esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation-Based Analysis on Operational Control of Batch Processors in Wafer Fabrication
## 4 Simulation Experiments and Results
### Results from Simulation Experiments
#### 4.2.2 Effect of the MBS Size on System Performance  
In MBS-based control schemes, the determination of the MBS threshold value is a primary decision. It is known from previous research studies that the performance of the MBS threshold values depends on the BP utilization levels. To examine the combined impact of MBS threshold values and BP utilization levels, simulation experiments are carried out with six different MBS threshold levels with four different BP utilizations. Here, to see the changes in waiting time and lead time over different BP utilization levels, the open-loop lot release rule, CONTIME, is used. The experimental results are shown in Figure 8. It is seen that overall, lower MBS threshold values provide less system lead time, as well as less waiting time at batch processors. This is especially true regarding lower BP utilization levels, such as 60% or 70%. However, when the BP utilization is high, such as 80% or 90%, the MBS values have little effect on the performance. This is because the batch size is large, with a high BP utilization level regardless of the MBS threshold values.  
Figure 7: Performance compared over different BP and DP utilization levels in terms of (**a**) throughput rate per nine months and (**b**) system lead time.  
One interesting finding is that as MBS levels increase from one to five, BP waiting time tends to increase slowly. However, with an MBS threshold of six, BP waiting time drops sharply compared to an MBS threshold of five. This is arguably due to the reentrant characteristics. Note that in the system under consideration, the batch capacity of a BP is six. Hence, in MBS6, only six wafer lots are formed as a batch to have a process service and are released at the same time to a downstream workstation. They visit several discrete workstations before arriving at the next batch processor where six lots are again required to form a batch. Their arrival at the next BP station should be within a relatively small period, so less waiting time is needed to form a batch at the BP station. When the BP utilization is high, the BP waiting time drops more sharply. In the case of a BP utilization of 80% and 90%, MBS6 provides the lowest waiting time at the BP station. However, from Figure 8b, it is seen that the sharp decrease in BP waiting time under MBS6 does not lead to less system lead time. The lead time is increased with MBS6 compared to MBS5 except in the case of high BP utilization. The results indicate that less BP waiting time at the local station does not always lead to less system lead time. Industries often utilize a full load control policy in the BP station. This may be a good idea when the BP station is under a heavy workload. However, in most cases, the BP station is not critically constrained in its capacity. In this case, a lower MBS threshold is recommended for better system performance.
# Delay-Sensitive NOMA-HARQ for Short Packet Communications
## 3 Delay-Sensitive NOMA-HARQ
### Reliability and Throughput Analysis of Two-User D-NOMA-HARQ  
We use an MM, as shown in Figure 2, whose states are represented by a vector \(\mathbf{J}=[J_{1},J_{2}]\), where \(J_{i}\in\{0,1,e\}\) is the current state of user \(i\). State 0 refers to a packet success  
Figure 1: Packetization model of two-user NOMA-HARQ with single retransmission. without any retransmission, State 1 refers to a packet success after single retransmission, and State \(e\) refers to packet failure after single retransmission. It is important to note that as each user can be in one of the possible three states, the MM for the proposed D-NOMA-HARQ when \(N_{u}=2\) with single retransmission will have 9 states. However, since we assume that user 1 is a near user and it will always transmit with a higher power and is decoded first at the receiver, and if unsuccessful user 2 will not be decoded, the number of possible states will reduce to 6. In particular, when \(J_{1}=0\), \(J_{2}\) can be \(0,1\), or \(e\), which corresponds to vector states \(\mathbf{J}_{1}\), \(\mathbf{J}_{2}\), and \(\mathbf{J}_{3}\), respectively. When \(J_{1}=1\), \(J_{2}\) can only be \(1\) or \(e\), which corresponds to states \(\mathbf{J}_{4}\) and \(\mathbf{J}_{5}\). This is because of the assumption that user 1 is always transmitting with a higher power, i.e., \(w_{1}>w_{2}\) and \(\alpha_{1}>\alpha_{2}\), where \(\alpha_{i}\) is the fraction of retransmission power. This assumption simplifies the SIC decoding order without compromising performance. Otherwise, if each user freely chose \(w_{i}\) and \(\alpha_{i}\) in the range \([0,1]\), the BS needs to find the optimal decoding order based on the received powers in each time slot with additional complexity [22]. Therefore, under this assumption, if user 1 is successful only after single retransmission, user 2 cannot be recovered with single transmission. Following a similar argument, when \(J_{1}=e\), \(J_{2}\) can be only \(e\), which corresponds to state \(\mathbf{J}_{6}\) in the MM in Figure 2. The state transition probabilities of the MM are given in the following lemma. **Lemma 1**.
