esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Model: RBF-Refined SVM Model
### Performance Measure  
Performance of the SVMs was measured based on the accuracy percentage, precision, or the number of positively predicted values, and sensitivity percentage of the relevant fractional values, otherwise termed \"recall\". Actual results in the prediction fell under the four categories true positive (TP), true negative (TN), false negative (FN), and false positive (FP), which were based on the confusion matrix, as described in Table 4. The confusion matrix was a way to show performance measures of classification models on the collection of test data. Confusion matrices consisted of the information given by classification models about predicted and actual classifications. When an event was positive and predicted positive, then it was deemed a true positive, while if predicted negative, was termed a false negative. Similarly, when an event was negative and predicted positive, then it was a false positive, and otherwise, termed a true negative.  
The calculations of precision and recall were made based on the true positive and false positive/negative values accordingly, as shown in Equations (3) and (4). The \(F\) measure gave the harmonic average of precision and recall, as shown in Equation (5). [PERSON]'s correlation coefficient (MCC) measures the acute accuracy quality by means of correlation. MCC is a measure which is relied upon as an accuracy rating; however, precision and recall do not consider the true negative values, as shown in Equation (6).  
\[Precision=\ \frac{TP}{TP+FP} \tag{3}\]  
\[Recall=\ \frac{TP}{TP+FN} \tag{4}\]  
\[F=\ \frac{2\times(Precision\times Recall)}{(Precision+recall)} \tag{5}\]  
\[MCC=\ \frac{(TP\times TN)-(FP\times FN)}{\sqrt{(TP+FP)(TP+FN)(TN+FN)(TN+FP)}} \tag{6}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Actual/Predicted** & **No** & **Yes** \\ \hline
**No** & TN & FP \\
**Yes** & FN & TP \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix. FN: false negative; false positive; TN: true negative; TP: true positive.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Average-Merit** & **Average-Ranking Score** & **Attribute \#/Name** \\ \hline \(1\pm 0\) & \(1\pm 0\) & \(14\) Bytes Received \\
0.999 \(\pm\) 0 & \(2\pm 0\) & \(15\) Signal Strength \\
0.982 \(\pm\) 0 & \(3\pm 0\) & \(16\) Noise Strength \\
0.474 \(\pm\) 0.003 & \(4\pm 0\) & \(8\) Sender Altitude (m) \\
0.468 \(\pm\) 0.002 & \(5\pm 0\) & \(7\) Sender Speed (km/h) \\
0.415 \(\pm\) 0.003 & \(6\pm 0\) & \(12\) Receiver Altitude (m) \\
0.336 \(\pm\) 0.001 & \(7\pm 0\) & \(1\) Packet-Sequence-No \# \\
0.335 \(\pm\) 0.004 & \(8\pm 0\) & \(2\) Time in Sec \\
0.104 \(\pm\) 0.001 & \(9\pm 0\) & \(9\) Receiver Latitude \\
0.091 \(\pm\) 0.002 & \(10\pm 0\) & \(10\) Receiver Longitude \\
0.078 \(\pm\) 0.001 & \(11\pm 0\) & \(6\) Sender Longitude \\
0.078 \(\pm\) 0.001 & \(12\pm 0\) & \(5\) Sender Latitude \\
0.017 \(\pm\) 0.003 & \(13\pm 0\) & \(3\) Time in unit sec \\
0 \(\pm\) 0 & \(14\pm 0\) & \(4\) Bytes Sent \\
0 \(\pm\) 0 & \(15\pm 0\) & \(11\) Receiver Speed \\ \hline \hline \end{tabular}
\end{table}
Table 3: Attribute ranking score card.
# DANet: Temporal Action Localization with Double Attention
## 2 Related Work
### Temporal Action Localization  
Existing TAL methods can be broadly divided into two categories, i.e., two-stage methods [6; 7; 17] and one-stage methods [8; 9; 10; 18]. The two-stage approaches [6; 7; 17] solve the TAL task by first proposing location proposals of action instances and subsequently using the location proposals to predict the semantic labels of actions and their corresponding start and end times. For example, BSN [4] first localizes the boundaries with a higher possibility, and then merges those boundaries to derive location proposals. Subsequently, BSN employs the temporal proposals to determine whether an action instance is present or not and computes the confidence of the temporal proposals. However, the efficiency of BSN is not high enough because feature extraction and confidence assessment are performed for each temporal proposal, leading to a potentially time-consuming process. To solve the corresponding limits, BMN [7] is proposed, which can simultaneously generate the probability of one-dimensional boundaries and the confidence maps. However, two-stage methods separate proposal and classification tasks, which may be inefficient and time-consuming [28].  
