esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
Furthermore, while applying the HRNet model for pose estimation, we proposed using Yolov5 Tiny instead of Yolov3 for detection of human objects in a frame to improve both speed and accuracy. Figure 7 shows an example of the human keypoints extracted from a key frame of one of the videos in the self-collected HNH dataset. Multiple human objects can be seen in Figure 7 along with other objects, and it can also be observed that the pose estimation model correctly identified the human objects and the relative positions of their joints for subsequent action recognition. In addition to action recognition, the proposed framework simultaneously performed face and emotion recognition on the selected key frame in real time, the results of which are depicted in the text labels of the bounding boxes in Figure 7. Moreover, it is pertinent to mention here that the video clip to which the frame in Figure 7 belongs was not used to train the DDNet model for action recognition. Instead, the DDNet model was pretrained using a subset of our self-collected HNH dataset for action recognition. Similarly, the face recognition model ArcFace [44] was pretrained using the self-collected HMTA dataset for face recognition. The training data for face recognition did not include the video clip to which the frame in Figure 7 belongs. Likewise, the residual  
Figure 6: The selected \(k\) frames for a video from the UCF Sports dataset, labeled as a _walking video_ based upon the highest \(k\) values of the gradient of the total motion metric. The red dots show the local minima, whereas the green dots show the local maxima. masking network [45] that we employed for emotion recognition was pretrained using a subset of the self-collected HMTA dataset and tested both on the FER2013 dataset [20] and the HMTA dataset. A description of these datasets is available in Section 4. The keypoints extracted using both HRNet and OpenPose were used to train the DDNet [30] for action recognition. The human skeletal keypoints were used to train the DDNet directly without any preprocessing. The DDNet model was trained using [PERSON] [49] as the optimizer with the parameters \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\). Different values were tried for the learning rate, including \(1\times 10^{-2}\), \(1\times 10^{-3}\) and \(1\times 10^{-4}\). The training was performed for a different number of epochs ranging from 50 to 600 with a batch size of 64. During training, the value of the loss function showed a steady decrease. On the validation set, the best accuracy was achieved at a learning rate of \(1\times 10^{-4}\) and at the 350 th epoch. Hence, we used a learning rate of \(1\times 10^{-4}\) and continued the training process for 350 epochs. Moreover, the human skeleton features extracted using HRNet yielded better results in terms of action recognition on several datasets; however, it was not as fast as OpenPose, especially when the number of human objects in a video frame increased.
# Neuroimaging of Pediatric Cerebellum in Inherited Neurodegenerative Diseases
## 2 Mitochondrial Disorders
### H Leukodystrophy  
RNA polymerase III (POLR3) is made of 17 subunits, with two major subunits, RPC1 and RPC2, encoded by the _POLR3A_ and _3B_ genes, respectively. POLR3-related leukodystrophy is a term that now encompasses all the hypomyelination diseases caused by mutations of these genes [26; 55]. Biallelic variants in POLR3A cause 4H leukodystrophy, an autosomal recessive disorder characterized by hypomyelination, hypogonadotropic hypogonadism, and hypodontia, associated with early cerebellar ataxia. Pyramidal signs, cognitive decline, endocrine features, and myopia are usually present, and tooth agenesis, including hypodontia or oligodontia, is a characteristic manifestation.  
Neuroimaging demonstrates cerebellum atrophy with normal basal ganglia and T2 hyperintensity of middle cerebellar pedunces. Cases with POLR3A demonstrate less cerebellar atrophy, while cases with POLR3B show subtler degrees of hypomyelination 27 (Figure 11).
