esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Location-Dependent Pricing and Request Mapping in ICN-Based Telco CDNs For 5G
## 5 Evaluation
### Experiment Setup  
#### 5.1.1 Traffic Model  
In simulation, we consider there to be 10 user regions and 10 CDN sites. End users from different regions consume traffic for three types of applications. To evaluate the proposed joint design under traffic that matches the trend of the practical traffic, we adopt the traffic generation approach in [45] to generate the traffic demand. Specifically, we use a Poisson process to simulate the process of end users' Internet access for different applications. User requests for application \(n\) (\(1\leq n\leq 3\)) arrive according to a Poisson process with \(\lambda_{mu}\) as intensity, the value of \(\lambda_{mu}\) is set according to the parameters in [45]. To evaluate the proposed mechanism in different traffic patterns, user requests from different access regions are generated according to Table 2, which gives the user number in different access regions. \(\lambda_{nl}\) is obtained according to Equation (6). According to the measurement of average latency of Internet access in China in [42], we set the latency parameters as follows. For site \(m\), \(t_{lm}\) (\(m\in M\)) is generated randomly according to a normal distribution with \(t_{m}\) as mean and 1 ms as standard deviation. \(t_{m}\) is generated randomly according to a continuous uniform distributions with 40 ms as the lower bound and 100 ms as the upper bound. \(C_{m}\) is set as 1.1 times the max bandwidth usage in Traffic Pattern 1. For the price-related parameters, we set \(P_{const}\) according to the bandwidth unit price in Ali CDN [35], set \(P_{min}\) as 0.2, \(P_{max}\) as 0.5 as default. According to the trend of traffic fluctuation throughout a day, 1 h is an appropriate length to distinguish the varied congestion level of a site [36; 46]. Therefore, the update period of LDP is set as 1 h. SM, PARM are both set as 10 min. Here, the relationship between periods of SM, LDP, and PARM conforms with Equation (1). The simulation time is set as 1 h. In summary, all parameters are shown in Table 3.  
\[\lambda_{nl}=\sum_{u\in I}\lambda_{mu},\forall n\in N,\forall l\in L. \tag{6}\]
## 3 State Estimation and Prediction  
A Kalman-filter-based fusion approach that was previously described in [23] is employed in this study for state estimation and trajectory prediction of the remote targets in the vehicle surroundings. At each time step, the measurements from the on-board remote sensors as well as the BSMs received from the remote targets are collected and processed with Kalman filter algorithms, which reduce the measurement noise and output the state and error covariance for each track. The types of information we used in this study to estimate the state and future trajectory of the remote target include the following: Position, speed, heading, yaw rate, and size information from the V2X communications; range and azimuth information from the radar system; and longitudinal and lateral distance information from the camera system. The state estimates from different tracks are then associated and fused together, where the weight for each track is determined based on the error covariance. The future trajectory of the remote target detected in the perception stage is estimated with the constant turn rate and velocity (CTRV) motion model. For performance evaluation in the context of safety applications, the future trajectory of the remote target is compared with the future trajectory of the host vehicle and an appropriate warning is generated when a possible collision is detected.
