esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Results  
#### 4.2.1 The Performance of Locating the Sound of Interest  
To verify the ability of our system in solving the first challenge in the cocktail party problem, we conducted experiments in the real world. We compared our AV-SSLT with three representative solutions in this field, the SEVD-MUSIC [18], the noise-robust GSVD-MUSIC [19] and the Multi-ACCDOA-based SELDnet [20; 21]. The two MUSIC-based algorithms took the audio data recorded via the four-channel microphone array as input. Two strategies were applied to select the direction of the target sound source from all DoA estimations, a random direction or the direction with the highest MUSIC spectrum response. The SELDnet was trained on the STARSS22 dataset [41] and a dataset synthesized using the method proposed in [42] with a batch size of 128 and a dropout rate of 0.05. The model was then fine-tuned on the real-world FOA format recordings from the experimental room generated via the 32-channel spherical microphone array. The target sound source of SELDnet was selected as the sound source whose sound event category was human speech and which was active at the same time as the keywords were detected. More detailed parameters of the MUSIC-based methods and SELDnet are presented in Tables 2 and 3, respectively.  
Figure 5: The configuration of an episode. The person marked by the yellow circle is the target speaker. The other people, identified by the red circles, are presented as the interfering sound sources. The robot highlighted by the green rectangle starts at a random position in the room. The sound from the television indicated by the blue rectangle served as the background noise.  
The results are summarized in Table 4. It can be seen that our AV-SSLT has a much higher success rate in locating the specific target and has the ability to get closer to the target than its counterparts relying solely on the microphone array. That is to say, by combining voiceprint and face features, our approach performs well in locating a specific speaker among multiple speakers, which is the first challenge in the cocktail party problem. Furthermore, in order to demonstrate the capability of our AV-SSLT system to work in real time, the average response time of each module was measured during the experiments and is presented in Table 5. It can be seen that the total response time of the entire system is about 0.5 s, which is sufficient for real-time interaction with humans.
# A Parallel Particle Swarm Optimisation for Selecting Optimal Virtual Machine on Cloud Environment
###### Abstract
It is therefore suitable to process parallel requests from different stakeholders with good execution time. Genetic algorithms can also be used to find high-quality solutions to optimisation and search problems inspired by biology. They have very good optimisation ability and internal implicit parallelism and can obtain the optimised solution and adjust the solution direction automatically through the optimisation method of probability [18,19]. They are part of evolutionary computing, which is inspired by [PERSON]'s theory about evolution. The algorithm is started with a set of solutions and tries to find the best solution through them according to their fitness--the more suitable they are, the more chances they have to reproduce, this is repeated until some condition is satisfied [20]. Currently, many healthcare applications that predict or diagnose diseases do not support real-time use, which maximises the time to respond to medical requests and remains a big challenge for most of the stakeholders in HCS [21]. Within this context, this paper aims to find the best choice of Virtual_Ms to reduce the total execution time of a medical request while easily accessing patient data and maximise the utilisation of resources. This paper proposes a new methodology for HCS based on cloud environment using parallel particle swarm optimisation (PPSO) and the genetic algorithm (GA) to optimise the Virtual_Ms selection. * Firstly, we run both algorithms to select the Virtual_Ms to minimise the total execution time of medical requests on the cloud environment and maximise the utilisation of resources. * Secondly, we compare both algorithms, and we select the best algorithm in terms of speedup and efficiency. * Finally, we apply the best algorithm in the CloudSim package to verify the performance of this algorithm in a cloud environment. The objective function relies on three parameters which are turnaround time, waiting time, and CPU utilisation. Most of the studies in related work have suggested the GA to select the optimal VM on the cloud environment. However, these studies did not reach optimal results to solve this problem. Therefore,this paper tries to explain GA results widely and compares it to another algorithm called PPSO to select the optimal algorithm between them. This paper is organised as follows: Section 2 discusses the background and concepts of this study. Section 3 discusses the related work, and Section 4 describes the methodology used in this study. Section 5 presents the results and discussion of the experiments and, finally, Section 6 concludes the paper.
# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 4 Experimental Setup
### Training Setup  
We implemented the proposed SDebrisNet in PyTorch 1.10. The network is trained on the Debian 10 system with a single A100 40 GB GPU, Intel Cascade Lake CPU. The network is initialized by PyTorch default setting and is not pretrained with any dataset. During the training, the batch size was set as 1. We adopted the Adam optimizer with a learning rate of \(1\times 10^{-4}\). The input images or video frames were resized to \(448\times 448\) before being fed into the network in both the training and inference phases. The sigmoid cross-entropy loss was used as the loss function to compute the loss between each input video frame and the corresponding ground truth saliency map. Moreover, the video clip length was set to 4 in both the training and test stages.
