esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Inverse Design of Silicon Photonics with Iterative Optimization Algorithms
### Optimization of Irregular Structures
Firstly, the derivative of total FOM to actual EM fields in the objective domain \(D_{o}\) is calculated as in Equation (4):  
\[\delta F(\mathbf{E},\mathbf{H})=2\text{Re}\int_{D_{o}}\bigg{[}\frac{\partial f} {\partial\mathbf{E}(\mathbf{x_{o}})}\delta\mathbf{E}(\mathbf{x_{o}})+\frac{ \partial f}{\partial\mathbf{H}(\mathbf{x_{o}})}\delta\mathbf{H}(\mathbf{x_{o}} )\bigg{]}\text{d}^{3}\mathbf{x_{o}}, \tag{4}\]  
where factors \(\frac{\partial f}{\partial\mathbf{E}(\mathbf{x_{o}})},\ \frac{\partial f}{ \partial\mathbf{H}(\mathbf{x_{o}})}\) are the constant values and are easy to get from Equation (2) as in Equation (5):  
\[ \begin{array}{c}\frac{\partial f}{\partial\mathbf{E}(\mathbf{x_{o}})}=\overset {\wedge}{\mathbf{n_{E}}}\times\overline{\mathbf{H_{T}}(\mathbf{x_{o}})}\\ \frac{\partial f}{\partial\mathbf{H}(\mathbf{x_{o}})}=\overline{\mathbf{E_{T} }(\mathbf{x_{o}})}\times\overset{\wedge}{\mathbf{n_{H}}}\end{array} . \tag{5}\]  
However, the terms \(\delta\mathbf{E},\ \delta\mathbf{H}\) cannot be calculated analytically, as the relationship between \(\mathbf{E},\mathbf{H}\) at objective domain and \(\epsilon,\ \mu\) at design domain cannot be expressed by an explicit function. Since the variation of permeability \(\delta\mu\) causes little change to EM fields, only the variation of permittivity \(\delta\epsilon\) is considered. In the design domain, the original geometry has electric field \(\mathbf{E}(\mathbf{x_{s}})\) at \(\mathbf{x_{s}}\). When a very small volume \(V\) at this position has a small permittivity change \(\delta\epsilon\), this will cause an induced dipole moment \(\mathbf{p^{ind}}(\mathbf{x_{s}})=V\epsilon_{0}\sigma\epsilon\mathbf{E}( \mathbf{x_{s}})\). Numerically, EM field variation \(\delta\mathbf{E}(\mathbf{x_{o}}),\ \delta\mathbf{H}(\mathbf{x_{o}})\) at \(\mathbf{x_{o}}\) in the objective domain come from the summation effects of this induced dipole moments in the design domain, which can be expressed with [PERSON]'s function [72] as in Equation (6):  
\[ \begin{array}{c}\delta\mathbf{E}(\mathbf{x_{o}})=\int_{\mathbf{ds}}\mathbf{ G}^{\mathbf{EP}}(\mathbf{x_{o}},\mathbf{x_{s}})\mathbf{p^{ind}}(\mathbf{x_{s}}) \text{d}^{3}\mathbf{x_{s}}\\ \delta\mathbf{H}(\mathbf{x_{o}})=\int_{\mathbf{ds}}\mathbf{G}^{\mathbf{HP}}( \mathbf{x_{o}},\mathbf{x_{s}})\frac{1}{\mu}\mathbf{p^{ind}}(\mathbf{x_{s}}) \text{d}^{3}\mathbf{x_{s}}\end{array} , \tag{6}\]  
where \(\mathbf{G}^{\mathbf{EP}}(\mathbf{x_{o}},\mathbf{x_{s}})\) and \(\mathbf{G}^{\mathbf{HP}}(\mathbf{x_{o}},\mathbf{x_{s}})\) represent the EM fields at \(\mathbf{x_{o}}\) in the objective domain from a unit dipole at \(\mathbf{x_{s}}\) in the design domain. The symmetry theory suggests the following relation as in Equation (7):  
\[ \begin{array}{c}\mathbf{G}^{\mathbf{EP}}(\mathbf{x_{o}},\mathbf{x_{s}})= \mathbf{G}^{\mathbf{EP}}(\mathbf{x_{s}},\mathbf{x_{o}})\\ \mathbf{G}^{\mathbf{HP}}(\mathbf{x_{o}},\mathbf{x_{s}})=-\mathbf{G}^{\mathbf{HP} }(\mathbf{x_{s}},\mathbf{x_{o}})\end{array} .
