esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The LOBIN Prototype: A Practical Study Case
### System Validation and Tests
#### 3.2.1 Healthcare Monitoring Subsystem Tests  
The Healthcare Monitoring Subsystem tests were planned twofold. The main goal of the first subset of tests was to verify that the sensors collect real data and that the WDAD processes and transmits these data correctly. In order to verify the correct processing of the bioelectric potentials obtained by the e-textile electrodes, the values sent by the WDAD were compared with the real values generated in the laboratory using the generator NI USB 628 from National Instrument. To verify that the body temperature sensor works properly, its values measures were compared with the values of a commercial thermometer, concluding that the temperature taken by the thermometer integrated into the WDAD takes about 5 minutes to acquire an accurate value. The accelerometer tests were performed manually by tilting the device to known angles.  
Next, the second subset of tests was devoted to verify that the sensors work properly when in direct contact with the human body. The main result from this subset of tests has to do with the quality of the acquired ECG signal. It was checked that the ECG signal quality is more than acceptable when patients stay still (_i.e._, normal conditions), but worsens when sudden movements take place (e.g., when patients get out of bed or walk around). Nevertheless, in the latter situations, the measured ECG signal is much more accurate if the electrodes are wet or covered with a conductive gel. In addition, the ECG signal quality in such situations can be further enhanced by filtering the noise taking advantage of the signals provided by the 3-axis accelerometer [42].
## 1 Introduction
In this paper, to identify the key influencing factors in complex systems, we propose a model based on heuristic causal inference, which consists of three modules: causal network learning, heuristic causal effect calculation, and key influencing factor identification. Causal network learning enables us to re-understand the concerned system from the perspective of causation; heuristic causal effect calculation enables us to analyse the interaction between system variables quantitatively; and key influencing factor identification enables us to grasp the core joints of the system accurately. Based on the observation dataset, we confirm the validity of the proposed method. The novel contributions of our work are summarized as follows:  
(1) We propose a complex system modelling and analysis method combining causal science and network science. This method can be used to identify the key influencing factors of complex systems. Compared with the traditional method based on experimental simulation, the proposed method does not need to establish a system simulation model, so it has better applicability. Compared with the DEMATEL method, the method proposed is based on scientific analysis of data, which avoids the problem that experts' subjective and qualitative judgements are difficult to quantify and lack persuasion. (2) We propose a causal network learning method that combines prior knowledge. Observation datasets of complex systems often belong to high-dimensional and heterogeneous datasets, and it is difficult for traditional methods to learn causal networks from these datasets directly. We propose adding the prior knowledge to the FCI algorithm, which includes the causal direction that can be inferred in time (the cause always occurs before the result) and the causal relationship between the variables that have been verified experimentally and so on. By merging prior knowledge, we can obtain causal networks underlying high-dimensional data at a lower cost. (3) We propose a heuristic causal effect calculation method to identify the key influencing factors of complex systems. Inspired by the ideas of network science, we define the concepts of causal pathway length and causal pathway contribution degree and propose a heuristic causal effect calculation method. This method draws on the idea of complex network topology analysis and takes the contribution degree of selected characteristic variables to the target variables on the causal pathway as an index to measure the approximate causal effect between variables. Depending on the size of the causal effect, the key influencing factors of the complex system can be identified effectively. The rest of this paper is organized as follows: In the second section, we briefly introduce the relevant work. In the third section, the overall structure of the proposed model and the detailed technical method are introduced; in the fourth section, we describe the process and results of the experiment. The fifth section contains the analyses of the experimental results, and in the sixth section, we summarize the content of the paper and look forward to the next steps.
