esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Effect of Laser Fluence on the Fabrication of Nanostructures on BiIn-Si  
To study the tuning effects of the laser parameters on ripple structures, experiments on the laser fluence and the pulse number dependences of surface structures were performed. The effect of the laser fluence on the period of the ripple structures is discussed first. Figure 5 shows the irradiated areas on the surface of BiIn-Si with laser fluences in the range from 1.53 to 2.55 J/cm\({}^{2}\) at a scan speed of 20 um/s. As shown in Figure 5a, no ripple was formed due to there being insufficient energy. With the laser fluence increased to 2.04 J/cm\({}^{2}\), ripple structures were generated (Figure 5b) and the period of ripples was 0.55 um, as seen in Figure 5e. When the laser fluence reached 2.55 J/cm\({}^{2}\), the period of the ripples was 0.54 um, which almost remained unchanged. The results indicate that the free electron density of the plasma layer was almost the same at two different laser fluences, which could be deduced from the mechanism discussed above. The possible reason for this phenomenon is that strong reflection, even up to a reflectivity of 60%, may have occurred at the early stage of ablation with a higher laser fluence [36]. Thus, the excitations of free electrons by the laser fluences of 2.04 J/cm\({}^{2}\) (Figure 5b) and 2.55 J/cm\({}^{2}\) (Figure 5c) were equivalent [37], with no obvious change observed in the ripple period.  
Figure 4: Schematic diagrams of the laser irradiation on samples. The laser beam was incident on the bare silicon (**a**) and on BiIn-Si (**b**). A sketch diagram of the energy distribution on bare silicon (**c**) and BiIn-Si (**d**).
# Fresh and Aromatic Virgin Olive Oil Obtained from Arbequina, Koroneiki, and Arbosana Cultivars
## 2 Results and Discussion
### LOX Pathway Volatile Compounds  
Among the agronomic factors studied, the stage of maturation had only a small effect on the total volatile compounds, probably because the maturation stages studied were very similar (Table 4). However, the differences between the oils obtained from irrigated and rainfed olives were significant,as were those among the oils derived from different cultivars. Similar differences were reported by [PERSON] et al. [14] in a study involving the Arbequina, Cornicabra, Morisca, Picolimon, Picudo, and Picual cultivars. Olive oils originating from irrigated crops had a higher content of volatile compounds than those from rainfed crops for a given cultivar. Comparing the three cultivars, the irrigated Arbequina (MI = 1.11) sample had the highest content of volatile compounds at 34.94 mg/kg; the sample with the lowest content was the rainfed Koroneiki sample (MI = 1.68) at 6.77 mg/kg, which was five times smaller. According to the results obtained by [PERSON] et al. [15], the oils from irrigated orchards had higher contents of (_E_)-2-hexenal, the major volatile compound, than oils from rainfed orchards. According to [PERSON] et al. [16], the volatile compounds most affected by irrigation are (_E_)-2-hexenal, (_Z_)-3-hexenol, and hexanol. The increase in the amount of water supplied to the olive trees significantly increases the contents of these volatile compounds. It is noteworthy that the (_E_)-2-hexenal content of the Arbequina cultivar samples was very high (29.39 mg/kg for the irrigated sample with MI = 1.11), compared with that of the Koroneiki samples (4.37 mg/kg for the irrigated sample with MI = 0.67); these compounds are very important contributors to the delicate green perception of EVOO. Our results are similar to those obtained by [PERSON] et al. [17], which showed that the contents of volatile compounds and (_E_)-2-hexenal increased with maturity, reaching a maximum concentration when the color of the olive fruit skin changed from purple to black. As the MI increased, the contents of linoleic and linoleic FA decreased, whereas the contents of volatile compounds from the LOX pathway increased. This could be attributed to the consumption of these two FA, which are used as substrates for the LOX enzyme present in the olive pulp [18].
