esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Benchmark Dataset: UCR Anomaly Archive
#### 2.2.1 Included Time Series  
The time series in the UCR Anomaly Archive can be classified into 12 types based on the domain they originate from: human medicine, meteorology, biology and industry. Figure 2a shows the distribution of time series types in the dataset.  
The distribution is highly imbalanced, with approximately 64% of the time series coming from human medicine applications, 22% from biology, 9% from industry and 5% being air-temperature measurements. Within a single type of time series (e.g., ECG), the time series are not unique, but differ in terms of injected anomalies or modifications to the original time series, such as the addition of Gaussian noise or baseline wander. Baseline wander, a low-frequency artifact commonly found in ECG caused by factors such as breathing or subject movement, refers to slow changes in the signal baseline [41].
# Assessment of Different Stochastic Models for Inter-System Bias between GPS and BDS
## 3 Experimental Data  
To validate the different proposed ISB estimation stochastic models and the influence of the extra parameter ISB on PPP performance, observations from 8 stations in the MGEX network, named as CUT0, KARR, MRO1, PERT, MOBS, NNOR, STR1, and YAR2, were chosen, covering a one-week period which is GPS week 1965 with the DOY (day of year) from 246-252, 2017. These 8 stations are all located in Australia, where we can usually observe more than 7 BDS satellites [5]. The distribution and the receiver types equipped in the stations are shown in Figure 1 and Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Schemes** & **Descriptions** \\ \hline ISB-OFF & Neglecting inter-system bias \\ ISB-PW & Estimating the ISB as piece-wise constant \\ ISB-RW & Estimating the ISB as a random-walk processing \\ ISB-AD & Estimating the ISB as a processing-arc-dependent constant \\ ISB-WN & Estimating the ISB as white noise \\ \hline \hline \end{tabular}
\end{table}
Table 2: The description of GPS/BDS ISB estimating schemes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Station ID**} & \multicolumn{4}{c}{**Location**} & \multirow{2}{*}{**Receiver Type**} \\ \cline{2-4}  & **Latitude (\({}^{\circ}\))** & & **Longitude (\({}^{\circ}\))** & **Height (m)** & \\ \hline CUT0 & \(-\)32.0039 & 115.8948 & 24.000 & TRIMBLE NETR9 \\ KARR & \(-\)20.9814 & 117.0972 & 109.247 & TRIMBLE NETR9 \\ MRO1 & \(-\)26.6966 & 116.6375 & 354.069 & TRIMBLE NETR9 \\ PERT & \(-\)31.8019 & 115.8852 & 12.920 & TRIMBLE NETR9 \\ MOBS & \(-\)37.8294 & 144.9753 & 40.578 & SEPT POLARX4 TR \\ NNOR & \(-\)31.0487 & 116.1927 & 234.984 & SEPT POLARX4 \\ STR1 & \(-\)35.3155 & 149.0109 & 800.032 & SEPT POLARX5 \\ YAR2 & \(-\)29.0466 & 115.3470 & 241.291 & SEPT POLARX4 TR \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details of 8 selected stations location and receiver types.  
The data of these 8 stations will be processed with the tight combined PPP model, and with the different stochastic models for ISB estimation mentioned above. The results in static and kinematic mode using _GBM_ and _WUIM_ precise products will be analyzed and explained in the following sections.
# 3D Face Model Super-Resolution Based on Radial Curve Estimation
## 5 Conclusions  
In this paper, we presented a super-resolution method for 3D face models, aiming to improve the resolution and resolution of 3D face model acquired by consumer depth cameras. We established a face model database which contains low resolution and high resolution face models of 111 participants acquired respectively using Kinect and Artec Eva. Based on this database, we estimated a radial curve database which includes low resolution radial curves and their corresponding high resolution ones. For a given high resolution 3D face model, we first extracted a set of radial curves on it, and then estimated their corresponding high resolution ones by utilizing the radial curve database. Finally, we deformed a reference high resolution 3D face model to generate a high-resolution face model by using radial curves as the main feature. We evaluated the method both quantitatively and qualitatively. The evaluation results show that the proposed method is effective. Our method has practical implications for improving the quality of 3D face models and promoting applications such as 3D face recognition and 3D games. However, our assumption in the radial curve estimation phase, i.e., if two radial curves from two low resolution models are similar, the corresponding curves from their high resolution models are similar too, is too strict. In the future, we will improve the radial curve estimation method to relax this assumption.  