## 5 Discussion
### SMAs as Actuators and Results Comparison  
The SMAs are encapsulated in a silicone layer so that external temperature has little influence on the alterations in the SMAs' state. Temperature monitoring with SMAs would be a future line of research, since crystal structure modification happens within austenite to martensite and vice versa, and two additional changes occur: The first one takes place with the change of its modulus of elasticity, and the other has to do with the alteration of its electrical resistance value, values that are directly  
Figure 18: Comparison of movements and variables of robotic jellyfish developed. Source: Author.  
proportional to the change in temperature. The resistance change value could be used to estimate water temperature using the SMA. SMA can then be deformed and heated 10 million times before failing.  
Among the advantages of using robotic jellyfish as a means of monitoring are mainly the quick contraction for propulsion, its low weight and the cost of implementation which is highly reduced compared to that of an underwater exploration structure or the hiring of diving equipment, with a constant and reliable monitoring.  
A comparison has been made between the displaced water volumes, with data obtained with CFD and quasi-analytical modeling, obtaining as main results that there is a difference between them, although both follow the same trend shown in Figure 9. In CFD, ejected volume is higher than in analytical model, because viscous of the effects associated with the vortex ring formation and added-mass-variation effects are neglected in the analytical model. A similar situation occurs with other parameters, such as the Reynolds number.  
The experimental results displayed in Figures 14 and 15 and Table 3 show the kinematic parameters evolution of the jellyfish. Where based on pulsations, modification in the displacement and the peaks of speed and acceleration for each case are shown.
# A Review on Wind Turbine Deterministic Power Curve Models +
## 3 Polynomial Models
### Linear Models  
Polynomial models where \(n_{j}\) is equal to or lower than \(1\) for all the intervals are called linear models (LM). The classic one is based on three intervals such as expressed in Equation (3) [12]:  
\[P=\left\{\begin{array}{cl}a_{01}&v_{1}\leq v<v_{2}\\ a_{02}+a_{12}\cdot v&v_{2}\leq v<v_{3}\\ a_{03}&v_{3}\leq v<v_{4}\end{array}\right. \tag{3}\]  
where the generally accepted assignation of parameters is that given in Table 1.  
The parameters of the WT used in Table 1 are as follows: \(P_{r}\) is rated power, and \(v_{ci}\), \(v_{r}\), and \(v_{co}\) represent the cut-in, rated, and cut-out wind speeds, respectively. For \(j=4\), there is no interval, but \(v_{4}=v_{co}\). In this case, the model loses accuracy in the interval between the cut-in and the rated wind speed.  
There is a second option for the simulation as a LM. It consists of dividing the wind speed range into five intervals, and in this case there is no relationship between the parameters of the model and the parameters of the WT [14]. Its mathematical expression is given by Equation (4):  
\[P=\left\{\begin{array}{cl}a_{01}&v_{1}\leq v<v_{2}\\ a_{02}+a_{12}\cdot v&v_{2}\leq v<v_{3}\\ a_{03}+a_{13}\cdot v&v_{3}\leq v<v_{4}\\ a_{04}+a_{14}\cdot v&v_{4}\leq v<v_{5}\\ a_{05}&v_{5}\leq v<v_{6}\end{array}\right. \tag{4}\]  
where \(a_{01}=0\), \(a_{05}=P_{r}\), \(v_{1}=0\), \(v_{5}=v_{r}\), and \(v_{6}=v_{co}\). The rest of the parameters of this model are not directly related with the parameters of the WT.