## 3 Results and Discussion
### Effect of Acid and Base Treatment on Heavy Metal Removal by \(H\). fusiformis  
The biomass from \(H\). _fusiformis_ was treated with acid (0.1 M and 1 M HCl) and base (0.1 M and 1 M NaOH) to investigate the effect of chemical treatment on heavy metal uptake capacity, and the effects were compared in Figure 2 (initial metal concentration: 100 mg/L; pH: 4.0; adsorbent dosage: 1.67 g/L; reaction time: 24 h; agitation speed: 100 rpm; temperature: 25 \(\pm\) 0.5 \({}^{\circ}\)C). The treatment of \(H\). _fusiformis_ with NaOH was effective in increasing the Cu\({}^{2+}\) uptake capacity, from 35.5 to 58.65 mg/g, whereas \(H\). _fusiformis_ modified by acid did not show such an effectiveness. In addition to Cu\({}^{2+}\) uptake, the uptake of other metals, namely, Cd\({}^{2+}\), Ni\({}^{2+}\), and Pb\({}^{2+}\), were also higher in NaOH-modified \(H\). _fusiformis_ than in untreated \(H\). _fusiformis_. [PERSON], et al. [39], reported that NaOH-treated biomass of _Penicillium digitatum_ also showed enhancement of Cd\({}^{2+}\) biosorption. This could be explained by the change in the cell wall structure and functional groups on the cell wall by chemical modification, which generally enhances the biosorption characteristics of biological materials, mainly due to hydrolysis reactions [40]. The hydrolysis reaction can produce more carboxylic (-COOH) and hydroxyl groups (-OH) on the surface of the cell wall, leading to enhanced metal-binding properties of algal biomass [41].
# Video Super-Resolution Network with Gated High-Low Resolution Frames
## 4 Experimental Results
### Comparisons with State-of-the-Arts  
#### 4.3.1 Comparison of Experimental Results on the Vid4 Test Set  
The Vid4 test set evaluation is a widely used baseline test set consisting of four video sequences: calendar, city, foliage, and walk.  
In comparison with existing methods including Bicubic, FRVSR [3], DUF [22], PFNL [24], RBPN [9], RLSP [14], TGA [29], RRN [21], BasicVSR [2], and GHLRF (our), FRVSR and TGA are suitable for explicit motion estimation and compensation. RBPN also calculates optical flow but uses it as an additional input rather than explicit motion compensation. DUF and PFNL use an advanced temporally integrated network to exploit motion information implicitly. RLSP and RRN propagate historical information in feature space. BasicVSR is the most relevant network at the feature level for motion alignment, but it uses a two-way recurrent network that can only perform the offline state of the video.  
As shown in Table 4, the model outperforms all other methods in terms of PSNR and SSIM indices, including the local sliding window method, the two-way and one-way cyclical methods, and the combined method. Specifically, our method outperforms the state-of-the-art method BasicVSR by 0.64 dB in the PSNR evaluation index. BasicVSR is an offline super-resolution model, which is unfairly compared to our online model. However, it is clear from the quantitative evaluation results that not only do we not limit the application scenario but we obtain better recovery results. This is because the gated high-low resolution neighboring frame alignment allows for inter-frame alignment and the selection of more useful information, and adaptive gating allows for the optimization of motion estimation. Scale-attentive U-shaped fusion networks, capable of detailed reconstruction for deeper features, are used to enhance the feature extraction and information fusion capabilities of the network as well as to enhance high-frequency detailed information through scale-attentive.  
Figures 10 and 11 illustrate the qualitative results for the scenes from the Vid4 test set. As we can see from the enlarged area, our frame recovers more reliable and finer detail on both regular and irregular patterns. The video frames in 'City' show that we do better on the buildings than the other models. On the buildings, only our model can clearly show the structure of the edges of the buildings, approaching the zoomed-in effect of GT. In the video frame of \"Calendar\" we can recover a clearer result, and, in the red box marked \"GT\", our method can recover a more realistic result.
## 2 Results and Discussion
### Aroma Compounds of 15 Brands of EVOOs Extracted by SPME and SAFE
#### 2.1.2. Aldehydes  
In this study, 22 aldehydes were identified from the 15 brands of EVOOs (Table 1). Among them, hexanal, (_E_)-2-hexenal, octanal, nonanal, (_E_,_E_)-2,4-heptadien, and (_E_)-2-decenal were detected in all the 15 brands of EVOOs. Also, these compounds were reported in the previous studies on EVOOs from Iran, Turkey, Greece, Tunisia, and Italy [2, 8, 9, 11, 15, 23]. The enzymatic breakdown of the 13-hydroperoxide of linolenic acid in leaf homogenates causes to produce the (_E_)-2-hexenal, while the action of the aldehyde-lyase generates hexanal [25]. Hexanal, (_E_)-2-hexenal, octanal, and nonanal present grass or green note, while (_E_,_E_)-2,4-heptadien and (_E_)-2-decenal show the fat or tallow note. (_E_)-2-Heprenal, (_E_)-2-ocenal, benzaldehyde, (_E_)-2-nonenal, and (_E_,_E_)-2,4-decaional were existed in most of the 15 brands of EVOOs. These aroma compounds also found in the EVOOs from Turkey and Spain [2, 16, 23]. (_E_)-2-Heprenal, (_E_)-2-ocenal, (_E_)-2-nonenal, and (_E_,_E_)-2,4-decadienal all possess the fat note in their flavor quality, and benzaldehyde showed almond or burnt sugar note. While the other compounds, for example (_E_)-2-pentenal, (_Z_)-2-hexenal, 2-furanaldehyde, and phenylacetaldehyde, only existed in some brands of EVOOs, most of the aldehydes have the low odor threshold values in common, which contribute to the whole flavor of EVOOs greatly. However, hexanal, (_E_)-2-hexenal, octanal, nonanal, (_E_,_E_)-2,4-heptadienal, and (_E_)-2-decenal were also reported as being potentially associated with off-flavor sensations and organoleptic defects in their elevated concentrations, such as winey, fusty, frozen, musty, or hay-wood as in note [26, 27]. In the next research, the panel test will be used to verify if the 15 brands of EVOOs present olfactory defects due to the compounds above, because the perceived intensity of the defect can compromise the quality of the oils and determine a downgrading based on the law.