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 3 Results
### Intensive Small Area Mapping by UAV
#### 3.1.3 Comparison of Classification Methods  
GEE implements a range of classification algorithms. The accuracy of many of these was tested for this application, using the 2 July UAV image from the C1 channel. In each case, the default classification parameters were used. The kappa value results (calculated from the error matrices of actual vs. classified values) are shown in Table 6. It can be seen that the classification and regression tree (CART) classification performed the best. In the following sections, the CART classifier will be used.
## Materials and Methods
### Laser Ablation-ICP-MS  
An Inductively Coupled Plasma Quadrupole Mass Spectroscopy ICP-QMS (Agilent 7900, Agilent Technologies, Hachioji, Japan) was coupled to a laser ablation system NWR 213 (New Wave Research, CA, USA) for the elemental imaging. The laser spot size was 60 \(\upmu\)m with 30 \(\upmu\)m between the lines, at a laser energy of about 29%. One measurement of each tissue sample was performed, with a scan speed of 70 \(\upmu\)m s\({}^{-1}\). The isotopes \({}^{13}\)C, \({}^{31}\)P, \({}^{34}\)S, \({}^{43}\)Ca, \({}^{44}\)Ca, \({}^{54}\)Fe, \({}^{56}\)Fe, \({}^{57}\)Fe, \({}^{63}\)Cu, \({}^{65}\)Cu, \({}^{64}\)Zn, \({}^{66}\)Zn, \({}^{66}\)Zn, \({}^{206}\)Pb, \({}^{207}\)Pb, and \({}^{208}\)Pb were monitored; in case of multiple available isotopes the most abundant was evaluated and the results were cross-checked with the additional isotopes of the same element, to check for possibly occurring spectral interferences. Quantification of the Fe, Cu, Zn, and Pb was performed, based on the rat brain-tissue standards. Blanks and five standards were prepared by spiking the homogenized rat brain tissue with elemental standard solutions. The resulting tissue, with the known concentrations (determined by the total elemental analysis), was frozen, cut in slices, and analyzed, in analogy to the samples. The calibration ranged from the blank tissue levels up to 19.8 mg/kg for Fe, up to 20 mg/kg for Cu, up to 30 mg/kg for Zn, and up to 2.5 mg/kg for Pb. The relative intensities of C, P, S, and Ca, were seen due to the lack of calibration standards. Image reconstruction was performed by a software developed in-house (Forschungszentrum Julich), giving the scale in mg/kg, for the elements with calibration. Due to the complex process of ablating and transferring the aerosol into the ICP-MS,  
Figure 1: **(A)** H&E staining section of the rostral brain (**Bregma 1.56 mm**) showing the three regions of interest (ROI): SSMC, CC, and CP. **(B)** Corresponding FTIR image. **(C–I)** Representative images of the biochemical components—Amide I, Amide II, CH\({}_{3}\) asymmetric, CH\({}_{2}\) symmetric, CH\({}_{2}\) asymmetric, Olefin, and Ester, accordingly. The color bar represents a high concentration, in red, and low concentration, in blue.  
there are several sources of uncertainty. Therefore in this work, the main focus was on identifying the elemental patterns within the tissue sections but not on an exact quantification.
# SDN Based Collaborative Scheme for Mitigation of DDoS Attacks
## 4 Testbed and Evaluations
### Performance of Central Control Platform
#### 4.4.1 Effect of Payload Size  
The performance of our system also depends upon the payload size of an attack definition which mainly consists of malicious IP addresses. We took various payloads and computed the time to verify and process the IP addresses to generate relevant flow table entries. Figure 10 display the effect of the increasing size of payloads. It took on average of 1.8 ms to process (verification of signature and insertion of flow table entry) a payload containing 1000 IP addresses. The processing time increases to 13 ms for payload with 10,000 IP addresses and around 137 ms to process 100,000 IP addresses.  
