esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Since surveillance cameras monitor large areas of construction sites, workers occupy only very small parts of the image. Besides the fact that detecting small objects with CNNs is a challenge, reducing the size of the image makes detection even more difficult. The downscaling may eliminate relevant features in the image required for a reasonable detection. As it is unclear whether CNNs can outperform classical machine learning methods on these terms, we conduct a comparison in this paper. The goal of the comparison is to find out whether one of the two approaches can satisfactorily track several construction workers when the amount of training data is small. In doing so, we restrict our focus to one view within one construction site at first. In this case, a camera could be mounted, e.g., on the mast of a tower crane. Alternatively, several cameras could be used to completely monitor a construction site, but this is not covered in this paper. Because no suitable dataset is publicly available for comparison, we assemble a small generic dataset ourselves. This shows pedestrian construction workers from a top view perspective under different conditions. As a representative CNN approach, we choose YOLOv3 [26], since it is a state-of-the-art detection network. For its counterpart from the field of classical machine learning, we rely on preliminary work [27] discussing diverse computer vision techniques for monitoring construction workers. In this work, we juxtapose eligible methods and develop a theoretical concept relying on a classical machine learning method. Based on these results, we implement a tracking approach based on a soft cascaded classifier in the course of this paper. A simple Kalman filter is applied to both approaches in order to track the detected workers within the recorded video sequence. In our experiments, we compared the detection and tracking results of our implemented approach with those of YOLOv3 trained on the same data. We found that our classical machine learning approach yields substantial better detection results on the small dataset than the CNN. However, the Kalman filter proves to be sufficiently robust to compensate for the lower detection quality of YOLOv3. Owing to this, both approaches perform similarly well on the tracking of pedestrian workers in general. Nevertheless, each approach possesses different tracking characteristics. A general recommendation can, thus, not be made. The appropriate tracking solution has to be determined with respect to the demands of the particular application.
## 4 NFV and SFC Frameworks Taxonomy
### Performance Tuning Frameworks
#### 4.2.5 Hypernf Framework  
HyperNF is a high-performance NFV platform proposed by [PERSON] et al. [82], which aims to properly utilise commodity server resources while scaling the number of network functions hosted by servers. The problem space addressed by HyperNF includes resource allocation, efficient utilisation, and high throughput when using everyday commodity servers to deploy virtual network functions.  
The proposed framework is aimed at large NF deployments, where utilisation is maximised for better throughput. The use of hypervisor-based I/O is employed, which helps reduce synchronisation overhead. HyperNF was designed using three core design objectives: (i) CPU cores are not reserved entirely for virtual I/O operations, thus providing high flexibility in terms of utilisation, (ii) proper accountability for virtual I/O tasks on respective VMs, thus offering a cohesive resource allocation strategy, and (iii) VM switches should not be used for packet switching; instead, the data path of software switches is exported to the hyper-visor for the purpose of forwarding and switching of packets.  
HyperNF was evaluated for scenarios involving a baseline setup using the VALE [86] switch for inter-VM communication, with each VM tied to a single CPU core, and second, a scenario that consolidates network functions in a shared CPU environment by varying the number of VMs (CPU cores are shared among the firewall VMs deployed using a round-robin scheme). Both scenarios outperformed the split and merge schemes compared with HyperNF. Other tests carried out include resource allocation, NFV throughput, and SFC chain composition. A chain of 50 NFs can achieve a delay as low as 2 ms, which makes the framework ideal for SFC deployments.
