esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Swarm Robotics for Area Coverage Problem
## 2 Methodology
### The Rationale for the Time Frame  
A period of tremendous technical developments occurred between 2015 and 2022, and this is why that is that time range was selected. During this time, swarm robotics controllers became more robust, and advancements in sensing technologies and hardware capabilities were substantial. Swarm robotics was widely acknowledged as an exciting new study area. During this time, swarm robotics emerged as a separate and significant field within robotics study.  
Advancements in swarm robotics algorithmic frameworks occurred between 2015 and 2022. Optimisation methods, coordination strategies, swarm intelligence system developments, and swarm robots are becoming more effective at tackling area coverage issues. During this time, swarm robotics moved from a theoretical study field to one with real-world applications. The practical applications of swarm robotics in agriculture, exploration, surveillance, and industry have recently attracted much attention from researchers and practitioners.  
Swarm robotics has recently seen an upsurge in academic publications, conferences, and group projects, all coinciding with the chosen time range. The survey covers all essential and relevant contributions by focusing on this time frame. During 2015-2022, swarm robotics reached a point of maturity. Scientists might improve previous efforts, test new approaches, and expand existing knowledge. The variety and depth of the surveyed papers demonstrate this level of development.  
The selected period captures foundational works and contemporary achievements while considering the historical context. This way, we know that the articles we are looking at are up-to-date and relevant to where swarm robotics is to ensure consistency when reviewing and comparing the selected publications; thus, the survey should be limited to 2015 to 2022. This makes it possible to concentrate on the most current trends and developments within the given period. Using this era as a starting point, the survey explores the history, development, and current uses of swarm robotics for coverage areas. This way, we may be sure that the chosen papers advance our knowledge of swarm robotics in the given setting. The conceptual framework of the work is shown in Figure 1.
## 2 Related Works
### Related Works on GEO
[37] proposed a novel medical image processing technique for analyzing different peripheral blood cells such as monocytes, lymphocytes, neutrophils, eosinophils, basophils and macrophages, using fuzzy c-mean clustering (MLWIFCM) based on GEO to perform cell nucleus segmentation. [PERSON] et al. [38] proposed a hybrid multilayer perceptron (MLP)-convolutional neural network (CNN) (MLP-CNN) technique to provide services to SUs even under active TCS constraints in order to address the spectrum scarcity problem in cognitive radio. [PERSON] et al. [39] improved the GEO algorithm by using the transfer function to transform GEO into discrete space, and used time-varying flight duration to balance the cruise and attack parts of GEO. Moreover, their improved GEO was applied to feature selection, achieving good results. GEO has been used for multi-objective optimization in the context of the problem of reducing robot power consumption, obtaining a better Pareto frontier solution [40]. [PERSON] et al. [41] proposed PELGEO with personal example learning in combination with the grey wolf algorithm. [PERSON] et al. [42] proposed GEO_DLS with personal example learning to enhance the search ability and mirror reflection learning to improve the optimization accuracy. Both PELGEO and GEO_DLS were applied for the 3D path planning of a UAV during power inspection. [PERSON] et al. [43] proposed S2 NA-GEO combined with a neural network learning algorithm. Later, a model for the uncertainty associated with renewable energy based on GEO was developed to relate the negative effects of variations in RES output for electric vehicles and intelligent charging [44].
# Knowledge-Aware Enhanced Network Combining Neighborhood Information for Recommendations
## 4 Experiments
### Baseline Model  
In this paper, to more comprehensively evaluate the performance of KCNR, we compare it with mainstream baseline methods, including graph network-based recommendation methods (FairGo, PER), knowledge graph-based recommendation methods (CKE, RippleNet, KGCN, MKR, CAKR), and other recommendation methods (LibFM, Wide&Deep). The descriptions of these methods are shown below:  
* **LibFM**[31] is used in the CTR recommendation scenario, and it does not use KG.
* **PER**[12] is a path-based recommendation method that regards the KG as a heterogeneous information network.
* **CKE**[13] introduces various types of heterogeneous data for the RS, combined with the initial item representation, to improve the recommendation quality.
* **Wide&Deep**[32] combines a deep model and linear model, to obtain more information, to improve the recommendation effect.
* **RippleNet**[10] proposed to use the user's prior information to propagate in the KG, to capture the user's personalized preferences.
* **KGCN**[15] propagates item entities in the KG and has a bias to aggregate the item's neighbor information to complement the item embedding.
