esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Local Energy Markets
### LEM Benefits and Challenges  
While a substantial number of articles have made valuable contributions to understanding the current state of the art of LEMs, there is still a need for further evaluation of the adoption of the LEM concept at the consumer level. This assessment is crucial to delineate the benefits and challenges it presents to all stakeholders and actors involved. LEMs introduce competition and price transparency, allowing consumers to choose from a variety of energy providers and tariffs. This competition can drive down energy costs and increase affordability for consumers. LEMs potentially enhance grid resiliency by enabling localized energy production and consumption. During power grid outages or disruptions, communities within LEMs can continue to operate independently, relying on local energy resources. This promotes energy security and reduces vulnerability to external factors. LEMs foster innovation and entrepreneurship in the energy sector. They encourage the development of new business models, technologies, and services, such as P2P energy trading platforms, virtual power plants, and smart energy management systems. These innovations can unlock new opportunities, drive economic growth, and accelerate the energy transition. The relationships between LEM benefits and corresponding beneficial stakeholders and LEM challenges and potential affected stakeholders are represented in Figure 7. LEMs often require supportive regulatory frameworks and policies to facilitate their adoption and operation. Existing regulations may not adequately accommodate decentralized energy generation, energy trading, and grid interactions, and the lack of well-defined rules and standards can hinder market development and create uncertaintiesfor participants. Establishing LEMs involves integrating various stakeholders, including energy producers, consumers, aggregators, and system operators. Ensuring seamless interoperability between different market participants and their systems can be complex, especially when dealing with diverse technologies, data formats, and communication protocols. LEMs rely heavily on data collection, management, and sharing among stakeholders, with the volume and complexity of data increasing as more devices, sensors, and systems are integrated into the market. Ensuring secure and reliable data communication, protecting consumer privacy, and addressing cybersecurity risks become critical considerations in LEM design and operation. LEMs should strive for inclusive participation, enabling all stakeholders, including residential consumers, businesses, and communities, to actively engage. However, barriers such as high transaction costs, lack of awareness, and limited financial resources can impede broad participation, preventing the full potential of LEMs from being realized.
# Wheeler Method for Evaluation of Antennas Submerged in Lossy Media
## 3 Analysis of the Wheeler Cap Method of Antenna Surrounded by the Lossy Media
### Simulation
#### 3.1.3 Radiation Efficiency in Lossy Media  
The dielectric constant of the material is fixed at 1 and the conductivity is set as a variable. The results are sampled at a single frequency at which resonance occurs at each medium. The efficiency \(\eta_{WR}\) calculated according to the metallic cavity radius \(r_{w}\) is compared with the efficiency \(\eta_{MR}\) calculated based on the field computation (Figure 2). As mentioned above, \(\eta_{MR}\) can obtain the uniform radiation efficiency in lossless media (Figure 5), but it cannot be applicable in lossy media. As radial distance \(d_{r}\) increases, the propagation loss increases and the radiation efficiency \(\eta_{MR}\) decreases rapidly (Figure 6).  
When the conductivity is as low as 0.01 S/m, the efficiency \(\eta_{WR}\) is almost similar to the radiation efficiency \(\eta_{MR}\) calculated by the field calculator (Figure 2) in a short propagation distance range. Additionally, the radiation efficiency of \(\eta_{WR}\) and \(\eta_{MR}\) show good agreement when the cavity radius \(r_{w}\) is equal to the radian length \(r_{l}\) of each medium. This clearly confirms that the Wheeler method can be applicable to lossy media.  
However, when the conductivity is relatively high, the error is significantly increased as \(r_{w}\) becomes larger than the radian length \(r_{l}\). The coupling resistance between the antenna and the cavity mode of the radiation shield causes errors in the efficiency measurement [29; 30]. When the cavity resonator is filled with lossy material, the quality factor is lowered [31]. Therefore, the increased \(Z_{\text{WC}}\) due to the cavity mode causes errors over a wide range. A radiation shield should be selected in an appropriate shape.  
Figure 5: The radiation efficiency \(\eta_{WR}\) versus cavity radius \(r_{w}\) with changes in the dielectric constant. \(\eta_{MR}\) is marked with a dash for the same color line. The radian length \(r_{l}\) is marked with black lines.  
