esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The residual magnetism of the iron core of power transformers can cause an excitation intrush current, posing a threat to the safe and stable operation of the power grid. This paper proposes a transformer remanence measurement method based on a demagnetization hysteresis loop to address the problems of large errors, complex operation, and poor universality in existing remanence measurement methods. This method is designed for off-grid transformers to avoid potential interference to the power grid caused by current pulses during the measurement process. This method constructs an RLC oscillation circuit that utilizes capacitor energy storage and iron core magnetic field energy conversion, combined with the dynamic characteristics of hysteresis loops, to achieve accurate measurement of residual magnetism and synchronous demagnetization. The effectiveness of this method has been verified through residual magnetism measurement experiments on ring transformers and large converter transformers, and it can be applied in specific engineering practice operations. Theoretical analysis shows that the charging range of energy storage capacitors is affected by the hysteresis characteristics of the iron core and the saturation magnetic flux, and the residual magnetization value can be directly calculated based on the difference in the intersection point of the longitudinal axis of the demagnetization hysteresis loop. Simulation and experimental results show that the measurement error of the proposed method is less than 5%--significantly better than traditional methods. This method does not require complex control strategies, has high precision and efficiency, and can provide reliable technical support for residual magnetism detection and suppression of off-grid power transformers.  
power transformer; remanence measurement; degaussing hysteresis loop; energy storage capacitors; RLC oscillation circuit 202525375
# Local Data Debiasing for Fairness Based on Generative Adversarial Training
## 5 Experimental Evaluation
### Execution Time of GANSan  
Using the available framework, we compared the execution of different approaches with ours. We used the Disparate Impact Remover (DIRM) [22] and Learning Fair Representation (LFR) [26] (with parameters \(K=50\), \(A_{x}=0.01\), \(A_{y}=1\), \(A_{z}=50\)) from the framework AIF360 [54] and we implemented FairGan and GANSan using the framework Pytorch [55]. All of the time measurements were carried out on the same computer (_Intel Core i7-8750H CPU@ 2.20 GHz_ with 30 Gi) using the dataset _Adult Census_. To accelerate the computation, we carried out our experiments on the _Compute Canada_ platform [56], which offers more resources.  
DIRM is the fastest to complete, only requiring 9.8 s while LFR needs 2563.33 s to complete. With respect to FairGan, we computed the time required to complete the computation of one epoch at the auto-encoding step (5.3 s), the distribution learning (2.75 s), and the fairness learning (5.19 s) step. Overall, given the parameters used in their original research paper, FairGan requires about 16,960 s. GANSan requires 460.37  
Figure 16: Distribution of attributes on a synthetic dataset with stronger discrimination. The sanitization (bottom) starts with the alignment of some distributions (_note\({}_{1}\)_) to match the decision criteria (_noteDec_), which is left untouched.  
Figure 17: Distribution of attributes on a synthetic dataset with increase protection of the sensitive attribute (from a BER of 0.1 to almost 0.32).  
s per epoch, leading to 18,414.8 s for 40 epochs, as used in our experiments. GANSan overhead is mainly due to the _vectorized_ formulation of the loss function and also the larger size of the networks. In fact, the layers of our discriminator are, respectively, matrices of size \((data\_input\_shape,data\_input\_shape*16)\) for the first layer and \((data\_input\_shape*\frac{16}{2^{i-2}},data\_input\_shape*\frac{16}{2^{i-1}})\) for the \(i^{th}\) subsequent ones until the last layer with size \((data\_input\_shape*2,output\_size)\). The retained shape was the one we empirically found to provide good results while running in a reasonable amount of time. Using the same layers structure as FairGan, our approach runs in 158.486 seconds per epoch (thus 6339 for all of 40 epochs).
# TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device
## 4 System Architecture
### Data Preprocessing
The before and after CSI sequences of human motion using OR-PCA under the through-wall condition are plotted, as shown in Figure 5. From Figure 5a, it can be seen that the original CSI contains more noise, and the waveform features are not obvious. At the same time, the amplitude of the subcarrier changes significantly, as shown in Figure 5b, after the noise from other environmental disturbances, such as walls, is processed away using the OR-PCA algorithm. Then, the selected optimal sub-carrier is converted into a Doppler frequency shift, as shown in Figure 5c. It can be seen that there is no change in the Doppler image when the previous action does not start, which is similar to the Doppler frequency when no one exists. This is also a critical performance to distinguish whether there is someone indoors.
