esa-sceva/satcom-chunk-collection huggingface dataset:
# Solving the Container Relocation Problem by Using a Metaheuristic Genetic Algorithm
## 4 A New Method for CRP Resolving Based on Genetic Algorithm
### A New Method for CRP Resolving  
As mentioned earlier, the focus is on solving the two-dimensional, static, offline, and restricted CRP. Figure 3 shows an example of a CRP on a bay with four tiers (\(t\) represents the numbers of tiers, i.e., the height of the stacks) and four stacks (\(s\) represents the number of stacks, i.e., the width of the bay). A bay consists of 12 containers.  
Figure 3: The example of CRP with yard bay with 4 tiers and 4 stacks.  
The stacks are indexed from left to right with number values from 0 to 3, similarly with the height of the stacks, i.e., tiers. The uppermost tier has index 0 (tier t\({}_{0}\)), and the lowermost tier has index 3 (tier t\({}_{3}\)). The position of the containers is determined by an ordered pair (t, s). For example, container 4 is located at position (1, 0). Each container has a priority retrieving number according to which it is shipped from the bay. The smaller the number, the sooner the container is shipped from the bay. The goal is to retrieve all containers from the bay with the minimum number of reshuffles (relocations of blocking containers) [17]. Only containers blocking the container currently being retrieved can be relocated to another stack. Containers can be relocated to a particular stack only if the stack is not fully occupied (the number of containers in the stack is 4). As mentioned earlier, we propose a new method based on a genetic algorithm to minimize the number of relocations within the bay. The genetic algorithm finds an optimal sequence of positions for relocating blocking containers. The process of determining an optimal sequence of container relocations using a genetic algorithm is described in detail below.
## 1 Introduction
### Structure of this Paper  
This research paper is structured to methodically explore the integration of artificial intelligence into cross-cultural language learning, culminating in developing and evaluating the CILS. The paper is organized to comprehensively analyze and present the research findings.  
The Introduction section outlines the relevance and urgency of enhancing language learning practices through AI, particularly in cross-cultural settings. This section also presents the research's overarching goals and objectives, establishing this study's academic and practical contexts.  
Following the introduction, the Literature Review section systematically examines existing research in language learning, AI applications in education, and cross-cultural communication. This review situates the current study within the broader field of educational technology and applied linguistics, highlighting significant advancements and identifying gaps that the CILS framework aims to address.  
The Theoretical Framework section discusses the foundational theories that underpin the design and function of the CILS. It delves into relevant language learning theories, AI technologies, and models of communicative competence that inform the system's development. This theoretical grounding ensures that the technological innovations proposed are deeply rooted in pedagogical principles.  
In the CILS Framework section, the paper details the proposed system's design, functionalities, and technical specifications. It describes how AI is utilized to adapt learning experiences to users' cultural and linguistic needs, illustrating the system's capabilities through scenarios and potential user interactions.  
The Case Studies section presents empirical research to validate the CILS's effectiveness. It includes detailed analyses of pilot studies involving platforms like Busuu and HelloTalk, providing insights into the system's performance in real-world educational settings.  
The Discussion section synthesizes the findings from the case studies and discusses the implications of these results for language learning and AI in education. This section critically examines the successes and challenges encountered during the study, offering a nuanced perspective on the potential and limitations of AI-enhanced language learning.  
The Conclusion section recapitulates this study's major contributions and outlines directions for future research. It reflects on the impact of the CILS on educational practices and provides recommendations for educators, policymakers, and technologists interested in the intersection of AI and language education.
# On the Virtual Cell Transmission in Ultra Dense Networks
## 1 Introduction
### Related Works
#### 1.1.3 Global Optimal Approach  
Besides the BS-centric and user-centric schemes, the third solution is to find the global optimum with respect to the combination of RBA/user pairing, transmitting power tuning as done in [16; 17; 18]. The basic idea is to reformulate the original intractable combinatorial optimization problem into a series of convex optimization problems or integer linear programs (ILP), and then, the global optimum can be acquired via existing optimization tools.  
In contrast to the simple BS-centric transmission scheme without coordination, the global optimum solution requires global coordination. Clearly, the anticipated ICI elimination comes at the cost of a large amount of information (including channel state information (CSI), control signaling, as well as user data) exchange, joint signal processing and series of large-scale optimization problem solving, the complexity of which could be prohibitively high if the number of RBAs is very large.
