esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Dopant Concentration  
The dopant concentration of the fiber laser is more complex than other parameters. A higher rear-earth-dopant concentration can increase the gain of the fiber, increase the slope efficiency, and therefore decrease the fiber length and reduce the fiber cost. However, a higher dopant concentration also leads to more heat generation and increases the probability of energy transfer processes. Heat generation and interionic processes were investigated and are shown in Figure 7. From Figure 7a,b, it is clear that regardless of other effects, a higher erbium concentration was definitely better for a high power output. To calculate the effect of the erbium concentration on the core temperature, the method given in [18,19] was utilized and the result is shown in Figure 7c. The DWCP scheme was feasible for much a higher erbium concentration in terms of heat generation. This temperature excursion calculated the heat caused by energy transfer processes at lasing. Considering the \(P_{1976}\) was focused on the clad rather than core of the fiber, the pump power density was lower in the DWCP scheme which was also good for heat dissipation and high-power generation.  
Figure 7d shows the effect of interionic processes, that is, \(W_{1103}\), \(W_{2206}\), \(W_{4251}\), and \(W_{5031}\) in this fiber laser. The higher erbium concentration made it easier for ions to interact with each other. To investigate how these processes affected the output power of the 3.5 \(\upmu\)m laser, the rates of these four processes were tuned higher separately and together and then compared with a normal fiber. The result shows \(W_{1103}\) and \(W_{5031}\) were positive for 3.5 \(\upmu\)m lasing, whereas \(W_{2206}\) and \(W_{4251}\) were negative. As the interionic processes enhanced together, the total effect was negative. In conclusion, erbium concentration cannot be given a simple optimized number, and the choice of concentration should consider the cooling capacity and measured power curve, but the appropriate concentration value of the DWCP scheme was clearly higher than that of the DWP scheme.  
Figure 6: Output power of a 3.5 \(\upmu\)m laser as a function of output coupler (OC) reflectivity at 5 W of \(P_{976}\) and different powers of \(P_{1976}\) under (**a**) the DWP scheme and (**b**) the DWCP scheme.
## 1 Introduction
### Adaptive Tuned Dynamic Vibration Absorber (ATDVA)  
An adaptive tunable dynamic vibration absorber tunes its natural frequency by varying the stiffness depending on the variable excitation frequency coming into a target object for vibration reduction. Accordingly, the dynamic vibration absorber's vibration-reducing performance was evaluated by varying the stiffness correspondingly to the variable excitation frequency applied to the target object.  
The target object for vibration reduction with the adaptive tunable dynamic vibration absorber on it was modeled as a two-degree-of-freedom system (Figure 8), which was formulated as shown in Equations (7) and (8).  
\[m\ddot{x}+kx+k_{mre}(x-x_{mre})=F(t) \tag{7}\]  
\[m_{mre}\ddot{x}_{mre}+k_{mre}(x_{mre}-x)=0 \tag{8}\]  
In this section, a simulation of vibration reduction by the ATDVA was conducted using a mass ratio of 0.15. A frequency range of 15-23 Hz was selected as the target range for vibration reduction. The simulation numerically computed the acceleration and frequency responses from the target object for vibration reduction.  
The simulation results showed that the adaptive tunable dynamic vibration absorber reduced vibrations at target frequencies during variable-frequency excitation (Figure 9). It exhibited outstanding vibration-reducing performance at the target frequency by overcoming the drawbacks of SDOF and dual dynamic vibration absorbers. This indicates that it was effective for vibration reduction at target frequencies to install an SDOF dynamic vibration absorber for each frequency.  
Three different types of dynamic vibration absorbers were compared for their vibration-reducing performance in terms of mean vibration reduction response in the target frequency range (15-23 Hz) (Table 1).  
From the comparison, it was found that the SDOF dynamic vibration absorber reduced vibrations at a designed frequency (20 Hz) but showed the poorest performance and that the best vibration-reducing performance was achieved by the adaptive tunable dynamic vibration absorber.  
\begin{table}
\begin{tabular}{c c} \hline
**Type** & **Reduction of Frequency Response (15–23 Hz, Mean)** \\ \hline SDOF absorber & 7 dB \\ Dual & 10 dB \\ ATDVA & 15.3 dB \\ \hline \end{tabular}
\end{table}
Table 1: Reduction of frequency responses due to vibration absorber type.  
Figure 8: Modeling of adaptive tuned vibration absorber and primary structure.