## 3 Results and Discussion
### Computational Calculations
#### 3.4.3 The Complex of [Cu\({}^{\rm II}\)(\(\beta\)-CD)(Ezm)\({}_{2}\)]\({}^{2+}\)
As is shown in Figure 8, the Cu(II) ion in _RSR_ complex stayed at the center of the small rim, while in SRS complex, the copper ion coordinated with the oxygen atom on the outside drug molecule all the time. Different configurations lead to different binding mode, which are related to their different stability. Compared with _SRS_-EZM, _RSR_-EZM cannot penetrate the cavity to coordinate with Cu(II) ion. H-bond is the only connection between _RSR_-EZM and \(\beta\)-CD. Thus, [Cu\({}^{\Pi}\)(\(\beta\)-CD)(_RSR_-EZM)\({}^{2}\)  
Figure 8: [Cu\({}^{\Pi}\)(\(\beta\)-CD)(EZM)\({}^{2}\)]\({}^{2+}\) structures in different running time from MD trajectory at 500 K. Color coding: copper, purple; fluorine, pink; nitrogen, dark blue; carbon, gray; oxygen, light red; hydrogen, white. squint towards to lose one \(RSR\)-EZM molecule. To further prove such a difference between the two enantiomers, three parallel simulations were performed for each drug molecule by placing them at different initial places. The distances between the EZM and [CuII(B-CD)]2+ are calculated from these MD trajectories, and the normalized distributions of the distances calculated from the three runs are shown in Figure 9. It is obvious that compared with \(RSR\)-EZM, \(SRS\)-EZM always keeps closer around the \(\beta\)-CD, starting from various initial structures. It further proves the better stability of the \(RSR\)-complex. To deeply understand the role of the drug enantiomers in the ternary complex and the application scope of our method, we studied the recognition of captopril and ibuprofen enantiomers by MS using Cu2+ and \(\beta\)-CD as ligand but failed. Captopril, \(\beta\)-CD and metal ions cannot form stable ternary complex because the sulfhydryl group of captopril is oxidated easily in the presence of metal ions. Ibuprofen, \(\beta\)-CD and metal ions cannot form ternary complex but can be recognized by D-glucose and metal ion complex, possibly due to the large opening size of \(\beta\)-CD relative to the ibuprofen molecule. Therefore, there are some limitations for MS with \(\beta\)-CD and the metal ions complex method for the recognition of enantiomers, such as suitable ligand, enantiomer stability, and molecular size.
# Emotionally Controllable Talking Face Generation from an Arbitrary Emotional Portrait
## 4 Experiment  
### Experimental Settings  
**Datasets.** Two audiovisual datasets: MEAD [10] and the Obama Weekly Address database [8] were employed. MEAD is a high-resolution emotional audiovisual dataset consisting of 60 actors from different continents with eight emotions, three intensities, and seven poses. A subset of this database was used for the pre-trained model of the _emotional landmark animation generation network_ and the _landmark-to-animation translation network_. Videos of the frontal pose were selected from the MEAD database to train our network. The dataset was divided into three sections: 60%, 20%, and 20% for training, hold-out validation, and testing, respectively. For the _emotional landmark animation generation network_, the reference was a video of a target portrait with emotional source expression. The Obama Weekly Address database contains high-quality video clips of weekly presidential speeches from a single speaker, former President [PERSON]. Its consistent frontal pose with little emotion is ideal for training the _lip-sync landmark animation generation network_.  
**Implementation Details.** All audio waveforms in the dataset were sampled at 16 KHz and all video was converted to 30 fps. All images of the network model were 256 \(\times\) 256 pixels in size. There are 68 key points of landmarks provided in [41]. The models were trained using PyTorch, including a 16 GB Nvidia Tesla V100 GPU from Santa Clara, CA, USA.  
**Comparing Methods.** Our approach was compared with two state-of-the-art representative algorithms for image-based face video generation, AVTG and MakeltTalk. The cascading network structure of AVTG based on face landmarks was proposed by [PERSON] et al. [13]. In addition, MakeltTalk by [PERSON] et al. [15] uses decoupled learning to generate spontaneous head movements. These two methods were used extensively for comparison.
# Advanced Progress of Optical Wireless Technologies for Power Industry: An Overview
## 2 Classification and Progress of Power Industry Optical Wireless Communications
### Channel Characteristics between Power Line and Indoor Wireless Optical Communications
#### 2.1.2 Channel Characteristics of Outdoor Wireless Optical Communications
However, once the IWOC system is intended to be integrated into the PLC system, the attenuation of the overall system at high frequencies will significantly reduce the available bandwidth of the system. Specifically, PLC channel uses a typical 6-diameter model, while the IWOC channel filters out stray light with blue light filters. Quantitative results show that the attenuation of the hybrid system is about 40 dB at 30 MHz, indicating that the available modulation bandwidth of the system will be severely limited.
