esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The geological structure in Japan is complicated, and excavating a tunnel in a mountainous area often makes construction difficult due to the presence of special ground conditions. Examples of special ground conditions are extremely low ground strength and unconsolidated ground due to ground inhomogeneity such as tunnel fracture zones and initial ground pressure such as expansive ground. When excavating a tunnel under such ground conditions, it is important to predict the impact on the tunnel to prevent its collapse.  
Since it has been reported that tunnel excavation work is difficult due to problems caused by ground heterogeneity, we focused on the heterogeneous ground conditions among such special ground conditions and tunnels. Figure 1 shows the inhomogeneous ground conditions where the tunnel was excavated. Figure 1a shows the case where a fault exists near the tunnel. In such a case, problems such as sudden spring water, face collapse, and cross-sectional deformation arise.  
Figure 1b shows the case where a geological boundary exists near the tunnel. If the geological condition is asymmetrical, an unbalanced pressure may occur in the tunnel support, and if the layer present in the stratum is weak, the tunnel may collapse due to excavation.  
Figure 1c shows the case where the sandy ground contains relatively large-sized gravel. If such a condition exists near the tunnel, gravel may fall out due to tunnel excavation, thereby leading to collapse.  
Furthermore, as shown in Figure 1d, clear inhomogeneities such as in Figure 1a-c are not expected in the same stratum, but weak parts may suddenly appear. If such a part of the ground unexpectedly appears around the tunnel during construction, appropriate support and auxiliary construction methods cannot be applied, and there is a possibility of largescale deformation of the tunnel and, in the worst case, collapse of the tunnel.  
If it is possible to identify the problems arising, such as the above-mentioned heterogeneous natural ground, by numerical analysis when drilling a tunnel before its construction, risks during construction can be considered to be greatly reduced.  
In this study, we propose tunnel excavation analysis techniques for the natural ground as shown in Figure 1c. In addition, an example of numerical analysis results is shown, and items to be noted when excavating under such ground conditions are shown.
## 4 Proposed Scheme
### Details of the Proposed Scheme
The index set (\(Z_{i}\), where \(Z_{i}\in[1,n]\)) is then where \(j\) is stored. It then extracts many keys from a collection of file indices (\(\{Z_{i}\}_{i\in[1,n]}\)) that are shared by \(l\) distinct data generators (\(dgs\)) in the manner described below:  
* \(P_{agg}=\sum_{j\in Z_{i}}E_{6 ij}\)
* \(S_{agg}=\sum_{j\in Z_{i}}E_{0 ij}\)  
Next, it selects \(w_{i}\in Z_{q}^{*}\) and computes multi-key aggregation as follows:  
* \(pp=w_{i}P\)
* \(PP_{agg}=\sum_{i\in l}P_{agg}\)
* \(SS_{agg}=\sum_{i\in l}S_{agg}\)
* \(SP_{agg}=H(w_{i}X)\oplus SS_{agg}\)  
Finally, it generates a trapdoor as follows:  
* \(\mu_{0}=PP_{agg}+T_{0i}\)
* \(\mu_{1}=E_{4 ij}\cdot\hat{e}(P{}_{agg},X^{-1})\)  
The blockchain sends \(\theta=(\mu_{0},\mu_{1},SP_{agg},pp)\) to the \(dr\). **Token. **  