Conceptualization, J.Z. and F.D.; methodology, F.Z., J.Z. and L.W.; software, F.Z.; validation, F.Z.; data curation, F.Z.; writing--original draft preparation, J.Z., L.W. and F.Z.; writing--review and editing, F.Z., J.Z., L.W. and F.D.; visualization, F.Z. and J.Z.; supervision, F.D.; project administration, F.D.; funding acquisition, F.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Natural Science Foundation of China, grant numbers 61572078, 61702293 and 61772050, and the Chinese Postdoctoral Science Foundation, 2017M622137.  
The authors declare no conflict of interest.
## 4 The Development of the MRFCO-ZTS Approach
### MRFCO-ZTS Approach
\tag{13}\]In Equation (13), \(t_{u}\) denotes the decision threshold, i.e., trust score threshold. By using the above equation, each user in the satellite network is efficiently classified as an attacker or detector (i.e., legitimate) according to their activity patterns (i.e., cost function). The Table 2 below shows detector actions based on classification results. The Algorithm 1 of the manta ray foraging cost-optimized zero-trust security approach for attacker/detector classification is given below. ```
1:Input: Number of satellites \(i_{1},i_{2},..,i_{M}\)'; Behavior logs, access pattern, trust score
2:Output: Minimum-cost classification model (deny, allow) for achieving higher security
3:Begin
4:Number of satellite users \(i_{1},i_{2},..,i_{M}\)'
5:For all satellite users \(i\) in the network
6:Define cost function \(C(M)=\alpha\cdot d_{i}\cdot r_{i}+\beta\cdot d_{i}+\gamma\cdot m_{i}\)
7:Determine Attack likelihood score '\(a_{i}\cdot a_{i}=a_{1}\cdot log\left(M_{p}+1\right)+a_{2}\cdot log\left(M_{np} 1\right)\)
8:Measure cumulative user risk \(r_{i}=\frac{1}{M}\left(\sum_{i=1}^{M_{p}}r_{p,i}+\sum_{i=1}^{M_{np}}r_{np,j}\right)\)
9:Calculate computational delay \(d_{i}=w1\cdot CD(M)+w2\cdot L(M)-w3\cdot S(M)\)
10:Define the Objective Function of optimization \(\mathrm{J}(. )=\min\limits_{\theta}\sum_{i=1}^{M}\left[\alpha\cdot a_{i}\cdot r _{i}+\beta\cdot d_{i}+\gamma\cdot m_{i}\right]\)
11:Apply MRFCO Concept, initialize population \(z_{i}\) by considering '\(t_{u}\)'
12:For each \(z_{i}\), Measure fitness \(\alpha\cdot a_{i}\cdot r_{i}+\beta\cdot d_{i}+\gamma\). 13:While not termination
14:Choose foraging mode (Chain, Cyclone, Somersault)
15:Update \(z_{i}\) position \(z_{i}^{t+1}=z_{i}^{t}+\alpha_{1}\cdot\left(z_{best}^{t}-z_{i}^{t}\right)+ \alpha_{1}\cdot\left(z_{i-1}^{t}-z_{i}^{t}\right)\)
16:Determine new fitness i.e., Re-evaluate cost, Update \(z_{best}^{t}\) if required
17:Return \(z_{best}^{t}\) as optimal parameters for ZTS classification
18:Cyclone Foraging \(z_{i}^{t+1}=z_{i}^{t}+r\cdot\left(z_{best}^{t}-z_{i}^{t}\right)\cdot\sin \left(\frac{\pi t}{t}\right)\)
19:Somersault Foraging \(z_{i}^{t+1}=z_{i}^{t}+S\cdot\left(\alpha_{1}\cdot z_{best}^{t}-\alpha_{2} \cdot z_{i}^{t}\right)\)
20:\(\hat{y}=\left\{ \begin{array}{l}\text{If }\alpha\cdot A_{i}\cdot R_{i}+\beta\cdot D_{i}+ \gamma\cdot m_{i}>t_{u}\text{, then }\hat{y}=1\text{ and user }i\text{ is classified as Attacker }\\ \text{Otherwise, then }\hat{y}=0\text{ and user }i\text{ is classified as detector }\\ \end{array} \right.\)
21:End While
22:End For
23:End
```  
**Algorithm 1**Manta ray foraging cost-optimized zero-trust security approach for attacker/detector classification algorithm
# An NDN Cache Management for MEC
## 4 A Limited Domain Cache Management Policy
### Limited Domain Caching Policy (LdC)  
For simple implementation, LdC utilizes a custodian-based NDN [19]. Each network domain has a custodian node that every _Interest/Data_ entering or leaving this domain must pass through. LdC proposes to utilize the custodian node as a representative node. By this assumption, when data is published in a named domain, all _Interests_ for the data are commonly forwarded via the custodian node of the domain. Hence, as shown in Figure 4, if a node having the identity \"/suv/research/security/david\" receives the _Interest_ for _Data_ having the data name \"/suv/research/security/david/doc/report.txt/s2/239\", the _Data_ are forwarded to the custodian nodes (i.e., representative nodes) of the domains named \"/suv/research/security\", \"/suv/research\", and \"/suv\" in order. These representative nodes gradually cache the _Data_ like LCD/WAVE.  
