensator compensates for the introduced DC bus voltage deviations. The low-pass filter (LPF) is adopted to obtain high-frequency power as the reference for the supercapacitor converter, ADRC is also utilized in the outer power control loop, and MPC is employed in the inner current control loop. The main contributions of this paper are as follows:  
1. The ADRC outer voltage control loop is presented for n battery converters. Compared with traditional observers, the voltage expansion state observer of the proposed ADRC control is independent of the system model and parameters and consequently has strong disturbance immunity, and significantly reduces voltage overshoots during power fluctuations. 2. Improved MPC is employed in inner current control loops of n battery converters and the supercapacitor converter. The MPC-based inner current control loop accelerates current response speed and significantly decreases switching losses. 3. For n parallel battery converters, a compensatory approach eliminating DC bus voltage deviations introduced by droop control is presented, to effectively increase accuracy and maintain constant DC bus voltage.