esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background  
### Definition of Concept Drift  
Concept drift has been previously studied by many researchers in different domains. The authors in [16; 17] provided a formal definition of concept drift in terms of the statistical properties of a target variable over a period of time and the joint probabilities of the feature vector and target variable. These researchers discovered three sources of CD in terms of the joint probabilities of a feature vector and a class variable. Using these relations between probabilities, the authors characterized two types of drifts i.e., virtual and real. In terms of statistical properties of target variable, we can define CD as follows--  
If \(D_{t}\) represents the statistical properties of target variable at time \(t\) and \(D_{t+1}\) represents the statistical properties of target variable at time \(t+1\), then there is CD if \(D_{t}\)!= \(D_{t+1}\).
# A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI
## 3 Experimental Results and Discussion
### Performance Comparison among Atlases  
A comparison of all performance metrics across all four atlases is represented in Figure 11 using the proposed Model-2 to determine which atlas had the most discriminative power in identifying autism and control cases.  
From the above graphical analysis, the following points can be demonstrated:  
* BASC atlas provided superior performance in terms of accuracy, sensitivity, F1, and AUC score using the proposed Model-2.
* AAL showed inconsistent results among various metrics. It had the lowest sensitivity value, which is very crucial and significant in medical diagnosis.
* CC200 and Power atlas depicted the lowest predictive power based on its performance value across all measures.  
From the aforementioned points, it can be concluded that the BASC atlas displays the best performance across all metrics. It exhibits the highest discriminative power in a balanced manner which is evident from its F1 score. Other models exceeding two hidden layers were also attempted, but experimental results deteriorated due to the limited dataset.  
Figure 11: Performance comparison among atlases using Model-2.  
Figure 10: Confusion matrix and AUC curve for AAL atlas using Model-2.  
Figure 9: Confusion matrix and AUC curve for BASC atlas using Model-2.
###### Abstract  
In this paper, a hybrid realization model is proposed for the controllers of autonomous underwater vehicles (AUVs). This model is based on the model-based systems engineering (MBSE) methodology, in combination with the model-driven architecture (MDA), the real-time unified modeling language (UML)/systems modeling language (SysML), the extended/unscented Kalman filter (EKF/UKF) algorithms, and hybrid automata, and it can be reused for designing controllers of various AUV types. The dynamic model and control structure of AUVs were combined with the specialization of MDA concepts as follows. The computation-independent model (CIM) was specified by the use-case model combined with the EKF/UKF algorithms and hybrid automata to intensively gather the control requirements. Then, the platform-independent model (PIM) was specialized using the real-time UML/SysML to design the capsule collaboration of control and its connections. The detailed PIM was subsequently converted into the platform-specific model (PSM) using open-source platforms to promptly realize the AUV controller. On the basis of the proposed hybrid model, a planar trajectory-tracking controller, which allows a miniature torpedo-shaped AUV to autonomously track the desired planar trajectory, was implemented and evaluated, and shown to have good feasibility.  
autonomous underwater vehicle (AUV); AUV control; extended/unscented Kalman filter (EKF/UKF); model-based systems engineering (MBSE); model-driven architecture (MDA); real-time UML/SysML; hybrid automata +
Footnote †: journal: Applied Sciences
## 3 Experimental Setup and Result Analysis  
### Dataset  
The datasets employed in this study were sourced from the open-source BreakHis dataset, which is organized into four groups based on different magnifications: 40\(\times\), 100\(\times\), 200\(\times\), and 400\(\times\). All the datasets underwent dye normalization preprocessing before being used in the experiments. For each magnification group, 80% of the images were randomly selected as the training set, while the remaining ones were used as the test set. The input image size was set to 224 \(\times\) 224 pixels. To mitigate overfitting, images in the training set were augmented by flipping them horizontally and vertically with a probability of 25%.
