esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Current Ripple Parameter-Free MPCC of Grid-Connected Inverters for PV Systems
## 3 Control Algorithm
### Comparison between Proposed Method and Known Ones  
A comparison between the proposed method and known ones is conducted, as summarized in Table 2. The future current is predicted by system parameters in [10,26], yielding weak robustness against the parameter variation. The former reduces the current ripples by multi-step prediction and the latter relies on virtual vectors instead. And Ref. [26] further reduces the computation by screening the candidate vectors. Ref. [11] controls grid-connected inverters by predicting power, which has extra tuning work and weak parameter robustness. The Kalman filter is utilized in [14] to predict the future current, which avoids the use of system parameters and has strong parameter robustness. However, the design logic of this strategy is complex, and only the basic voltage vectors are chosen for optimization, so the quality of grid-connected current should be improved.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **[10]** & **[11]** & **[14]** & **[26]** & **Proposed** \\ \hline Requires a model & Yes & Yes & Yes & Yes & Ultralocal data driven \\ Requires gain tuning & Yes & Yes & No & Yes & No \\ Current ripples & Low & High & High & Low & Low \\ Parameter robustness & Low & Low & High & Low & High \\ Relative Simplicity of algorithm & Middle & Low & Middle & High & Middle \\ Computational burden & High & Low & High & Middle & Low \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between proposed strategy and known ones.  
Figure 8: Proposed parameter-free MPCC control algorithm.
## 3 Evaluation Method
### Classifiers  
We evaluate decision maps using four classifiers--logistic regression [47], random forest (200 estimators) [48], neural network (with three hidden layers, each with 200 units), and support vector machine (SVM, with an RBF kernel) [49]--which are all extensively used in machine learning. They represent different families of algorithms: logistic regression is a linear classification model; random forest is an ensemble method; neural network represents deep learning; and SVM is a maximum margin classifier. Importantly, these classifiers are frequently studied in existing decision-map research, thus allowing for meaningful comparisons. The four machine learning classifiers are implemented using _scikit-learn_[50].  
We train all four classifiers on 5000 samples from the four real-world datasets, using the remaining samples for testing and constructing the corresponding three decision maps for each combination. As a result, we obtain \(4\times 4\times 3=48\) combinations of datasets, classifiers, and decision maps.
## 5 Applications Across Diverse Sectors
### Recent Innovations
#### 5.1.5 Two-Phonton Polymerization  
In the world of additive manufacturing, two-photon polymerization (TPP) is used for a variety of purposes. It allows for the crafting of complex microstructures found in microelectronics, such as photonic crystals and microelectromechanical systems (MEMSs), which are crucial for small devices and sensors. Furthermore, TPP is employed in optics to create top-notch micro-optical components, such as lenses and waveguides. These components offer meticulous management of light passage, improving systems used in optical communication and imaging.  
TPP plays a vital role in tissue engineering by creating intricate scaffolds for cell growth and organization in regenerative medicine. Additionally, it enables precise fluid manipulation in microfluidics by producing microchannels, valves, and pumps. Its versatility extends to various fields, especially those requiring intricate microstructures and nanostructures for advanced applications [46; 69].