## 3 Methodology
It should be noted that, where the application of the formula leads to a \(C_{M}\) value greater than 0.50, it is recommended that this value is adopted as a maximum, taking into account indications of the European Standard EN 16941-1 [37]. Assuming its validity in Portugal, Formula (1) was applied to 12 weather stations (Figures 3 and 4), where it is possible to obtain temperature and precipitation values, to calculate theoretical values for the monthly runoff coefficients for green roofs with characteristics similar to those of the pilot system [32; 33; 34; 35; 36; 37; 38] and find patterns related to the climatic nuances in the country. For these weather stations, the historical record of monthly rainfall and temperatures is available on the Portuguese government's SNIRH - Sistema Nacional de Informacao de Recursos Hidricos website ([[https://snirh.apambiente.pt/](https://snirh.apambiente.pt/)]([https://snirh.apambiente.pt/](https://snirh.apambiente.pt/))). Most of Portugal has a Mediterranean climate, but northern Portugal has a significant Atlantic influence, where the Mediterranean climate is less dominant. This is the case for stations 2, 7, and 10. Station 5, although also located in the north of Portugal, is situated in the Douro valley, which has a very specific local climate. The long mountain range on the west rim of this region protects the valley from the cold and humid winds that sweep in from the Atlantic, creating drier conditions than along the coast, but also colder winters and warmer summers. It is due to its special climatic conditions that this valley has become the birth of one of the world's most well-known drinks, Port Wine. Figure 3: Location of Portugal close to the Atlantic Ocean and the Mediterranean Sea.
# Parameter Estimation of a Class of Neural Systems with Limit Cycles
## 5 Simulations
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\sigma^{2}\) & \(k\) & \(\mu\) & \((a+b)\mu\) & \(ab\mu\) & \(J\) & \(c_{1}\) & \(c_{2}\) & \(\delta\) (\%) \\ \hline  & 500 & 19.1634 & \(-\)11.8220 & 23.2437 & 16.5106 & 1.1374 & 0.4206 & 95.8047 \\  & 1000 & 35.0060 & \(-\)8.8759 & 18.5691 & 37.0688 & 1.0426 & 0.5921 & 86.7670 \\ \(0.2^{2}\) & 5000 & 85.1399 & 57.0311 & \(-\)0.6404 & 42.6241 & 1.0992 & 0.5409 & 35.9599 \\  & 10,000 & 94.1694 & 90.4475 & 6.3213 & 47.8311 & 1.0994 & 0.5577 & 13.2635 \\  & 15,000 & 97.3389 & 103.1461 & 8.3135 & 49.9311 & 1.0338 & 0.6045 & 4.8003 \\  & 20,000 & 99.1619 & 107.5047 & 9.5947 & 49.7100 & 0.9374 & 0.2108 & 1.7150 \\ \hline  & 500 & 23.1916 & \(-\)11.9815 & 24.6474 & 12.4417 & 1.2785 & 0.2591 & 95.2371 \\  & 1000 & 35.3666 & \(-\)7.5615 & 17.1582 & 39.9121 & 1.1419 & 0.7780 & 85.7224 \\ \(0.5^{2}\) & 5000 & 85.5678 & 59.2415 & \(-\)0.6955 & 44.1461 & 1.2350 & 0.6827 & 34.4612 \\  & 10,000 & 95.2375 & 91.8121 & 6.4314 & 47.7921 & 0.9014 & 0.6650 & 12.2575 \\  & 15,000 & 98.4425 & 103.4602 & 8.5951 & 49.2526 & 1.0869 & 0.7208 & 4.3983 \\  & 20,000 & 99.6694 & 108.1012 & 9.7009 & 50.1496 & 0.9396 & \(-\)0.2527 & 1.3341 \\ \hline True values & 100.0000 & 110.0000 & 10.0000 & 50.0000 & 1.0000 & 0.5000 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The MISG estimates and errors (\(p=3\)). Figure 2: Parameter estimation errors \(\delta\) versus \(k\) using RLS and MIRLS. From Tables 1 and 2, it is seen that both the RLS and MIRLS algorithms have a fast convergence rate and a high estimation accuracy, which can also be observed from Figures 2 and 3. On the other hand, by using the SG and MISG algorithms, it is shown in Tables 3 and 4 that it requires a data length around \(L_{d}=20,000\) to achieve an acceptable estimation accuracy. Though not shown in table, the MIRLS algorithm with \(p=5\) enjoys the most accurate parameter estimate and the fastest convergence rate. From Figures 2 and 3, we can see that for the same batch of data, compared with the RLS algorithm, the SG algorithm extracts less information from the measured data and uses information inefficiently, which results in a much slower convergence. Hence, to show the effect clearly, the results by the RLS and SG algorithms are shown in two figures, respectively. Meanwhile, from Figure 2, it can be found that due to the high efficiency of the RLS algorithm, the MIRLS algorithm has limited improvement potential for the accuracy of parameter estimation. However, the computation efficiency of the MIRLS algorithm will be revealed in the case of data missing, which remains our future work. From Figure 3, it is clear that the MISG algorithm has a faster convergence rate than the SG algorithm or the MISO algorithm with \(p=1\) and the MISO estimates with \(p=3\) and \(p=5\) have higher accuracy  
Figure 3: Parameter estimation errors \(\delta\) versus \(k\) using SG and MISG. than the SG estimates. In fact, the parameter estimation errors by the MISG algorithm become smaller and smaller as the innovation length \(p\) increases.
