esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Examples in 3D Case  
To demonstrate the proposed method for the design of 3D FGM, this example gives several 3D FGM with a volume fraction gradient. The 3D CBSs are optimized with maximum bulk modulus and maximum shear modulus. Each of the cellular structure is discretized with \(20\times 20\times 20\) eight-node cubic elements. The Young's modulus and [PERSON]'s ratio of the solid material in the optimization are selected as \(\mathrm{E}=10,000\) MPa and \(\
u=0.3\). The [PERSON]'s modulus and [PERSON]'s ratio for the void part in the optimization are \(\mathrm{E_{void}}=0.001\) MPa and \(\
u_{\mathrm{void}}=0.3\). In the same way, the 3D transitional cellular structures are constructed using the proposed method. The 3D FGM with two different extreme performances CBS are shown in Figure 16, and the volume fraction of each CBS or TCS are shown in Table 7. The design results show that the boundaries between different cellular structures can be connected smoothly. From the connection modality, it clearly demonstrates that the proposed transitional connection method also applies to the 3D case.  
with a single type of CBS in the 3D scenario. It implies that the gradient structure can also improve the mechanical performance in the 3D case. Particularly, in this study, the volume fraction is selected as the key parameter to induce the gradient property. The TCS can ensure a precise control on its volume fraction, which is highly related to its structural stiffness. However, the proposed approach relies uniquely on pure geometrical considerations, and it is restricted to the design of the layer-wise cellular FGMs. Although we use the strain energy to evaluate the performance of the FGMs, the proposed method does not consider the strong coupling on the macro and micro scales. A future extension of this approach will be the solution of multiscale design for the FGMs with C\({}^{1}\) connection.  
In addition, the proposed transitional connection method is also applied to the design of 3D FGM with different types of CBSs. In this example, two types of CBSs are considered, including the CBS with maximum bulk modulus and the CBS with maximum shear modulus. The final design is presented in Figure 20. To make the structure joints more visible, the configuration of transitional cellular structure is exhibited in Figure 21 with different views. On both sides of the transitional cellular structure, its surface shape is the same with that of the connected cellular structures, so the geometry of different cells can fit perfectly. Figure 22 shows a sectional view. It can be seen that the transitional cellular structure has the geometric features of both CBSs. Therefore, even in the 3D cases, the transitional connection method can be used to tackle the different types of CBSs, so as to guarantee a smooth interface between adjacent cells in the cellular FGMs.
## 6 Discussion and Conclusions
Although redesigning the Kerr comb to meet our application requirements may take a long turnaround time, the current results indicate that compared to the independent lasers, the reduction in the optical carrier power from +10 dBm to \(-\)10 dBm and decreased OSNR from <35 dB to <31 dB for the Kerr comb carriers lead to increased penalty from \(\sim\)1 dB to 7-8 dB for the LFM pulse with 1 GHz of bandwidth. One potential solution to address this issue would be, for example, using non-solitonic combs, which demonstrate higher pump-to-comb conversion efficiency and high power-per-line [39]. Moreover, by extending our analysis by increasing the LFM pulse bandwidth by a factor of 10 from 1 GHz to 10 GHz, we can conclude that the minimum penalty would increase from \(\sim\)1 dB to 4-6 dB. In our concept demonstration, one can observe that the actual latency greatly exceeds the minimum viable one. This added latency is due to using off-the-shelf components with long internal fiber jumpers connecting the elements. Luckily, most of the components have the potential to be integrated into a single photonic integrated circuit. For example, the chirped gratings are designed and fabricated on-chip [40; 41; 42]. Moreover, the amplifier can be replaced with an integrated semiconductor optical amplifier [43; 44]. In addition, the LCoS filter can be replaced with any integrated photonic technology filter such as ring resonator tunable filters [45; 46]. Conceptualization, A.A. and M.T. ; methodology, A.A., Y.C. and M.T. ; software, A.A. and M.T. ; validation, A.A., Y.C. and M.T. ; formal analysis A.A. and M.T. ; investigation, A.A. and M.T. ; resources, P.L. and A.W. ; data curation, A.A. and M.T. ; writing--original draft preparation, A.A.; writing--review and editing, A.W. and M.T. ; visualization, A.A. and M.T. ; project administration, M.T. All authors have read and agreed to the published version of the manuscript.