To limit a cache area to a publisher-side name domain, if the representative node of a named domain receives _Data_ with a name prefix that does not match its node identity, the node forwards the Data without caching the Data. To implement this functionality, LdC adds two fields, a cache flag (CF), and a cache remove flag (RF), to the _Data_ structure.  
* If the CF of the _Data_ has '1' and the domain name of a node receiving the _Data_ matches the name prefix of the _Data_, the node caches the _Data_. Otherwise, the node does not cache it.
* The RF is an optional field. It is used to delete the content that is cached in a node after the node forwards the _Data_. If the RF has '1', except for the custodian node with the layer 1 name domain of a data publisher, each node deletes the cached _Data_ after forwarding the _Data_. Figure 5 describes in detail the LdC procedure. Assume that the CF of the _Data_ has '1'.
* If a node receives the _Interest_ for _Data_ that it has cached, the node forwards the _Data_ back to the requestors. Then, if the node is neither a custodian node of a name domain nor the publisher of the _Data_, it checks whether the RF of the _Data_ has '1'. If it does, the node deletes the cached _Data_ from its CS.
* If a node receives _Data_ from its neighboring node, the node confirms the CF of the _Data_. If CF = 0, it forwards the _Data_ without caching the _Data_. Otherwise, the node compares its node name with the name domain hierarchy of the _Data_. If these two names match, it caches the _Data_ with CF = 1. Otherwise, it does not cache the _Data_.
* The node sets the CF of the Data to '0'. Then, it sends the _Data_ to the requestors. However, it still has a cache of the _Data_ that has a CF = 1.  
Figure 5: LdC (a limited domain cache policy) procedure using custodian-based NDN.  
Using LdC, the cache of _Data_ gradually moves toward a node matching the highest layer name prefix of the _Data_. Additionally, to enhance the performance of LdC, it is possible to increase the amount of cached _Data_ per each content transmission time like in WAVE.
## 2 Materials and Methods
### Quantitative Parameters  
Fourteen quantitative parameters were measured (OCT \(n=1\), SDF \(n=6\), LSCI \(n=2\), and FA \(n=5\)). The quantitative parameter for OCT was [14]:  
1. The percentage of speckle contrast pixels (speckle %) indicative of the flow obtained in the M-mode of the scans; Quantitative parameters derived for SDF included [15]:
2. Total vessel density (TVD): ratio between the total vessel length and the area of ROI (mm/mm\({}^{2}\));
3. Perfused vessel density (PVD): ratio between the perfused scored vessel length and the area of ROI (mm/mm\({}^{2}\));
4. Microvascular flow index (MFI): sum of the qualitative determination (0 no, 1 intermittent, 2 sluggish, 3 continuous) of the predominate flow in four quadrants of the SDF image, divided by four;
5. De Backer Score (DBS): number of vessels crossing a grid overlay divided by the total length of the vessels;
6. Proportion of perfused vessels (PPV): perfused vessel length divided by the total vessel length of the ROI in %;
7. Velocity (\(\upmu\)m/s): assessed per selected vessels by a space-time diagram; The quantitative parameters calculated for LSCI were [17]:Flux in laser speckle perfusion units (LSPU);
* \(\Delta\)%Flux defined as the percentage difference in Flux relative to Loc1 (positive number when there is a decrease in Flux);  
FA was quantified by the assessment of the time-dependent change in the fluorescence signal at the ROIs in the form of the software-derived fluorescence-time curves [8; 19]. The quantitative parameters for FA included:  
10. Influx time point (\(\tau\)): time in seconds between the occurrence of the peak fluorescence in the right gastroeipiloic artery and the time point at which the fluorescence intensity in the ROI was statistically significantly larger than the background;
11. F\({}_{\text{max}}\): maximal intensity in arbitrary units (AU) within 50 s after the influx time point. Fmax was corrected by subtracting the median background value measured from the start of imaging to the influx time point (\(\tau\));
12. T\({}_{\text{max}}\): time in seconds after the influx time point (\(\tau\)) at which the background-corrected fluorescence intensity reached Fmax, also known as the time-to-peak;
13. T\({}_{1/2}\): time in seconds after the influx time point (\(\tau\)) at which the background-corrected fluorescence intensity was half of the Fmax;
