esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Bacteriostatic Capacity of Extracts from Fresh and Aged Garlic  
Table 3 details the bacteriostatic capacity of extracts from fresh and aged garlic. All samples exhibited bacteriostatic capacity against _B. subtilis_ (ATCC 6051), _S. aureus_ (ATCC 6538), and _P. aeruginosa_ (ATCC 9027). However, the fresh garlic and that aged for 5-10 days did not exhibit bacteriostatic capacity against _E. coli_ (ATCC 10536); only the garlic aged for longer than 15 days was bacteriostatic against _E. coli_, but the diameter of the inhibition zone was only up to 8.33 mm, which showed little inhibitory effect. The diameter of the inhibition zone against _B. subtilis_ and _S. aureus_ for the garlic aged for 35 days was 23.00 and 25.33 mm, respectively, and the MIC and MBC were 0.2 and 0.4 mg, respectively. In contrast, the diameter of the inhibition zone against _B. subtilis_ and _S. aureus_ for the fresh garlic extract was 12.90 and 11.00 mm, respectively, the MIC and MBC for _B. subtilis_ were 0.4 and 0.8 mg, and the MIC and MBC for _S. aureus_ were 0.8 and 1.0 mg, respectively. Therefore, the bacteriostatic capacity of the BG extracts was higher against _B. subtilis_ and _S. aureus_ than that of the fresh garlic extract. The bacteriostatic capacity against _P. aeruginosa_ was highest for the garlic aged for 35 days, with the diameter of the inhibition zone being 12.67 mm and the MIC and MBC being 0.4 and 0.8 mg, respectively. The diameter of the inhibition zone for the fresh garlic extract against _E. coli_ was only 8.33 mm, and the MIC and MBC were 1.0 and 2.0 mg, respectively. These results demonstrated that the extracts from fresh garlic and BG had higher bacteriostatic capacity against Gram-positive bacteria such as _B. subtilis_ (ATCC 6051) and _S. aureus_ (ATCC 6538) than against Gram-negative bacteria such as _P. aeruginosa_ (ATCC 9027) and _E. coli_ (ATCC 10536).
# Modeling of Lane Changing Behavior of Connected Driving Based on Vehicle Interaction Potential
## 3 Acceptable Clearance for Lane Changing
### Research on Acceptable Clearance of Lane Changing
#### 3.2.3 Acceptable Lane Changing Clearance between Vehicle \(M\) and Vehicle \(L\)  
There is a collision risk between lane changing vehicle \(M\) and leading vehicle \(L\) in the lane changing process, so it is necessary to study their interaction state. As shown in Figure 8, in order to avoid angle collision between vehicle \(M\) and vehicle \(L\), the two vehicles should meet the conditions as follows,  
\[G_{a}^{M-L}\geq S_{M}-S_{L}+G_{\min}+w\sin(\theta) \tag{14}\]  
where: \(G_{a}^{M-L}\) represents the acceptable lane changing gap between vehicle \(M\) and vehicle \(TF\); \(S_{M}\) indicates the distance traveled by lane changing vehicle \(M\) after the end of lane changing; and \(S_{L}\) indicates the distance traveled by the leading vehicle \(L\) in the current lane during the lane changing.  
Since the motion state of the leading vehicle \(L\) in the current lane is basically not affected by the lane changing vehicle \(M\), the leading vehicle \(L\) may perform uniform motion, accelerating motion, or decelerating motion. The initial lane changing speed and acceleration of vehicle \(M\) are set to be \(v_{M}\) and \(a_{M}\), respectively, and the initial lane changing speed and acceleration of vehicle \(TF\) are set to be \(v_{L}\) and \(a_{L}\), respectively. According to the generation mechanism of lane changing intention, it can be seen that vehicle \(M\) is subject to the repulsive force of the leading vehicle before it generates the lane changing intention, so this paper assumes that the initial lane changing speed relation is \(v_{M}>v_{L}\).  
Figure 7: Lane changing process between vehicle \(M\) and vehicle \(TF\).  