## 3 Results and Discussion
### Microwave Treatment
#### 3.3.1 Effect of Microwave Treatment on Impurities Phases
Furthermore, the 50 minutes' X-ray intensities of microwave treatment, Figure 9e, was relatively similar to those obtained for 30 and 40 min, suggesting a consistent rate of increase in sample temperature. The phase change that was shown after 50 min was similar to what was seen after 40 and 30 min, indicating that the effects of the microwave treatment were similar after 50 min. Figure 10: SEM images of untreated and microwave-treated pyrophylike samples. Figure 10a-f shows the SEM micrographs of the untreated and microwave-treated samples at various durations (10, 20, 30, 40, and 50 min). The untreated sample displays a fine schistose texture, and the particles are highly foliated, with have anhedral flakes arranged, as shown in Figure 10a. For the 10 minute microwave treatment, cracks developed mostly within the grain boundary, as shown in Figure 10b. The observations agree with previous studies, which reported that microwave irradiation is more significant at the boundaries and the surfaces of the grains [21; 30]. The 20 min treated samples display a very fine schistose texture and the particles are highly foliated, as in Figure 10c. Moreover, the phase transition of pyrite into pyrrhotite may potentially be attributed to the intense impact of microwave irradiation induction on the grain rims and surface. SEM micrograph of 30 min microwave-treated sample is shown in Figure 10d. The surface of the particles appears significantly induced by microwave irradiation compared to the samples treated for 10 and 20 min. Figure 10e illustrates that the notable impact of microwave irradiation induction for 40 min on the surface and rims of the grains is comparatively similar to that induced by 30 min of microwave treatment. The results of the 50 min microwave irradiation treatment showed similar results as the 40 and 30 min treatments, as shown in Figure 10f. The SEM findings are supported the XRD, suggesting that the microwave treatment's effects were comparable for 30, 40, and 50 min.
## 5 Discussion  
### Comments on the Preliminary Test Results  
Let us resume the presented typical test results as follows:  
The targeted throughput of 200 Mb/s (Figure 18) was projected, based on past experience, with a traffic load of 10 BSes. The throughput for the internet access was measured in conditions of traffic load with 2 RAN Bes (Trnovski privilege and Berkovici). The internet traffic was categorized as best effort (i.e., with lowest priority in case of congestion).  
As is already elaborated above, the expectations of longer delay with longer packets is confirmed in Figure 19, where the packets of 1500 bytes length exhibited the maximal delay value of 3.7 ms, which is below the optimal values for LTE Bes (Table 1). Moreover, since the conducted measurements were round-trip, the actual one-way packet latency was about half of the displayed one.  
No packet loss occurred at this data rate and the low bit error rate (BER) (Figure 20), whereas the jitter values in Table 3 of maximal 59 us for the largest packets of 1500 Bytes are far below the allowed figure in Table 1.  
Based on these particular measurements, it came out that the end-to-end QoS satisfied the target values, which is in accordance with the end-to-end RR performance prediction being in-service monitored by means of the network monitoring and control tool also tracking changes of the modulation order throughout 24 h, during 7 successive days.
## 2 Materials and Methods
### GNSS-RO Dataset  
The GNSS-RO dataset used in this study includes data from several R.O. missions (COSMIC-1, KOMPSAT-5, MetOp-A, -B GRACE, TanDEM-X, and TerraSAR-X), all freely available from the COSMIC Data Analysis and Archive Center (CDAAC) ([[https://www.cosmic.ucar.edu/what-we-do/data-processing-center/data](https://www.cosmic.ucar.edu/what-we-do/data-processing-center/data)]([https://www.cosmic.ucar.edu/what-we-do/data-processing-center/data](https://www.cosmic.ucar.edu/what-we-do/data-processing-center/data))) (accessed on 12 April 2024). This study used the atmospheric profiles with moisture (wetPrf format) from the post-processed datasets (level 2). The CDAAC uses the one-dimensional variational approach (1D var) and the ERA-Interim model reanalysis as a first guest to inverse the observed refractivity to vertical profiles of temperature, atmospheric pressure, and water vapor pressure [40; 41]. The vertical resolution is 100 m from the Earth's surface to 40 km altitude. \(\rho_{v}\) is calculated using Equation (2), and after checking the quality control flag, 30 profiles were selected to assess the tomography solution.