## 3 Experimental Results
### Slip Behavior
#### 3.6.1 Load-Slip Response  
The load-slip curves of all specimens are plotted in Figure 26 before the headed bars are yielded and 'BS' in the figure stands for bond slip while 'HB' denotes the concrete compression deformation by head bearing. In general, it can be seen that the slips due to bond behavior are larger than the concrete compression deformations by head bearing for all the specimens.  
For specimens with a different lap length and the same head size (Figure 26a), specimen M4-D12-6d with the minimum lap length shows the minimum bond slip with maximum head compression deformation while the other specimens show a similar load-slip response.  
Figure 26: Load–slip curves (**a**) Comparison of different lap length; (**b**) Comparison of different head size.  
Figure 25: Bond behavior with different head size (**a**) Bond force; (**b**) Average bond stress.  
When the headed bar is yielding, the lap length of six times the headed bar diameter is increased by 25% and 50%, and the bond slip is increased by 43.1% and 48.4%, respectively.  
For specimens with the same lap length and a different head size (Figure 26b), specimen M3-D12-7.5d with the minimum head size shows the maximum bond slip and head compression deformation. When the headed bar is yielding, the bond slip of specimen M3-D12-7.5d is 31.5% larger than that of specimen M4-D12-7.5d. In addition, the values of concrete compression deformation decrease with the increase in head size.
# Survey on Power-Aware Optimization Solutions for MANETs
###### Abstract  
Mobile ad hoc networks (MANETs) possess numerous and unique characteristics, such as high channel error-rate, severe link-layer contentions, frequent link breakage (due to node mobility), and dissimilar path properties (e.g., bandwidth, delay, and loss rate) that make these networks different from the traditional ones. These characteristics seriously interfere with communication and hence, ultimately degrade the overall performance in terms of end-to-end delay, packet delivery ratio, network throughput, and network overhead. The traditional referenced layered strict architecture is not capable of dealing with MANET characteristics. Along with this, the most important apprehension in the intent of MANETs is the battery-power consumption, which relies on non-renewable sources of energy. Even though improvements in battery design have not yet reached that great a level, the majority of the routing protocols have not emphasized energy consumption at all. Such a challenging aspect has gained remarkable attention from the researchers, which inspired us to accomplish an extensive literature survey on power-aware optimization approaches in MANETs. This survey comprehensively covers power-aware state-of-the-art schemes for each suggested group, major findings, crucial structures, advantages, and design challenges. In this survey, we assess the suggested power-aware policies in the past in every aspect so that, in the future, other researchers can find new potential research directions.  
MANET; energy-efficient routing; transmission power control; power-aware routing metrics; power-aware optimization; cross-layer optimization; hybrid optimization +
Footnote †: journal: Electronics Electronics
# Free Surface Energy and Hansen Solubility Parameter Vector Field. Interface Thickness
## 3 Data and Discussion
### Contact Angle and Interaction Vectors Angle  
Contact angles and interaction vector angles work in two different vector spaces, but they can be linked. Figure 13 depicts an image comparing between the interaction vector angles in the Hansen field, \(\alpha\), \(\beta\), \(\gamma\), and the contact angle, \(\theta\).  
Inside the Hansen field, the triangle formed by the interaction vectors that link solid, \(S\), liquid, \(L\), and gas, \(G\), where the angles are \(\alpha\), \(\beta\), \(\gamma\), and the sides opposite to these angles are \(a\), \(b\), and \(c\), respectively, the opposite sides to the angles. The law of cosines is expressed as follows:  
\[a^{2}=b^{2}+c^{2}-2 bc\cdot cos\alpha \tag{75}\]  
Reorganizing:  
\[c^{2}-a^{2}=2 bc\cdot cos\alpha-b^{2} \tag{76}\]  
(72) can also be enunciated as follows:  
\[cos\theta=\frac{c^{2}-a^{2}}{b^{2}} \tag{77}\]  
Substituting (76) into (77) and operating, the following is obtained:  
\[cos\theta=2\frac{c}{b^{2}}\cdot cos\alpha-1 \tag{78}\]  
Figure 12: Calculated contact angle versus experimental contact angle.  
Figure 13: Angles between interaction vectors and contact angle.  
\[cos\theta=2\frac{\left|\overset{\rightarrow}{S}_{SG}\right|}{\overset{ \rightarrow}{S}_{LC}}\cdot cos\alpha-1 \tag{79}\]  
Figure 14 shows three relevant instances where the contact angle, \(\theta\), and vectors are related.  
