esa-sceva/satcom-chunk-collection huggingface dataset:
# Joints Trajectory Planning of Robot Based on Slime Mould Whale Optimization Algorithm
## 3 Slime mould While Optimization Algorithm (SMVOA)
### Principle of the SMWOA
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Functions** & **Expression** & **Dimension** & \
\begin{tabular}{c} **Search** \\ **Space** \\ \end{tabular} &
\begin{tabular}{c} **Optimal** \\ **Value** \\ \end{tabular} \\ \hline Sphere & \(F_{1}(x)=\sum\limits_{k=1}^{D}x_{k}^{2}\) & 30 & \([-100,100]\) & 0 \\ Schwefel 2.22 & \(F_{2}(x)=\sum\limits_{k=1}^{D}|x_{k}|+\frac{D}{k=1}|x_{k}|\) & 30 & \([-10,10]\) & 0 \\ Schwefel 1.12 & \(F_{3}(x)=\sum\limits_{k=1}^{D}\left(\sum\limits_{l=1}^{k}x_{l}\right)^{2}\) & 30 & \([-100,100]\) & 0 \\ Schwefel 2.21 & \(F_{4}(x)=\max\limits_{k}\left|x_{k}\right|,1\leq k\leq D\right]\) & 30 & \([-100,100]\) & 0 \\ Rosenbrock & \(F_{5}(x)=\sum\limits_{k=1}^{D-1}\left[100((x_{k+1}-x_{k}^{2}))^{2}+(x_{k}-1)^{ 2}\right]\) & 30 & \([-30,30]\) & 0 \\ Rastrigin & \(F_{6}(x)=\sum\limits_{k=1}^{D}\left[x_{k}^{2}-10\cos(2\pi x)+10\right]\) & 30 & \([-5.12,\) & 0 \\ Ackley & \(F_{7}(x)=\) & \(-20\exp(-0.2\sqrt{\frac{1}{D}\sum\limits_{k=1}^{D}x_{k}^{2}-\exp(\sum \limits_{k=1}^{D}\cos 2\pi x_{k})})\) & 30 & \([-32,32]\) & 0 \\  & \(+20+e\) & & & \\ Alpine & \(F_{8}(x)=\sum\limits_{k=1}^{D}|x_{k}\sin(x_{k})+0.1x_{k}|\) & & \([-10,10]\) & 0 \\  & \(F_{9}(x)=\) & \(\frac{\pi}{D}\cdot\left(10\sin(\pi y_{1})+(y_{k}-1)^{2}\right)+\) & & & \\  & \(\frac{\pi}{D}\cdot\sum\limits_{k=1}^{D-1}(y_{k}-1)^{2}[1+10\sin^{2}(\pi y_{k +1})]\) & & & \\ Penalized 1.1 & \(+\sum\limits_{k=1}^{D}\mu(x_{k},10,100,4)\) & 30 & \([-50,50]\) & 0 \\  & \(y_{k}=1+\frac{\pi_{k}+1}{4},\mu(x_{k},p,a,m)=\left\{\begin{array}{l}p(x_{k}-a)^ {m},x_{k}>a\\ 0,-a<x_{k}<a\\ p(-x_{k}-a)^{m},x_{k}<-a\end{array}\right.\) & & & \\ \(F_{10}(x)=0.1\bigg{\{}\sin^{2}(3\pi x_{1})+\frac{D}{k=1}\left(x_{k}-1\right)^{ 2}[1+\sin^{2}(3\pi x_{k}+1)]+\) & & & \\ Penalized 1.2 & \((x_{k}-1)^{2}[1+\sin^{2}(2\pi x_{k})]+\sum\limits_{k=1}^{D}\mu(x_{k},5,100,4) \bigg{\}}\) & 30 & \([-50,50]\) & 0 \\  & \(\mu(x_{k},p,a,m)=\left\{\begin{array}{l}p(x_{k}-a)^{m},x_{k}>a\\ 0,-a<x_{k}<a\\ p(-x_{k}-a)^{m},x_{k}<-a\end{array}\right.\) & & & \\ [PERSON] & \(F_{11}(x)=(\frac{1}{500}+\frac{25}{1+\sum\limits_{l=1}^{D-1}(x_{k}-a_{l})})\) & 2 & \([-65.536,\) & 1 \\ [PERSON] & \(F_{12}(x)=(x_{2}-\frac{5.1}{4\pi x_{1}^{2}}+\frac{5}{\pi}x_{1}-6)^{2}+10(1- \frac{1}{8\pi})\cos x_{1}+10\) & 2 & \([-5,5]\) & 0.398 \\ [PERSON]- & \(F_{13}(x)=[1+(x_{1}+x_{2}+1)^{2}(19-14x_{1}+3x_{1}^{2}-14x_{2}+6x_{1}x_{2}+3x_ {2}^{2})]\) & 2 & \([-2,2]\) & 3 \\ Price & \(\times[30+(2x_{1}-3x_{2})^{2}\times(18-32x_{1}+12x_{1}^{2}+48x_{2}-36x_{1}x_{ 2}+27x_{2}^{2})]\) & & & \\ [PERSON]- 3D & \(F_{14}(x)=-\sum\limits_{k=1}^{4}c_{k}\exp(-\sum\limits_{l=1}^{3}a_{kl}(x_{l}-p_{ kl})^{2})\) & 3 & \([0,1]\) & \(-3.86\) \\ Shekel 5 & \(F_{15}(x)=-\sum\limits_{k=1}^{5}\left[(X-a_{k})(X-a_{k})^{T}c_{k}\right]^{-1}\) & 4 & \([0,10]\) & \(-10.1532\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Tested benchmark functions.
