esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Contrast Learning Approach for Address Semantic Matching
## 2 Materials and Methods
### Address Semantic Contrast Learning Model
#### 2.3.2 Bi-LSTM  
LSTM is a kind of RNN, mainly to solve the problem of gradient disappearance and gradient explosion in the training process. LSTM has better performance in long sequences [32]. The LSTM neural network uses three gate structures: input gate, forget gate and output gate to maintain and update the increase and decrease in information in the cell. However, a one-way LSTM can only process information in one direction, and cannot process information in another direction. The bidirectional LSTM is a further extension to solve the defects of LSTM. This paper uses bidirectional LSTM to extract feature information to learn address features fully. Specifically, two different LSTM neural network layers are used to traverse from the front and the back of the Chinese address, respectively, so that the address information of the two directions can be saved. Compared with the one-way LSTM, Bi-LSTM cannot only save the previous context address information, but also consider the future context address information. Therefore, the semantic representation is extracted more completely.  
Figure 2: (**a**) Overall framework of the contrast learning model. (**b**) The structure of ABLC model.  
First, the forget gate generates a value \(f_{t}\) between 0 and 1 based on the output \(h_{t-1}\) from the previous memory unit and input data \(x_{t}\), to determine how much information is lost in the last long-term state. \(h_{t-1}\) and \(x_{t}\) through the input gate to determine the update information to \(i_{t}\), and in addition, through a \(\tan h\) layer to get the new candidate memory unit information \(C_{t}\). Additionally, the last long-term status \(C_{t-1}\) is updated to \(C_{t}\) through the operation of the forget gate and the input gate. Finally, the judgment is obtained from the output gate, to multiply the value \(o_{t}\) between \(-1\) and 1. The multiply result \(h_{t}\) is used to determine which state characteristics of the current memory cell are output. As shown in the following formula:  
\[f_{t}=\sigma(W_{f^{\prime}}[h_{t-1},x_{t}]+b_{f}) \tag{2}\]  
\[i_{t}=\sigma(W_{i^{\prime}}[h_{t-1},x_{t}]+b_{i}) \tag{3}\]  
\[{C_{t}}^{\prime}=\tanh(W_{C^{\prime}}[h_{t-1},x_{t}]+b_{C}) \tag{4}\]  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*{C_{t}}^{\prime} \tag{5}\]  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{6}\]  
\[h_{t}=o_{t}*\tanh(C_{t}) \tag{7}\]  
This model uses LSTM to solve long-term dependence, and combines the complementary information of the positive and negative directions of Bi-LSTM to fully learn the address text characteristics as shown in Figure 3. In this experiment, the number of hidden neurons is 100, and the dropout parameter is set to 0.5.  
Figure 3: The Structure of Bi-LSTM module.
## 4 Bone Grafts and Substitutes for Bone Repair
### Natural Bone Grafts and Substitute Materials
#### 4.1.5 Bone Graft Material Derived from Extracted Tooth Used in Dentistry  
Bones, dentin and enamel have a similar composition to hydroxyapatite in the inorganic component as well as type 1 collagen and other proteins in the organic component but with different percentages [65; 66]. The potential osteoinduction characteristics of the demineralized dentin matrix have been demonstrated in several studies, as well as the presence of bone-morphogenetic proteins in the human dentin matrix after a demineralization process [67; 68].  
In 2017, [PERSON] theorized how the process of dentin demineralization of autologous extracted teeth allows better bone augmentation through the increased availability of bone morphogenetic proteins [69]. Other studies have confirmed the efficacy of a partially demineralized autologous dentin matrix prepared in real time for human bone regeneration clinical procedures [70; 71].  
Since bone and dentin are mineralized tissues with an almost similar chemical composition, and the morphogenetic proteins in dentin and bone have a major stimulating effect with osteoinductive properties, the regenerative properties of autogenous demineralized dentin matrix (DDM) have been highlighted in several studies. It was found that the dental collagen matrix, similar to the bone matrix, can also induce bone formation. They are currently in development, and there have already been a few clinical uses of such demineralized dentin (DDM) matrices, produced from the patient's extracted teeth, to repair alveolar bone defects. The materials obtained are processed and then applied in the form of powders, bone blocks or moldable pastes, and in the future, they could be an option for use as a vehicle for growth factors and stem cells [72; 73].