Here we choose 100,000 IP addresses to stress test the computation overhead due to large payload processing on low-end commodity hardware. Unlike routers, the TCAM space in switches are expensive, so it would be more realistic to apply reasonable cap or exchange malicious subnets information instead of IP addresses, in case switches are used as the network component.  
Figure 9: Linear vs. Mesh or Central: Shows a time based comparison of Policy Propagation delay between linear and mesh approach. Due to direct connection in mesh or central approach the mitigation effect is immediate.
# Scenario-Based Emergency Material Scheduling Using V2X Communications
## 1 Introduction  
Emergency logistics is receiving increasing attention from academics as well as practitioners [1; 2; 3]. Effective and efficient delivery of relief resources to victims is critical. However, unreasonable distribution of emergency material seriously affects the efficiency of disaster rescue. Emergency material scheduling refers to a quick response to the urgent need for relief in affected areas right after disasters, especially emergency logistics distribution. Traditional emergency material scheduling has many shortcomings, for example, the simplification of dynamic information on traffic such as speed, road traffic capacity, road network repairing, lack of fairness, and ignorance of differences of disaster scenarios, including air transportation, making it hard to implement an optimal scheduling plan, especially when the supply is insufficient. In recent years, vehicle-to-everything (V2X) communication has provided a useful tool for making more precise relief logistics decisions by collecting and transmitting disaster video and vehicle data in real time.  
Vehicle-to-everything communication incorporates communications such as V2I (vehicle-to-infrastructure), V2N (vehicle-to-network), V2V (vehicle-to-vehicle), V2P (vehicle-to-pedestrian), V2D (vehicle-to-device) and V2G (vehicle-to-grid). The main motivations for V2X are road safety, traffic efficiency and energy savings [4]. V2I and V2V communication can be used to share relevant information,such as vehicle position, damage to the roads, and surrounding environment, especially in an earthquake [5; 6]. A vehicle-mounted communication system can collect the vehicle running status by using Controller Area Network (CAN) bus and sensors, such as vehicle location, running direction, running speed, acceleration, and surrounding video. With V2X communications, the urgency of a disaster scenario can be evaluated accurately and considered in the optimization of emergency material scheduling, which is a novelty of our method.  
This paper studies a scenario-based emergency material scheduling method (SEMS), taking into account the urgency of a disaster scenario and the fairness of material scheduling. The SEMS takes fairness as the main objective and the transport time as the secondary objective, and fairness also considers the urgency of a disaster scenario besides traditional demand satisfaction of material scheduling.  
The remainder of the paper is organized as follows. Section 2 reviews related works. Section 3 presents the SEMS method. Section 4 presents the experiment to verify the SEMS model. Section 5 presents conclusions and future work.
## 3 Finite Element Analysis of a Railway Track Slab with a Gap
### Analysis of Computed Results by Finite Element Analysis
#### 3.2.1 Comparison of Acceleration Level with Time  
Figure 3 illustrates the variation of acceleration amplitudes with time obtained for 0.05 s generated on the top surface of the concrete slab. The acceleration amplitudes were computed at the surface of the slab 1.0 m away from the point of impact loading. It was clearly identified that the peak amplitude generated in the case of with a gap was six times larger than that of the case of without a gap. In the case of with a gap, immediately after peak acceleration was generated in a relatively short time after impacting, it decreased much more greatly in a short time compared to the case of without a gap.  
Figure 4 describes the variation of acceleration amplitudes of leaked Rayleigh waves into air computed 0.4 m above the concrete slab surface and 1.0 m away from the impact loading point for the cases of without a gap and with a gap separately. For the case of with a gap, the generated maximum amplitude was about seven times larger than that of the case of without a gap. In the case of without a gap, the peak value was generated at 0.027 s after impact loading. However, in the case of with a gap, the peak value was generated at 0.016 s after impact loading. This means that the peak value appeared much more quickly in the case of with a gap than without a gap.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Modulus E (MN/m\({}^{2}\))** & **Poisson’s Ratio \(\
u\)** & **Density \(\rho\) (kg/m\({}^{3}\))** \\ \hline Air & 0.1 & 0.4 & 1.130 \\ Concrete Slab & 30,000 & 0.2 & 2300 \\ Soil & 40 & 0.3 & 2000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input parameters for finite element modeling of railway track section.  
