esa-sceva/satcom-chunk-collection huggingface dataset:
# Ensemble Learning for Skeleton-Based Body Mass Index Classification
## 6 Results
### Results of Five-Fold Cross-Validation
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**\# of \(k\)-NNs**} & \multicolumn{3}{c}{**Macro-Average**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Running Time**} \\ \cline{3-3} \cline{5-6}  & & **TPR** & **PPV** & & & \\ \hline Comparison model \#1 & 3 & 0.9588 & 0.8676 & 0.9001 & 0.9441 & 0.5565 s \\ Comparison model \#2 & 6 & 0.9677 & 0.9092 & 0.9315 & 0.9640 & 0.9856 s \\ Comparison model \#3 & 10 & 0.9790 & 0.9428 & 0.9585 & 0.9748 & 1.5512 s \\ Proposed ensemble model & 15 & 0.9862 & 0.9603 & 0.9721 & 0.9820 & 2.2610 s \\ Comparison model \#4 & 16 & 0.9758 & 0.9566 & 0.9646 & 0.9730 & 2.3994 s \\ Comparison model \#5 & 17 & 0.9746 & 0.9575 & 0.9643 & 0.9730 & 2.5386 s \\ Comparison model \#6 & 18 & 0.9776 & 0.9595 & 0.9669 & 0.9766 & 2.6790 s \\ Comparison model \#7 & 19 & 0.9746 & 0.9575 & 0.9643 & 0.9730 & 2.8217 s \\ Comparison model \#8 & 20 & 0.9686 & 0.9585 & 0.9627 & 0.9766 & 2.9629 s \\ Comparisons model \#9 & 21 & 0.9809 & 0.9594 & 0.9689 & 0.9784 & 3.1046 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Five-fold cross-validation performance comparisons between ensemble models with different numbers of \(k\)-NN models. TPR: true positive rate; PPV: positive predictive value. \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Item**} & \multicolumn{1}{c}{**Explanation**} \\ \hline Operating System & Windows 10 Pro \\ Processor & Inter(R) Core(TM) i5-8250U CPU @ 1.60 GHz \\ Memory & 8.00 GB \\ GPU & Intel(R) UHD Graphics 620 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental setup. Table 4 lists the optimized parameter settings for the ensemble model consisting of 15 \(k\)-NN models in each cross-validation fold. Table 5 lists the five-fold cross-validation results of the five benchmark methods discussed above. Here, instead of SMOTE, the undersampling technique was used for performance evaluations. Therefore, during the training phase of each cross-validation fold, for class 2, 268 (i.e, \(73\times 4-6\times 4\)) anthropometric feature vectors were randomly selected among the 292 total vectors and removed from the training dataset. For class 3, 104 (i.e., \(32\times 4-6\times 4\)) vectors were randomly selected among the 128 total vectors and removed from the training dataset. The remaining 72 (i.e., 24 anthropometric feature vectors per class \(\times\) 3 classes) vectors were used to train the algorithm for each method. As shown in the table, for each method, there were significant variations in the TPR, PPV and \(F_{1}\) values over the three classes. These results demonstrate that the undersampling technique used in [28] is not effective in overcoming the class imbalance problem.
## 5 Case Studies
### Analysis of the Regulation Efficiency of Distributed Energy Storage
#### 5.6.2 Accommodation Effect  
The effect of DES regulation on promoting renewable energy accommodation is analyzed based on the accommodation performance of renewable energy. As shown in Table 5, the introduction of the energy storage system significantly improves the accommodation of renewable energy. Before the participation of energy storage, the accommodation rates across the scenarios were 95.90%, 96.67%, 97.36%, 96.20%, and 96.58%, indicating that a portion of renewable energy was not fully utilized. However, after the participation of energy storage, the accommodation rates were significantly improved to 100%, 100%, 100%, 99.14%, and 98.95%. This demonstrates that energy storage substantially enhances the grid's regulation capability by absorbing excess power and balancing supply and demand. It not only effectively reduces the waste of renewable energy but also increases the efficiency of energy utilization, laying a solid foundation for the further development of renewable energy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenarios** & **Voltage Overrun** & **Power Overrun** & **Overload** \\ \hline Before DES Integration & 2.47\% & 0.65\% & 6.46\% \\ After DES Integration & 0\% & 0\% & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Supply Assurance Effect Before and After DES Integration.  
Figure 11: DES cluster SOC change results.