# Evaluation and Validation of Viscous Oil Cavitation Model Used in Torque Converter
## 4 Parameter Study Results and Discussion
### Analysis of Parameter Sensitivity  
We analyzed the characteristics and internal flow field of torque converter under stall by establishing and solving the cavitation CFD models with different parameters. After the analysis and comparison of various parameters, the influence degree (the change rate of the torque converter performance caused by the parameter modification of the cavitation model) was calculated to express the relationship between cavitation model parameters and CFD results, as shown in Figure 9.  
By comparing the additive influence degree of each parameter, we found out that the influence degree of them follows \(NVF>F_{\text{vap}}>R_{\text{B}}>MDR>CRRF>F_{\text{cond}}>P_{\text{v}}\).  
It can be concluded from Figure 9. that \(NVF\) and \(F_{\text{vap}}\) both have a great influence on the performance of torque converter, especially on capacity constant and mass flow rate, yet they have limited influence on stall torque ratio. While \(R_{\text{B}}\), \(MDR\), and \(CRRF\) have limited influence, which is also mainly reflected in capacity constant and mass flow rate. Other cavitation parameters have little effects on the performance of the torque converter,  
Figure 8: Error comparison of turbine torque, torque ratio, efficiency, and capacity constant between no cavitation and cavitation at various speed ratio under cavitating conditions.  
Figure 9: Influence degree of all cavitation parameters on capacity constant, torque ratio, and mass flow rate of torque converter at stall.  
such as \(F_{\text{cond}}\) and \(P_{\text{v}}\). It is worth noting that the capacity constant and mass flow rate of the torque converter varied pronouncedly under different cavitation parameters, while the torque ratio under stall is mildly affected by the cavitation parameter settings.
# A Cognitive Anycast Routing Method for Delay-Tolerant Networks
## 5 Performance Measurements
### Scenario 1: Homogeneous Propagation Delays and Random Packet Drops  
The case of links sharing identical features was evaluated first. In this scenario, the propagation delay values that are depicted by the graph's edges in Figure 1 were replaced with a constant value of 100 ms. These delays were emulated by introducing controlled buffering to the outbound links of the nodes through the Traffic Control tool (NetEm). Three of the links were configured to randomly drop packets with a small probability: 0.01 (g22-g28), 0.02 (g23-g26), and 0.03 (g25-g27). These links emulate the use of wireless channels in the topology (e.g., via medium-orbit satellites). Figure 2a depicts the average response time achieved by the proposed anyscal routing method. For comparison purposes, the results obtained with CGR (unicast as the protocol does not support anycast) to either of the anycast group members are also shown. Addressing the whole traffic with CGR to g27 rather than g30 produced better results. The performance achieved by the CSG method was observed to be better than either choice sent with CGR. CSG dynamically divided the flow delivering a number of the bundles to g27 and the rest to g30 as the SNN adapted to the expected routing cost. This effect can be observed in Figure 2b, which depicts the average path of the flows and sub-flows. The average observed split between the two sub-flows is depicted in Figure 2d. It is interesting to note that under low traffic levels, CSG attempted to balance in about the same proportion the workload of both anycast group  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline SNN simulation time, step & 100 ms \\ SNN simulation step & 0.5 ms \\ Time constant (\(\tau\)) & 10 ms \\ Resistance (\(R\)) & 10 M\(\Omega\) \\ Maximum depolarization (spike voltage) & 35 mV \\ Spike fire threshold (\(\theta\)) & \(-\)50 mV \\ Reset & \(-\)90 mV \\ Rest & \(-\)70 mV \\ Bootstrap current (\(I_{ref}\)) & 2.1 nA \\ Random walk probability (\(P_{w}\)) & 0.1 \\ Learning factor (\(\eta\)) & 0.1 \\ Smoothing factor (\(\alpha\)) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the CNC parameters used in the tests.  
members. However, as the traffic level was increased, there was a clearer preference for the g27 node because of the larger end-to-end bandwidth to that node. The response time deviation of the resulting sub-flow from the aggregate average was observed to be very small in this experiment. The average response time of the sub-flows is depicted by the discontinuous lines in the figure. The addition of both sub-flow throughputs yielded the final CSG throughput.  