Compared with two-stage approaches, one-stage ones [8; 9; 10; 18] possess a more notable and simple pipeline, because one-stage methods eliminate the proposal generation process utilized in the two-stage methods. Furthermore, one-stage methods can be broadly divided into two categories, namely, anchor-based [8] and anchor-free methods [5]. For instance, SSAD [18] is a monolithic anchor-based network that directly predicts the timing boundaries and the confidence of actions. Conversely, the anchor-free approach is frame-level, wherein each time node is generally predicted, and eventually merged at the frame-level to generate the final prediction. For example, ActionFormer [10], an anchor-free approach, combines multi-scale feature representation with self-attention, uses a weighted decoder to classify each time step, and estimates the corresponding action boundaries. However, ActionFormer limits self-attention in a local window, which may be unable to integrate global information of a video effectively and capture all relevant time intervals (see Section 4.2).  
Our model is a one-stage, anchor-free approach, which classifies each time step and generates behavior boundaries, followed by the merging of frame-level results to produce the final output. Specifically, we model temporal context at both local and global levels simultaneously.
# Student Performance Prediction with Regression Approach and Data Generation
## 3 Experiment and Analysis of Results
### Datasets and Experiment by [PERSON]  
The datasets used in this research are collected from two secondary schools in Portugal. The datasets consist of a mathematics performance dataset with 395 pieces of student data and a Portuguese performance dataset with 649 pieces of student data. Both datasets have 33 features--3 grades in 3 periods of a semester and 30 datapoints of personal information, such as school, age, gender, family education, etc. [PERSON] used a naive predictor as the baseline. NN, SVM, and Random Forest (RF) are also used to predict the final performance of a student based on the collected student data. The prediction accuracy reported in the research [6] using all data features except the final grade is shown in Table 1.  
[PERSON]'s research uses a classification approach to predict student performance. The result shows that in [PERSON]'s research, the prediction accuracy of a pass/fail grading system is better than a 5-level grading system, the accuracy of a pass/fail grading prediction is, on average, 21.7% higher on the mathematics dataset, and 22.1% higher on the Portuguese dataset.
# Tissue Engineering Strategies for Retina Regeneration
## 7 Challenges and Future Directions  
Transplanted cells in the retina perform better in terms of physiology and cell survival when they are supported by a scaffold, compared to cell suspension. Support from factors provided by a cell monolayer (such as extracellular matrix and adhesion molecules) can help the cells to function better when transplanted along with the substrate. There are different methods to construct a scaffold including spun, machined, printed, assembled stepwise, or casted. New methods to create microscale niches for cocultured stem cells are also being explored. In the future, robotic and 3D bioprinting will allow several multiple types of cells and tissue layers to be combined with new generation scaffolds, to construct complex implants. Many scaffolds discussed here have not been assessed in vivo and therefore, evaluation of each type of scaffold is required in animal models to establish \"the proof of concept\". Implanting polymer scaffolds thicker than the size of the retina may result in retinal trauma and detachment during surgery or during the post-surgery period. It is also important to rule out inflammation caused due to scaffolds and their by-products.  
Using fast degrading polymers for clinical applications is limited mostly due to toxicity issues [127]. Even though many fast degrading polymers are in the development stage, slow degrading polymers might show lesser adverse events after transplantation. PLGA and PGA have faster rates of degradation compared to PCL or PLA polymers (over 2 years).  