Case (a): Maximum moisturizing happens when \(cos\theta=1\). In this situation:  
\[\left|\overset{\rightarrow}{S}_{SG}\right|=\left|\overset{\rightarrow}{S}_ {LG}\right|\quad\alpha=0 \tag{80}\]  
This means that maximum moisturization happens when the solid and liquid Hansen solubility parameters are the same because their vectors overlap each other.  
Case (b): The contact angle is \(90^{\circ}\) when \(cos\theta=0\), and this also implies the following:  
\[\left|\overset{\rightarrow}{S}_{SG}\right|\cdot cos\alpha=\frac{1}{2} \tag{81}\]  
According to this expression, multiple configurations of \(\overset{\rightarrow}{S}_{SG}\) and \(\overset{\rightarrow}{S}_{LG}\) vectors, and the angles between them, are possible. One of the potential combinations is when \(\left|\overset{\rightarrow}{S}_{SG}\right|=\left|\overset{\rightarrow}{S}_ {LG}\right|\) and \(\alpha=60^{\circ}\).  
Case (c): Moisture is zero when \(\theta=180^{\circ}\), or, in other words, \(cos\theta=-1\). This happens when \(\overset{\rightarrow}{S}_{SG}\) and \(\overset{\rightarrow}{S}_{LG}\) vectors are perpendicular: \(\alpha=90^{\circ}\).
## 3 Results
### Upregulation of EBNA1 and MICA Mediates the DHT-Mediated Cytotoxicity
Specifically, UL4 indicates the percentage of NKG2D-positive and CD56-negative cells, LL4 represents the percentage of NKG2D-negative and CD56-negative cells, UR4 corresponds to the percentage of NKG2D-positive and CD56-positive cells, and LR4 denotes the percentage of NKG2D-negative and CD56-positive cells. 1e1 means \(1\times 10^{1}\), which equals 10. 1e2 means \(1\times 10^{2}\), which equals 100. 1e3 means \(1\times 10^{3}\), which equals 1000. Next, we investigated the upregulation of EBNA1 in response to DHT by Western blot (Figure 5B). In SNU719 cells treated with 100 nM DHT, _EBNA1_, _MHCA/B_, and _AR_ expression increased compared to the control. However, at 5000 nM DHT, there was no increase in _EBNA1_, _AR_, or _MHCA/B_ expression. We also utilized FACS to investigate whether _MICA_ upregulation in DHT-treated SNU719 cells was associated with increased EBNA1 expression (Figure 5C). As the concentration of DHT increased, the number of MICA\({}^{+}\) SNU719 cells rose, while the number of EBNA1\({}^{+}\) cells decreased. However, the population of MICA\({}^{+}\)/EBNA1\({}^{+}\) cells showed a slight increase at 100 nM and more than doubled significantly at 5000 nM. Additionally, FACS analysis was used to assess HLA-A and EBNA1 expression in SNU719 cells treated with DHT (Figure 5D). The number of HLA-A\({}^{+}\) cells exhibited minimal change with increasing DHT concentration, whereas EBNA1\({}^{+}\) cells showed concentration-independent fluctuations. The HLA-A\({}^{+}\)/EBNA1\({}^{+}\) cell population slightly decreased at 100 nM but significantly increased, more than doubling, at 5000 nM. Lastly, we used FACS to examine CD56 and NKG2D expression on NK-92 cells treated with DHT (Figure 5E). The number of CD56\({}^{+}\) cells increased with rising DHT concentrations, while NKG2D\({}^{+}\) cells showed a slight increase at 5000 nM. The CD56\({}^{+}\)/NKG2D\({}^{+}\) cell population also increased marginally with increasing DHT concentrations. These findings suggest that the enhanced cytotoxicity of NK-92 cells and PBMCs against SNU719 cells is due to the DHT-induced upregulation of _EBNA1_ and _MICA_ in SNU719 cells. The upregulation of _NKG2D_ by DHT is not considered a contributing factor to the increased cytotoxicity of NK-92 cells. Specifically, _MICA_ upregulation by DHT enhances cytotoxicity by promoting NKG2D receptor recognition on NK cells. Similarly, DHT-induced _EBNA1_ upregulation enhances CD8\({}^{+}\) T-cell recognition via MHC-I antigen presentation, leading to increased cytotoxicity.
