esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Tuning Lam Annealing: Learning Hyperparameters While Problem Solving
## 2 Methods
### Extracting Hyperparameters from the Modified Lam  
Although the Modified Lam annealing schedule is often argued to be parameter-free, it has several constants that control its runtime behavior. For sufficiently long runs of SA, the values for these constants do not matter much, which is why the potential for tuning is often overlooked and why they are treated as constants rather than parameters. Here we replace several of these constants with hyperparameters, and later show how to effectively auto-tune these during the search.  
In Section 2.1, we saw that the Modified Lam must approximate the acceptance rate as it changes throughout the run. Recall that AcceptRate\((i)\) is the estimate of the acceptance rate at iteration \(i\). The Modified Lam has three constants associated with the definition of AcceptRate\((i)\), including the 0.998 and 0.002 in Equation (2), as well as the value of AcceptRate\((0)\). Our Self-Tuning Lam introduces hyperparameters for these.  
First note that Equation (2) is equivalent to an Exponential Moving Average (EMA), \(A_{t}\), of a time-series \(Y\), where \(Y_{t}=1\) if the random neighbor at time \(t\) was accepted, and \(Y_{t}=0\) if the random neighbor at time \(t\) was rejected. Thus define \(A_{t}\) as follows:  
\[A_{t}=(1-\alpha)\cdot A_{t-1}+\alpha\cdot Y_{t}. \tag{4}\]  
Since \(Y_{t}\) can only be 1 or 0, we can rewrite this as:  
\[A_{t}=\begin{cases}(1-\alpha)\cdot A_{t-1}+\alpha&\text{if neighbor accepted}\\ (1-\alpha)\cdot A_{t-1}&\text{if neighbor not accepted}.\end{cases} \tag{5}\]A simple rewriting to change time \(t\) to iteration \(i\) and to rename \(A_{t}\) arrives at:  
\[\text{AcceptRate}(i)=\begin{cases}(1-\alpha)\cdot\text{AcceptRate}(i-1)+\alpha& \text{if neighbor accepted}\\ (1-\alpha)\cdot\text{AcceptRate}(i-1)&\text{if neighbor not accepted},\end{cases} \tag{6}\]  
which when \(\alpha=0.002\) is the same as the original Equation (2).  
We also saw in Section 2.1 that the Modified Lam uses a temperature adjustment (Equation (3)) much like the classic exponential cooling schedule, where the 0.999 is essentially the \"cooling\" rate, but such that the temperature can be adjusted up or down by that rate as necessary. Additionally, we saw that it uses a predefined initial temperature \(T_{0}=0.5\). We extract \(T_{0}\) as a hyperparameter to tune, and introduce a hyperparameter \(\beta\) by redefining the temperature adjustment as follows:  
\[T_{i}=\begin{cases}\beta\cdot T_{i-1}&\text{if AcceptRate}(i)>\text{LamRate}( i)\\ T_{i-1}/\beta&\text{otherwise}.\end{cases} \tag{7}\]  
We extracted the following hyperparameters, which the Self-Tuning Lam auto-tunes during the search: AcceptRate\((0)\), the initial value of the EMA estimate of the acceptance rate; \(\alpha\) from Equation (6), which is a discount factor controlling the impact of earlier observations on the acceptance rate estimate at the current iteration of the search; \(T_{0}\), the initial temperature; and \(\beta\), the rate of temperature change from Equation (7).
# Accuracy Analysis of the Measurement of Centre of Gravity and Moment of Inertia with a Swing
## 4 Discussion
A great benefit of this experimental set-up is to provide the distance of the centre of gravity with a very high accuracy in a standardised and repeatable way. Furthermore, the dynamic tests on the swing replace previous measurements hanging from a pivot point on the ceiling. It is indisputable that a more complex measurement including multiple load cells is able to quantify the full location of the centre of gravity and the moments of inertia also with a high accuracy and in less time [1]. The presented swing has a clear limitation to one degree of freedom for the oscillation, as well as a simple, stable and repeatable positioning of the model in relation to the pivot axis. Nevertheless, it is a good compromise providing an accurate and standardised measurement, which is suitable for the typical projects investigated at FloWave. The main aim is to provide a standardised measurement, which can be used not only as a simple check of the theoretical value based on CAD but also to provide a very high accuracy for the moment of inertia, which can be included in the analysis of the results from the wave and current tank.