Natural polymers have limited processability and it is difficult to control their batch to batch variability and mechanical properties. There can be changes in the constituents of natural polymers with age which can cause accumulation of debris in BM leading to the dysfunction of the transplanted cells [73]. The poor mechanical properties of most of the natural polymers make them difficult to handle surgical procedures. Methods like cross-linking are used to improve mechanical properties but this can make them thicker, poorly permeable, and non-biodegradable. Transplantation of tissue-engineered scaffolds into the retina needs immunosuppression at least provisionally until the blood-retinal barrier heals [128]. In the future, different HLA-matched, genetically screened, cGMP grade PSC-derived cells from the initial passages can be made available in cell banks, which will make cross-matching easy, to find the most suitable cells to avoid an immune response. Bringing together appropriately layered RPE with the multi-layered neural retina and establishing connections with the retinal ganglion cells for the visual signals to reach the brain through the optic nerve are the major challenges in tissue engineering the retina. Fine-tuning of the combined aspects of advancements in material science, stem cell biology, and clinical expertise along with the inputs from the ongoing clinical trials can resolve the hurdles in developing a final clinical-grade protocol for the therapy of retinal degenerative disease.
# Faulty Links' Fast Recovery Method Based on Deep Reinforcement Learning
## 3 Faulty-Path Recovery Scheme Based on DDPG-LBBP
### DDPG-LBBP Data Transmission Path Optimization Algorithm
\begin{array}{ll}\
abla_{\theta^{\mu}}G(\mu_{\theta^{\mu}})&= grad[Q]*grad[\mu]\\ &\approx\frac{1}{N}\sum_{i}\
abla_{a}Q(s,a|Q^{a})\left|{}_{s=s_{t},a=\mu(s_{t})} \
abla_{\theta^{\mu}}\mu(s|\theta^{\mu})|s_{t}\right.\end{array}  \tag{5}\]  
In Equation (5), the variable \(grad[Q]\) represents the action gradient derived from the online Q-network. This gradient serves as a crucial factor in guiding the adjustment of the online policy network's parameters, thereby fine-tuning the policy's direction. \(grad[\mu]\) represents the parameter gradient of the online policy network. This gradient ensures that  
Figure 2: DDPG-LBBP algorithm framework. the online policy network learns to select actions that yield high rewards. Meanwhile, \(N\) represents the number of randomly sampled experiences from the experience replay buffer. 2. Critic network updates  
The critic network comprises the online Q-network and the target Q-network, both tasked with estimating the action-value function. Specifically, the online Q-network computes the Q-value for a given state \(s_{t}\) and action \(a_{t}\), sampled from the experience replay buffer. The target Q-network is used to calculate the Q value of state \(s_{t+1}\) sampled from the experience replay pool and action \(a_{t+1}\) selected from the target Policy network. The online Q-network updates the network parameters based on two Q values to minimize errors. The updating process is shown in Equation (6):  
\[L=\frac{1}{N}{\sum_{i}}\Big{(}y_{t}-Q\Big{(}s_{t},a_{t}\Big{|}\theta^{Q}\Big{)} \Big{)}^{2} \tag{6}\]  
In Equation (6), \(N\) represents the number of samples taken at random, and \(y_{t}\) represents the Q value of selecting action \(a_{t+1}\) in the target Q-network when calculating the state \(s_{t+1}\). The target Q-network performs iterative training based on state \(s_{t+1}\) and action \(a_{t+1}\) to obtain the target value function \(Q^{\prime}\Big{(}s_{t+1},\mu^{\prime}\Big{(}s_{t+1}\Big{|}\theta^{\mu^{\prime }}\Big{)}\Big{|}\theta^{Q^{\prime}}\Big{)}\). The parameter \(\theta^{Q^{\prime}}\) is obtained by regularly copying the online policy network parameters \(\theta^{Q^{\prime}}\). The target Q network calculates the target return value \(y_{t}\) for the online Q-network. The calculation process of \(y_{t}\) is shown in Equation (7):  
\[y_{t}=r_{t}+\gamma Q^{\prime}\Big{(}s_{t+1},\mu^{\prime}\Big{(}s_{t+1}\Big{|} \theta^{\mu^{\prime}}\Big{)}\Big{|}\theta^{Q^{\prime}}\Big{)} \tag{7}\]  
In Equation (7), \(r_{t}\) signifies the reward value accrued through executing action \(a_{t}\) in state \(s_{t}\). Furthermore, \(\theta^{Q^{\prime}}\) denotes the parameters of the target Q-network, \(\theta^{\mu^{\prime}}\) represents the parameters of the target policy network, and \(\gamma\in[0,1]\) stands for the discount factor, which factors in the temporal decay of future rewards.