# Experimental Investigation on Jet Vector Deflection Jumping Phenomenon of Coanda Effect Nozzle
## 4 Conclusions  
This paper investigates the vectoring deflection jumping phenomenon of the Coanda effect nozzle experimentally and numerically. The PIV technique is characterized the dynamic characteristics of jet deflection flow. According to the force measurement, pressure measurement, and DMD analysis of PIV measurement results, the phenomenon of jet deflection jump and the evolution law of vortex in a dynamic jet deflection detachment condition are investigated and discussed. Finally, according to the control object, control position, and control frequency obtained from the analysis results, the flow field's active flow control is carried out. According to the experimental model and conditions, the results are as follows:  
The jumping phenomenon in the Coanda effect nozzle is caused by the different stability of the jet detachment condition and the jet attachment condition. The stability of detachment is steady, while the stability of the attachment flow is periodic. The vector deflection process is a process of mutual transformation between steady and periodic stable systems. The stability of the jet attachment is worse than that of the jet detachment state, so the jumping phenomenon will occur when the jet leaves the Coanda wall.  
The DMD method can accurately analyze the jumping phenomenon in the unsteady flow field under the working conditions of this paper. The DMD method can extract the dominant frequency and the corresponding flow field modal structure in the unsteady flow field. The modal stability analysis of PIV results can accurately extract the shedding characteristic frequency of separated bubble structures. The DMD method provides a technical means for analyzing the flow field structure of the unsteady flow.  
In the experimental conditions of this paper, by injecting a 78.16 Hz pulsed jet into the Coanda wall, the jumping phenomenon can be effectively suppressed, and the angular velocity of the jet vector changes linearly. The pulsed jet injection control position can effectively enhance the strength of the separation bubble structure and avoid the sudden rupture of the separation bubble structure. This active flow control method can provide a technical means for designing a new fluid thrust vectoring nozzle, by finding the characteristic frequency form of flow structure in the unsteady flow field.  
Funding acquisition, Y.G.; Writing\(-\)original draft, S.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, the National Natural Science Foundation of China, No. 11972017.  
The authors declare no conflict of interest.
# Fine Recognition of MEO SAR Ship Targets Based on a Multi-Level Focusing-Classification Strategy
###### Abstract  
The Medium Earth Orbit (MEO) spaceborne Synthetic Aperture Radar (SAR) has great coverage ability, which can improve maritime ship target surveillance performance significantly. However, due to the huge computational load required for imaging processing and the severe defocusing caused by ship motions, traditional ship recognition conducted in focused image domains cannot process MEO SAR data efficiently. To address this issue, a multi-level focusing-classification strategy for MEO SAR ship recognition is proposed, which is applied to the range-compressed ship data domain. Firstly, global fast coarse-focusing is conducted to compensate for sailing motion errors. Then, a coarse-classification network is designed to realize major target category classification, based on which local region image slices are extracted. Next, fine-focusing is performed to correct high-order motion errors, followed by applying fine-classification applied to the image slices to realize final ship classification. Equivalent MEO SAR ship images generated by real LEO SAR data are utilized to construct training and testing datasets. Simulated MEO SAR ship data are also used to evaluate the generalization of the whole method. The experimental results demonstrate that the proposed method can achieve high classification precision. Since only local region slices are used during the second-level processing step, the complex computations induced by fine-focusing for the full image can be avoided, thereby significantly improving overall efficiency.  
MEO SAR; ship target recognition; ship target focusing; multi-level classification
## 4 Discussion
### Research Limitations  
This research was based on diagnostic imaging data acquired from the KFHU, and the results were limited to the radiographic examinations of a single department (i.e., one facility with two scanners). To achieve more accurate results, this research requires extension to other hospitals in the Kingdom of Saudi Arabia so that a large-scale analysis can be conducted, and any probability of error in recorded dosimetry can be eradicated through the examination of a larger sample of recorded DRLs and ADs for both mammography and radiography procedures.  