# PPG-Based Systolic Blood Pressure Estimation Method Using PLS and Level-Crossing Feature
###### Abstract  
This paper proposes a cuff-less systolic blood pressure (SBP) estimation method using partial least-squares (PLS) regression. Level-crossing features (LCFs) were used in this method, which were extracted from the contour lines arbitrarily drawn on the second-derivative photoplethysmography waveform. Unlike conventional height ratio features (HRFs), which are extracted on the basis of the peaks in the waveform, LCFs can be reliably extracted even if there are missing peaks in the waveform. However, the features extracted from adjacent contour lines show similar trends; thus, there is a strong correlation between the features, which leads to multicollinearity when conventional multiple regression analysis (MRA) is used. Hence, we developed a multivariate estimation method based on PLS regression to address this issue and estimate the SBP on the basis of the LCFs. Two-hundred-and-sistxty-five subjects (95 males and 170 females [(Mean \(\pm\) Standard Deviation) SBP: 133.1 \(\pm\) 18.4 mmHg; age: 62.8 \(\pm\) 16.8 years] participated in the experiments. Of the total number of subjects, 180 were considered as learning data, while 85 were considered as testing data. The values of the correlation coefficient between the measured and estimated values were found to be 0.78 for the proposed method (LCFs + PLS), 0.58 for comparison method 1 (HRFs + MRA), and 0.62 for comparison method 2 (HRFs + MRA). The proposed method was therefore found to demonstrate the highest accuracy among the three methods being compared.  
blood pressure estimation; photoplethysmography; partial least-squares regression Article
## 3 Self-Calibration of Camera Intrinsic Parameters
### Self-Calibration of the Scaled Focal Length in \(x\) Direction  
As the interaction matrix in (4) shows, pure rotation of the mobile robot do not depend on the depth but depends on the scaled focal length in \(x\) direction. Therefore, an focal length observer designed in [21] is exploited to the camera installed on the mobile robot. Let \(\xi=(\bar{u},\bar{v},f_{u},\frac{1}{f_{u}})\) be the state vector, where \(\bar{u}=u-u_{0},\bar{v}=v-v_{0}\) are the partially normalized image feature. Assume a pure rotational motion is applied to the mobile robot, the dynamic equations are expressed as  
\[\dot{\xi}=\left[\begin{array}{c}-(\xi_{3}+\xi_{1}^{2}\xi_{4})\\ -\xi_{1}\xi_{2}\xi_{4}\\ 0\\ 0\end{array}\right]w,y=\left[\begin{array}{c}\xi_{1}\\ \xi_{2}\end{array}\right]. \tag{9}\]  
Let \(\dot{\xi}\in\mathcal{R}^{4}\) be the estimation of the state \(\dot{\xi}\). By defining \(e=\xi-\dot{\xi}\) as the estimate error vector, the nonlinear observer is designed as  
\[\dot{\xi}=\left[\begin{array}{c}-(\dot{\xi}_{3}+y_{1}^{2}\dot{\xi}_{4})\\ -y_{1}y_{2}\dot{\xi}_{4}\\ 0\\ 0\end{array}\right]w+\left[\begin{array}{c}k_{1}e_{1}\\ k_{2}e_{2}\\ -k_{3}we_{1}\\ -k_{4}(y_{1}^{2}we_{1}+y_{1}y_{2}we_{2})\end{array}\right] \tag{10}\]  
with \(k_{1},k_{2},k_{3},k_{4}\in\mathcal{R}^{+}\) are positive parameters. Then we obtain the error dynamics  
\[\dot{e}=\underbrace{\left[\begin{array}{cccc}-k_{1}&0&-w&-y_{1}^{2}w\\ 0&-k_{2}&0&-y_{1}y_{2}w\\ k_{3}w&0&0&0\\ k_{4}y_{1}^{2}w&k_{4}y_{1}y_{2}w&0&0\end{array}\right]}_{H}e. \tag{11}\]  
Since a pure rotational motion is imposed on the mobile robot, it ensures \(w^{2}\geq 0\), \(y_{1}^{2}y_{2}^{2}w^{2}\geq 0\). The matrix \(H\) is a Hurwitz matrix. Thus the exponential convergence of the error system can be guaranteed. The partially normalized image features are obtained by applying the estimated principle point from the previous subsection, which are used in the observer (10). Then the result is \(\hat{f}_{u}=830.5416/\)pixels and the evolution is shown in Figure 3.  