## 3 Results
### Concentrations of Endogenous Hormone and Photosynthetic Key Enzyme in Maize Leave  
Plant endogenous hormones are essential for plant growth and development. At the jointing stage, the concentrations of endogenous hormones in maize leave were significantly different between the treatments (Figure 3). The IAA, GA, and ZR concentrations were significantly higher by 68.86%, 50.58%, and 55.22%, respectively, whereas the ABA concentration was significantly lower by 32.78% in the U+FA treatment than in the U treatment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Maize Growth Stages**} \\ \cline{3-6}
**Variable** & **Treatment** & **Seedling** & **Jounting** & **12-Leaf** & **Silking** \\ \hline \multirow{3}{*}{plant height (cm)} & CK & 51.60 b & 93.23 b & 139.80 c & 189.90 c \\  & U & 60.86 a & 109.33 a & 227.08 b & 231.45 b \\  & U+FA & 60.28 a & 111.15 a & 246.33 a & 250.40 a \\  & CK & 10.47 b & 22.02 b & 20.56 b & 20.49 b \\ stem diameter (mm) & U & 13.06 a & 23.31 b & 26.50 a & 24.68 a \\  & U+FA & 13.47 a & 27.41 a & 26.77 a & 25.06 a \\ \hline \hline \end{tabular} Treatments: no nitrogen fertilizer (CK); application of urea (U); combined application of urea and fulvic acid (U+FA). Values within a same column followed by different letters are significantly different between treatments for a same year according to least significant difference test (\(p<0.05\)).  
\end{table}
Table 4: Effects of different fertilization treatments on plant height and stem diameter at the different growth stages of maize, in 2017.  
Figure 2: Effects of different fertilization treatments on SPAD value (**A**), net photosynthetic rate (Pn, (**A**)), stomatal conductance (Gs, (**B**)), and transpiration rate (Tr, (**B**)) in maize leave at the jointing stage, in 2018. The treatments include: no nitrogen fertilizer (CK); application of urea (U); combined application of urea and fulvic acid solution (U+FA). The values are means of four replicates, and the error bars are standard errors. Different letters indicate significant differences between treatments for a same parameter according to SNK’s test at \(p<0.05\).  
The PEPC and PPDK are two key enzymes in carbon assimilation. Their activities were significantly different between the treatments during the jointing and V12 stages (Figure 4). At the jointing stage, PEPC and PPDK activities were not significantly distinct between the U+FA and U treatments. However, at the V12 stage, the concentrations of PEPC and PPDK mostly in the U+FA treatment were significantly higher by 42.96% and 29.40%, respectively.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 4 Experiments and Results
### Analysis of Results
#### 4.1.4 Maximum Height (\(H_{Max}\))  
\(H_{max}\) is the maximum attainable height for a drone with sufficient network strength. The graph in Figure 18e suggests that the number of drones decreases exponentially with increasing \(H_{max}\), while the plot in Figure 18f shows the linear relationship between \(H_{max}\) and the average leftover energy. These two plots again lead to a double optimization and, in our case, the optimal value for \(H_{max}\) is 2 km.
# Emotion Classification Algorithm for Audiovisual Scenes Based on Low-Frequency Signals
## 3 Numerical Results  
### Data Results and Analysis of Subjective Evaluation Experiments  
#### 3.1.1 Data Results and Analysis of Subjective Evaluation Experiments  
The subjective evaluation scores of video stimuli without audio were used as reference values for each participant, and the subjective evaluation scores of the same visual stimuli with the addition of different LFS signals were considered outcome values. The relative  
Figure 11: Near-miss undersampling process for handling data imbalance.  
values of subjective emotion evaluation were obtained by subtracting the reference values from the outcome values, which indicated the enhancement or reduction in perceived emotion intensity for each participant under the three emotion states caused by the different LFS signals. A positive relative value indicated that the addition of LFS signals increased the perceived intensity of the known emotion. A negative relative value indicated a trend of reduced intensity in the perceived emotion due to the addition of the corresponding LFS signal. A relative value of zero indicated no effect of the LFS on the perceived emotion intensity. As is shown in Figure 12, the relative values of the subjective ratings of all subjects were counted according to 5 video signals of 3 emotions.  
