esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
We calculated the energy bandgap dependence versus strain in T2 SLs InAs/InAsSb (samples 4, 5, and 6) based on an 8 \(\times\) 8 kp solver. The results are presented in Figure 8. Assuming average strain 1.27%, 0.6%, and 0.05% (Figure 6), our simulation results read the following measurements:  
* \(\sim\)12.3 \(\upmu\)m for 1.27%--(measured-12.8 \(\upmu\)m);
* \(\sim\)10.9 \(\upmu\)m for 0.6%--(measured-11.5 \(\upmu\)m);
* \(\sim\)9.8 \(\upmu\)m for 0.05%--(measured-9.4 \(\upmu\)m). The presented theoretical energy bandgap and \(\lambda_{100\%\text{scut-off}}\) trends versus \(T_{growth}\) followed the experimental results. The discrepancy is believed to be connected to the assumed abrupt interfaces in the T2 SLs InAs/InAsSb. In addition, assuming strain-balanced conditions, the Sb composition drop increased the effective T2 SLs InAs/InAsSb energy bandgap, which was also presented previously by [PERSON] et al. [28; 29]. Our simulation results also followed the trend presented by [PERSON] et al. for the T2 SLs (3n, n) InAs/InAsSb\({}_{0.4}\)[30]. According to the Sb composition distribution presented in Figure 6, the average Sb composition distribution decreases with an increase in the \(R\) parameter. In addition, it must be stressed that we made a theoretical assumption on strain distribution in the InAsSb layer (similar distribution was presented by [PERSON] et al. [22]). In addition, it is revealed from data presented in Figure 6a-d that Sb distribution created extra strain in the InAs layer.
## 3 Home Area Network
### Home Communication General Architecture
#### 3.1.3. HAN Internet Protocol (IP)-Based Solutions  
Regardless of the initial doubt shown by many researchers about the suitability of the Internet architecture for wireless networks, in the present good performing implementations of IPv6 stacks exist already for HAN. In addition, IPv6 presents solutions prepared for network statelessness and auto-configuration, and satisfies the great address space needed for such networks.  
At the same time, the Internet Engineering Task Force (IETF) has been developing the standardization of mechanisms in order to encompass the Internet for actuator and sensor networks. In addition, the utilization of IP for such type of devices is being endorsed by the recently created IP for Smart Objects (IPSO) Alliance. Despite the fact that the work prepared by the IETF is at present in progress, IP-based sensor networks are emerging and could radically increase the capillarity of the Internet. In the near future, entirely standardized IP-based solutions for wireless HANs will be possible and accessible [49].
## 3 Results and Discussion
### Sensory Analyses  
The chemical differences collected in Table 1, and previously discussed, were perceived by the tasting panel, which was not capable of distinguishing between wines from the VC10 and VC12 conditions because of the high ethyl acetate content. The blendings between wines of different fermentation strategies were displayed because of the differences in ethyl acetate content between them. This ester is considered to impart negative sensorial properties when its concentration in wine exceeds 200 mg/L [1].  
The tasting panel noted distinct characteristics among the wines presented (Figure 5); they first noticed a nail polish aroma and a strong olfactory intensity and flavor in the VC10 and VC12 conditions. These wines were qualified as unbalanced and defective for most of the tasting panel components. The SF condition achieved the highest score in the herbaceous and vegetable smell, considered a balanced wine by the tasting panel, but most of the judges scored it as mediocre. The blended wines (SF + 10% of VC10/VC12 and SF + 20% of VC10/VC12 (Section 2.8)) (Figure 5) showed a more diverse sensory profile than SF and VC10/VC12, finding floral, herbaceous, vegetables and fruity aromas, being the last one the most outstanding for the tasting panel. While SF + 10% was positively evaluated by most of the components, reaching the highest score in the general gustatory and olfactory score, in SF + 20%, most of the judges rated the wine as unbalanced, highlighting a polish smell.  
Although blending was positively evaluated, the use of different inoculation techniques, such as yeast immobilization or sequential fermentation, could be useful to reduce the ethyl acetate production by _W. anomalous_ to desirable values and should be tested in further studies [32; 45].  
Figure 5: Sensory profile plot in wines produced with different fermentation strategies; SF: Spontaneous fermentation; VC10/VC12: Fermentation with _W. anomalous_ strains; SF +10%: Blending of SF (90%), VC10 (5%) and VC12 (5%); SF +20%: Blending of SF (80%), VC10 (10%) and VC12 (10%). Values represent averages from the evaluation made by the tasting panel (n = 12). General scores are represented in (**A**), and specific attributes are represented in (**B,C**).