## 5 Case Study and Results: An Assessment of Earthquake Hazards in Portugal  
### Scope, Context, and Criteria  
Considering the application of the RIACT process to our case study, the '**scope**' encompasses all municipalities of Portugal, as we set out to compare and analyse their varying degrees of exposure to earthquake risks. This comprehensive scope is most important to understand and illustrate the different levels of earthquake susceptibility across the municipalities, ranging from high to very low. The '**context**' of this study is defined by the 308 municipalities, each with its unique position in relation to seismic threats. This contextual framework is very important as it allows for a detailed examination of how different regions are affected by and respond to earthquake hazards. The '**criteria**' for the study are centred on the socio-economic vulnerability and resilience of these municipalities. This focus is key to addressing the broader implications of seismic activity. It helps in evaluating not just the physical robustness of infrastructure, but also the socio-economic readiness and adaptive capacities of communities. Such a nuanced approach enables a more holistic understanding of urban resilience at a macro scale, offering valuable insights into how different areas might effectively prepare for, withstand, and recover from seismic events. The case study presented in the following sub-sections serves as an exemplar for comprehensively evaluating urban resilience, taking into account the varied degrees of earthquake risk and socio-economic dynamics across a broad geographic area.
## 3 Results and Discussion
### The Charge Balance Through the Thickness Adjustment of Both ETL and HTL
Data in Figure 5d suggest that turn-on voltage (Von) can be optimized by properly selecting the thicknesses of PVK and ZnO. As compared to the measured Von of Ref, a significant reduction of Von (i.e., from 5.5 to 5.0 V) was observed in Samples 1 and 2. It is interesting to note that the value of Von is more sensitive to the thickness change of PVK and ZnO. We speculate that this phenomenon is due to the degree of charge-imbalance improvement. As shown in Figure 5e,f, as compared to Ref, luminance was improved by 2.9 and 4.4 times for Sample 1 and Sample 2, respectively. Additionally, efficiency that was converted from the data listed in Figure 5d,e was increased by 2.5 and 2.7 times for Samples 1 and 2, respectively. In addition, from the results of Samples 2 and 3, it was found that when QD and HMDS were not blended, luminance and efficiency were poor under the same HTL and ETL thickness conditions. Although the properties such as Von, luminance, and efficiency of QD-LEDs were improved by properly adjusting the thicknesses of PVK and ZnO, the emission center for the EL spectra was hardly changed as shown in Figure 5a,b. This result suggests that we can improve the EL characteristics without any side effects (e.g., unexpected color change) via the emission profiling study regardless of the QD-LED types. Until now, the highest luminance among Green CdSe/ZnS QDs is known as 614,000 cd/m\({}^{2}\) published by [PERSON] et al. [25]. Our research also seems to require further structural optimization.
# A Long-Distance Communication Architecture for Medical Devices Based on LoRaWAN Protocol
## 5 Conclusions  
In this paper, we proposed a long-distance communication architecture for medical devices based on LoRaWAN Protocol, which is dedicated to long-term health monitoring of users/patients in urban/rural environments without assistance and is integrated into a broader IoT infrastructure. For practical implementation, an ultra-low power high-performance microcontroller from ARM Cortex-M3 (see Figure 3) will be used. The connectivity with the system can be made using Bluetooth Low Energy (BLE) or LoRaWAN(r) module to connect the device with a smartphone or other mobile device that can display the acquisition of vital signals.  