# Double-Consensus Based Distributed Optimal Energy Management for Multiple Energy Hubs
## 4 Application Examples  
In this section, several simulations have been studied to verify the effectiveness of the proposed algorithm. The first case is used to show the effectiveness of the proposed DDC algorithm via comparison to a centralized algorithm. The second case shows the performance of the proposed algorithm under time-varying demand. The third case demonstrates the plug and play properties of the proposed algorithm. The configuration and communication structures of the test system with five energy hubs are shown in Figure 3. The energy transformer efficiencies \(\eta^{ee}\), \(\eta^{e,clp}\), \(\eta^{h,clp}\) and \(\eta^{boil}\) are selected as 0.98, 0.35, 0.4 and 0.9, respectively. The cost parameters and constraints of each hub are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Hub1** & **Hub2** & **Hub3** & **Hub4** & **Hub5** \\ \hline \(a^{e}\) & 0.12 & 0.08 & 0.09 & 0.05 & 0.13 \\ \(b^{e}\) & 12.0 & 13.0 & 12.5 & 13.5 & 11.5 \\ \(a^{\mathcal{S}}\) & 0.033 & 0.023 & 0.042 & 0.033 & 0.012 \\ \(b^{\mathcal{S}}\) & 5.8 & 6.0 & 5.5 & 6.3 & 8.6 \\ \(\omega^{e}\) & 0.011 & 0.012 & 0.009 & 0.012 & 0.008 \\ \(\omega^{h}\) & 0.021 & 0.023 & 0.031 & 0.025 & 0.026 \\ \(E^{e,min}\) & 0 & 0 & 0 & 0 & 0 \\ \(E^{e,max}\) & 200 & 150 & 175 & 210 & 200 \\ \(E^{g,min}\) & 0 & 0 & 0 & 0 & 0 \\ \(E^{e,max}\) & 200 & 275 & 150 & 175 & 375 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the test system.  
Figure 3: Structure of multiple energy hub system.
# Two-Dimensional Digital Beam Steering Based on Liquid Crystal Phase Gratings
## 2 Materials and Methods
### Manufacturing of LC Cells  
The 1D liquid crystal cell is made up of an ITO coated glass sandwich that is filled up with nematic liquid crystal. One of the substrates is pixelated using a DLW technique, which allows generating high resolution structures in wafers with transparent ITO (Figure 5). The electrodes (pixels) are separated by less than 3 \(\upmu\)m, leading to devices with a high fill factor [25]. The DLW installation (Lasing S.A.), consists of a UV laser with a CNC controlled XYZ-stage that shifts the substrate in XY while maintaining the focal distance using a second laser feedback system.  
Figure 4: Schematic of the blaze grating for 72 pixels. The purple phase step structure corresponds with a topology \(+12\) and the orange structure corresponds with a topology \(-1\).  
Figure 3: Schematic of the blaze grating structure over 12 pixels. The full colored orange, green, and purple rectangles present the wrapped phase delay of each pixel, corresponding to different integer and fractional topologies: (**a**) Topology \(+4\), (**b**) Topology \(+4.2\), (**c**) Topology \(+5\). In all pictures, the sign of the topology is positive, corresponding to a deviation to the right or up.  
Both the pixelated and the continuous glass wafers were conditioned with rubbed polyimide PIA-2304(Chisso LIXON aligner, Tokyo, Japan), ensuring a uniform homogenous LC alignment. The cell thickness was controlled using randomly positioned 6.4 \(\upmu\)m diameter cylindrical silica spacers. Once the cell is mounted and sealed, it is filled in vacuum with the positive nematic high birefringence liquid crystal MDA-98-1600 (Merck KGaA, Darmstadt, Germany). The thickness of the cell and the birefringence determined at 632 nm of the LC allow for an optical retardation variation of \(\Delta\)n\({}_{632\mathrm{nm}}\cdot\mathrm{L}=0.266\cdot 6.4\)\(\upmu\)m = 1.7 \(\upmu\)m, which means that, assuming that the reduction in birefringence does not exceed 10%, the manufactured cell will be applicable to wavelengths close to 1550 nm, which in turn would allow for twice the steering angle. The light transmission of the substrates limits the minimum working wavelength to about 400 nm.  
Interconnections between the employed flex connector and the ITO electrodes were done using an Anisotropic Conductive Adhesive (Hitachi Chemical, Tokyo, Japan). In the contact area, the ITO between electrodes was eliminated by multiple ablation lines.  
The free end of the flex connector was connected directly to the driving electronics, providing full and independent control of each of the electrodes.
# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## 5 Discussion
Moreover, melting snow is mapped using ancillary S2 data if cloud cover does not hamper snow detection, whereas desert areas are not present in Italy. The use of a buffer zone of fixed buffering distance (1 km), done to classify pixels belonging only to the TDWA/TDFA and not to the CDWA/CDFA (Sections 3.2.2 and 3.2.3), can be considered as a critical aspect, because the choice of 1 km, done based on our previous experience with SAR images of inundations, is subjective and may be questioned. However, this situation generally regards a few pixels (often forming objects smaller than the minimum mapping unit), as demonstrated in Figure 12, and does not have a significant impact on the final map.
# Identifying Synthetic Faces through GAN Inversion and Biometric Traits Analysis
## 2 Related Work
### GAN Image Generation and Inversion  
The GANs' typical inner mechanism entails that (random) vectors lying in a latent space are transformed into semantically meaningful images. This procedure can be also inverted to some extent, by back-projecting an image into a point in the latent space that corresponds to similar content in the image space.  
The inversion of generative models has recently drawn strong attention in the computer vision community [12; 16]. In this context, an interesting property is that the latent space can be queried and browsed along specific directions corresponding to visual attributes. As a matter of fact, inversion processes are mainly investigated for fast image editing applications. To the best of our knowledge, the only work that exploits inversion properties for inferring information on the image source is [17]. However, in that case, the authors analyze synthetic images only, with the goal of identifying the correct generator among a set of candidates. Moreover, a single distance-based indicator is used, and earlier, less compelling GANs are considered in the experimental analysis.  
In our work, we analyze the outcome of the GAN inversion process, which given an image under investigation, finds the point in the GAN latent space that leads to the closest possible generated output in the image domain [12]. We expect that the application of this kind of process to images synthesized by that generator will produce a point in the latent space that leads to an equal or highly similar image, given that such a point exists for sure. On the contrary, the inversion process applied to natural images can only provide a latent vector that is associated with some approximation of the image, according to the considered generative model.  
The proposed detector was tested on the widely known and highly realistic StyleGAN2 face generator [4], among the best-performing GAN image synthesizers currently available. The images under investigation are compared to their closest reconstruction obtained through the inversion process available in StyleGAN2, and their biometric facial traits are encoded through different face representations (including deep embeddings and landmark-based features) and learned by conventional classifiers such as SVMs. Conceptually, our work shares similarities with differential morphing detection pipelines, as in [18; 19], where face image pairs containing authentic or morped faces in biometric verification scenarios need to be distinguished, thus also requiring the characterization of subtle differences in facial traits. The use of handcrafted features and lightweight classifiers for detecting synthetic images has also been explored in [20], which however, did not include semantic features. The use of semantic cues for the detection of GAN-generated images has been explored and advocated by several works [21; 22; 23; 24] but, in those cases, semantic artifacts are characterized in a post hoc analysis, thus not relying on the architecture of the generator.
## 4 Conclusions  
A 10 MHz QCM based e-nose containing polystyrene MIP layers has been successfully applied to generate sensitivity and selectivity for terpenes released from fresh and dried species of Lamiaceae family _i.e._, rosemary (_Rosmarinus Officialis L._), basil (_Ocimum Basilicum_) and sage (_Salvia officinalis_). For this purpose, a unified approach of synthesis process is adapted to optimize the composition of polymer. Layer heights (40-240 nm) were optimized, which showed selectivity at 50 ppm of the selected terpenes. A wide range of sensor signals from <20-1200 Hz were obtained from terpenes emanated from fresh and dried herbs. Sensor responses recorded for isomers _i.e._, \(\alpha\)-pinene (111-1183 Hz) and \(\beta\)-pinene (10-74 Hz) emanated from fresh and dried herbs also emphasized the selective behavior of e-nose. Similarly sensor responses of limonene, eucalypttol, terpinene and estragole attained from fresh and dried herbs also revealed sensitivity and selectivity. The sensor data obtained from the e-nose was validated by GC-MS which reveals that the designed e-nose is sensitive enough to detect <20 ppm of emanated terpenes. Fresh herbs showed 6-10 time higher sensor signals for emanated terpenes than dried herbs. MIP-QCM sensors possess high potential for solving problems of analyzing resembling constituents of a complex mixture. Moreover, this miniaturized multichannel QCM based e-nose elegantly behaved as an artificial receptor which can be best compared to human olfactory systems. Furthermore, a remarkable sensitivity and selectivity is achieved while comparing fresh and dried herbs, even at the isomeric level e.g., \(\alpha\)-pinene and \(\beta\)-pinene. As emanation pattern of terpenes obtained from herbs is the function of time so, it can be useful to determine the freshness, usability and shelf-life of herbs. Simplicity and efficacy of this system suggests that arrays should be used for real time analysis of complex mixtures e.g., composting exhaust, herbaceous odorant emanation, or air quality as a whole. For this purpose, sensors will have to be calibrated by selecting marker compounds of major significance and data analysis systems will have to be installed for real time evaluation. Electronic noses, thus manufactured, can be used for a large number of industrial applications.
