esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Raman Analysis  
As revealed from Figure 5, the band relative to the E\({}_{2}\)(high) mode dominates all the Raman spectra. It confirms the good crystallinity of the hexagonal wurtzite structure of ZnO NCs [28]. This band, centered at 437 cm\({}^{-1}\), does not show any shift with doping, but its width decreases with the Eu\({}^{3+}\) monodoping and increases with Na\({}^{+}\) codoping. The absence of the peak relating to sodium and its compounds point out that the wurtzite phase of ZnO remains dominant and an effective substitution of Zn\({}^{2+}\) by Na\({}^{+}\) has been achieved following doping [10]. The large peak at around 330 cm\({}^{-1}\) is attributed to E\({}_{2}\)(high)-E\({}_{2}\)(low) mode. Its presence shows the good crystalline quality of all the samples [9]. However, a band at 378 cm\({}^{-1}\) appears only for the monodoping with Eu\({}^{3+}\) ion. This band corresponds to the vibration mode A\({}_{1}\)(TO) and its presence confirms the good crystalline quality of the sample. Indeed, the monodoping allows the Eu\({}^{3+}\) ion to fill the vacant site of Zn\({}^{2+}\) and therefore reduces the density of defects in crystal lattice [2]. In addition, a weak abnormal peak appears around 251 cm\({}^{-1}\) in the spectrum of the highly Na\({}^{+}\) doped sample (ZnEuNa5). We think it would be related to the B\({}_{1}\)(low) silent mode [17]. In fact, the high Na\({}^{+}\) doping can deform the crystal lattice and break the selection rules, which activates the silent Raman modes in the lattice [29].  
Figure 4: (**a**) TEM image of ZnEuNa4 sample; (**b**) EDS spectrum of ZnEuNa4 sample.  
Figure 5: Raman spectra of undoped and Eu, Na codoped ZnO NCs.  
The band located around the frequency 580 cm\({}^{-1}\), related to A\({}_{1}\)(LO) mode, decreases in intensity with Eu\({}^{3+}\) monodoping, which implies a reduction in the density of defects. However, it increases with the Na\({}^{+}\) content which leads to the creation of other defects in the structure due to, on the one hand, the large ion radius of Na\({}^{+}\) ion compared to Zn\({}^{2+}\) ion, and on the other hand to the low solubility of transition metals in the ZnO lattice.  
The non-appearance of additional vibration modes, corresponding to secondary phases, confirms the insertion of Eu\({}^{3+}\) and Na\({}^{+}\) ions in the ZnO structure. In addition, the fact that the Raman peaks do not show a shift is a sign that doping causes little deformation in the ZnO lattice. Thus, the Raman and XRD results are in good agreement.
## 3 Results and Discussion
### Rain Intensity Assessment  
Figure 7 shows the probability distribution function (PDF) of rainfall intensity for the selected regions in Malaysia. Based on the 342 rain gauges, the occurrence of precipitation sharply decreased from 56.4% at 0-1 mm/day to 4.4% at 1-2 mm/day and reached a plateau from 8.1% to 10% at 2-50 mm/day, to subsequently decrease to 3.3% at \(\geq\)50 mm/day (Figure 7a). The 3B42V7 and 3B42 RT showed slight underestimation in no/tiny rainfall (0-1 mm/day), by 17.7% and 18.3%, respectively, while the PERSIANN-CDR showed a dramatic underestimation of 62.3%. The underestimation of PERSIANN-CDR in the no/tiny rainfall class was also found over the contiguous United States [23]. The difference between precipitation products and gauge measurement in the no/tiny rainfall class was quite large. This differs from the findings of [PERSON] _et al._[64], which evaluated TRMM 3B42 and PERSIANN performance at Poyang Lake Basin, China. The main reasons for such differences could be due to differences in climate and geographical conditions, the Poyang Lake Basin being relatively far from the ocean.  