## 3 Considering the ALS Workflow and Its Impact on the Efficacy of Data Products
### Data Processing
#### 3.2.4 Point Cloud Classification
It is also important to note that the classification parameters cannot generally be applied to an entire project area. Parameters more often need to be adjusted for specific areas. Also further processing starting from the Axelsson classification algorithm or other specific algorithms can be developed that accounts for stepped pyramids and other features with vertical or nearly vertical facets. Figure 18: [PERSON]’s ground classification algorithm parameters. Figure 19: Illustration of tight **(top)**_vs._ loose (**bottom**) ground classification results. (**a**) First return DSM and cross section coloring returns based on its ordinal attribute; (**b**) ground return DEM and cross section coloring returns based on its class from a very tight classification; (**c**) results from a balanced classification; (**d**) results from a loose classification; (**e**) results from a very loose classification. It does not matter what ground classification algorithm is used to classify the returns, all of them make the classification in a probabilistic rather than deterministic fashion. Because they are based on a probabilistic analysis, the classification algorithm can produce false positives (points that do not come from the ground but are classified as coming from it) and false negatives (points that are returns from the ground surface but are not classified as coming from it). As illustrated with Figure 19 the use of the classification algorithms parameters can be set to produce a tight classification (images towards the top of the figure) that minimizes false negatives but at the same time produces more false positives, or it can be adjusted so that it produces a loose classification (images towards the bottom) that produces more false positives and minimizes false negatives. It is the goal of an experienced ALS data processor, working in conjunction with the researcher, to produce a good classification of the returns by achieving a balance that minimizes both false positives and false negatives (images in the center) for a given application and research question. In Figure 19 red arrows are used to mark features that have been eroded by the tight classification and green arrows are used to point out vegetation returns that have been allowed into the ground class and produce noise on the DEM. However, it can also be observed that loose classifications can erode features by allowing vegetation returns to mix with the ground returns blurring the features. The adequacy of the classification is easily assessed by cutting cross sections on the classified point clouds, as illustrated in the images on the right of Figure 19, where the returns classified as ground are depicted in orange and the non-ground returns are depicted in green.
# Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing
## 1 Introduction
### Deep Learning Segmentation  
Deep learning methods applied to error detection are composed of (1) classification and (2) segmentation methods. The same defects on the tire surface follow an atypical shape [34; 35]. For example, in the case of a vent spew error, it is difficult to have the same shape because the type of tire, the location of the error, the size of the tire, and the length of the protruding hair are different. The classification method is not suitable for training and classifying such unstructured data [36]. Since the segmentation method detects errors in units of pixels, more accurate fault detection is possible. We train and detect tire error data using a network called DeepLab, among several deep learning-based segmentation networks.  
DeepLab is a deep learning network used in fields that require precise prediction on a pixel-by-pixel basis as a semantic segmentation algorithm [37]. Semantic segmentation classifies each image pixel differently from the existing CNN that classifies the entire image [38; 39]. Therefore, it is possible to classify several classes existing in one image in units of pixels. Because of these characteristics, the semantic segmentation method is used in various fields, such as autonomous driving and medical care [40]. The semantic segmentation algorithm extracts abstract semantic features that are global and resistant to change through filters for training. This process may lose features that are not necessarily global but can be important factors in error detection. The DeepLab network uses atrous convolution and fully connected conditional random field (CRF) to compensate for these shortcomings [41]. In this paper, we train and detect tire defects, using the latest version of the DeepLab network (V3+) model [42].  
Unlike the classification method, the segmentation method must also label pixels on the normal tire surface. This means that during the training process, the deep learning classification model also trains normal pixels. The rate of occurrence of defective tires in the tire manufacturing process is quite low. In addition, the area occupied by the defect in the overall image of the defective tire is very small. Therefore, the tire error detection process is similar to the anomaly detection process.
## 4 Path Tracking
### Trajectory Blending
Define five homogeneous coordinate matrices: \(\mathbf{N}_{j}=[\mathbf{b}_{j}\ \mathbf{e}_{j}\ \mathbf{a}_{j}]\), for \(i=1,2,3,4\), and \(\mathbf{N}_{5}=[\mathbf{c}_{k}\ \mathbf{m}_{k}\ \mathbf{t}_{k}]\), whose vector blocks are all three dimensional -- their entries are the homogeneous coordinates of the corresponding points in the platform frame \(\mathcal{F}\). Then, find a homogeneous displacement matrix \(\mathbf{D}\) satisfying \(\mathbf{N}_{5}=\mathbf{D}\mathbf{N}_{4}\); hence, \(\mathbf{D}=\mathbf{N}  
According to Equations (24)-(26), the composite affine transformation matrix is calculated as  
\[\mathbf{T}_{5}\equiv\mathbf{N}_{5}\mathbf{N}_{3}^{-1}\mathbf{S}_{\psi}(\tan\psi, 1)\mathbf{R}(\pi/4)= \begin{bmatrix}0&-l_{3}&l_{3}\\ l_{2}&-l_{4}&l_{4}\\ 0&0&1\end{bmatrix}  \tag{27}\]  
with \(l_{3}\) and \(l_{4}\) defined as  
\[l_{3}=l_{2}\sin e_{\theta},\quad l_{4}=l_{2}\cos e_{\theta} \tag{28}\]
# Offloading of Atomic Tasks in Satellite Networks:
## 3 Task Offloading Model
### Atomic Task Offloading Model  
To avoid the repetition of the same tasks throughout the satellite network, we design an offloading model for the atomic task queues on the satellite.  