## 3 Case Study
### Experimental Setup  
The spot welding testbed is shown in Figure 14. The testbed was manufactured by Chowel (Ansan-si, Korea) and Hubis (Daejeon, Korea). The spot welder of the testbed was controlled by the Welcom-1000 software. To accelerate the degradation of the welding electrode tips, the electrical current used for welding was intentionally increased above that of normal conditions. The waveforms and images were recorded for individual spot welding, including dynamic resistance, pressure, current, and images of the upper and lower welding electrodes. A single set of data was collected every 2.2 s. The sampling rate was 50 kHz. A dataset was collected until 100 weld points were obtained. Then, the degraded welding electrode was replaced with a new one. Five welding electrodes were tested. Therefore, five datasets were generated for the five welding electrode tests.  
Another dataset was collected until 500 weld points were obtained. For this particular dataset, the experiment was terminated when the symptoms of failure were observed by domain experts in the field. As shown in Figure 15, the maximum value and the slope of the dynamic resistance decreased from one to 375 weld points. After 375 weld points, the dynamic resistance waveform shape changed  
Figure 13: CNN architecture for unsupervised feature extraction.  
drastically. The database collected from the accelerated testing of the welding electrodes is summarized in Table 4.  
Figure 14: Spot welding testbed.  
Figure 15: Dynamic resistance data by weld number: (**a**) full view and (**b**) magnified view between 0.71 s and 0.95 s.  
CNN was trained using an Adam optimizer [29]. The learning rate was 0.001, while the batch size was 16. The number of training images was 500, while the number of test images was 1000. The shape of the activation maps was \(12\times 1\). The activation maps were used to calculate the health indicator using MD. The graphical processing unit was an NVIDIA Geforce Titan V GPU. Python 3.6 and TensorFlow 1.8 were used on the Linux operating system (Ubuntu 18.04).
# Alternating Direction Method of Multipliers for Generalized Low-Rank Tensor Recovery
## 2 Notations and Preliminaries
Tensor \(\mathbf{\mathcal{A}}\) is low-rank if and only if \(\mathbf{rank}(\mathbf{A}_{(n)})\ll\mathbf{I}_{n}\) for some \(n\). Then, the Tucker decomposition of \(\mathbf{\mathcal{A}}\) is defined as  
\[\mathbf{\mathcal{A}}\approx\mathbf{\mathcal{C}}\times_{\mathbf{1}}\mathbf{U}^{(1)} \times_{\mathbf{2}}\mathbf{U}^{(2)}\cdots\times_{\mathbf{N}}\mathbf{U}^{(N)} \triangleq\mathbf{\mathcal{C}}\times_{\mathbf{n-1}}^{\mathbf{N}}\mathbf{U}^{(n)}, \tag{1}\]  
where the core tensor \(\mathbf{\mathcal{C}}\in\mathbb{R}^{\mathbf{I}_{1}\times\mathbf{I}_{2}\times\cdots \times I_{N}}\), mode matrices \(\mathbf{U}^{(n)}\in\mathbb{R}^{\mathbf{I}_{n}\times\mathbf{I}_{n}}\) and \(\mathbf{J}_{n}\leq\mathbf{I}_{n}\). The multi-linear rank of a tensor has superiority over other definitions (e.g., the rank used in PARAFAC decomposition) because it is easy to compute. Refer to the survey [12] for further understanding of tensor algebra. Within the field of low-rank matrix/tensor recovery, two proximal minimization problems are extensively employed:  