## 2 Materials and Methods
### Estimation of the Photocatalytic Activity  
The photocatalytic activity of PMMA + TOCs foils (TOCs = (**1**)-(**5**)) was estimated by monitoring the concentration changes of methylene blue (MB) and crystal violet (CV) in aqueous solution. Sample preparation included irradiating the samples with UVA or Vis radiation for 28 h and placing them in quartz cuvettes with dye solution (V = 3.5 mL and C = 2.0 \(\times\) 10\({}^{-5}\) M). After 12 h in the dark, the solution was replaced by MB solution (C = 1.0 \(\times\) 10\({}^{-5}\) M). Samples with dye solution were exposed to UVA irradiation (18 W lamp, 340-395 nm range) and Vis light (77 W tungsten halogen lamp, range of 350-2200 nm). Absorbance was measured at 664 nm for MB and 590 nm for CV (Metertech SP-830 PLUS, Metertech, Inc., Taipei, Taiwan) every 7 h during 38 h of irradiation. The percentage of MB decolorization caused during photodegradation was calculated using the same equation as the adsorption capacity [54]. Linear changes of the studied dyes' concentration in time (zero-order kinetics) allowed for the assumption that the dye concentration does not affect the monitored process in the studied reaction time, as the process rate reached its maximum in the applied conditions. Thus, calculation of the observed rate constants can be performed with a simple equation after the following mathematical transformations:  
\[-\frac{dc_{dye}}{dt}=k \tag{2}\]  
\[dc_{dye}=-kdt \tag{3}\]  
\[c_{t}-c_{0}=-kt \tag{4}\]  
\[c_{0}-c_{t}=kt \tag{5}\]  
Taking into account the definition of the dye decolorization percentage, the above equation can be transformed as follows:  
\[\%\text{ dye decolorization}=\frac{c_{0}-c_{t}}{c_{0}}100\%=\frac{kt}{c_{0}}10 0\%=\frac{100\%k}{c_{0}}t=k_{obs}t \tag{6}\]  
Application of the same starting concentration of the used dyes (c\({}_{0}\)) allows for estimation of the relative photoactivity of the studied materials and their mutual comparison.
## 3 Proposed Protocol: Co-DNAR
### Data Forwarding  
After deciding destination and relay nodes based on the weighting function, the source node embeds the IDs of these nodes and broadcasts data packets. After receiving the source broadcasted packets, the destination and relay nodes know that they have been selected by the source for further data forwarding by looking at their IDs in the data packets. The relay nodes amplify the packets and further forward them towards the destination. As can be seen in Figure 9, a set of three data copies is received at the destination point: one copy is directly from source or sender and two copies from two relays (relays 1 and 2 in the depicted Figure), one copy from each relay.  
The MRC technique is implied at the destination, which calculates BER of received data from the source and relay nodes. The destination node only accepts the data, when the BER \(\leq\) 0.5. Otherwise, it drops the packet and sends the requests to the source to send the data again. This process continues unless the destination gets data within the acceptable BER limit or drops the packets.  
The flow chart for the Co-DNAR routing scheme is shown in Figure 10.  
Figure 9: Cooperation of relay nodes.
## 3 Results and Discussion
### Analysis of Microbial Community during Fermentation of BBP-Meju with Different Salt Concentrations  
#### 3.6.1 Analysis of Abundance of Microbial Community  
To elucidate the effect of salt-reduced fermentation on the microbial community during BBP-meju fermentation, 18 meju samples with different salt concentrations were subjected to high-throughput sequencing using the Illumina NovaSeq platform. After quality filtering, primer sequence identification and removal, assembly, and removal of chimeras, the high-quality sequences for bacteria and fungi were 37,236-70,391 and 61,232-77,852, respectively (Table 3). They accounted for 80.27-91.45% and 95.04-97.50% of the effective sequences, respectively (Table 3). The Shannon index curve and rarefaction curve can reflect the microbial diversity of each sample at different sequencing depths and indirectly indicate species abundance. A flat curve indicates sufficient sequencing depth where the feature abundance does not increase with additional sequencing. In contrast, a non-flat curve indicates unsaturation, suggesting that increasing data volume may reveal more features ([PERSON] et al., 2018). The results showed that the Shannon index curve and the rarefaction curve flattened for the different salt concentrations of BBP-meju, indicating that the sequencing depth could characterize the microbial diversity within the samples (Figure S2).  