###### Abstract
The study developed a CADx system for the Hiroshima classification. In a similar fashion in [PERSON] et al. [32], a CADx was developed for CRP classification from images, obtained with NBI and stained endocytoscopy. The study employed SVM which required a three-step process to perform the polyp prediction. As the availability of medical data increased, alternative approaches using more recent deep learning frameworks allowed the design of end-to-end predictions. In [PERSON] et al. [33], a framework was developed to classify diminutive polyps using magnified NBI. The magnified modalities allow for detailed imaging of lesions but require a high-level of precise movement of the endoscope, which makes it a less desirable technique in clinical usage. More recently, in [PERSON] et al. [34], a deep learning framework was developed to classify unaltered video frames of nonmagnified NBI to classify polyps using the NICE classification. One main arising issue is that the NICE classification does not incorporate SSAs polyps, which causes doctors to consider them as dangerous as ADs. Despite this aspect, the study presents a great method to accurately classify polyps during real-time colonoscopy. The current CADx studies have focused their potential around NBI, whereas BLI and LCI have not yet seen significant developments with the capabilities of artificial intelligence. In our previous work [35], we collected a dataset of 203 patients with WL, BLI, and LCI. The limited dataset constrained the classification of our data. Therefore, we have explored and extracted features from a pretrained ResNet50 with the aim to classify the dataset of polyps between benign and pre-malignant. We have trained the features by combining single SVMs for each modality and evaluated the results via Leave-One-Patient-Out Cross-Validation (LOPOCV). Our study was finalized by incorporating the information of WL, BLI, and LCI and combining the posterior probabilities of the trained model. In this work, we present a new study which builds further on our previous research in the following points. 1. Our dataset is improved with 458 new patients resulting in a total of 2919 images obtained from three different hospitals. 2. We perform a benchmark with mostly used state-of-the-art colorectal polyp deep learning architectures, in order to train an end-to-end CNN, evaluated with a test set of 60 patients obtained with WL, BLI, and LCI. 3. We build a CADx system to classify CRPs between benign and pre-malignant and we compare our results with the knowledge and expertise of 19 endoscopists (13 novices and 6 experts). 4. We present a probability score to the endoscopists, which is computed from the average prediction of WL, BLI, and LCI. 5. Our developed CADx systems provides explainable visual data from the CNN to contribute to smooth decision-making. Our study concludes with showcasing how our CADx system could perform in clinical routine and how the outcomes can offer benefits to the endoscopists during real-time colonoscopy.
# Remediation of Metal/Metalloid-Polluted Soils: A Short Review
## 4 Removal Techniques
### Chemical Processes
#### 4.2.1 Chemical Stabilization  
Chemical stabilization reduces the mobility, bioavailability, and bioaccessibility of heavy metals and metalloids in soil. It consists of adding specific immobilizing agents. These agents promote precipitation, complexation, or adsorption to immobilize the pollutants.  
In general, chelating agents with functional groups able to donate nitrogen, oxygen, sulfur, or phosphorus atoms can more likely react with heavy metal ions [82].  
As a low-cost alternative, phosphates and carbonates have been demonstrated to be efficient [121].  
Several materials including organic ones containing sulfur (hair or cysteine), manganese compounds, zeolite, or iron oxide [70] can also be applied. Nowadays, one of the aims is to use waste-derived resources, that are cost-effective and environmentally-friendly, such as biochar [122; 123]. Biochar is a carbonaceous material produced by pyrolysis, that is  
Figure 3: Chemical processes to remove heavy metals and metalloids.  
able to stabilize heavy metals and metalloids [124]. [PERSON] et al. [71] used waste (eggshells and cockle shells) to immobilize Cd, Pb, and Zn in mine tailing soil.  
These stabilizers can be sprayed in aqueous solutions over a site if they are water-soluble; alternatively, the chemical agents can be mixed with contaminated soil.  
This process aims to immobilize heavy metals [82], but the used chemicals will remain in the soil, and their concentrations will have to be monitored after the remediation.
# TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
## 4 Experiment
### Comparisons of Different Regions  
In this section, we applied the TD-LSTM method to different regions, including the Coral Sea, the Equatorial Pacific Region, and the South China Sea. The performance of the method was evaluated by comparing the average of MSEs of a plurality of randomly selected points. Figure 8 shows the comparison of various methods in the Coral Sea. The results show that LSTM is better than SVR and MLPR. Between LSTM variants, TD-LSTM is better than 8-LSTM and 24-LSTM, and TD-LSTM has an input sequence length less than 24-LSTM, which reduces the need for historical-data collection and reduces the computational overhead of the model. Figure 9 shows the comparison of the various methods in the Equatorial Pacific Region. The results show that the LSTM method is better than SVR and MLPR at 0 m, 200 m, and 500 m. Among the variants of LSTM, TD-LSTM is better than 8-LSTM and 24-LSTM. At 50 m and 100 m, LSTM is not as good as SVR. Figure 10 shows the comparison of the various methods in the South China Sea. The results are similar to those in the Pacific equatorial region. At 0 m, 200 m, and 500 m, the LSTM method is better than SVR and MLPR. Among the variants of LSTM, TD-LSTM performs best. At 50 m and 100 m, SVR performs better than LSTM.  
