esa-sceva/satcom-chunk-collection huggingface dataset:
# Reflection Enhancement and Giant Lateral Shift in Defective Photonic Crystals with Graphene
## 1 Introduction  
It is well known that the most typical property of photonic crystals (PCs) is the photonic bandgap structure [1]. As a light beam impinges upon PCs, there is a large lateral shift of the reflected beam on the bandgap edge [2]. The shift of reflected beam, which can be spatial and angular, was firstly confirmed experimentally by [PERSON] and [PERSON] [3]. The spatial shift and angular deviation from the prediction of geometrical optics essentially result from the dispersion of reflection coefficients, and could be estimated by the famous Artmann formula [4]. Giant lateral shift has also been reported inside the reflection band, and become extremely large at exceptional points (EPs) in parity-time-symmetric (PT-symmetric) PCs [5]. Furthermore, a great lateral shift may also exist in non-Hermitian systems and periodic dielectric multilayers with PT-symmetry [6, 7]. Meanwhile, low loss in slabs and defects embedded in PCs can greatly enhance the lateral shift [8, 9]. The defective mode (DM) locates at the middle of the bandgap in defective PCs. The lateral shift of the reflected beam near the DM is generally dozens of incident wavelengths, and the reflectivity of the DM can be as low as zero in passive PCs. On the other hand, non-Hermitian systems can induce many unique optical phenomena, and have become a hot topic in optics and other fields [10, 11, 12, 13, 14, 15, 16, 17]. In non-Hermitian systems, large lateral shift can be obtained near exceptional point (EP) and coherent-perfect-absorption (CPA)-laser state [5, 6, 18], but the reflectivity of the EP is also zero and the reflectivity is very low near it [19, 20, 21] while the CPA-laser state is unstable, so these are not conducive to the observation of lateral shift for reflected beams.  
Graphene is an ultrathin two-dimensional material, and it has been studied intensively for its fascinating optical, electronic, and mechanical properties [22, 23, 24, 25, 26]. The surface conductivity ofgraphene could be flexibly modulated by chemical doping or external gate voltages, and the low loss in graphene exists across a wide frequency range [27; 28]. Therefore, we constructed a complex system composed of graphene and defective PCs, of which the surface current of graphene could be utilized to enhance the reflectance of the DM and a giant lateral shift could be induced to obtain low loss in graphene.  
In this study, we investigated the enhancement of reflectance and the lateral shift of reflected beams in defective PCs incorporated with graphene. Graphene was embedded in the PCs to enhance the reflectance of the DM by utilizing the transition conversion of electrons in graphene. Subsequently, we calculated the reflection of the DM for different Bragg periodic numbers. Finally, we studied the lateral shift of reflected beams around the DM as well as the tunability of the lateral shift.
# Efficient Parallel FDTD Method Based on Non-Uniform Conformal Mesh
###### Abstract  
The finite-difference time-domain (FDTD) method is a versatile electromagnetic simulation technique, widely used for solving various broadband problems. However, when dealing with complex structures and large dimensions, especially when applying perfectly matched layer (PML) absorbing boundaries, tremendous computational burdens will occur. To reduce the computational time and memory, this paper presents a Message Passing Interface (MPI) parallel scheme based on non-uniform conformal FDTD, which is suitable for convolutional perfectly matched layer (CPML) absorbing boundaries, and adopts a domain decomposition approach, dividing the entire computational domain into several subdomains. More importantly, only one magnetic field exchange is required during the iterations, and the electric field update is divided into internal and external parts, facilitating the synchronous communication of magnetic fields between adjacent subdomains and internal electric field updates. Finally, unmanned helicopters, helical antennas, 100-period folded waveguides, and 16 \(\times\) 16 phased array antennas are designed to verify the accuracy and efficiency of the algorithm. Moreover, we conducted parallel tests on a supercomputing platform, showing its satisfactory reduction in computational time and excellent parallel efficiency.  