At a similarity level of 97% using USEARCH, sequences were clustered. OTUs were filtered at a threshold of 0.005% of all sequences, resulting in a total of 8148 bacterial OTUs and 3870 fungal OTUs (Table S1). The coverage of all BBP-meju samples exceeded 99.90%, indicating that almost all sequences in the samples were detected (Table S1). This result could effectively reflect the microbial community characteristics of BBP-meju samples with different salt concentrations (Table S1 and Figure 6). The species abundance of microbial communities in BBP-meju samples with different salt concentrations was further characterized by the ACE index and Chao1 index. Meanwhile, the Simpson and Shannon indices were utilized to characterize species diversity ([PERSON] et al., 2018). The results indicated that bacterial OTUs were greater than fungal OTUs in all meju samples, suggesting a higher abundance of bacteria than fungi, consistent with previous reports ([PERSON] et al., 2018). During the fermentation, the abundance and diversity of bacterial communities in medium-salt meju were consistently higher than those in low- and high-salt meju (Figure 6b-e). In contrast, the highest abundance and diversity of fungal communities were found in low-salt meju, followed by high-salt meju, and the lowest in medium-salt meju (Figure 6g-j). These findings suggested that moderate salt-reduced fermentation contributed to the proliferation and succession of bacterial and fungal communities in BBP-meju. However,deficient salt levels could lead to dysbiosis of the internal flora of the meju, resulting in reduced levels of abundance and diversity.
## 4 Discussion
### Tensile Strength
#### 4.2.1 Tensile Strength of BTC Tests  
The crack initiation position and corresponding tensile strength are summarised in Table 4, after carefully checking all BTC using the DIC method. Obviously, only three specimens appeared with a crack initiation at the centre of the disc and five specimens at the boundary. Interestingly, it was observed that the average nominal tensile strength of specimens cracking at the boundary was larger than that of specimens cracking at the centre. An appropriate explanation for this might be that the tensile strength was related to the failure process of discs. As can be seen from Figure 11a, the strain concentration formed at the upper loading point in the BTC-01 test was exactly calculating the vertical compressing strain field by the DIC method. The significant compressing strain concentration resulted in a change of boundary conditions from a concentrated force to a flatten distributed pressure as shown in Figure 11b,c. However, the boundary of the specimens cracking at the centre approximately maintained the state of concentrated force boundary as shown in Figure 11d-f. The nominal tensile strength of a crack at the boundary was thus larger than that of a crack at the centre.  
Figure 9: The history curves of the horizontal strain, \(\varepsilon_{x}\), at the point of \(P_{up}\) near the upper boundary as the black line, \(P_{down}\) near the bottom boundary as the blue line, and the central point of \(P_{0}\) as the red line in the BTC-01 (**a**) and BTC-03 (**b**) tests.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Loading** & **Maximum** & \multirow{2}{*}{**Crack Initiation**} & **Tensile** \\  & **Rate/kN/s** & **Load/kN** & & **Strength/MPa** \\ \hline
03 & 0.478 & 14.53 & & 3.291 \\
04 & 0.513 & 15.12 & & 3.424 \\
07 & 0.509 & 15.45 & & 3.499 \\ Average & 0.500 & 15.03 & & 3.405 \\ \hline
01 & 0.515 & 16.25 & & 3.680 \\
02 & 0.544 & 16.90 & & 3.490 \\
05 & 0.480 & 15.41 & & 3.691 \\
06 & 0.476 & 16.30 & & 3.506 \\
08 & 0.514 & 15.48 & & 3.827 \\ Average & 0.506 & 16.07 & & 3.639 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics of the maximum of load, crack initiation position, and the tensile strength in BTC.  
Figure 10: The load–displacement curves of the BTD-01 (**a**) and BTD-02 (**b**) tests and the developing maps of \(U\) to determine the initiation and propagation of cracks in the BTD-01 (**c–e**) and BTD-02 (**f–h**) tests corresponding to the upper load–displacement curves.
## 4 Results and Discussion
### Access Management, Urban Logistics, and Sustainable Development Goals
This includes the intelligent management of parking, as discussed previously, but extends to the broader allocation of urban resources, prioritizing green spaces, pedestrian and cycling infrastructure, and efficient public transport. The goal is to create cities where logistics and transportation systems seamlessly integrate with the needs of residents, businesses, and the environment, fostering economic vitality through efficient goods movement while simultaneously improving air quality, reducing noise pollution, and enhancing social equity through inclusive mobility options.
