esa-sceva/satcom-chunk-collection huggingface dataset:
# Inter-Satellite Link Prediction with Supervised Learning: An Application in Polar Orbits
## 4 Supervised Learning Model
### Weighted Loss Function  
Given the nature of satellite encounters in sparse constellations, it is expected that communication opportunities will be scarce. Specifically, for a set of 20,000 different satellite pairs, the average time they are within 2000 km of proximity is only 2%. Consequently, the dataset exhibits a significant imbalance between zeros (no encounters) and non-zeros, posing challenges during training. To address this imbalance, Equation (2) presents the weighted loss function (\(L\)) used for training. This loss function has proven to work considerably better than other default functions such as mean square error [22], mean absolute error [23], and cross entropy [24].  
\[L=(AY_{t}+1)\cdot(Y_{t}-Y_{p})^{2} \tag{2}\]  
where \(Y_{t}\) is the true value, \(Y_{p}\) is the predicted value, and \(A\) is a positive constant.  
\(L\) represents the squared error between the true value and the predicted value, scaled by a factor greater than one. This scaling ensures that the loss increases proportionally to \(Y_{t}\). In other words, for a given squared error, a higher \(Y_{t}\) implies a higher loss. Therefore, more importance is given to higher \(Y_{t}\) values, which are rarely found during training. The scalar parameter \(A\) is used to adjust the importance given to non-zero values, having a significant impact on the training process. Various values greater than 0 are compared.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Value** & \(\mathbf{B^{*}}\) [\(\mathbf{R_{E}^{-1}}\)] & \(\mathbf{a}\) [km] & \(\mathbf{e}\) [-] & \(\mathbf{i}\) [\(\mathbf{{}^{*}}\)] & \(\mathbf{\Omega}\) [\(\mathbf{{}^{*}}\)] & \(\mathbf{\omega}\) [\(\mathbf{{}^{*}}\)] & \(\mathbf{M}\) [\(\mathbf{{}^{*}}\)] \\ \hline From & \(-0.03\) &The best performance is observed with \(A\) values lower than one. For datasets with stronger imbalances, higher \(A\) values might be necessary.  
Figure 4 shows the loss function for different true values from 0 to 1 for \(A=0.5\). Notice that the minimum loss is always found when \(Y_{p}=Y_{t}\). As \(Y_{t}\) increases, the loss for a given square error increases. For instance, for \(Y_{t}=0.2\) and \(Y_{p}=0.8\), the loss is 0.4, while for the same square error with \(Y_{t}=0.8\) and \(Y_{p}=0.2\) the loss increases to 0.5. Notice that the highest loss is obtained when the true value is 1 and the prediction is 0.
# Visual and Phonological Feature Enhanced Siamese BERT for Chinese Spelling Error Correction
## 2 Related Work
### Supervised Methods  
Several supervised methods have been proposed to tackle this problem. They regard the CSC task as a sequence labeling problem [14; 15; 16], in which RNN based models are adopted. Confusion set guided Pointer Networks [7] used a pointer network to copy similar characters from the confusion set directly. A chunk-based framework was proposed in [17] to correct single-character and multi-character word errors uniformly.  
Recently, BERT[9], the masked language representation model, has been successfully applied in CSC task. FASPell [11] fine-tunes a pre-trained BERT to produce character candidates and then use a confidence-similarity decoder to filter the candidates. Candidate characters with high contextual confidence and similarity from the original character are more likely to be the best. Confusionset-guided Pointer Networks [7] proposed a Seq2 Seq model to copy an input sentence to a corrected new sentence through a pointer network. Soft-Masked BERT [12] consists of a network for error detection based on GRU [18] and a network for error correction based on BERT, with the former being connected to the latter with a soft-masking technique. The character embeddings of potential error characters are masked via a soft pointer to help BERT select the best candidate.  
Graph-based methods have also been proposed to utilize external information of characters for CSC task. SpellGCN[8] with graph convolutional network (GCN) aims to capture similarity information between characters and it is able to achieve the SOTA results on benchmarks. Specifically, two graphs are built for pronunciation and shape similarities correspondingly. An attentive graph is used to combine node representations of the two graphs. For characters in a given input text, SpellGCN uses BERT to extract context representations. Then, similarities between these context representations and node representations extracted by GCN are measured to find the correct answer. SpellGCN relies on the confusion set, which requires human annotation and only covers a subset of Chinese characters.