The 3B42 RT and 3B42V7 showed slight overestimation at 1-2 mm/day rain class, of 39.7% and 27.8%, respectively, while APHRODITE showed significant overestimation of 150.6%. All precipitation products showed overestimates at 2 to 20 mm/day, ranging from 20.2% to 166.1%. These overestimations at moderate daily rainfall intensity are consistent with other studies [45]. In addition, precipitation products showed large underestimation on the 0-1 mm/day rain amounts, whereas slightly underestimation was found at the \(\geq\)50 mm/day rainfall ones. As indicated by Figure 7, both 3B42 RT and 3B42V7 showed good performance during high heavy and violent rains, most likely because of the high  
Figure 7: The occurrence probability distribution functions (PDF) of daily precipitation (2003–2007) aggregated from 342 rain gauges (**a**) over Malaysia as a whole and (**b–h**) in different regions of Malaysia.  
signal-to-noise ratio of PMW-based precipitation sensors [61]. Accurate estimates of high daily precipitation amounts are vital for flood analysis, and therefore the 3B42 RT and 3B42V7 products are suitable for this purpose over Malaysia.
# High-Throughput Line Buffer Microarchitecture for Arbitrary Sized Streaming Image Processing
## 5 Evaluation
### Experiment Setup and Evaluation Metric  
The hardware generator of D-SWIM was realized using Verilog RTL and implemented with Xilinx Vivado. Our selected target FPGA device was Xilinx-XC7 VX690, where the tool synthesizes the Verilog into logic circuits and generates FPGA-specific mapping. Meanwhile, it also gives the resource utilization and timing performance, which are generally employed as the evaluation metrics of FPGA implementation. The resource utilization can be broken down into four FPGA building-blocks: the look-up table (LUT), Flip-Flop Register (REG), BRAM, and DSP. On the timing performance, the worst negative slack (WNS) of the critical path is given by Vivado and can be translated to the maximum operating frequency (\(f_{max}\)). The vendor tool gives the power consumption of the D-SWIM module as well. Besides the hardware tools, we implemented the D-SWIM instruction generator on the server to provide the control codes for any given image width (\(N_{line}\)) and block size (\(N_{blk}\)).
# Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues
## 4 Results
### Implementation Details  
During the training phase, we opted for the PyTorch [33] framework and employed the Adam optimizer [34] for model optimization. Regarding the learning rate, we set the initial learning rate to \(1\times 10^{-5}\) and adopted a warm-up strategy, starting with a smaller learning rate and gradually increasing it. This strategy aimed to promote more robust convergence of the model. Similar to prior research [2], we applied deformation operations to static images by transforming one image into three different ones, thereby generating virtual videos for pretraining [35]. For the main training on the YouTube-VOS [14] and DAVIS [13] datasets, we utilized the same data processing methods as described in Ref. [36]. We resized all input image masks to 384 \(\times\) 384 with padding to preserve their aspect ratios. Similar to STM [1], batch normalization layers were frozen during the training process. We supervised the training of the shape extraction module by generating real mask edges from ground truth object masks. The training approach for the motion prediction module remained consistent with Ref. [20]. Additionally, we adopted the same training frame sampling method as MiVOS [37], initially using a smaller sampling interval, gradually increasing it to 25, and finally restoring it to the original value as training approached completion. Similar strategies were employed for the shape space constraint module's sampling of the previous frame mask. In the inference phase, we utilized the first \(k\) filters [37], setting \(k\) to 80 on the DAVIS 2017 [13] validation set and 20 on other datasets. Additionally, we employed kernelized memory reading [9], with a kernel size of 7. Memory storage operations were performed every five frames, and no temporary frames were stored. We used the same implementation as STCN [2] for our backbone network, and the gated convolutional layer implementation in the object shape extraction module was the same as in [25]. In our object motion prediction module, the mask encoder used two 2D-Conv layers, followed by 3-layer Conv-LSTMs. The encoders (\(p_{\theta}\) and \(q_{\phi}\)) employed three 3D-Conv layers. For the mask decoder, we used three 2D-DeConv layers. During both the training and inference phases, we leveraged automatic mixed precision in PyTorch [33] to accelerate the training and inference processes.