## 2 Method
### Knowledge Graph for the Identification of Ship Illegal Behaviors
#### 2.3.5 Step 9: Fake Ship License Plates Inference  
As shown in Figure 9, the rules for fake ship license plates can be defined in order of whether the ship is an inland ship, MMSI or ship name information does not match, certificate expiration or invalidity, inconsistent or non-reporting of ports and trajectories, and ship focus monitoring. Using ship knowledge graph feature analysis technology, ship feature vectors can be extracted from ship information from monitoring chokepoints before combining them with feature weights for weighted assignment. The calculation process includes the following four sub-steps:  
_Step 9-1:_ Discover the ships that may be snared from the mapping of ship behavior characteristics in the study area, mainly acquiring the ships with multiple trajectories (this ship is usually a sea ship), and extract the abnormal trajectory data of the ship at sea;  
_Step 9-2:_ Retrieve the surveillance chokepoints within 50 km of the surrounding area with this ship as the center. Assume the longitude and latitude of the ship \(i\) (\(i=1\), \(2\), \(3\)) are \(lat_{i}\) and \(lat_{j}\) respectively; the longitude and latitude of the monitoring chokepoint \(j\) (\(j=1\), \(2\), \(3\)) are \(lon_{i}\) and \(lat_{i}\), respectively. The distance \(D_{ij}\) between ship \(i\) and monitoring chokepoint \(j\) can be calculated as Equations (5)-(7).  
\[D_{ij}=R*2a*tan^{2}(\sqrt{a},\sqrt{1-a}), \tag{5}\]  
\[a=sin^{2}(\Delta lat/2)+cos(lat_{i})*cos(lat_{j})*sin^{2}(\Delta lon/2)\;. \tag{6}\]  
\[\Delta lon=lon_{i}-lon_{j},\;\Delta lat=lat_{i}-lat_{j}. \tag{7}\]  
where \(R\) is the radius of the Earth and takes the value of about 6371 km. After finding the distance between any point and the current position, determine whether it is less than or equal to 50 km (1 km is about 1.852 km) to obtain the corresponding result;_Step 9-3:_ Calculate the spatial distance between the multi-track ship and the surveillance chokepoint, calculate the average speed of the ship, combine the two to calculate the time difference, and collect the ship's information before the time difference of the surveillance chokepoint. The calculation formula of average ship speed is as Equation (8):  
\[V_{n}=\sum_{i=1}^{n}v_{i}/n=(v_{1}+v_{2}+...+v_{n})/n \tag{8}\]  
\(V_{n}\) denotes the average speed, \(V_{i}\) is the ship sailing speed at each point, and \(n\) denotes the total number of points;  
_Step 9-4:_ The collected ship information set is correlated and compared with the ship behavior characteristic graph in the current region, and combined with the fake license plate rules, according to the weight order of whether the ship is a river ship, whether the ship information does not match (ship name/MMSI), whether the ship certificate is invalid, whether the ship does not have the reported port record (if so, also judge whether the ship trajectory is inconsistent with the reported port), or whether the ship is a key monitoring, etc. The highest possible ship is finally deduced.
## 2 Materials and Methods
### Extraction Procedure
#### 2.2.1 Pulsed Electric Fields (PEF) Extraction  
For the PEF pre-treatment of the spirulina 2% (\(w/v\)) solution was used with the PEF-Cellcrack III equipment (German Institute for Food Technology (DIL)) (ELEA, Germany). A treatment chamber of 900 mL capacity was used, the distance between the electrodes was fixed at 10 cm, and the total mass added to the treatment chamber was 202 g (198 g of water + 4 g of microalgae). A 100 kJ/kg treatment was applied with an electric field of 3 kV/cm according to a previous work [24]. Before and after the treatment, the temperature and conductivity of each sample was measured with a portable conductivity meter, ProfiLine Cond 3310 (WTW, Xylem Analytics, Weilheim in Oberbayern, Germany). The minimum electric field strength needed to produce changes in the cells is 1 kV/cm, and it has been shown that with a pulse duration of milliseconds, an electric field of 3-4 kV/cm is sufficient to create electroporation [14; 32].
# Seismic Response and Damage Analysis of an Isolated Bridge Subjected to Near-Fault Ground Motion
## 3 Results and Discussion
### Seismic Response Analysis of Bridge
#### 3.1.1 Girder Acceleration  
The acceleration time histories of the girder B2 point are shown in Figure 10. In the X-direction, the isolation bearing decreases the acceleration peak value of the girder B2 point from 6.7 m/s\({}^{2}\) to 5.6 m/s\({}^{2}\). The decreasing percentage of the acceleration peak value is 16.4%. In the Z-direction, the isolation bearing decreases the acceleration peak value of the girder B2 point from 6.5 m/s\({}^{2}\) to 3.4 m/s\({}^{2}\). The decreasing percentage of the acceleration peak value is 47.7%. The isolation bearing plays a role in isolation, and the isolation effect in the Z-direction is better.
