esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Result
### SELMWK Algorithm Effect Comparison Experiment
#### 5.3.2 Algorithm Stability Comparison  
Figure 5 shows the results of the SSELM algorithm running 100 times on the above two datasets without changing any conditions. The number of labeled samples is 4 and 6, respectively:  
As can be seen from Figure 5, the recognition effect of the SSELM algorithm is not stable. In Batch10, the highest recognition accuracy of the SSELM algorithm differs from the lowest recognition accuracy by about 7%. The difference between the highest and lowest recognition accuracy of the SSELM algorithm in Dataset2 is about 10%. This is because SSELM uses random hidden layer mapping, so even if the data and hyperparameters are not changed, the classifier trained by the SSELM algorithm will be different each time, so the recognition effect will continue to change. In the SELMWK algorithm, the kernel matrix is generated according to the input data after the parameters are set. The result is the same every time, so the SELMWK algorithm is more stable than the SSELM algorithm.
## 2 Apparatus
### Temperature Measurement and Control  
There are many NMR material science experiments which are far more informative if carried out as a function of temperature. In particular, highly useful NMR T\({}_{1}\) and T\({}_{2}\) minima measurements may then be made, which provide additional information on the motional dynamics in the sample. NMR Cryoporometry also ideally needs very well controlled wide-range temperature ramps (typically from ~100 K to ~350 K). The temperature measurement needs to reflect as true a temperature measurement of the sample as possible, and this apparatus appears to give a precision better than 10 mK [28; 29]. To aid this precision, \"sample\" and \"control\" copper-constant thermocouples were used, the control thermocouple offering a faster thermal response time for dynamic control purposes, while the sample thermocouple offers longer term precision, being in very close contact with the sample.  
Two different 0C thermocouple junction references have been used in this work:  
1. Referenced to a cell of distilled water maintained half frozen (Omega IceCell Model TRCIII), with a third thermocouple attached to a thermal mass in a Dewar provided a short-term temperature reference to help reduce the effect of the temperature cycling in the IceCell.
2. Electronic diode junction 0C references have in recent years improved greatly, and these are under evaluation. They have the advantage of no temperature cycling.  
These thermal EMFs were measured and averaged using a USB interfaced 5 channel, 24 bit Digital Volt Meter (DVM, Lab-Tools, Ramsgate, Kent, UK). During each measurement of NMR signal amplitude, the DVM measurements were further averaged. Thermal EMFs were converted to temperature using a 18 segment quadratic piece-wise polynomial algorithm, with segments contiguous in amplitude and gradient. The agreement with the original NIST thermocouple data are believed to be good to better than 1 mK [42].  
The Peltier cooling offers extreme uniformity of warming rate needed by NMR Cryoporometry, particularly near the probe liquid bulk melting point, as well as the ability to have temperature ramps that last over days. NMRC measurement of pore diameters in excess of 1 micron were measured, with acceptable repeatability at 1 micron pore diameter.