Figure 6: Graphic view of an ECG acquisition on PC.  
Figure 7: Graphic view of an ECG acquisition on smartphone.  
For this long-distance communication architecture for medical devices based on LoRaWAN protocol, preliminary tests were performed within the University, and no other parameters that could affect the system were taken into account.  
The tests were performed over distances that did not encounter obstacles (such as geographical obstacles or buildings); no optimizations were made in terms of coverage and energy consumption, transmission distance, and how many nodes the LoRaWAN gateway can accept (for example, a channel with an SF7 module supports a smaller number of nodes compared to a channel with an SF12 module, and this is due to the large number of packet collisions caused by a large number of inter-packet interferences). The operation of the system was taken into account, the optimizations being planned in the next stage, which will be published in an extension of this communication. This extension will include several scenarios in terms of data volume, power consumption, distance, and obstacles between the device and the gateway. Therefore, in this paper, I have proposed only testing long-distance communication architecture for medical devices based on LoRaWAN protocol.  
There is a need for further exploitation and integration of clinically usable devices in such intelligent systems. Correct estimation of clinical symptoms, such as ECG waves, is vital. These new approaches will have a direct clinical impact because they will be used for the diagnosis, monitoring, and treatment of the patients, using the newest and most advanced acquisition and communication technologies using LoRa.  
The infrastructure used for this work was partially supported by the project \"Integrated Center for research, development and innovation in Advanced Materials, Nanotechnologies, and Distributed Systems for fabrication and control\", Contract No. 671/09.04.2015, Sectoral Operational Program for Increase of the Economic Competitiveness co-funded from the European Regional Development Fund.  
The author declares no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems
## 2 System Model
### Channel Model
Note that Equation (7) can be further expressed by a more compact way  
\[\mathbf{H}=\mathbf{A}_{R}\mathbf{\Delta}_{d}\mathbf{A}_{T}^{H} \tag{8}\]  
where \(\Delta_{d}\in\mathbb{C}^{L\times L}\) is a diagonal matrix that contains non-zero elements of channel gains, and \(\mathbf{A}_{R}\in\mathbb{C}^{N_{R}\times L}\) and \(\mathbf{A}_{T}\in\mathbb{C}^{N_{T}\times L}\) contain the columns \(\mathbf{a}_{R}\left(\theta_{l}^{R}\right)\) and \(\mathbf{a}_{T}\left(\theta_{l}^{T}\right)\), respectively. With the multipath sparsity of physical channel, a discrete virtual angular domain (VAD) channel model is widely used [9; 10; 11; 12; 13]. Assume that the AoAs and AoDs are taken from a uniform grid of size \(G\), i.e., \(\theta_{l}^{T},\theta_{l}^{R}\in\left\{0,\frac{2\pi}{G},\cdots,\frac{2\pi(G-1 )}{G}\right\}\) with \(G\gg L\). Then, Equation (8) can be rewritten as  
\[\mathbf{H}=\tilde{\mathbf{A}}_{R}\mathbf{\Delta}_{d}^{v}\tilde{\mathbf{A}}_{ T}^{H}+\mathbf{E}_{Q} \tag{9}\]  
where \(\mathbf{\Delta}_{d}^{v}\in\mathbb{C}^{G\times G}\) is a \(L\)-sparse VAD channel matrix with \(L\) non-zero elements in the positions corresponding to the AoAs and AoDs, \(\tilde{\mathbf{A}}_{T}\in\mathbb{C}^{N_{T}\times G}\) and \(\tilde{\mathbf{A}}_{R}\in\mathbb{C}^{N_{R}\times G}\) are the transformation dictionaries that conation the array response vectors corresponding to the angles in the grid, and \(\mathbf{E}_{Q}\) denotes the quantization error that is bounded, as analyzed in [25]. In addition, a suitable choice of \(G\) is crucial as a larger \(G\) can reduce the quantization error but increases the computational complexity, vice versa. Typically, it is assumed that the number of grids \(G\) is larger than the number of antennas [25].
# Design of a Dynamic Secondary Mirror Truss Adjustment Mechanism for Large Aperture Telescopes
## 5 Experimental Validations
### Motion Accuracy Tests
After the accuracy test, the repeatable positioning accuracy of the SMAM is acceptable, and the difference between the absolute and repeatable positioning accuracy is found to be significant. When the stroke range and step length are more prominent, the SMAM's absolute positioning accuracy error also becomes larger. The analysis shows that this is caused by the assembly errors of the adjustment mechanism, the cam zeroing errors, and the errors during measurement. Therefore, it is necessary to analyze the absolute positioning accuracy of the SMAM.