## 3 Bispectrum Analysis of EEG Signals for an Emotion Recognition System
### Probabilistic Neural Networks  
PNN is an ANN classifier that is based on Bayes theorem. With a Bayes classifier, the datum \(X\) belongs to the class \(C_{j}\) when \(P(C_{j}\,|\,X)\) has the biggest probability value.  
\[P(C_{j}|X)=\frac{P(X|C_{j})P(C_{j})}{P(X)} \tag{6}\]  
To calculate the probability \(P(C_{j}\,|\,X)\), it is necessary to estimate the conditional probability \(P(X|\,C_{j})\) and the a priori probability of \(P(C_{j})\) of each class \(C_{j}\). The calculation of \(P(X)\) is not required because \(P(X)\) exists in every class. The a priori probability \(P(C_{j})\) could be calculated by including the entire training data. The conditional probability \(P(X|\,C_{j})\) is estimated using the Parzen window p.d.f. estimation. By assuming Gaussian distribution, the Parzen window p.d.f. estimates \(p_{j}(x)\) as:  
\[p_{j}(x)=\frac{1}{N}{\sum_{k=1}^{N}\frac{1}{\left(\sigma\sqrt{2\pi}\right)^{d} }}\mathrm{e}^{(-\frac{1}{2}\left(\frac{x-x_{k}}{\sigma}\right)^{2})} \tag{7}\]  
Here, \(x\) is the sample data with the probability being estimated, \(j\) is the class number, \(N\) is the number of training data in class \(C_{j}\) and \(d\) is the dimension of the feature vector. The value of \(\sigma\) (sigma) in the Gaussian distribution is the standard deviation or the smoothing parameter of the Gaussian curve; however, the actual standard distribution was unknown and should be determined.  
The PNN consists of four layers: an input layer, a pattern layer, a summation layer and a decision layer. In the input layer, which consists of one neuron, the input vector was received and then forwarded to the pattern layer. Each neuron at the pattern layer represents the training data that belong to each class. We used 50% of the data samples as the training data. Thus, the number of neurons in the pattern layer was equal to 50% of the number of data samples. In the pattern layer, the vector input was compared to the training data in each class. In the summation layer, the Parzen window p.d.f. formula was used to determine the class of the data. The biggest probability value determined where the datum should belong.
## 4 Signal Feature Extraction Method Based on Intrinsic Mode Function Energy Permutation Entropy
### Permutation Entropy Analysis of Intrinsic Mode Functions
This empirical evidence substantiates that energy-based prioritization of shaft-rate electric field components enables selective extraction of discriminative electromagnetic signatures, thereby facilitating vessel classification through enhanced feature separability in the dominant energy regimes.
## 7 Simulation Results Discussion: Analysis and Evaluation  
In this section, we delve into the key findings from our simulation of vehicular communication in the Peak District. Our focus is on understanding the impact of our approach within a rural setting, with limited availability of RSUs. The following analysis synthesizes the data collected from various 24 h simulation scenarios, providing insights into the effectiveness of our proposed Pre-Signature scheme in enhancing connectivity under diverse conditions.  
The following figures derived from SUMO simulation GUI provide a visual representation of the communication scenarios enabled by the Pre-Signature scheme, illustrating its application and impact.  
1. V2R Communication, Figure 11: Vehicles communicate with RSUs to update their RVs and obtain preliminary authentication credentials, ensuring network integrity within the RSU's service area.
2. Online V2V Communication, Figure 12: Vehicles within the RSU's range exchange information based on their RVs, guaranteeing the reliability of the communication.
3. Offline V2V Communication, Figure 13: Vehicles communicate outside the RSU's range, utilizing a Pre-Signature system to maintain dependable communication without RSU real-time supervision. Whether the communication has an up-to-date reputation available depends on whether the sender obtained a Pre-Signature prior.  