# On the Use of Deep Learning for Video Classification
## 6 Conclusions  
This article reviews deep learning approaches for the task of video classification. Some of the notable studies are summarized in detail, and the key findings in these studies are highlighted. The key findings are reported as an effort to help the research community in developing new deep learning models for video classification.  
The latest developments in deep learning models have demonstrated the potential of these approaches for the video classification task. However, most of the existing deep learning architectures for video classification are basically adopted from the favored deep learning architectures in image/speech domain. Therefore, most of the existing architectures remain insufficient to deal with the more complicated nature of video data that contain rich information in the form of spatial, temporal, and acoustic clues. This calls for attention towards the need for a tailored network capable of effectively modeling the spatial, temporal, and acoustic information. Moreover, training CNN/RNN models requires labeled datasets, and acquiring those datasets is usually time-consuming and expensive, and hence, a promising research direction is to utilize the considerable amount of unlabeled video data to derive better video representations.  
Furthermore, the deep learning approaches are outperforming other state-of-the-art approaches for video classification. The deep learning Google trend is still growing, and it is still above the trend for some other very well-known machine learning algorithms, as shown in Figure 9a. However, the recent developments in deep learning approaches are still under-evaluated and require further investigations for the video classification task. One such example is geometric deep learning approaches, and the worldwide research interest in this specific topic is shown in Figure 9b, which describes that this topic is still confined to some states of U.S., Europe, and India. Therefore, it has yet to be developed and investigated further. The use of geometric deep learning in extracting rich spatial information from videos can also be a new research direction as a future work for better accuracy in the video classification task.  
**Author Contributions**: Conceptualization, A.u.R. and S.B.B.; methodology, M.A.K.; validation, A.K.; formal analysis, S.B.B.; data curation, A.u.R.; writing--original draft preparation, A.u.R.; writing--review and editing, M.A.K.; visualization, A.u.R.; supervision, S.B.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
**Acknowledgments:** We would like to acknowledge Malardalen University for supporting the publication charges.  
**Conflicts of Interest**: The authors declare no conflict of interest.
# Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information
## 5 Static Detection Algorithm
### Self-Adaptive MUSIC Algorithm
* Construct a Gaussian mask \(\mathrm{g}_{\alpha}\left(\theta\right)\) with an expected value \(\alpha\) and a variance according to the desired level of error tolerance. Set \(\tilde{\mathrm{g}}_{\alpha}\left(\theta\right)=1-\mathrm{g}_{\alpha}\left( \theta\right).\)
* Calculate \(\mu\left(\rho\right)=\frac{\mathrm{g}_{\alpha}(\theta)\rho^{\prime}(\theta) \mathrm{d}\theta}{\mathrm{g}_{\alpha}(\theta)\rho^{\prime}(\theta)\mathrm{d} \theta}\). Our algorithm estimates the best deviation for each packet and then calculates the offset of all packets. We will generate two phase deviation values \(\langle\delta_{0},\delta_{1}\rangle\) in the multiple clustering algorithm and select the most frequent phase deviation combination as our final estimate. Calibrate Antenna 2 and Antenna 3 while applying the calibrated CFR to the traditional MUSIC algorithm. This inherent deviation occurs when the device is started and does not change when the device is running, but the phase deviation is reset when the device is restarted. We apply this algorithm to the spatial smoothing MUSIC algorithm to automatically correct each phase offset at the start of the device. At this point, we eliminate the time offset in the different CSI packets generated by the STO, make the direct path more accurate and evaluate the optimal phase offset by the self-calibration method, which is generated by the SFO. In the fourth quarter, a large number of experiments shows that for different angles of the AP, the self-calibration algorithm can effectively detect its position. What we want is only the direct path. Therefore, we adopt spatial smoothing to get the direct path accurately. Though our witnessing AP only has a three-antenna array, it can detect two AoA in the indoor environment. Therefore, we group our antennas into two groups \(\{\mathrm{antenna1,2;antenna2,3}\}\) as the input signal to detect the direct path. In detail, spatial smoothing generating signals \(x_{1}\),\(x_{2}\),\(x_{3}\) would output two signals \(\dot{x}_{1}\), \(\dot{x}_{2}\) where \(\dot{x}_{i}=\frac{1}{2}(\dot{x}_{1}+\dot{x}_{2}),i=1,2\). At last, we implemented two algorithms using only a three-antenna service AP. Testing the same situation as the traditional algorithm, that is the \(90^{\circ}\) AP on the LoS, our self-adaptive algorithm can accurately detect the LoS, and the detection error is \(1^{\circ}\), which shown in Figure 2b. At the same time, the impact of NLoS is eliminated. Compared to Phaser [14] and ArrayTrack [44], we use fewer antenna arrays and achieve the total average error of \(6.3^{\circ}\); accurately enhancing \(5.6^{\circ}\) compared to traditional algorithms. Similarly, compared with the SpotFi [10] with a certain complexity, we do not need the clustering algorithm to calculate the AP position and ToF estimation.