# Cause Analysis and Preventive Measures against False Bottoms in Echosounder Data
## 2 Materials and Methods
### The Influence of False Bottoms on Echosounder Detection and Its Causes  
The term \"false bottom\" refers to a spurious representation of the ocean floor. Within echosounder images, false bottoms exhibit continuity and certain thickness characteristics, resembling the actual bottom. These false bottoms can significantly impede the accurate determination of the bottom position. Moreover, if a false-bottom image appears above the actual bottom, it can be erroneously identified as an echosounder image of the target object, thereby compromising the accuracy and reliability of echosounder detection. However, false bottoms do not manifest randomly or spontaneously. They typically arise due to the reception of multiple reflections, primarily secondary reflections, by the echosounder's receiver, resulting in detection-related effects. Additionally, a false bottom can originate from the reception of echo signals occurring beyond the detection range. Common false bottoms can be categorized into the following three types [17]:
## 1 Introduction
Therefore, in order to control the shield attitude within a reasonable range under complex geological conditions, it is necessary to predict both the shield attitude and total thrust. In addition, for the temporal attention-LSTM model, the selection of hyperparameters is particularly challenging due to the numerous hyperparameters involved and the increased complexity in multi-ring prediction scenarios. The manual tuning process lacks rigor and does not guarantee that the model achieves optimal performance. Therefore, Bayesian optimization is chosen to optimize the model because it can reach the optimal solution more quickly by automatically selecting the hyperparameter combinations and considering the loss values from previous steps [34; 35; 36]. This paper proposes a Bayesian optimization-based temporal attention-LSTM model (BOTA-LSTM) to predict the multi-ring shield attitude and total thrust. The main contributions of this study in shield attitude prediction and correction are as follows. First, the model achieves the multi-ring shield attitude and total thrust prediction by incorporating the temporal attention mechanism, which provides sufficient time for decision makers to draw up a reasonable attitude correction plan. Second, based on the multi-ring prediction results, an intelligent correction method is developed, considering both the shield attitude and total thrust. This method enables more effective control of the shield attitude and prevents excessive thrust that could lead to tunnel collapse. The proposed multi-ring correction method also allows for further adjustments, if the initial correction does not meet expectations. Moreover, the hyperparameters of the proposed model are automatically selected through Bayesian hyperparameter optimization, which can effectively address the issue of complex parameter selection and optimization in multi-ring and multi-objective tasks.
# Evaluating the Efficacy of a Passive Exoskeleton for Enhancing Ergonomics in Manufacturing
## 3 Results and Discussion
### BExo Impact on PPE in Different Tasks
#### 3.3.1 Lifting a Box  
As shown in Figure 6, the most impacted body parts benefiting from the use of an exoskeleton during the task of lifting a box were the lower back, knee, and shoulder. The lower back, in particular, showed a significant reduction in perceived physical exertion (PPE) when using the exoskeleton, with the PPE scores dropping from around 60 to 20. The knee also exhibited a notable decrease, with the PPE scores decreasing from approximately 35 to 15. Similarly, the shoulder benefited substantially from the exoskeleton support, with the PPE scores reduced from around 30 to 15. These findings highlight the effectiveness of exoskeletons in alleviating physical strain on the lower back, knees, and shoulders during lifting tasks, potentially reducing the risk of occupational injuries and enhancing the ergonomic support in high-exertion activities.  
Figure 5: Effect of BExo support on PPE for different tasks.  
Figure 6: Participants’ average sum of PPE per body part for lifting a box.
## 2 Methodology
### Observation Data Description
#### 2.3.3 Joint Research Center (JRC) FPAR  
The Joint Research Center (JRC) generic FPAR algorithm has been used to develop FPAR products for both Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Medium Resolution Imaging Spectrometer (MERIS) [30, 46]. It is not biome-specific, but defines FPAR as \"green\" instantaneous FPAR under direct illumination. Its input data are top of atmosphere (TOA) bidirectional reflectance factors (BRFs) in blue, red, and near-infrared bands. Blue band is used to remove the atmospheric effects [47]. For the monthly composite, median values which are the closest to the temporal average estimated over the compositing period are selected to generate the statistics [48].