These illustrative figures are a testament to the Pre-Signature scheme's critical role in enhancing vehicular network resilience, demonstrating the feasibility of reliable communication under varying RSU support conditions.  
Figure 11: V2R Communication for Updating RV and obtaining Pre-Signature from RSU.
# Detecting and Solving Tube Entanglement in Bin Picking Operations
## 3 Bin Picking Algorithm
### Tube Classification  
This phase aims to select the tubes that have the highest potential of being picked without facing any issues of entanglement, where other tubes are also brought up by the robot arm when it performs the lifting motion.  
Firstly, if a value is provided for the tubes' length, then all the virtual tubes whose length is below a certain minimum threshold (which subtracts from the expected length a tolerance value) are immediately discarded since they correspond to incomplete models of real-life tubes. The value of a virtual tube's length is typically smaller than the corresponding real-life tube's length (due to the approximation of the curve as a set of segments). This should be taken into account when defining the minimum acceptability threshold.  
Secondly, the number of occlusions for each tube is counted from the sensor's point of view. Since the sensor's line of sight is vertical, this provides a computationally efficient manner of detecting overlaps between tubes. Occlusions can only occur in joints since cylinders correspond to approximation of continuous, visible portions of a tube.  
Figure 2: Two examples of each step of the tube modeling phase.  
Definition 8: An **occluded joint** is a joint for which the segments used to create both of its connected cylinders are different.  
At the end of this phase, two disjoint sets of tubes are created: the non-occluded tubes, the weakly-occluded tubes and the strongly-occluded tubes. In this paper, only the non-occluded and weakly-occluded tubes are considered as candidates to be picked.  
Definition 9: A **non-occluded tube** is a virtual tube with no occluded joints.  
Definition 10: A **weakly-occluded tube** is a virtual tube with a single occluded joint. The linked list of cylinders in an occluded tube is partitioned into two **sections** by the occluded joint.  
Definition 11: A **strongly-occluded tube** is a virtual tube with two or more occluded joints.
# Securing IoT Data Using Steganography: A Practical Implementation Approach
## 5 Results and Discussion  
### Scenario-1: Perceptual Undetectability and Payload Capacity  
In this scenario, the effectiveness of the proposed algorithm is evaluated on signal frames sampled at 64. We set the algorithm keys' values to maximize the hiding capacity while maintaining the speech quality, i.e., threshold = \(-\)20 dB, \(\rho\) = 15 dB, \(F_{HDmin}\) and \(F_{HDmax}\) were set to 1 and 28 for 4 ms frame length. The distortion between the stego and cover signals is calculated and averaged over several frames. \(SegSNR\) value for one modified speech frame of 4 ms is given by the following equation:  
\[SegSNR_{dB}=10\log_{10}\left(\frac{\sum_{k=1}^{28}|S_{c}(m,k)|^{2}}{\sum_{k=1} ^{28}|S_{c}(m,k)-S_{s}(m,k)|^{2}}\right) \tag{5}\]  
The summation was performed over the signal on a frame basis. To evaluate the results, the following criteria were used. First, the capability of embedding a larger quantity of secret data (Kpps) was sought while the naturalness of the stego signal was retained. Second, the hidden data were fully recovered from the stego channel after being sent over the wireless channel.  
Figure 7 shows that for \(SegSNR\), the values range from 42 to 48 dB and from 4.38 to 4.41 for the \(PESQ\) while registering a payload up to 24 Kpps. These results guarantee a high payload with very good quality of the stego channel regardless of the LSB layer depth.  
Additional analysis to Figure 7 indicates that the quality of the stego channels is maintained even when the embedding rate is maximized. Hence, to achieve a higher secret traffic rate required in IoT applications and maintain the stego channel quality, hiding at the first LSB layer is to be adopted if the payload survives channel noise as we discuss in the next section.
# Optimized Gateway Placement for Interference Cancellation in Transmit-Only LPWA Networks
## 5 Numerical and Simulation Results
### Sensor Contention  
The reason for applying TO to SNs for LPWAN is mainly to increase energy efficiency. However, the packet loss ratio (PLR) is impacted by many factors such as the duty cycle of each SN and the number of SNs in the network. Obviously, the PLR in a system with unchanged number of SNs will increase with the increased duty cycle. On the other hand, the PLR will decrease with fewer SNs for the same duty cycle. Following [8], we take SN contentions as the target metric to measure each SN and the whole TO system since it is not affected by traffic load, but only by the network topology.  