* **MKR**[14] is a multi-task recommendation model trained by combining the recommendation task and the KGE task.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MovieLens-1M** & **Book-Crossing** & **Last.FM** \\ \hline \#users & 6036 & 17,860 & 1872 \\ \#items & 2445 & 14,910 & 3846 \\ \#inter. & 753,772 & 139,746 & 42,346 \\ \#entity & 182,011 & 77,903 & 9366 \\ \#relation & 2,483,990 & 303,000 & 31,036 \\ \#triplets & 20,195 & 19,793 & 15,518 \\ \#sparsity & 0.9489 & 0.9994 & 0.9941 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical information for the three datasets.  
* **FairGo**[33] uses adversarial learning techniques to consider the fairness of graph recommendation.
* **CAKR**[34] improves the interaction unit of MKR, to better capture characteristic interactions between entities.
## 2 Materials and Methods
### Time Series Similarity
#### 2.3.1 Dynamic Time Warping  
Dynamic Time Warping (DTW) is an algorithm used to measure the similarity between two time series by finding the optimal alignment between them by warping the time axis. DTW is based on Euclidean distance, in which a cost matrix between two sequences is built, and a path through the matrix that minimizes the cost path [23]:  
* Given two time series or sequences \(A=(a_{1},a_{2},\ldots,a_{n})\) and \(B=(b_{1},b_{2},\ldots,b_{n})\), DTW finds the warping path between these series that minimizes the total distance.
* A cost matrix \(D\in\ \mathbb{R}^{n\ x\ m}\), where each element is the distance between points (typically Euclidean): \[D(i,j)=dist\big{(}a_{i},b_{j}\big{)}\] (4)
* A warping path W is a sequence of matrix indices: \[W=(\omega_{1},\omega_{2},\ \ldots,\omega_{L}),\ \omega_{k}=(i_{k},j_{k})\] (5) Subject to boundary condition \(\omega_{1}=(1,1)\) and \(\omega_{L}=(n,m)\), monotonicity \(i_{k+1}\geq i_{k},j_{k+1}\geq j_{k}\), and continuity \(i_{k+1}-i_{k}\leq 1,j_{k+1}-j_{k}\leq 1\).
* The optimal is found using dynamic programming: \[DTW(i,j)=D(i,j)+min\left\{\begin{aligned} DTW(i-1,j)\\ DTW(i,\ j-1)\\ DTW(i-1,\ j-1)\end{aligned}\right.\] (6) With initializations \(DTW(0,0)=0\) and \(DTW(i,0)=DTW(0,j)=\infty\) for \(i,j>0\).
* The final DTW distance is \(DTW(n,m)\). In our R code, we used the corresponding functions of the library \(dtw\) to estimate the DTW distances between time series [24].
# Value of Information Based Data Retrieval in UWSNs
## 5 Performance Measures, Simulation Setup and Results
### Results  
In this section, we attempt to make a comparative analysis of the path planning algorithms. We ascertain the performance of algorithms that take into consideration VoI or time or both, versus those algorithms, that do not. The regions where a high-priority event takes place are deemed as hot-spots. The effect of parameters such as valuation ratio \(A_{H}/A_{L}\) and number of hot-spots \(N_{HS}\) is assessed.  
We make a performance comparison among six path planners; RPP, LPP, GPP, HPP, GIPP, and HIPP. At various points in this section, we normalize results. The normalization is with respect to RPP as it is our base-case. Except for in anomalous cases, RPP is the worst performing among all path planning algorithms; this is expected as it does not take into account time or information valuation for generating the node visitation sequence. Each reading in the results has been averaged over 100 different VoI profiles across the same UWSN map.
## 2 Materials and Methods
### Removal Efficiency and Environmental Risk Assessment  
Average removal efficiency (RE) of PhACs in WWTPs was calculated using the concentrations in the influent and effluent samples using the following equation:  
\[RE\;(\%)=\frac{C_{inf}-C_{eff}}{C_{inf}}\times 100 \tag{3}\]  
where \(C_{inf}\) = concentration in the influent samples, \(\mu\)g/L; \(C_{eff}\) = concentration in the effluent samples, \(\mu\)g/L.  
