esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
In light of the experimental results presented in this study, it is evident that the Tm,Ho:YAP SDWL demonstrates remarkable capabilities in terms of switchability and intensity-controllability. The switchability of the 2 \(\upmu\)m laser is based on the influence of temperature on the stimulated emission cross-section, allowing the laser to work in stable dual-wavelength operation or switch between two wavelengths by adjusting the temperature controlled by the cryostat. The optimal temperature for balanced output will continuously decrease with increase in pump power, making temperature control at cryogenic temperatures crucial for achieving higher output power in the Tm,Ho:YAP SDWL. The method in this study may also potentially be applied to other SDWLs based on Tm and Ho ion doping.  
Achieving balanced dual-wavelength laser output holds immense potential for advancement in multiple fields, including trace-gas detection, remote sensing and biomedical research. By enabling the simultaneous emission and precise control of two distinct wavelengths, these lasers offer new possibilities for innovation and technological development.  
In future work, we will explore the implementation of Q-switching and mode-locking in the Tm,Ho:YAP SDWL. Moreover, based on fluorescence spectra from previous studies,  
Figure 8: Dual-wavelength output power stability of the Tm,Ho:YAP SDWL.  
Figure 7: The beam quality of the Tm,Ho:YAP SDWL operated at optimal temperatures for balanced output. (**a**) Transverse distributions on beam waist and (**b**) M\({}^{2}\) factors for the SDWL at 2000 nm and 2119 nm with respect to the pump powers at corresponding optimal temperatures.  
the dual wavelength should exhibit \(\pi\) polarizations for 2000 nm and \(\sigma\) polarizations for 2119 nm. We plan to investigate the polarization characteristics of both wavelengths using suitable polarization beam splitters in subsequent studies. In this paper, the observed variations in total output power with temperature were likely related to the absorption spectra and absorption cross-sections of the laser crystal at different temperatures. We intend to conduct a detailed investigation of this aspect once an appropriate broadband light source becomes available. Based on the optimal temperature identified during the experiments, we will further investigate cost-effective, modular cooling solutions in order to develop a stable and user-friendly SDWL.
# Analysing Near-Miss Incidents in Construction: A Systematic Literature Review
## 4 Results
### Key aspects of near-miss investigations in the construction industry
#### 4.4.1 Occupational Risk Assessment  
[PERSON] et al. [12] propose that the analysis of near misses should be conducted in four areas: (a) determining the course of the incident; (b) characterizing each incident in terms of physical characteristics (e.g., falling objects); (c) evaluating the feedback provided on the safety management system, whether positive or negative; and (d) conducting a risk analysis based on an assessment of the probability and consequences of each incident.  
[PERSON] et al. [90] created a database containing information on subway construction incidents to improve occupational safety management and expand the capabilities of quantitative risk assessment (QRA). The study analysed the differences between an incident, an accident, an accident-prone situation, and unsafe worker behaviour. A subway construction incident database (SCID) was created using the Microsoft Access 2007 software platform, with consideration given to three key aspects: supervision, investigation, and implementation. Furthermore, an exemplar of the deployment of the SCID in the domain of safety management during the construction of subways is provided. The findings of the study suggest that the SCID has the potential to address the shortcomings in safety management during subway construction. It can be employed as a qualitative instrument for the identification of preliminary scenarios and as a quantitative instrument for the estimation of risks.  
A quantitative risk assessment was conducted using real-time worker location-tracking technology [45]. The technology facilitated the automatic aggregation of data pertaining to the geographical coordinates of workers and construction equipment, which was subsequently subjected to an analytical process aimed at identifying locations and scenarios characterised by an elevated risk of collisions or other hazardous interactions.  
In contrast, [PERSON] et al. [18] conducted a quantitative risk assessment using the Analytic Hierarchy Process (AHP) method, applying it to evaluate the risk of near misses associated with tower cranes. The study identified the risk potential by assigning weights to different levels of event impacts, which were then used to calculate the total risk potential for each reported event. The results showed how technical and human factors affect risk in the field of tower crane operation. Cluster analysis (k-means clustering) was used to group incidents into homogeneous groups based on their similarity. Clusters were evaluated by comparing specific incident severity levels within each cluster to the overall database. This analysis determined which clusters had a higher or lower risk of specific incident severity levels. This method facilitated a comprehensive examination of potential risks and the discernment of clusters of events exhibiting analogous risk profiles.