After receiving \(\theta\) from the blockchain, the \(dr\) utilizes its private key (\(x\)) and selects \(u_{i}\in Z_{q}^{*}\) randomly to compute  
* \(\tau_{1}=\mu_{1i}^{u_{1}}\)
* \(\tau_{2}=\frac{u_{1}}{x}\mu_{0}\)  
Then, the \(dr\) sends \(\tau=(\tau_{1},\tau_{2})\) to the FS. **Phase 5: Search. **  
Upon acquiring the trapdoor (\(\tau\)) from the \(dr\), the FS initially confirms its legitimacy by calculating the subsequent equation:  
\[\alpha=\frac{\tau_{1}}{\hat{e}(E_{3 ij}+\tau_{2},P)}=1 \tag{3}\]The FS combines several ciphertexts (\(E^{1}_{agg}=\sum_{i\in[1,n],i\in Z_{i}}E_{1 ij}\), \(E^{2}_{agg}=\sum_{i\in[1,n],i\in Z_{i}}E_{2 ij}\)) if the equation is true. Then, it sends the result (\(E^{1}_{agg},E^{2}_{agg}\)) to the \(dr\), granting permission to access the original sensing data. If not, it rejects access to the data and aborts the outcome. **Correctness**: The correctness of \(\alpha\) is determined as follows:  
\[\alpha =\frac{\tau_{1}}{\tilde{e}(\sum_{i\in[1,n],i\in Z_{i}}E_{3 ij}+\tau_ {2},P)}\] \[=\frac{[E_{4 ij}\cdot\delta(PP_{agg},X^{-1})]^{u_{i}}}{\tilde{e}(E_ {3 ij}+\tau_{2},P)}\] \[=\frac{\tilde{e}(E_{3 ij},(y_{i}+k_{i})P)^{u_{i}}\cdot\tilde{e}(PP _{agg},X^{-1})^{u_{i}}}{\tilde{e}(E_{3 ij},P)\tilde{e}(\tau_{2},P)}\] \[=\frac{\tilde{e}(r_{ij}P,(y_{i}+k_{i})P)^{u_{i}}\cdot\tilde{e}( \Sigma_{j\in Z_{i}}E_{6 ij},\frac{1}{x}P)^{u_{i}}}{\tilde{e}(E_{3 ij},P)\tilde{e }(\frac{u_{i}}{x}\mu_{0},P)}\] \[\varphi_{i} =\frac{\hat{e}((y_{i}+k)P,P)}{\hat{e}(T_{1i}+T_{0i},P)}\]0 \[\varphi_{i} =\frac{\hat{e}((y_{i}+k)P,P)}{\hat{e}(T_{1i}+T_{0i},P)}\]1 \[\varphi_{i} =\frac{\hat{e}((y_{i}+k)P,P)}{\hat{e}(T_{1i}+T_{0i},P)}\]2 \[\varphi_{i} =\frac{\hat{e}((y_{i}+k)P,P)}{\hat{e}(T_{1i}+T_{0i},P)}\]3 \[\varphi_{i} =\frac{\hat{e}((y_{i}+k)P,P)}{\hat{e}(T_{1i}+T_{0i},P)}\]4  
**Phase 6: Decryption. **  
Upon receiving ciphertexts (\(E^{1}_{agg},E^{2}_{agg}\)) from the FS, the \(dr\) is able to access encrypted sensing data by using its private key (\(x\)) to perform the following computation:  
* \(SS_{agg}=SP_{agg}H(xpp)\)
* \(d_{i}=SS_{agg}\oplus E^{1}_{agg}\)  
This algorithm provides the \(dr\) with a lightweight decryption process.
# Distantly Supervised Named Entity Recognition with Self-Adaptive Label Correction
## 4 Experiment
### Ablation Study
#### 4.4.2 Effectiveness of Noisy Example Detector  
The following variants of SALO are conducted to further validate the effectiveness of the noisy example detector. The negative detector is exploited to search for negative noisy examples (Negative: The O is recognized as non-O tag) to facilitate a more effective training example. Otherwise, the positive detector aims at augmenting the training set via detecting positive noisy examples (Positive: All entity mention tags). The random detector (\(\alpha\), \(1-\alpha\)) corrects all predictions of the denoising model via \(\epsilon=\alpha,\hat{y}=\alpha*\hat{y}+(1-\alpha)*v.\)\(\alpha\) is a hyperparameter learned by self-adaptive training. The random detector(\(\alpha,\beta\)) constructs pseudolabels via randomization, \(\epsilon=\alpha,1-\epsilon=\beta,\hat{y}=\alpha*\hat{y}+\beta*v.\)\(\alpha\) and \(\beta\) are two hyperparameters learned by self-adaptive training.  