As shown in Definition 2, We first define the offloading decision variable on the satellite s.  
Given a satellite network \(G=(V,E,T)\), the atomic task request queue \(Q\) received by satellite \(s,s\in V\), \(q_{i}\) represents one of the atomic task requests. At a slot \(t\), decision variable \(\varphi^{s}_{q_{i}}(t)\) will be offloading to the high-orbit satellite or not have a value range in \(\{0,1\}\).  
There are two main stages in the offloading decision of satellite tasks. \(\varphi^{s}_{q_{i}}(t)=0\) denotes that the current task is calculated locally. \(\varphi^{s}_{q_{i}}(t)=1\) means that the task will be offloaded to the high-orbit satellite for execution. Therefore,the decision variable on the satellite s can be expressed as \(\varphi^{s}_{Q}(t)=\{\varphi^{s}_{q_{1}},\varphi^{s}_{q_{2}},\cdots,\varphi^{s }_{q}\}\).  
Then we describe the calculation of the number of tasks remaining in the on-star task queue, taking into account the unloading of duplicate tasks in the task.Assuming that the atomic task request queue received by satellite \(s\) is \(Q_{s}=\{q_{1},q_{2},\ldots,q_{i}\}\), some atomic task requests set \(\xi\) that processed by satellite \(s\) at time \(t\) is expressed as \(Q^{\xi}_{s}(t)\), then the remaining atomic tasks waiting to be executed can be expressed as  
\[Q_{rest}(t)=Q_{s}(t)-Q^{\xi}_{s}(t) \tag{4}\]  
If an atomic mission already exists on a high-orbiting satellite, then the same atomic mission on a low- orbiting or medium-orbiting satellite does not need to be repeatedly offloaded, so the atomic computing mission to be offloaded is represented as follows,  
\[Q_{offloading}(t)=Q_{rest}(t)-Q_{repeat}(t), \tag{5}\]  
where \(Q_{offloading}(t)\) denotes the atomic task queue that needs to be offloaded at time \(t\), \(Q_{rest}(t)\) indicates the atomic task queue waiting at time \(t\), \(Q_{repeat}(t)\) denotes the atomic task queue that has been buffered on the high-orbit satellite at time \(t\).  
Equations (4) and (5) give the process and calculation for updating the on-satellite task queue when a task is unloaded in the paper. The task queue remaining to be executed on the satellite is the original task queue minus the task queue that has already been executed on the satellite. Meanwhile, the tasks need to be calculated before they can be offloaded to ensure that all pending tasks have been executed and that the calculation results are kept. If there are tasks that have already been executed and the computation results have been retained, these tasks will not be offloaded.
# Periodic Monitoring and Filtering Suppression of Signal Interference in Mine 5G Communication
## 1 Introduction  
The fifth-generation mobile communication system (5G) provides information-transmission channels with high bandwidth, low latency, and massive connections for diverse IoT applications [1]. The popularization of 5G has brought profound changes to the field of IoT [2, 3] and promoted the vigorous development of smart transportation, smart factories, and smart cities [4, 5]. Currently, IoT applications have achieved unprecedented prosperity, accounting for more than half of all 5G applications. With the development of intelligent mines, 5G systems have been successfully deployed in demonstration mines in China. At the same time, 5G-based mine IoT applications, such as unmanned driving, intelligent video, unmanned work surfaces, industrial control, and intelligent robot inspection, have been or are about to be successfully implemented [6, 7, 8].  