Combining the results of the above three sea areas, it is not difficult to find that LSTM has better performance than SVR and MLPR at 0, 200, and 500 m. In the variant of LSTM, TD-LSTM performs better than 8-LSTM and 24-LSTM. Table 2 shows the improvement rate of TD-LSTM relative to other methods at 0, 200, and 500 m. It can be seen from the numbers that the overall performance of TD-LSTM at three depths is the best, far better than the variants of SVR and MLPR.
# Active Learning Based on Crowdsourced Data
## 2 The State of the Art
### The CenHive System
The total number of answers for different mapping types is shown in Table 1. During the tests, we established a procedure for dealing with malicious users. As in the crowdsourcing approach, we have no control over the user actions, and we decided to consider only tasks with multiple solutions. To eliminate blind shots, we took into account only questions that were displayed for at least 5 --the time needed to actually read the question. * Image tagging/verification--using 2048 clone ([[https://kask.eti.pg.gda.pl/cenhive2/2048/](https://kask.eti.pg.gda.pl/cenhive2/2048/)]([https://kask.eti.pg.gda.pl/cenhive2/2048/](https://kask.eti.pg.gda.pl/cenhive2/2048/)) accessed on 21 December 2021). The player, during the game, donates some of his or her computer computation power to create new data in the volunteer computing approach. The downloaded photos were analyzed using Viola-Jones Feature Detection Algorithm using the Haar Cascades [22] algorithm (more specifically using HAAR.js implementation ([[https://github.com/foo123/HAAR.js](https://github.com/foo123/HAAR.js)]([https://github.com/foo123/HAAR.js](https://github.com/foo123/HAAR.js)) accessed on 21 December 2021)). Detected faces were sent back to CenHive and stored as tags, where the original image was tagged with coordinates of the rectangle containing the detected face. The player, when he or she wanted to undo a move, needed to verify the correctness of faces detects. He or she was presented with a random image selected from all of the detection done by any player. During the test period, where tests were done with the help of the research team and students, for 64 multi-person photos, the game players generated 628 face-detects (giving 303 distinct face detects). In this case, multiple face-detects are not necessary as they are done arithmetically using the player's device's processing power. The Two other clients are implemented that support general image tagging and aim at producing data for training neural networks using an active learning approach. Through both clients, users can verify bee detection done by a neural network and have the ability to tag bees on random images. One of the clients is a captcha element, which in its current form can be seen on the CenHive registration page ([[https://kask.eti.pg.gda.pl/cenhive2/?page=register](https://kask.eti.pg.gda.pl/cenhive2/?page=register)]([https://kask.eti.pg.gda.pl/cenhive2/?page=register](https://kask.eti.pg.gda.pl/cenhive2/?page=register)) accessed on 21 December 2021). The other client is an Android game that should be available for the general public in the coming weeks. This work is a part of our wider idea for a beehive monitoring system using both audio and video for hive supervision [23]. The approach was validated and shows promising results [18], which allows us to propose a generalized model for crowdsourcing supported Active Learning approach.
## 3 Methodology
### DESTformer Architecture
#### 3.2.3 Encoder  
In the encoder, representation learning [13] and time series decomposition [27] are used to extract representations for seasonal and trend components, respectively. Suppose there are \(N\) encoder layers. The \(l\)-th encoder layer is represented as \(X_{en}^{l}=Encoder(X_{en}^{l-1})\). The detailed process of each layer is represented as  
\[\begin{split}& S_{en}^{l,1}=MVI-Attention(X_{en}^{l-1}),\\ & T_{en}^{l,1}=MSC-Attention(X_{en}^{l-1}-S_{en}^{l,1}),\\ & S_{en}^{l,2}=S_{en}^{l,1}+FeedForward(S_{en}^{l,1}),\\ & T_{en}^{l,2}=T_{en}^{l,1}+FeedForward(T_{en}^{l,1}),\\ & X_{en}^{l}=S_{en}^{l,2}+T_{en}^{l,2},\end{split} \tag{3}\]  
where \(FeedForward()\) is composed of stacked one-dimensional convolutional and linear layers. In the last encoder layer, the seasonal representation \(S_{en}^{l,2}\) and trend representation \(T_{en}^{l,2}\) are no longer added together but directly input to the decoder. Detailed descriptions of MVI-Attention and MSC-Attention are provided in Sections 3.3 and 3.4, respectively. They can replace self-attention to extract seasonal and trend representations, respectively.