In this lane changing scenario, the collision that very easily happens is angle collision, and the time for the two vehicles to present the illustrated state is defined as \(t_{j}\), which is usually taken as 1.8-3 s according to experience. If there is no angle collision between the two vehicles at this time, there will be no further collision risk. In this case, we get a distance difference of \(\Delta S=(v_{M}-v_{L})t_{j}+\frac{a_{M}t_{j}^{2}}{2}\).  
In summary, the expression of acceptable lane changing clearance between vehicle \(M\) and vehicle \(L\) can be obtained as  
\[\left\{\begin{aligned} G_{a}^{M-L}&\geq(v_{M}-v_{L}) t_{j}+\frac{a_{M}t_{j}^{2}}{2}+G_{\text{min}}+w\sin(\theta)\\ G_{\text{min}}&=[t_{0}-c_{v}(v_{L}-v_{M})-c_{a}(a_{L }-a_{M})]v_{M}\end{aligned}\right. \tag{15}\]
# Flocculation Harvesting Techniques for Microalgae: A Review
## 5 Particle-Based Flocculation
### Magnetic Particle
The changes in hydrophobicity for the SA-Fe\({}_{3}\)O\({}_{4}\)-ZnO composite gradually increased the number of algal cells migrating into the bulk suspension from the flocs, as a function of UV irradiation [121]. Development of low-density flocculant of sizes comparable to algal cells is ideal for easy detachment. [PERSON] et al. [103] reported successful detachment of BaFe\({}_{12}\)O\({}_{19}\) from algae-BaFe\({}_{12}\)O\({}_{19}\) flocs based on pH control and the re-usability of the magnetic flocculant. According to their results, the detachment efficiency of _Chlorella_ sp. KR-1 was sharply increased from 13% to 85% after increasing the flocculant size from about 100 nm to 1.2 \(\upmu\)m. The same algal species could be harvested and detached by commercial Fe\({}_{3}\)O\({}_{4}\) microparticles (Sigma-Aldrich, St. Louis, MO, USA) through only pH control [108], with pH 7 being used for harvesting and pH 12 for detaching. In addition, the supernatant of the spent culture medium was suitable for a second cultivation cycle after supplementation with 50% fresh medium. Despite the short time required for algae harvesting using magnetic particles, the manufacturing costs could be the main limiting factor for its scalability to biofuel production. Therefore, more studies are needed to reduce these costs through the utilization of low-cost materials as well as to enable the reusability of the flocculants after biomass recovery.
## 2 Methodology
### Spatial Modelling and Geographical Weighted Regression for Bacterial Distribution  
A spatial model has been developed through hexagons which shows a number of grids in an area. In this research, we used hexagon tessellates, because many hexagons will cover a surface without overlapping or leaving any gaps. The most basic reason that regular hexagons tessellate is that the degree measure of the angle at each vertex is a divisor of 360.  
Geographically weighted regression (GWR) is a technique that analyzes the variables in spatially and models their local relationships [29; 30]. GW identifies spatial heterogeneities in regression models [31; 32; 33; 34] of geo-referenced data. The development of GWR includes a number of techniques like local regression and smoothing, spatial autocorrelation, kernel bandwidths, generalized linear modelling and Bayesian GWR [35]. It is a combination of a generalized linear model and a standard linear model (SLM). SLM assumes that the response \(y\) is normally distributed for its expected value \(\mu\) with constant variance \(\sigma^{2}\)  
\[yN\left(\mu,\sigma^{2}\right)\]  
while in turn, \(\mu\) can be expressed as a linear combination of \(p\) predictor variables \(x_{1},x_{2},\cdots,x_{p}\)  
\[\mu=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\cdots+\beta_{p}x_{p}.\]  
where \(\beta_{0},\beta_{1},\cdots,\beta_{p}\) are regression coefficients.  
The GWR extends the linear model  
\[\mu=\beta_{0}W_{0}+\beta_{1}x_{1}xw_{1}+\beta_{2}x_{2}w_{2}+\cdots+\beta_{p} x_{p}w_{p}.\]  
where W is a matrix of location-specific weights.  
In this paper, the relationship between bacterial diversity and water temperature across the set of hexagons is shown, with providing a smooth fit.