###### Abstract  
Low-carbon design has become increasingly important in the cement and concrete industry. Previous studies have primarily focused on the impact of different types of admixtures on the carbon emissions of concrete while overlooking the influence of the water-to-ceentitious materials ratio on concrete carbon emissions. To address this gap, this study aims to investigate the synergistic effects of the water-to-binder ratio and quartz powder dosage on concrete hydration, strength, and carbon emissions and to propose a comprehensive performance prediction model. Our proposed performance prediction model highlights the critical role of the water-to-cementitious materials ratio in low-carbon concrete design. It distinguishes between the dilution and nucleation effects of the quartz filler and evaluates the impact of quartz content (10% and 20%) and water-to-binder ratios (0.5 and 0.2) on the cement hydration rate; consequently, it is able to predict the concrete's thermal, chemical, mechanical, and environmental properties. The model specifics are as follows: the parameters were determined using hydration heat data from a paste with a water-to-binder ratio of 0.5 over the first 3 days, and the chemically combined water and portlandite production was calculated up to 28 days. The water availability coefficient, derived from hydration product measurements with a ratio of 0.2, shows that lower water-to-binder ratios slow hydration as the coefficient exceeds 1. A linear equation predicts strength development based on the mix ratio and reaction degree. The CO\({}_{2}\) emission analysis shows that when the water/binder ratio is 0.50, with a compressive strength of 1 MPa, the carbon emissions change little with an increase in the quartz powder replacement rate. However, when the water/binder ratio is 0.2, the percentage reductions in CO\({}_{2}\) emissions per unit strength are 11% and 20% for the 10% and 20% quartz powder replacement rates, respectively, compared with the specimen without quartz powder. The model's regression method is simple, versatile across mix ratios, and capable of predicting hydration heat, product composition, strength, and CO\({}_{2}\) emissions.  
caption = title]Applied sciences
# Performance Analysis of MIMO System with Single RF Link Based on Switched Parasitic Antenna
## 3 Modeling of MIMO System in SystemVue
### Flow of the Simulation  
The whole system is built from the simple to the complex, from the local to the whole. The overall construction and operation process is as follows:  
1. The single link can realize the modulation and demodulation process, which is not related to the RF link, channel or noise. The first signal by IQ modulation in transmitter directly passed the SPDT switch. In the ideal state, if the measured bit error rate is 0, the demodulated signal  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Elliptic Band-pass Filter & 2380–2420 & MHz \\ Amplifier 1 Gain & 60 & dB \\ Isolator Isolation & 30 & dB \\ Local Oscillator Power & 2 & dBm \\ Resonant Frequency & 2620 & MHz \\ Linear Attenuator & 2 & dB \\ Chebyshev Bandpass Filter & 210–230 & MHz \\ Filter Cut-off Frequency & 2700 & MHz \\ Amplifier 2 Gain & 33 & dB \\ Amplifier 3 Gain & 30 & dB \\ Controllable Attenuator & 3 & dB \\ \hline \hline \end{tabular}
\end{table}
Table 3: RF receiver module parameter.  
Figure 7: The input and output spectrum of the receiver link chain. (**a**) The input spectrum; (**b**) The output spectrum.  
constellation distribution will be concentrated in [\(-\)1,0] and [1,0], which means the correctness of the system design.
2. Design the transmitter and the receiver separately to get the radio link budget. Then measure the RF link power and the input and output spectrum distribution in all nodes to ensure the RF link design is correct. It can be added as a separate module to the MIMO system for further investigation.
3. The transmitting end and the receiving end RF link design need to be incorporated into the system in (1), which will introduce the synchronization module. The feedback time measured by the CorrDelay component gives the BER of the system in the software.
4. On the basis of (3), the channel and Gauss white noise are added to construct the single RF link \(2\times 2\) MIMO system based on a switched parasitic antenna. Using the parameter scanning function in SystemVue, we can measure the change of system BER with the signal to noise ratio and the channel capacity, which includes the characteristics of single pole double throw switches, switching speed, isolation and insertion loss, system delay, and different encoding modes at the receiver.