## 8 Simulation and Evaluation
### Energy Analysis  
In this part, we focus on and analyse the energy consumption of nodes by using NCR and ANCR respectively to finish the transmission of 1000 packets in the case of thirty data streams. Figure 7 shows the energy consumption of nodes under using NCR and ANCR respectively. The X-axis and Y-axis represent the geographical location of nodes and the vertical axis represents the energy consumption of nodes. We define the initial energy of each node as 0.2.  
From Figure 7, we can come to the conclusion that the energy consumption of ANCR is less than that of NCR. The main reason is that ANCR chooses the nodes with coding opportunities and high transmission success rates to encode packets and transmit them, but NCR only considers the coding opportunities of nodes and ignores the transmission success rates of the nodes. Consequently, under the same network situation, the total number of transmission of ANCR is less than that of NCR, which means that the energy consumption of ANCR is lower than that of NCR. Moreover, the nodes whose energy consumption decreases all have coding opportunities.  
Figure 6: Comparison of throughput between NCR and ANCR. (**a**) 2 data streams; (**b**) 5 data streams; (**c**) 20 data streams.  
Figure 7: Energy consumption of nodes under using NCR and ANCR. (**a**) Energy consumption of nodes under using NCR. (**b**) Energy consumption of nodes under using ANCR.  
In a word, with the number of data streams and packets increasing, the advantage of ANCR is becoming more and more obvious.
## 5 Nuclear Mechanotransduction  
As described above, some mechanosensors can shuttle between cytosol and nucleus. The crosstalk between the cytoskeleton and nucleus is very important for the transmission of the mechanical signal from the cytosolic membrane to the nucleus in order to activate a gene response for the adaptation to the external stimuli. Important for this function is LINC (linker of nucleoskeleton and cytoskeleton), which is represented by different proteins, such as SUN1/2 and nesprin1/2. The SUN family of protein can interact with nuclear pore complexes (NPC), thus controlling the morphology and the organization of the nuclear envelope [75].  
Emerin has been identified as a tension sensor thanks to its phosphorylation operated by Src that cause its accumulation in the nucleus [68; 69]. Recently a new signalling pathway involved in mechanotransduction has been identified: the Hippo family composed of Yap and Taz. These factors are involved in stiffness-promoted transcriptional changes. Several pieces of evidence demonstrated that Yap and Taz are localized in the nuclei of epithelial and other cells when cultured on rigid substrates, but are degraded when the same cells are grown on soft substrates [83]. These changes in substrate rigidity cause not only the shift in the localization of Yap and Taz but also the formation of stress fibres, a clear adaptation to the change of substrate. Furthermore, Yap and Taz do not bind actin or other cytoskeletal subunits directly. Instead, they bind directly to the angiomotin that is strictly connected with actin filaments [110]. Ankyrin repeat domain 2 (Ankrd2), also called Ankyrin repeat, the PEST sequence, the proline-rich region (Arpp), Ankyrin repeat domain 1 (Ankrd1)/cardiac Ankyrin repeat protein (CARP), and the diabetes ankyrin repeat protein (DARP) are involved in muscle Ankyrin repeat protein (MARP) mechanosensing that forms a complex with titin/calpain 3 protease/myopalladin [65]. Moreover, cardiac tissue is involved in mechanosensor activity, through the presence of Muscle LIM protein or MLP that translocates to the nucleus where it can bind cardiac transcription factors, such as GATA4. MLP creates a complex with telethonin (TCAP), with titin that can sense the stretching stimuli in cardiac muscle, with \(\alpha\)-actinin, cofilin-2, and calcineurin. It also creates a tight bound with \(\beta\)1-spectrin and zyxin in constamere, specialized structures that sense specific mechanical cues [74]. Finally, PKC\(\varepsilon\) is necessary to produce the autophosphorylation of FAK and localizes to Z-bands of cardiomyocytes. PKC\(\varepsilon\) shuttles to the nucleus after mechanical stimuli, thus regulating the transcription of \(\alpha\)-actin [111].
# A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition
## 4 Experimental Evaluations
### Experimental Setup  
All experiments were performed on a powerful PC with 64G RAM running under Windows 10. CPU speed was 2.10 GHz, core was 40, and logic processor was 80. To accelerate computing, 2 RTX 2080 Ti GPUs were used. All models were implemented with TensorFlow toolkit [39].  
To prevent possible overfitting, during the training stage, dropout was implemented in all layers. Dropout rate was 0.5, batch size was 32, and epoch was 100. In addition, Adam [40] was adopted as optimizer.  