## 3 Results
### Evaluation of Dose Distributions  
Results of water phantom validation are shown in Figure 4. Root mean squared differences (denoted RMS) between profiles generated from the PRIMO phase spaces and GAN phase spaces were calculated for all test settings. Some profiles with the largest and smallest differences are shown in Figure 4. Depth profiles were computed along the beam axis, where the phase space with the highest error was \(P_{3}\) for the CGAN model and \(P_{4}\) for the RoCGAN model. The lateral profiles were computed along the Y axis at 6.9 cm depth, which was greater than the depth at which the maximum dose occurred and represented about 80% of the maximum dose. In this case, the phase spaces with the highest errors were \(P_{3}\) for the CGAN model and \(P_{6}\) for the RoCGAN model, respectively. Next, gamma passing rates (GPR) for 2%/2 mm tolerance [26] and relative dose differences to 5% and 95% of the region volume (denoted, respectively, \(\Delta D_{5}\) and \(\Delta D_{95}\)) [25] in selected regions of interest were calculated for each phase space and plan. These regions  
Figure 3: Probability density estimates for the training space used in the InfoVAE model. Densities calculated from PRIMO-generated phase space are depicted in blue, while densities for the InfoVAE-generated phase space are shown in orange. Figure 2: Histograms of test phase spaces. Solid color denotes median of kernel density estimates for training and test phase spaces, respectively. Green dashed lines denote first and third quartiles of probability densities. Solid lines denote median absolute differences between PRIMO-generated and GAN-generated probability estimates. Dashed lines correspond to first and third quartiles of absolute differences. included the planned target volume (PTV), gross target volume (GTV), clinical target volume (CTV), brain, brain stem, spinal cord, prostate, mandible, and submandibular glands. Relative dose differences to \(N\%\) were calculated as \(\Delta D_{N}=|\frac{D_{\textit{cmp},N}-D_{\textit{prf},N}}{D_{\textit{prf},N}}|\), where \(D_{\textit{ref},N}\) is the smallest reference dose to be received in the \(N\%\) most irradiated volume; similarly, \(D_{\textit{cmp},N}\) refers to the same quantity in the compared dose distribution. Results are presented in Table 2 for both models. In an ideal situation, the GPR is equal to 100% and relative dose differences are equal to 0%. The speed of phase space generation was compared by the requirement to generate 350 million photons to the given phase space. PRIMO required about 24 h on 48 CPU cores to complete the task with splitting roulette variance reduction activated. With this option disabled, the time increased to 60 h on 48 CPU cores. The RoCGAN model can generate a phase space of the same size in about 1 h on a single CPU core; thus, the GAN generates photons over 1000-times faster than MC with variance reduction and over 2800-times faster than MC without variance reduction.
# Consensus Algorithms Based Multi-Robot Formation Control under Noise and Time Delay Conditions
###### Abstract  
In recent years, the formation control of multi-mobile robots has been widely investigated by researchers. With increasing numbers of robots in the formation, distributed formation control has become the development trend of multi-mobile robot formation control, and the consensus problem is the most basic problem in the distributed multi-mobile robot control algorithm. Therefore, it is very important to analyze the consensus of multi-mobile robot systems. There are already mature and sophisticated strategies solving the consensus problem in ideal environments. However, in practical applications, uncertain factors like communication noise, communication delay and measurement errors will still lead to many problems in multi-robot formation control. In this paper, the consensus problem of second-order multi-robot systems with multiple time delays and noises is analyzed. The characteristic equation of the system is transformed into a quadratic polynomial of pure imaginary eigenvalues using the frequency domain analysis method, and then the critical stability state of the maximum time delay under noisy conditions is obtained. When all robot delays are less than the maximum time delay, the system can be stabilized and achieve consensus. Compared with the traditional Lyapunov method, this algorithm has lower conservativeness, and it is easier to extend the results to higher-order multi-robot systems. Finally, the results are verified by numerical simulation using MATLAB/Simulink. At the same time, a multi-mobile robot platform is built, and the proposed algorithm is applied to an actual multi-robot system. The experimental results show that the proposed algorithm is finally able to achieve the consensus of the second-order multi-robot system under delay and noise interference.  