## 5 Demonstration of Machine Learning Model
### Shear Capacity Curve  
The shear capacity curve for RC columns was determined using Equation (9) from ASCE 41-23 [31, 32], which accounts for a decrease in shear strength with increasing displacement ductility. The failure mode of RC columns can be determined based on the relationship between the analytically calculated shear demand curve and the shear capacity curve determined using the standard equation. The shear demand curve is derived from pushover analysis and the yield displacement may be calculated. Based on the calculated yield displacement, the displacement ductility (k) is determined to derive the shear capacity curve. When comparing the shear demand curve with the shear capacity curve, as shown in Figure 9a, if there is no intersection between the two curves, it is determined that flexure failure has occurred. In Figure 9b, if there is an intersection between the shear demand and capacity curves, shear failure is indicated. If the intersection occurs before the yielding of the reinforcement, it is classified as shear failure; if it occurs after, it is classified as flexure-shear failure.  
Figure 8: Validation of modeling method (experiment vs. simulation) [1, 2, 3].  
Figure 7: Shear-governed column modeling method.  
The relationship between the shear demand and shear capacity curves for previous experimental studies [1; 2; 3] used for validating the modeling methodology for each mode of failure is illustrated in Figure 10. For the C3-1 specimen in [PERSON] and [PERSON]'s study [1], the absence of an intersection between the shear demand and capacity curves indicates high ductility and flexure failure. The 3 SLH18 specimen in [PERSON] et al.'s study [2] shows an early intersection between the two curves. Because the intersection occurs before the yielding of the reinforcement, it is classified as shear failure. Lastly, for Specim 1 in [PERSON] and [PERSON]'s study [3], the intersection of the shear demand and capacity curves after the yielding of the reinforcement indicates flexure-shear failure.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols
### Security Analysis of the PQC Standalone PKE Protocol
#### 5.1.3 Formal Statement  
If SHA-256 is collision-resistant and HMAC is unforgeable under chosen-message attacks, an adversary cannot impersonate either party or inject their own public key into the key exchange process.
## 4 Applications
### Dual-band Antennas Based on SRs  
It has been recently demonstrated that dual-band antennas with very closely spaced operating frequencies (such as those required, for instance, in UHF-RFID applications to cover different regulated bands worldwide), can be implemented by coupling electrically small resonators to mono-band antennas [47]. This perturbs the impedance of the antenna and conjugate impedance matching between the antenna and the integrated circuit at two required frequencies can be achieved. For this application, the key aspect is the resonator's size. For this reason, the use of a 2-SR is of interest.  
**Figure 21.** Photographs of the mono-band (**a**) and dual-band (**b**) fabricated UHF-RFID tags. (**c**) Measured and simulated read ranges. The dimensions of the tag are \(48\text{ mm}\times 48\text{ mm}\), and the strip width is \(1.4\text{ mm}\). The other relevant dimensions are \(l_{m}=16.3\text{ mm}\), \(w_{m}=4.8\text{ mm}\), \(d_{l}=7.3\text{ mm}\), \(d_{r}=33.9\text{ mm}\) and \(d_{f}=14.2\text{ mm}\). The final dimensions concerning the 2-SR and its relative position inside the antenna (shown in Figure 21b) are \(l_{r}=16.1\text{ mm}\), \(w_{r}=6.8\text{ mm}\), \(s_{h}=8.3\text{ mm}\) and \(s_{v}=5.4\text{mm}\). The strip width of the 2-SR is 0.5 mm and the separation between spirals is 0.3 mm. The considered substrate is the _Rogers RO3010_ with the dielectric constant \(\varepsilon_{r}=10.2\) and thickness \(h=0.127\text{ mm}\). From [47], copyright \(\copyright\) 2011, IEEE; reprinted with permission.  