# Cyber Insurance for Energy Economic Risks
## 2 Modeling of Cyber Intrusions in Energy Systems
\[L\big{(}\hat{\psi}\big{)}=\sum_{m=1}^{M}\!\left(\frac{r_{m}}{\sigma_{m}}\right) ^{2}=\sum_{m=1}^{M}\!\left(\frac{z_{m}-H_{m}\hat{\psi}}{\sigma_{m}}\right)^{2 }=\!\!\left(z\ -H\ \hat{\psi}\ \right)^{T}\!D^{-1}\!\left(z\ -H\ \hat{\psi}\ \right) \tag{4}\]  
The attacker strategically crafts a vector that will alter the system's measurements in a subtle--yet impactful--way. This attack vector, denoted as \(a\), is designed to seamlessly integrate with the normal data flow, making detection challenging. It is computed by multiplying the Jacobian matrix \(H\) by a nonzero vector, \(c\), which represents specific alterations in state variables that are intended to mislead the state estimation process. \[a=Hc \tag{5}\]  
During an FDI attack, the actual measurements \(z\) are altered by the addition of the attack vector \(a\), resulting in manipulated measurements, \(z_{a}\). This alteration impacts the estimated state, \(\hat{\psi}_{a}\), which adjusts to \(\hat{\psi}+c\) due to the deceptive data. Consequently, the residual \(\rho_{a}\), which reflects the difference between manipulated measurements and the recalculated estimates, remains ostensibly unchanged, masking the presence of the attack. \[\rho_{a}=z_{a}-H\hat{\psi}_{a}=(z+a)-H\big{(}\hat{\psi}+c\big{)}=\rho \tag{6}\]  
When \(L\big{(}\hat{\psi}\big{)}\), corresponding to \(\rho\), is smaller than the threshold \(\zeta\), then \(L\big{(}\hat{\psi}_{a}\big{)}\) is also smaller than the threshold \(\zeta\), since \(\rho_{a}=\rho\) and \(L\big{(}\hat{\psi}_{a}\big{)}=L\big{(}\hat{\psi}\big{)}\). Accordingly, the attack is successful, evading the detection of the bad data.
## 4 Classification of MAC Protocols Based on Channel
### Cluster-Based Multi-Channel MAC Protocols
#### 4.9.1 Cb Mac  
Cluster-based MAC eliminates the hidden terminal problem for non-safety applications and efficient hand-over (shifting of data sessions from one base station to another). Each cluster head assigns bandwidths to the members of the cluster for efficient communication. As IEEE 802.11 is not cluster-based, the control packets are modified in CB-MAC with new control packets containing RTCF (request to cluster formation), ReTCI (registration to cluster), RCLM (request to cluster merging) [96]. For cluster member, a vehicle RTCF message is broadcast in the network; after any cluster head has received the message, it transmits ReTCI, which includes a cluster member ID(CM-ID), a cluster ID (Cl-ID), and a cluster head address (CHA). Then, the cluster head updates the cluster member list and broadcasts it to all cluster members. If the new vehicle that wants to enter a cluster does not receive any ReTCI, it forms a new cluster and has to be the cluster head itself. If a CH does not receive any CTS when it has sends an RTS during the SIFS time interval, it assumes that the cluster member is out of the range of transmission and deletes it from the cluster member list. Then, again, the updated information is broadcast to all other members. A CH with a more significant number of cluster members becomes the CH.  
This protocol is contention-free and provides high throughput, better PDR, and better resource utilization. When the number of vehicles increases rapidly, there might be chances of collision, leading to less throughput and a reduction in system performance.  
Figure 15: Clustering in PDMAC in the bidirectional traffic scenario [95].
## 7 Conclusions  
In this study, we designed an embedded gateway with wired/wireless communication capabilities for military warehouse monitoring systems. In addition to extending the communication range of the ZigBee monitoring system, this gateway can also solve the problem of military facilities being located in communication blind spots or lacking cable networks. We tested the ability of the system to monitor the temperature and humidity of a military warehouse in order to verify the effectiveness of the embedded gateway. The results showed that the gateway is capable of automatically switching to communicating via GSM/SMS when the Ethernet connection fails. Data takes longer to transmit when using GSM/SMS, compared to the Ethernet. However, in a scenario where speed of transmission is not a priority and the Ethernet connection has failed, the GSM/SMS functionality ensures that the system remains operational.  
Finally, an embedded gateway with communication extension and backup capabilities for ZigBee-based monitoring and control systems was constructed to monitor the temperature, humidity, and pressure of the weapons in the storage tank at the National Chung-Shan Institute of Science & Technology to validate the feasibility and practicality of the proposed architecture. The results of this research will be a useful reference for developing long-distance or large-scale ZigBee monitoring systems in the future.  