The statistical results showed that when the video signal clearly induced happy emotions, the relative values of subjective emotion ratings were negative after adding LFS, which indicated that the subjects' happiness was overwhelmingly diminished. This implies that LFS signals generally have an inhibitory effect on happy emotions. Among these three LFS signals, the S3 signal exhibited the strongest inhibitory effect on happy emotions, while the S1 signal had the weakest effect. The inhibitory effect of LFS signals on happy emotions was relatively more pronounced in dynamic videos. When subjects were known to induce sadness, the relative values of subjective affect scores were almost always positive after the addition of LFS, which suggested that LFS can bring enhancement to subjects' known levels of sadness. The enhancing effect of the S4 signal was relatively weak, and in some cases, no enhancement was observed, whereas the S6 signal had the strongest effect on sad emotions. The addition of LFS to visual signals known to induce shock emotions in subjects revealed mostly positive relative values for subjective emotion scores, which indicated that the intensity of shock emotions from audiovisual signals were overwhelmingly higher than those without LFS, suggesting that LFS signals can enhance shock to varying degrees. No significant differences were observed between the results of still images and moving videos in terms of sadness or shock (Table 4).
## 1 Introduction  
The compressed air foam system (CAFS) is a type of fire-extinguishing equipment which generates foam in the form of uniformly sized bubbles of a relatively small size which are injected forcibly with compressed air as an aqueous foam solution. The fixed fire-extinguishing system was initially developed by the National Research Center of Canada (NRCC) in the late 1990s. Currently, this system forms the basis of all foam fire-extinguishing systems. The main action of the foam fire-extinguishing agent is to choke the flame by covering the surface of the combustion product. Foam is created from a mix of an aqueous solution and air. A cooling effect due to the moisture included in the aqueous foam solution is an additional effect. This agent has been used for nearly one hundred years [1]; however, given the disadvantage of secondary damage caused by water, systems which minimize this problem by reducing the use of fire water and through long-distance discharges at high speeds have been developed and commercialized in Canada and the United States. In addition, Korea recently developed such a system, which has reached the stage of commercialization. The Korea Fire Institute (KFI) has developed an approval program to certify the fire-extinguishing performance of the CAFS [2].  
Various experimental and analytical studies of CAFSs have been conducted. [PERSON] et al. [3] proposed an experimental method to measure the drainage rate of liquid from fire-extinguishing foam for an aqueous film forming foam (AFFF) product and a film-forming fluoroprotein foam product. [PERSON] et al. [4] studied the fire-extinguishing performance for the oil fire between the general air mixture system (GAMS) and the CAFS in accordance with the discharge flow rate. [PERSON] [5] analyzed the contributions of various gas-liquid mixing chambers and gas-liquid mixing procedures with regard to the performance levels of CAFSs. [PERSON] et al. [6] compared the fire-extinguishing performance using the nozzle only for the compressed air foam and the fixed nozzle, respectively. [PERSON] and [PERSON] [7] carried out experimental research on the extinguishing effects of the nozzle shape, air pressure and mixing ratio as foam-performance parameters for non-aqueous liquid fuel fires.  
In this paper, in order to comprehend the relationship between the mixing ratio of the air-to-aqueous foam solution, which is an important performance factor of the CAFS, and the fire-extinguishing performance of the class B type for oil fires in the CAFS, the fire-extinguishing performance with the air-to-aqueous foam solution is evaluated through a fire-extinguishing experiment.
## 4 Standard Methods Based on Ion Chromatography  
The ideal methods for anion and cation determinations should meet the following criteria: determination of target ions within a limit of determination of 25% (or even 10% in some cases) of maximum acceptable concentration; short analysis time; simple sample pre-treatment; low cost of a single analysis; and method availability. Ion chromatography methods meet these requirements. Hence, they are used for routine applications in wastewater laboratories. Thanks to many advantages ion chromatography has when compared to the manual wet methods, it quickly became accepted world-wide as a reference method for the analysis of anions and cations in water and wastewater [52].  