Figure 20: Layout (**a**), photograph (**b**), and frequency response (**c**) of the fabricated elliptic lowpass filter. Dimensions are: \(W=5\text{ mm}\), \(G=0.55\text{ mm}\), \(l=6.6\text{ mm}\), \(b=2.42\text{ mm}\), \(c=2.7\text{ mm}\). The strips of the meander lines are of 0.2 mm. The filter has been fabricated on the _Rogers RO3010_ substrate with measured dielectric constant \(\varepsilon_{r}=11.2\) and thickness \(h=254\text{ \mu m}\). The circuit simulation corresponds to an ideal elliptic filter with the following characteristics: order-3 elliptic-function LPF with a passband ripple of \(L_{Ar}=0.1\text{ dB}\), a cutoff frequency of \(f_{c}=1\text{ GHz}\) and a stopband attenuation of \(L_{As}=30.52\text{ dB}\) with the equal-ripple stopband starting normalized frequency \(\Omega_{s}=2.5\). From [46], copyright \(\copyright\) 2010, IEEE; reprinted with permission.  
Figure 21 depicts a conventional mono-band meander line antenna, as well as its dual-band counterpart. The dual-band antenna has been designed to cover the UHF-RFID USA and European regulated bands. As can be seen from the measured read ranges, dual-band functionality, with very reasonable read ranges, is obtained [47].
## 3 Results
### Experiment III  
In Figure 9 and 10, the results of Experiment III.1 are presented, mirroring the methodology of Experiment II.1. In this iteration, the locations of the two objects were exchanged, and the phantom environment transitioned from a water-based medium to a ground-meat environment. The reconstructed image in Figure 9 shows the normalized electrical conductivity distribution of the cross-sectional phantom.  
In Figure 10A, the profile line depicts the normalized electrical conductivity intensities along the 128 th pixel row, spanning a frequency range of 1-220 kHz. The aluminum object occupies pixels 1 to 32, while the copper object occupies pixels 224 and 256. In particular, the peaks at the 18 th (with a two-pixel deviation) and 240 th positions in the profile lines correspond to the centers of the two objects. These findings highlight the effective signal-acquisition capability of the system in a non-homogeneous medium.  
Figure 10B illustrates the normalized contrast values across the frequency spectrum from 1 to 220 kHz, using a threshold of 0.95 to identify a practical frequency range from 14 to 37 kHz. This frequency range aligns with normalized sharpness parameters, ranging from 15 to 38 kHz, and normalized CNR parameters, ranging from 16 to 37 kHz, as shown in Figures 10C and 10D, respectively. These results suggest that the identified frequency range to achieve a higher image contrast under the given experimental conditions provides valuable insights for EIT imaging in a heterogeneous environment.  
Figure 9: Analysis of Experiment III.1 with a normalized conductivity reconstruction image with a resolution of 256 \(\times\) 256 pixels and a scale of 10 mm/16 pixels.  
Figure 10: Graph of analytical indices in Experiment III.1: (**A**) the profile line at the 128 th pixel row. The red-dashed squares represent the positions of the objects; (**B**) the normalized contrast values; (**C**) the normalized sharpness values; (**D**) the normalized CNR values corresponding to the surveyed frequency range.
# Eco-Friendly Metadome-Antenna Innovations for Wearable Millimeter Wave Radar Sensing
## 2 Materials and Methods
### Eco-Friendly Materials Used for the Antenna and the Metasurface
#### 2.1.2 Eco-Friendly Conductor: Aluminum (Al)  
Aluminum (Al) is widely used in many day-to-day objects since it is highly abundant, relatively cheap, has adequate mechanical strength and low density, and resists corrosion. It is soft, ductile and malleable.  
For electronics, it is highly conductive, lighter and more flexible than copper, recyclable and eco-friendly. In fact, it is one of the most environmentally friendly metals on earth and can be recycled infinitely to create the exact same product, requiring less energy and time than other metals (recycling aluminum only requires 5% of the energy it takes to produce it the first time around and its CO\({}_{2}\) footprint is close to zero) [35], which paves the way for a truly circular economy. Hence, it is a good alternative for conductive parts of wearable antennas and metaradomes, despite the fact that it cannot be soldered with ordinary soldering irons.