Figure 19: The testing results of communicating delay time over Ethernet an SMS.  
**Author Contributions:** [PERSON] and [PERSON] performed the experiments. [PERSON] and [PERSON] wrote the manuscript. [PERSON], [PERSON] and [PERSON] provided the concept and experimental design of the study and reviewed the paper prior to submission. All authors discussed the results, analyzed the data and commented on the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This study was supported by grants from the Ministry of National Defense-Medical Affairs Bureau (MAB-106-067, MAB-106-068).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Results and Discussion  
### Sensitivity Analysis  
The evolutionary algorithms had random parameters, with values that had to be initialized at the beginning before applying them to the real case study. Therefore, a sensitivity analysis was considered for the near-accurate determination of these parameters. In fact, the sensitivity analysis could be carried out to study the variation in the objective function of the system versus the variation in the initial values of the algorithm's parameters. Several trial and error procedures were carried out to examine the proper initial values for the algorithm's parameters. For example, in cases where the main target was used to maximize hydropower production, the procedure was identifying a particular range for the domain of the parameter values. In this research, several trial and error procedures were performed. First, the population was changed for the constant values of two other parameters. Second, the other values of other parameters were changed versus constant population size.  
As seen in Table 3, a normal year was considered, and changes in the population size ranging from 20 to 80 were considered. It could be found that when the population size was 60, the objective function had a maximum value. In other words, to achieve an optimal value of the objective function, it was recommended to initialize the value of the population size to be equal to 60. In the same fashion, the flight length (\(f^{i,iter}\)) for the normal year changed from 1 to 4, and the maximum value for the objective function was 106.23 (10\({}^{8}\) kwh). From Table 3, the best initialization value for the flight length might have been equal to 3. The range of changes in the awareness probability (\(AP^{i,iter}\)) was selected to be between 0.10 and 0.40. The maximum value for the objective function, 106.24 (10\({}^{8}\) kwh), occurredwhen the initial value of this parameter might have been equal to 0.30. A complete sensitivity analysis for all other cases of dry and wet years were carried out in a similar sequence, and all of the best initial values for the algorithm's parameters are presented in Table 3.
## 3 Experimental Setup
### Data Pre-Processing
#### 3.3.1 Noise Removal  
In order to remove the noise from sleep EEG signals, we applied a Butterworth low-pass filter. A sample of filtered data is shown in Figure 2.
## 6 Network Simulation and Results
### Setting  
NS2 (Network Simulator 2) simulation results are presented in Section 6.2. Simulations were made of a field of 1000 \(\times\) 1000 m, with varying numbers of routers placed randomly, equipped with a 802.11b radio. For the purpose of this study, router mobility was not considered. The metric is based on hop-count.  
Each simulation lasts for 100 s. Thirty random routers in the network initiate route discovery to another random destination every two seconds. The number of collisions, average overhead, average route discovery delay and average path length are measured.  
Different jitter settings are compared:  
* No jitter.
* Standard RFC 5148 jitter, \(J_{m}=\) 100 ms. Jitter is selected within \([0,100]\) ms (mean, \(50\) ms).
* Standard RFC 5148 jitter, \(J_{m}=\) 200 ms. Jitter is selected within \([0,200]\) ms (mean, \(100\) ms).
* Window jitter, \(\alpha=\frac{1}{2}\), \(J_{m}\)= 100 ms. Jitter is selected within \([50,100]\) ms (mean, \(75\) ms).
* Window jitter, \(\alpha=\frac{2}{3}\), \(J_{m}\)= 150 ms. Jitter is selected within \([100,150]\) ms (mean, \(125\) ms).  
Figure 15 shows the probability density function (pdf) for the different jitter settings.  
For each of the five settings, the shortest-path and shortest-delay strategies for the RREQ forwarding scheme are evaluated.