# Evaluating Deep Learning Techniques for Natural Language Inference
## 5 Experimental Results
### Results on RTE  
The best performance of DAM on the RTE development set was 68.20%. This accuracy was achieved by training the model with a batch size of 64, a hidden layer size of 200, and the Adam optimizer with a learning rate of 0.0001 and a dropout of 0.5. The model achieved the highest accuracy on the 25 th epoch, and improvement stopped after that. With the inclusion of intra-attention, we observed similar patterns, with the highest accuracy score being 63.48%.  
The ESIM model performed even worse than DAM, scoring an accuracy of 61.49%. This was the best score and was achieved by training with a batch size of 64, hidden state size of 200, and the Adam optimizer with a learning rate of 0.0001 and a dropout of 0.5 for 16 epochs. The highest accuracy was achieved on the 11 th epoch.  
With BERT-base, as with all transformers, we first trained the model on MNLI and then on RTE training data. This improved performance on the evaluation data of RTE. BERT-base achieved an accuracy of 83.75% on RTE. We trained the model with a batch size of 64, a learning rate of 2 \(\times\) 10\({}^{-5}\), and 24 warmup steps for 240 steps. BERT-large's highest accuracy on RTE was 84.48%. We trained BERT-large with a batch size of 16, a learning rate of 2 \(\times\) 10\({}^{-5}\), and 30 warmup steps for 300 steps in total.  
The best performance with RoBERTa-base was 87.36%, a large improvement compared to BERT-base. We trained RoBERTa-base with a batch size of 64 and a learning rate of 2 \(\times\) 10\({}^{-5}\) for 280 steps, with the first 28 being warmup steps. The large version's best performance was 90.97% (Figure 7), a 6.5% increase from BERT-large. RoBERTa-large  
Figure 6: (a) Validation accuracy graph and (b) training loss graph of best model (ALBERT-Large) for QNLI.  
was trained with a batch size of 16 and a learning rate of \(10^{-5}\) for 200 steps with 20 warmup steps.  
ALBERT base's best performance on RTE was 84.48%, 0.73% higher than BERT-base and equal to BERT-large. It was trained with a batch size of 64, a learning rate of \(10^{-5}\), and 20 warmup steps for 200 steps. The large version of ALBERT performed equally well as the base version scoring 84.47% accuracy on the development set. It was trained with a batch size of 16, a learning rate of \(2\times 10^{-5}\), and 60 warmup steps for 600 steps. We believe the reason the accuracy is not higher is that the model was not finetuned well enough on MNLI.
## 7 Conclusions  
This paper proposes a resource allocation scheme in a sidelink JCS system, named consecutive collision mitigation semi-persistent scheduling (CCM-SPS). By employing collision detection referring to the echo power threshold and Q-learning to train the \(RC\) decreasing step size, this scheme can effectively suppress the consecutive collision probability. Compared with traditional SB-SPS and the FD-enhanced scheme, CCM-SPS can achieve both superior sensing and communication performance even in high-density vehicle scenarios. Furthermore, CCM-SPS can support services with large packet sizes and achieve accurate sensing, and the cost of communication reliability is smaller as the distance increases. It is particularly meaningful for CCM-SPS from the perspective of enabling sidelinks to support sensing and communication collaboration in 6G networks. In future work, there are interesting topics to be studied, such as practical full-duplex impacts from interference and cross-layer optimization. In addition to the V2X network studied in this paper, there is still room to explore the CCM-SPS scheme to be used in various JCS applications, such as the AIOT network. Additionally, the integration of edge computing with the CCM-SPS scheme can further enhance the performance of the Sidelink JCS system to support rich and broad JCS application tasks.  
Conceptualization, Z.L. and P.W.; methodology, Z.L. and Y.S.; software, Z.L.; validation, Z.L. and S.L.; formal analysis, P.W.; investigation, Z.L., P.W. and Y.S.; resources, Z.L.; data curation, Z.L.; writing--original draft preparation, Z.L.; visualization, Z.L.; supervision, P.W.; project administration, P.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData are contained within the article.  
The authors declare no conflict of interest.
