esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging
## 3 Experimental Results and Discussions
### Feature Analysis  
#### 3.2.1 Feature Exploration and Visualization  
For optimization, this study uses a custom function (Figure 1) to automate the statistical analysis and visualization process for key variables in the dataset. This function produces a single plot that overlays the feature distributions for the surviving group and the non-surviving group. The distributions are plotted using histograms and kernel density estimates (KDEs), allowing a visual comparison of the behavior of the features across the two groups.  
The four plots illustrate how key characteristics--Age, SibSp (siblings/spouse aboard), Parch (parents/children aboard), and Fare--are distributed based on Titanic passengers' survival status (Figure 2): (a) survivors tend to be slightly younger than non-survivors, but the difference is not statistically significant (\(p\)\(\sim\)0.053), and both groups show a non-normal distribution; (b) while survivors generally have fewer siblings or spouses on board, the difference between survivors and non-survivors is not significant (\(p\)\(\sim\)0.292), with both distributions being non-normal; (c) survivors tend to have higher Parch values, indicating they traveled with more family members, and this difference is statistically significant (\(p\)\(\sim\)0.015), though both groups are non-normally distributed; and (d) survivors paid significantly higher fares, indicating that passengers in higher classes had better chances of survival, with the difference being highly significant (\(p\) = 0.000) and both groups showing non-normal distributions.  
Figure 2: Survival standard deviation distribution: (a) Age distribution by survival, (b) SibSp distribution by survival, (c) Parch distribution by survival, (d) Fare distribution by survival.  
In summary, Fare and Pareh show significant differences between survivors and non-survivors, while Age and SibSp do not. All features exhibit non-normal distributions, which is typical for these types of data. This automated feature exploration ensures a consistent analysis of multiple variables, providing insight into key survival predictors.
## 3 Results
### Pixel Size
#### 3.1.2 Rectangular Pixels  
Rectangular pixels (2.3 \(\times\) 7 km\({}^{2}\)) are compared to square pixels of approximately equal area (4 \(\times\) 4 km\({}^{2}\)), with no other biases applied. Over the ensemble of 30 simulations, rectangular pixels performed consistently worse than square pixels. Figure 2 shows the simulated observations, and Figure 9 shows the emission estimates for all three source emission rates and three pixel sizes and orientations. While the accuracy of each pixel is similar, the precision of the rectangular pixels is worse. Moreover, estimates from pixels with the long edge oriented perpendicular to the plume were significantly less precise than estimates from pixels with the long edge oriented parallel to the plume. One contributing factor can be found by studying Figure 2. When the pixels have their long edge across the plume, they average the areas of very high enhancements near the source with areas outside the plume, resulting in relatively low maximum enhancements. In contrast, pixels with their short edge perpendicular to the wind mostly average along the wind direction, which decays much slower, and so they maintain higher maximum enhancement values, and allow for more precise emission estimation. In this case, the impact on emission estimates is similar to that of having a narrow field of view (Figure S11).  
Due to the absence of imposed biases, the difference between the median estimated and true emissions is negligible. However, the rectangular pixels have significantly worse precision than the square pixels. The square pixels have approximately 2 Mt yr\({}^{-1}\) precision in emission estimates, while the rectangular pixels oriented across the plume have only 4 Mt yr\({}^{-1}\) precision, and the rectangular pixels oriented along the plume pixels have approximately 3 Mt yr\({}^{-1}\) precision. This corresponds to up to a two times more precision estimate with square pixels than rectangular pixels. These results are consistent with those from [PERSON] et al. (2010). They found that the number of images required to reduce the error in the estimated rate of CO\({}_{2}\) emissions to below 1 Mt yr\({}^{-1}\) was much greater if the pixels were smeared across the wind direction than along the wind direction.  
Figure 9: Emission estimates from an ensemble of 30 simulations for square pixels (4 \(\times\) 4 km\({}^{2}\)), and rectangular pixels with the long side oriented both perpendicular to (7 \(\times\) 2.3 km\({}^{2}\)) and along (2.3 \(\times\) 7 km\({}^{2}\)) the wind, as in Figure 5.