Unlike the conditions on the ground, the problem of multifactorial interference is faced by mine 5G [9, 10]. In main transport roadways, a large amount of strong electrical equipment, such as mining cable cars, high-voltage inverters, power boxes, and coal transportation systems, is stored. At the moment of turn-on or turn-off, an alternating magneticfield will be caused by a huge inrush current, disrupting the base station and terminal of mine 5G systems from those under normal operation. In fully mechanized mining faces, the signal propagation path will be blocked by large-scale mechanical equipment, such as trains, scrapers, and shearers, resulting in reflection, scattering, phase mismatch, and waveform distortion, which will weaken the 5G signal. In the underground substation, due to the storage of a large number of high-voltage distribution boxes, power boxes, and relays, it is easy to generate coupling circuits, which will generate electromagnetic interference with the 5G signals and affect the accuracy of signal transmission. Furthermore, turning power boxes and relays on or off can cause signal interference. After research and analysis, spurs and crosstalk in the frequency domain are considered to be the biggest culprits leading to the instability and inaccuracy of 5G signals. Therefore, how to suppress these interference sources has become the top priority.  
The existing mine 5G anti-interference solutions still follow the overground model, mainly suppressing the interference between cells, which cannot deal with the problem of multifactorial interference underground [11; 12]. Furthermore, if each of these disturbances are solved respectively, the direct superposition of multiple solutions will result in an excessive amount of computation, which will drag down the speed of signal processing and introduce additional delays.
## 2 Theory  
Pseudothermal ghost imaging relies on correlating a measured 2-D intensity speckle pattern to a transmission or reflection scalar value of an object illuminated by the same speckle pattern. By varying the speckle pattern over time and simultaneously measuring the transmitted light through the object, a statistical reconstruction of the image is possible. Thus, the measured 2-D speckle pattern is weighted with the corresponding scalar value of an object in every iteration and is added to the ghost image. The ghost imaging algorithm and the measurements for the quality of a ghost image used in this work were published in [19; 20] and consider the nonlinear background and fluctuations of the light source given by  
\[G\left(\overrightarrow{p}\right)=\left\langle\left(\frac{I\left( \overrightarrow{p}\right)}{\left\langle\left(\overrightarrow{p}\right) \right\rangle}-1\right)\left(S-\frac{\left\langle S\right\rangle S_{I}}{ \left\langle S_{I}\right\rangle}\right)\right\rangle, \tag{1}\]  
where the ghost image \(G\left(\overrightarrow{p}\right)\) with the spatial coordinate \(\overrightarrow{p}\) depends on the measured intensity pattern \(I\left(\overrightarrow{p}\right)\), the scalar value \(S\), and the integrated intensity \(S_{I}=\int I\left(\overrightarrow{p}\right)d\overrightarrow{p}\) of the object. The brackets represent the mean over all iterations.  
The randomness of the rotating ground glass diffuser in pseudothermal ghost imaging will impose the field fluctuations that arise. The CNR is an important quantity that specifies the quality of an image and can represent the noise and fluctuations in the fields that illuminate the two detectors. For object signal and background noise in a ghost image to be distinguishable, a good CNR value is necessary. Here, we use the CNR to measure the quality of a ghost image. The CNR is calculated from the object signal strength and the background signal strength and normalized to the image noise [37; 38]:  
\[CNR=\frac{\langle I_{o}\rangle-\langle I_{b}\rangle}{\sigma_{GI}}, \tag{2}\]  
where \(\langle I_{o}\rangle\) is the mean signal of the object area, \(\langle I_{b}\rangle\) is the mean signal of the background in the ghost image, and \(\sigma_{GI}\) is the standard derivation of the image considering noise, which is generally taken as that of the background.