# DDS: A Delay-Based Differentiated Service Virtual Network Embedding Algorithm
## 1 Introduction
Some meta-heuristic solutions, like simulated annealing [23], particle swarm optimization [24], ant colony optimization [25], or genetic algorithm [26], are also used in the field of VNE. However, in real network scenarios, different tenants have different network requirements. For example, a virtual network that provides VoIP services has high CPU, medium bandwidth, and low link propagation delay requirements, and a virtual network that provide P2P services has medium CPU, medium bandwidth, and no propagation delay constraints [3]. Considering that different virtual network requests have different resource requirements and QoS requirements, a single virtual network embedding method cannot effectively serve differentiated requests. To solve the inefficient utilization of underlying network resources caused by the single virtual network embedding method, this paper proposes an algorithm that distinguishes network types and adopts different mapping strategies to efficiently map differentiated virtual network requests. The advantage of this method is that it provides a personalized mapping scheme for virtual network requests by classifying network types and flexibly responds to the resource requirements and QoS requirements of the virtual network. Experimental results show that our method can accept more virtual network requests and improve the utilization of underlying network resources. The main contributions of this paper are as follows:  
1. For the sake of efficiently mapping differentiated virtual network requests, maximizing the utilization of substrate network resources, and improving the success ratio of virtual network embedding, we classify virtual network requests. The classification standard of virtual network is whether the virtual network has link delay constraints, which is divided into ordinary requests and delay-sensitive requests. 2. A two-stage embedding sub-algorithm is used for ordinary virtual network requests. In the node mapping stage, the resource-rich nodes are selected based on the greedy strategy, and the adjacent link resources are considered in the resource measurement to improve the success ratio of the next stage. Moreover, the consumption balance between different resources of nodes is considered to avoid the early exhaustion of a single resource affecting the embedding of subsequent virtual requests. Then the k-shortest path algorithm [27] is used for link mapping. 3. A constrained integer linear programming sub-algorithm is used for delay-sensitive virtual network requests. Inspired by Ref. [14], we construct candidate node set and candidate link set to reduce the solution scale of linear programming, aiming to find the optimal embedding solution with delay constraints in polynomial time. The rest of this paper is organized as follows. The related work is introduced in Section 2. In Section 3, we introduce the problem and model of virtual network embedding. In Section 4, we describe the proposed VNE algorithm in detail. The Section 5 shows the evaluation results. Finally, Section 6 concludes this paper.