# Numerical Simulation of Rock-Breaking Mechanism by Spherical Tooth Impact in Granite Formation
## 3 Results and Discussion
### Layout Optimization Research  
It is important to calculate the minimum number of ball teeth on a down-the-hole hammer head with a diameter of \(\varnothing\) within the range of fracture stress to reduce economic costs. The following formula calculates the maximum number of ball teeth. It is worth mentioning that due to the presence of flow channels on the hammer head, the actual distribution of ball teeth is smaller.  
\[N(\phi)_{\text{max}}=1+\sum_{k=1}^{\lfloor\frac{\phi/2-x_{d}}{x_{l}}\rfloor} \Bigg{[}\frac{\pi}{\arcsin\biggl{(}\frac{x_{l}}{2(x_{d}+x_{l}(k-1))}\biggr{)}} \Bigg{]} \tag{21}\]  
\[k=\left[\frac{\phi/2-x_{d}}{x_{l}}+1\right] \tag{22}\]  
where \(x_{d}\) is the pitch between two spherical tooth; \(x_{l}\) is the spacing between two spherical tooth; k represents the maximum number of rings.  
Analysis of Rock Fragmentation Performance Under Multi-Button Scenarios Based on Single-Button Studies. According to the analysis of single-button rock fragmentation, the 22 mm button bit demonstrates superior rock-breaking performance under the conditions of an impact energy of 35 J and a drilling pressure of 1.4 kN. Based on these findings, this study further analyzes the rock-breaking effect in multi-button scenarios. The analysis primarily considers two different motion patterns of the button bits on the drill bit: circular spacing (radial distance) and pitch spacing (circumferential displacement). The drilling parameters used are derived from the single-button simulation. The optimal stress influence range for a single button bit, which is 43.2 mm, is taken as the starting distance, with increments of 4 mm. Considering that button bits are positioned on different radial circles, their rotational speeds vary accordingly. For this study, a commonly observed rotational speed of 8 r/min is used. Since the duration of a single impact is extremely short, the arc displacement of the button bit during this period can be approximated as a linear displacement. Therefore, a linear velocity is applied to simulate the cutting motion of the button bits. For the outermost button bit, which is located at a radial distance of 300 mm from the central axis, a linear velocity of 0.25 m/s is applied. For the button bits on the inner radial circle, located 256.7 mm from the central axis, a linear velocity of 0.22 m/s is applied.
# Residential Demand Response Scheduling with Consideration of Consumer Preferences
## 4 Computational Experiments
In the case of both objective functions, Figure 6 shows the relative production cost (given in percent) compared to the one corresponding to the case when all appliances are used at the highest preference level (1). From the computational experiments, several conclusion can be made. First, an increase in the number of households that are scheduled together produces a higher level of savings. The results also indicate that increasing the number of households above a certain limit starts having a neglectful effect. This is similar to the original problem when satisfaction levels are not used. The potential savings are higher in the case that production costs are observed as a convex function for an hourly pricing scheme. In the case of the former, the savings have reached close to 12% in the case of 250 households for the lowest maximal allowed dissatisfaction levels. In the case of an hourly pricing scheme, the savings were less, and the highest level was lower than 6%. It is important to note that in the case of \(ProdCost_{LP}\), the savings are dependent on the selected convex function. When observing the PF for the relation between the average household satisfaction levels and the production costs, several interesting conclusions can be made. The first is that decreasing the average satisfaction over a certain level has no effect on lowering the production cost for a red fixed maximal level of allowed dissatisfaction. Beyond this point, savings can only be achieved by reducing the allowed level of dissatisfaction. It is important to note that several percents of savings can be achieved only by reducing the maximal allowed dissatisfaction and maintaining the same average satisfaction. This can be exploited in defining pricing schemes for household groups. Another interesting aspect of the performed test is that even for a high average satisfaction (1.5), more than 50% of potential savings are achieved if the maximal dissatisfaction is allowed to reach three.
## 3 Results and Discussion
### Drying Shrinkage  
Shrinkage plays a critical role in the long-term dimensional stability of structures and their susceptibility to cracking. Research indicates [54; 55] that this property is influenced by the volume of hydrated cement paste and the restraining effect of aggregates. Notably, 3 DPC differs from traditional concrete in that it utilizes formwork-free construction techniques, leading to an increased surface area of concrete exposed to air, which is more likely to cause concrete drying shrinkage. In this study, three specimens for each mix design were tested to investigate the influence of different amounts of FA and GGBFS cement replacement on the drying shrinkage of 3 DPC. The test results are shown in Figure 20.  