## 3 Multiple Reconfigurable Features in Antenna  
Multi-reconfigurable antennas can have two or more characteristics modified independently:  
* frequency and bandwidth
* frequency and radiation pattern
* frequency and polarization
* radiation pattern and polarization
* frequency, radiation pattern, and polarization.
# Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems
## 3 System Model
### Proposed SSA-CoSaMP Algorithm
If the setting is too small, the number of iterations of the algorithm will increase, which will cause the algorithm to take too long, which is not conducive to engineering applications. Conversely, if the value is too large, it will cause the algorithm iteration to end prematurely. The reconstructed result is too large, which reduces the reconstruction accuracy of the algorithm. Hence, the setting of \(\beta\) has a crucial impact on the reconstruction accuracy and iteration time. Therefore, in the next section, we determine the parameter \(\beta\) through simulation experiments, which will reduce the computation time while satisfying the reconstruction accuracy.
# Improved Low-Complexity, Pilot-Based Channel Estimation for Large Intelligent Surface Systems
## 2 System Characterization
### Channel Model  
As depicted in Figure 1, the \(n\)-th transmitted block of \(N\) data symbols, sent by the \(t\)-th UE (User Equipment), is denoted as \(\mathbf{s}_{n}^{(t)}\), while the received block at the \(r\)-th antenna of the LIS system is represented as \(\mathbf{y}_{n}^{(r)}\). The mapping between the time-domain signal and the frequency-domain signal for the \(k\)-th subcarrier (assumed to remain invariant during the transmission of a given block) is defined by the Discrete Fourier Transform \(DFT\Big{\{}\mathbf{s}_{n}^{(t)};n=0,1,...,N-1\Big{\}}=\Big{\{}\mathbf{S}_{k}^{(t)};k=0,1,...,N-1\Big{\}}\), (a similar process applies, mutatis mutandis, to the received signal block, the channel, and the noise).  
The received frequency-domain signal, in matrix-vector representation, is expressed as [21]:  
\[\mathbf{Y}_{k}=\left[\mathcal{Y}_{k}^{(1)},\ldots,\mathcal{Y}_{k}^{(R)}\right]= \mathbf{S}_{k}\mathbf{H}_{k}+\mathbf{N}_{k} \tag{1}\]  
where \(\mathbf{S}_{k}=\left[\mathbf{S}_{k}^{(1)},...,\mathbf{S}_{k}^{(T)}\right]^{T}\) represents the frequency-domain transmitted data symbols. \(\mathbf{H}_{k}\) denotes the \(T\times R\) channel frequency response for the \(k\)-th subcarrier, assumed invariant during the transmission of a given block. The \((r,t)\)-th element of \(\mathbf{H}_{k}\) is represented as \(\mathbf{H}_{k}^{(r,r)}.\mathbf{N}_{k}\) corresponds to the frequency-domain noise block for the same subcarrier [22].
## 4 Conclusions  
In summary, a facile one-pot synthesis strategy was successfully developed for fabricating magnetically separable core-shell Fe\({}_{3}\)O\({}_{4}\)@C-Pt nanocatalysts. Pt nanoparticles (\(\sim\)2 nm) were uniformly dispersed on the Fe\({}_{3}\)O\({}_{4}\)@C support surface. Systematic characterization techniques, including X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and nitrogen adsorption-desorption analysis (BET), confirmed the well-defined core-shell architecture and chemical composition of the Fe\({}_{3}\)O\({}_{4}\)@C-Pt catalyst. A yield of \(\geq\)93% was achieved in the catalytic hydrogenation of 20 substituted nitroarenes to aromatic amines using this catalyst, with water employed as a green solvent and hydrazine hydrate utilized as the reductant. Rapid magnetic separation from the reaction mixture was enabled by the embedded Fe\({}_{3}\)O\({}_{4}\) core catalyst (Fe\({}_{3}\)O\({}_{4}\)@C-Pt). Robust stability was confirmed through recycling experiments, with catalytic activity retained over 10 consecutive cycles.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app15105773/s1](https://www.mdpi.com/article/10.3390/app15105773/s1)]([https://www.mdpi.com/article/10.3390/app15105773/s1](https://www.mdpi.com/article/10.3390/app15105773/s1)). Figure S1: TGA curve of the Fe\({}_{3}\)O\({}_{4}\)@C in air. Figure S2: Hot filtration experiment of hydrogenation of nitrobenzene. Table S1: BET results (surface area and pore volume) of Precursor, Fe\({}_{3}\)O\({}_{4}\)@C, and Fe\({}_{3}\)O\({}_{4}\)@C-Pt. Table S2: Optimization of catalyst and reducing agent. Table S3: Optimization of reaction conditions. Figures S3-S22: \({}^{1}\)H NMR spectrum of **2a-2t**.  