# Abnormal Activity Detection Using Pyroelectric Infrared Sensors
## 8 Conclusions  
In this paper, we propose a novel approach for detecting a human's abnormal activities. By employing the FOV modulation for PIR sensors, the human activity is encoded into low-dimensional data streams, which can be used to extract the tempo-spatial feature of the human motion. A self-turning spectral clustering algorithm is used to cluster the training samples with modified KL distance. The HMMs are trained to profile each cluster of activity. One-class SVM is setup to classify whether the testing samples are abnormal or not. A major advantage of our approach is that it does not need abnormal samples for training in advance. This is critical in real deployment because it is unrealistic to train the system by providing all kinds of abnormal activities. Another advantage is that our training procedure is an unsupervised learning fashion, which does not need to specify the number of kinds of normal activities. In other words, it is not necessary to label the training samples. This advantage will facilitate the mass deployment in different locations, and it can greatly reduce the human labor spent on training sample preprocessing. We demonstrate the effectiveness of our approach using real data collected from PIR sensors attached to the ceiling of the monitoring region. It shows that, as the number of training samples increases, the performance of our system will improve accordingly.  
In the future, we wish to continue in the direction of detecting abnormal activities from continuous data streams. We will investigate how to incorporate the location information with the motion information to improve the performance of our system. A robust and reliable abnormal activity detection system is the most important prerequisite of the home-based assisted living paradigm.  
This work is supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61301294 and 61401174, the Natural Science Foundation of Guangdong under Grant No. 2014A030310462, and the Youth Elite Project of Guangzhou University of Chinese Medicine.  
Study concept and design: [PERSON]. Acquisition of data: [PERSON], [PERSON], [PERSON] and [PERSON]. Analysis and interpretation of data: [PERSON] and [PERSON]. Drafting of the manuscript: [PERSON]. Discussion and reviewing the manuscript: [PERSON] and [PERSON]. All authors read and approved the final manuscript.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **240 Samples** & **160 Samples** & **80 Samples** \\ \hline SC+OSVM & 0.863 & 0.433 & 0.710 \\ SC+iForest & 0.375 & 0.404 & 0.332 \\ OneHMM & 0.354 & 0.379 & 0.360 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Area under the ROC curve (AUC) values with different algorithms and different numbers of training samples.
## 3 Monitoring Scheme of Large Section Bifurcated Small Clear Distance Tunnel
### Layout of Monitoring Points  
#### 3.2.1. Advanced Geology Prediction  
Advanced GPR detection was adopted in the section with complex geological conditions of grade IV and V surrounding rock. It can accurately judge the geological surrounding rock conditions in front of construction and improve the quality and safety of construction. GPR is an electronic piece of equipment that penetrates underground objects using high-frequency electromagnetic waves. The GPR transmitters emit a pulse electromagnetic wave signal. When this signal meets targets such as karst caves and faults in the rock stratum, it generates a reflection signal. The reflected signal is inputted to the receiver through the receiving antenna and displayed by the oscilloscope after amplification. Based on the presence or absence of reflected signals by the oscilloscope, it can determine whether there is a target to be measured. Based on the arrival time of the reflected signal, the distance of the target can be calculated. The layout of its survey line is shown in Figure 3.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Measurement** & & & \\
**Items** & & & \\ \hline Methods and tools & & & \\ \hline The layout of measuring points & One section every 10 \(\sim\) 20 m, and 1 \(\sim\) 3 pairs of measuring points for each section & One section every 10 \(\sim\) 20 m \\ \hline Construction period & 1 to 15 days & 16 days to 1 month 3 months & More than 3 months & 1 to 15 days & 6 days to 1 month & 1 month to 3 months \\ \hline Measurement interval & Once or twice a day & Once every two days & Once or twice a week & Once or twice a day & Once every two days & Once or twice a week & One to three times were month \\ \hline \hline \end{tabular}
\end{table}
Table 2: Monitoring items and monitoring methods.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Management Level** & **Managed Displacement** & **Construction State** \\ \hline III & \(\text{U}_{0}<\frac{\text{U}_{n}}{3}\) & Normal construction \\ II & \(\frac{\text{U}_{n}}{3}\leq\text{U}_{0}<\frac{2\text{U}_{n}}{3}\) & Strengthen monitoring \\ I & \(\frac{2\text{U}_{n}}{3}\leq\text{U}_{0}\) & Take measures to strengthen support \\ \hline \hline \end{tabular} Note: \(\text{U}_{0}\) is the measured displacement value, and \(\text{U}_{n}\) is the allowable displacement value.  
\end{table}
Table 3: Monitoring data management.