FDTD; non-uniform conformal mesh; CPML absorption boundary; MPI parallel
# PowerBridge: Covert Air-Gap Exfiltration/Infiltration via Smart Plug
## 4 Power Line Communication and Smart Plug
### Smart Plug  
A smart plug is an Internet-connected device designed to remotely control and automate the use of appliances and devices in homes and businesses, common smart socket usage scenarios, as shown in Figure 12. These small devices are installed on standard power plugs, allowing users to easily control their appliances from anywhere without physical interaction. Smart plugs can also provide energy consumption information, notify users of  
Figure 11: Images of common electric cats.  
Figure 8: Power line WiFi cross-layer coverage.  
Figure 10: Power line communication principle diagram.  
Figure 9: Power line WiFi schematic diagram.  
excessive electricity usage, and offer timer functions that can be programmed according to specific events or conditions.  
With the increasing popularity of smart home technology and the proliferation of Internet-connected devices, it is anticipated that the market for smart plugs will experience significant growth in the coming years.  
The global market for smart plugs is projected to reach a value of 9.24 billion US dollars by 2030, exhibiting a compound annual growth rate of 26.1% from 2023 to 2030 [35]. Furthermore, it is anticipated that the shipment volume will attain approximately 104 million units by the year 2030 [36]. In the context of smart homes, these plugs can be directly integrated into wall plugs or connected in series between power lines and appliances through standard power plugs, thereby enabling users to conveniently manage electrical equipment remotely at their convenience.  
The structure of smart plugs primarily comprises power conversion modules and energy acquisition modules; their electrical structure is shown in Figure 13. The electric energy acquisition module transmits the collected current information to the WiFi communication module for external device transmission [37]. This enables intelligent measuring plugs to monitor real-time data on the current, electricity, voltage, and power consumption of electrical equipment while also offering early warning capabilities for voltage peaks. Many smart plugs provide detailed reports on energy consumption and usage patterns of appliances or devices, which facilitate informed decision-making regarding energy conservation strategies or upgrades.  
In this paper, we propose a novel bidirectional communication channel called Power-Bridge for air-gapped computers, which combines a controlled smart plug with [PERSON] et al.'s PowerHammer model.
## 4 Experiments
### Music Identification Experiment  
The music identification experiment has the structure shown in Figure 9. The separation model is trained using mixed music and speech signals as inputs and each original signals as a reference in the training process. In the test process, the mixed signal is separated into the speech and music signal by the trained each separation model, and fingerprinting features are extracted from the separated music signal. The separated speech signal is only used to measure the separation performance. Music identification is performed in the fingerprinting database using the landmark-based fingerprinting feature [33] of the separated music signal. We can obtain the identification result, and we can also calculate the separation performance from the separated signal.
# Blind Deconvolution with Scale Ambiguity
## 3 The Proposed Approach
### Joint Prior
#### 3.1.2 Statistical Analysis  
By introducing the \(L^{p}\) normalization constraint, we obtain the transformed deconvolution formulation (4), in which the regularization term can be regarded as a joint prior of \(u\) and \(k\). Naturally, there is a question that whether this joint regularization term facilitates the deblurring process to generate a pair of pleasing solutions. To this end, we study the statistical property of the joint prior. We find that the joint prior statistically favors ground-truth sharp solutions over trivial solutions.  
To verify, we calculate the average prior ratios of ground-truth pairs \((u,k)\) and trivial pairs \((f,\delta)\) of different \(p\) on the benchmark dataset [19]. Specifically, we first choose 20 different values of \(p\) varying from 0.5 to 5. For each fixed \(p\), we compute the prior ratios \(\|\delta\|_{p}^{0.5}\|\
abla f\|_{0.5}/\|k\|_{p}^{0.5}\|\
abla u\|_{0.5}\) on each sample in the dataset. Then, we calculate the average prior ratios of 32 images for each fixed \(p\) to analyze how often the prior favors the ground-truth pairs. The average prior ratios curve is shown in Figure 1. It can be observed that for \(p>1.2\), the trivial pairs have larger prior values than the ground-truth ones. This indicates that the sharp solutions are encouraged favorably rather than the blurred ones, which further ensure the success of the proposed deconvolution.