## 6 Evaluation
### Results and Discussion
#### 6.2.4 Feature Selection for MM-LDA Model  
In order to select the most significant feature that can achieve the optimal estimation of the cause of scan failure/delay, first, we find the correlation between the scan response RTT and other scan response features. As shown in Table 5, we observed that the scan minimum RTT and scan maximum RTT were correlated to the scan response RTT with an average of 0.87 and 0.99 correlation coefficients, respectively. Furthermore, we apply the feature selection method to the correlated features. Figure 9 shows the results of feature selection of each scenario. The number of features ranked, as shown in Figure 9, vary based on the correlated feature in each scenario. Only one feature is correlated with the scan response RTT in the normal and low SNR scenarios, while two features are correlated with the scan response RTT in the congestion scenario and when the training data are used for the feature selection. The correlated feature is used for the feature selection. The minimum RTT is selected for a normal scenario with a 1.0 feature ranking score, as shown in Figure 9. On the other hand, maximum RTT is selected as the best feature for the low SNR scenario, with 1.0 feature ranking score, and for the congestion scenario, with 0.56 ranking score, as shown in Figure 9,c, respectively. Since there is no common feature among all scenarios and as explained above, only one feature can be linearly separable from other features; therefore, the training data are used to select the best feature, and the uncorrelated mode of the estimation algorithm is used for failure factor estimation.  
Figure 9: Feature selection using scan traffic: (**a**) Normal scenario; (**b**) low SNR scenario; (**c**) congestion scenario; (**d**) combined scan response—training data.  
Moreover, using the training data will prevent bias selection, as different features may be more sensitive to a particular cause of scan failure/delay than the others if features are selected based on each scenario without a common feature among the scenarios. We apply the feature selection method to the correlated features of the training data shown in Table 5. Figure 9d shows the result of the feature that is selected as useful for cause estimation when the training data are utilized. Here, the maximum RTT is selected as the best feature that is useful for the estimation algorithm, with a ranking score of 0.65 feature ranking scores.
# Cooperative SWIPT MIMO-NOMA for Reliable THz 6G Communications
## 6 Simulation Results
### System Simulation
#### 6.2.1 Achievable Users' Rates versus Transmit Power  
Figure 6 demonstrates the system users' performance versus the dedicated power. For the two users, the MIMO technique provides remarkable leverage to system performance compared to the SISO scheme. The NU performs much better than the FU, despite the EH technique allocating the essential energy to achieve the target rate and harvesting the remainder for retransmission, thus clarifying the impact of comparative THz losses. However, the FU still achieves a data rate greater than the target despite the interference it experiences (as the FU does not perform the SIC process). Furthermore, we can make use of the harvested power for further EH processes.
# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 4 Results
### Limitation of Search  
There are some limitations that may have affected our literature review. Our query string exclusively on the term 'digital twin', excluding aliases such as AI, ML, DL simulation, CPS, DSS, IoT, Digital Model, and other similar terms. AI has long been utilized in smart farming and precision agriculture. Simulations have been employed for water quality and crop irrigation for many years [107]. However, the emergence of the term 'digital twin' (DT) marks a significant advancement in this field. DTs mostly utilize AI, simulations, CPS, and the IoT to create dynamic models of physical agricultural systems, but not all simulation models and IoT systems qualify as DTs.  
To limit the articles and focus on our objective, we ensured that the term 'digital twin' appeared at least once in the title, abstract, and keywords of the selected studies. There may be some other articles on DT technology in agriculture water management, but it is unlikely that an article would omit the term 'digital twin' from the title, abstract, and keywords. We searched for articles in all well-known databases. While it is possible that additional articles exist in other databases, we believe that if well-known databases contain limited papers on this topic, other databases are likely to have even fewer. Therefore, we think finding additional papers elsewhere will not significantly affect our review. Additionally, PRISMA guidelines do not impose a minimum number of articles for a systematic review.