Lower bundle response times imply a more balanced use of the parallel paths that are available for forwarding bundles in the DTN. Both routing methods use this metric as the minimization goal. By reducing the response times, it is also possible to improve the end-to-end throughput as indicated by the results in Figure 2c compared to what can be achieved with unicast CGR to a selected member.
## 3 Results
### Results for Cadaver Heads 2-4  
#### 3.2.1 Pre-Filtration with 0.2 vs. 0.3 mm Copper Filter  
Based on the results of cadaver head 1 121 kV protocols were further evaluated with additional copper pre-filtration. Image quality was comparable between different data sets scanned without, with 0.2 mm or 0.3 mm copper filters. The radiation dose could be reduced to 40% compared to the dose without pre-filtering by using a 0.2 mm thick copper filter, and to 42 to 60% of the original dose by using a 0.3 mm thick copper filter. See Table 1 for detailed results and Figure 4 for representative images with copper pre-filtration.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scan Protocol at 121 kV** & \multicolumn{4}{c}{**Minimum Score of Each Reader**} & \multicolumn{2}{c}{**Overall**} & \multicolumn{2}{c}{**Average DAP**} & \multicolumn{2}{c}{**Mean CTDI\({}_{\text{vol}}\)**} \\ \hline
**DL (nGy)** & **Pre-Filtration** & **Skull 1** & **Skull 2** & **Skull 3** & **Skull 4** & **Minimum Score** & **(cGycm\({}^{2}\))** & **(mGy)** \\ \hline
278 & 0.2 mm Cu & 2 & 2 & 2 & 2 & 2 & 13.1 & 2.4 \\
278 & 0.3 mm Cu & 2 & 2 & 2 & 2 & 2 & 12.6 & 1.7 \\
548 & 0.2 mm Cu & 2/3 * & 2 & 2/3 * & 2 & 23.9 & 3.2 \\
548 & 0.3 mm Cu & & 2/3 * & 2 & 2/3 * & 2 & 21.0 & 2.8 \\
1090 & 0.2 mm Cu & 3 & 3 & 3/4 * & 3 & 46.5 & 5.2 \\
1090 & 0.3 mm Cu & & 3 & 3/4 * & 3 & 40.5 & 4.7 \\
2180 & 0.2 mm Cu & 3 & 4 & 3/4 * & 4 & 3 & 122.0 & 8.1 \\
2180 & 0.3 mm Cu & 4 & 3/4 * & 4 & 3 & 153.9 & 5.6 \\
278 & 0.4 mm Sn & 1 & 1 & 1 & 1 & 49.3 & 1.3 \\
548 & 0.4 mm Sn & 1 & 1 & 1 & 1 & 88.0 & 1.9 \\
1090 & 0.4 mm Sn & 1 & 1 & 1/2 * & 1 & 214.0 & 3.2 \\
2180 & 0.4 mm Sn & 1 & 1/2 * & 2 & 1 & 400.4 & 5.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Display of the minimum achieved scores of the four skulls depending on the scan protocol (mean value of both readers, stars * mark a difference in the minimum scores of the two readers). Additional information: average radiation exposure values of each protocol (DAP = Dose Area Product, CTDI\({}_{\text{vol}}\) = mean computed tomography volume index).  
Figure 3: Post-processed data set of a scan with 121 kV/DL 278 and pre-filtration with 0.2 mm copper, raw datasets. Higher image noise in (**A**) than in (**B**) and (**C**). Blurred visualisation of bony edges in (**C**) compared to (**A**) and (**B**). (**B**) was rated as the best compromise.