As can be seen, the 3 DPC specimens with a larger amount of FA cement replacement had higher drying shrinkage, with a water-to-binder ratio of 0.4 and a sand-to-binder ratio of 1.5. Specim 50F5 showed the most significant change, with a drying shrinkage of 0.034% at the age of 28 days. In contrast, the other two 3 DPC specimens, SF25 and S5F0, had similar drying shrinkage, 0.025% and 0.0235%, respectively, at the age of 28 days. FA typically increases shrinkage due to its finer particles and slower pozzolanic reaction, which can lead to increased porosity and delayed hydration. The greater difference observed with 50% FA replacement suggests that there exists a threshold where these effects become more pronounced, whereas with 25% replacement, the balance between FA and PC likely mitigates significant shrinkage differences.  
The FA used in this study had a finer texture compared with the GGBFS; thus, the 50% FA replacement in S0F5 resulted in relatively lower density compared with the other two specimens (SF25 and S5F0), which contained larger amounts of GGBFS. Consequently, S0F5 was better at absorbing excess moisture within the 3 DPC paste, leading to higher water absorption and more pronounced drying shrinkage.
# Non-Uniform Input-Based Adaptive Growing Neural Gas for Unstructured Environment Map Construction
## 3 Agng-Nu  
### The Operating Mechanism of the Growing Neural Gas Network  
The GNG algorithm is a growing SOM algorithm. It also has four stages, that are competition, collaboration, adaption and neuron growth and deletion. However, compared with the SOM algorithm, the calculation of the GNG algorithm in the collaboration and adaption stages is much simpler. The calculation of the neuron growth is also much simpler compared with the GCS algorithm. The operation mechanism of the GNG algorithm is as follows [20; 21; 22; 23]:  
(1) Competition stage  
GNG neurons compete with each other under the stimulation of the input sample \(\xi=(\xi_{x},\xi_{y})^{T}\in X\). The purpose of the competition is to find the neuron closest to the input node and identify it as the winning neuron. As shown in Formula (3), the neuron with the smallest value in Formula (3) wins.  
\[\left|\left|\xi-w_{i}\right|\right|=(\xi_{x}-w_{ix})^{2}+\left(\xi_{y}-w_{iy} \right)^{2} \tag{3}\]where the serial number of the winning neuron is \(i(\xi)\) and its position coordinate is \(w_{i}=(w_{ix},w_{iy})^{T}\in X\).
2. Collaboration stage  
In the collaboration stage, the neuron that wins the competition does not complete the task of constructing the topological map in isolation. Instead, it excites nearby neurons, causing them to excite together with itself, as shown in Figure 4. The domain function is usually used to determine which neurons are excited with the winning neuron, and finally complete the construction of the topological map together. The domain function of the GNG algorithm \(\theta_{i(x),j}\) is shown in Formula (4).  
\[\theta_{i(x),j}=\left\{\begin{array}{ll}1&j\text{ connected to }i\\ 0&j\text{ not connected to }i\end{array}\right. \tag{4}\]  
3. Adaption stage  
In the adaption stage, the Hebb learning rate can be used, and the weight adjustment method of neurons is that the neurons are adjusted closer to the input node, as shown in Formulas (5)-(8).  
\[\Delta w_{i}(t)=eb(\xi(t)-w_{i}(t)) \tag{5}\]  
\[w_{i}(t+1)=w_{i}(t)+\Delta w_{i}(t) \tag{6}\]  
\[\Delta w_{j}(t)=en(\xi(t)-w_{j}(t)) \tag{7}\]  
\[w_{j}(t+1)=w_{j}(t)+\Delta w_{j}(t) \tag{8}\]  
where \(0\prec eb\prec 1\), \(0\prec en\prec 1\), and \(en\ll eb\).
4. Neuron growth and deletion stage  
When the number of input samples increases to a certain value \(\lambda\), a new neuron will be inserted. The process is shown in Figure 5. If there is a neuron that cannot always win or has no connected edges, the neuron will be deleted. In addition, if the age of the connecting edge reaches the set maximum age value, the connecting edge will be deleted.  
The GNG algorithm completes the analysis and calculation of the input data and establishes a topological structure map after adjusting through the four stages of competition, collaboration, adaptation, and neuron growth and deletion.  
Figure 4: Diagram of collaboration stage.  
Figure 5: Diagram of inserting a new neuron.