Figure 2: Schematic of generating and measuring reflected waves by impact loading: (**a**) without gap, (**b**) with gap.
# Parameter Optimization for Ship Antiroll Gyros
## 4 Mathematical Model for Parameter Optimization of Antiroll Gyro
### Objective Functions  
Different ships have different working environments and different requirements for their antiroll gyros, which can be roughly summarized by two points: (1) the efficiency principle, which refers to the antirolling effect that antiroll gyro can achieve and is typically expressed as the roll reduction rate; and (2) the lightweight principle, which refers to the gyro's overall mass that is as small as possible because the mass of the antiroll gyro is mainly concentrated on the rotor and can also be expressed as the mass of the rotor.  
1. Highest roll reduction rate  
The roll reduction rate was expressed in Equation (19), and the objective function was expressed as follows:  
\[\text{min}Z_{1}=1-TT \tag{20}\]  
2. Minimum rotor mass  
The structure and size of the rotor are displayed in Figure 4. Therefore, the mass of the rotor could be expressed as follows:  
\[M\text{s}=\rho V=\rho[H_{1}(D_{2}-D_{1})+2H_{3}D_{1}+H_{2}D_{3}] \tag{21}\]  
where \(V\) is the volume of the rotor in Figure 4.  
The objective function was expressed as follows:  
\[\text{min}Z_{2}=M\text{s} \tag{22}\]
# Compensation of Frequency Drift in Frequency-Sweep Polarization-Modulation Ranging System
###### Abstract  
In frequency-sweep polarization-modulation ranging, distance is determined by the frequency of modulated waves and the corresponding wavelength multiple when emitted and returned waves are in phase. However, measurement of the frequency and the wavelength multiple is affected by thermally induced phase delay of the polarized wave. In this article we systematically discuss the principle of the ranging method and analyze the influences of thermally induced phase delay. New approaches to measurement are proposed to eliminate the impact on frequency and the wavelength multiple. Theoretical analysis and experimental results proved the efficiency and applicability of the methods.  
absolute distance measurement; frequency-sweep polarization-modulation ranging; frequency drift; error compensation Article
# Aggression Detection in Social Media from Textual Data Using Deep Learning Models
## 3 Methodology
### Feature Extraction
#### 3.4.2 Word2 Vec  
Word-embedding methods, especially the Word2 Vec algorithm [45], introduced new ideas that had an enormous impact on the natural language processing. Word2 Vec is one of the most popular methods when processing text. Word2 Vec is based on word embedding, i.e., when dealing with words, it creates vectors. The Word2 Vec-embedding technique generates vectors that can be used to find relationships between words having the same meanings in the context of other words. Some of the researchers [46; 47; 48] used the TF-IDF embedding technique. TF-IDF, while simple and efficient, fails to capture several key linguistic concepts, such as synonymy and polysemy, since it reveals little of the connections between terms [49]. It also falls short of capturing the sentiments of the words [50]. The motivation behind using Word2 Vec is that it is pre-trained on a large corpus and reduces computational complexity. It is based on the Skip-gram model trained by negative sampling to predict surrounding words given a center word, and the idea is: if words are similar to each other, they tend to have similar properties. So, the words can be represented as points in vector space. Features extracted using Word2 Vec are combined with emotional features before feeding them to deep learning models.