## 4 Discussion
### Differences in Vegetation Regulations of LUE Models  
The LUE-type models are predominantly applied in satellite-based models, where the fraction of solar radiation intercepted by terrestrial vegetation (FPAR) is calculated from remote sensing data. In this study, FPAR was either a MODIS product (MOD15) or was derived from NDVI and EVI. Although NDVI and EVI are complementary vegetation indices [68], there was a remarkable difference in the long-term change between NDVI and EVI over the global scale (Figure 6f,g). The NDVI is the integration of near infrared (NIR) and red reflectance bands, while the EVI is the composition of three reflectance bands including NIR, red, and blue bands. Difference of the interannual variability of the reflectance bands may cause different NDVI and EVI trends [68; 69; 70]. We recommend that future studies may quantitative analyze the possible reasons for the differences in the long-term changes of vegetation indices.
## 3 Results
### Inferential Statistical Analysis  
When comparing HC and MCS subjects, a significant 'between-groups' effect (\(p<0.01\), corrected) was found, highlighting statistically significant higher values in MCS patients in: (i) PS recorded on both X plane (X) and Y plane (Y) within low (L) and middle (M) frequency intervals recorded on SP and during CE and OE conditions (Figure 2A, Table 2); (ii) classic posturography parameters mainly in CE condition and on foam carpet (Table 2); and (iii) RDT measurements, in both CW and \(+40^{\circ}\) and CCW and \(-40^{\circ}\) conditions (Figure 2A, Table 2). Conversely, MCS subjects were found to be significantly impaired in VOR gain and reslo values, bilaterally (Figure 2B, Table 2). Mean, SDs and both significant and not significant effects for all variables measurements are shown in Table 2.  
Figure 1: In (**A**) histograms highlighting dizziness handicap inventory (DHI) total scores in Multiple Chemical Sensitivity (MCS) and healthy subjects (HC); In (**B**) star diagram showing MCS and HC quick environment exposure sensitivity inventory (QEESI) sub-items results in head-related (HEAD), cognitive-related (COG), affective-related (AFF), neuromuscular-related (NM), musculoskeletal-related (MS), skin-related (SKIN), genitourinary-related (GU), gastrointestinal-related (GI), heart/chest-related (COR), airway or mucous membrane-related (AIR/MM) symptoms.  
Figure 2: This represents all statistical significant differences in Visual Dependency scores, and spectral values of body oscillations (**A**) and video Head Impulse Test measurements (**B**) when comparing Multiple Chemical Sensitivity (MCS) and healthy subjects (HC). Acronyms represent tags for Rod and Disc test, RDT (counter-clockwise, CCW; clockwise, CW), spectral values of body oscillations on X plane (X) and Y plane (Y) within low (L) and middle (M) frequency intervals recorded on stiff platform and during closed (CE) and opened eyes (OE) conditions and for vestibulo-ocular reflex (VOR) gain and regression slope, reslo; L, left; R, right; med, median.  
Within the vHIT results, analysed using the 36 ears of the 18 MCS patients, fifteen ears (41.6%) had both covert and overt saccade components, and only one overt or covert saccade was observed in the remaining twelve (33.3%) and nine (25%) ears, respectively. Head peak velocity of ten extracted measures per subject was 162\({}^{\circ}\)/s \(\pm\) 11.  
For illustrative purposes, Figure 3 depicts an example of a single MCS and HC subject's patient changes in postural and VOR parameters.