In a TO network, the contention from the perspective of a SN is the number of other SNs which would prevent its packet from being decoded by any GW if the collision will happen. If there are \(N\) SNs in a network, and we say that SN A has a contention of \(n\), it means that the packets transmitted by A can be decoded successfully (assuming two-packet collisions here), whether by capture directly or after IC, despite interference from any one of \(N-n\) sensors in \(N-1\) sensors. In other words, SN A's packet will be lost if interfered by any one of the other n sensors. For instance, assume there are 100 SNs (including SN A) in a TO network with one GW and no capture or IC. If [PERSON] loses a packet when that packet collides with a packet transmitted by any one of the other 99 SNs, then, A's contention is 99. As another example, if we build several GWs and each of the GW can perform capture and IC during a collision, and suppose [PERSON]'s packet can be decoded if interfered with a packet from any one of 50 sensors, but not for the other 49 sensors, then [PERSON]'s contention is cut down to 49, halved from the original setting. In our simulations, we compute the average contention, which is the average over the contentions of the SNs.
# A Survey of Full-Cycle Cross-Modal Retrieval: From a Representation Learning Perspective
## 6 Benchmark Datasets
### Cross-Modal Datasets
The Visual Genome [101] dataset labels all visual objects in the images with the Objects Categories. Wikipedia [95] is the most-extensively used for cross-modal retrieval. However, it contains limited samples and semantic categories based on Wikipedia articles. SBU Captions [96] contains images with one-million user-generated titles. However, these pairs are still insufficient to train models with hundreds of millions of parameters. The Flickr 8K [97] and Flickr 30K [99] datasets come from Flickr, Yahoo's photo album site. The images in both datasets typically depict individuals engaged in an activity. The handwritten annotation for each image is five words. Because the two databases derive from the same root, the syntax of the annotations is comparable. Besides, twelve million image-text data pairs named Conceptual 12M [105] are suitable for training vision-and-language models. The researchers explore and compare this dataset to the previously popular dataset. The new dataset emphasizes long-tail visual identification, and the quantitative and qualitative findings indicate the benefits for visual and language tasks. Data from multiple media types are distributed and represented differently in fine-grained cross-media retrieval. Some researchers created a fine-grained cross-media retrieval dataset called PKU FG-XMedia. Moreover, the Object365 [104] dataset, which contains objects in natural situations,primarily handles large-scale detection problems, including 365 object types, and it provides a diverse and useful baseline for target detection research. Open Images V6 ([[https://storage.googleapis.com/openimages/web/index.html](https://storage.googleapis.com/openimages/web/index.html)]([https://storage.googleapis.com/openimages/web/index.html](https://storage.googleapis.com/openimages/web/index.html)), accessed on 1 January 2022) significantly increases the annotation of the Open Images dataset when compared to V5, introducing many new visual associations, human activity annotations, and horizontal picture labels. Some academics focus on creating large-scale Chinese datasets to undertake large-scale multi-modal pre-training in Chinese, such as M6 [50]. The M6 dataset aids large-scale pre-training models in learning sophisticated global information in Chinese, including sports, politics, science, and other disciplines. Researchers can leverage benchmark datasets to develop more effective and efficient cross-modal retrieval systems, resulting in advancements in image retrieval, text retrieval, and cross-modal retrieval, among other applications.
## 3 Results and Analysis
### Validation of Single-Frequency Cycle Slip Detection  
Correct cycle slip detection is one important factor in achieving a reliable estimation. The GNSS receiver on board the Luojia-1A satellite relies on a standard temperature-compensated oscillator without discipline, and the clock steering events occur frequently. The GNSS receiver clock steering of the LEO satellite at high speeds makes single-frequency cycle slip detection ineffective and difficult. Figure 9 shows the number of cycle slips detected by the HTD algorithm and the proposed algorithm on each epoch.  
