esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Mixed Appliance Operation, Excluding Microwave Oven  
This scenario followed a similar process of signal segmentation and event determination. Sub-events can be represented between points A and B, C and D, and E and F based on the power trends of mixed appliance operation, excluding the microwave oven, as shown in Figure 12. Figure 13 shows the results of the analysis conducted to determine central clusters for the 1 st, 3 rd, 5 th, and 7 th harmonics, and Table 8 lists the central cluster positions for the current fundamental and odd harmonics, while Table 9 lists the Euclidean distances of each current signal.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**1 st Harmonic**} & \multicolumn{3}{c}{**3 rd Harmonic**} & \multicolumn{3}{c}{**5 th Harmonic**} & \multicolumn{3}{c}{**7 th Harmonic**} \\ \hline I/A & 0.5167 & 3.682 & 10.39 & 0.3764 & 0.6029 & 1.937 & 0.1356 & 0.3352 & 0.501 & 0.1366 & 0.2236 & 0.235 \\ U/A & 220.1 & 219.7 & 218.8 & 220 & 218.7 & 219.3 & 220.1 & 218.8 & 219.8 & 220.1 & 218.8 & 219.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Central cluster positions of harmonics of mixed appliance operation, including microwave oven.  
Figure 12: Power trends of mixed appliance operation, excluding microwave oven.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **1 st Harmonic** & **3 rd Harmonic** & **5 th Harmonic** & **7 th Harmonic** \\ \hline \multirow{3}{*}{Low mode} & 0.424837 & 0.421136 & 0.167565 & 0.166757 \\  & 1.392695 & 1.434236 & 1.200377 & 1.006421 \\  & 5.502466 & 0.360555 & 0.202358 & 0.225934 \\ \hline \multirow{3}{*}{Medium mode} & 0.452118 & 0.423428 & 0.168143 & 0.168948 \\  & 1.725017 & 1.426522 & 1.300276 & 1.308579 \\  & 5.47295 & 0.29 & 0.300943 & 0.224554 \\ \hline \multirow{3}{*}{High mode} & 0.519417 & 0.426236 & 0.168485 & 0.168769 \\  & 1.334077 & 1.242477 & 1.201858 & 1.105954 \\ \cline{1-1}  & 5.547343 & 0.372285 & 0.302224 & 0.222325 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The Euclidean distances of each current signal to the predetermined center of mixed appliance operation, including microwave oven.  
Figure 13: Cont.  
The central cluster positions for the current fundamental and odd harmonics are detailed in the table below. Using the established Bayesian network model, the probability values of 32.4% for low mode, 30.2% for medium mode, and 37.7% for high mode for each power setting of the microwave oven were calculated. These values indicated that the microwave oven was not in operation during this scenario.
# Computing Idle Times in Fuzzy Flexible Job Shop Scheduling
## 3 Idle Times with Fuzzy Durations
### A coarser Approach  
Both the expressions from (17) and (19) correspond to a fine-grained approach where idle times are computed for each operation, and then they are aggregated to obtain the resource core idle time. However, it is sometimes the case that individual idle times for operations are of no interest, and only the resource idle time as a whole is necessary. In these situations, where not so much granularity is necessary, it is possible to compute the core idle time for resource \(r\) in a coarser manner, as follows:  
\[\widehat{\text{CIT}}_{\phi}^{C}(r)=\begin{cases}(0,0,0)&\text{if }\{o\in \mathbf{O}:\tau_{\phi}(o)=r\}=\emptyset\\ \max\{(0,0,0),\left(\widehat{c}_{\phi}(\omega_{\phi}(r))-\widehat{s}_{\phi}( \alpha_{\phi}(r))\right)-\widehat{W}_{\phi}(r)\}&\text{otherwise}.\end{cases} \tag{20}\]  
where  
\[\widehat{W}_{\phi}(r)=\sum_{o\in\mathbf{O}:\tau_{\phi}(o)=r}\widehat{p}_{ \phi}(o) \tag{21}\]  
is the workload for resource \(r\), that is, the time in which it is active processing operations, defined as the sum of the processing times of all operations assigned to the resource.  
The rationale behind this expression, assuming that at least one operation is scheduled in resource \(r\), is to calculate the time span between the starting time of the first operation to be processed in the resource and the completion time of the last operation in that resource and then subtract the total time in which the resource is actually active. Again, we discount negative values, which we know are not possible, and the result will be the core idle time of the resource.