## 4 Results
### Case Study Application to Singtel Building  
Real-world validation took place at the Singtel Serangoon TEPL office building, which features diverse workforce demographics and fluctuating usage patterns.
# Deep-Learning-Based Models for Pain Recognition: A Systematic Review
## 3 Review Papers
### Single-Model-Based Pain Recognition
#### 3.1.3 Facial Expressions
The cumulative attribute is defined as 'an intermediate representation Ci obtained by transforming the original labels yi into a vector'. In this study, the deep CNN is used with the cumulative attributes in two steps. In the first step, the cumulative attribute vector is outputted from a trained CNN. In the second step, the regression model is trained to produce the final real output. Their approach performed tests on a pain estimation dataset and estimated the age. Their pain estimation results obtained higher values for CA-CNN experiments compared with non-CA-CNN experiments. In addition to the use of a CA layer trained with a log-loss function, it significantly outperforms a CA layer trained with the Euclidean loss. Their approach has the advantage of using the CNN framework without any additional annotations. However, the requirement to build the annotated dataset for pain estimation is important to overcome most of the problems in classification tasks. Therefore, [PERSON] et al. [11] built a new database with RGB, depth and thermal (RGBDT) images of the face for pain-level recognition in sequences. Their approach of elicitation is different from previous datasets obtained by stimulated healthy people with electrical pulses. Twenty subjects participated in the data collection to determine the pain recognition based on five levels of pain (0 for no pain and four for severe pain). After collecting the data sets, they constructed a baseline model for pain recognition based on spatio-temporal features and deep learning. First, they preprocessed the video frames by cropping only the face region based on their previous proposed method [12] for RGB images. Then, they used homography matrix codes to crop other depth and thermal images. After this, they applied deep learning based on two approaches for individual modalities or the fusion between them. The main idea of the proposed method is based on two steps. First, they used 2D-CNN for frame-features extraction and pain recognition. Second, they used LSTM to find the temporal relation between frames and sequence level pain recognition. From the results, the fusion approach exhibited the greatest performance compared with the individual modalities. In addition, early fusion, which is achieved by integrating the input from all models to be fed to classifiers is better than late fusion, which integrates the output of each model to input the second classifier.
# Pas: Privacy-Preserving Authentication Scheme Based on SDN for VANETs
## 4 The Proposed Scheme
### Network Architecture  
This part describes the secure communication network architecture based on SDN. As depicted in Figure 3, the below entities are incorporated for deploying the system.  
Department of Motor Vehicles (DMV) provides necessary management service for vehicles, such as registration, user change, transfer, mortgage, and cancellation registration. Vehicles need to apply for registration with DMV.  
Trust authority (TA) is halide for computing and broadcasting system parameters. Meanwhile, the TA provides public keys and private keys for vehicles registered in DMV.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(ID_{A}\) & The true identity of A \\ \(PK_{A}/SK_{A}\) & The public and private key of A \\ \(K_{A-B}\) & The shared key between A and B \\ \(C_{A-B}\) & The ciphertext generated by A and sent to B \\ \(SigTA\) & The signature generated by [PERSON] \\ \(PS_{A}\) & [PERSON]’s pseudonym \\ \(TS\) & Timestamp \\ \(EXP\) & Expiration \\ \(N\) & Challenge value \\ \(H_{i}\) & Hash function \\ \(||\) & Concatenations between messages \\ \(Enc\_ALG\_PK_{A}\{M\}\) & Using \(PK_{A}\) to encrypt message \(M\) through ALG algorithm \\ \(Sign\_ALG\_SK_{A}\{M\}\) & Using \(SK_{A}\) to sign message \(M\) through ALG algorithm \\ \(Enc\_ALG\_K_{A-B}\{M\}\) & Using \(K_{A-B}\) to encrypt message \(M\) through ALG algorithm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Symbols and descriptions.  
Figure 3: Network architecture.  