## 3 Results and Discussion
### Synthetic Data
#### 3.2.2 Root Mean Square Error  
The root mean square error (RMSE) is used to quantify the stability and measure how different the generated data is from the original data, as shown in Equation (11).  
\[RMSE=\sqrt{n\sum_{i=1}^{n}(Ori_{i}-Syn_{i})^{2}}. \tag{11}\]
# OCT Capillary Depth Measurement in Copper Micro Welding Using Green Lasers
## 3 Results
### Welding Depth and Process Stability
(**a1,a2**) heat conduction welding (\(P=1000\) W, \(v=250\)\(\frac{\text{mm}}{\text{s}}\)); (**b1,b2**) deep-penetration welding (\(P=1500\) W, \(v=250\)\(\frac{\text{mm}}{\text{s}}\)); (**c1,c2**) deep-penetration welding with melt jet effects (\(P=1750\) W, \(v=750\)\(\frac{\text{mm}}{\text{s}}\)), (**d1,d2**) deep-penetration welding with humping (\(P=3000\) W, \(v=750\)\(\frac{\text{mm}}{\text{s}}\)). described by the background color (heat conduction welding = white; deep penetration welding = grey). Additionally, the process stability criteria are marked, where a cross pattern represents an increase in spatter formation with spatter sizes \(>100\) um, the dotted area represents experiments with melt-jet effects present and vertical lines represent the humping regime. For a laser power of \(1000\,\mathrm{W}\), one can observe heat conduction welding for all three investigated welding speeds. For a welding speed of \(250\,\frac{\mathrm{mm}}{\mathrm{s}}\) (red triangles), increasing the laser power to \(1250\,\mathrm{W}\) results in a change from heat conduction to deep penetration welding. By increasing the laser power further, the welding depth increases up to \(\sim\)1070 um at \(P=3000\,\mathrm{W}\). At the same time, an increase in spatter formation can be observed. With an increase of the welding speed to \(500\,\frac{\mathrm{mm}}{\mathrm{s}}\) (green diamonds), the change from heat conduction welding to deep penetration welding shifts to \(P=1500\,\mathrm{W}\). With increasing laser power, the welding depth rises up to \(522\,\mathrm{\SIUnitSymbolMicro m}\) at \(P=3000\,\mathrm{W}\), while the process stays stable regarding melt flow effects and spatter formation. For a welding speed of \(750\,\frac{\mathrm{mm}}{\mathrm{s}}\) (blue circles), the deep penetration welding regime starts at \(1750\,\mathrm{W}\) and is accompanied by the prior presented melt-jet effect behavior of the molten material (dotted area). With increasing laser power, the process changes into the humping regime (vertical lines), while the welding depth increases up to \(333\,\mathrm{\SIUnitSymbolMicro m}\) at \(P=3000\,\mathrm{W}\). By the determination of the welding depth in the performed experiments, the correlation between the measured signals of the OCT and the welding depth can be determined. Furthermore, the classification of the process regimes and stability can be used to improve the validity of the results.
# Experimental Study of the Effect of Ultrasound on the Freezing Process of Bo Chinh Ginseng
## 3 Results and Discussions
### Experimental Effect of Interruption Rate on Freezing Time of Bo Chinth Ginseng  
Materials exposed for a long time to ultrasound are at risk of experiencing thermal effects. [PERSON] et al. [21] studied how the duration of ultrasound irradiation affects the freezing of agar gel. The study showed that higher duty cycles resulted in larger crystals as they reduced the freezing rate, and the elongation of the duration of ultrasound produced heat that melted the ice crystals. The other study also showed that a long duration of ultrasound irradiation can result in heat generation in the material, which is not good for the nucleation process [22]. When the time of ultrasound irradiation is prolonged and the ultrasonic thermal effect is enhanced, the heat can hinder the formation of ice crystals and slow down the freezing rate [3]. Therefore, the effect of the intermittency ratio of ultrasound irradiation was studied.  