The datasets EMODB, CASIA, and SAVEE do not provide a separate training and testing set, therefore, speaker-independent (SI) strategy was employed to do train-test partition. The samples of each database were randomly divided into 5 equal parts, and 4 parts were used as the training data while the remaining one was used as the testing set. Experiments were repeated 10 times and the average value of all trials was computed. Confusion matrix and such evaluation measures as precision, unweighted average recall (UAR), accuracy, and F1-score were employed to evaluate the performance.
## 5 Orthogonal Design-Based Neural Network Inverse Analysis  
### Forward Calculation Scheme with Orthogonal Design  
With reference to the range of mechanical parameters derived from geological exploration and laboratory experiments during the project's initial phase, the ranges of forward calculation values for each parameter were determined. The factors were divided into 10 distinct levels, as depicted in Table 5. An orthogonal experimental design method was used to design different numerical calculation schemes for the factors, determining various combinations of mechanical parameters. These combinations served as inputs for finite difference calculations to obtain the surface settlement values after tunnel excavation. A total of 50 experiments were conducted, and the resulting sets of mechanical parameters and corresponding surface displacement changes were used as training samples for the BP neural network, as shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample No.**} & \multicolumn{2}{c}{**Input**} & \multicolumn{5}{c}{**Output**} \\ \cline{2-9}  & _u/m_ & _h/m_ & \(c_{1}\)/kPa_ & \(\varphi_{1}\)/\(c_{2}\)/kPa & \(\varphi_{2}\)/\(c_{3}\)/kPa & \(\varphi_{3}\)/\(c_{4}\) \\ \hline
1 & 2.018 & 207.8 &  
Through testing, it was found that when \(n_{1}=10\), the system error is minimal, and the training effect is ideal. The schematic diagram of the neural network model is shown in Figure 7.  
(3) The process of neural network inverse analysis  
Using the BP neural network for the inversion analysis of tunnel soil mechanics parameters involves the following basic steps [20; 21; 22]: (1) Determine the parameters to be inverted and the experimental scheme through the systematic sensitivity analysis method and orthogonal experimental method; 2 solve the forward problem using finite difference software to obtain samples reflecting the input-output relationship; (3) design a BP network to learn and train the above samples to determine an appropriate network structure; 4 apply the trained BP neural network to simulate the measured displacement and obtain the values of tunnel soil mechanics parameters.
# TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing
## 2 Background
### Blockchain Overview
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Comparison Features**} & \multicolumn{1}{c}{\begin{tabular}{c} **Public Blockchain** \\ **(Permissionless)** \\ \end{tabular} } & **Private Blockchain** & **Permissioned Blockchain** \\ \hline Read & It is an open network, no permission needed, anyone can download the protocol and read & Only specific participants in the organization can read, verify and add new nodes & Under a legal contract, the public and participants are permissible \\ \hline Write & It is an open network, no permission needed, anyone can download protocol and write & Only specific participants in the organization can write & Participants are permissible under some legal contracts \\ \hline \multirow{2}{*}{Consensus Operational} & No conditions are needed to join consensus; the process needs more energy and resources. & Only those who are pre-selected can conduct the consensus within the organization & Pre-selected nodes within consortium \\ \hline \multicolumn{2}{c}{**Examples**} & **Bitcoin** & **Ethereum** & **Hyperledger Fabric** \\ \hline Network & Permissionless & Permissioned or Permissionless & Permissioned \\ \hline Classification & Public & Public or Private & Private \\ \hline Governance & Decentralized & Ethereum Developers & Linux Foundation \\ \hline Currency & Yes & Yes, Ether Tokens (smart contract) & None, Currency Tokens (chaincode) \\ \hline Operation Pattern & Order–execute & Order–execute & Execute–order–validate \\ \hline Cost & Yes, Satoshi (It is synonymous with Bitcoin). & Yes, Gas (the amount of computational power). & None \\ \hline Smart Contracts & No & Smart Contracts (Solidity, Serpent, and LLL) & Chaincodes (Go, JavaScript, Java, and more) \\ \hline Consensus Algorithms & Proof of Work & \begin{tabular}{c} Proof of Work or Proof of Stake \\ (new versions) \\ \end{tabular} & \begin{tabular}{c} Normal operation or Practical \\ Byzantine Fault Tolerance (PBFT) \\ \end{tabular} \\ \hline Encryption of transaction data & No & No & Yes \\ \hline TPS & 3.3–4.6 & 15 & Up to 5000 \\ \hline Block Size & \begin{tabular}{c} 1–2 MBs 4 MB SegWit \\ (Segregated Witness) \\ \end{tabular} & 20–30 KB & \begin{tabular}{c} By default: 512 KB (Preferred) \\ 98 MB (Absolute Maximum) \\ \end{tabular} \\ \hline Transactions per Block & 3500 & 70 & 10 (default) \\ \hline Block Time & 10 min & 15 s & 1 s \\ \hline Currency Capitalization & 21 million & 5 every 14 s & No Currency \\ \hline Current Block Reward & 12.5 BTC & 3 ETH & N/A \\ \hline \multirow{2}{*}{Applications} & \begin{tabular}{c} Track ownership of Digital \\ Currency (Mostly) \\ \end{tabular} & \
\begin{tabular}{c} DApps (Games, IoT, Fintech, Supply Chain, and so on) \\ \end{tabular} &
\begin{tabular}{c} Private Blockchain requires High \\ Performance, resiliency, \\ and privacy \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of different blockchain types.