# Optimal Design of a New Rotating Magnetic Beacon Structure Based on Halbach Array
## 4 The Effect of Changing the Size of the Permanent Magnet on the Magnetic Field Distribution
In summary, the beacon structure that can produce the best characterized magnetic field is obtained through a series of optimizations and comparisons: width of each permanent magnet is \(w=2\) cm; height of each permanent magnet is \(h=2\) cm; lengths of each permanent magnet are \(l_{1}=l_{7}=1.55\) cm, \(l_{2}=l_{6}=1.95\) cm, \(l_{3}=l_{5}=2.2\) cm, \(l_{4}=2.6\) cm; position difference between adjacent permanent magnets in the Z-axis \(\Delta\)h = 1.5 cm; the angles between the direction of magnetization of each permanent magnet and the positive  
Figure 17: Comparison of magnetic field between linear array and non-linear array. Figure 14:0 The magnetic field strengths corresponding to different \(\Delta\)h.  
direction of the Z-axis are \(\alpha_{1}=0\), \(\alpha_{2}=45^{\circ}\), \(\alpha_{3}=90^{\circ}\), \(\alpha_{4}=180^{\circ}\), \(\alpha_{5}=-90^{\circ}\), \(\alpha_{6}=-45^{\circ}\), \(\alpha_{7}=0\).
# Exploring the Effectiveness of Evaluation Practices for Computer-Generated Nonverbal Behaviour
## 5 Results
### User Studies
#### 5.1.2 Study 2: Appropriateness for Gesticulation  
The purpose of this study was to investigate the ability of the participants to select the correct matching segment belonging to a conversation. For this, we used the match/mismatch paradigm initially proposed in [20] and later also used in [13]. The participants were presented with pairs of matching and mismatching videos and asked to choose which one featured the correct gesturing motion. They also had the option to choose whether the videos were equal. Details on the demographics can be found in Table 2.  
Figure 6 shows the percentage of votes for matched, equal, and mismatched per condition. For SG, 62 videos were reported as matching, 56 were mismatched, and 65 were equal. For the baseline condition, 74 were reported as matching, 69 were mismatching, and 40 were equal. For the ground truth, 120 were matched, 30 were mismatched, and 24 were reported as equal.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **N** & **Mean Age (SD)** & **Male** & **Female** & **Nationality** & **Education** \\ \hline
1 & 22 & 35.2 (12.4) & 16 & 6 & UK (20), USA (1), & P: 0, HS: 0, \\  & & & & & IE (1) & DS: 12, M: 1, \\  & & & & & & & D: 1, O: 8 \\
2 & 27 & 40 (12.54) & 14 & 13 & UK (21), CA (2), & P: 0, HS: 0, \\  & & & & & & & BS: 14, M: 3, \\  & & & & & & & D: 0, O: 10 \\
3 & 22 & 41.8 (13.94) & 13 & 9 & UK (16), USA (1), & P: 0, HS: 0, \\  & & & & & & IE (2), NZ (2), & BS: 10, M: 1, \\  & & & & & & & CAN (1) & D: 0, O: 8 \\
4 & 26 & 39 (11.55) & 10 & 16 & UK (15), CA (5), & P: 0, HS: 0, \\  & & & & & & IE (3), AU (3) & B: 9, M: 7, \\
5 & 46 & 42 (14.6) & 22 & 24 & UK (40), USA (1), & P: 0, HS: 0, \\  & & & & & & & BS: 19, M: 7, \\  & & & & & & & D: 0, O: 22 \\
6 & 48 & 37 (13) & 27 & 21 & UK (39), IE (3), & P: 0, HS: 0, \\  & & & & & & & CB: 20, M: 11, \\  & & & & & & & D: 0, O: 15 \\ \hline \hline \end{tabular} Abbreviations: P = primary school, HS = high school, BS = bachelors, M = masters, D = doctorate, and O = other.  
\end{table}
Table 2: Participant demographics for each study.  
Figure 5: Boxplots of human-likeness scores on gesturing for StyleGestures (SG), baseline (BL), and ground truth (GT) conditions.  