## 3 Results
### Larger versus Regular Maximillary Advancement in the One-Jaw Simulation  
Advancement of 6 mm was set as the dividing distance in the one-jaw plan. Twenty-six patients (63%) needed larger maxillary advancement and 15 patients (37%) needed regular advancement in the one-jaw virtual surgical plan. In patients with larger maxillary advancement in the one-jaw plan, the two-jaw approach had significantly lower SNA, SNB, and A-N perpendicular data compared to the one-jaw plan (\(p<0.01\)), but the ANB angle was not significantly different. However, in the regular maxillary advancement design, there was no significant difference in terms of SNA and A-N perpendicular between one-jaw and two-jaw surgery plans, but ANB angle in the two-jaw surgery was significantly larger than the one-jaw surgery by 3.28 degrees (\(p<0.01\)) (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**One-Jaw**} & \multicolumn{2}{c}{**Two-Jaw**} & \multicolumn{2}{c}{**One-Jaw vs. Two-Jaw**} \\ \cline{2-6}  & **Mean** & **SD** & **Mean** & **SD** & \(p\) **Value** \\ \hline SNA & 83.48 & 5.25 & 80.79 & 3.61 & 0.000 ** \\ SNB & 80.44 & 4.70 & 77.57 & 3.45 & 0.000 ** \\ ANB & 3.12 & 4.22 & 3.37 & 3.44 & 0.706 \\ A-N perpendicular & 2.48 & 4.32 & \(-0.18\) & 3.04 & 0.000 ** \\ Pog-N perpendicular & \(-0.11\) & 7.36 & \(-1.76\) & 4.28 & 0.099 \\ Anterior cant & 4.64 & 3.80 & 3.10 & 2.67 & 0.000 ** \\ Posterior cant & 2.48 & 1.81 & 0.90 & 0.89 & 0.000 ** \\ \multicolumn{6}{l}{**The midline discrepancy**} \\ A & 2.27 & 1.60 & 1.75 & 1.19 & 0.039 * \\ Me & 2.52 & 2.20 & 1.51 & 1.01 & 0.012 * \\ U1 & 2.77 & 2.20 & 1.37 & 1.41 & 0.001 ** \\ L1 & 2.16 & 1.88 & 1.45 & 1.06 & 0.038 * \\ \hline \end{tabular}  
* \(p<0.05\). ** \(p<0.01\).  
\end{table}
Table 3: The 3D cephalometric measurements of one-jaw and two-jaw designs.
## 2 Background
### Heavy-Hitter Flows  
A flow is defined as a set of packets passing an observation point during a specific time interval [26]. Packets sharing certain attributes belong to the same flow. Usually, such attributes are the source and destination IP addresses, source and destination port numbers, and the protocol identifier. Studies about flow behaviour show that a very small percentage of flows carry the bulk of the traffic, especially in DCN. These flows are often termed as HH flows [27; 28; 29]. Generally, HHs can be classified by using different metrics based on its duration, size, rate and burstiness. Each flow can be classified into two groups--HH and non-HH. This dichotomy of the flow types is achieved using a threshold which varies depending on the classification metric used.  
[PERSON] and [PERSON] [28] provide a definition of flow types within each category with a zoological fair. For instance, flows that have a duration longer than a certain time period are _tortoises_, otherwise they are termed as _dragonflies_. Flows that have a size larger than \(s\)\(B\) (_bytes_) are _eleplants_, in turn, _mice_ are flows that have a size less than or equal to \(s\). _Cheetahs_ are flows with a rate greater than \(r\)_Bps_ while _snails_ are flows with a rate less than or equal to \(r\). Flows with burstiness greater than \(b\)_ms_ are called _porcupines_ while those with burstiness less than or equal to \(b\) are _stingrays_. Table 1 summarises the main characteristics of HH flows as described by [PERSON] and [PERSON] [28]. In general, tortoise flows do not consume a lot of bandwidth. Elephants flows are long-lived and have a large size, but they are neither fast nor bursty. Cheetahs are small and bursty. The occurrence of porcupine flows is very likely due to the increasing trends in downloading large files over fast links. The rest of the paper uses the flow size feature for defining HHs.
# Deep Learning for Detecting Verticillium Fungus in Olive Trees: Using YOLO in UAV Imagery
## 1 Introduction  
The Verticillium fungus [1] is one of the largest and most widespread causes of destruction in olive trees around the world. The fungus survives in soil and can transmit through water [2, 3]. Controlling it is extremely challenging since it has a wide selection of alternative hosts and is able to initially manifest asymptomatically [4]. In order to protect the crops, it is of the utmost importance to accurately detect and assess tree health at scale.  