14. Mean slope: rate at which the fluorescence intensity increased (AU/s).
## 4 Spatiotemporal Feature Extraction Techniques
### Feature Extraction Methods Future Research Directions
#### 4.5.4 Spatio-Temporal Convolutional Networks  
Spatiotemporal Convolutional Network is a neural network model that combines spatial and temporal convolutions. It uses traditional two-dimensional convolution in the spatial dimension and introduces one-dimensional convolution in the temporal dimension. This structure enables the network to capture temporal and spatial data information [81] effectively.
# Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images
## 1 Introduction
According to disease symptoms, hyperspectral data of rice leaves infected with ShB were carefully processed to explore the spectrum response mechanism of rice ShB. The objectives of this study were to analyze variations of the reflectance spectrum curves of rice leaf blades with different levels of ShB. According to spectrum change rules, \"three-edge\" parameters and vegetation indices closely related to rice ShB were selected to construct the spectral characteristic library of rice ShB. Then, we examined the applicability of these spectral features to identify rice ShB at the leaf scale. The spectral differences between leaf sheath lesions and leaf blade lesions were also discussed in order to establish a basis for multi-angle remote sensing monitoring of ShB in rice.
## 5 Experimental Setup
### Classification Model  
We used a two-layer neural network model for aspect category detection. Vector representation of sentences \(\overrightarrow{s}\) and output labels \(\overrightarrow{y}\) were used to train our neural network model. All of the proposed sentence representation techniques were applied incrementally to train multiple models and then each was evaluated on the test dataset. The dataset was divided into training and validation sets with a ratio of 85 and 15. For training our neural network, we used 1708 sentences from SemEval-2016 Task 5. The neural network training parameters are given in Table 4.  
We applied adaptive learning rates that gradually slow down the step size to achieve fast and optimal convergence. The adaptive learning rate of the neural network is controlled by the decay rate, which gradually reduces the learning rate by a small factor depending on the number of epochs. \(L2\)-regularization was used to control over-fitting of the model. Each epoch consists of multiple batches across the whole dataset instances. Batch size was fixed, and contained 80 examples in each set of training examples. Before starting each epoch, the training dataset was shuffled. Loss and accuracy was observed after every 5 epochs, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Epochs & 100 \\ Hidden layer units & layer 1: 300, layer 2: 250 \\ Batch size & 80 \\ Base learning rate & 0.002 \\ Decay rate & 0.96 \\ Regularization & 0.003 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Neural network model tuned parameters.  
Our ACD model was evaluated by computing \(F1\) (micro-averaging) scores based on the ratio between correctly classified labels and the set of predictions, and the gold standard as discussed in SemEval-2016 Task 5 [34] and SemEval-2015 Task 12 [35]. We compared our experimental results with the current best scores. F1 scores were calculated under the known definitions of precision (\(p\)), and recall (\(r\)), which are:  
\[F1=2\frac{pr}{p+r}\text{ where }p=\frac{tp}{tp+fp},\quad r=\frac{tp}{tp+fn}\]
# An Improved YOLOX Model and Domain Transfer Strategy for Nighttime Pedestrian and Vehicle Detection
## 5 Experimental Comparison and Discussion
### Analysis
#### 5.3.3 Effect of CA Module Location and Number of Layers  
In this paper, we further investigate the effect of the additional position and number of layers of CA modules in the backbone network. As shown in Table 3, a total of eight comparative experiments are conducted, including four different location addition schemes and three different layer number analyses.  
In Table 3, the \"\(\sqrt{\text{\tiny{}}}\)\" indicates that the CA module was used at that location in the network, and the bolded in the table indicates the best results. From the results in Table 3, we know that the CA module has the best accuracy of 80.1% when it was added after the CSP components at positions P3, P4, and P5 in the backbone network. Upon further reducing the number of layers of the CA module from the initial three layers to one layer, the mAP is reduced by only 0.5%, but the inference time was reduced by 5.3 ms and the model size was also reduced by 0.2 MB, which is more advantageous than adding three layers of the CA module. The final mAP of the improved YOLOX algorithm is 79.6%. Compared with the original YOLOX model, the mAP improved by 3.1% and model size was reduced by 1.6 MB.