Ion chromatography was quickly accepted by many other organizations, such as the United States Environmental Protection Agency (USEPA), American Society for Testing and Materials (ASTM), or National Institute for Occupational Safety and Heath (NIOSH), as a reference method for water and wastewater analysis. After the publications of the international standard methods based on ion chromatography, the number of laboratories applying this technique increased rapidly. In 1993, the USEPA published Method 300.0, the first USEPA method widely accepted as the standard for common inorganic anions [53]. Many regulatory agencies use the same methodology as the USEPA Method 300.0. During the standardization process, the draft standard methods have to be validated in the range of (at least) accuracy, precision, recovery, and inter-laboratory trials. The normative part of an analytical standard method usually includes: scope, principles, normative references, requirements, chemicals, instruments, interferences, performance of the separation column, sampling and sample pre-treatment procedures, calculation, expression of results and, finally, test reports. However, each agency can use a unique method format and style, as well as a quality control mode. Standard methods are usually adopted as recommended on a voluntary basis by any laboratory around the world. The governments can incorporate the existing standards into their national standards. The overview of selected international standard methods for the determination of selected ions in wastewater is given in Table 3.
## 5 Automation and Teleoperation
### Technology Description
#### 5.2.2 LTE and 5G Cellular Networks
Currently, combinations of 4G LTE and mid-band 5G-based networks can be found, for example, at coal mines in China [34] as well as in current research activities at underground test facilities in Kvantrop, Sweden. Various use cases exist/are being tested, such as teleoperation, fleet control, drone-based remote inspections and mine ventilation. Another aspect to consider is the use of 5G as a private 5G network. It describes a local area network for dedicated wireless connectivity within a specific area alongside provider licensing having a frequency slice allocated for a specific application [62]. Specific management of the network can be applied to a 5G private network. This includes data periodization and assured latency for certain applications that can be a key factor of the technology and a necessity for safe and efficient teleoperation especially in high-density areas and in the advancing use of connectivity in mining. Furthermore, enterprise users have the ability to define their own data policies and keep sensitive and proprietary information local [63]. Another aspect to consider is a private 5G network. It describes a local area network for dedicated wireless connectivity within a specific area [62]. That leaves the opportunity, regardless of provider licensing, to utilize the network for industrial purposes. A 5G private network can be managed; enterprise users also have the ability to define their own data policies and keep data local [63]. In summary, 5G is suitable for a fully teleoperated or autonomous mining fleet from the specification side; however, the feasibility of the hardware and application in the field of an active mine still needs to be researched. Furthermore, it has the potential to be a WCT that can leverage other technologies (Figure 7).
# FPGA Implementation of a Real-Time Edge Detection System Based on an Improved Canny Algorithm
## 2 Proposed Design Strategy  
### Conventional Canny Edge Detection Algorithm  
Canny developed an approach to derive an optimal edge detector to deal with step edges corrupted by white Gaussian noise. Figure 1 summarizes the original Canny edge detection algorithm [5] for an input image of size \(M\times M\) with n bits each. It consists of the following steps:  
1. Calculate the horizontal gradient \(G_{x}\) and vertical gradient \(G_{y}\) at each pixel location by convolving with gradient masks.
2. Compute the gradient magnitude \(G\) and direction \(\theta_{G}\) at each pixel location.
3. Non maximum suppression (NMS): Convert blurred edges of the image into sharp edges and suppress minima (preserving local maxima) in the gradient image.
4. Computation of threshold: Potential edges are determined by high and low thresholds and are calculated based on the gradient magnitude histogram of the whole image.  
Hysteresis thresholding: Creates a continuous edge map by comparing the values of the gradient magnitude of each pixel with low and high threshold values. This removes edge pixels caused by noise and illumination variation.
# A New Association Scheme for Handling Node Mobility in Cluster-Tree Wireless Sensor Networks
## 4 Simulation Assessment
### Results and Discussion
#### 4.2.2 Mobile Nodes at 5 m/s  
In the same way, as in the previous scenario, a new set of tests was performed, but with mobile nodes moving at 5 m/s. Figure 10 shows the related average packet delivery rate.  