Figure 6: The radiation efficiency \(\eta_{WR}\) versus cavity radius \(r_{w}\) with changes in the conductivity and \(\eta_{MR}\) versus radial distance \(d_{r}\) is marked with a dash for the same color line. The radian length \(r_{l}\) is marked with black lines.
## 3 Dynamic Analysis and Design for General VSM with Energy Recovery Control
### Bandwidth Separation Design for Frequency and SoC Control  
Combining the model of conventional synchronous generator and VSM, we obtain the simplified frequency control as sketched in Figure 7, where the total inertia and damping are calculated by \(H_{\text{SG}}+H_{\text{VSM}}\) and \(D_{\text{SG}}+D_{\text{VSM}}\), respectively. Setting \(s=0\) in governor control, the bandwidth of primary control (\(\omega_{\text{bw,primary}}\)) can be roughly estimated by Equation (7), indicating larger inertia will result in slower primary control.  
\[\omega_{\text{bw,primary}}=-\frac{k_{p,\text{VSM}}+k_{p,\text{SG}}+D_{\text{ VSM}}+D_{\text{SG}}}{H_{\text{VSM}}+H_{\text{SG}}} \tag{7}\]  
The bandwidth of secondary frequency control, \(\omega_{\text{bw,secondary}}\), can be evaluated from \(G_{f}(s)/s\). Since \(\omega_{\text{bw,secondary}}\) should be much smaller than \(\omega_{\text{bw,primary}}\), the simplified secondary control model can be obtained and \(\omega_{\text{bw,secondary}}\) is then estimated as Equation (8), indicating larger governor gain will lead to slower frequency recovery.  
\[\omega_{\text{bw,secondary}}=\frac{k_{i,\text{SG}}}{k_{p,\text{SG}}+k_{p, \text{VSM}}+D_{\text{SG}}+D_{\text{VSM}}} \tag{8}\]  
Compared with conventional frequency regulation scheme, energy recovery control of VSM is also considered in the model, where \(E_{\text{nom}}\) and \(SoC_{\text{init}}\) are the energy capacity and the initial SoC of ESS, \(k_{p,e}\) and \(k_{i,e}\) denote the proportional and integral gain of SoC recovery control, respectively. By including the effect of frequency control as power disturbance, the SoC recovery control loop of VSM can be redrawn as Figure 8. As the outermost loop, the bandwidth (\(\omega_{\text{bw,SoC}}\)) of the simplified SoC control loop \(G_{\text{e}}(s)\) should be lower than that of frequency regulation (\(\omega_{\text{bw,secondary}}\)), which is approximated by Equation (9). Obviously, larger energy capacity will result in slower energy recovery.  
\[\omega_{\text{bw,Soc}}=\frac{k_{p,e}}{E_{nom}} \tag{9}\]  
Since the control plant in energy recovery is an integral block, a proportional block can normally achieve zero steady-state error. However, considering the parasitic losses of ESS, an integral block (\(k_{i,e}/s\)) is added in the SoC recovery control. For the parameters in Table 2, a small \(k_{i,e}\) value (0.002) is selected so as to ensure proper damping and keep the original bandwidth. As observed, faster energy recovery can be achieved by larger values of \(k_{p,e}\)  
Figure 7: Simplified diagram of frequency regulation in the power system with SG and VSM.
## 3 Simulation of SMP-Microstrip Vertical Transition and Stripline Power Divider for Ku-Band  
In this module, a vertical transition of SMP-to-microstrip is adopted, as illustrated in Figure 4. The SMP pin passes through the CLTE-XT substrate with a metallized via. Simulation was carried out to optimize the three critical parameters: the radius of the metallized via, the radius of the annular ring and the width between the annular ring and the ground, and the width of the microstrip line.  
Figure 4: SMP-to-microstrip vertical transition model: (**a**) front side of the simulation model; (**b**) back side of the simulation model.  
Figure 3: A photo of the 8-channel T/R module with 7-bit TTD.  
LTCC substrates are adopted for the T/R modules and TTD modules. A 2-way Wilkinson power divider and a 4-way Wilkinson power divider are realized by means of LTCC-based stripline to further increase integration. The simulation model of the LTCC-based stripline 2-way power divider is shown in Figure 6. Alongside the stripline, grounding vias are adopted to constrain the transmission of the electromagnetic wave to decrease the loss of electromagnetic energy. Based on the 2-way power divider, the 4-way power divider is realized through a cascading two-level 2-way power divider.  