Second, the cohort was limited by the fact that the data monitoring system only recorded the patient's body mass index, and the exact weight of a patient was missing. Patient weight (kg) is a key indicator of the size of a patient in medical imaging and it affects the DRL values of the research. Moreover, the estimations made in this study are subject to a considerable level of uncertainty and may affect the principal results of the research. This research was confined to the use of two radiography scanners and one mammography scanner, which contributed to the lack of sufficient systems to monitor the data acquired from diagnostic medical imaging. Finally, this study did not assess image quality, which was beyond the scope of our study.
# Compressed Communication Complexity of _Hamming Distance_
## 4 Conclusions and Open Questions  
This paper showed a randomized public-coin protocol for a joint computation of the _Hamming distance_ of two compressed strings. Our _Hamming distance_ protocol relies on [PERSON] et al.'s LCP protocol for two strings that are compressed by non-self-referencing LZ77,while our communication complexity analysis is based on new combinatorial properties of non-self-referencing LZ77 factorization.  
As a further research, it would be interesting to consider the communication complexity of the _Hamming distance_ problem using self-referencing LZ77. The main question to this regard is whether \(\mathsf{zs}(S[i..])=O(\mathrm{poly}(\mathsf{zs}(S)))\) holds for any suffix \(S[i..]\) of any string \(S\). In the case of non-self-referencing LZ77, \(\mathsf{zn}(S[i..])=O(\mathsf{zn}(S)^{2})\) holds due to Lemma 2.  
Conceptualization, Y.N, S.I., H.B. and M.T.; methodology, S.M., Y.N., and S.I.; writing--original draft preparation, S.I.; writing--review and editing, Y.N. and H.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by JSPS KAKENHI Grant Numbers JP18K18002 (YN), JP20H04141 (HB), JP18 Fl04098 (MT), and JST PRESTO Grant Number JPMJPR1922 (SI).  
The authors declare no conflict of interest.
## 3 Anaerobic Digestion Plant Technologies
### Economic Considerations about Biogas Plant  
Total costs for the realization of a biogas plant must be distinguished between initial cost (capital cost) and operating cost. The capital costs are those necessary for the construction (components, sensors, building work), and are strictly related to the characteristics of the plant (batch, continuous or semi-continuous). For AD process, the following voices are taken into account: (a) feedstock handling and storage; (b) building and operating the digester, with heat and power generation; (c) handling and storage of the digestate.  
Several studies reported the results of the estimation of total cost related to biogas plant and equipment [33]. Capital cost could be estimated in a range between USD 300 and 800 per m\({}^{3}\), USD 2400-7500 per kWe and USD 5000-7500, referred to the digester volume, to the installed electric power, and to the hourly produced biogas volume, expressed as Nm\({}^{3}\)/h, respectively.  
Considering the biogas upgrading, the investment cost varies from USD 1950 to 2600 per Nm\({}^{3}\)/h, for raw gas capacities larger than 800-1000 Nm\({}^{3}\)/h. Generally higher costs are observed for smaller-scale equipment [34].  
The operating cost of the plant depends both on the ordinary and extraordinary management costs. The first cost item includes the cost for the production (for energy crops), management and storage of substrates to convert, and the energy cost for operating the plant (e.g., maintaining a constant temperature). The second cost item comprises the costs for periodic maintenance, commonly estimated in the range 3-10% of the investment cost.  
Incomes refer to the price recognized by the national Electrical Service Authority for the amount of electricity introduced into the grid, plus any production incentives, now present in different countries, but destined to disappear completely in the future.  
As for plant technology, AD operating in wet conditions (\(\leq\)10% dry matter) requires less capital investment and anaerobic digesters based on wet biomass input typically convert 30% to 60% of the biomass to methane.  
In dry conditions (dry matter between 15-20%), facilities require less operating energy, due to the total amount of water to be heated, if compared to wet conditions, and the system provides more gas per unit of feedstock.  
Moreover, digesters can operate in continuous flow or in batch flow, and digestion can occur in single- or multiple-step digesters; although multiple digesters allow higher efficiencies for each digestion phase, they require higher investment costs [35].