Figure 2: Image paths of four points and the calibrated principle point.
## 6 Results and Discussion
### Sea Experiment Design  
To verify the effectiveness of the proposed algorithm in a real underwater environment, the corresponding sea experiments are designed. The topology of the experiments is shown in Figure 12. The experiment includes a central control node and three sensing nodes (SNs); the transmission distances from the central control node to SN1, SN2, and SN3 are 936 m, 1120 m, and 1254 m. The developed underwater communication unit is used at both the transmitter and receiver ends. The unit has four input channels and one output channel, which can be selected according to different applications.  
Figure 13 shows the time-frequency diagram of the request-to-send (RTS) packets received by the three nodes for spectrum sensing. We observed that there are multiple narrowband interferences in the underwater environment, but only three of them affecting the communication, that is, in the communication band (21-27 kHz) range. Table 4 summarizes the BER performance of the three nodes for different schemes. As Table 4 shows, the AND criteria collaboration detection scheme still shows the worst performance, and the BER of  
the proposed algorithm is reduced compared to the traditional OR criterion collaborative detection scheme, which proves the effectiveness of the proposed spectrum-sensing scheme.  
Figure 12: The topology of the experiments.  
Figure 13: The time-frequency diagram of the RTS packets received by the three nodes for spectrum sensing: (**a**) node 1; (**b**) node 2; and (**c**) node 3.
## 2 Materials and Methods
### Assurance and Validation of Quality  
We validated the proposed method by calculating the recovery, linearity range, repeatability, and limits of quantification (LOQs) [10; 11]. For determining the recovery and repeatability, we spiked blank samples in triplicate by using the following standard mixture of analytes at two concentrations (low and high levels): 1 and 5 ng/g for determination of chloramphenicol classes, dyes, and nitrofuran metabolites; and 5 and 25 ng/g for determination of quinolone classes. Extraction and treatment of the samples followed a previously reported protocol [2; 8; 9]. The aforementioned recovery validation method was employed to determine the method's repeatability, which was calculated as the percentage of the relative standard deviation (RSD%). The recovery and repeatability (expressed as the percentage of relative standard deviation) of veterinary drugs ranged from 88.67% to 92.35% (repeatability range: 3.79-9.67%) for chloramphenicol classes, 75.21% to 103.31% (repeatability range: 6.72-14.58%) for quinolone classes, 98.81% to 100.31% (repeatability range: 3.58-8.13%) for MG and LMG, and 99.29% to 100.52% (repeatability range: 0.98-5.58%) for nitrofuran metabolites in shrimp samples (Supplementary Table S2). Matrix-matched calibration executed through the use of blank sample extracts and addition of the corresponding amount of working solution (with target compounds at a concentration of 0.5-500 ng/mL) was performed to evaluate the linearity. The calibration curves obtained had high linearity and reproducibility, with the analytical matrix-matched calibration achieving favorable correlation coefficients (R\({}^{2}\) > 0.990). The LOQs were defined as being the concentrations of analyte that yielded peak signals 3\(\times\) and 10\(\times\) the intensity of background noise from the chromatogram. The florfenicol, thiamphenicol, chloramphenicol, LMG, MG, and nitrofuran metabolite LOQ was 0.25 ng/mL in shrimp samples. Compared with these chemicals, the LOQ of other veterinary drugs, including dinoflagoxin, difloxacin, enrofloxacin, flumequine, and sarafloxacin, was 1 ng/g (Supplementary Table S2); concentrations lower than these LOQs indicated that the chemicals and drugs were considered undetectable.