## 3 Results and Discussion
### 1L Scale Batch Fermentation
#### 3.3.1 Fermentation of \(L\). casei  
As reported in Figure 2, in 72 h complete depletion of fermentable sugars occurred, with L-lactic acid production of 30.0 g/L. In this experiment in particular, we did not measure the concentration of d-lactic acid. As expected, sorbitol was not consumed at all as a carbon source, since most Lactobacillus lack the proper metabolic pathway to break it down, except under certain genetic conditions [44]. It is worthwhile noting that, in experimental conditions similar to ours, \(L\). _casei_ has been reported to express diauvic growth [45], with uptake of lactose starting only when monosaccharides are completely depleted and after a lag phase of a few hours to allow the rearrangement of enzymatic assets. The trend of lactic acid concentration reflected the change in the metabolic pathway of carbon source utilization. Glucose and fructose were promptly depleted in 20 h. No accumulation of galactose and glucose was observed from lactose hydrolysis, indicating that both were produced and immediately consumed.  
Overall, in 72 h, uptake of almost 97% of the sugars present in the waste mixtures occurred with \(L\). _casei_ to produce L-lactic acid with a yield factor (Y\({}_{\text{LA/S}}\)) of 90%, which became 87% when calculated considering also sorbitol. Sugar uptake rates were 100% (Table 5). Ninety percent of yield factor for the sole L-lactic acid could be likely explained taking into account the unavoidable small quantity of d-lactic produced by \(L\). _casei_ in microaerophilic conditions.  
In 1L batch fermentation, the overall volumetric rate of L-lactic acid was 0.42 grams per hour, while the specific productivity was 0.17 grammes of product per gramme of cell per hour. Yield factor for biomass was 8%, corresponding to 2.50 g/L of cells.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Microorganism** & \(\boldsymbol{Y_{\text{LA/S}}}\) & \(\boldsymbol{Y_{\text{LA/S}}}\) & \(\boldsymbol{Y_{\text{X/S}}}\) & \(\boldsymbol{S_{\text{up}}}\)**GLU** & \(\boldsymbol{S_{\text{up}}}\)**FRU** & \(\boldsymbol{S_{\text{up}}}\)**LAC** & \(\boldsymbol{Q_{\text{SGLU}}}\) & \(\boldsymbol{Q_{\text{SFRU}}}\) & \(\boldsymbol{Q_{\text{SLAC}}}\) & \(\boldsymbol{q_{\text{LA}}}\) & \(\boldsymbol{Q_{\text{LA}}}\) \\  & & & & & & & & & & & **(g/L)** & **(g/L)** & **(g/L)** & **(g/g-h)** & **(g/L-h)** \\ \hline \(L\). _casei_ & 90\% & 87\% & 8\% & 100\% & 100\% & 100\% & 0.11 & 0.18 & 0.50 & 0.17 & 0.42 \\ \(L\). _factrinitis_ & 98\% & 18\% & 15\% & 100\% & 100\% & 0\% & 0.11 & 0.09 & 0.00 & 0.14 & 0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Best-fit kinetic parameters calculated from experimental data and statistical analysis.  
In the waste mixture, 2.5 g/L of L-lactic acid was already present, probably derived from side fermentation occurring in the non-sterile RCW by other LAB.
## 3 Results and Discussion
### Fitting of Model Parameters
There was a certain error in the fitted minimum creep rate, which caused a large gap between the predicted value of individual points and the experimental value. However, in general, the model curve was in good agreement with most of the experimental results, with 73% of the data points within the double error band. The degree of dispersion between the predicted and experimental values was small. Consequently, it has been proved that the DMTS creep model can accurately describe the minimum creep rate of CGI under a wide range of temperatures and stress. It can be seen that although the method of fitting the parameters of the creep-constitutive model by linear regression had been well used in the literature, the linear regression method was not suitable in the case of a few data points. It would cause one or some parameters to be unreasonable or even unable to fit the parameters so that the model could not accurately express the creep characteristics of CGI. In small samples, the multiobjective optimization method could still obtain more accurate parameters. In addition, by comparing the two methods of obtaining model parameters, it can be seen that the process of obtaining parameters through the multiobjective optimization method was more concise and efficient. As a consequence, the multiobjective and multivariable optimization technique has a certain significance in controlling research costs and improving model accuracy.