# A Novel Implicit Neural Representation for Volume Data
###### Abstract  
The storage of medical images is one of the challenges in the medical imaging field. There are variable works that use implicit neural representation (INR) to compress volumetric medical images. However, there is room to improve the compression rate for volumetric medical images. Most of the INR techniques need a huge amount of GPU memory and a long training time for high-quality medical volume rendering. In this paper, we present a novel implicit neural representation to compress volume data using our proposed architecture, that is, the Lanczos downsampling scheme, SIREN deep network, and SRDenseNet high-resolution scheme. Our architecture can effectively reduce training time, and gain a high compression rate while retaining the final rendering quality. Moreover, it can save GPU memory in comparison with the existing works. The experiments show that the quality of reconstructed images and training speed using our architecture is higher than current works which use the SIREN only. Besides, the GPU memory cost is evidently decreased.  
implicit neural representation; volumetric medical image; super-resolution; SIREN +
Footnote †: journal: Medical Image Processing  
1
Footnote 1: email: _Applied_  
Footnote †: journal: Medical Image Processing  
of high-resolution (HR) and low-resolution (LR) images, which can fit the downsampled volume data in usual GPUs with low graphical memory. As a result, the SIREN's layers can be reduced. Training can be accelerated accordingly. The following super-resolution (SR) reconstruction network can recover images up to the quality of the original HR.  
In summary, our contributions include:  
* Our architecture consists of the Lanczos downsampling scheme, SIREN deep network, and SRDenseNet upsampling scheme, which increase the speed of training and decrease the demand for GPU memory in comparison with existing INR-based compression techniques;
* Our architecture can reach both a high compression rate and high quality of the final volume data rendering.  
The following part of this paper is organized as follows. Section 2 presents the related work. Section 3 illustrates the methodology. Section 4 shows the experiments and results from the analysis. Section 5 draws our conclusions and presents future works.
# Occupational Risk Prediction for Miners Based on Stacking Health Data Fusion
## 3 Introduction to Methods and Data Processing
### Database
#### 3.1.1 Removal of Outliers Based on a Priori Conditions  
One approach to outlier removal based on prior conditions involves using the mean and standard deviation. This method assumes that the data follow a normal distribution and compares individual data points to the mean. Outliers can be defined by the standard deviation method. Low outliers are defined as values less than \(\mu-3\sigma\), and high outliers are defined as values greater than \(\mu+3\sigma\), where \(\mu\) is the mean and standard deviation of the data. If the data point deviates from the average value by more than 3 times the standard deviation, the data point will be marked as abnormal and removed due to noise, sensor failure or other atypical factors.  
Suppose \(X\) is a data matrix of \(n\) rows and \(m\) columns, where \(n\) denotes the number of samples and \(m\) denotes the number of features. We use \(x_{ij}\) to denote the data in \(i\) rows and \(j\) columns of \(X\). For each feature \(j\), first, calculate its mean \(\mu_{j}\) and standard deviation \(\sigma_{j}\).  