Recently, image data collection has been facilitated due to advances in technology, such as the improvement of camera resolutions and Unmanned Aerial Vehicle (UAV) technologies that enable automatic collection of data. Machine learning and especially deep learning has made it possible to analyse and classify such data accurately [5]. YOLO is one of the most used algorithms for such tasks. It has been used effectively in tasks of tree damage detection from UAV imagery, both for parasite infestations [6] and for environmental damage detection [7].  
The timely detection of Verticillium infections is a complex and time intensive task, due to the nature of the disease, that as previously stated, initially manifests asymptomatically. Until recently, skilled labor was needed to determine whether infection has occurred. Still, in cases where the wild had manifested in areas not visible to the examiner, like the top of the tree, infection could go undetected and thus lead to disease spreading. In this context the objective of this paper is to explore the potential of the application of the YOLO algorithm to detect Verticillium infections in olive trees. We have used RGB images captured with UAVs from three fields in northern Greece during October and November, when the symptoms of the disease are more pronounced. The images were used to train three different architectures of the YOLO version 5 algorithm with promising results. By employing UAVs, we attempted to eliminate cases where infections were not visible to an examiner, and by using the YOLO algorithm we introduced an objective wayof determining tree infection with high accuracy and without the need for time intensive human labor.  
The structure of this paper is organised as follows. Section 2 contains background information on the Verticillium fungus and the YOLO algorithm. Section 3 lists the work performed by other researchers on similar tasks, focusing on the application of YOLO on UAV tree imagery data. Then, Section 4 describes the materials and methods used to conduct the experiment as well as information on the evaluation processes that took place. In Section 5, the results of this research effort are presented both regarding the produced dataset and the YOLO model performance reached. Lastly, Section 6 contains discussion of the results and future work that is to be performed as an extension of this present work, concluding the paper.
## 4 Securing Artificial Intelligence Accompanying the WBAN
### AI on the Sensors within the WBAN  
Possible tasks for artificial intelligence directly at the sensor include the preprocessing, pruning, and general orchestrating of the data acquisition. In general, it should be considered that the on-sensor computing power is extremely low.  
For an assessment of security requirements in this context, a distinction needs to be made between two cases:  
Case A: The AI is specialized only for a single sensor and its vital parameters. Accordingly, the AI system operates locally on a computing unit at the sensor directly with the raw data captured by the sensor. In this case, no further security measures are necessary since the AI performs onboard preprocessing. For the transmission of the processed data by AI, encryption can again be employed, although it is not relevant for the on-sensor AI.  
Case B: The AI system on the sensor collaborates with similar systems on other sensors either by sharing network parameters directly in the context of distributed learning of artificial neural networks or by adjusting itself based on contextual information deducted by other networks within the WBAN [40].  
In general, due to the low computational power of the sensor carriers within the WBAN, it is expected to be more feasible to employ crucial AI functionalities on the first reachable edge computing instance within the clinic but outside the WBAN. The most relevant adversarial attack vector for AI on the sensor in machine learning will most likely be data poisoning [41]. An attacker tries to maliciously infuse data to invoke a specific output of the AI, e.g., suppressing features that might indicate diseases resulting in a wrong diagnosis for the specific patient. In the described multi-hop setup, the prevention of man-in-the-middle attacks is of particular importance, as each sensor receives from another sensor and sends data to another [42].  
Figure 3: AI abstraction levels concerning WBAN systems in the healthcare structure, from bottom to top: AI models and threats on sensor nodes, WBAN edge devices, each with their own set of sensor nodes, local clinic servers, and AI models spanning different clinics.