## 4 Analysis of Nonlinear Tuned Mass Damper Systems Based on Different Conditions
### NTMD Control Performance Analysis Based on 1:1 Resonance Conditions
It is also clear from the figure that the control performance of the linear-based NTMD is better than that of the nonlinear-based NTMD at certain frequency ratios. However, when the frequency ratio is in the range of 0.85-1.05, the control performance is weaker than that of the nonlinear-based NTMD. Therefore, the control effect of the NTMD based on the nonlinear design, although weaker than that of the NTMD with a linear design in some frequency ratio cases, is better overall in terms of the amplitude-frequency response curve control effect. The amplitude will not change to a larger extent, and the stability of the amplitude-frequency response curve is better. Figure 11 shows the time-history response curves obtained by selecting the frequency ratio at the maximum amplitude of the controlled structure. The blue solid line shows the displacement time-history of the controlled structure without the damper installed, the black dashed line shows the displacement time-history of the controlled structure controlled by the linearly designed TMD, and the red solid line shows the displacement time-history of the controlled structure controlled by the nonlinearly designed NTMD. It is observed that the transient response and steady-state response of the structure with the NTMD installed are smaller than those of the uncontrolled structure. The amplitude at the maximum peak of the amplitude-frequency response curve is reduced by 20% compared to the amplitude at the maximum peak of the uncontrolled structure when the NTMD is designed using the linear design method. Using the nonlinear design formulation for the NTMD, the amplitude of the amplitude response curve at the maximum peak is reduced by 40%. Figure 12a depicts the three-dimensional relationship between the nonlinear coefficients, the frequency ratio and the steady-state amplitude of the primary structure, while Figure 12b illustrates the \(\alpha_{2}-\Omega_{2}\) parameter plane. From these two plots, it can be observed that the depression represents the optimal design frequency ratio. As the nonlinear coefficient increases, the optimal design frequency ratio gradually decreases, while the corresponding minimum controlled structural steady-state amplitude remains unchanged in size. Therefore, the nonlinear coefficient can be utilized as a design parameter to adjust the optimal frequency ratio of the structure, assuming other conditions remain unchanged. In the nonlinear design process, the nonlinear coefficients generally cannot be too large or the structure will become unstable [33]. Figure 10: Amplitude and frequency response curves under different designs. Figure 11: Time range response curves under different designs. Figure 12: Frequency–mass ratio of mass-damper-controlled structural steady-state amplitude.
# Type Hierarchy Enhanced Event Detection without Triggers
## 3 Methodology
### Instance Encoding
#### 3.3.2 Context-Level Representation  
Long short-term memory (LSTM) [36] is the most widely used method to deal with long-term dependence. It consists of three gates (i.e., an input gate, a forget gate and an output gate) and a storage unit to hold the state of each neuron. The standard LSTM can only utilize the historical information of the sequences, which are single-directional features. Due to the complexity of natural language sentence structure, it is also necessary to consider future information.  
As Figure 3 shows, we exploit bi-LSTM [37], which uses two LSTMs to work in the forward and backward time directions to obtain contextual information. First, we feed sequence embedding \(x\) to forward propagation, which runs \(x\) from the past to the future. Similarly, we feed the reverse order to backward propagation, which considers the contextual information from the future to the past. Therefore, we can preserve information in two directions at any point in time.  
Given the input token \(w\), the input of bi-LSTM is \(x_{t}\), which includes three parts (i.e., a word embedding \(e_{wt}\), an entity embedding \(e_{et}\), and a pos tag embedding \(e_{pt}\)), and its hidden state is \(h_{t}\) at time \(t\). The formula is expressed as follows:  
\[x_{t}=concat(e_{wt};e_{et};e_{pt}) \tag{1}\]  
\[\overrightarrow{h_{t}}=LSTM(x_{t},\overrightarrow{h_{t-1}}) \tag{2}\]  
\[\overleftarrow{h_{t}}=LSTM(x_{t},\overleftarrow{h_{t+1}}) \tag{3}\]  
\[h_{t}=[\overrightarrow{h_{t}}\oplus\overleftarrow{h_{t}}] \tag{4}\]  
Here, we obtain the hidden embeddings \(h=\{h_{1},h_{2}\cdots h_{i}\cdots h_{n}\}\) of the input tokens, where we use the last hidden state \(h_{n}\) to denote the context-level representation, because it contains all the information in the forward and backward directions.