Figure 10: Average packet delivery rate at 5 m/s.  
Figure 9: Average end-to-end delay at 1.4 m/s.  
The results show that using the IEEE 802.15.4 standard association mechanisms, the packet delivery rate was below 31% with just one mobile node, while using the proposed anticipation method, it reached more than 90% of packet delivery rate. It can also be observed that with the proposed anticipation method and up to 40 active mobile nodes, the average packet delivery rate was always over 55%. The use of alternative MAC parameters did not make a significant difference in the packet delivery rate for a small number of mobile nodes. It was, however, possible to identify that this method, together with the anticipation mechanism, presents better results for a large number of mobile nodes. On the other hand, it is possible to conclude that, with an increasing speed, the IEEE 802.15.4 standard association mechanisms were highly inefficient to support mobile nodes.  
Regarding the time during which the mobile nodes remained disconnect from the network, the results were very similar to the first scenario, with mobile nodes at 1.4 m/s. Figure 11 illustrates the average percentage time during which the mobile nodes remained disconnect from the network.  
Using the standard association mechanism, the simulation results showed that with 10 or more mobile nodes, there is a large increase in the percentage during which the nodes were without access to the network. On the other hand, with the proposed anticipation method, the mobile nodes rarely lose synchronization with the network. It is possible to conclude that, with the proposed anticipation method and for the presented scenario, even increasing the node speeds and the number of mobile nodes, the mechanism maintained the synchronization of the nodes with the network.  
Finally, Figure 12 illustrates the end-to-end communication delays for mobile nodes at 5 m/s. Results are similar to the previous scenario, which show that a higher number of travelling messages implies higher end-to-end delays due to network congestion. It can be attributed to the evident gain in the packet delivery rates, even when the number of mobile nodes increases (Figure 10).  
Figure 11: Average percentage of disconnect time at 5 m/s.
## 2 Materials and Methods
### Experimental Procedure  
Before commencing the experiment, researchers provided participants with detailed explanations of the experimental procedures, specific body positions, and required sitting postures. Participants were given the opportunity to adjust the height of the chair, standing support, and desk at each angle. They fine-tuned the height of the working table for each task according to their individual height and body proportions, following ergonomic principles. Additionally, participants practiced the required postures to ensure they were comfortable and correctly aligned for the experiment.  
The experiment will concurrently utilize body pressure distribution measurement equipment and electromyographic (EMG) signal acquisition devices. Prior to the start of the experiment, for the pressure sensor data, several calibrations were performed to ensure the accuracy and consistency of the sensors. Body pressure distribution testing pads were positioned on the standing chair, while electrodes were placed on the bilateral erect spinae, bilateral vastus lateralis, and bilateral gastrocnemius muscles, following SENIAM international standards [34]. Participants sat on the standing chair with seat incline angles of 0\({}^{\circ}\), 20\({}^{\circ}\), and 40\({}^{\circ}\), respectively (as depicted in Figure 3), and engaged in the following three sets of experimental tasks.  
1. The participant sits on the standing chair with both hands resting relaxed on their thighs. After receiving the start instruction, they begin typing continuously on the keyboard on the desk for 20 min. Recording starts after this period and lasts for 20 s (the typing content has been printed and placed on the task desk).  
Figure 2: Experimental equipment and scene: (**a**) EMG signal collector; (**b**) human body pressure distribution acquisition instrument; (**c**) experimental scenarios.  
2. The participant sits on the standing chair with both hands resting relaxed on their thighs. After receiving the start instruction, they begin writing continuously on the desk surface for 20 min. Recording starts after this period and lasts for 20 s (the writing content has been printed and placed on the task desk).
3. The participant sits on the standing chair with both hands resting relaxed on their thighs. After receiving the start instruction, they begin ironing clothes continuously on the desk for 20 min. Recording starts after this period and lasts for 20 s (the ironing task requires the participant to use their right hand).  
During the experiments, we used standardized postures and measurement positions, ensuring that all measurements were taken under the same environmental conditions to minimize the influence of external factors. Additionally, we used high-resolution pressure transducers to ensure the stability and consistency of the data.