# Preparation, Characterization and Modeling of Unbound Granular Materials for Road Foundations
## 3 Results and Discussion
### Analytical Hierarchy Process Approach  
To compute a suitable predictive model for CBR versus maximum dry density, we have used Matlab AHP Analytical Hierarchy Process in solving the key issue of the CBR estimation. The model has been developed herein, consisting of an interconnected assembly of simple processing elements, which are organized in a layered fashion. Our model comprised three layers with a set of input nodes and a set of output nodes. The empirically obtained relationships between moisture and density were inserted as input nodes that the processing elements computed in order to estimate an output, according with the CBR-moisture data collected in this work. Because an universal equation relating CBR to MDD regardless of soil type was not evident, AHP was used due to its advantages in  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Soil** & \(\mathbf{MDD=\alpha+\beta_{1}OMC+\beta_{2}OMC^{2}}\) & \(\mathbf{CBR=a+bMDD}\) \\ \hline \multirow{3}{*}{Selected} & \(\alpha=2.017\) & \multirow{3}{*}{\(R^{2}=0.94\)} & \(a=-1645.485\) & \multirow{3}{*}{\(R^{2}=0.89\)} \\  & \(\beta_{1}=0.088\) & & & \\  & \(\beta_{2}=0.008\) & & & \\ \hline \multirow{3}{*}{Suitable} & \(\alpha=1.697\) & \multirow{3}{*}{\(R^{2}=0.93\)} & \(a=-4645.711\) & \multirow{3}{*}{\(R^{2}=0.86\)} \\  & \(\beta_{1}=0.123\) & & & \\  & \(\beta_{2}=-0.007\) & & & \\ \hline \multirow{3}{*}{Z25} & \(\alpha=1.970\) & \multirow{3}{*}{\(R^{2}=0.99\)} & \(a=-9426.522\) & \multirow{3}{*}{\(R^{2}=0.91\)} \\  & \(\beta_{1}=0.076\) & & & \\  & \(\beta_{2}=-0.005\) & & & \\ \hline \multirow{3}{*}{Tolerable} & \(\alpha=1.081\) & \multirow{3}{*}{\(R^{2}=0.92\)} & \(a=-343.056\) & \multirow{3}{*}{\(R^{2}=0.90\)} \\  & \(\beta_{1}=0.108\) & & & \\  & \(\beta_{2}=-0.004\) & & & \\ \hline \multirow{3}{*}{NGA + 3\% Lime} & \(\alpha=1.720\) & \multirow{3}{*}{\(R^{2}=0.92\)} & \(a=-411.299\) & \multirow{3}{*}{\(R^{2}=0.93\)} \\  & \(\beta_{1}=0.087\) & & & \\ \cline{1-1}  & \(\beta_{2}=-0.005\) & & & \\ \hline \multirow{3}{*}{Tolerable + 3\% Lime} & \(\alpha=1.412\) & \multirow{3}{*}{\(R^{2}=0.99\)} & \(a=-228.043\) & \multirow{3}{*}{\(R^{2}=0.97\)} \\  & \(\beta_{1}=0.066\) & & & \\ \cline{1-1}  & \(\beta_{2}=-0.003\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of analytical correlation between CBR and MDD.  
determining evaluation objectives. The process involved proceeding by soil type using first-, second-, and third-order equations, as well as allometric fitting. Confirmation of the model (CBR) was done by considering correlation coefficient and the \(F\) test. Regression analysis indicated the strongest correlations (\(R^{2}=0.86\)-\(0.97\)) between maximum dry density (MDD) and California bearing ratio (CBR). The data plots are provided in Figure 7A,B for all of the soil types analyzed. Experimental data are also shown in the plots. It can be noted that the correlation equations obtained as a result of regression analysis are in satisfactory agreement with the test results.
# Nonlinear Control Design of a Half-Car Model Using Feedback Linearization and an LQR Controller
###### Abstract  
Effective control of ride quality and handling performance are challenges for active vehicle suspension systems, particularly for off-road applications. The nonlinearities tend to degrade the performance of active suspension systems; these introduce harshness to the ride quality and reduce off-road mobility. Typical control strategies rely on linear models of the suspension dynamics. While these models are convenient, nominally accurate, and controllable due to the abundance of linear control techniques, they neglect the nonlinearities present in real suspension systems. The techniques already implemented and methods used to cope with problem of Half-Car model were studied. Every method and technique had some drawbacks in terms of complexity, cost-effectiveness, and ease of real time implementation. In this paper, an improved control method for Half-Car model was proposed. First, input/output feedback linearization was performed to convert the nonlinear system of Half-Car model into an equivalent linear system. This was followed by a Linear Quadratic Regulator (LQR) controller. This controller had minimized the effects of road disturbances by designing a gain matrix with optimal robustness properties. The proposed control technique was applied in the presence of the deterministic road disturbance. The results were verified using the Matlab/Simulink toolbox.  