## 2 Methods
### Experimental Setup  
The software is implemented using the Python V3.9.13 programmingguage. It uses well-known open-sourceibraries such as OpenCV, NumPy, and Matplotlibheries. Theseibraries are essential to enable computer vision, numerical, and visualization operations, respectively.  
The proposed embedded system is based on three main units: Raspberry Pi, an IR emitter, and an IR camera, as presented in Figure 1a. The following details show the description of the system's components:  
* Acquisition box is a black box with dimensions 20 \(\times\) 60 \(\times\) 60 cm (width \(\times\) depth \(\times\) height). It provides a controlled environment for vein injection where the patient's arm is placed inside it as depicted in Figure 1b.
* Raspberry Pi-TBD is the microprocessor embedded system. It is a quad-core 64-bit ARM-Cortex A72 running at 1.5 GHz. It hosts and executes the proposed vein injection software and controls both the IR emitter and IR camera.  
* IR emitter source, Model: LZ1-10R602-0000 [41]. It is eight source with an 850 nm wavelength and 3.2 W of power. It is used during the detection and injection of the vein. It is connected to the Raspberry Pi using an H-bridge (DRV8871) to control its intensity using pulse-width modulation (PWM). This emitter is sufficient for the experimental setup since it has single-color infrared with a high-power LED. Since the IR emitter has power, it is connected to an external power source and is controlled by the Raspberry Pi.
* IR camera, Model: NoIR Rpi Camera V2. It is connected to the Raspberry Pi board by the camera serial interface (CSI) cable. It is used to capture the infraredight band with an 850 nm wavelength.  
The system unit is a compact device such that both the IR emitter source and the IR camera are firmly fixed inside the acquisition box from the upper side as illustrated in Figure 1c. They are placed pointing towards the patient's arm. The costs for the Raspberry Pi, IR emitter, and NoIR camera are USD 70, USD 11, and USD 34, respectively. Thus, the cost for the three main units together for the proposed device is around USD 115. That is fairly costly for the proposed embedded system.  
Figure 1: Embedded system units: (**a**) system hardware diagram; (**b**) acquisition box; (**c**) system hardware actual model.
###### Abstract  
We focused on the researches of two models used for Jason-3 precise orbit determination (POD)--Jason-3 attitude modes and receiver phase center variation (PCV) model. A combined attitude mode for the Jason-3 satellite is designed based on experimental analysis used in some special cases, such as in the absence of quaternions or when inconvenient to use. We researched the linking of satellite attitude with antenna phase center. Specially, to verify the validity of the combined attitude, we analyzed the effects of different attitude modes on receiver phase center offset (PCO) estimation, PCO correction and POD. Meanwhile, the difference analysis of PCO correction based on attitude modes also contains the combined attitude modeling processes. The POD results showed that the orbital accuracies with the combined attitude are slightly more stable than those with attitude event file. By introducing receiver PCVs into POD, the mean residuals root-mean-square (RMS) is reduced by 1.9 mm and orbital 3D-RMS position difference is improved by 5.7 mm. The eight schemes were designed to integratedly verify the effectiveness of different attitude modes and receiver PCVs model. The results conclude that the accuracy using the combined attitude is higher than that of event file, which also prove the feasibility of the combined attitude in integrated POD and it can be as a revision of attitude event file. Using all mentioned attitude modes, the orbital accuracy by introducing PCVs can be improved by the millimeter level. The integrated effects of attitude modes and receiver PCVs on POD are almost consistent with the effects of a single variable. The optimal results of Jason-3 POD indicate that orbital mean radial RMS is close to 1 cm, and the 3D-RMS position difference is within 3 cm.  
Jason-3 satellite; attitude modes; receiver phase center offset and variation (PCO/PCV); spaceborne GPS; precise orbit determination (POD); accuracy validation 2019/19, 2408; doi:10.3390/s19102408