multi-robot; consensus problem; formation control; noise; time delay +
Footnote †: journal: Applied Physics and Mechanics Letters
# Stamping Tool Conditions Diagnosis: A Deep Metric Learning Approach
## 1 Introduction
At its core, DML is a \(W\) projection matrix [32]; the FEM transforms objects into the \(W\) projection matrix and then learns according to its projection matrix similarity. The distances between these projection matrices demonstrate the similarities of the objects. A contrastive loss method [33] can be used to minimize the distance between the projection matrices for same samples and maximize it for different samples. A selection of samples for batches in training [34] is also can contribute to a better result in DML. On the basis of its advantages, DML has been used to develop robust algorithms that respond to changes in working conditions. Previously, [PERSON] et al. [35] proposed novel deep distance metric learning combined with representation clustering to diagnose a rolling element bearing with limited data required to learn. In addition, [PERSON] et al. [36] used deep metric learning combined with meta-learning to diagnose bearing faults with few-shot samples. On the other hand, [PERSON] et al. [37] proposed deep metric learning with a probability method to diagnose faults in bearings using a limited number of samples. [PERSON] et al. [38] proposed a similar method to [PERSON]. It was used to classify Raman spectroscopy and able to add new classes. In the method proved by [PERSON] et al. [37], the DML was able to recognize an unseen class, whereas [PERSON] et al. [38] proposed a model able to dynamically add a class with as few as one example. The DML itself has several variations of techniques to implement ranging from batch selections to loss functions, and its application can be found in many fields, such as facial or signal recognition [33; 34; 35; 36; 37; 38]. However, to our knowledge, a thorough investigation involving batch selection strategies and loss functions to determine the most effective DML method for stamping tool condition diagnosis has not been conducted. Herein, we propose a solution for stamping tool condition diagnosis that offers improvements over the method introduced in our previous work [29], the main improvement that we aim to achieve is the flexibility of adding new class while still retaining the advantages gained from the previous work. The solution is based on the incorporation of DMLwith several distance metric loss and batch strategies. The contributions of this study are as follows:  
1. We developed a DML method for stamping tool condition diagnosis that incorporates several distance metric loss and batch strategies. 2. We investigated the performance of each distance metric loss and batch selection method. 3. We investigated the robustness of each loss and mining method based on the degree of training data variation, noise injection, and capability of adding new classes. The remainder of this paper is organized as follows: Section II describes metric learning and DML. Section III presents the proposed experiments, their results, and a discussion. Finally, section IV presents the research conclusions.
### Vision of the Tactile Internet  
For a wide range of application areas such as Industry 4.0, smart e-learning and education, and Healthcare 4.0, the Tactile Internet (TI) envisions the interaction of human-to-machine (H2M) and relies upon it to add another paradigm to see its influence on society. The envisioned TI desires to meet the stringent design requirements by empowering a suitable communication framework. First, it needs to give an extremely low round-trip time (delay) of 1 ms or below, and possible ultra-high reliability to avoid motion sickness. It likewise needs to guarantee data security and high availability of a framework without compromising the ultra-low latency requirement. These crucial design goals of TI can be achieved by placing tactile devices close to each other, which is often based on distributed or decentralised architecture dependent on recent technological advancements such as Edge/Fog Computing, Software-Defined Networking (SDN), and Network Coding, Network Function Virtualisation (NFV), and suitable multiple-access (MA) techniques. Moreover, adaptable techniques at all protocol layers are expected to minimise the RTT from sensors to actuators.  
Significantly, the TI will establish vital prerequisites for next-generation access networks as far as latency, availability, and reliability are concerned. Wired access networks are ultimately fulfilling these prerequisites already, and wireless access networks are not yet proposed to fulfil these prerequisites. As per the International Telecommunication Union--Telecommunication Standardisation Sector (ITU-T) Technology Watch Report on the TI [1], research in these areas will need to speed up substantially, introducing new thoughts and designs to promote access network characteristic redundancy, and a decent variety to resolve RTT issues and reliability constraints of TI applications.  