## 3 Results
### Content of Caffe and Phenolic Compounds (HPLC-UV Analysis)  
All three coffee types contained similar levels of gallic, cinnamic, and caffeic acids (Table 2). Significant differences (\(p\leq\) 0.01), however, were observed in their caffeine and chlorogenic acid content. Elephant coffee had the lowest levels of both compounds, with 0.93% caffeine and 0.33% chlorogenic acid. Conventional coffee contained the highest amount of caffeine (1.33%), while civet coffee had the highest concentration of chlorogenic acid (0.44%).  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Sample** & **DPPH** & **ORAC** & **ABTS** & **TPC** & **TFC** \\ \hline \multicolumn{5}{c}{\(\mu\)g TE/mg} & \multicolumn{3}{c}{\(\mu\)g GAE/mg} \\ \hline Conventional coffee & 44.14 \(\pm\) 13.28 \({}^{\text{a}}\) & 289.99 \(\pm\) 75.93 \({}^{\text{a}}\) & 125.97 \(\pm\) 14.22 \({}^{\text{a}}\) & 34.91 \(\pm\) 4.88 \({}^{\text{a}}\) & 2.15 \(\pm\) 0.46 \({}^{\text{a}}\) \\ Civet coffee & 32.26 \(\pm\) 7.97 \({}^{\text{ab}}\) & 314.19 \(\pm\) 79.60 \({}^{\text{a}}\) & 104.78 \(\pm\) 15.76 \({}^{\text{b}}\) & 32.69 \(\pm\) 10.64 \({}^{\text{b}}\) & 1.73 \(\pm\) 0.40 \({}^{\text{ab}}\) \\ Elephant coffee & 19.67 \(\pm\) 2.86 \({}^{\text{b}}\) & 250.85 \(\pm\) 60.35 \({}^{\text{a}}\) & 81.84 \(\pm\) 7.78 \({}^{\text{c}}\) & 26.35 \(\pm\) 3.08 \({}^{\text{b}}\) & 1.38 \(\pm\) 0.28 \({}^{\text{b}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Antioxidant activity and total phenolic and flavonoid contents (mean \(\pm\) SD) of the tested coffee samples.
## 5 Experimental Results
### Evaluation Metrics  
* Accuracy (\(ACC\)) is a standard metric used to assess the performance of classification models. It represents the percentage of samples correctly classified out of the total number of evaluated samples. It is computed as follows: \[ACC(\%)=\frac{TP+TN}{TP+TN+FP+FN}\times 100,\] where true positives (\(TP\)) and true negatives (\(TN\)) correspond to correctly identified malicious and benign samples, respectively, while false positives (\(FP\)) and false negatives (\(FN\)) represent misclassified samples.
* Precision (\(PR\)) evaluates the accuracy of positive predictions made by a model. It quantifies the proportion of true positives (i.e., correctly identified positive instances) among all instances classified as positive. It is calculated as follows: \[PR=\frac{TP}{TP+FP}\times 100,\]  
Figure 7: Our proposed one-class classifier.  
* The detection rate (\(DR\)) measures the proportion of the true positive samples among all the samples that are truly positive. This metric provides insight into how well the model avoids false negatives. It is computed as follows: \[DR=\frac{TP}{TP+FN}\times 100,\]
* The F1 score (\(F1\)) serves as a harmonic mean, providing a balance between two key classification metrics, precision (\(PR\)) and the detection rate (\(DR\)). It can be calculated as follows: \[F1(\%)=\frac{2*PR*DR}{PR+DR}\times 100,\]
* The mean arctangent absolute percentage error (\(MAAPE\)) evaluates the prediction error as a percentage. It is computed as follows: \[MAAPE=\frac{1}{N}\sum_{i=1}^{N}\arctan(\left|\frac{x_{i}-\hat{x}_{i}}{x_{i}} \right|)\times 100\%.\]  
To evaluate our one-class detector, we use the _ACC_, \(PR\), \(DR\), and \(F1\) score. Higher values for these metrics indicate better detector performance. For evaluating our predictor, we utilize the _MAAPE_, a well-known scale-free metric that measures error as a percentage [57]. Lower values of MAAPE indicate better predictor performance.
## 2 Principle of SM Using a Novel Active Transmitter Detection Scheme with SSD
### The Novel Active Transmitter Detection Scheme  
The proposed novel active transmitter detection scheme consists of the signal pre-distortion technique at the transmitter and a novel power-based statistical method for active transmitter detection at the receiver. The signal pre-distortion technique shapes the CAP signal power at the transmitter to facilitate active transmitter detection. The power-based statistical method is then implemented to reduce the transmitter detection error statistically.