## Materials and Methods
### Membrane Characterization
#### 2.5.2 Membrane Surface Charge and Hydrophilicity  
Water contact angle measurements were recorded in order to evaluate the hydrophilicity and wettability of the synthesized membranes. To that end, a KRUSS DCA-25 Drop Shape Analyzer (Germany) was used. The volume of each water droplet was set at 1 \(\mu\)L and three readings were taken at three different randomly chosen positions on each membrane sample. An average of the nine readings was calculated as the contact angle for each membrane. Angles were measured using sessile drop orientation with a Young Laplace fitting method.  
The surface charge for the membranes was calculated via the streaming potential method in a SurPASS(tm) 3 electrokinetic solid surface analyser ([PERSON], Austria). The fabricated membranes were cut to fit the plated sample cell holder, whilst a 100 \(\mu\)m gap was maintained in between the membrane samples. An aqueous solution of 1 mM KCl was passed through the measuring cell. Zeta potential values were obtained for a range of pH values at room temperature using 0.1 M NaOH and 0.1 M HCl buffers to alter the pH of aqueous KCl.
## 3 Results
### GHG Mitigation Scenarios  
As mentioned earlier, the following two scenarios were developed to assess the potential of GHG mitigation in the energy sector of Chiang Mai province: (i) BAU and (ii) NDCs mitigation plan. In the BAU scenario, Chiang Mai's total GHG emission is expected to increase from 2,042,583 tCO\({}_{2}\)eq in 2015 to 3,248,243 tCO\({}_{2}\)eq in 2030 assuming the forecasted annual economic growth rate of 4.0% (Figure 5). In 2015, GHG emissions from energy consumption in residential buildings and manufacturing and industrial sectors contributed the highest fractions of the total emissions at 31.87% (650,983 tCO\({}_{2}\)eq) and 27.69% (565,597 tCO\({}_{2}\)eq), respectively. This was followed by GHG emissions from energy usage in commercial and institutional buildings (493,425 tCO\({}_{2}\)eq), which accounted for 24.16% of the total GHG emissions in 2015. Under the BAU condition, GHG emissions from residential sectors (approximately 1,211,942 tCO\({}_{2}\)eq) are expected to contribute 37.31% of the total GHG emissions in 2030. This is followed by the manufacturing sector, which is estimated to emit around 1,087,147 tCO\({}_{2}\)eq or 33.47% of the total. Emissions associated with energy demand in commercial and institutional buildings are projected to be around 742,273 tCO\({}_{2}\)eq, which is approximately 22.86% of the total predicted GHG emissions in 2030. Potential GHG mitigation options were proposed based primarily on Thailand's NDC.  
More specifically, if all policy interventions as indicated in Thailand's NDC Roadmap on Climate Change Mitigation (2021-2030) are \"fully\" implemented in Chiang Mai, the total GHG emissions would be reduced by approximately 189,378 tCO\({}_{2}\)e or 5.83% of the total expected GHG emissions in 2030.
# Evaluation of VoIP QoS Performance in Wireless Mesh Networks
## 2 VoIP Application and Scenarios
### First Responders  
Every emergency relief starts with a group of people reaching the affected area after the incident. The first responders need to communicate among themselves to locate people, move medics, send support teams, and coordinate their efforts [46]. They also need to send and retrieve data to/from a central command. In case of large-scale multinational emergency response efforts, the communication becomes more difficult since teams may use different communication technologies [19]. In such cases, a unique communication system through which first responders, rescue teams, and affected people could get connected and communicate is very much important in order to save lives, protect public infrastructure and coordinate efforts in a timely and efficient way.  
Earthquake/Flooding/Cyclones/Fires  
Earthquakes of high magnitude, large-scale flooding, cyclones, and fires destroy cities, damage infrastructure and forests, and put people's lives in danger. Large-scale natural disasters usually affect existing wired and wireless communication infrastructure [47]. Survivors of such large-scale disasters and rescue teams need to communicate with each other frequently using battery-powered mobile devices until the proper infrastructure is rebuilt or fixed. As an example, Figure 2 shows a small village affected by flooding where the existing communication infrastructure was damaged.