As TI is going to serve critical applications of society, it should have competencies such as ultra-reliability [6], very low latencies, and be able to connect to numerous devices for communicating with each other simultaneously and autonomously. It should also be feasible to interact with ongoing and conventional wired Internet, mobile Internet and the IoT, thus forming a network of entirely new possibilities and opportunities.
# Voice Pathology Detection and Classification Using Convolutional Neural Network Model
## 3 Proposed Methodology  
### Dataset Used  
As already stated, we have opted to use continuous vowel /a/ phonation as the base for our experiments. A speaker is asked to maintain vowel phoning during this specific speech task, to maintain the amplitude and frequency at a realistic rate [21]. The benefit of this speech task is that it is free of articulative and other linguistic confusions compared with other language standard tasks such as reading or speaking activities. This uniqueness makes it an ideal alternative for this mission for building the large database required for supervised deep learning models [43]. Thus, the only speech task used in this process is sustained /a/ vowel phoning.  
The Saarbruecken Voice Database (SVD) is built based on 2000 speakers [10] and voice and electroglottography (EGG) signal sets are included in this dataset. It comprises records of 687 healthy individuals (259 men and428 women) and 1356 individuals (629 men and 727 women) with different pathologies. The recording procedure involves: (a) vowels /i, a, u/ formed in normal speech, (b) high and low pitches; vowels /i, a, u/ with rising-falling pitch; and (c) the German sentence \"Guten Morgen, wiegeht es Innen?\" (\"Good morning, how are you?\"). Each recorded SVD voice was sampled with a resolution of 16-bit at 50 kHz. This dataset is fairly recent and has therefore been used by very few studies in the field of voice pathology. Following the three diseases criteria, we downloaded files from the website listed in [44] and selected only the continuous vowel /a/ samples generated at normal pitch.
# Detection of Electromagnetic Seismic Precursors from Swarm Data by Enhanced Martingale Analytics
## 4 Quantitative Experiment
### Mexico 2017
#### 4.1.1 \(500\times 300\) km  
Figure 4 illustrates the EMF intensity activity leading up to and following the seismic event. As described earlier, each data point represents the mean value aggregated across daily recordings within a \(500\times 300\) km grid, calculated according to Equation (11).  
In Figure 5a, we illustrate the outcomes obtained from applying the original Martingale framework to the EMF data depicted in Figure 4. Despite the presence of approximately 15 significant anomalies in the ground truth data represented in Figure 4, the Martingale framework identifies only one anomaly in the EMF signals leading up to the seismic event. Remarkably, this sole anomaly is detected within the EMF data originating from the Alpha satellite. Conversely, subsequent to the seismic event, the Martingale framework identifies two anomalies. Intriguingly, both of these anomalies are observed within the EMF data derived from the Beta satellite and coincide temporally with the anticipated aftershock spikes, as depicted in Figure 4.  
Figure 4: A plot of the original data occurring within the \(500\times 300\) km grid obtained using the original Swarm alongside Equation (10). The color coding is Alpha (red), Beta (green), and Charlie (blue).  
Figure 5: _Cont._Figure 5b depicts the anomalies identified by our novel approach applied to the data visualized in Figure 4. Notably, there is a discernible escalation in the number of detected anomalies as the seismic event date approaches. Around nine months preceding the event, aberrant patterns in the time-series of Charlie are detected, followed by anomalies in the Beta time-series just under six months before. Subsequently, there is a surge in anomalies detected in the Charlie time-series just under three months before the event, accompanied by several anomalies in the Alpha time-series. Post the seismic event, anomalies are observed in the Beta time-series.  
In contrast to the original Martingale framework illustrated in Figure 5a, our approach demonstrates a higher capability in detecting abnormalities across the time-series of all three satellites. Intriguingly, some detected spikes originate from regions that exhibit no apparent spikes or other anomalies in Figure 5. This observation suggests the presence of underlying data behavior in those regions that contravene the null hypothesis, serving as the triggering mechanism for an anomaly label.