The simulation results are shown in Figure 7. The insertion loss is below \(-3.5\) dB and the difference between that of port-2 and port-3 is less than 0.1 dB. The return loss of port-1 is less than \(-20\) dB and the return loss of port-2 and port-3 is less than \(-30\) dB. The isolation between port-2 and port-3 is less than \(-20\) dB.  
Figure 5: Simulation results of the SMP-to-microstrip vertical transition. (**a**) Return loss S11 and S22; (**b**) Insertion loss S21.  
Figure 6: LTCC-based stripline 2-way power divider for Ku-band.
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 3 Review of Edge Computing Technologies
### Traffic Offloading  
Traffic offloading refers to offloading traffic that meets specific offloading rules to mobile edge networks. It is vital in mobile edge computing as it focuses on providing high bandwidth and low latency.  
[PERSON] et al. [67] presented an edge cloud architecture called REPLISOM. REPLISOM is a long-term evolution (LTE)-aware edge cloud architecture with optimized LTE memory replication protocol. This protocol enables end devices to transmit the memory replica to its neighbor devices by using device-to-device technology. The receiving devices compress the received replicas. The edge servers pull the completed compressed data from the receiving devices. This architecture alleviates LTE bottlenecks and effectively schedules memory replication to avoid resource-allocating conflicts. Evaluation results demonstrate that this architecture can reduce delay and cost for traffic offloading of IoT applications.  
To alleviate network bottlenecks and reduce data latency, [PERSON] et al. [68] investigated vehicular delay-tolerant networks (VDTNs). This architecture enables effective data transmission to various devices using a store-and-carry-forward mechanism. Massive computations, such as decisions about charging and discharging, are performed by mobile devices at the edge of the network. Experimental results demonstrate an increase in throughput and a decrease in response time and latency.  
[PERSON] et al. [69] proposed a further improvement of communication in mobile edge computing by introducing an energy-efficient device-to-device (D2D) offloading network scheme. The D2D technique is used for traffic offloading as well as task balancing. Experimental results show that the proposed MEC-D2D model and MEC-D2D-Relay model reduce energy consumption and inter interference. Additionally, both models can avoid traffic congestion and provide a rapid response to end users.
## 5 Discussion and Implications
### Implications and Recommendations
#### 5.4.3 Institutions and Policy  
Institutions must establish ethical frameworks and data governance policies to guide the responsible use of LA and GenAI. Bridging digital gaps in under-resourced contexts is essential to prevent widening educational inequities. Professional development in AI and data literacy should be prioritized for educators, advisors, and policymakers to support informed implementation.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Challenge** & **Description** & **Future Direction** \\ \hline Model opacity & Deep models lack transparency, limiting educational use. Retrospective models restrict real-time interventions. & Advance Explainable AI (XAI) in LA. Develop online/incremental modeling and real-time feedback systems. Create modality-aware models (e.g., sensor, LMS, multimodal). Broaden global datasets and include non-Western case studies. Study adoption, co-design with educators, and report implementations. \\ Narrow evaluation metrics & Accuracy often outweighs pedagogical value in evaluations. Fairness, consent, and bias remain underexplored. & Create modality-aware models (e.g., sensor, LMS, multimodal). Broaden global datasets and include non-Western case studies. Study adoption, co-design with educators, and report implementations. \\ Ethical blind spots & Femaxes, consent, and bias remain underexplored. GenAI lacks pedagogical grounding and deep integration into LA. & Apply ethical audits and promote fairness-aware ML and data literacy. Explore adaptive, generative, and co-creative GenAI applications. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Challenges and future directions in ML and GenAI for learning Analytics.