# Shipborne GNSS-Determined Sea Surface Heights Using Geoid Model and Realistic Dynamic Topography
## 4 Derivation of Offshore Dynamic Topography
### Correction of Hydrodynamic Model Based Dynamic Topography  
With the inclusion of determined uncertainties, the DB estimates were predicted offshore (with the NEMO-Nordic spatial resolution) by employing least-squares collocation [55] with the second-order Markov model covariance function [56]. In Figure 4, the mean predicted DB and standard deviation estimates of predicted DB for campaigns C1, C2, and C6 are presented. The estimated surfaces smoothly follow the long-wavelength DB trends determined at TG locations, but do not include the erratic behavior and offsets described in Section 4.1. It appears that the primary variations of DB occur in the Gulf of Finland and Gulf of Riga, while DB is more stable in the Baltic Proper (Figure 4b,d,f; refer to Figure 2 for basin locations). Additionally, notice how the mean DB surfaces differ for campaigns C1, C2, and C6 (Figure 4a,c,e). These results demonstrate the spatiotemporal changes of the NEMO-Nordic contained DB well.  
The predicted offshore DB was used to correct the initial HDM (at hourly temporal resolution \(t^{H}\)) by employing Equation (4). Notice that the comparisons between the corrected NEMO-Nordic HDM and TG readings now yield improved correlation coefficients, except for TG\({}_{6}\), which shows a slightly reduced correlation (Figure 3). Since this study aimed to derive and validate shipborne GNSS-based SSH, the DT estimates were finally determined at the GNSS measurement locations via bilinear interpolation at observation epochs \(t\) (interpolated linearly from the hourly resolution data). These values are shown  
Figure 5: Uncertainty estimates of DB (**a**) at each TG station (refer to Figure 2) during the C1 campaign (i.e., \(\sigma_{DB}(t^{H})\); cf. Equation (12)) and (**b**) averaged for all campaigns.  
in Figure 6b and denoted as \(\hat{DT}(\varphi,\lambda,t)\). See also the predicted DB during marine survey campaigns in Figure 6a.
# Hysteresis Compensation and Trajectory Tracking Control Model for Pneumatic Artificial Muscles
## 3 Position Control Strategy of PAM
### Feedforward-Incremental PID-Type Iterative Learning Feedback Composite Control  
The output value of the PAM is influenced by the previous output value, so iterative learning is added to the incremental PID algorithm. The combination of QUPI inverse model feedforward control with incremental PID-type iterative learning feedback control is defined as Control Method 2. This approach fully leverages the fast response characteristics of feedforward control and the error correction capability of iterative learning, forming an efficient control method that can both respond quickly and optimize gradually. By using error information during each iteration of system operation, iterative learning control can continuously optimize the control input, enabling the system to approach the ideal control target through repeated operations. During this process, incremental PID control ensures system stability and robustness, avoiding issues such as integral saturation, which can occur with traditional PID control. Particularly when facing large disturbances or load variations, an incremental PID can rapidly adjust the system output, maintaining overall system stability. The key advantage of this composite control strategy lies in its combination of fast response and error correction capabilities, allowing the system not only to quickly reach the desired target but also to continuously optimize control performance during operation. Especially in PAM applications, this method effectively addresses the system's nonlinearity and complex dynamic behavior, ensuring precise trajectory tracking and stable dynamic performance under various working conditions. The block diagram of its control system is shown in Figure 8, where \(Z_{d}\) represents the target displacement of the PAM, and \(Z\) represents the actual displacement of the PAM.  