feedback linearization; Half-Car model; Linear-Quadratic-Regulator; multiple input multiple output; nonlinear system +
Footnote †: journal: Applied Sciences  
1
# A Fine-Tuning Based Approach for Daily Activity Recognition between Smart Homes
## 1 Introduction  
Daily activity recognition in smart homes can identify activities of daily living (ADL) and assist people in their daily lives without violating residents' privacy. It greatly reduces the cost of care for people with cognitive impairment and is a study of great medical value. Meanwhile, it is also one of the main implementations of ambient assisted living (AAL). Daily activity recognition in smart homes is achieved by placing different types of sensors in the home environment. Residents trigger a corresponding stream of sensor events when they perform their daily activities. Through these event streams, the daily activities performed can be inferred by the residents and thus enhance the role of ambient assisted living.  
A common problem with daily activity recognition in smart home environments is the lack of data tagging. Data tagging is a time- and cost-consuming task [1; 2]. For a new home environment, it faces the problem of cold start, whereas transfer learning can learn existing knowledge to recognize unknown daily activities, which can greatly reduce the task of labeling data [3; 4; 5].  
Fine-tuning, as one of the main implementations of model migration, has played a great role in the field of deep migration learning. Especially in the field of image recognition, it has achieved great success. This is due to the richness of image datasets, which enables the training of excellent network models. Additionally, deep networks have also been shown to have good learning performance on one-dimensional data, and [PERSON] et al. [6] made a commendable contribution by using fine-tuning on the UCR dataset [7]. At the same time, they also believe that there is still much work to be explored in building deep neural networks for mining time series data.  
Inspired by the success that fine-tuning has applied to image processing, this paper presents a fine-tuning-based approach for daily activity recognition. The following contributions are made.  
We found a network structure suitable for identifying sensor data streams in smart homes.
2. We performed a small data-based network model training on a public dataset [8] and obtained highly informative parameter metrics.  
The remaining structure of this paper is as follows. In Section 2, we summarize existing feature-based transfer learning methods, instance-based transfer learning methods and fine-tuning efforts. In Section 3, we detail our network framework and fine-tuning methods. Additionally, in Section 4, the experimental setup and the comparison of results are presented. Finally, in Section 5, we summarize our work and present the future outlook.
## 3 Detection Approaches Review
### CBR for Sentiment Analysis  
The CBR approach has successfully been applied in the past to predict sentiment. In Reference [27] explicit customer needs are extracted from customer reviews of products, by means of performing sentiment analysis on them using fuzzy SVMs. Then, a CBR module constructs a case on its case base according to the ordinary use cases of products detected in the previous step. When extraordinary use cases are detected, the CBR module searches for the most similar ordinary case in the case base, and then elicits the extraordinary customer needs in the extraordinary use case based on the ordinary case employing substitution, rule-based adaptation, and design engineer evaluation of the adapted extraordinary cases. In Reference [28] sentiment classification is addressed using a CBR-based approach. First, the case base is populated using labeled customer reviews and five different sentiment lexicons. If a document is correctly classified by at least one lexicon, a case is created containing document statistics and writing style of the review that generated it, and also the case solution which is the information about which sentiment lexicons generated a successful prediction on the review associated to the case. Prediction on new reviews is performed retrieving the k most similar cases (1, 2, or 3 in the reported experiments), and querying the lexicons of the retrieved solutions for sentiment information on the terms of the new review. Domain ontology with natural language processing techniques are combined in Reference [29] to perform sentiment analysis. Case-based reasoning is also used to learn from past sentiment polarizations. The authors claim that the accuracy obtained by the proposed model overcomes standard statistical approaches. Different ways of using a CBR-based approach for sentiment analysis are shown in the works presented in this section. While in Reference [27] CBR is used for generating extraordinary cases (cases of use of products not frequently found), based on ordinary or frequent cases, in Reference [28] the CBR approach is combined with sentiment lexicons to form cases of document statistics and writing style associated to lexicons that correctly classified the document that generated the case. Finally in Reference [29] CBR is used to learn from past sentiment polarizations while using domain ontology combined with NLP techniques for performing sentiment analysis. In the results can be observed that the later work outperforms the CBR approach for generating extraordinary use cases in precision and recall, and also outperforms the lexicon CBR approach in terms of accuracy. The inclusion/exclusion criteria was applied in this section selecting works that applied a CBR-based approach for sentiment analysis in different ways.