## 6 Results
### Comparison with Related Work
These values suggest that some existing methods are relatively effective in conserving energy, while others may have limitations in this regard. In contrast, S-2 EC-ERF demonstrated notably higher energy remaining at 94.7%, thereby indicating its efficiency in energy management and surpassing most of the related works. When considering accuracy, a similar pattern emerged. The related works showed a range of accuracy percentages from 31.5% to 92%. This wide variance underscored the diversity in the effectiveness of different approaches in accurately detecting and mitigating attacks. S-2 EC-ERF, on the other hand, boasted a significantly higher accuracy of 98.193%, thereby surpassing the accuracy levels of all the related works. These results suggest that S-2 EC-ERF exhibits high percentages of energy remaining and accuracy. Its superior performance indicates better energy efficiency and more reliable attack detection capabilities, thus positioning it as a leading solution. While the S-2 EC-ERF framework displayed potential in simulations, accurately replicating real-world complexities in such environments proved challenging due to various resource constraints, including financial, security, and time limitations. However, the components of the proposed framework consistently exhibited behavior in both simulated and real-world settings, thereby mitigating the impact of differences between simulation and reality on evaluating the system's effectiveness.
# Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning
## 2 Literature Review
### Productivity Improvement  
A few studies aimed to explore construction workers' activity analysis for productivity improvement. [PERSON] et al. integrated an RGB image stream, optical flow stream and gray image stream and trained them separately with CNNs to achieve workforce activity recognition [56; 57]. [PERSON] et al. proposed a system for measuring construction worker's productivity through analyzing worker poses and classifying worker activity into three domains: effective, ineffective, and contributory work [58]. [PERSON] et al. employed a depth camera for activity analysis and developed a bag of poses activity classifier and a Hidden Markov Model (HMM) for classification [22]. [PERSON] et al. synthesized pose sequences for the vision-based activity analysis of an excavator [59]. Apart from a vision-based approach, a mechanical approach that basically attaches different mechanical sensors to the human body to capture the signals of body and limb movement can also be regarded as a useful tool for motion capture [60]. Passive RFID and IMUs including gyroscopes and magnetometers were studied as practical tools [61]. [PERSON] and [PERSON] studied activity recognition in construction by using accelerometers [62]. Although magnetic sensors were also adopted, they are susceptible to metal surroundings, which is not applicable for scaffolding work [63]. To increase accuracy, [PERSON] et al. combined IMUs with video cameras to study productive masons [53]. The mechanical method is feasible under lab environments but it might face difficulties while being widely applied to construction sites due to the inconvenience for clothes wearing and washing caused by the attachment of sensors to the body.  
To facilitate the procedure of workface assessment, many studies have been devoted to approaches to automate it. The positioning technologies including Ultra Wideband (UWB) systems, Global Positioning Systems (GPS) and Radio Frequency Identification (RFID) only enable researchers to track the locations or positions of construction equipment and workers [64; 65; 66; 67]. Besides, computer vision via video surveillance became another option for object detection and tracking [68; 69; 70]. These approaches remain at the stage where they infer the pattern of construction equipment and workers by tracking their positions in certain construction areas, which seems to be applicable to the equipment such as trucks or earth excavators, but not effective to onsite workers [71]. Without interpreting the construction activity that construction workers performed, retrieving useful data for workface assessment is challenging.
# Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles
## 3 Collective Perception Mechanism for Autonomous Vehicles  
This section describes the VILE mechanism, which considers three phases in the CP process, namely, knowledge, selection, and perception. The knowledge phase divides the scenario into a set of regions (i.e., hexagonal perception regions) and receives beacons from the CAVs to build a dynamic graph. Next, in the selection phase, the RSU selects, in each region, the vehicle closest to the hexagonal's center and sends a message requesting the perception of the surrounding environment. Finally, in the perception phase, the chosen CAVs broadcast their perception with others CAVs in the scenario. In the following, we introduce the considered the network and system model, as well as the mechanism operation.
## 3 Monitoring System Architecture and Software Architecture
### Software Architecture of the Cloud Server  
The cloud server consists of a front-end application layer, a back-end service layer, and a database. The workflow of the cloud server is shown in Figure 5.  
As shown in Figure 5, the workflow of the cloud server is as follows:  
Step 1. The software in the front-end application layer sends a request to the back-end service layer (1).  