## 6 The Controller Design
### The Allocator Design
\(\delta_{r}\) and \(\delta_{\psi}\) are the rudder deflection angle and thrust vector nozzle deflection angle, whose control efficiencies are \(M_{\delta_{r}}\) and \(M_{n_{\psi}}\). The control efficiency can be calculated by Equation (37). \[\left\{ \begin{array}{l}M_{\delta_{e}}=\frac{\hat{q}_{xx}Sb}{ly_{y}}C_{m\delta _{e}}\\ M_{n_{\theta}}=\frac{\hat{r}_{xx}d_{xII}}{ly_{y}}\end{array} \right.\left\{ \begin{array}{l}M_{\delta_{e}}=\frac{\hat{q}_{yy}SL}{I_{xx}}C_{l\delta_{e}} \\ M_{n_{\theta}}=\frac{\hat{r}_{xx}}{I_{xx}}\end{array} \right.\left\{ \begin{array} []{l}M_{\delta_{e}}=\frac{\hat{q}_{yy}SL}{I_{xx}}C_{l\delta_{e}}\\ M_{\delta_{e}}=\frac{\hat{q}_{yy}SL}{I_{zz}}\end{array} \right.\left\{ \begin{array} []{l}M_{\delta_{e}}=\frac{\hat{q}_{yy}SL}{I_{zz}}C_{n\delta_{e}},\\ M_{\delta_{e}}=\frac{\hat{q}_{yy}SL}{I_{zz}}\cos\delta_{1}\end{array} \right. \tag{37}\]  
Here, \(C_{m\delta_{e}}\) is the pitch control derivatives, \(C_{l\delta_{a}}\) is the roll control derivatives, and \(C_{n\delta_{e}}\) is the yaw control derivatives. \(\delta_{TN}\) is the pitch control efficiency of the propulsion system, which is defined as the differential force of the lift fan and ducted nozzle, and \(d_{Lift}\) is the length from the center of gravity to the lift-fan fix point. \(\delta_{L}\) is the roll control efficiency of the side nozzles, which is defined as the roll moment generated by each differential flow unit. \(L\), \(S\), and \(b\) are the reference length, area, and span, respectively. \(I_{xx}\), \(I_{yy}\), and \(I_{zz}\) are the inertia moments around the body \(x\), \(y\), and \(z\) axes, respectively. Besides the attitude channels, the speed channel also requires an allocator in the transition mode. Due to the peculiarity of the thrust-vector and lift-fan system, the descending rate can be controlled more precisely by the direct lift at a low airspeed, which is also the guaranty of SRVL security. According to the SRVL deceleration performance analysis, the positive boundary of the forward speed acceleration surpasses the negative boundary significantly. Since the propulsion system only impacts the positive boundary, the control efficiency requirements of the propulsion system are not an issue. This work let the highly efficient propulsion system control the descending rate. The control inputs were \(n_{T}\). This work let the thrust vector deflection angle, \(\delta_{1}\), control the forward speed:  
\[\left\{ \begin{array}{l}n_{T}=\frac{\hat{v}_{xg}}{M_{xyz}}\\ \delta_{1}=\frac{\hat{v}_{xg}}{0.5g}+\delta_{trim}\end{array} \right. \tag{38}\]  
where \(M_{xyz}\) is the forward flight channel control efficiency, defined as the forward speed acceleration generated by the engine speed unit. \(\delta_{trim}\) is the current trimmed thrust vector deflection angle.
## 2 Electro-Thermal Modeling and Analysis of Composite Materials Under Lightning Strike
### Finite Element Model
#### 2.2.1 Mesh Generation and Boundary Condition  
The composite laminate model established in this study measured 100 mm \(\times\) 100 mm, with individual ply thicknesses of 0.2 mm and a layup sequence of [45/0/-45/90]\({}_{2S}\), totaling 16 plies, as depicted in Figure 1. The mesh was generated using DC3D8E elements, which are eight-node linear electro-thermal coupled hexahedral elements; a mesh convergence analysis was conducted by progressively refining the mesh density until the temperature field and damage area predictions stabilized. The optimal mesh size was determined when further refinement produced a less than 2% change in the predicted maximum temperature and damage area. An implicit solver (Abaqus/Standard) was used in the simulation. To enhance the simulation accuracy and precisely model local effects, the mesh was locally refined in the central region, resulting in a total of 33,728 elements. Based on the operating conditions described in the experiment, precise electrical and thermal boundary conditions were applied during the simulation. The side and bottom surfaces of the specimen were in full contact with the metal components of the experimental apparatus, ensuring effectivegrounding. Consequently, the electric potential for the side and bottom surfaces was set to 0 V. Due to Joule heating, significant temperature increases occurred along the current flow paths, leading to substantial temperature differences between the top and side surfaces and the environment. Therefore, a third-type boundary condition with a thermal emissivity of 0.9 was applied. The bottom surface of the laminate exhibited minimal temperature variation and was treated as adiabatic, employing a second-type boundary condition with the ambient temperature set to 25 \({}^{\circ}\)C, considering the short duration of lightning strikes and the limited heat diffusion to the bottom layer.