## 2 Integration and Excitation of Plasmonic Structures
### Localized Surface Plasmons
#### 2.2.2 Strong Coupling  
In the configuration presented in Figure 5a, the strong coupling [84] between the plasmonic and SOI waveguides induces additional distinctive characteristics specific to this system. Firstly, over a wide range of frequencies, the supermodes propagate in a vortex-like manner. These vortices are related to slow modes [85] and can be also characterized by their phase profile along the chain [86]. Secondly, due to strong coupling, supermodes can also be excited beyond the light lines of the different materials surrounding the plasmonic chain. Thus, they can be excited as radiative modes above the light line of the confinement layer (silica here) and/or below the highest index material light line (silicon here) in the non-guided mode region (see the dispersion diagrams in Figure 5b,c). As a consequence, the plasmonic mode hybridization can modify their radiative or guided nature, or even extend their possible frequency range.  
Moreover, nanoantenna arrays integrated on waveguides can take advantage of tapering their dimensions within the integration plane to achieve enhanced control over coupling characteristics. For instance, tapering the period of the plasmonic array enables efficient mode conversion and excitation of higher-order modes in the waveguide [87].
# Coexistence in Wireless Networks: Challenges and Opportunities
## 3 Types of Wireless Communication
### Satellite Communication Systems  
Satellite communication systems are advanced technologies that enable the transmission of data, voice, and video signals over long distances by utilizing satellites in the Earth's orbit. These systems play a crucial role in connecting remote and inaccessible areas where terrestrial communication infrastructure is limited or unavailable. Satellite communications operate through a network of ground stations, user terminals, and orbiting satellites, using radio frequencies in various bands, such as the C-band, Ku-band, Ka-band, and L-band, to provide global coverage [48]. Particularly in regions with sparse or nonexistent terrestrial communication infrastructure, these systems are essential for enabling global connectivity [49]. Wide-area coverage is made possible through satellite communication, which is utilized for many purposes such as navigation, internet access, military communication, television transmission, and more [50]. Depending on the use and coverage region, communication satellites are deployed in a variety of orbits, including medium Earth Orbit (MEO), geostationary Orbit (GEO), and low Earth Orbit (LEO) [51], as shown in Figure 4.  
Satellite communication systems are widely used in various domains, including broadcasting, global positioning systems (GPS), disaster management, remote sensing, and military operations [52]. The emergence of high-throughput satellites (HTS) and satellite constellations like SpaceX's Starlink and OneWeb has revolutionized the industry, enabling high-speed internet access even in the most remote locations. These systems are pivotal in bridging the digital divide and supporting the global communication infrastructure.
## 3 Results and Discussion
### GC-IMS Topographic Plots  
The topographic plot map of the four stages of Zhenba bacon in GC-IMS is shown in Figure 2. The horizontal axis represents the migration time and the vertical axis represents each point on both sides of the reactive ion on peak (RIP peak) during retention of gas phase color spectrum representing a volatile organic matter. Colors represent the concentration of compounds; the white represents the low concentration and the red represents the high concentration. Meanwhile, deeper color indicates increased concentration. Most of the signals appeared in the retention time range from 100-400 s and drift time from 1.0-1.5 s. It can be seen from this plot that VOCs of samples at different stages are well separated from each other. The amounts of VOCs detected in the yellow box area of the fresh sample had the least content from Figure 2. The type and content of VOCs always increase with processing time. The VOCs in the red box area were the most diverse in YZ samples, while the differences were significant in other stage samples. From the GC-MS data, it is also evident that accumulation of the major constituents in YZ sample are esters. Figure 2 shows that the differences in VOCs of Zhenba bacon in different processing periods are mainly manifested in the position, number, intensity, and time of ion peaks. Compared with CP sample, the XZ sample is relatively close on the topographic map. Smoking led to the unique flavor, such as guaiacol. Combined with the data of GC-MS, it can be seen that a large number of phenolic substances are mainly produced. Meanwhile, the two stages only carried out a short burning and washing process, the influence on the change of flavor substances is less. In summary, compared with these samples, the signal intensities of substances are different among the four stages. As evident from the figure, VOCs of the different processing stages display significant differences in their topographic plots. [PERSON] et al. (2018) found that fermentation can increase the VOCs, which can be seen intuitively in the topographic plot. Similarly, the results suggested that the identified number and concentration of VOCs in the processed samples were higher than that in the fresh samples.  
Figure 1: Changes of POV and TBARS content in different stages. Fresh: raw sample; YZ: cured sample; XZ: smoking sample; CP: finished sample; POV: peroxide value; TBARS: thiobarbituric acid reactive substance.