The potential risks of residual PhACs in the effluent to aquatic organisms were assessed using risk quotient (RQ), which is calculated as the ratio of the measured environmental concentration (MEC) to the predicted no effect concentration (PNEC) of a target compound [29]. In this study, the strictest standard, that is, the lowest PNEC value of a specific compound ever reported in water was adopted for calculation. The weighted measured environmental concentration (WMEC) of a PhAC in the final effluent and the downstream environment of the five WWTPs was calculated as follows:  
\[WMEC=C_{ave}\;\times R_{tot}\;+\;C_{max}\;\times\;R_{90\%max} \tag{4}\]  
where \(C_{ave}\;\) and \(C_{max}\;\) represent the average and maximum concentrations, respectively; \(R_{tot}\;\) represents the detection frequency (\(\%\)); and \(R_{90\%max}\) represents the ratio of the number of samples with concentrations higher than 90% maximum concentration to the total number of samples.  
Hence, the RQ was calculated by Equation (5):  
\[RQ=\frac{WMEC}{PNEC} \tag{5}\]  
The specific values of PNEC used for RQ calculations are detailed in Table S1 [30; 31; 32; 33]. Common ranking criteria for evaluation of environmental risks were adopted in this work: RQ < 0.1, low risk; 0.1 \(\leq\) RQ < 1, medium risk; and RQ \(\geq\) 1, high risk [30].
## 2 Calculation Theory and Model
### Finite Element Analysis Model  
Analytical calculation method [25] can be used in the analysis of medium and short span bridges. Nevertheless, the differential equilibrium equation can neither consider the coupling effects of various factors nor the geometrical non-linearity for a suspension bridge, which has complex stress and deformation. Since FEM has the above two advantages, this paper adopts ANSYS (a commercial FEM software) to establish the spatial coupling model of CWR on suspension bridges based on bridge-track interaction. When calculating the dynamic responses of REJ under train load, ANSYS is used for preprocessing and postprocessing, while LS-DYNA is used for explicit dynamic solving.
# Multi-LEO Satellite Stereo Winds
###### Abstract  
The stereo-winds method follows trackable atmospheric cloud features from multiple viewing perspectives over multiple times, generally involving multiple satellite platforms. Multi-temporal observations provide information about the wind velocity and the observed parallax between viewing perspectives provides information about the height. The stereo-winds method requires no prior assumptions about the thermal profile of the atmosphere to assign a wind height, since the height of the tracked feature is directly determined from the viewing geometry. The method is well developed for pairs of Geostationary (GEO) satellites and a GEO paired with a Low Earth Orbiting (LEO) satellite. However, neither GEO-GEO nor GEO-LEO configurations provide coverage of the poles. In this paper, we develop the stereo-winds method for multi-LEO configurations, to extend coverage from pole to pole. The most promising multi-LEO constellation studied consists of Terra/MODIS and Sentinel-3/SLSTR. Stereo-wind products are validated using clear-sky terrain measurements, spaceborne LiDAR, and reanalysis winds for winter and summer over both poles. Applications of multi-LEO polar stereo winds range from polar atmospheric circulation to nighttime cloud identification. Low cloud detection during polar nighttime is extremely challenging for satellite remote sensing. The stereo-winds method can improve polar cloud observations in otherwise challenging conditions.  
3D-winds; atmospheric motion vectors (AMVs); MODIS; SLSTR; stereo imaging; parallax; LiDAR; polar clouds; planetary boundary layer; polar circulation 2023
# Seismic Effectiveness of Multiple Seismic Measures on a Continuous Gider Bridge
## 1 Introduction
(2018) proposed applying shock-absorbing devices along with restrainers to counter bounding effects and to improve the serviceability of elevated bridges during a seismic event; their results indicated that rubber shock-absorbing devices were effective in mitigating the impact forces as well as the acceleration impulse caused by bounding. [PERSON] (2018) investigated three different configurations of a hybrid device with a rubber bumper and restrainers to prevent unseating and mitigate the bounding effect at the expansion joint of isolated multi-span bridges, and the results showed that restrainers are substantially effective at reducing the relative displacements and impact forces due to bounding at the expansion joints. The sudden changes of stiffness during poundings can be smoothed by using a natural rubber shock absorber, which prevents the acceleration peaks due to impact. [PERSON] et al. (2018) discussed the merits and limitations of various pounding and unseating mitigation devices that have been proposed by multiple researchers, and the application of rubber bumpers with restrainers and the combination of dampers with restrainers has received considerable attention in applications to reduce the damages as well as improve the serviceability of the bridge. The above research shows that, under the action of multi-factor coupling, the influence of seismic load on various measures is not a simple linear superposition, which also means that the effectiveness of different coupling measures on the seismic response of bridges is diverse and complex. A satisfactory aspect is that it provides specific stiffness against service-level loads, dissipates substantial energy during moderate earthquakes, and prevents collapse during a strong earthquake ([PERSON] et al., 2018; [PERSON] et al., 2018). This paper focuses on the seismic effectiveness of multiple seismic measures (such as expansion devices, restrainers, and unseating prevention devices) on preventing unseating, pounding, and collapse damage to a continuous girder bridge under earthquakes of various magnitudes to reveal the catastrophe process of the bridge structure and the cooperation law of seismic measures in an earthquake.