Step 2. The back-end service layer finds the corresponding method (2) to process the request (3) and executes the program. It then accesses the database through business logic to perform operations such as CRUD (create, read, update, and delete) (4).  
Step 3. The back-end service layer retrieves the data (known as \"dynamic resources\") from the database and processes them (5). It then encapsulates the database results (6) and returns them to the front-end application layer (7).  
Step 4. The front-end application layer displays the results of the request in the form of charts (known as \"static resources\").
# A New Ice Accretion Model for Aircraft Icing Based on Phase-Field Method
## 5 Conclusions  
In this paper, a new numerical simulation method for predicting ice shape is proposed. The ice and water in this study are regarded as one fluid, and the method is implemented based on the phase-field model. The main advantage of the phase-field model is predicting the ice shapes under glaze ice or mixed ice conditions, which mean the ambient temperature is high. By comparing three node layout schemes under different icing conditions, the sensitivity of the mesh was verified. Then, comparing the simulation results, the ice shapes predicted by the phase-field icing model are found to be more consistent with the experimental ice shapes than those predicted by LEWICE, especially the mixed ice and the glaze ice. Taking the stagnation thickness as the ice shape characteristic, the deviation between the ice shape provided by the phase-field model and experimental ice shape is the smallest. Therefore, the validity and accuracy of the ice accretion model based on field phase method is verified.  
Conceptualization, H.D., C.Z., H.Z. and S.L.; methodology, H.D.; software, H.D.; validation, H.D.; formal analysis, H.D.; investigation, H.D.; resources, H.D.; data curation, H.D.; writing--original draft preparation, H.D.; writing--review and editing, H.D. and C.Z.; visualization, H.D.; supervision, C.Z.; project administration, C.Z.; funding acquisition, C.Z. and S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No.11832012, 51806105), Open Fund of the Key Laboratory of Icing and Anti/De-icing (Grant No. IADL20190302, IADL20190309) and the Natural Science Foundation of Jiangsu Province (Grant No. BK20180442).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Stroke Dataset Modeling: Comparative Study of Machine Learning Classification Methods
## 2 Materials and Methods  
### Comprehensive Models for Stroke Prevention  
Advancements in stroke prevention modeling have enriched our understanding and refined preventive strategies. Several essential modeling techniques are utilized in this area:  
1. Risk Assessment Models: Risk assessment models are invaluable for identifying individuals at high risk for stroke early. These models consider various demographic factors (e.g., age, gender) and lifestyle habits (e.g., smoking, alcohol consumption). By evaluating the probability of stroke occurrence, these models enable the development of personalized prevention plans [7].
2. Pharmacological Intervention Models: Pharmacological intervention models evaluate the effectiveness of anticoagulants, antiplatelet, and antihypertensives in stroke prevention [8]. These models help determine the optimal dosing regimens and identify patient profiles that would benefit most from these treatments, ensuring that therapies are effective and appropriately dosed [9].
3. Lifestyle Intervention Models: These models assess the impact of lifestyle modifications, such as dietary changes and physical activity, on stroke risk [10]. Based on individual health profiles, they provide tailored recommendations aimed at reducing stroke incidence and contribute to the formulation of personalized prevention strategies.
4. Screening Program Models: Screening models compare the cost-effectiveness of various screening approaches, including those targeting atrial fibrillation, a known stroke risk factor [11; 12]. These models guide public health officials in developing effective community-based screening protocols to reduce stroke incidence.
5. Patient Education Models: Patient education models evaluate the impact of different educational initiatives on stroke prevention [13]. These models identify the most effective educational strategies and assist healthcare providers in implementing programs that enhance public knowledge about stroke risk factors and prevention.
6. Models for Atrial Fibrillation (AF) Management: The Atrial Fibrillation Better Care (ABC) pathway provides a systematic framework for managing AF, aiming to reduce stroke and adverse events through continuous risk assessment. Tools like the CHA\({}_{2}\)DS\({}_{2}\)-VASc score [14] are used for selecting patients for anticoagulation therapy. However, recent approaches emphasize dynamic risk assessment to manage bleeding risks and target at-risk patients for early intervention [15].  