# A Survey: Network Feature Measurement Based on Machine Learning
## 2 Machine Learning Method in Network Measurement
### Semi-Supervised Learning in Network Measurement  
Unsupervised and supervised learning are combined in semi-supervised learning. It performs pattern recognition using both labeled and a sizable amount of unlabeled data. In practice, there are far more unlabeled training samples than labeled training samples. In this situation, where untagged data are easily accessible, and tagged instancesare frequently challenging, expensive, and time-consuming, the SSL technique is better suitable for real-world applications. To make up for the lack of markup training data, SSL can create superior classifiers [59].  
There are still some flaws in the theory that semi-supervised learning has proposed because it has only recently been developed. Three reliability-related presumptions are made, and they are as follows: Semi-supervised learning depends on relative simplicity, and data processing does not account for noise interference to samples, which is unavoidable in real life. Additionally, semi-supervised learning is unable to pinpoint the conditions under which the classification performance of unlabeled data can be enhanced. Semi-supervised learning performs worse than supervised learning when the model or parameters are incorrectly chosen. As a result, semi-supervised learning is currently primarily experimental on synthetic datasets, and further study is necessary before it can be applied to real datasets. As a result, network measurement has not yet used semi-supervised learning.
# A Comprehensive Survey for Deep-Learning-Based
## 3 Abnormal Detection with Images over Short Distances
### Visible Light
#### 3.1.3 Vegetation Encroachment Monitoring  
Each growing season, the overgrown vegetation encroaching on high-voltage transmission lines can cause severe blackouts/flashovers because of the short circuit faults among the conductors, posing a major threat to the security and stability of smart grids [74; 75]. In these circumstances, it is critical to monitor the vegetation encroachment so as to inspect potential circuit failures of the transmission lines. In the case of vegetation encroachment monitoring, the tasks mainly include detecting and classifying vegetation that is near the power lines and estimating its height and relative distance to the transmission lines [47].  
[PERSON] et al. [74] proposed a method for vegetation encroachment monitoring using a 2D camera integrated on transmission poles. With image frames acquired from cameras, the algorithm is first initialized by reference and scene images. Then, they filter out images with uneven illumination caused by rain, foggy weather, and so on. After that, the Hough transform algorithm is applied to recognize the horizontal and vertical lines far away from transmission poles. In the end, they use motion tracking to detect the encroached vegetation in the current scene image relative to reference images.  
As deep learning techniques have achieved tremendous success on image recognition, several deep-learning-based detection frameworks have been proposed to monitor vegetation encroachment. Based on faster R-CNN, [PERSON] et al. [75] proposed a detection framework with advanced stereo vision. First, they use faster R-CNN and Hough transform to detect the vegetation regions and power lines, respectively. Next, in order to obtain the precise geographical locations of the vegetation and power lines, an advanced stereovision (SV) algorithm is applied to convert the detected two-dimensional (2D) image data into three-dimensional (3D) height and location information for vegetation encroachment detection. Combining GIS data and aerial and satellite imagery, [PERSON] et al. [76] introduced a vegetation analysis system to estimate the vegetation profile within a buffer zone. The residual U-Net architecture from [79] is utilized for vegetation segmentation.  
To conclude, visible light, especially the aerial image, is an accessible and effective data source modality for abnormality detection in smart grids. With the help of conventional image processing and novel deep learning techniques, abnormality detection based on visible light has shown promising potential in smart grids, such as self-blast glass insulator location, icing detection and measurement, vegetation encroachment monitoring, and so on.