## 4 The Vehicle-to-Bridge Mass Ratio on Frequency Deviation Rate  
### Numerical Examples  
The frequency of the vehicle is generally between 0-15 Hz in reality [2; 49]. Based on the numerical examples in [PERSON]'s study [5], the range of each dimensionless parameter is as follows: \(\mu_{1}=0.2\)\(\sim 0.9,1.2\)\(\sim\) 15, \(\alpha_{mv}\)\(=0.01\)\(\sim\) 0.5, \(\alpha_{v}=0.03\)\(\sim\) 0.75, and \(\alpha_{v}=0.001\)Substituting the above parameters into Equations (5) and (6), the acceleration time history curve of vehicles passing the bridge can be obtained. Then Fast Fourier transform can be used to obtain the vehicle acceleration spectrum. Using the method described Section 3, the dimensionless bridge fundamental frequency can be identified, and then the corresponding frequency deviation rate can be obtained using Equation (10). The main results of the numerical calculation are shown in Figures 5 and 6, and the shaded part in the figure indicates that the recognition failed. The colors in Figures 5 and 6 gradually change from blue to red, representing the gradual change of the bridge fundamental frequency deviation rate from the minimum value to the maximum value. Blue and red represent the minimum and maximum values in their respective graphs. The different colors indicate the relative magnitude of the bridge fundamental frequency deviation rate in each graph. Therefore, the absolute values of the bridge fundamental frequency deviation rate represented by the colors in different pictures are not the same.
## 1 Introduction  
Many online sound effects repositories exist, such as freesound.org, freeSFX.co.uk, and zapsplat.com, where users can upload sounds to be hosted under a Creative Commons (CC) licence. The Audio Commons project is aimed at promoting the creative reuse of CC-licensed audio content. One method of encouraging reuse is to make it easier for users to search for desired sound effects.  
Most online sound effect repositories employ some form of keyword searching--matching user search queries to titles and/or tags that uploaders have manually ascribed to the sounds. Therefore, the ability to find a desired sound effect is limited by the quality and quantity of the user-supplied metadata, which may be sparse or inconsistent. Some curated repositories also provide technical metadata, such as sample rate and bit depth, or musical descriptors, such as tempo or genre. The technical descriptors, whilst useful, describe the audio file format rather than the sonic content and many of the musical descriptors are not applicable to sound effects (e.g., a recording of a door slam has no tempo).  
The freesound.org repository can additionally provide metadata calculated with the essentia toolbox [1]. However, most of these metadata are technical measurements, such as spectral flux, spectral kurtosis, or silence rate, which are unlikely to be meaningful to the non-acoustician. For such a user, timbral descriptors would be more readily understood and would provide more useful assistance in the search for a desired sound. Therefore, it would be beneficial if timbral metadata could be generated automatically, using perceptual models to tag audio files with their timbral characteristics.
## 4 Results and Discussion
### Variation of Ventilation Volume with Solar Radiation Intensity  
Figure 7 shows the variation curve of solar chimney ventilation with solar radiation intensity at different internal heat source heat flow densities, where: internal heat source heat flow density \(W\) is 0-1500 W/m\({}^{2}\); solar radiation intensity \(I\) is 200-1000 W/m\({}^{2}\); internal heat source geometry size is 4000 \(\times\) 2000 mm.  
As can be seen from Figure 7, when the outdoor wind speed is 1 m/s, the trend of the ventilation in the solar chimney is approximately the same for different heat flow densities of the internal heat source. As the intensity of solar radiation increases, the amount of ventilation in the solar chimney also increases. When the solar radiation intensity increases from 200 to 1000 W/m\({}^{2}\) and the heat flow density of the indoor heat source is 0, the solar chimney ventilation increases from 0.393 to 0.523 m\({}^{3}\)/s, a relative increase of 33.1% in ventilation; with a heat flow density of 1500 W/m\({}^{2}\) from the indoor heat source, the solar chimney ventilation volume increases from 0.519 to 0.617 m\({}^{3}\)/s, a relative increase of 18.9% in ventilation volume. The results show that the effect of solar radiation intensity on ventilation is diminished as the heat flow density of the indoor heat source increases. In buildings with solar chimneys and internal high-temperature heat sources, the solar  
Figure 7: Variation of ventilation volume with solar radiation intensity.  
chimney and the heat source have a coupling effect on natural ventilation. As the intensity of solar radiation increases, the heat collecting wall absorbs more heat, which increases the heat gain of the air in the chimney channel. The temperature rises, the density difference increases, and the airflow float driving force generated by the density difference gradually increases, which in turn leads to an increase in ventilation. With the gradual increase in the density of heat flow from the indoor heat source, the heat plume generated on the surface of the heat source and the indoor air undergo natural convective heat exchange, increasing the impact of the buoyant heat plume on ventilation, increasing the impact of the internal heat source on natural ventilation, and relatively decreasing the impact of solar radiation intensity on ventilation.