## 3 System Architecture
### Remote Maintenance Sessions  
As long as the shop-floor/field technicians identify a malfunctioning machine, then, with the use of a mobile device, they use the cloud platform so as to communicate with an expert engineer. In the cloud platform, this form of communication is realized through the creation of online sessions. For each session created, a communication domain is created. Upon the establishment of the communication domain, the two parties are capable of bilaterally exchanging information.
## 4 Control Design
### PD Controller and Compensation Unit  
PD control is a linear control method, which is based on the error \(e(s)\) between the input angle \(\theta_{\text{in}}(s)\) and the output angle \(\theta(s)\), and there is  
\[e(s)=\theta_{\text{in}}(s)-\theta(s). \tag{21}\]  
The PD controller performs proportional and differential operations on the error \(e(s)\), and adds the two operation results to obtain the control input \(u(s)\). The expressions of the PD controller can be written as  
\[u(s)=k_{\text{p}}e(s)+k_{\text{d}}se(s), \tag{22}\]  
where \(k_{\text{p}}\) is the proportional coefficient, and \(k_{\text{d}}\) is the differential coefficient. Through multiple feedback processes, the error will gradually shrink, which will improve the output accuracy.  
Ideally, the friction value of the identified friction model is equal to that of the friction model. Therefore, the friction feedforward compensation satisfies the complete compensation condition:  
\[G_{\text{f}}(s)K_{\text{a}}K_{\text{v}}-\frac{\frac{V_{0}}{\kappa R_{\text{s} }}s+c_{2}}{4c_{1}A_{\text{P}}}=0. \tag{23}\]  
So the transfer function \(G_{\text{f}}(s)\) of the feedforward compensation term is designed as  
\[G_{\text{f}}(s)=\frac{\frac{V_{0}}{\kappa R_{\text{s}}}s+c_{2}}{4c_{1}A_{ \text{P}}K_{\text{a}}K_{\text{v}}}. \tag{24}\]
# A High Resolution Vernier Digital-to-Time Converter Implemented with 65 nm FPGA
## 2 Operation Principle of the Proposed DTC
### Circuit Description of 3D Vernier DTC
For a given time width \(X\Delta T\), the corresponding value of \(\alpha\), \(\beta\) and \(\gamma\) can be derived from Equation (6). However, it consumes a lot of logic gates and power to process division and remainder operation in the digital circuit design. For the sacrifice of some design flexibility, we can design the computation unit just with some adders in a binary system, as shown in Equation (8). \[q=\left\lfloor\frac{a[K:0]}{b}\right\rfloor=\left\lfloor a[K:0]\cdot\frac{1}{b} \right\rfloor=b_{1}\times a[K:1]+b_{2}\times a[K:2]+\cdots\cdots+b_{K}\times a[K] \tag{8}\]  
where \(q\) denotes the integer quotient of \(a\) and \(b\), \(b_{x}\) denotes the \(n\)th fractional place of \(1/b\) in binary. For a given \(b\), the \(b_{x}\) is determined as \(0\) or \(1\). For example, when \(b\) equal to \(31\) and \(x\) is an integral multiple of five, \(b_{x}=1\), otherwise, \(b_{x}=0\). To reduce the design complexity and the cost of the computation unit, the \(n\) in Equations (5) and (6) is recommended to be selected as \(2^{M}-1\). Thus, the remainder can be calculated as:  
\[r=a[K:0]-b\times 2^{M}+b \tag{9}\]  
In this case, all of the computation units can be realized by very few adders, as shown in Figure 6. The maximum number of \(\alpha\) needs to be \((n+1)/2-1\), which is the ratio of medium and fine resolution in Equation (5). The maximum number of \(\beta\) and \(\gamma\) is \(n-1\) and \(2^{K\ -2M\ +\ 1}\), respectively. Therefore, the number of delay stages in PDL is designed as \(2^{M}\), the input bits width of CNTM is \(2M\) and the input bits width of CNTS is \(K-2M+2\) for a K bits DTC. Figure 5: Schematic diagram of a pulse generator.