Moreover, the TA communicates with the SDN controller to provide the vehicles' public keys for supporting anonymous authentication.  
SDN controller is considered as the global intelligence to control all network behaviors. SDN controller communicates with TA to obtain vehicles' public keys and relay them to SDN RSU controllers.  
SDN RSU controllers are deployed into base stations, which are mainly responsible for vehicle identity management. Furthermore, in order to support the authentication protocol, SDN RSU controllers periodically send vehicles' information to RSUs.  
RSUs use the information stored from SDN RSU controller to verify the legality of vehicles. Once the vehicles are thought to be legal, RSUs are able to provide the services for the vehicles.  
Vehicles equipped with OBUs adopt DSRC and IEEE WAVE standard to communicate with the surrounding vehicles and RSUs to obtain various services.
## 3 Results and Discussion
### Lipid Oxidation
On the other hand, some authors have found that Se supplementation did not affect oxidative meat stability [14; 44; 45] or have described a positive Se effect in retarding oxidation processes in plasma or liver but not in meat [41; 46]. However, to the best of the authors' knowledge, the effects of Se on oxidative processes have been evaluated mainly through TBARS analysis. In the present study, the comparison of the results obtained from the two parameters of lipid peroxidation, TBARS and hexanal, seemed to highlight the following main aspects. In general, both methods could give a good indication of the oxidation processes, which seemed to be reduced by the presence of the antioxidants administered in the diet. In particular, the effects of the antioxidants were highlighted more clearly by the TBARS data, while the hexanal data showed a containment effect of the degradative processes only in the VE 100 dietary group. This could have indicated a more specific correlation of this index with the lipid oxidative processes. In fact, hexanal is a typical and well-known end product of the oxidation of fatty acids, in particular _n_-6. The values found for TBARS, which other authors have also considered to be less specific in detecting the presence of oxidative processes [2], could in fact be due to the simultaneous evolution of other degradative processes, including those related to proteins, as also described by other authors [6]. As is known, for example, the Maillard reaction leads to the production of carbonyl compounds. Moreover, the advances in meat science research have indicated that the same oxidants which initiate lipid oxidation can cause and propagate protein oxidation, and carbonyl formation is a common reaction pathway of the oxidation process. Moreover, recent studies have shown that MDA, as another by-product of lipid oxidation, can affect heme pigment, protein carbonylation and reactive oxygen species-generating system, promoting myofibrillar oxidation [47; 48]. In addition, protein oxidation occurs by means of a chain reaction of free radicals, such as the oxidation of lipids in animal muscle [11; 49]. Hence, the almost complete absence of variation in the hexanal content in the EcoE dietary groups could have indicated that Se dietary supplementation could prevent the degradative processes but with a more extensive effect and not specifically directed only to lipid oxidation. Moreover, Se intake could modulate the antioxidant enzyme glutathione peroxidase, thus contributing to the overall antioxidant defence of muscle, as has also been suggested in other studies [11].
# XR Technology Deployment in Value Creation
## 4 Deployment Purposes of XR Technologies in Value Creation
### Visualization  
Visualization is central to any XR technology composition [77]. Thus, because of these technologies, there are countless possibilities for depicting details and designs in three dimensions for the users. Of course, as in the assistance already described, showing content is a major component of this deployment purpose. The focus within assistance is rather on adding information to a manual flow and supporting the user, with less emphasis on graphical fidelity. The value-creating aspect of visualizing content in value creation is virtually presenting the future appearance of a physical state or product in a highly detailed manner to enable and improve the decision-making process [54].  