## 2 Method
### PCA Algorithm  
PCA transforms the original data into a set of linearly independent data vectors in various dimensions through linear transformation, which can be used to extract the main feature components of the data and often be used for dimensionality reduction of high-dimensional data. Therefore, before EEG signals were input into the neural network, we used the PCA algorithm to reduce the signal dimension. After preprocessing of the raw signals, a data matrix X is obtained, of which the abscissa and ordinate are the time and space domains, respectively. We input the matrix X into the PCA algorithm and calculated the covariance matrix (Cov(X)); then, the eigenvalue eigenvectors of the covariance matrix were obtained. We could select a matrix of eigenvectors corresponding to the features with the largest eigenvalues. In this way, the data matrix could be transformed into a new space, and the dimension reduction of the data features could be realized. Through dimensionality reduction using PCA, the size of feature vectors changed from 64*240 and 30*160 to 64*120 and 30*80 (two datasets respectively). The mathematical formula is as follows:  
Input the matrix \(X(x1,x2,x3,...,xn)\), and reduce the matrix X to K dimensions, \(0\leq K\leq n\).  
Find the covariance matrix of matrix X:  
\[\text{Cov}\,\text{X}=\frac{1}{n}XX^{T} \tag{1}\]  
Find the eigenvalues and eigenvectors of the covariance matrix CovX:  
\[\text{Cov}\,\text{X}=\Lambda L \tag{2}\]  
where \(\Lambda=diag[\lambda 1,\lambda 2,\dots,\lambda n]\) is the eigenvalue of the \(X\) covariance matrix, and \(L\) is the eigenvector of the \(X\) covariance matrix.  
Figure 1: Experimental set-up. **(a)** 6*6 GUI used in the brain–computer interfaces (BCI) competition; **(b)** 4*10 graphical user interface (GUI) used in the laboratory.  
Sort the eigenvalues \(\Lambda(set\lambda 1\geq\lambda 2\geq...\geq\lambda n\geq 0)\) from large to small, and select the largest k of them. Then, use the corresponding k feature vectors as row vectors to form a feature vector matrix \(P\) and the data are transformed into a new matrix \(Y\) constructed by k feature vectors:  
\[Y=PX \tag{3}\]  
where \(Y\) is the matrix after dimensionality reduction.
## 7 Conclusions
The developed approach of operational forecasting makes it possible to reduce the total error by almost 1.5 times in comparison with the short-term forecast. The mean average percentage error was calculated to be about 13%. At the same time, the operational forecast makes it possible to obtain more robust estimates of the PVPP generation predicted values: the percentage of operational forecasts meeting the confidence interval of \(\pm\)1 MW (for 15 MW PVPP) is more than 88%, and the percentage of the forecasts meeting the confidence interval of \(\pm\)2 MW is more than 97%. Thus, this confirms the effectiveness of the proposed methodology for an operational forecast based on retrospective data on short-term forecasting errors. The proposed forecasting software was installed on a low-cost distributed computing system, characterized by robust and maintenance-free operation, which is of great importance for power generation facilities operated in the autonomous mode and/or providing system service at the wholesale energy market. Moreover, in the course of PVPP operation, the retrospective dataset is being permanently updated with the newly introduced measurements and calculation results. The proposed hardware system has outstanding scaling properties, so there is no need to introduce high-performance computing facilities even for the Big Data sets. Conceptualization, S.A.E. and D.A.S. ; data curation, S.A.E., A.I.K., D.A.S. and A.M.R. ; formal analysis, V.V.D. and A.M.R. ; funding acquisition, A.I.K. ; investigation, S.A.E., D.A.S. and D.N.B. ; methodology, S.A.E., A.I.K. and D.A.S. ; project administration, A.I.K. ; resources, S.A.E., V.V.D. and D.N.B. ; software, S.A.E., A.I.K., V.V.D. and A.M.R. ; Supervision, S.A.E. and A.I.K. ; validation, D.A.S., V.V.D., A.M.R. and D.N.B. ; visualization, D.A.S. ; writing--original draft, S.A.E., A.I.K. and D.A.S. ; writing--review and editing, D.N.B. All authors have read and agreed to the published version of the manuscript. No funding was received for this study. The authors declare no conflict of interest.