Table 3 summarizes the experimental results. SALO achieves up to 3.84% and 2.83% \(F_{1}\) score improvements compared with the random detector (\(\alpha\), \(1-\alpha\)) and random detector (\(\alpha,\beta\)), respectively, which verifies that the proposed method is able to distinguish challenging problematic labels. The positive detector achieves a 53.54% \(F_{1}\) score, which is narrowly worse than the best \(F_{1}\) score on Webpage 54.90 (SALO). The negative detector obtains the worst performance of all other detectors, especially leading a significant drop compared with the positive detector. The results confirm that SALO can successfully detect the corrupted labels of these highly difficult entity mentions and also demonstrates the top gains of SALO come from detecting problematic positive examples. Accurately correcting corrupted labels of entity mentions makes a much larger impact than rectifying the error O tags.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision** & **Recall** & \(F_{1}\) \\ \hline w/o denoising & 61.54 & 27.03 & 37.56 \\ hard denoising & 64.15 & 45.95 & 53.54 \\ SALO & 65.42 & 47.30 & 54.90 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The effectiveness of implicit noise denoising on Webpage: Precision, Recall and \(F_{1}\) score (in %). Basemodel: BiLSTM-CRF.
# Residual Vibration Reduction in Flexible Systems Based on Trapezoidal Velocity Profiles
## 3 Optimization of Parameters for Vibration Suppression
### Tuning Procedure
#### 3.2.1 Optimization with Condition C1  
We use the hat signal to distinguish the parameters after tuning. If condition C1 is chosen, the value of \(t_{1}\) is modulated as follows:  
\[\hat{t}_{1}=ceil\left(\frac{t_{1}}{T_{n}}\right)T_{n} \tag{16}\]  
where \(ceil()\) returns the nearest integer that is greater than or equal to the input value. Note that since \(\hat{T}_{v}\geq 0\), the constraint \(\hat{t}_{2}\geq\hat{t}_{1}\) must hold. To ensure that \(\hat{t}_{2}\) meets this constraint, the value of \(t_{2}\) is modulated as follows:  
\[\hat{t}_{2}=\max\left\{\hat{t}_{1},t_{2}\right\} \tag{17}\]  
Then, \(\hat{T}_{a}\) and \(\hat{T}_{v}\) can be obtained, and the peak acceleration is recomputed by:  
\[\hat{a}=\frac{h}{\hat{t}_{1}\hat{t}_{2}} \tag{18}\]  
The peak velocity is \(\hat{v}=h/\hat{t}_{2}\). Since \(\hat{t}_{1}\) and \(\hat{t}_{2}\) are not smaller than their original values, the peak acceleration and velocity after tuning definitely fulfill the actuator limits, guaranteeing the feasibility of the trajectory.
# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 2 Related Works
### Data-Driven Intent Recognition Methods
#### 2.2.1 Artificial Neural Networks  
Artificial Neural Networks (ANNs) have been widely used in intention recognition because of their strong ability in feature learning and pattern recognition.  
In the field of human motion intention recognition, Leap Motion Controller (LMC) combined with ANN are used to infer human operation intention [36]. By capturing hand movement data, the ANN model is used to predict which target boxes the operator will select during the stacking boxes task. In order to improve the synchronization of man-machine haptic collaboration, [PERSON] and [PERSON] propose an intention recognition method based on a radial basis function neural network (RBFNN) [37]. The method predicts the motion intention of a human partner by analyzing the interaction force and contact point motion characteristics (such as position and speed) in real time. The experimental results show that this model can improve the synchronization of cooperation and decrease the cooperation force significantly while eliminating the difficulty of estimating the impedance parameters of the human body. In the field of network security, an attack intent recognition method (SAIRF) based on the Fuzzy Min-Max Neural Network (FMMNN) is proposed [38]. By analyzing the attack characteristics, the network attack intent is divided into general intent (breach of security indicators such as confidentiality and integrity) and specific intent (specific attack mode).  