The mean, computed as the summation of all observed values in a dataset divided by the sample size, serves as a fundamental measure of central tendency in statistical analysis. This parameter is extensively employed to characterize the central position of  
Figure 3: Health threat assessment model for miner operators.  
data distributions and quantify their overall trends, providing critical insights for data interpretation and hypothesis testing in empirical research.  
\[\mu_{j}=\frac{1}{n}\sum_{i}^{n}=1^{x_{ij}} \tag{1}\]  
Standard deviation refers to the average deviation between each data point in the dataset and the average value. It is used to measure the degree of data dispersion.  
\[\sigma_{j}=\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}\left(x_{ij}-\mu_{j}\right)^{2}} \tag{2}\]  
Standardization is to transform data into a distribution with a mean value of 0 and a standard deviation of 1. Normalize the data matrix to obtain the normalized data matrix \(Z\):  
\[z_{ij}=\frac{x_{ij}-\mu_{j}}{\sigma_{j}} \tag{3}\]  
Next, for each feature \(j\), it is assumed to follow a normal distribution \(N\) (\(\mu_{j}\), \(\sigma_{j}^{2}\)). Based on the 3\(\sigma\) criterion, a threshold \(T_{j}=3\sigma_{j}\) is defined, where data points exceeding this threshold will be considered outliers. Finally, for each sample \(i\), it is determined whether it is an outlier. For each feature \(j\), the standardized value \(z_{ij}\) is checked to see if it lies within the interval \([-T_{j},T_{j}]\). If, for all the features \(j\), \(z_{ij}\) falls within this interval, the sample \(i\) is considered normal; otherwise, the sample \(i\) is considered abnormal.
# Optimizing Reinforcement Learning Using a Generative Action-Translator Transformer
## 3 Action-Translator Transformer
### Model Formulation  
#### 3.2.1 Basic Introduction of the Model  
We have devised a novel reinforcement learning sequence decision model called the Action-Translator Transformer (ATT) for the task of generating reinforcement learning sequence trajectories. This model leverages existing trajectory data to learn the relationship between the target reward and the trajectory. It learns sequences that lead to the target reward, allowing the generation of corresponding trajectories based on a user-defined target reward value. This approach aims to achieve the maximum target reward in the current environment, completing the reinforcement learning task. Similar to DT, ATT directly models trajectory data and avoids the training instability associated with bootstrapping  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Environment** & **Dataset** & **DT (\(L_{1}\))** & **DT (\(L_{2}\))** \\ \hline \multirow{3}{*}{Walker} & Medium & 74.0 & 73.1 \\  & Medium-Expert & 108.1 & 108.3 \\  & Medium-Replay & 66.6 & 67.2 \\ \hline \multirow{3}{*}{Hopper} & Medium & 67.6 & 67.0 \\  & Medium-Expert & 107.6 & 108.0 \\ \cline{1-1}  & Medium-Replay & 82.7 & 78.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance results of DT with different types of loss functions.  
and iteration, eliminating issues related to MDP-based reinforcement learning errors (refer to Appendix A).  
To ensure that our model focuses solely on predicting actions without providing additional environment and reward information, we draw inspiration from the field of NLP, specifically the text translation problem. We conceptualize the training process as a translation task, transforming state and reward information into the corresponding actions. As illustrated in Figure 4, we define a target reward value (the maximum reward value of the environment) based on environment-specific information or prior knowledge. This target reward value, along with the current environment state information, is inputted into our ATT model, which then predicts the action to be performed. After interacting with the environment to update the state and receive instant rewards, we update the historical trajectory information, the current target reward value, and the current state information. This process is repeated, predicting the next action until reaching a termination state or satisfying the target reward. This outlines the fundamental process of using ATT to predict optimal actions and obtain optimal trajectories.  
Figure 4: Schematic of the overall framework of the ATT model, abstracting the predicted actions into text translation task solving. The details of the Action-Translator are shown in Figure 5.  
Figure 5: Action-Translator Transformer overall model architecture.
## 1 Introduction  
### Motivation  
IEEE 802.15.4 has become the _de facto_ standard for wireless sensor networks [1] in a broad spectrum of applications such as home automation [2] and vehicle/satellite communications [3]. The flexibility of the carrier sensing multiple access with collision avoidance (CSMA/CA) enables the implementation of distributed networks with heterogeneous devices [4]. Many of these networks involve sensing and control tasks, which typically have lower traffics compared with normal wireless data network, but require each node to sustain certain throughput demand to guarantee system performance. For example, in a satellite attitude determination and control system (ADCS), various sensor nodes need to successfully transmit a number of packets containing sensor measurements to the controller within a certain time interval in order for the control signal to be properly computed and executed.  