# Research on Multicore Key-Value Storage System for Domain Name Storage
## 3 Model Description
### Cache Synchronization Method  
As shown in the Figure 6, our method uses a main-proxy replication model. Asynchronous methods are used to synchronize messages, which may reduce the hit rate. The protocol parsing module writes cache entries in the main table and reads cache entries from the proxy table. The protocol parser module reads the proxy cache table on the self core when parsing network traffic. This read is done without locking the proxy cache table, but read indication is sent to the main cache table synchronously. The main cache table adjusts the distribution of the accessed cache items based on this read operation and combined with our proposed cache replacement algorithm.  
When the protocol parser module parses the traffic and obtains the information it needs to store, it writes the cache entries to the main cache table. This write operation requires locking because it is a multicore operation. In this way, the main cache table updates the read operations and write operations. The main cache table is adjusted the cache table according to the cache replacement algorithm based on all these operations.  
The proxy cache table working on the parsing core is not updated in real time, and only the read indication is forwarded to the main cache table. This design can avoid frequent lock operations caused by cache replacement and can ensure high throughput and low system latency. At the same time, the main cache table accepts write operation instructions and read operation instructions, and the cache table must be replaced and updated according to these instructions.  
Figure 6: One-main and multi-proxy synchronization method.  
Because the cache table is split into cache sub-tables, we only need to synchronize the sub-tables whose information has changed during synchronization. The sub-tables with the same information do not need to be synchronized. This design greatly reduces the amount of data that needs to be synchronized each time. The data flows from the parsing module to the main cache table and then to the proxy cache table. The flow direction of the data flow is one-way. When synchronizing data, it can only be synchronized from the main cache table to the proxy cache table. Therefore, there are no data inconsistencies.  
The main cache table periodically (for example, every second) synchronizes data to the proxy cache table. After receiving the synchronization information, the proxy cache table starts to update its own cache table when it is not busy.
## 5 Discussion  
### The Proportion of Accurate Vectors  
Aiming at the mismatch phenomenon of the MCC algorithm in the SSC inversion in high turbidity areas, we propose to use the greatest three candidate vectors obtained by the multi-correlation coefficient algorithm as potential vectors. The rotation direction of the vectors within the tidal oscillation is used to identify and substitute the spurious vector, to improve the accuracy of SSC inversion. The method was verified using drifting buoy data.  
Figure 12: OSU-derived SSC field in the Yellow Sea at seven intervals on 5 August 2012.  
Figure 13a shows the vector proportion obtained under each correlation coefficient after the algorithm processing. We can see that the vector proportion under the 1 st correlation coefficient is the largest, reaching 82.54%, indicating that the MCC algorithm has strong applicability for SSC field inversion in the research area. The proportions of vectors under the 2 nd and 3 rd correlation coefficients are 11.11% and 6.35%, respectively, which are smaller and show a decreasing trend compared with the 1 st correlation coefficient. On the one hand, the multi-correlation coefficient algorithm can identify and substitute the spurious vector of SSC inversion; at the same time, it also shows that when using this algorithm, considering the effect of computational efficiency and accuracy improvement, it is better to obtain the greatest three candidate vectors as a potential vector. The vectors under the 2 nd and 3 rd correlation coefficients are smaller than the replacement vectors. The distributions of the two in each period are shown in Figure 13b. It can be seen that the first and last period vectors account for a large proportion, indicating that the GOCI inversion had more error vectors in these two periods. The reason may be that the solar zenith angle during dawn and dusk is larger and less water color information is obtained by satellite sensors, resulting in poor inversion results [47]. In this study, since the spurious vectors are distributed in the study area with the case data (red and blue crosses in Figure 8a), the spatial distribution of the corresponding replacement vector is the same as the case data. It can be seen that there are more case data distributed in offshore areas, indicating that there are more wrong inversion results in the offshore areas.  