\[\min_{\mathbf{\mathcal{X}}}\lambda\left|\left|\mathbf{\mathcal{X}}\right|\right|_{ \mathbf{1}}+\frac{\mathbf{1}}{2}\left|\left|\mathbf{\mathcal{X}}-\mathbf{\mathcal{A}} \right|\right|_{\mathbf{F}}^{2}, \tag{2}\]  
\[\min_{\mathbf{\mathcal{X}}}\lambda\left|\left|\left|\mathbf{\mathcal{X}}\right|\right|_{ \mathbf{*}}+\frac{\mathbf{1}}{2}\left|\left|\mathbf{\mathcal{X}}-\mathbf{\mathcal{A}} \right|\right|_{\mathbf{F}}^{2}, \tag{3}\]  
where \(\mathbf{\mathcal{A}}\) and \(\mathbf{\mathcal{A}}\) are a given data tensor and matrix respectively, \(\mathbf{\lambda}\geq\mathbf{0}\) is a regularization factor. To address the above two optimization problems, we first define an absolute thresholding operator \(\mathbb{S}_{\mathbf{\lambda}}():\mathbb{R}^{\mathbf{I}_{1}\times\mathbf{I}_{2} \times\cdots\times I_{N}}\rightarrow\mathbb{R}^{\mathbf{I}_{1}\times\mathbf{ I}_{2}\times\cdots\times I_{N}}\) as below: \((\mathbb{S}_{\mathbf{\lambda}}(\mathbf{\mathcal{A}}))_{i\mathbf{I}_{2}\cdots i\mathbf{ }_{N}}=\max\left(\left|\mathbf{a}_{i\mathbf{I}_{i}\mathbf{i}_{2}\cdots i \mathbf{}_{N}}\right|-\mathbf{\lambda},\mathbf{0}\right)\). It has been proven that problems (2) and (3) have closed-form solutions denoted by \(\mathbb{S}_{\mathbf{\lambda}}(\mathbf{\mathcal{A}})\)[2] and \(\mathbf{T}_{\mathbf{\lambda}}(\mathbf{\mathcal{A}})\)[23] respectively, where \(\mathbf{T}_{\mathbf{\lambda}}(\mathbf{\mathcal{A}})=\mathbf{US}_{\mathbf{\lambda}}(\mathbf{ \Sigma})\mathbf{V}^{\text{T}}\) and \(\mathbf{U}\mathbf{U}\mathbf{U}^{\text{T}}\) is the singular value decomposition of \(\mathbf{\mathcal{A}}\).
## Materials and Methods
### Material Characterization
#### 2.3.3 Sorption Analysis  
CO\({}_{2}\) and N\({}_{2}\) sorption isotherms were obtained at 20 \({}^{\circ}\)C using a gravimetric analyzer (Hiden Isochema IGA-003, UK) with a resolution of 0.2 \(\upmu\)g and the buoyancy force correction (Archimedes principle) [52; 53; 54]. The chosen experimental CO\({}_{2}\) pressure range was 0-5 bar for all samples, and the N\({}_{2}\) pressure range was 0-5 bar for **P\({}_{9}\)IL\({}_{0}\)Zn\({}_{0}\)** and **P\({}_{9}\)IL\({}_{6}\)Zn\({}_{9}\)**, and 0-18 bar for **P\({}_{9}\)IL\({}_{6}\)Zn\({}_{0}\)** and **P\({}_{9}\)IL\({}_{6}\)Zn\({}_{1}\)**. Samples were degassed under vacuum at 70 \({}^{\circ}\)C for 10-70 h before the measurements. The time required to obtain each experimental value equalled 360 min for all samples. A detailed explaination of the methodology is reported in the electronic supporting information.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample** & \multicolumn{3}{c}{**Composition**} & \multicolumn{3}{c}{**Ratios**} & **Label** \\ \cline{2-9}  & **PIL** & **IL** & **Zn\({}^{2+}\) Salt** & **PIL** & **IL** & **Zn\({}^{2+}\) Salt** & \\
**[Name]** & **[wt \%]** & **[wt \%]** & **[wt \%]** & **[–]** & **[–]** & **[–]** & **[–]** & **[–]** \\ \hline PIL & 100 & – & – & 9 & 0 & 0 & P\({}_{9}\)IL\({}_{0}\)Zn\({}_{0}\) \\ PIL/IL & 60 & 40 & – & 9 & 6 & 0 & P\({}_{9}\)IL\({}_{6}\)Zn\({}_{0}\) \\ PIL/IL/Zn[Tf\({}_{2}\)N]\({}_{2}\) ratio 9:1 & 56.3 & 37.5 & 6.2 & 9 & 6 & 1 & P\({}_{9}\)IL\({}_{6}\)Zn\({}_{1}\) \\ PIL/IL/Zn[Tf\({}_{2}\)N]\({}_{2}\) ratio 1:1 & 37.5 & 25.2 & 37.3 & 9 & 6 & 9 & P\({}_{9}\)IL\({}_{6}\)Zn\({}_{9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composite PIL/IL/Zn\({}^{2+}\) materials.