# The Simulation Analysis of Cutting Characteristics and Chip Formation Mechanism in Gear Skiving
## 2 Kinematic Model and Chip Morphology Analysis of Gear Skiving
For each cut of the skiving tool, there will be a swept surface formed inside the workpiece. As the workpiece moves axially, a series of swept surfaces are formed inside the workpiece; thus, the undeformed skiving chips can be bounded by two swept surfaces. The tool cuts into the workpiece starting from the meshing line on one tooth flank of the workpiece, and thereafter, the cutting thickness gradually increases from 0 to the maximum and then exits the cut. Figure 3 shows the cutting pattern obtained by the tool's rake face during the cutting process with a sliding velocity \(\mathbf{v}\) of the tool relative to the workpiece. In the initial stage of cutting, the cutting area is divided into two segments. One is near the root of approaching edge A, and the other is near the node of the recessing edge R and top edge T. As the tool continues to cut, the cutting width increases, and the two segments of chips merge at the node of the approaching edge A and top edge T, forming a V-shape. As the tool continues its cutting motion, the V-shaped chip thickness increases, and the top edge and recessing edge apparently contribute the most. Just before the tool exits the workpiece, the cutting thickness reaches its maximum, then drops to zero as the tool exits. Figure 2 illustrates the cutting graph of the single feed gear skiving method, which comprises three regions: 'R', 'T', and 'A'. These regions are formed by the recessing edge \(R\), top edge \(T\), and approaching edge \(A\), respectively. As shown in the figure, the majority of the material of the workpiece is removed by the recessing edge and the top edge of the tool, while the approaching edge only removes a small portion of the material. The different cutting loads assigned to the edges of the skiving tool may lead to uneven wear across the tool edges.
## 2 Methodology
### Assessment of Consistency between Model and Observation Data Sets
#### 2.4.4 Zonal Patterns  
Similar to monthly FPAR, zonal FPAR were also calculated by the grid area and land unit fraction:  
\[FPAR_{a(i)}=\frac{\sum_{j}FPAR_{a(ij)}\times Area_{i,j}\times frac_{i,j}}{ \sum_{j}Area_{i,j}\times frac_{i,j}} \tag{9}\]  
where \(FPAR_{a(i)}\) is the averaged FPAR for latitude band \(i\). \(FPAR_{a(i,j)}\) is FPAR at computational grid \((i,j)\), \(Area_{i,j}\) is the area for the grid \((i,j)\), \(frac_{i,j}\) is the fraction of land unit at computational grid \((i,j)\). Zonal FPAR anomalies at global or biome level were calculated by:  
\[FPAR_{ano(i)}=FPAR_{a(i)}-FPAR_{mean(i)} \tag{10}\]  
where \(FPAR_{ano(i)}\) is FPAR anomaly for computational latitude band \(i\), \(FPAR_{mean\ (lat)}\) is the mean value for the averaged FPAR of all latitude bands.  
The Angular Effect in Fraction of Absorbed Photosynthetically Active Radiation (FPAR) under Direct Solar Radiation  
To illustrate the angular effect in FPAR with direct solar radiation, we regard tree crowns as spheroids-on-sticks (Figure 1). \(K_{g}\) and \(K_{c}\) are the sunlit background portion and the sunlit canopy portion, respectively. They correspond to the areal fraction of ground and canopy under direct solar radiation.  
According to the Boolean Scene Model introduced in [54], in a sparse random distribution of the spheroids, the sunlit background portion is:  
\[K_{g}=e^{-\lambda A_{\
u}(\theta)} \tag{11}\]  
where \(\lambda=\frac{n}{A}\) is the number of canopies in a unit of surface area, and \(A_{\
u}(\theta)\) is the average areal projection of the canopy onto the background at the zenith angle \(\theta\). From this expression for \(K_{g}\), the proportion of sunlit canopy portion can be immediately obtained, as both sum to 1.  
\[K_{c}=1-e^{-\lambda A_{\
u}(\theta)} \tag{12}\]  
For a single canopy area at vertical angle, \(A_{\
u}(0)=\pi rb\), where r and b are the horizontal and vertical radius of the spheroid, respectively. For the off-vertical angle, \(A_{\
u}(\theta)=\pi rb/cos\theta\). Thus, we have:  
\[K_{c}=1-e^{-\lambda\pi rb/cos\theta} \tag{13}\]  
Therefore, as the solar zenith angle decrease, the canopy intercepts less direct solar radiation, resulting in the angular effects in FPAR under direct solar radiation. It is important to note that such an effect is restricted to sparse vegetation. For a fully-vegetated area under direct solar radiation, the angular effect is negligible, because the bare soil is thoroughly covered by the vegetation and thus receives little solar radiation, regardless of how the solar zenith angle changes.  
Figure 1: Geometry of a spheroid-on-a-stick, with three parameters: \(h\), stem height from ground to the bottom of crown, and \(r\) and \(b\), the horizontal and vertical radius of the spheroid, redrawn from [54].