# Virtual Marker Technique to Enhance User Interactions in a Marker-Based AR System
## 4 Virtual Marker Technique in Marker-Based AR System
### Level 1: Virtual Marker Programming  
In our system, virtual markers can be used in conjunction with marker-based tangible programming to provide scalability and customization. We named this virtual marker programming. Markers and hand gestures will be used in virtual marker programming. Each marker contains information, and multiple markers will be placed in the scene in a specific order. The arrangement will be read and recognized by the system and a visualization program will be generated. Since it is difficult to accurately identify a large number of markers at the same time in a limited FOV, we propose an information combination method. Information from multiple markers will be merged and become the content of the first marker. After that, the user can use a single marker to replace the previous multiple markers to make more complex programming. In the AR scene, virtual and physical markers can be observed at the same time. These markers will be located in the same coordinate system. Therefore, the system can obtain a marker sequence composed of virtual and physical markers. Language syntax structures are provided for virtual marker programming, such as condition structure and loop structure. The architecture of the system can be expanded on this basis and more structures can be introduced.  
We use an example to illustrate how gestures and virtual markers are used (see Figure 6). By using a defining gesture, the user can merge the content of multiple markers into the first marker. The user can copy the marker using selecting and copy gestures. When the marker is selected and the user performs a value change gesture, the value of the selected marker will be modified. In this example, we show how the information of the virtual markers can be combined.  
Based on this example, users can complete more complex virtual marker programming (see Figure 7). The user can merge the combined information into the first marker using a defining gesture. The final function definition can be formed through multiple markers that already contain combined information.  
Figure 6: An example to illustrate how to combine the content of multiple markers, operate markers, and modify the marker parameter. First, the user creates function and variable virtual markers. In step 1, the user uses a defining gesture to combine the contents of the two virtual markers into the first virtual marker. In step 2, the user selects a variable virtual marker and uses a deleting gesture to delete the variable virtual marker. In step 3, the user selects the virtual marker and uses the copy gesture to copy it. After completing the copy, the user places the two virtual markers in different positions through dragging gesture. In step 4, the user selects the virtual markers by selecting gesture and uses the parameter/value change gesture to modify the parameter of the selected marker.
## 3 Results  
### Dataset Creation and Enhancement  
The research focused on the Buertai coal mine in Erdos City, where uniform sensor models and standardized sampling procedures were implemented across all working faces. The investigation leveraged FEMR signal data spanning January 2019 through May 2023, extracted from the mine's comprehensive database. Given the intermittent nature of rock burst precursor manifestations during extraction activities, the potential for model overfitting due to limited data samples was mitigated through strategic data expansion methodologies. These approaches were specifically designed to strengthen the predictive model's resilience. To account for geographical variations in frequency distribution patterns that might affect precursor identification, the research team implemented advanced time-frequency characteristic enhancement strategies. These included the spatial transformation of graphical representations, selective image segmentation, and the incorporation of controlled Gaussian noise at varying intensities (0.05 and 0.005). Through these techniques, the repository of enhanced time-frequency diagrams was expanded to 2600 instances, substantially boosting the model's detection accuracy.  
The methodology incorporated a rigorous field data acquisition process coupled with meticulous data labeling, followed by the systematic partitioning of the dataset into distinct groups for model training, evaluation, and final assessment. Following established research conventions, the FEMR records from the Buertai mine were proportioned into training and validation cohorts at an 80:20 distribution. The comprehensive documentation of EMR signal classifications within the database, including specific numerical distributions for both training and validation groups, is presented in Table 1.  