###### Abstract  
Relay-aided free-space optical (FSO) communication systems have the ability of mitigating the adverse effects of link disruption by dividing a long link into several short links. In order to solve the relay selection (RS) problem in a decode and forward (DF) relay-aided FSO system, we model the relay selection scheme as a Markov decision process (MDP). Based on a dueling deep Q-network (DQN), the DQN-RS algorithm is proposed, which aims at maximizing the average capacity. Different from relevant works, the switching loss between relay nodes is considered. Thanks to the advantage of maximizing cumulative rewards by deep reinforcement learning (DRL), our simulation results demonstrate that the proposed DQN-RS algorithm outperforms the traditional greedy method.  
free space optics; relay selection; deep reinforcement learning +
Footnote †: journal: Applied sciences
# Tamed Warping Network for High-Resolution Semantic Video Segmentation
## 1 Introduction
KFC processes key frames in the same way as per-frame models do except that KFC also sends the features of the current key frame to the next frame (a non-key frame). NKFC extracts spatial features of the current non-key frame and warps context features from the preceding frame. Then, these features are fed into our two correction modules, context feature rectification (CFR) and residual-guided attention (RGA). CFR fuses warped context features with the spatial details of the current frame to learn feature space residuals under the guidance of KFC. Furthermore, RGA utilizes the compressed-domain residuals to correct features learned from CFR. At the end, the corrected features are also sent to the next non-key frame. Experiments show that TWNet is generic to backbone choices and significantly increases the mIoU of the baseline from 67.3% to 71.6%. For non-rigid categories, such as human, the improvements are even higher than 18 percentage points, which is important for the safety of autonomous driving. The contributions are summarized as follows:  
Figure 1: Comparison between warping only and warping with correction. (**a**) The walking people’s limbs can be occluded by their own bodies a few frames prior but appear later, thus becoming severely deformed in later warped frames. (**b**) A moving car occludes distant objects that cannot be warped from previous frames. By adding a correction stage, errors can be significantly alleviated. * We propose a fast high-resolution semantic video segmentation framework utilizing compressed-domain motion vectors to carry out warping and fusing warped context features with current spatial details for the following feature correction. * To alleviate the errors incurred by warping, we propose two correction modules to learn the feature space residuals. CFR fuses warped context features with the current spatial details to correct the warped features under the guidance of a per-frame model. Furthermore, RGA utilizes compressed-domain residuals to correct features learned from CFR. * Experiments show that TWNet significantly increases the mIoU of the baselines. For non-rigid categories, the improvements are even higher.
## 3 Results and Discussion
### Fitting Results
Initially, the Ca\({}^{2+}\) concentration was lower than the model-derived value, but it gradually increased and eventually became greater than the model-derived Ca\({}^{2+}\) concentration (Figure 9a). The SSE, MSE, and RMS error of the HCO\({}_{3}\)\({}^{-}\) concentration of the theoretical model and the experimental model were calculated to be 2.04 \(\times\) 10\({}^{-8}\), 4.08 \(\times\) 10\({}^{-8}\), and 6.38 \(\times\) 10\({}^{-5}\), respectively. As can be seen from Figure 9b, among the five points, except for the second point, the gap is large, and the other points are well fitted. Figure 8: Actual toluene consumption versus theoretical consumption. Figure 9: The fitting diagram of experimental data and model data. (**a**) The fitting diagram of Ca\({}^{2+}\); (**b**) the fitting diagram of HCO\({}_{3}\)\({}^{-}\). After the above rough analysis, the fitting situation of the ions was studied. Temperature affects the dissolution of calcium carbonate in groundwater. At the first point, the calcium carbonate may not be completely dissolved. Toluene is converted to metabolic intermediates, such as benzylsuccinate and benzene carboxylic acid, which affect the pH value in the aqueous carbonate system [47; 48]. The experimental data of the Ca\({}^{2+}\) concentration showed slightly higher values than the model data. This may be because the intermediate product is acidic, which affects the ionization of carbon dioxide and promotes the dissolution of calcium carbonate. The first two points may have belonged to the stage when microorganisms were adapting to their environment. The second to third points marked the period of rapid conversion of nitrate to nitrite, when the concentration of nitrate and nitrite changed rapidly, so there may have been a great difference in monitoring concentration. In addition, this stage also represented the exponential period of microbial growth and reproduction, and the toluene consumption of microorganisms may have been much higher than 5%, which may be the reason for the significant difference between the third point and the model. In the later period, microorganisms were in the decline period, and microorganisms had almost stopped consuming toluene, so the fitting results of the latter two points were slightly poor. To ensure the survival of microorganisms, small amounts of Mg\({}^{2+}\) and phosphate plasma were added to the experiment as necessary nutrients. The influence of various ions in the system also caused a difference between the model and the experiment.