# ChatGPT as a Virtual Dietitian: Exploring Its Potential as a Tool for Improving Nutrition Knowledge
## 3 ChatGPT as a Nutrition Tool
### Can ChatGPT Effectively Replace Dietitians and Nutrition Professionals?
#### 3.3.3. Coordinated Nutrition Services  
In complex cases involving multiple concurrent medical conditions, intricate medication regimens, or specialized dietary requirements, ChatGPT may not have the capacity to provide comprehensive and tailored nutrition guidance. These situations often require the involvement of coordinated nutrition services that collaborate with a multidisciplinary team of specialized professionals [62]. For instance, a patient with concurrent diabetes, hypertension, and renal disease who is taking multiple medications with varying interactions and has specific dietary restrictions would require comprehensive nutrition guidance which may be beyond the capabilities of ChatGPT. In such cases, coordinated nutrition services would involve a registered dietitian working closely with the patient's primary care physician, endocrinologist, nephrologist, and potentially other specialists. This collaborative approach ensures that the patient receives tailored and well-coordinated nutrition guidance to effectively manage their health conditions [53].
## 3 Applications to a Waste Separation System  
The proposed methodology for expert knowledge aggregation, and its use for parameter estimation by consistency analysis introduced in Section 2, is verified through a case study of a waste separation system. These systems consist of multiple operating units that eliminate some components of the waste based on different principles. They are usually poorly instrumented; thus, measurement data are not available. However, modeling these systems and determining their parameters is essential to be able to conduct simulation experiments that support the adaptability of these systems to the changing environment, feed composition, and ensuring the achievement of quality requirements.  
Due to the importance of this issue, our investigations were conducted on an operating waste separation system in Hungary. This gives the opportunity to gather information from experts who have worked with the technology for a long time. The system, the handling of expert knowledge, and the output estimation of the system were thoroughly described in [29]. In the present paper, the parameter estimation and the evaluation of expert knowledge are investigated.  
In the following, the technology is described in detail in Section 3.1. Section 3.2 contains a short description of the available expert-based data, Sections 3.3 and 3.4 the principles and results of parameter estimation, respectively. Section 3.5 introduces how the results can be used for the evaluation of expert knowledge. Section 3.6 presents the results applying an outer iteration circle to eliminate incorrect expert knowledge from the estimation. Section 3.7 shows a comparative analysis of the results obtained by different methods.
# A Study Comparing Waiting Times in Global and Local Queuing Systems with Heterogeneous Workers
## 3 Models and Simulation Setup
### Local Queues Systems  
In the local queues setting (Figure 1b), the dispatcher assigns the arriving tasks according to their order of arrival using a routing policy. The worker fetches the next task from her dedicated queue once it is ready. Routing policies target to keep each task's waiting time as short as possible and determine the arrival rate \(\lambda\)i for each worker i. We expect the arrival rate \(\lambda\)i to conform to the worker i efficiency level reflected by the processing time.  
In a homogeneous environment, we expect the arrival rates \(\lambda\)i to be equal. Assuming that the routing policy is JSQ, TB events may occur when the routing policy needs to be decided between workers, such as when there is more than one idle worker or when there are workers whose queue sizes are the same. Since there are more possible \"wrong\" decisions in local queue systems, the global queue systems have been shown in many studies to outperform local queue systems. [PERSON] et al. (2018) provided a formal model and analysis of JSQ when used by the routing policy for homogenous server farms. They used a processor-sharing (PS) scheduler rather than a first-come-first-serve (FCFS) server farm in their model. They show the equivalence between M/G/K/JSQ/PS for \(K\) workers with local queues and Mn/M/1/PS for each worker in the system. It means that if each worker's assignment rate, \(\lambda\)i, is conditioned by the queue length, as in JSQ, we can assume \(\lambda\)i = \(\lambda\)/K, which ensures the required load balance and schedule optimization we wish to have while allocating the tasks. Since our systems follow the M/M/K model, we can use [PERSON]'s calculation to show that all the queues in the local queue system have the same number of tasks and similar waiting times. [PERSON] (2018) describes the basics of queueing theory that are particularly useful in healthcare. For an M/M/K system, they observe the relationship between system workers' utilization and task waiting times using the coefficient variance (CV) metric. CV(A) and CV(S) are the ratios between the standard deviation and the mean of inter-arrival and service times, respectively. They show that if the coefficient of variation (CV) is close to one, which is the case for homogenous workers, the waiting times will be shorter than when the CV is far from one, as seen with heterogenous workers. We checked the CV(A) of the arrival rates when the workers were homogeneous and found that it was close to one.