# GIS-Based Cluster and Suitability Analysis of Crop Residues: A Case Study in Yangon Region, Myanmar
## 2 Materials and Method
### Cluster Analysis
#### 2.3.1 Calculation of Crop Residue  
Equation (1) computes the feedstock for the crop residue potential. The RPR values of different crops are shown in Table 11; Appendix A. Grain production data are collected from township profiles from the Myanmar Information Management Unit. The data are processed to connect the attribute table of the vector file of the townships in the study region in the ArcGIS 10.8.1 environment after calculating the biomass waste generation potential from crop residue, as indicated in Equations (1) below:  
\[\sum_{i=1}^{n}Gi*RPRi \tag{1}\]  
where,  
\(Gi=\) Grain production  
\(RPRi=\) residue to crop ratio  
Thus, waste generation potential is equal to total grain production given by (\(Gi\)) times residue to crop ratio denoted by (\(RPRi\)).
# A Simple Experimental Stand for Studying the Network Security Level of Power Electronic Devices
## 2 Types of Cyber-Attacks and Vulnerabilities Applicable to Power Electronic Devices  
Since the communication link between the PED and the user is through the Internet or some other IP network, the information exchange/data flow can be attacked. Since PEDs already have network functionalities, they can be considered as typical network devices. Due to this functionality, the damage/manipulation of the PED can now be done remotely without the need for physical access to it. The attacks that are inherent to IP networks and modern communication networks in general are the same for PEDs. These attacks can lead to various situations, such as incorrect battery charging, unwanted turning on or off of various devices, intentional damage to devices, theft of money during a payment transaction for the energy consumed by the electric vehicle during the charge, etc.
## 2 Working Principle of Electromagnetic-Ring Balancing Actuator  
### Balancing System  
As shown in Figure 1, the electromagnetic-ring active balancing system is mainly composed of four parts: the data collection unit, the control unit, the power supply unit and the actuator. The working principle of the system is as follows: The vibrations of the machine tool spindle are measured by a set of sensors, such as displacement probes or accelerometers. Through the analog input module of the data collection unit, these vibration signals are sampled to a manageable data form and then transmitted to the control unit. The control unit continuously monitors the unbalance vibration amplitude of the spindle and compares it to a set value. When the amplitude is higher than the set value, the control unit forms control commands according to the unbalance vibration parameters and sends them to the power supply unit through the I/O module of the data collection unit. The required electrical signals are supplied by the power supply unit and fed to the balancing actuator installed in the hollow rotor of the machine tool spindle. According to the electrical signals, the mass distribution of the actuator is changed, a compensation mass is produced to balance the rotor system and the unbalance vibration is decreased accordingly. When the amplitude of the unbalance vibration is lower than the set value again, the control unit stops to output the control commands and continuously monitors the vibration of the machine tool spindle.
## 5 Discussion
### On Overall User Experience  
The presented user experience evaluation of Four Seasons focused on collecting and analyzing the participants' perceptions of presence and using hand gestures to control the immersive VR experience. Although the questionnaire responses measuring user experience (Figures 3 and 4) were mostly positive, a few interesting findings motivate further discussion.  
The participants who responded to the questionnaire experienced few symptoms of cybersickness (statement 3 in Figure 4). We expect that this was contributed to by a design decision to keep the user stationary in the virtual world. We did not implement any teleportation or continuous movement methods for the user to travel in the virtual environment because rapid artificial movement and rotation are among the key causes of cybersickness in immersive VR [43]. However, Four Seasons still caused cybersickness in four participants who discontinued the experience and did not respond to the questionnaire. Therefore, cybersickness remains an issue to be resolved in Four Seasons and VR in general [44].  
Four Seasons aimed at providing an immersive and realistic experience through the simulation of a natural environment with flora and fauna, which can be manipulated by hand gestures. Although the affective effects of Four Seasons are not explored in depth in this study, we expected that the participants might have some emotional responses to the content. However, the user experience evaluation indicated that only some of the users felt a change in their emotional state after playing Four Seasons (statement 12 in Figure 4). Previous research has shown emotional engagement to be an important part of user experience in immersive VR [45].  
The interview results showed that wearing the VR equipment (HTC VIVE) caused discomfort in some participants. In a previous study, VR technology was used to perform a specific function to improve a user's skill training [46]. The results of the study showed that users tend to be willing to put on complex and uncomfortable wearable gear when the VR content is meaningful to them and presented attractively. This result has also been observed in user acceptance models using information technology [47].