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 2 Background
### Nature-Inspired Approaches to Finding the Shortest Path
#### 2.3.3 Ant Colony Optimization (ACO)  
In nature, several insects and animals have an inert sense of swarming together to accomplish a task more efficiently. Swarming, when used as a means for collaboration, can help optimize both the time and the energy spent. Adding this feature to our research approach would be beneficial, as it has the same setup of a search in an unknown environment guided by an external stimulus. The stimulus, which in the natural swarming process may be e.g., the smell of food, is in our case the beacon (cf. Section 3.3.1 for details).  
Ant systems are an example of accomplishing a task by collaborating via _stigmergy_, i.e., using the environment to guide the actions of the individual. Ants effectively use the world as a sort of shared memory. They do so by depositing a trail of pheromones on their way. When using the aggregated pheromone levels in their environment to influence their path planning, the collective manages to converge on the shortest path to the food source, despite all obstacles, and can possibly do so in optimal search time.  
Ant travel is generally a positive feedback mechanism. An ant's decision at every fork is affected by its likelihood to adhere to paths with more pheromone concentration, in addition to a random element. This random element encourages exploration of other less-traveled trails that could possibly lead to better solutions. Furthermore, the pheromone also evaporates over time. So, in the context of a newly emerged shorter path to the food source, most ants would slowly migrate to the new path due to the decay of pheromone and the new pheromone deposits. For a simulation of pheromone-led ant travel, the probability of an ant taking a particular path is represented by its density function, which is given by  
\[P_{A}(t+1)=\frac{(c+n_{A}(t))^{\alpha}}{(c+n_{A}(t))^{\alpha}+((c+n_{B}(t))^{ \alpha}} \tag{1}\]  
where \(c\) is the degree of attraction to an unexplored path (the random element), \(\alpha\) is the bias to using a pheromone concentrated path, and \(A\) and \(B\) are the two paths to choose from at a fork [102]. Given the specific nature of the application we discuss in this article, we have explored the ranges of values for \(\alpha\) and \(c\), namely alpha in the range [1.8, 2.2], and \(c\) in the range [18, 21] A parameter space exploration (omitted here) demonstrated empirically that \(\alpha\approx 2\) and \(c\approx 20\) provide the best fit to experimentally observed behavior.
## 3 Analysis of Success Probability
### Conditional Success Probability
#### 3.2.2 Conditional Success Probability in Secondary Network  
Similar to the case of the primary network, the conditional success probability at any typical SR \(y_{k}^{s}\) is given as  
\[\mu_{y_{k}^{s}}^{\dagger}=P(SINR_{y_{k}^{s}}>\theta_{s}|\Phi,\forall\Phi=\Phi_ {p}^{I}\cup\Phi_{s}^{I}). \tag{21}\]In the conventional underlay mode, the conditional success probability as \(\sigma^{2}\to 0\) is obtained as  
\[\mu_{y_{k}^{\prime}}^{\prime UU}=\prod_{x_{i}^{\prime}\in\Phi_{s}^{\prime}|x_{k}^ {\prime}}\Big{(}\frac{v_{s}}{1+\theta_{s}r_{s}^{a}||x_{i}^{s}||^{-a}}+1-v_{s} \Big{)}\prod_{x_{i}^{\prime}\in\Phi_{p}^{\prime}}\Big{(}\frac{v_{p}}{1+\theta_ {s}r_{s}^{a}||x_{i}^{p}||^{-a}\frac{P_{p}}{p_{s}^{\prime}}}+1-v_{p}\Big{)}. \tag{22}\]  
Its b-th moment is similarly obtained as  
\[M_{b}=E_{y_{k}^{\prime}}[(u_{y_{k}^{\prime}}^{\prime U})^{b}]=E_{r_{s}}\Big{[} E\prod_{x_{i}^{\prime}\in\Phi_{s}^{\prime}|x_{k}^{\prime}}\Big{(}1-\frac{E[v_{s}| \Phi]}{1+\frac{||x_{i}^{s}||^{a}}{\theta_{s}r_{s}^{a}}}\Big{)}^{b}E\prod_{x_{ i}^{\prime}\in\Phi_{p}^{\prime}}\Big{(}1-\frac{E[v_{p}|\Phi]}{1+\frac{||x_{i}^{s}||^{a} }{\theta_{s}r_{s}^{a}}\frac{P_{p}}{p_{s}^{\prime}}}\Big{)}^{b}\Big{]}. \tag{23}\]  
From (23), the approximation for the conditional success probability in the secondary network can as well be obtained for the conventional underlay, conventional overlay and the case of misdetection. The analysis for the hybrid is then straightforward from (10).
# GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques
## 4 Detection, Exclusion and De-Weighting Strategies  
Specific detection, exclusion and de-weighting strategies are now discussed. Besides a customized implementation of time-averaging and a _M of N_ detection strategy, three new geometry-based exclusion algorithms are investigated.
## 4 The Proposed 3-Tier AKA Mutual Authentication and Key Agreement (AKA) Protocol
### Initialization Phase  
Figure 2 shows the UML activity diagram for the initialization phase. In the initialization phase, each entity (the edge devices \(\delta_{d}\), fog nodes \(\zeta_{z}\), and cloud data centers) requests an eSIM from the 5G service provider \(u_{v}\). The eSIM of the edge device includes the device ID (\(\delta_{d}\) ID) and a temporary secret key \(S_{\delta_{d}}\). The fog node's eSIM includes the node ID (\(\zeta_{z}\) ID) and a temporary secret key \(S_{\zeta_{z}}\). Similarly, the cloud data center's eSIM includes its ID (\(\gamma_{c}\) ID) and a temporary secret key \(S_{\gamma_{c}}\). The 5G service provider \(u_{v}\) maintains a master secret key \(MS_{u_{v}}\). This key does not leave the \(u_{v}\), and there will be one master key per application. The 5G service provider also knows these temporary secret keys.
# Label-Guided Data Augmentation for Chinese Named Entity Recognition
## 2 Related Work
### Data Augmentation  
Data augmentation is a widely used technique to address data scarcity, originating in Computer Vision and later adapted to NLP tasks [2]. Traditional data augmentation methods, such as deletion, replacement, insertion, and back translation, typically preserve semantic labels at the sentence level. However, for NER tasks, where labels are assigned to each character or token, these methods often disrupt character-level semantics, resulting in misaligned or inaccurate entity labels.  
To counter this issue, researchers have proposed advanced augmentation techniques tailored to NER. For instance, [PERSON] et al. [6] explored data augmentation through the use of multilingual training data and heuristic rules that explicitly inject entity labels into the sentence context. This approach reduces the risk of label misalignment and enriches the augmented data with meaningful entity information.  
In recent years, prompt-based data augmentation methods have been successfully applied to sentence-level low-resource learning tasks, largely due to the careful design of templates and label words. By constructing templates in the form of cloze tests, the likelihood of introducing meaningless or erroneous noise samples can be reduced, thereby improving the quality of the augmented samples [22; 23; 24]. [PERSON] et al. [22] systematically analyzed how to construct templates and select examples for prompt learning when applied to NER, proposing the combination of contextually learned task templates for input augmentation. However, when applied to character-level sequence-labeling tasks such as NER, predicting the entity type of each text span in a sentence requires manually designing templates, which is the inefficient and time-consuming process of enumerating all potential entities for template querying. To address this issue, [PERSON] et al. [25] proposed a more convenient method that reconstructs the NER task into a language model problem without the need for templates. Specifically, this work explored rules that help language models automatically search for suitable label words, retaining the prior knowledge of entity labels provided by prompt-based data augmentation methods while avoiding the complex process of template construction.  
In low-resource sample data, the long-tail distribution phenomenon is more likely to occur. Current NER methods based on traditional data augmentation merely perform simple augmentation operations on the original samples, which do not yield satisfactory results. Simple sentence-level addition or deletion may disrupt the samples of token-level classification tasks or cause label misalignment, potentially exacerbating the imbalance of the sample distribution.