## 4 Results
### Parameterization of Random Forest  
As stated in Section 3.4, two important parameters including _mtv_ (the number of randomly selected predicable variables) and _ntree_ (the number of individual trees) should be determined. Since the input image consisted of 13 bands, _i.e._, five spectral bands, four fraction bands, one NDWI band and three topographical bands, thus _mtv_ was set to be 3.61, which was the square root of the number of input variables. As for _ntree_, the optimal value should make the OOB error of RF converge. We set it to be 200 according to our previous studies [29,30], and the plot of _ntree vs._ OOB error is depicted in Figure 6 to justify whether 200 trees is enough for the classifier.  
Figure 6 depicts clearly that the OOB error has already been convergent when _ntree_ reaches 200. Specifically, the OOB error decreases sharply from 17.4% to 3.1% while _ntree_ increases from 1 to 15. After that, the OOB error drops slowly and begins to show convergence. Besides, Figure 6 also indicates that _ntree_ = 50 or 100 witnessed a similar OOB error to that of _ntree_ = 200, however, the OOB error curve showed greater fluctuations at _ntree_ = 50 or 100 than _ntree_ = 200. Therefore, we rendered that a number of 200 decision trees shows more robustness and _ntree_ was then set to be 200 in the Random Forest classifier.  
Figure 6: Out-of-bag (OOB) error _vs. ntree_.
## 4 Related Work in the ML and DL Domain
### MRI-Based Diagnosis of ASD
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Citation** & **Dataset** & **Technique** & **Reported Metrics** & **Limitations** \\ \hline
[96] & ABIDE dataset, fMRI images & DNN & Accuracy: & 87.40\% & Lack of practical context, small sample size, and limited generalizability. \\ \hline
[95] & ABIDE dataset, rs-fMRI images: Public & Transfer learning autoencoder & Accuracy: & 70.00\% Sensitivity: & Limited sample size, limited accuracy, lack of augmentation, and insufficient preprocessing. \\ \hline
[98] & ABIDE dataset, MRI images: Public & DL with SVM, MLP, ATM and Auto ASD encoder method & Accuracy: & 80.00\% & Low sample size, missing data states, absent hyperparameter tuning, and incomplete implementation details. \\ \hline
[97] & ABIDE-1 Dataset, rs-fMRI images of ASD and non-ASD patients: Public & kNN, SVM, LR, ANN, LDA & Accuracy: & 77.70\% & Small data samples, lack of augmentation, feature insufficiency, and accuracy constraints. \\ \hline
[99] & ABIDE 1, s-MRI Images (public) & VGG16, SVM, kNN, MLP, Linear Discriminant Analysis & Accuracy: & 65.00\% & Data dependency, integration challenges, and dataset diversity. \\ \hline
[100] & ABIDE, rs-fMRI images of ASD and non-ASD patients: Public & LeNet-5, VGG16, ResNet-50 & Accuracy: & 95.00\% & No data augmentation comparison, missing evaluation metrics, and limited performance analysis. \\ \hline
[102] & ABIDE dataset, 4D rs-fMRI images of ASD and non-ASD patients: Private & Graph DNN & Accuracy: & 97.66\% Precision: Recall: & 98.00\% & Limited data size, no data augmentation, and insufficient validation data. \\ \hline
[101] & ABIDE-1 dataset, fMRI images: Private & Autoencoder & Accuracy: & 70.90\% Sensitivity: & Limited dataset size, missing data augmentation, lack of model comparison, and incomplete preprocessing. \\ \hline
[103] & ABIDE I, ABIDE II, KAU dataset, rs-fMRI Images of ASD and non-ASD individuals: Public & SVM, NB, DT, ANN, XGBoost, MLP & Accuracy: & 62.70\% Sensitivity: & Limited data, lack of augmentation, and accuracy constraints. \\ \hline \end{tabular}
\end{table} 
Table 3: A summary of works using AI techniques for detecting ASD through MRI image data.