In terms of visualization, XR technologies that generate AR overlay virtual content within the real world [78; 81]. Based on this understanding, [PERSON] and [PERSON] analyze AR-based product presentations and nominate a potential utilization of AR for virtual try-ons [82]. The visualization aspect is the superimposing of a computer-generated representation of products on a user's body reflection through an image. The value-generating benefit results from the superiority of the simultaneous visual representation of user and product over the traditional 2D product representation on the web [82]. [PERSON] et al. list 26 different use cases across industries that apply AR to provide the user with a visual impression by simulating the appearance of a physical product in reality [83]. The utilization of XR technologies for VR is a mature tool for visualization in product design and manufacturing, especially for detailed previewing of a future physical state [54]. [PERSON] et al. further reviewed VR applications in the manufacturing industries [84]. They pointed out that the visualization aspect of VR adds value by intuitively depicting a situation to convey relevant information with the right details to the user [84]. Especially in the context of design reviews, concepts exist that provide realistic prototypes and product impressions while reinforcing the relevance of VR visualization for improved decision-making [66].  
XR technology deployment for the purpose of visualization is the use of XR technologies for the immersive, interactive, and intelligent presentation of content relevant to value creation prepared for a target group relevant to value creation. This primarily includes the three-dimensional presentation of content to the user. Visualization with the help of XR technologies enables the user to perceive details through the three-dimensional depiction as well as through interaction, which would not be possible without XR technologies. Therefore, the value-adding component enables early-stage decisions, either on a product design or on the buying action of an end-customer itself.
## 2 Optimization Model
### Set of Constraints
#### 2.2.1 Power Balance Equation  
The nodal power balance equations for node \(k\) with regard to active and reactive power require calculating the sums of power generated or demanded, i.e., for active and reactive power. These sums are then equalized to the flow of injection power, be it active or reactive. The equations can be expressed as follows:  
\[p^{g}_{kh}-p^{d}_{kh} =\sum_{l\in L}\left(A^{+}_{kl}p^{s}_{lh}+A^{-}_{kl}p^{r}_{lh} \right),\,\forall\,k\in\mathcal{N},\,\forall\,h\in\mathcal{H}, \tag{2}\] \[q^{g}_{kh}-q^{d}_{kh}+q^{\mathrm{FACT}}_{kh} =\sum_{l\in L}\left(A^{+}_{kl}q^{s}_{lh}+A^{-}_{kl}q^{r}_{lh} \right),\,\forall\,k\in\mathcal{N},\,\forall\,h\in\mathcal{H} \tag{3}\]  
where \(p^{g}_{kh}\) and \(p^{d}_{kh}\) are the active power generated and demanded; \(q^{g}_{kh}\) and \(q^{d}_{kh}\) are the reactive power generated and demanded; \(p^{s}_{lh}\) and \(q^{s}_{lh}\) are the sending active and reactive power flows of the distribution line \(l\); \(p^{r}_{lh}\) and \(q^{r}_{lh}\) are the receiving active and reactive power flows of the distribution line \(l\); \(q^{\mathrm{FACT}}_{kh}\) is the reactive power injected by the FACTS at node  
Figure 1: Generic branch connection with FACTS devices in an electrical network.  
\(k\) and time \(h\); and \(A^{+}\) and \(A^{-}\) are the positive and negative values of the node-to-branch incidence matrix \(A\), i.e., \(A=A^{+}+A^{-}\).
# Audio Steganalysis Estimation with the Goertzel Algorithm
## 3 Performance Evaluation
### Frequency Analysis Decomposition
#### 3.4.2 Audio Vector Comparison  
Using the frequency spectrum in each of the information channels, the original audios and corresponding possible stegoaudios were compared. Figure 10a-f shows the comparative graphics, which illustrate that the information contained by the audio vectors differs; this discrepancy is visible only in the frequency domain. The blue line graphic corresponds to the frequency spectrum of the original audio, while the red line graphic corresponds to that of the possible stegoaudio vector. The most notable differences in the red lines are illustrated in the left and right parts of each graph.  
Notably, in contrast to the time domain comparison of channels shown in Figure 7, this analysis reveals that information is hidden in the vectors.
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## 6 Testbed and Performance Evaluation
### Implementation and Testbed Deployment  
The proposed solution was evaluated through a small-scale PoC, comprising an Open Network Operating System (ONOS) SDN controller, a simplified data plane composed of Stratum BMv2 switches emulated through Mininet, and three MEC Hosts running Docker as the virtualization technology.