esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Preliminary Experimental Results  
In Figures 8 and 9 some preliminary experimental results concerning AHX1 are shown. In the first graph the difference between the temperatures of the hot HX (the heater) and of the ambient HX is reported as a function of the measured input heat transfer rate (\(\dot{Q}_{H}\)). The thermocouple (K-type thermocouple) of the heater was placed inside a pore of the ceramic honeycomb support so it presumably measured a mean value (\(T_{H}\)) between the gas and the solid wall temperatures.  
Figure 8: The temperature difference between the hot and ambient heat exchanger with rectangular pores as a function of the input heat.  
Figure 7: The drive ratio and peak-to-peak particle displacement amplitude as a function of the input heat flux for **(a)** the HX with rectangular pore geometry and **(b)** the HX with circular pore geometry. The operation temperatures of the heater, \(T_{H}\), are also indicate on the horizontal scale on the top.  
The thermocouple of the ambient HX was suspended by a little spacer in the gap between two fins so in this case the gas temperature (\(T_{S}\)) was measured. The input heat flux \(\dot{Q}_{H}\) was calculated by measuring the voltage drop across the heater and the flowing current. The heat flux extracted by the ambient HX, \(\dot{Q}_{A}\), was calculated through Equation (2) from the measured values of water mass flow rate (\(\dot{m}_{w}\approx 0.052\) kg/s) and of its temperature at the inlet (\(T_{wi}\approx 300\) K) and outlet sections of the HX measured by two thermocouples. The acoustic pressure values reported in Figure 9 were measured by a condenser microphone (sensitivity 1.589 mV/Pa) flush mounted in the wall of the narrow duct at a distance of 3 cm from the ambient HX (see Figure 1).  
The air starts to oscillate spontaneously in the engine for \(T_{H}\approx 200\)\({}^{\circ}\)C at a (resonance) frequency of 156.5 Hz (which slightly grew with \(\dot{Q}_{H}\)). Figure 8 shows how the temperature difference (\(T_{H}\)-\(T_{S}\)) grows quickly and almost linearly with \(\dot{Q}_{H}\) in the investigated range. A linear behavior is also found in Figure 9 where \(\dot{Q}_{H}\) and \(\dot{Q}_{A}\) are reported as a function of the square of the acoustic pressure. Note, however, as the difference between \(\dot{Q}_{H}\) and \(\dot{Q}_{A}\) (increasing with the heater power) is a clear evidence of heat and/or acoustic power leakage toward the external environment, so an improvement of the engine insulation is required.  
These findings are in accordance with the linear theory of thermoacoustics and let suppose that in the investigated operation range non linear effects are negligible. This result is somehow expected when considering that at the highest \(\dot{Q}_{H}\) values shown in Figure 9 the measured acoustic pressure reached 0.6% of the mean pressure. Since the microphone is located around halfway a pressure node and antinode this value should roughly correspond to a drive ratio of about 1.2%, that is well below the onset level of non linear effects.
## 2 Fundamentals and Theory  
The wavelet transform is a method based on signal processing that was developed from Fourier and short-Fourier transforms for suitable specific applications. The time width and frequency can be adjusted for optimal analysis.  
The wavelet theory based on mathematics integrates small signals into one signal. The small signal is the wavelet, which has a specific character because the wavelet is a wave-like oscillation with an amplitude that begins at zero, increases, and then decreases back to zero. Therefore, any signals in the wavelet theory are a combination of wavelet groups that are structured from the same function. This function is the origin wavelet called the mother wavelet. The signals are caused by the mother wavelet stretching (scaling: a) or shrinking (shifting: k) the wavelet itself, which occurs as a position change along the time axis.  
DWT signal analysis can select many scales (m) and many positions (k) for integrated wavelets and can form a signal at the interest scale. When integrating the signal at all resolutions, the actual input signal is received. In multilevel analysis, the scale of analysis is reduced by two times. Hence, this is a DWT-type dyadic wavelet transform, as described in Equation (1) [24].  
\[DWT(m,n)=\frac{1}{\sqrt{2^{m}}}\sum_{k}f(k)\psi\left[\frac{n-k2^{m}}{2^{m}}\right] \tag{1}\]  
where  
\begin{tabular}{l l} m, n, k & = Integers \\ \(\Psi\) & = Mother wavelet \\ n & = Number of data points \\ m & = Scaling \\ k & = Shifting \\ \end{tabular}  
Signals are decomposed by the wavelet. The mother wavelet is a filter signal that separates the following two channels:  
* High-frequency component (detail coefficient: cD).
* Low-frequency component (approximation coefficient: cA).  
The high-frequency component will be used to analyze signal during transient state.  
The mother wavelets used in the DWT are db, sym, coif, and bior [24]. Each mother wavelet provides a differential coefficient because of its differential characteristic signal, as shown in Figure 1.  
The Daubechies (db) mother wavelet has asymmetric basic functions, while the symlets (Sym) mother wavelet has the least asymmetric basic functions. The biorthogonal (Bior) mother wavelet has symmetric basic functions, and the coif1 mother wavelet has nearly asymmetric basic functions. It is necessary to choose a suitable mother wavelet for increased efficiency.