# Comprehensive Review of UAV Detection, Security, and Communication Advancements to Prevent Threats
## 3 Classification of UAVs
### Classification of UAVs Based on Ground Command and Control  
It is already shown in Figure 2 that any UAV can be controlled remotely using a ground command and control mechanism, either by mobile phone, radio channel frequency, or the internet of things [21]. Therefore, these UAVs are classified based on their ability to fly over long distances without any intervention. These types are mentioned below as:  
* Fully autonomous controlled UAVs: These are the UAVs that can perform different tasks without any intervention from human beings and are fully automated.
* Remotely operated UAVs: These UAVs are designed to execute the task as directed by a human being. Thus, they have a human as their main operator.
* Remotely pilot-controlled UAVs: Drones where all tasks and maneuvers are performed by the human-based remote control from the GCR.  
The above-referred classification is summarized in Table 2[22] along with the pros and cons of the UAVs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Factors**} & \multicolumn{3}{c}{**Based on Wing Type**} & \multicolumn{3}{c}{**Based on Altitude**} \\ \cline{2-6}  & **Fixed-Wing Type** & **Rotary-Wing Type** & **Hybrid-Wing Type** & **Low Altitude Below 400 ft** & **High Altitude Above 400 ft** \\ \hline Hovering & No & Yes & Yes & Yes & Yes \\ \hline Small Size & No & Yes & Yes & Yes & No \\ \hline Transport goods & Yes & Low weight & Yes & Yes & No \\ \hline Battery time & \multirow{2}{*}{\(>\)1 h} & \multirow{2}{*}{1 h} & \multirow{2}{*}{\(>\)1 h} & \multirow{2}{*}{\(>\)1 h} & \multirow{2}{*}{\(>\)1 h} & \multirow{2}{*}{\(>\)1 h} \\ (in hour) & & & & & \\ \hline Maneuver speed & High Speed & Low speed & High speed & High Speed & High Speed \\ \hline Flexible & \multirow{2}{*}{No} & \multirow{2}{*}{No} & \multirow{2}{*}{No} & \multirow{2}{*}{Yes} & \multirow{2}{*}{No} \\ deployment of communication & & & & & \\ \hline Cost effective & Expensive & Cheap & Expensive & Cheap & Cheap \\ \hline Endurance & High & Low & Medium & Low & High \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification of UAVs based on wing type and altitude.  
In the above, the reader might be confused with the term of flexible deployment of communication. This states the proper coverage at which the drone can be controlled and stabilized for any sort of task. There are some research contributions that have classified the drones based on their altitude, as demonstrated in Figure 3 along with their examples [23; 24].
# Mixed-Integer-Based Path and Morphing Planning for a Tensegrity Drone
## 2 State of the Art
### Mechanical Design of Tensegrity Structures  
The design of tensegrity structures has been studied since the seminal work of [PERSON] and [PERSON] in the 1940s and 1950s. A number of well-behaved tensegrity structures, such as tensegrity prisms and towers, various symmetrical sphere-like structures, and tensegrity trusses have been proposed and studied [18; 19; 20]. One of such structures is the six-bar tensegrity structure, most notably used in the design of NASA's planetary probe prototype SUPERBall [21; 22; 23]. Simulation and experiments with the prototype demonstrated its ability to withstand impact without mechanical damage, as well as its ability to move by rolling, using controlled deformation of the structure to change the relative position of its center of mass.  
The design of new tensegrity structures can be done in a variety of ways, including combinations of known structures and direct topology optimization [24; 25]. Once the topology of the structure (which element is connected to which) is known, configuration (position of structs and cables) and pre-stress (defined by rest lengths of the deformable elements) need to be determined [26]. A configuration with a given pre-stress is statically stable if it corresponds to a local minimum in potential energy of the structure. Given different rest lengths of deformable elements, a different configuration would correspond to a local minimum in potential energy. Thus, by changing the rest lengths of deformable elements, one can control the shape of the structure. The task of finding force distribution that proves that the configuration corresponds to a local minimum of potential energy can be solved in a number of ways, including the force density method [27; 28]. The task of finding a statically stable configuration, given rest lengths of deformable elements, can be solved as a nonlinear optimization problem [29].  