## 5 The Realization of ATC-Based Distributed Coordinated Optimization  
### Test System and Data  
The case study is conducted on the modified IEEE-33 bus distribution system to coordinate the energy management and optimal operation between RMGs and ADN during 24 h of operation as shown in Figure 6.  
Figure 6: Modified IEEE-33 bus distribution system.  
The RMGs are connected to bus 1-18 in the IEEE 33-bus system. Every RMG includes 50 households andare considered to have the same load-demand profile. The 24-h load-demand profile for a typical household is shown in Figure 7. The daily load curve for the remaining buses of the IEEE 33-bus system and time-of-use (TOU) electricity price are illustrated in Figure 8. Their controllable DGs are connected to buses 22, 23 and 27 respectively, and their corresponding rated active power is 3 MW, 3.5 MW and 4.5 MW. The corresponding parameter settings for the ATC algorithm are: \(\alpha_{m}=\beta_{m}=1\), \(\gamma=2\), \(\delta_{1}=0.01\), \(\delta_{2}=0.001\). The proposed optimization problems are implemented in MATLAB2015 utilizing the CPLEX12.5 solver [31].
## 3 Results
### End-to-End Communication System Emulation in Interfering Model
#### 3.5.1 Test 1 Results (No Interference)  
Signals configuration at HDR CORTEX ('IF TEST'): \(C\) = \(-\)9.6 dBm; \(N_{0}\) = \(-\)102 dBm/Hz. Then, 'IF NOMINAL' gives: \(C\) = \(-\)28.6 dBm and \(N_{0}\) = \(-\)121 dBm/Hz.  
\[\therefore\frac{C}{N_{0}}=-28.6+121=92.4\ dBmHz. \tag{1}\]  
Since \(R_{b}\) = 50 Mbps (WFI-AMZ-1), and  
\[\frac{E_{b}}{N_{0}}=92.4-10\log_{10}(R_{b}),\therefore\frac{E_{b}}{N_{0}}=15.4\ dB, \tag{2}\]  
\(E_{b}\)/\(N_{0}\) = 15.4 dB is equal to the link budget parameter of the AMZ-1 satellite WFI camera. Cortex measurements:  
\(N_{0(\text{CRTX})}=-99.7\ dBm/Hz\) = \(-\)19 dB = \(-\)118.7 dBm/Hz, and \(C\) = \(-\)29 dBm. Since,  
\[\frac{E_{b}}{N_{0}}=89.7-10\log_{10}(R_{B}),\therefore\frac{E_{b}}{N_{0}}=12.7\ dB. \tag{3}\]  
Spectrum analyzer measurements (with interference):  
\(I_{(\text{SPECT})}=-36.8\ dBm\) and \(N_{0}\) = \(-\)118.3 dBm/Hz,  
\[\implies I_{0}=-36.8-77=-113.8\ dBm/Hz.\]  
Since,  
Figure 13: Moving window of WFI images with no failures.  
\[\frac{I_{0}}{N_{0}}=-113.8+118.3,\ \therefore\frac{I_{0}}{N_{0}}=4.5\ \mathrm{dB}=INR\, \tag{4}\]  
and,  
\[\frac{E_{b}}{N_{0}+I_{0}}=\frac{SNR}{1+INR}\therefore\frac{E_{b}}{N_{0}+I_{0}}=12.7 -10\mathrm{log}_{10}\,3.88\approx 6.9\ \mathrm{dB}. \tag{5}\]  
Measuring the relation \(E_{b}/(N_{0}+I_{0})\) on CORTEX, we have:  
\[\frac{E_{b}}{N_{0}+I_{0}}=7\ \mathrm{dB}. \tag{6}\]  
Measure (6) is close to that calculated in (5). So,  
\[1+INR=10^{\frac{12.7-7}{10}},\therefore INR\approx 4.3\ \mathrm{dB}. \tag{7}\]  
Comparing (7) with the result in (4), we can conclude that the INR values calculated by the CORTEX and spectrum analyzer measurements have extremely low variation, validating our test setup.