## 4 Conclusions  
To improve the survivability and effective strike capability of underwater vehicles, this study used various approaches to simulate the radiation characteristics of their sound fields. This study mainly focuses on fluid-radiated noise and structure-radiated noise, and some necessary comparisons and analyses were made.  
The head and tail of an underwater vehicle are the main sources of fluid-radiated noise, especially in the trailing vortex region of trailing edge section. Pressure pulsations generated by transient characteristics as well as the formation, development, and breaking of the vortex itself are relatively important sources of noise. In most cases, it is reasonable to ignore the quadrupole source such as volume excitation and consider only the wall pulsation pressure as the dipole source for excitation. However, in the case under the conditions of this study, ignoring the quadrupole source may have a bad effect, which needs to be considered and traded off in some practical applications.  
Figure 13: Sound pressure level distribution of structure-radiated noise at specific frequencies.  
The frequency domain distribution curve of structure-radiated noise is very different from that of fluid-radiated noise. In the high frequency part, peaks in the sound pressure levels occur, and these peaks are not lower than those in the low-frequency part. The peaks appear at approximately the same frequency at each monitoring point, which is related to the structure of underwater vehicle and is an inherent characteristic of the underwater vehicle. The sound pressure level diagram of structure-radiated noise is also very different from that of fluid-radiated noise, which shows obvious flap-like distribution characteristics at higher frequencies, and the number of flaps increases with frequency and gradually develops to both sides of the torpedo. The sound pressure level of the high-frequency part in the direction of torpedo axis is relatively low, corresponding to the frequency domain distribution curve. For the conditions studied in this paper, the structure-radiated noise and fluid-radiated noise have the same order of magnitude and do not differ much.  
In the future, we will carry out multiconditions and multimorphology underwater vehicle flow field and sound field calculation, and explore a more universal trend from changing the flow velocity and changing the model, to provide guidance to general underwater vehicle researchers for model design and noise reduction design.  
Conceptualization, H.C.; Software, H.C.; Writing--original draft, H.C.; Writing--review & editing, H.C.; Supervision, L.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors have no conflicts to disclose.
# Escalator: An Autonomous Scheduling Scheme for Convergecast in TSCH
## 3 Related Work
### Scheduling of WSNs based on TDMA and Channel Hopping
Packets received in duplicate during packet transmission are discarded. These multipath packet transmissions may improve the delay and reliability of the packet transmission. Multihop and Blacklist-based Optimized TSCH protocol (MABO-TSCH) [22] proposes a link-specific channel offset allocation scheme using graph coloring and an improved channel blacklisting scheme. Blacklist information of the communication channel is generated at one node of each parent/child pair, and this information is embedded in data orACK frames exchanged between the two nodes. [PERSON] et al. [23] proposed a distributed scheduling based on the spectrum, which means the node group of the same hop count from the root. This scheme allocates a time-frequency block for each spectrum for collision avoidance. The time-frequency blocks are arranged in a sequential manner to reduce the packet transmission delays. These studies propose efficient distributed scheduling schemes that reduce delays through sequential schedules and parallel transmission. However, these distributed scheduling schemes use signaling or overhearing for information gathering from neighboring nodes. Our proposed scheme generates sequential schedules that support parallel transmission, such as the distributed scheduling schemes, but uses only routing information without additional overheads for scheduling. This is the difference between the existing distributed scheduling schemes and Escalator. The state of the art solution is autonomous scheduling. In autonomous scheduling, each node in the network determines the communication schedule like distributed scheduling. However, unlike distributed scheduling, each node schedules according to predefined rules without any signaling and resources to the schedule creation or modification. The autonomous scheduling has the advantage of less overheads for scheduling than the centralized and distributed schemes. However, there is a disadvantage that it is difficult to generate an optimal schedule. Orchestra [8] is an autonomous scheduling scheme based on the routing information. Orchestra uses multiple slotframes separated by traffic types. Orchestra uses the node ID and routing information for the three slotframes to generate a schedule that considers the routing topology. However, this schedule is not suitable for data collection, which is a typical application of WSNs because the delay and traffic requirements are not considered. 6 TiSCH-minimal [24] can be seen as an autonomous scheduling scheme in a large category because it does not use a separate scheduler. 6 TiSCH-minimal provides a schedule with a single shared timeslot in that all nodes in the network use it in a slotted-aloha fashion. 6 TiSCH minimal is used as the baseline schedule of 6 TiSCH and can be used with other scheduling schemes.