ANNs excel in intent recognition due to their strong feature extraction and nonlinear modeling capabilities, making them highly effective in complex data scenarios. They are versatile, demonstrating cross-domain applicability from physical human-robot interaction to cybersecurity, and support efficient processing of temporal data, enabling real-time applications in dynamic environments. However, ANNs require large labeled datasets for training, limiting their use in data-scarce domains. Their black box nature reduces interpretability, posing challenges in scenarios requiring high transparency and trust. Additionally, the computational resources needed for complex neural architectures can be a bottleneck, especially in resource-constrained settings.
# Chinese Text Classification Model Based on Deep Learning
## 5 Results
### Comparison between English and Chinese  
To validate the ability of our model on different languages, our BLSTM-C model is compared with a simple LSTM model on the English news dataset as well as the Chinese news dataset that has the same categories as the English one. The five categories include technique, sports, business,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **SST-1(\%)** & **SST-1(\%)** & **THUCNews(\%)** & **Reported in** \\ \hline SVM & 40.7 & 79.4 & 77.5 & [PERSON] et al.(2013b) ([PERSON] et al., 2015) \\ NBoW & 42.4 & 80.5 & 75.5 & [PERSON] and [PERSON] (2014) ([PERSON] and [PERSON], 2014) ([PERSON] and [PERSON], 2014) \\ Paragraph Vector & 48.7 & 48.7 & 48.7 & [PERSON] and [PERSON] (2014) ([PERSON] and [PERSON], 2014) \\ \hline DCNN & 48.5 & 86.8 & - & [PERSON] et al. (2014) ([PERSON] et al., 2016) \\ CNN-non-static & 48.0 & 87.2 & - & [PERSON] (2014) ([PERSON] et al., 2016) \\ Modeling-CNN & **51.2** & 88.6 & - & [PERSON] et al. (2015) ([PERSON] et al., 2016) \\ CNN-multichannel & 47.4 & 88.1 & - & [PERSON] (2014) ([PERSON] et al., 2016) \\ \hline RCNN & 47.21 & - & - & [PERSON] et al. (2015) ([PERSON] et al., 2016) \\ S-LSTM & - & 81.9 & - & [PERSON] et al. (2015) ([PERSON] et al., 2015) ([PERSON] et al., 2015) \\ BLSTM & 49.1 & 87.5 & - & [PERSON] et al. (2015) ([PERSON] et al., 2015) \\ Tree-LSTM & 51.0 & 87.5 & - & [PERSON] et al. (2015) ([PERSON] et al., 2015) \\ \hline MV-RNN & 44.4 & 82.9 & - & [PERSON] et al. (2012) (Neural Network, 2016) \\ RNTN & 45.7 & 85.4 & - & [PERSON] et al. (2013b) ([PERSON] et al., 2015) \\ DRNN & 49.8 & 86.6 & - & [PERSON] and [PERSON] (2014) ([PERSON] and [PERSON], 2014) \\ \hline LSTM & 47.1 & 87.0 & 83.4 & Our implementation \\ B-LSTM & 47.3 & 88.1 & 86.5 & Our implementation \\ CNN & 46.5 & 85.5 & 82.5 & Our implementation \\ BLSTM-C & 50.2 & **89.5** & **90.8** & Our implementation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with baseline models on Stanford Sentiment Treebank and THUCNews.  
It can be concluded from the experiments that our BLSTM-C is more suitable for the Chinese language because of the unique structure of Chinese.
# Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage
## 5 Modules of Hardware Acceleration Kernel
### Majority Voting Module  
A dedicated hardware circuit module was designed to implement the majority voting process (Figure 7). The module includes majority voting units, copy count memory, FSM, and a bit concatenation/splitting unit, enabling the processing of four sequencing read replicas per clock cycle. The 512-bit input signal, which contains 4 copies, is divided into four groups of 120 single-bit signals by the bit concatenation/splitting unit. The module features 120 majority voting units, enabling the parallel processing of 120 bits of payload. The input signal \(S_{n}^{l}[i]\) corresponds to the \(i\)-th bit of the \(l\)-th sequencing copy from the \(n\)-th reference sequence. The number of occurrences of bit '1' in \(l\) copies is calculated using a parallel adder tree, and the result is accumulated and stored in Reg_1. Every four cycles of valid input signals constitutes a complete round of majority voting, and Reg_1 is updated to reflect the total count of bit '1' occurrences in all of the copies at that position. The value in Reg_1 is left-shifted by one bit (denoted as \(V_{N}\)) and compared with the copy count (\(C_{N}\)) stored in Reg_2. If \(V_{N}\) exceeds \(C_{N}\), the output \(Q_{n}[i]\) is set to '1'; otherwise, it is set to '0'.  
Figure 6: Index check module.  
At the start of each new voting cycle, the FSM controls a multiplexer to right-shift Reg_3 by 4 bits and store the result, while the lower 4 bits of Reg_3 are saved to Reg_2. After 16 voting cycles, the RAM read address is updated, and the FSM controls the multiplexer to save the output from the RAM to Reg_3.  
The erasure mark is computed during the fourth cycle of the voting process, with two distinct scenarios considered. The first scenario occurs when a value of '0' is stored in Reg_2, indicating the absence of sequencing read copies for the current reference sequence. The second scenario arises when the number of bit '0' and bit '1' occurrences are equal (\(V_{N}=C_{N}\)). In both of these cases, the bit \(E_{n}[i]\) is set to '1', marking it as an erasure error.  
The majority voting module merges these erroneous sequence copies by counting the number of occurrences of '0' and '1' bits at the same position to determine the consensus sequence. Positions where the ratio of '0' and '1' bits is equal are marked as erasure errors. In addition, if there is no copy of the sequencing read corresponding to a certain primer ID and index, the entire sequence is marked as containing 120 erasure errors.
## 6 Conclusions  
A numerical study to investigate the seismic behavior of dual frames with MRFs and EBFs is presented in this paper. The examined dual frames are equipped with short links, and their seismic capacity is evaluated by means of non-linear static analyses. From the observed results the following conclusions can be pointed out:  
1. The dual frames exhibit a better response than the EBFs because of their greater ductility and post-yield stiffness.
2. The presence of the MRFs allows a better plastic distribution among the links for each of the stories.
3. Increasing the redundancy of dual frames (i.e., two MRFs in place of one) is beneficial, guaranteeing ductility and preventing unfavorable failure modes.
4. The frames with shorter links exhibited higher resistance and rigidity. However, the shorter links do not guarantee satisfactory overall displacement capacity because the deformation demand in the links is almost twice the demand seen in those with the investigated longer links, thus their ultimate deformations are attained at smaller drifts.
5. The advantage of very short links is mainly limited to the smaller design forces expected on the connections at the link ends, and thus the simpler connection details. This aspect can be economically convenient, especially if the detachable links are used with deep sections. However, structures with very short links can be characterized by limited displacement capacity because the link deformation demand is higher than in the case of short links, and the link failure is anticipated.
6. This study highlights that a lower-bound value associated with the length of short links would be desirable because a structure with very short links can exhibit a limited overall displacement capacity that does not comply with the demand at the Near Collapse limit state.
7. Although very short links can be convenient, if detachable links are designed, because of the relatively smaller moments at the ends of the link, the global response of the building would have to be verified through non-linear analysis to control the global displacement capacity and the evolution of the plastic mode of the building.
8. Considering the limits of pushover analyses as well as the features of the investigated cases, further experimental and numerical investigations are deemed necessary to develop new rules for the use of such very short elements.  
Figure 6: Static non-linear analysis results in terms of force–displacement curves of the 8-story structures (**a**), 4-story structures (**b**), and 2-story structures (**c**).  