## 3 What Is SDN?
### Challenges and Solutions  
SDN offers the advantage of easy network programmability and the ability to create dynamic traffic flow policies. However, this advantage also introduces potential security vulnerabilities. The overall security of SDN was analyzed by [PERSON] et al. [82]. It was concluded that the centralized nature of the controller and the programmability of the network introduce new threats related to network security, necessitating novel countermeasures. In SDN architecture, the centralized view of the network has been identified as a potential security drawback [83]. For instance, a Denial-of-Service (DoS) attack on a centralized controller managing a large network of multiple devices would be more destructive than a targeted attack on an individual router. To mitigate these challenges, several techniques have been proposed, including replication, diversity, and secure components [84].  
Ensuring high-quality services in advanced SDN-enabled cellular networks remains a challenge. SDN is utilized to create network slicing, enabling support for different applications. Automated Quality-of-Service (QoS) provisioning per application or service is required. Automating QoS is particularly crucial when multiple wired and wireless technologies share the same network slice [85]. A Software-Defined Low Latency (SDLL) framework based on SDN was proposed by [PERSON] et al. [86] to provision QoS and guarantee ultra-low latency in 5G and beyond networks.  
In SDN-enabled cellular networks, effective monitoring of performance metrics for both physical and virtual networks is essential. To address this challenge, Network Tomography (NT) has been explored as a solution. NT estimates network performance by analyzing data from a selected subset of network elements rather than requiring measurements from every part of the network. This targeted approach provides a cost-effective and efficient method for monitoring network health and diagnosing issues [87].  
Figure 8: The use case for OpenFlow.
## 5 Electromagnetic Fields and Surface Current Analysis  
[PERSON]'s law of electromagnetic induction predicted that moving the charged particle can generate a magnetic field. According to [PERSON]'s laws of induction, the electromagnetic  
Figure 6: Simulation setup for changing field propagation.  
Figure 7: Transmission coefficient (S\({}_{21}\)) for changing field direction.  
interface turns it into an electric field. The EM fields produced by time-varying charged particles can be expressed using [PERSON]'s curl equation [30]:  
\[\stackrel{{\rightarrow}}{{\
abla}}\times\stackrel{{ \rightarrow}}{{\mathrm{H}}}=\stackrel{{\rightarrow}}{{\mathrm{J}}}+ \frac{\stackrel{{\rightarrow}}{{\partial}}\mathrm{D}}{\frac{ \partial}{\mathrm{t}}} \tag{6}\]  
\[\stackrel{{\rightarrow}}{{\
abla}}\times\stackrel{{ \rightarrow}}{{\mathrm{E}}}=\frac{\stackrel{{\rightarrow}}{{ \partial}}\mathrm{H}}{\frac{\mathrm{t}}{\mathrm{t}}} \tag{7}\]  
where the operator is:  
\[\
abla=[\frac{\partial\mathrm{x}}{\partial\mathrm{t}},\frac{\partial\mathrm{y} }{\partial\mathrm{t}},\frac{\partial\mathrm{z}}{\partial\mathrm{t}}] \tag{8}\]  
Equations (6) and (7) are insufficient to describe the correlation between the produced fields and the material. The following equations can be used to alleviate this drawback:  
\[\mathrm{E(t)}=\frac{\mathrm{D(t)}}{\varepsilon\left(\mathrm{t}\right)} \tag{9}\]  
\[\mathrm{H(t)}=\frac{\mathrm{B(t)}}{\mu\left(\mathrm{t}\right)} \tag{10}\]  
The three important features that can be described using the aforementioned equations include the electric field, magnetic field and surface current. Figures 8 and 9 represent the electric field distribution and magnetic field distributions, respectively, for the 3 resonance frequencies of 2.38 GHz, 4.55 GHz and 9.42 GHz. The current flow changes with a change in frequency. Two SRRs of the unit cell are more influenced by surface current flow than the other resonators, which are depicted in Figure 10. Thus, it can be noted that the first 2 frequencies, 2.38 GHz and 4.55 GHz, belong to the 2 SRRs, whereas the two epsilon-shaped resonators, which are placed in the middle of the unit cell, produced a low current flow with a frequency of 9.42 GHz.  
Figure 8: Electric field distribution at **(a)** 2.38 GHz, **(b)** 4.55 GHz and **(c)** 9.42 GHz.  
The induced magnetic field intensity (B) depends on the current flow (I) or charge flow and the distance (r) of the conductor, which can be expressed using [PERSON]'s law, \(\text{B}=\frac{\mu_{0}\text{I}}{2\pi\text{r}}\)[31], where \(\mu_{0}\) is the permeability in free space. With the increase in the current flow, the strength of the magnetic field also increases. The same effect is also observed in the E field, as presented in Figure 8. When the electric field pattern (Figure 8) is compared to that of the H field presented in Figure 9, it can be observed that the electric field (E) decreases when the magnetic field becomes positive. This changing pattern of the H and E fields support Equations (6) and (7). Moreover, it is also found that the electric field intensity was high in the split gaps as the splits in the resonator function as of capacitors.