In the displacement tracking control process of the PAM actuator, the error function is:  
\[e(k)=l^{g}(k)-l^{r}(k) \tag{16}\]  
In the equation, \(l^{g}(k)\) represents the target displacement of the PAM and \(l^{r}(k)\) represents the actual displacement of the PAM.  
Combining iterative learning, the control law for the PAM pressure can be expressed as:  
\[p(k)=IQUPI(p(k-1),l^{g}(k))+u(k) \tag{17}\]  
\[u(k)=u(k-1)+K_{\text{p}}(e(k)-e(k-1))+K_{\text{i}}e(k)+K_{\text{d}}(e(k)-2e(k- 1)+e(k-2)) \tag{18}\]  
In the equation, \(p(k)\) represents the pressure of the PAM, \(IQUPI(p,l)\) is the output pressure value from the QUPI inverse model, and \(u(k)\) is the pressure compensation value from the incremental PID feedback. KP, KI, and KD are the proportional, integral, and derivative gains of the incremental PID, respectively.  
Figure 8: Block graph on feedforward-incremental PID-type iterative learning feedback composite control system.
###### Abstract  
YTHDC1 (YTH domain containing 1), a crucial reader protein of N6-methyladenosine (m6A) mRNA, plays a critical role in various cellular functions and is considered a promising target for therapeutic intervention in acute myeloid leukemia and other cancers. In this study, we identified orthosteric small-molecule ligands for YTHDC1. Using a molecular docking approach, we screened the eMolecules database and recognized 15 top-ranked ligands. Subsequently, molecular dynamics simulations and MM/PBSA analysis were used to assess the stability and binding free energy of these potential hit compounds in complex with YTHDC1. Notably, five compounds with IDs of ZINC82121447, ZINC02170552, ZINC65274016, ZINC10763862, and ZINC02412146 exhibited high binding affinities and favorable binding free energies. The results also showed that these compounds formed strong hydrogen bonds with residues SER378, ASN363, and ASN367 and interacted with the aromatic cage of the YTHDC1 reader protein through TRP377, TRP428, and hydrophobic residue LEU439. To assess their viability as lead compounds, we conducted absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies to reveal promising features for these identified small molecules, shedding light on their pharmacokinetic and safety profiles.  
m6A-RNA; YTHDC1 reader protein; molecular docking; molecular dynamics simulations; MM/PBSA; ADMET; virtual screening Article  
Virtual Screening and Molecular Dynamics Simulation to Identify Inhibitors of the m6A-RNA Reader Protein YTHDC1. _Applied_
# Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal
## 3 Methods
### Validation
#### 4.2.1 Model A
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**NF**} & \multirow{2}{*}{**Best Features**} & \multirow{2}{*}{**TN**} & \multirow{2}{*}{**FP**} & \multirow{2}{*}{**TP**} & \multirow{2}{*}{**FN**} & \multicolumn{2}{l}{**Prec**} & \multicolumn{2}{l}{**Sens**} & \multicolumn{2}{l}{**Spec**} & \multicolumn{2}{l}{**Acc**} & \multirow{2}{*}{**AUC**} \\  & & & & & & & & & & & & \\ \hline SVM & 5 & TOTPWR, PLE/PHF, SD1, \(\alpha_{2}\), SampEn & 70 & 2 & 40 & 1 & 95.24 & 97.56 & 97.22 & 97.35 & 0.963 \\ LDA & 4 & PNN50, PLE/(PLF+PHF), \(\Delta\), SD1/SD2 & 70 & 2 & 35 & 6 & 94.60 & 85.37 & 97.22 & 92.92 & 0.907 \\ DT & 7 & PNN30, RMSSD, Triangular Index, PHF, PLE/PHF, Mean of CD4, FuzzyMEn & 70 & 2 & 39 & 2 & 95.12 & 95.12 & 97.22 & 96.46 & 0.952 \\ NB & 5 & PNN50, SDNN, PLE/(PLF+PHF), Std of CD2, FuzzyMEn & 69 & 3 & 37 & 4 & 92.50 & 90.24 & 95.83 & 93.80 & 0.922 \\ l-NN & 6 & PVAE, PHE/PLF, PLE/(PLF+PHF), SD2, \(\Delta\), & & & & & & & & & \\  & & SD1/SD2, \(\alpha_{2}\) & 71 & 1 & 39 & 2 & 97.50 & 95.12 & 98.61 & 97.35 & 0.959 \\
3-NN & 5 & TOTPWR, Mean of CD4, SD1/SD2, \(\alpha_{2}\), & & & & & & & & & \\  & & SampEn & & & & & & & & & & & \\
5-NN & 4 & PVLE, PLE/