In this experiment, freezing occurred at a power level of 100 W (irradiation temperature of \(-0.5\)\({}^{\circ}\)C) with an intermittency radio of 0, 0.2, 0.4, 0.6 and 0.8. The intermittency ratio is calculated as [23]:  
\[\mathrm{A=}\frac{\mathrm{T_{off}}}{\mathrm{T_{on}+T_{off}}} \tag{2}\]  
in which: \(\mathrm{T_{on}}\)--freezing time with ultrasound assistance;  
\(\mathrm{T_{off}}\)--freezing time without ultrasound assistance;  
\(\mathrm{A=0}\): continuous ultrasound transmitting;  
\(\mathrm{A=0.2}\): 60 s on 15 s off;  
\(\mathrm{A=0.4}\): 60 s on 40 s off;  
\(\mathrm{A=0.6}\): 60 s on 90 s off;  
\(\mathrm{A=0.8}\): 60 s on 240 s off;  
\(\mathrm{A=1}\): no ultrasound.  
Previous studies have shown that continuous transmitting affects freezing time. Extending the airing time will generate heat, which increases the freezing time. As a result, the freezing time is not as reduced as that of the non-ultrasound freezing regime [8]. Some studies have examined how interrupting the transmitting rate affects freezing time [5; 15; 17; 24; 25]. These research results also show that the rate of the transmitting intermittency affects the freezing time. Figure 10 indicates that at an intermittency ratio of 0.2, 0.4 and 0.8, the freezing time is lower than that of the unsupported mode of ultrasound irradiation, but higher than that of the continuous broadcasting mode. At the rate of 0.6, the freezing time is the lowest among the freezing modes. This shows that choosing the appropriate ratio helps to shorten the freezing time, reduce the air time and ensure product quality.  
Figure 10: Effect of intermittent transmitting rate on material freezing time.
## 10 Discussion and Significance
### Artificial Intelligence (AI)  
Resource allocation systems can be efficiently and adaptively managed using artificial intelligence techniques, such as optimization strategies and reinforcement learning. Artificial intelligence prediction algorithms are very good at correctly predicting congestion patterns in wireless sensor networks and then applying appropriate congestion mitigation methods based on the expected data. The aforementioned designs include the application of efficient routing techniques, frequency allocation, and power resource allocation. The effectiveness of AI is based on its ability to efficiently reduce congestion in extremely dense wireless sensor networks through the prediction, optimization, and dynamic allocation of network resources.
# Implications of Metamaterial on Ultra-Wide Band Microstrip Antenna Performance
###### Abstract  
This study is addressing the slotted ring resonator effect on the performance of the ultra-wide band (UWB) microstrip antenna. Two types of metamaterial with double slotted ring resonators (SRR), circular (C-SRR) and square (S-SRR), are studied and implemented on back of the antenna. The design examines the effect of the number of the SRR and its position with respect to the antenna's ground plane and the rotation of the inner and outer C-SRR rings on different antenna characteristics. The dimensions of the antenna are \(45\) mm \(\times\) 31 mm \(\times\) 1.27 mm. The implementation of the SRR increased the antenna bandwidth to cover the range from 2.2 GHz to 9.8 GHz with rejected bands and frequencies. Antenna simulated characteristics like return loss, maximum gain and radiation pattern are obtained utilizing HFSS. The return loss measurement and the VSWR of the antenna with all SRR configuration studied are in good agreement with simulated results.  
microstrip antenna; metamaterial; SRR; circular SRR; square SRR; UWB Article
# High-Speed, High-Performance DQPSK Optical Links with Reduced Complexity VDEE Equalizers
## 2 DQPSK Equalization
The formulation of the pertinent equalization schemes follow the guideline described above for the case of joint constructive/destructive processing, noting however, that the signal diversity at the receiver is limited to half of that in the former case, as instead of eight, four signals are now  
Figure 2: **(a)** Schematic illustration of utilized signals sampled with fractional spacing f and (**b**) signal flow graph or the DQPSK single-ended joint VDFE equalizer. available for digital processing, namely \(y_{1,1}(n)\triangleq I(nT_{s})\), \(y_{2,1}(n)\triangleq I(nT_{s}+T_{s}/2)\), \(y_{1,2}(n)\triangleq Q(nT_{s})\), \(y_{2,2}(n)\triangleq Q(nT_{s}+T_{s}/2)\). As a consequence, the complexity of the corresponding balanced DQPSK equalizers are reduced to about half of that required for the joint processing case, however, at the expense of a significant deterioration in the attained performance [2; 21].