Figure 13: (**a**) The vector proportion obtained under each correlation coefficient after algorithm processing. MCC 1 st, MCC 2 cd, and MCC 3 rd represent the vectors obtained under the first, second and third correlation coefficients, respectively. (**b**) The proportion of replacement vectors in each period after multi-correlation coefficient algorithm processing.
## 4 Classification of MAC Protocols Based on Channel
### Random Selection Based
#### 4.11.2 Self-Sorting-Based MAC
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**SR.** & **Protocols** & **Comparison** & **Objective** & **Methodology** & **Simulator** & **Classification** & **Fros** & **Cons** \\ \hline
19 & SAFE & standard & Fair channel access & Batch selection and reselice time & Numerical & Contention-based, CSMA/CA, and time synchronized & Minimized & Fairness issue not handled for V2V and V2D \\ \hline
20 & ReMAC [87] & & & & DSRC standard, and & & & \\  & & VeMAC [53], & & & CCH and SCH & & Hybrid, multi-channel, and delay and high & Communication overhead and energy consumption \\  & & CFR MAC [121], & & & & & & \\  & VAT MAC [75] & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline
21 & QCH MAC [88] & & & & EPCA combined with & & & & \\  & & & & & & & \\  & & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline
22 & TSGS & Random & & Minimizes collision & & & & \\  & MAC [124] & sending & and reduces & & & & \\  & & & overlapping & & & & \\  & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline
23 & CoMACAV [90] & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline
24 & CR MAC [89] & & & & & & & \\  & & & & & & & \\  & & & & & & \\  & & & & & & & \\  & & & & & & \\  & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**SR.** & **Protocols** & **Comparison** & **Objective** & **Methodology** & **Simulation** & **Classification** & **Pros** & **Cons** \\ \hline
35 & ACB & Traditional CB & Collision free and authenticated reliable communication & Cluster based, blockchain framework, and prioritizing safety message & MATLAB & Cluster-based and multi-channel MAC & Minimized for safety messages and secure & Energy consumption and less \\ \hline
36 & Relia MAC [98] & DTB MAC [105] & DTB MAC [105] & Timely and reliable transmission of safety data, cope with dynamic mobility pattern, and error prone communication & MAC fragmentation and segmentation, encoding FEC, and checksum calculator for ARQ & N52 & Virtual token, distributed, and dynamic environment & High throughput and PDR & Overhead and energy \\ \hline
37 & OGC & VeMAC [53], MAC [100] & Proper resource allocation and channel access, and reliability broadcasting & Random selection, contention strategy and probability for collision free beacon broadcasting & PAMelled contention mechanism and OFDM & High throughput & Scalability issue and delays due to CW overload. \\ \hline
38 & Self-Sorting-Based & Hassen’s Model, DMDMAC [134], and & Minimizes channel collision and & Data sent periodically, Self sorting by Markov chain, channel reservation, and probability of successful queuing is calculated & DAMA, contention free, random access, and throughput & Reduce packet loss and delay, dynamic scenario and high throughput & Handling dynamic scenario and high throughput & Handling dynamic problem \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cost.
# Effective 5G Wireless Downlink Scheduling and Resource Allocation in Cyber-Physical Systems +
## 5 Performance Evaluation
### Goodput Measurements  
Goodput measurements are important at the application-level, since it shows the rate of successful data packet delivery observed on the UE side. Different from throughput, a goodput measurement excludes packet retransmissions; hence, the goodput can be comparatively lower than the throughput. However, in the case of noisy environments, it is important to measure the goodput to verify the successful delivery of packets received at a UE in a cell. To measure the goodput, we consider the delay and Doppler parameters as shown in Table 2.  
As shown in Table 2, we consider three delay spread channel models: (1) the Extended Pedestrian A (EPA), (2) the Extended Vehicular A (EVA) and (3) the Extended Typical Urban (ETU) models. Each of these channel models has different delay profiles. EPA has the lowest delay and interference. EVA and ETU have higher delay and interference, in that order. We have also considered different correlation profiles as: (1) low, (2) medium and (3) high between the base station \(\alpha\) and mobile station \(\beta\), as shown in Table 2.