# Control Strategy Development of Driveline Vibration Reduction for Power-Split Hybrid Vehicles
## 1 Introduction
The torque feedback would be sent back to the controller changing the motor torque output signal to an equal but reverse torque to cause vibration neutralization. [PERSON] et al. [17] focused on the driveline vibration caused by the traction motor in the launch situation, and proposed a superposition control strategy to suppress the driveline vibration. [PERSON] et al. [18] provided an improved genetic algorithm method to reduce the speed-up transient vibration of planetary power-split driveline system. For PID control, [PERSON] [19] introduced the active disturbance rejection control (ADRC) as an alternative for PID control. This research used a discrete-time case to speed up real time implementation in dynamic system. [PERSON] [20] compared the current rotation speed with the expected rotation speed of the counter shaft when the internal combustion engine ran, and sent the difference to the PID controller. The torque feedback could neutralize the remaining vibration. However, the above two control methods were only for the internal combustion engine when starting. Thus, this research further explored the vibration reduction effect such as adding feedback control when a greater change in power source torque existed. This research focused on improving the elastomer vibration in the TMH driveline caused by the instantaneous large torque from each power source. Two control methods were implemented to neutralize the vibration. One was using the motor output torque feedback generated by predicting the torque when the internal combustion engine started and shut down. The other one was adjusting the motor speed by comparing the monitored rotation speed of the output axis and the estimated rotation speed based on the stable vehicle speed. Finally, the characteristics of two control methods were combined to reduce vibration. The remaining sections are as follows: the theoretical background and modeling of two-mode power-split hybrid system, the control strategies of vibration reduction, the results and comparison of vibration reduction, and the conclusions.
# Advanced Methods for Photovoltaic Output Power Forecasting: A Review
## 2 Artificial Intelligence Techniques  
There are many definitions of artificial intelligence and one of the most used is \"imitating intelligent human behavior\" that includes systems with the following characteristics: they have a \"thinking process\" similar to the one human beings have, they are able to act like humans, their thinking process is rational, and their acting is rational too. For these reasons a number of intelligent computing technologies are today more and more used as an alternative to conventional techniques [22].  
There are different branches of AI techniques that can be classified as follows [31]: machine learning (ML) including supervised, unsupervised, reinforcement learning which regards algorithms/statistical models that computer systems use in order to effectively perform a specific task without using explicit instructions, and deep learning (DL) which is seen as a ML subset. DL allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction.  
Other branches are expert systems (ESs), which allow computers to \"make decisions\" by interpreting data and selecting from a list of alternatives, computational intelligence (CI) covering the theory, and development of biologically and linguistically motivated computational paradigms (the main three pillars of CI are: artificial neural networks (ANNs), genetic algorithms (GAs), and fuzzy logic (FL)).  
These main groups often are adopted in order to accomplish specific tasks or are widely used in many fields crosswise such as: problem solving and planning (PSP), nonmonotonic reasoning (NMR), logic programming (LP), pattern recognition (PR), knowledge representation (KP), Common sense knowledge and reasoning (CSKR), natural language processing (NLP), computer vision (CV), robotics, epistemology, ontology, and others.  
Some of the techniques used in the literature for the forecasting of the PV power are [23]: GA including different meta-heuristic methods such as particle swarm optimization (SWO), ACO, and etc.; ML including support vector machine (SVM), k-nearest neighbors (k-NN), linear regression (LR), decisions trees (DT), naive Bayes (NB), and etc.; NNs including MLP, RNN, RBFN, etc.; FL and hybrid systems that combine two or more branch of AI (such as for example ANFIS, ANN-GA, etc.  
This review paper focuses on the most used AI techniques for the PV output power forecasting that are ML and DL that, as shown in Figure 1, are a subset of AI. With reference to AI, the next breakthrough in the field of PV forecasting will come from systems that combine representation learning and complex reasoning [32].  
Figure 1: A Venn-diagram of artificial intelligence: link between artificial intelligence, machine learning and deep learning.