# SHPB Experiment and MPM Simulation on Dynamic Properties of Unsaturated Clay Under Impact Loading
## 3 MPM Simulation on SHPB Test of Unsaturated Clay
### Constitutive Law for Unsaturated Clay
#### 3.2.1 Soil Water Retention Curve (SWRC)  
To represent the hydraulic behavior of unsaturated soil, the well-known VG model [44] is adopted to predict the moisture-suction relationship. The expression of VG model is defined as Equation (7).  
\[S_{l}=S_{min}+(S_{max}-S_{min})\left[1+\left(\frac{s}{P_{0}}\right)^{\frac{1}{ 1+\lambda}}\right]^{-\lambda} \tag{7}\]  
where \(S_{max}=1\), \(S_{min}=0.01\), \(P_{0}=1950\) kPa, and \(\lambda=0.4\) are fitting parameters for lateritic clay, whose water retention behavior [45] is similar to the clay sample investigated in this paper (as shown in Figure 7).
# An Emerging Role of Long Noncoding RNAs as Novel Biomarkers for Breast Cancer Metastasis
## 5 Potential lncRNA-Based Biomarkers
### H19  
The involvement of lncRNA H19 in BC cancer metastasis further underscores the diverse roles of lncRNAs in tumor progression. As a transcript of the H19 gene, located close to the telomeric region of chromosome 11p15.5, H19 undergoes reciprocal imprinting, meaning its expression is regulated in coordination with its neighboring gene of Insulin-like Growth Factor 2 (IGF2). H19 mostly plays a dual role: it acts as a storage site for miR-675, which suppresses specific targets, and it also binds to miRNAs or proteins. However, its role varies across different types of cancers [53].  
[PERSON] et al. compared two Single-Nucleotide Polymorphisms (SNPs) in the lncRNA H19, rs217727, and rs2839698 and found that elevated levels of the former were associated with an increased risk of BC, particularly in patients with ER+, HER2-, and ER+/HER2 subtypes [54]. H19 promotes the G1-S cell cycle transition in BC cells by binding to the E2F transcription factor 1 (E2F1). H19 binds to and inhibits S-adenosylhomocysteine hydrolase (SAHH), impacting broad epigenetic alterations and suggesting its involvement in gene methylation dynamics in development and diseases, including BC. Moreover, the overexpression of miR-675-5p in MDA-MB-231 cells downregulates ubiquitin ligase E3 family proteins (c-Cbl and Cbl-b), activating epidermal growth factor receptor (EGFR) and c-Met pathways, thereby enhancing proliferation and metastasis abilities in BC [55]. In a study by [PERSON] et al., H19 expression in BC tissue samples was notably higher in cases with LNM compared to those without, and it was also elevated in grades III-IV compared to grades I-II [56]. [PERSON] et al. observed elevated expression levels of H19 in both BC tissues and plasma samples compared to healthy controls. Plasma H19 levels correlated significantly with ER, PR, HER-2, and LNM. The AUC was estimated to be 0.810 (sensitivity 56.7% and specificity, 86.7%), which exceeded the values obtained for CEA and CA153. Furthermore, postoperative plasma H19 levels were significantly reduced compared to preoperative levels [57]. In another study, similar results showed a plasma H19 AUC of 0.870 (with a sensitivity of 87.0% and specificity of 70.6%). Exosomal H19 expression levels correlated with LNM, distant metastasis, TNM stages, ER, PR, and HER-2 and were diminished post-operation. These findings establish H19 as a promising biomarker for BC metastasis [58].
# Virtual Reality or Videoconferencing for Online Learning? Evidence from Comparative Meta-Analyses
## 5 Comparative Discussion
#### Implications for Practice and Research  
Drawing upon these findings, we propose several implications for online teaching practice. First, we recommend that teachers using VC platforms conduct online learning for smaller classes with younger students owing to its superiority in this context. However, as class sizes expand and students mature, VR emerges as a more viable and effective alternative, particularly when financial considerations permit. Second, we encourage teachers to choose more recent VC platforms for online learning due to their more advanced functions; students should also be encouraged to turn on their cameras for improved effectiveness. For VR-supported online learning, we favor desktop computers over HMDs, as they offer a similar learning experience, but are more affordable and accessible. Third, in the VR environment, we recommend that teachers adopt more student-centered pedagogies to capture the affordances of this technology in full. Conversely, in the VC environment, a lecture-based approach may be more appropriate, given the characteristics of this medium.  