## 4 Theoretical Analysis
### Impact of the Stretch Factor
#### 4.2.2 Load Balance
Then, obviously, there must exist such a Euclidean routing path \(\mathcal{R}_{2}\) obtained in the case \(\epsilon=\epsilon_{2}\) that \(\mathcal{R}_{1}\) is an image of \(\mathcal{R}_{2}\) through a homothetic transformation with the scale factor \(\frac{\xi_{1}}{\xi_{2}}\) (because they are image of a base path through homothetic transformations with the scale factors of \(\xi_{1}\) and \(\xi_{2}\), respectively). It is trivial that the length of \(\mathcal{R}_{1}\) does not exceed \(\frac{\xi_{1}}{\xi_{2}}\) times that of \(\mathcal{R}_{2}\). Consequently, let \(T_{1}\) and \(T_{2}\) be the total hopcounts of all packets from \(s\) to \(t\) in the cases \(\epsilon=\epsilon_{1}\) and \(\epsilon=\epsilon_{2}\), respectively. Then, we can deduce that:  
\[L_{1}\leq\frac{\xi_{1}}{\xi_{2}}L_{2} \tag{3}\]  
Moreover, let us divide \(\mathcal{B}_{1}\) and \(\mathcal{B}_{2}\) into sub-regions by the segments connecting their vertices as shown in Figure 5. Denote the i-th sub-region of \(\mathcal{B}_{1}\) as \(S_{i}^{(1)}\) and the corresponding sub-region of \(\mathcal{B}_{2}\) as \(S_{i}^{(2)}\) (Figure 5). Obviously, the area of \(S_{i}^{(1)}\) is \(\frac{\xi_{1}-1}{\xi_{2}-2}\) times that of the area of \(S_{i}^{(2)}\). Let \(n_{1}\) and \(n_{2}\) be the number of nodes participating in forwarding packets in the case \(\epsilon=\epsilon_{1}\) and \(\epsilon=\epsilon_{2}\), respectively. If we assume that the nodes are evenly distributed in the network, then \(n_{1}\) and \(n_{2}\) are proportional to the areas of \(\mathcal{B}_{1}\) and \(\mathcal{B}_{2}\), respectively. Accordingly, we have the following:  
\[n_{1}=\frac{\xi_{1}-1}{\xi_{2}-1}n_{2} \tag{4}\]  
Denote by \(p_{1}\) and \(p_{2}\) the probabilities for a node to forward a packet in the cases \(\epsilon=\epsilon_{1}\) and \(\epsilon=\epsilon_{2}\), respectively. Then, we have:  
\[p_{1} = \frac{L_{1}}{n_{1}} \tag{5}\] \[p_{1} = \frac{L_{2}}{n_{2}} \tag{6}\]  
Figure 3.0 Comparison of the areas of \(\mathcal{B}_{1}\) and \(\mathcal{B}_{2}\). In order to compare \(\mathcal{B}_{1}\) and \(\mathcal{B}_{2}\), we divide them into sub-regions by dotted lines [38]. From Equations (3)-(5), it can be deduced that \(p_{1}\leq p_{2}\). It means that when \(\epsilon\) is sufficiently small such that the trajectory of all routing paths from \(s\) to \(t\) stays inside the network, then increasing \(\epsilon\) tends to improve the load balance. However, when \(\epsilon\) is too large, the trajectory of all routing paths from \(s\) to \(t\) may exceed the network boundary as shown in Figure 3.1 In this case, the area of the trajectory becomes almost stable when increasing \(\epsilon\). It means that increasing \(\epsilon\) cannot increase the number of sensors participating in the routing process. In the meanwhile, the routing path stretch still gradually increases when increasing \(\epsilon\). Consequently, increasing \(\epsilon\) tends to decrease the load balance.
# Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control
## 3 Quadrotor Integrated Control  
### Height and Vertical Velocity Estimations  
As the error of the accelerometer will accumulate with the integration, it is not reliable to obtain the vertical velocity by using the accelerometer integration alone. Therefore, the optimal vertical velocity is estimated by integrating the differential data of the range-finder and the integral data of the accelerometer. The sampling period is set as T = 1 and the block diagram of the discretized fusion speed measurement system is shown in Figure 3.  
In Figure 3, \(a_{k}\) is the speed increment from time \(t_{k-1}\) to time \(t_{k}\), \(\vartheta(k-1)\) is the optimal estimation of vertical velocity. \(Z_{k}\) is the observations of multi-sensor increment from time \(t_{k-1}\) to time \(t_{k}\), which are used as the estimated value of the observation correction of the speed measurement system.  
Assuming that both the system and the measurement noise are Gaussian white noise with an expectation of zero, the discretized extended Kalman filter equation of the velocity measurement system [46; 47] is:  
\[\left\{\begin{array}{l}X_{k}=\hat{X}_{k-1}+a_{k}\\ P_{k}=\Phi\hat{P}_{k-1}\Phi^{\mathrm{T}