Subject to the throughput constraint, each node has the incentive to minimize its transmission attempts as more frequent transmission consumes more energy and occupies more channel resources. Thus, it is essential to tune each node's transmission rate for both \"private\" and \"public\" benefits. This, in a homogeneous IEEE 802.15.4 CSMA/CA network, can be achieved using Markov modeling techniques [5; 6; 7; 8], from which several key performance metrics such as packet success rate, packet delay can be derived and subsequently the optimal transmission rate can be carefully selected with respect to the given requirements.  
However, in many wireless sensor network (WSN) and Internet of Things (IoT) applications, nodes are heterogeneous in that they have different throughput demands and thus different traffic loads. In addition, the traffic generated by each node may change over time. For example, a sensor monitoring a plant may refrain from sending its measurements to the controller unless the sensor readings are abnormal [9; 10]. Consequently, a centralized Markov chain framework may not be flexible enough or may be too computationally expensive to be implemented online. Therefore, in order to fully exploit the flexibility of IEEE 802.15.4 protocol, it is strongly required to (a) devise a distributed mathematical framework to characterize the heterogeneous traffics and (b) to tune the transmission rate of each node to meet the specific performance requirements in a network with time-varying traffics.
# Comprehensive Comparison of VNE Solutions Based on Different Coordination Approaches
## 4 Evaluation of Examined Solutions
### Performance Metrics  
For the evaluation of our experimental results, we used the following performance metrics. The acceptance ratio (AR) calculates the successfulness of an embedding solution by dividing the number of completely mapped VNRs by the total number of processed VNRs. To avoid any overly simplifications, we should mention that a high AR value by itself does not necessarily translate to an absolutely efficient embedding strategy. The cost to revenue measurement (COSTREV) represents the relationship between the cost of SN resources spent and the revenue grossed after the completion of the mappings. However, the higher the number of the rejected VNRs the lower the eventual cost may seem to be, thus, determining a respectively low COSTREV value, which would fail to depict the real successfulness of the embedding algorithm.  
In this context, the COSTREV metric would prove to be a lesser quality indicator of the executed mappings. A modified version of COSTREV could be the CR*MR metric [24], which multiplies the former by the mapped revenue ratio. By dividing the number of the mapped nodes of the successfully embedded VNRs to the total number of the residual nodes of a SN that actively serve those VNRs, we can calculate the active node stress metric (ANSTRESS). The running time (RT) is the time spent by each embedding process in order to achieve a complete and successful VNR mapping.
## 4 Discussion
### Personalized Planning in Plan Development  
Personalized planning is a new generation of advanced optimization algorithms for inverse planning included in the new version of the Pinnacle TPS.  
The use of the aforementioned engine can lead to fully automated planning based on the development of a specific template (referred to as techniques in Pinnacle) for each treatment site and protocol. In their study, [PERSON] et al. [20] highlighted the applicability of the Pinnacle personalized engine for high-risk prostate patients by performing a comprehensive dosimetric evaluation. In particular, 26 clinical plans were obtained by applying personalized planning on 13 high-risk and 13 low-risk cancer patients, and the results were compared with the plans obtained by the previous version of the autoplanning algorithm and with the ones manually generated by expert medical physicists. These comparisons showed increased plan quality in terms of dosimetric evaluations for target volumes (i.e., D98%, D95%, D2%, HI, and conformity index) and dose constraints for OARs for the plans developed using personalized planning compared to the autoplanning and manual ones. In accordance with these results, the plans generated for the prostate case in our study showed high homogeneity and high dose conformity for all the investigated targets. Of note, the AAPM constraints themselves required extremely high homogeneity inside the target volumes as the prescription on the high-risk PTV was 68 Gy to 95% of the volume with a maximum acceptable overdosage of 71.5 Gy, corresponding to the 105% of the prescription dose. In our center, more variability of PTV coverage is considered acceptable in clinical settings. PTV coverage is evaluated using 95% of the prescription dose, which has to cover at least 95% of the volume.  
Furthermore, in another work from [PERSON] et al. [23], the application of personalized planning on a population of 15 high-risk head and neck patients with various tumor sites and stages was investigated in terms of clinical and dosimetric evaluations. The implemented approach demonstrated an overall significant improvement in plan quality in target coverage, dose conformity, and dose reduction to OARs with respect to the ones directly derived from manual planning.  
In our study, personalized planning successfully generated a higher number of clinically acceptable and deliverable plans for the PCa case than for HNCa cases, and larger PTVs overlapped on several OARs and specific anatomical features. This result is in line with the plan complexity in several cases, ultimately affecting the deliverability of plans for HNCa.