By integrating these diverse modeling frameworks, clinicians and researchers can develop more effective stroke prevention strategies tailored to individual patient needs and dynamic clinical conditions.
## 2 Derivation of the Estimation Algorithm
### Preparations--Orthogonal Expansion  
Consider two orthogonal and normalized (orthonormal) sequences \(v_{k}(x),x\in[S_{1},\,S_{2}]\,k=1,2,\ldots\) and \(T_{j}(r),r\in[R_{1},R_{2}]\), \(j=1,2,\ldots\) that are complete in \(L_{2}(S_{1},\,S_{2}])\) and \(L_{2}([R_{1},R_{2}])\), respectively. Then, \(f(x;r)\) can be represented by the series (convergent in \(L_{2}[S_{1},\,S_{2}]\)) with the following coefficients:  
\[a_{k}(r)=\int_{S_{1}}^{S_{2}}v_{k}(x)\,f(x;r)\,dx,\quad k=1,2,\ldots \tag{1}\]  
Notice that \(a_{k}(r)\)'s can be interpreted as follows:  
\[a_{k}(r)=E_{r}\left[v_{k}(X(r))\right], \tag{2}\]  
where \(E_{r}\) stands for the expectations with respect to random variable \(X(r)\), having p.d.f \(f(x;r)\). Furthermore, each \(a_{k}(r)\) can be represented by the series:  
\[a_{k}(r)=\sum_{j=1}^{\infty}\alpha_{kj}T_{j}(r) \tag{3}\]  
with constant coefficients \(\alpha_{kj}\), defined as follows:  
\[\alpha_{kj}=\int_{R_{1}}^{R_{2}}a_{k}(r)\,T_{j}(r)dr,\quad k,\,j=1,2,\ldots \tag{4}\]  
Series (3) is convergent in \(L_{2}([R_{1},R_{2}])\). By back substitution, we obtain the following series representation of \(f\):  
\[f(x;r)=\sum_{k=1}^{\infty}\sum_{j=1}^{\infty}\alpha_{kj}\,v_{k}(x)\,T_{j}(r) \tag{5}\]  
The series in (5), convergent in in \(L_{2}([S_{1},\,S_{2}]\times[R_{1},\,R_{2}])\), forms a base for constructing estimators for \(\mathcal{F}\). They differ in the way of estimating \(\alpha_{kj}\)'s and in the way of resolving the so called bias-variance dilemma. The latter can be resolved by appropriate down-weighting \(\alpha_{kj}\)'s estimators. In this paper, we confine ourselves to the simplest way of down-weighting, namely, to the truncation of the both sums in a way described later on.
###### Abstract  
In point-cloud scenes, semantic segmentation is the basis for achieving an understanding of a 3D scene. The disorderly and irregular nature of 3D point clouds makes it impossible for traditional convolutional neural networks to be applied directly, and most deep learning point-cloud models often suffer from an inadequate utilization of spatial information and of other related point-cloud features. Therefore, to facilitate the capture of spatial point neighborhood information and obtain better performance in point-cloud analysis for point-cloud semantic segmentation, a multiscale, multi-feature PointNet (MSMF-PointNet) deep learning point-cloud model is proposed in this paper. MSMF-PointNet is based on the classical point-cloud model PointNet, and two small feature-extraction networks called Mini-PointNets are added to operate in parallel with the modified PointNet; these additional networks extract multiscale, multi-neighborhood features for classification. In this paper, we use the spherical neighborhood method to obtain the local neighborhood features of the point cloud, and then we adjust the radius of the spherical neighborhood to obtain the multiscale point-cloud features. The obtained multiscale neighborhood feature point set is used as the input of the network. In this paper, a cross-sectional comparison analysis is conducted on the Vaihingen urban test dataset from the single-scale and single-feature perspectives.  
Keywords:airborne LiDAR; PointNet; semantic segmentation; multiscale and multi-feature