## 3 Man-In-The-Middle Attacks
### MITM as Message Tampered  
In this attack, the attacker particularly targets the content of the received message. Whenever a message is received at the malicious node, attacker changes the content of the message. This form of attack has severe impact on the network as the content may contain sensitive information. For instance, legitimate vehicle broadcast a message during heavy rain that \"there is steep-curve ahead, slow down\". The message is received at the attacker node, where the content is intentionally altered to \"there is no steep-curve, speed up\". This message is misleading for the legitimate vehicles and it can create disaster (such as accident occurrence) in the network.  
Further, every transmitted message in VANET contains three important information, i.e., (1) data, (2) time, and (3) location. In this attack, the attacker have the ability to change \"data\", \"transmission time\" or \"transmission location\". In this particular attack, the attacker can change,  
* \"data\" into misleading \"compromised data\"
* \"transmission time\" into compromised transmission time by changing it with garbage time \(t_{g}\)
* coordinates of sender location \((loc(x,y,z))\) into unknown location \((loc(x_{a},x_{b},x_{c}))\)  
In this paper, we implemented the above three versions (message delay, message drop, message alter) of the MITM attack. It is worth mentioning that MITM nodes can violate integrity, authentication, confidentiality and availability security requirement for VANET. Further, to evaluate the impact caused by these attacks, we considered two different strategies of attackers performing MITM.  
1. First, the attackers are distributed randomly across the network
2. Second, the attackers exist in fleet structure where the attacks are launched in a collaborative manner.  
Figure 4 highlights the difference of attacker pattern in the network. In the next section, we describe the simulation environment for these MITM attacks in VANET.
# Edge-Aided Reliable Data Transmission for Heterogeneous Edge-IoT Sensor Networks
## 5 Conclusions  
In this paper, we investigate the problem of data transmissions in edge-aided sensor networks. Unlike the existing works that exploit the computing resources of edge servers, we use the communication resources of edge servers to significantly reduce the transmission and delay overheads of data transmissions in sensor networks. We first adaptively employ edge servers for data forwarding to save transmissions and then identify poor links by multi-hop overhearing. By retransmitting packets for the temporarily poor links, the transmission and delay overheads can be further reduced. We conduct simulation experiments and the results show that the performance of data transmission in sensor networks is largely improved in terms of both transmission count and delivery delay.  
**Author Contributions:** Formal analysis, T.H.; Investigation, Z.X., M.C., X.L. and Q.S.; Methodology, Z.X. and M.C.; Validation, T.H.; Visualization, X.L.; Writing--original draft, Z.X.; Writing--review & editing, Q.S.  
**Funding:** This work was support by National Natural Science Foundation of China (Grant Nos. 51775496, 51875503). The postdoctoral number of the first author is 207641.  
**Acknowledgments:** The authors would like to thank all the colleagues that have supported this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: The delivery delay with a varying number of edge nodes.  
Figure 10: The delivery delay with a varying number of destination nodes.
# An Active-Clamp Forward Inverter Featuring Soft Switching and Electrical Isolation
## 4 Grid-Connection System and Control Circuit  
The circuit diagram of a PV grid-connection power system using the proposed active clamp forward inverter is shown in Figure 7, which mainly includes a PV array, a dc-link capacitor \(C_{dc}\), and an active clamp forward inverter, system controller, load and utility-line. The core of the system controller is the digital signal processor (DSP) dsPIC33 FJ16 GS504. Except for extremely fast operations, this DSP has built-in analog/digital conversion (ADC) and multiple pulse-width-modulation (PWM) control signals. Due to the Harvard structure, it can analyze load current components, and sample voltage and current signals, simultaneously. Digital design can simplify hardware circuits and reduce the number of components, resulting in small size, light weight, high flexibility and high reliability.  