Signal Super Prediction and Precursor Recognition Framework Based on Guided Diffusion Model with Transformer
## 6 Conclusions  
We have presented and implemented an FEC-based framework for H.264/AVC compressed video bitstream. The proposed FEC integrates H.264 syntax element-based selective encryption that improves the quality of videos that have been affected by various types of channel errors during transmission and provides protection against unauthorized access. It is a block coding FEC method that corrects the error bit by finding its location within a macroblock of H.264 encoded video bitstream. It has been tested on multiple test video sequences of CIF and HD resolution at different QP values. QP is the quality of perception and it is reciprocal to the video quality. The results are presented at three different QP values (12, 34, and 48). The effectiveness of the proposed FEC method was analyzed by evaluating the PSNR and PSNR\({}_{611}\) values of selectively encrypted, noisy encrypted, crypto-encoded, decoded without FEC, and recovered with FEC videos. These results exhibited a significant improvement in the PSNR values of each component (Y, U, and V) and PSNR\({}_{611}\) values of the videos using the proposed FEC mechanism. The performance of our FEC algorithm was best observed at QP 34. Our method can correct up to 90-95% errors from a 10-15% affected erroneous video by correctly guessing the error bits and reversing those bits to recover the error. It is a simple method that reduces the computational complexity of the codec as it does not involve complex mathematical operations. It combines the encryption process with error correction to avoid the additional computation cost. Moreover, it does not require any backchannel for retransmitting the complete data in case of error occurrence, avoiding transmission delay and making it suitable for real-time applications where low latency is crucial. Therefore, it can be used practically to preserve the objective quality of transmitted videos that has been compromised during transmission due to noisy channels.  
**Author Contributions:** Conceptualization, M.B.I. and R.K.; Methodology, M.B.I. and R.K.; Software, M.B.I.; Writing--original draft, M.B.I. and R.K.; Supervision, M.B.I. 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:** Data are available upon request from the authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Materials and Methods
### Evaluation of Model Accuracy  
To minimize accidental errors resulting from splitting the training and test sets, this study used a ten-fold cross-validation method to split the dataset of fifty-two ground observation samples into training and validation sets. The principle of cross-validation involves dividing the dataset into ten groups of equal sample sizes. The other nine groups were utilized as the training set while one group was chosen consecutively as the verification set to test the model's ability of estimation. For each cross-validation, the determination coefficient (R\({}^{2}\)) and root mean square error (RMSE) were calculated for the forecasted and realized values. This process was repeated ten times to calculate the average values [41]. A higher R\({}^{2}\) value indicated greater accuracy of the model, while a smaller RMSE value is signified a more accurate regression model. The calculation of each indicator is as follows:  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_ {i}-\overline{y})^{2}} \tag{5}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{n-1}} \tag{6}\]  
where \(n\) is the sample size, \(y_{i}\) is the actual carbon storage of the plot \(i\), \(\overline{y}\) is the mean of the measured carbon storage, and \(\hat{y}_{i}\) is the estimated value of carbon storage at plot \(i\).
## 1 Introduction
The main contributions of this article are summarized as follows:  
* the DN\({}^{2}\)PCIoT algorithm that optimizes partitionings aiming for inference rate maximization or communication reduction while properly accounting for the memory required by the CNNs' shared parameters and biases;
* a case study whose results show that the DN\({}^{2}\)PCIoT algorithm is capable of producing partitionings that achieve higher inference rates and that it is also capable of producing valid partitionings for very constrained IoT setups;
* a case study of popular ML tools such as TensorFlow, DIANNE, and DeepX, which may not be able to execute DNN models on very constrained devices due to their per-layer partitioning approach;
* a study of the METIS tool, which indicates that it is not an appropriate tool to partition DNNs for constrained IoT setups because it may not provide valid partitionings under these conditions;
* an analysis of the DNN model granularity results to show that our DNN with more grouping minimally affects the partitioning result;
* an analysis of how profitable it is to distribute the inference rate execution among multiple constrained devices; and
* a greedy algorithm to reduce the number of communications based on the available amount of memory of the devices. This paper is organized as follows. Section 2 provides the background in CNNs and neural networks represented as a dataflow graph; it also presents the related work in ML and IoT tools and in general-purpose, automatic partitioning algorithms. Section 3 presents the DN\({}^{2}\)PCIoT algorithm. Section 4 explains how LeNet was modeled, the adopted approaches, and the experiment setups. Section 5 presents and discusses the results. Finally, Section 6 provides the conclusions.