In this work, a six-bar tensegrity structure is used to build the world's first tensegrity drone (referring to the drone's frame structure; the first drone to use tensegrity as a protective shield, to the best of our knowledge, was reported in [17]). A dataset of stable configurations is generated using energy minimization cast as a single nonlinear optimization problem, as proposed in [29], using different rest lengths of the deformable elements to generate different configurations.
## 3 Bispectrum Analysis of EEG Signals for an Emotion Recognition System
### EEG Channel Selection Related to Human Emotion  
The brain is the operations center of the human nervous system and can be divided into three major parts: the brainstem, the cerebellum (hindbrain) and the cerebrum (front brain). The largest part of the brain is the cerebrum, and the outer part of the cerebrum is the cerebral cortex, which generates most of the voltage measured on the surface of the head. The cerebral cortex is divided into four lobes, which are the temporal, the frontal, the parietal and the occipital lobes. Each lobe is associated with various functions, and the frontal lobe is where we can observe brain signals associated with emotions.  
An EEG captures the electrical activity of the brain by measuring the voltage fluctuations due to the flow of the electric current between neurons in the brain. The EEG electrodes are named according to their corresponding area of the cerebral cortex: the frontal (F), the central (C), the parietal (P), the occipital (O) and the temporal (T). Some electrodes are placed in pairs on the left side and right side of the head, with odd numbers given to the left electrode positions and even numbers given to the right electrode positions. The electrodes in the middle positions are given zero (z) subscripts.  
In this study, we document the effect of channel selection on the recognition rate of the emotion classification system; then, aside from the initial 32 channels, we also assess the effect of channel selection on the brain emotion theory, which involves all eight channels on the frontal, frontal parietal and anterior-frontal regions. In addition to studying the 32 channels and the reduced eight channels, we also compared our results with the 14 channels corresponding to the channels available in the BCI Emotiv device.
## 4 Anomalies and Sources
### AGV under Cyber-Attack  
In this section, a cyber-attack is considered as a possible anomaly source that results in an immobilized AGV that is still able to communicate. For the sake of simplicity, the following assumptions are made: first, the attacker has already obtained network access to the AGV fleet, e.g., by a phishing attack (see Section 2.1). Second, prior knowledge of a vulnerability in the firmware of the targeted AGVs allowed the attacker to prepare a weaponized payload that compromises AGVs when deployed, handing over control to the attacker. Both these assumptions are realistic in an industrial context, especially due to the accessible fleet design that allows dynamic addition or removal of AGVs. These assumptions make it possible to focus directly on the relevant anomaly as well as its detection and mitigation. When trying to understand the source of the anomaly in the context of a cyber-attack, it is also relevant to discuss potential threat actors and their motivations. Due to the complex and proprietary nature of industrial systems as well as their significance with regard to critical infrastructure and national security, the majority of active threat actors in this environment constitute nation-states or state-sponsored groups [53]. As opposed to ordinary cyber-criminals who primarily seek monetary profit, nation-state interests comprise espionage, sabotage and the acquisition of offensive capabilities against critical infrastructure (see Section 2.2 for more details). In this context, the above-mentioned cyber-attack against an individual AGV in the fleet is likely to constitute only a minor part of a larger attack. For instance, immobilizing an AGV but keeping its communication capacities operational could allow attackers to learn about anomaly detection and incident response systems and processes that are in place to protect the fleet. This knowledge could then be used for further attacks against more significant targets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Anomaly Source** & \\ \hline \multirow{4}{*}{**Process indicator**} & \multirow{2}{*}{**Process**} & **Defects** & **Process Changes** & **Cyber-Attack** \\  & & No transport jobs from broken machine & High number of transport jobs from suddenly high active machine & Malicious or dangerous manipulated process data \\ \cline{2-4}  & \multirow{2}{*}{**Component indicator**} & Lower driven distance of AGV with broken self-localization module & Length of local queue of each robot longer than usual due to unexpected transport request peak & Deviation of AGV-internal software list \\ \cline{2-4}  & \multirow{2}{*}{**Communication indicator**} & No messages received from AGV with broken WIFI module & High number of messages from MES received & Wrong agent (e.g., AGV) publishes transport job \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of possible anomalies classified by anomaly indicators and sources.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 3 Architecture of the Network Emulation Platform
### Physical Implementation Model
#### 3.2.2 Main Controller  
The main controller interacts with the Web UI and is used for parameter calculation, SDN switch control and node management. The calculation results are written in the database for subsequent emulation operations.  