The main function of the PV grid-connected power system is to deliver solar energy to utility-line. Since the maximum PV power changes with sunlight intensity and temperature, the inverter output current should regulate accordingly. The control circuit diagram of the PV grid-connection power system is shown in Figure 8. By inputting the feedback voltage \(V_{PVf}\) and the feedback current \(I_{PVf}\) from the PV panel to the MPPT controller, the level of the output current command \(i_{0}\)' is changed accordingly. The MPPT controller uses the perturbation & observation (P&O) algorithm [8; 9] and is implemented by the DSP, which can ensure that the system can draw the maximum power from the PV panel and feed it into the utility-line. The current command \(i_{0}\)' is a half-wave, and its frequency is twice the frequency of the line voltage \(v_{s}\). By comparing the output current feedback signal \(i_{0f}\) with the output current command \(i_{0}\)', the obtained error signal \(i_{err}\) will adjust the SPWM switch driving signal through the compensator \(G_{cc}\). Therefore, the output current \(i_{0}\) can have the same frequency and the same phase as the line voltage \(v_{s}\), and the solar energy can be completely f or the load. In addition, a square wave signal \(v_{P}\) with the same frequency as line voltage \(v_{s}\) can be obtained by the phase detector, which can be used to generate the driving signal of the low-frequency bridge unfolding circuit.
# The Role of Cryptogams in Soil Property Regulation and Vascular Plant Regeneration: A Review
## 4 Challenges and Opportunities  
Through a discussion of the last two decades of studies with cryptogam, we found that, in the absence of artificial irrigation and other sources of water, there appears to be a boundary between the average annual precipitation of 500-600 mm: moss promotes the accumulation of soil water in areas where annual precipitation is above this value, whereas cryptogams accelerate soil water loss in areas with levels below it. Moreover, the effect of cryptogam on soil water content will further affect the regeneration of vascular plants. Thus, we might ask: what is driving this phenomenon whereby cryptogam plants intercept the thin precipitation in arid regions? Is it the regulation of evaporative dispersion by cryptogam? Or changes in soil structure caused by cryptogam? Perhaps something else? This remains an open question.  
A large number of studies have shown that cryptogams (or their biological soil crust) can improve the fertility of soil and the availability of nutrients (TOC, TN, TP, TK, and micronutrients) in deserts, sandy land, drylands, karst, waste tailings, burned land, high-latitude and high-altitude zones, and other harsh environments. However, there are only a few studies on the effects of cryptogam plants on soil nutrient cycling and land fertility in grasslands, forests, and other ecosystems. It has to be said that the effects of cryptogam plants on soil nutrients have been neglected in these areas. Since soil nutrients are closely related to plant growth, it is necessary to comprehensively and systematically study the effects of cryptogam on soil nutrient content in these ecosystems.  
Now studies have found that cryptogam increases the ability of ecosystems to resist invasion by exotic species. However, these studies have generally occurred in ecologically fragile areas such as deserts [26], burned land [95], and semi-arid areas [96; 97]. In 2019, there is limited research on whether cryptogam can inhibit the invasion of exotic species in stable ecosystems such as grasslands, woodlands, and even farmland.
## 3 Theoretical Background
### Rationale 2: Market Failure  
Based on neoclassical environmental economic theory, double externalities of eco-innovation from knowledge spillover and environmental protection endow the public sector and regulations with crucial roles to deal with market failure problems. The value generated from the environmental innovation investment of a company is appropriated partly by other firms because of knowledge spillover and additionally by the whole society due to decreased environmental impact. As a result, the companies do not have enough required enthusiasm and motivation to improve eco-innovation. Compared with traditional innovation, more regulatory offset is necessary to reach the social equilibrium of the eco-innovation amount. The important role of environmental regulation in eco-innovation promotion is also a lesson from evolutionary economics. Regulation is one facet of the environment where the firms struggle to survive, and it can pose an effect.  
Moreover, some scholars explain the public procurement's effect through \"lead in role\", \"early adopter\", and impact on the private market. Large sales to public sectors enable innovative firms to achieve cost reductions more quickly. Then, the price would reduce and private demand would be extended. If public demand is large enough and the likelihood of additional private demand is sufficient, suppliers will realize significant economies of scale and be motivated to innovate. Public procurement may also increase the environmental awareness of firms, as well as private purchasers. What is discussed above can enhance the commercialization and diffusion of innovation [29; 47].  
General innovation theory has not recognized this regulatory effect on eco-innovation development, while Environmental Innovation Theory has. The \"regulatory effect\" has evolved into drivers of eco-innovation, as important as demand-side and supply-side factors. As indicated by \"arrow #2\" in Figure 1, this rationale is also embedded in the coercive pressure of the Neo-institutional Theory. From the view of Stakeholder Theory, the government is always a core influencer as a regulator with power.