## 3 Detailed Problem Formulation  
The segment-downloading sequence affects the viewing experience of the user in the P2P-based VoD system. Therefore, many segment selection strategies were proposed. To support the particularity of the VCR operation in the P2P-based VoD system, data-prefetching strategies were introduced as a specific segment selection strategy, i.e., downloading the data before the current playing point in advance, which is designed to reduce the playback delay after VCR operations. The most mature data-prefetching strategy is the anchor point strategy [6]. When the user watches a video, the CN with the anchor-prefetching strategy downloads the anchor data with the residual download bandwidth after downloading the urgent data. When the user jumps to a new point of the video using a VCR operation, the playback point is adjusted to the beginning of the closest anchor. If the anchor has been downloaded, the CN will resume playing the video with no delay. In this paper, we hope to improve the viewing experience of the user after a VCR operation by improving the anchor-prefetching strategy. To improve this strategy, we consider two aspects: anchor duration and anchor interval.  
First, we discuss the anchor duration. Each anchor must be a block that can be independently played. Based on the content fragmentation strategy in the P2P-based VoD system in Section 2, a chunk size is approximately 2 M [14]. An Android native player must download approximately 2 chunks before it begins playing a video, i.e., the data block has at least 4 M bits that can be independently played. According to the research of PPLive, the video code rate is 381-700 kbps. Therefore, to satisfy the requirement of the player to play the video segments, each anchor duration is 6-10 s. To ensure the normal playback of video with different bit rates, we set the anchor duration to 10 s.  
Second, to study the anchor interval, we must consider two factors: playback delay and positioning satisfaction.
## 4 Test Systems
### Test System: 33-Node AC MG  
This test system is a 33-node AC MG capable of grid-connected and islanded operation. This system was selected since it has been previously used in the literature to validate intelligent optimization strategies [49]. It is composed of 33 nodes and 32 distribution lines, operating with a base voltage of 12.66 kV and a base power of 1000 kVA.  
Figure 6 presents the electrical diagram of the 33-node MG. This system includes electrical loads at each node, all of which have both active and reactive power demands,  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Methodology** & **Parameter** & **Grid-Connected** & **Islanded** \\ \hline MC & No. of individuals & 1000 & 1000 \\  & No. of iterations & 1000 & 1000 \\ \hline PGA & No. of individuals & 103 & 80 \\  & No. of iterations & 5000 & 6000 \\  & No. of mutations & 1 & 2 \\ \hline PSO & No. of individuals & 413 & 295 \\  & No. of iterations & 1326 & 3571 \\  & Minimum inertia & 0.4179 & 0.3863 \\  & Maximum inertia & 0.4855 & 0.5580 \\  & Cognitive factor & 2 & 2 \\  & Social factor & 1.6871 & 1.6105 \\  & Velocity limit factor & 0.0689 & 0.0526 \\ \hline JAYA & No. of individuals & 45 & 45 \\  & No. of iterations & 9870 & 15000 \\ \hline GNDO & No. of individuals & 491 & 311 \\  & No. of iterations & 1440 & 1500 \\ \hline MVO & No. of individuals & 178 & 109 \\  & No. of iterations & 2500 & 7700 \\  & Min. WEP & 0 & 0.0043 \\  & Max. WEP & 0.0381 & 0.0684 \\  & \(p\) & 8.3440 & 1.7483 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tuned parameters for the optimization methodologies implemented in the 33- and 69-node MGs (grid-connected and islanded modes).  
as detailed in Table 3. Node 1 serves as the slack bus and can be energized either by the main electrical grid (during grid-connected operation) or by a 4000 kW diesel generator (when operating in islanded mode or during grid failures or planned disconnections). This generator operates within a power limit range of 40-80% of its rated capacity. Moreover, the red markers in the figure indicate the WTs connected at nodes 12, 15, and 31, each with an installed capacity of 1300 kW. In this test system, the power converters used for each WT featured a power capacity of 1300 kW.  
Table 3[49] presents, from left to right, the line number \(l\)  
Table 4 presents the cost and \(CO_{2}\) emission values associated with the operation of the MG in its two operating modes. In this table, \(C_{kWh}\) represents the energy purchase or generation cost stemming from the conventional generators, \(C_{cg}\) denotes the \(CO_{2}\) emissions factor resulting from the MG's operation, and \(C_{OkM}\) refers to the operation and maintenance costs of the WTs [50].  
Figure 7 illustrates the generation curves of the WTs and the demand profile of the MG for a typical day of operation. These curves were obtained based on the wind generation patterns and electricity demand behavior of a specific region in Colombia [31].