The parameter calculation refers to the process that the main controller reads the network model parameters, obtains the relationships of visibility and link characteristics between nodes, filters the connection relationships, and assigns the network configuration of emulation nodes. The SDN switch control [27] refers to the process that the main controller sends topology control flow tables to SDN switches in real time according to the target network topology. The node management refers to the process in which the main controller creates emulation nodes, binds nodes with SDN switches, and allocates emulation resources. Moreover, the main controller is also responsible for generating the experimental start time and transmitting the operation state of the experiment to the Web UI.
# Deep Learning-Based Method for Accurate Real-Time Seed Detection in Glass Bottle Manufacturing
## 3 Materials and Methods
### Proposed SW Solution: The Image Algorithm
#### 3.3.2 Candidate Selection Approach  
The baseline of the solution aims at detecting the seeds and blisters present on the surface of the bottle by means of classical image processing techniques. In the case of detecting the presence of seeds, the algorithm will calculate their corresponding height and width dimensions. This was the initially deployed solution that arose from the need for a deep learning-based solution for the filtering of false positives and more accurate output.  
This candidate selection approach relies on thresholding, filtering and morphological operations to detect the seeds. The stages of the algorithm are as follows. First, an adaptive and local thresholding takes place. For a segmentation from the light foreground for a pixel at position (_r,c_), a local threshold \(T\)(_r,c_) is calculated within a window of size _mask_size_\(\times\)_mask_size_ by means of the following expression,  
\[T(r,c)=\mu(r,c)(1+k(\frac{\theta(r,c)}{R}-1))\]  
where \(\mu(r,c)\) is the local mean value within the window and \(\theta(r,c)\) denotes the corresponding standard deviation. The parameter \(R\) is the assumed maximum value of the standard deviation (\(R=128\) for byte images) and \(k\) is a parameter that controls how much the threshold value \(T(r,c)\) differs from the mean value. If there is high contrast in the neighborhood of a point (_r,c_), the standard deviation has a value close to \(R\) which yields a threshold value \(T\)(_r,c_) close to the local mean. Dark structures on a light background are segmented. Every pixel \(p(r,c)\) whose gray value is smaller than the calculated local threshold \(T(r,c)\) is selected. Value for step is 0.1 and mask size of 21 was fixed.  
The blobs above the threshold are connected in a \(3\times 3\) neighborhood and analyzed. Those blobs not having the features expected for seeds in glass are discarded. The identified features for seeds are sizes ranging between 3 and 200 pixels in both width and height, Euler number being \(-1\) or 0 for detection of inner regions, and not having eccentricity. Relation between width and height is also considered in order to remove the joints or other linear marks in the bottle that cannot be seeds. The blobs that remain after these filtering steps are the final identified potential seeds. Figure 5 shows the processing algorithm.  
Figure 5: The sequence of the processing algorithm for the candidate selection: (**a**) input image of the bottle; (**b**) region of the bottle zoomed in for better understanding; (**c**) local threshold is applied; (**d**) tblobs after filtering by size and shape; (**e**) connected regions after dilation operation; (**f**) final blobs named as “candidates” for next steps.
## 5 Discussion  
### Unconformity and Reservoir Roof and Floor  
According to the profile of the even well (Figure 11), the reservoir profile of the Ma131 well area is drawn (Figure 12). The reservoir is located at the top of the Baikouquan Formation, in which the reservoir has good connectivity. P/T overlap uncomorformity is developed at the bottom of the reservoir (Baikouquan Formation), which belongs to sand-mud-mud type. The bottom plate of the reservoir corresponds to the weathered clay layer and the weathered leaching zone within the unconformity structure. The uncomorformity structure has a large thickness and a high fracture pressure gradient, contributing to its sealing role in the lower part of the reservoir in the Ma 131 well area. Combined with the good sealing property of mudstone in the