To analyse these results, chi-squared tests were conducted with Holm-Bonferroni correction applied for multiple comparisons. For SG, matching differed significantly from mismatching (\(\chi^{2}=179,p<0.0001\)). For baseline, matching differed significantly from mismatching \(\chi^{2}=179\), \(p<0.0001\), as well as the ground truth (\(\chi^{2}=155\), \(p<0.0001\)).  
Lastly, we tested for differences between the conditions, in which ties were split equally over matching and mismatching. For SG versus the ground truth, there was a significant difference (\(\chi^{2}=21.99,p<0.0001\). For baseline versus the ground truth, there was a significant difference \(\chi^{2}=21.99,p<0.0001\). SG and baseline did not differ significantly.
# Scalable Block Preconditioners for Linearized Navier-Stokes Equations at High Reynolds Number
## 7 Numerical Results
### Handling the Low Viscosity Case. Two-Direction GS/BFBt-c
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Matrix**} & \multicolumn{3}{c}{\(\
u\) = 0.1} & \multicolumn{3}{c}{\(\
u\) = 0.01} & \multicolumn{3}{c}{\(\
u\) = 0.005} & \multicolumn{3}{c}{\(\
u\) = 0.001} \\ \cline{2-13}  & **iter** & **time** & **tres** & **iter** & **time** & **tres** & **iter** & **time** & **tres** & **iter** & **time** & **tres** \\ \hline M40 & 46 & 14.08 & 1.8e-5 & 52 & 21.14 & 4.6e-5 & 60 & 18.53 & 3.0e-4 & 80 & 24.49 & 1.5e-2 \\ M80 & 49 & 33.99 & 1.5e-5 & 55 & 49.64 & 2.3e-5 & 65 & 42.35 & 7.6e-5 & 85 & 56.16 & 6.9e-3 \\ M160 & 52 & 125.01 & 8.9e-6 & 53 & 127.17 & 1.4e-5 & 66 & 158.49 & 2.1e-5 & 93 & 224.67 & 9.3e-4 \\ M320 & 52 & 589.25 & 1.1e-6 & 52 & 614.96 & 1.1e-5 & 66 & 651.42 & 1.3e-5 & 99 & 1128.95 & 1.4e-4 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results using 2 dir-GS and BFBt-c.  
Figure 8: On the left, the spectra of the preconditioned Schur complement for BFBt and BFBt-c preconditioners. On the right the spectra of the preconditioned \((1,1)\) block and the Schur complement using BFBT-c. Plots referring to matrix M40 and \(\
u=0.005\). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Matrix**} & \multicolumn{3}{c}{\(\
u\) = 0.1} & \multicolumn{3}{c}{\(\
u\) = 0.01} & \multicolumn{3}{c}{\(\
u\) = 0.005} & \multicolumn{3}{c}{\(\
u\) = 0.001} \\ \cline{2-13}  & **iter** & **time** & **tres** & **iter** & **time** & **tres** & **iter** & **time** & **tres** & **iter** & **time** & **tres** \\ \hline M40 & 47 & 13.13 & 1.6e-4 & 53 & 14.76 & 1.5e-4 & 62 & 16.97 & 4.7e-4 & 82 & 22.31 & 1.5e-2 \\ M80 & 50 & 27.43 & 1.7e-4 & 52 & 28.10 & 8.2e-5 & 64 & 35.47 & 5.2e-4 & 89 & 49.34 & 5.5e-3 \\ M160 & 52 & 75.46 & 3.2e-4 & 56 & 81.91 & 5.7e-6 & 67 & 98.77 & 1.4e-4 & 94 & 140.11 & 6.4e-3 \\ M320 & 55 & 312.79 & 3.3e-4 & 58 & 332.21 & 1.5e-5 & 67 & 384.49 & 1.2e-4 & 99 & 697.55 & 1.9e-3 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results using ICP with split-GS and M20 as the coarsest mesh. The low-rank update, whose effects will be described next, will mitigate this effect.