**Author Contributions:** All authors contributed equally. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Experimental Investigations
### Subjective Experiment for Auditory Localization  
#### 4.3.1 Experimental Protocols  
The experiment in Section 4.2 indicated that the directional information of sound sources with individualized HRTFs were easier to be detected when the azimuth had been known before. To further verify the superiority of the individualized HRTFs in perceiving the spatialization cues when the directional information was unknown for the listeners, the subjective localization experiment was also carried in this section. The participants employed in this experiment were the same as Section 4.2. The localization experiment also tested two HRTF species: individualized HRTFs (obtained from the presented method) and non-individualized HRTFs (measured from KEMAR dummy head). There were totally 22 testing azimuths on the horizontal plane. The testing stimuli were produced by convolving the signal shown in Figure 8 with the HRIRs of the testing azimuths. The testing azimuths were: 0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 65\({}^{\circ}\), 80\({}^{\circ}\), 100\({}^{\circ}\), 115\({}^{\circ}\), 135\({}^{\circ}\), 150\({}^{\circ}\), 165\({}^{\circ}\), 180\({}^{\circ}\), 195\({}^{\circ}\), 210\({}^{\circ}\), 225\({}^{\circ}\), 245\({}^{\circ}\), 260\({}^{\circ}\), 280\({}^{\circ}\), 295\({}^{\circ}\), 315\({}^{\circ}\), 330\({}^{\circ}\) and 345\({}^{\circ}\). 0\({}^{\circ}\) denotes the front of the subject, and the azimuth increases clockwise.
# Link-State Aware Hybrid Routing in the Terrestrial-Satellite Integrated Network +
## 2 Related Work
### Satellite Routing Schemes  
Some researchers presumed that propagation delay is the dominating factor in satellite communication, and a number of pioneering satellite routing mechanisms have been proposed to find the minimum propagation delay paths with minimal hop count for communication. According to the periodic motion of satellites, [PERSON] et al. [18] proposed a routing strategy based on a finite state to calculate the global optimal path of each state. However, the routing table for each slot is calculated on ground and stored onboard in advance, which can not avoid the link interruption caused by the time-varying topology. In [19], based on a dynamic virtual topology, a priority-based adaptive routing algorithm is proposed to find the minimum distance path by the Dijkstra algorithm. [PERSON] et al. [20] proposed a routing strategy based on geographic distance with minimized propagation delay between satellites. [PERSON] et al. [21] proposed a Netgrid-based Shortest path Routing (NSR) algorithm to find the optimal path from the source netgrid to any other reachable netgrids. However, the search for the distance path would cause congestion in some satellites, which leads to not only unfair distribution of the network traffic, but also higher queuing delay at some satellites. Therefore, in [22; 23], agent-based dynamic routing schemes are presented to collect queuing delays of all neighbor links and deliver them to all satellites. In these schemes, the satellite operation period can be divided into multiple time intervals, queuing delay is collected at the beginning of each interval, and the Dijkstra Shortest-Path (DSP) algorithm is used to calculate the shortest path. Since the gathering of global link takes longer distances, the signaling exchange time would increase. Thus, it occupies more channel resources, which is reflected in a larger communication overhead.  
To cope with this issue, a distributed routing strategy is proposed, in which the routing decisions are made independently for each packet. For example, to avoid traffic congestion, the Explicit Load Balancing (ELB) routing method [24] focuses on making a choice for the next best hop, in which the congestion status is exchanged among neighboring satellites.  
Based on current outgoing links, the Traffic-Light-based Routing (TLR) algorithm [25] is proposed to achieve load balance by deriving the queue length. In TLR, the ISL congestion is predicted to re-calculate routing. Another proactive distributed routing method is the selective split load balancing strategy (SSLB) [26], in which the traffic flow of congestion nodes is diverted to neighboring nodes. However, all ISLs are assumed to have equal length, it can hardly reflect the time-varying feature of propagation delay in real satellite networks.