Conceptualization, A.J. and Q.Z.; methodology, J.Q.; formal analysis, Y.G. and K.Z.; investigation, J.Q. and Y.G.; resources, J.Q.; writing--original draft preparation, J.Q.; writing--review and editing, J.Q., C.S., and A.J.; supervision, C.S., A.J., and Q.Z.; project administration, C.S. and Q.Z.; funding acquisition, C.S. and Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Zhejiang Provincial National Natural Science Foundation of China (No. LQ21E020003) and the Zhejiang Shuren University Basic Scientific Research Special Funds (2020 XZ011).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We thank [PERSON] and [PERSON] for the TGA, NMR spectra, and XRD analysis. The authors would like to thank Shiyanjia Lab (www.shiyanjia.com) for the XPS, TEM, and VSM analysis. [[https://www.shiyanjia.com/all.html](https://www.shiyanjia.com/all.html)]([https://www.shiyanjia.com/all.html](https://www.shiyanjia.com/all.html)) (accessed on 19 May 2025).  
The authors declare no conflicts of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 Multiple Response Characterization and Modeling of DR Resources
### Multi-Response Characteristics of DR Resources  
DR can generally be classified into two types: incentive-based DR (IDR) and price-based DR (PDR) [2]. Here, for IDR, we consider interruptible load (IL), direct load control (DLC) and transferable load (TL).  
As the system has a problem of supply-demand balance due to insufficient idle capacity, excessive peak load, transmission line fault or clean energy forecast deviation, the IL and DLC involved in system regulation adjust their own load according to the contract with the power company. The response costs of IL and DLC can be expressed as follows:  
\[D=D(Q,\tau,t_{sus},t_{\text{int}},f_{num}) \tag{1}\]  
where \(D\) and \(Q\) are the total scheduling cost and response amount of DR resources, respectively. \(\tau\) represents the type of load resources involved in the dispatch process. \(t_{sus}\) and \(t_{\text{int}}\) are the duration of response and the interval hours between two adjacent response, and \(f_{num}\) is the maximum responding number.  
The weight values in the day-ahead costing are different because of different IDR resources. They are integrated in Equation (2):  
\[D_{m}(t)=\mu_{m}\bullet f_{m.num}\bullet|P_{m}(t)|\bullet X_{m}(t)/t_{m.\text {int}} \tag{2}\]  
where \(D_{m}(t)\) is the dispatch cost of the DR resource at time \(t\). \(\mu_{m}\) represents the cost weight factor of  
where \(C_{z}(t)\) and \(Q_{z}(t)\) are the response cost and the volume of PDR at time \(t\), respectively. \(W_{z}(t)\) represents the price of the dispatcher's compensation for the response amount of PDR at time \(t\). \(X_{z}(t)\) represents the state variable indicating whether PDR \(z\)th participates in the responding process.
## 2 Methodology
### Subject Selection and Experiment Site  
To ensure an independent and quiet testing environment, the experiments were conducted in the physics laboratory of the School of Architecture and Art at Beijing Jiaotong University. A VR headset (HTC VIVE Pro eye) was selected as the experiment apparatus, and an accompanying eye-tracking data processing platform was used. A total of 94 college students from different majors were recruited as subjects to make the findings more credible.  
After the experiment, data collation and summary statistics for each subject were conducted and derived, including the collection of all subjects' information and statistics for the paper version of the record files. A total of 94 sets of data were collected, of which 92 were valid. The eye-tracking data recorded in the experiment were used to generate heat and tracking maps. The heat map used warm and cool colors to indicate the time and position changes of the subjects' fixation; warmer colors indicated a higher fixation and, conversely, cooler colors indicated a lower fixation. The tracking map indicated the locations of the subjects' fixation points, as well as the fixation duration sequence (by connecting the dots).