Based on our meta-analyses, we derived three limitations for future research. First, we observed that many reviewed articles lacked sufficient statistical data. We therefore strongly recommend that future studies enhance the rigor of their data and carry out comprehensive reporting of relevant data. Second, our findings revealed that current studies on VR or VC online learning interventions focus on cognitive outcomes, such as knowledge and skills acquisition. We thus encourage researchers to consider non-cognitive outcomes as well, such as behavioral changes and emotional development, in their future investigations. Third, the analyses were conducted using CMA software, which does not support multilevel modeling or robust variance estimation. As a result, in some cases, the statistical dependence among multiple effect sizes extracted from the same study may not have been fully addressed. Future research could adopt more advanced statistical tools to better account for this issue.
## 2 Miniaturized X-ray/Gamma-ray Imaging Payload
### Deployment as Space Radiation Monitor  
Besides the primary function of the payload as focal-plane X-ray imager, the device can operate also as a high-resolution space radiation monitor [28]. The embedded Timepix detector enables to characterize the mixed-radiation field environment in wide-range along the spacecraft orbit. The composition and spectral characterization of the charged particle field of both light and heavy charged particles can be provided in wide range in terms of particle-event types, energy loss and direction [29; 30]. This secondary function is used in the Timepix X-ray telescope payload [9] onboard the VZLUSAT-1 CubeSat, launched in 2017 and operating in LEO, now entering its 5 th year of operation in open space. Together with navigation data, the Timepix detector provides detailed space radiation maps which serve to determine the background and unwanted event component in the X-ray imaging data of the VZLUSAT-1 Timepix X-ray telescope.
# Quality of Tomato Juice as Influenced by Non-Thermal Air Plasma Treatment
###### Abstract  
This paper presents the results of an experiment based on using a Glide-arc type plasma reactor operating at atmospheric pressure for the quality of fresh pressed tomato juice, variety Bekas. The impact of after-glow plasma gas (air) on the physicochemical, microbiological properties and morphology of the product's samples was investigated. Five groups of juices characterized by different exposure times (30, 60, 120, 300 and 600 s), as well as untreated juice (as control) were used. The juice quality was assessed on days 1, 3, 5, and 10 of refrigerated storage. Significant increases were observed when Cold Atmospheric Plasma (CAP)-treated tomato juice was tested against total soluble solids, pH, lycopene, and vitamin C in comparison to the control treatments. Moreover, changes in the tested physicochemical values during the storage of juice subjected to the action of cold plasma did not progress as quickly as in the case of the control juice. A significant decrease was observed in total plate count, yeast, and mold after 300-600 s CAP treatment. The findings of the current study suggested that CAP treatment is a promising technique that could provide improved quality and stability during the processing of tomato juice with better physicochemical properties and bioavailable nutrients.  
atmospheric pressure plasma; Glide-arc reactor; microbial bio-decontamination; physicochemical and morphological properties; fresh pressed tomato juice; electrotechnology for food +
Footnote †: journal: Applied Sciences
# An Application of the Associate Hopfield Network for Pattern Matching in Chart Analysis
## 3 Materials and Methods
### Pattern Representation
#### 3.1.2 Template Grid  
In [PERSON] paper [13], they introduced their novel template grid representation methodology: The time series data is encoded by the pattern identification code (PIC), a one-dimensional array that represents the position of the data points in a given template grid. An example is given to illustrate the PIC in Figure 4:  
After generating the PIC, each cell of the template grid would be assigned a weight by applying the following approach: the weights of each column are calculated as the equation \(w_{j,c}=1-|p-j|D\), where \(j=1\cdots M\), \(D=\frac{2M}{(M-p)(M-p+1)+(p-1)p}\), \(p\) is the vertical position of the data point, \(M\) is the dimension of the template grid.  
We constructed the predefined patterns like H&S, Double Top, Triple Top, Spike Top, etc. using the TG method. In order to generate the stored pattern that could be properly learned by the HNN, we present three different representation: (**a**) PIP-TG: To reduce the processing time, we firstly utilize PIP to process the data and then extract the data points to generate PIC, such that a simplified TG can be formed. (**b**) N-equal-part TG: Preset the dimension \(N\) of the template grid, the time sequence would be split evenly into \(N\) parts, each part represents the data point on the TG. For the predefined pattern, we increase the data points of the template to \(N\) and then apply the TG representation. (**c**) Scaling PIP: After reducing fluctuant points by PIP, simply scale up the number of the data points between the PIPs.