Figure 8: Variation of the multipath and observation noise with the C/N0. The dashed line shows that the weighting model agrees well with the multipath and observation noise.  
In general, the number of cycle slips is very small compared to that of observations. However, many cycle slips are incorrectly identified by the HTD algorithm since the cycle slip detection based on the carrier phase is impacted by the frequent receiver clock steering events, although the HTD algorithm can resist high dynamics of the satellite platform. The new algorithm is independent of the clock steering events and provides more reasonable cycle slip detection. The number of actual cycle slips is 59 and 21, accounting for 0.020% and 0.018% of all GPS and BDS observations, respectively. Considering the carrier phase outliers, the number of cycle slips and outliers increases to 168 and 27 for GPS and BDS, and the percentage is 0.056% and 0.024%, respectively. If cycle slips are detected, new ambiguity parameters will be introduced into the KOD estimator. This can protect against the erroneous estimation of the ambiguity parameter and contamination of KOD results.
## 3 PV-Grid Tied Charging Stations for EVs and PHEVs
### Operating Modes
#### 3.2.3 Mode 3 (PV and Grid to Battery)  
As shown in Figure 5c, in this operating mode the power required to charge the EV is provided by the grid and the PV at the same time. This mode comes into operation when the PV cannot provide enough power to independently charge the EV battery but is able to supply a certain part of the required power. Depending on how much energy the PV can distribute, the amount of energy supplied from the grid varies; any deficiency is reimbursed by the grid. As the nature of irradiance conditions is variable, the system controller has to properly distribute the amount of power delivered by the PV and the grid, ensuring that the power required by the EV is reached.
# Image Splicing Location Based on Illumination Maps and Cluster Region Proposal Network
## 4 Discussion
### Experiments and Comparative Analysis  
We compare the proposed framework with traditional methods CFA [1], ELA [2], NI [3] and CNN-based methods SPAN [17], RGB-N [19], LSTM-EnDec [21], LSTM-EnDec-Skip [22]. The \(F1\) score comparison results are shown in Table 3, where the results of CFA, ELA, NOI1 comes from [19]. Among them, the dual-stream R-CNN uses the GE as 'Ours (GE)', and the as 'Ours (IIC)'. The top performance values in the table are all expressed in bold font.  
Our method is significantly better than other methods, and even reaches 81.0% on the NIST16 dataset when using IIC. Compared with RGB-N [19], the \(F1\) score is increased by at least 2.9% using GE and 4.4% using ICC. This shows that the illumination maps (GE and IIC) can better represent the tampered area compared to the noise feature map. Since the LSTM-EnDec [21] and LSTM-EnDec-Skip [22] have no experiments on Columbia and NIST16 datasets, we compare the results on CASIA, which demonstrates that our method on IIC and GE has a better performance. However, our method does not reach the best performance on the Columbia dataset, since most tampering on Columbia occur on background. The visualization results are shown in Figure 7, where the first two rows are from the CASIA dataset and the last two rows are from the NIST16 dataset. It shows better localization of pixel-level by using illumination maps (IIC and GE) on the CASIA and NIST16 datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Columbia** & **NIST16** & **CASIA** \\ \hline CFA [1] & 46.7 & 17.4 & 20.7 \\ ELA [2] & 47.0 & 23.6 & 21.4 \\ NI [3] & 57.4 & 28.5 & 26.3 \\ SPAN [17] & **81.5** & 58.2 & 38.2 \\ RGB-N [19] & 61.2 & 72.2 & 40.8 \\ LSTM-EnDec [21] & – & – & 39.1 \\ LSTM-EnDec-Skip [22] & – & – & 43.2 \\ Ours (GE) & 73.1 & 79.4 & 43.9 \\ Ours (IIC) & 73.2 & **81.0** & **45.2** \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(F1\) score comparison on NIST16 and CASIA datasets (%).  
Figure 6: Exemplar visual results on NIST16 dataset. From left to right: the first two columns are tampered images and Ground-Truth, the third to fifth columns are results of the tampered images, tampered images with noise (0, 0.02), blur (\(3\times 3\), 0.5) and JPEG (50).