upper part of the Baisan Member (Figure 11), it provides good roof and floor conditions for the formation and preservation of oil and gas in the Ma 131 well area. The Ma132 well north fault, Ma3 well north fault, and Xia 13 well fault are developed in the updip direction. These faults, together with the glutenite above the unconformity surface, provide a good migration channel for the upward migration of oil and gas. Because the study area is a stratigraphic oil and gas reservoir, the underground structure is relatively simple. After the formation of oil and gas, the fault activity makes the tight rock layer move up and directly contact with the oil and gas reservoir, blocking the lateral migration of oil and gas, and providing a good blocking effect for the lateral direction of the oil and gas, so that the oil and gas pool forms a complete, enclosed space. Adversely,  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & & **Roof** & \multicolumn{4}{c}{**Middle Baseplate**} & \multicolumn{2}{c}{**Baseplate**} \\ \hline
**Well** & **FG** & **H (m)** & **FG\({}^{*}\)H** & **FG** & **H (m)** & **FG\({}^{*}\)H** & **FG** & **H (m)** & **FG\({}^{*}\)H** & **Oil** \\ \hline Ma 131 & 2.27 & 40.7 & 92.4 & 2.23 & 24.3 & 54.2 & 2.28 & 26.1 & 59.5 & 9.24 t/d \\ Ma 133 & 2 & 24.9 & 49.8 & 2.01 & 19.1 & 38.4 & 2.02 & 27.4 & 55.3 & 6.69 t/d \\ Ma 154 & 1.96 & 12 & 23.5 & 2.15 & 3.3 & 7.1 & 1.98 & 26.3 & 52.1 & 2.81 t/d \\ Ma 158 & 1.95 & 27.15 & 52.9 & 1.91 & 4 & 7.64 & 1.99 & 7.5 & 14.9 & 3.55 t/d \\ Xia 89 & 2.1 & 10.5 & 22.1 & 1.91 & 10.1 & 19.3 & 2.07 & 18.2 & 37.7 & 7.06 t/d \\ Xia 94 & 2.11 & 20.9 & 44.1 & 2.12 & 5.1 & 10.8 & 2.13 & 30.1 & 64.1 & 4.36 t/d \\ Xia 723 & 2.06 & 7.2 & 14.8 & 2.04 & 4.9 & 10 & 2.09 & 25.7 & 53.7 & 3.5 t/d \\ \hline \end{tabular}
\end{table}
Table 1: Statistical table of fracture pressure and thickness in the roof and floor of the Ma 131 reservoir.  
Figure 11: Even well profile of the Ma131 well area.  
due to the small fracture pressure of the roof and poor sealing conditions, the annex of Ma 17 well cannot effectively preserve oil and gas, so no reservoir has been formed.
## 3 Results and Discussions
### Coupled Flow and Noise Simulations for a Wind Turbine Array
#### 3.4.4 Wind Turbine ANPropagation across Multi-Wake
It indicates that the high frequency noise will be heavily attenuated over long propagation distance except in special cases such as sound propagation over water (hard surface), stronger downward refraction under special climate conditions. Finally, it is interesting to predict the noise spectra at different receivers. The receiver height is again fixed at \(z=2\) m, and the horizontal distances measured from wind turbine #1 are: \(x=[1620,\) 1820, 2020, 2220, 2420] m. The noise spectra are shown in Figure 20 and marked with different colors. The general trend is that the noise level decreases as observer distance increases. However, it does not always hold true at some distances and frequencies. For example, at \(x=1820\) m (22.75D), except at low frequencies, increase of noise level is observed as compared to the \(x=1620\) m case. The simulations show that the low frequency noise might be the major wind turbine noise source in the farfield. Since the spectra are obtained in farfield, the overall SPLs are quite low. According to the wind turbine noise regulations in many countries (such as 45 dB), the calculated noise evidently fulfill the limit. From the results, it is seen that SPLs over 1000 Hz is below 20 dB. For future studies, it is indicated that more focus should be paid on long-path ANPropagation at low frequencies. \begin{table}
\begin{tabular}{c c c c} \hline  & **Case 3 (Figure 19c)** & **Case 3 (Figure 19d)** & \(L_{w}-10\log_{10}4\pi D^{2}-\alpha D\) (Figure 19d)** \\ \hline
20 (Hz) & 34.64 dB & 37.92 dB & 33.89 dB \\
100 (Hz) & 12.61 dB & 15.05 dB & 28.53 dB \\
250 (Hz) & 26.25 dB & 28.33 dB & 27.57 dB \\
1000 (Hz) & 17.10 dB & 17.99 dB & 18.00 dB \\ \hline \end{tabular}
\end{table} 
Table 1: Comparisons of SPL at some selected frequenciesFigure 19: Receiver is fixed at \(x=2500\) m away from wind turbine #1, and the receiver height is 2 m. (**a**) SPL from wind turbine #1 (Source: Lp) to the receiver; (**b**) SPL from wind turbine #2 (Source: Lp) to the receiver; (**c**) SPL from wind turbine #3 (Source: Lp) to the receiver; (**d**) Total SPL from wind turbines #1, 2, 3 (Source: Lp1, Lp2, Lp3). Figure 20: Total SPL from wind turbines #1, 2, 3 received at various distance.