## 5 Case Study
### The NS100 KCOLOR Dataset  
In the literature, the Noscope (Jackson Town Square) ([[https://github.com/stanford-futuredata/noscope](https://github.com/stanford-futuredata/noscope)]([https://github.com/stanford-futuredata/noscope](https://github.com/stanford-futuredata/noscope)) (accessed on 16 December 2023)) [3] is one of the most popular datasets used in query experiments about traffic surveillance in general. It contains 60 h of surveillance video and the respective labeled CSV dataset with annotations for each object (event) found in each frame. The CSV file contains 1,064,237 events (object detections) in 6,426,647 frames with the following fields: _frame_, _objectname_, _confidence_, _xmin_, _ymin_, _xmax_, and _ymax_.  
The Noscope dataset is adequate for simple object-related queries and spatiotemporal attributes. However, the query expressed in Section 4.2 needs more information: the object's color and the ID of the objects. The last is required by the DQC (Section 4.3) to create the connections between _FBEs_ dynamically. It is worth mentioning that other related works in the literature also use the ID and color of objects [33; 53; 54].  
Therefore, a new dataset was created for this case study from around 10% of the original Noscope dataset. The new NS100 KCOLORID dataset contains 102,835 labeled events in 858,806 frames. This subset was improved by including two more fields: the predominant color of the vehicle (red, blue, green, yellow, white, gray, black, pink, and teal) and a unique Id of each object obtained using the DeepSort algorithm [67]. Furthermore, aiming at improving the data quality, only the events with at least 50% confidence were added, following the literature approach to computing objects above this confidence in a video stream [54].  
The NS100 KCOLORID dataset offers more data to explore the capabilities of the NOP Query. Several tests can be performed focused on CPU usage, memory consumption, and reactivity to different streaming video queries. The dataset generated during the current study is available in the GitHub repository at the following link ([[https://github.com/bioinfolabic/NS100](https://github.com/bioinfolabic/NS100) KCOLORID]([https://github.com/bioinfolabic/NS100](https://github.com/bioinfolabic/NS100) KCOLORID) (accessed on 16 December 2023)). It is another contribution of this paper.  
There are no other large, public datasets available in the related literature. Most of the authors used streams from surveillance cameras on Youtube that are no longer available.
###### Abstract  
To analyze the dynamic characteristics of single-tooth rock-cutting behavior, this paper proposes that the height of the extrusion zone (\(h_{\text{c}}\)) is not constant and does not equal the cutting depth in real rock-cutting behavior, and introduces a new dynamic cutting model (NDCM). By analyzing the single-tooth rock-cutting process, the concepts of the single cutting process and cutting frequency (\(f_{\text{c}}\)) are defined. A method for determining \(f_{\text{c}}\) based on the tangential cutting force (\(F_{\text{c}}\)') is also proposed. A series of single-tooth rock-cutting tests were conducted using numerical simulation, and the influence of rake angle (\(a\)), cutting speed (\(v\)), and cutting depth (\(h\)) on \(f_{\text{c}}\) was analyzed. The geometric difference coefficient (\(GDC\)) is introduced in the new dynamic cutting model, defined as the ratio of \(h_{\text{c}}\) to \(h\). The determination method of \(GDC\) and its relationship with cutting parameters are explored from both theoretical and experimental perspectives. The results show that the cutting frequency corresponds to the main frequency of the tangential cutting force. \(f_{\text{c}}\) is linearly proportional to \(v\) and decreases nonlinearly with increasing \(h\), while the rake angle has little effect on cutting frequency within the range of 10-20\({}^{\circ}\). \(h_{\text{c}}\) exhibits a nonlinear relationship with \(h\): when the cutting depth is small, \(GDC\) is close to 1.0; as \(h\) increases, \(GDC\) gradually decreases and eventually stabilizes, which aligns with experimental findings. The results of this study provide valuable insights for engineers to better understand the dynamic characteristics of tool-rock interaction in single-tooth rock cutting and offer new perspectives for applying cutting force and optimizing rock-cutting models.  
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Footnote †: journal: Applied sciences  
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