# A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications
## 4 Optimization Algorithms Used for IRS-Assisted UAV Communications
### ML Algorithms
In particular, UAV active beamforming, coefficients of IRS elements, and UAV trajectories were jointly optimized to maximize the sum secrecy rate of all legitimate users in the presence of multiple eavesdroppers. A deep deterministic policy gradient (DDPG) framework was adopted to solve the accurate CSI estimation problem. In [89], the authors considered a UAV-mounted IRS system with a single BS that communicates with various IoT devices on the ground. The authors focused on optimizing the UAV altitude and phase shift of the IRS to minimize the AoI, which indicates the latency of the state-update system and application. Because it is difficult to analyze the activation pattern of IoT devices, the authors devised a DRL-based proximal policy optimization (PPO) algorithm to understand the randomness of IoT devices' behavior. In [77], the authors considered multiple UAV-mounted IRSs for a HetNet supported by dual connectivity. The authors developed a DQN-based distributed algorithm to optimize the trajectory and speed of UAVs, phase shift of IRS, subcarrier allocation, and active beamforming at BSs. The authors of [99] proposed a DL-based channel-tracking mechanism to track the time-varying channel. The proposed algorithm consists of two modules: channel pre-estimation and channel tracking. The pre-estimation was achieved by using a DNN with offline training on a pre-collected training dataset. The tracking module was designed using a stacked bidirectional long short-term memory that can track the CSI over a time-varying channel in a data-driven manner. The proposed channel-tracking algorithm was shown to provide fast convergence during the offline training phase. The author of [100] proposed a resource allocation model for the aerial IRS based on a cooperative DNN rather than a universal DNN. As the proposed collaborative and distributed learning framework makes decisions by considering the information on the entire environment, it provides better performance and reliability. In summary, research on the application of ML techniques in IRS-assisted UAV communications systems is still in its early stage. Nevertheless, the previous works introduced above confirmed that ML is promising to optimize various parameters of the complex IRS-assisted UAV communications systems. Table 5 summarizes the main features of the previous studies based on ML for IRS-assisted UAV communication systems.
# Evaluation of Remote Sensing-Based Evapotranspiration
## 2 Study Area and Data  
### Study Area  
The Jinhua River is a tributary of the Giantang River and is located in the midwestern region of Zhejiang Province, East China (Figure 1). The length of this river is 195 km and its catchment area above the Jinhua discharge station (control station) is 5996 km\({}^{2}\)[30]. The Jinhua River Basin is subject to an Asian monsoon climate, which is characterized by abundant precipitation and high temperatures in summer and a rainless, cold winter. The annual mean temperature is 17 \({}^{\circ}\)C. Based on fifty years (1962-2011) of precipitation data, the annual mean precipitation is 1404.9 mm and is unevenly distributed, with more than 50% of annual rainfall falling from May to July [31]. The uneven distribution of precipitation has led to frequent floods and droughts. Solid hydrological simulation can provide support to disaster prediction and prevention, and water resource management.
## 3 Natural Products: A New Source of Molecules with Antitumor Activity
### Polyphenols
#### 3.1.3 Kaempfer  
Kaempfer is a flavanol that is present in vegetables and plants such as broccoli, grapes, apples, brussels sprouts, or black tea. This bioactive compound possesses cardio-protective, neuroprotective, anti-inflammatory, antidiabetic, antioxidant, and antitumor properties [82]. The mechanisms of action underlying the anticancer effect of kaempfer are based on inhibition of the cell cycle in the G2/M phase to prevent tumor growth and proliferation, as well as induction of apoptosis by acting on different cell signaling pathways. Several in vitro studies have also observed its anti-angiogenic effect and anti-metastatic, inhibiting VEGF production and decreasing the expression of markers that are involved in epithelial-mesenchymal transition (EMT) at a dose of 0.1 uM in MDA cell lines [83; 84]. Pharmacokine studies that were performed in rats determined that the bioavailable fraction of this compound after oral doses was 2% of the total administered [85]. To overcome this obstacle, the combination with quercetin has been tested, resulting in an increase in its bioavailability, in addition to improving efficacy [82]. In this way, this bioactive compound would act selectively on tumor cells, without affecting healthy ones. This compound exhibits cytotoxic effects on various CRC cell lines, such as HCT116, HT-29, HCT-15, LS174-R, and SW480, both when used alone and in combination with other chemotherapeutic agents. In HT-29 cell lines, the ability of this compound to inhibit the cell cycle has been studied, resulting in cell cycle arrest in G1 and G2/M phases through inhibition of CDK2, CDK4, and Cdc2 activity [86].  
In vitro studies in the 5-FU-resistant LS174-R cell line and in other CRC lines such as HCT116 and HT-29 have shown that the antiproliferative activity that is exerted by the combination of kaempfer with this chemotherapeutic agent is through cell cycle arrest and induction of apoptosis, acting on signaling pathways such as JAK/STAT3, PI3K/AKT, MAPK, or NF-kB [87; 88]. Other studies that were carried out in oxaliplatin-resistant HCT116, and HT-29 cell lines have shown that the combination of kaempfer with this drug generates a greater cytotoxic response, decreasing cell proliferation. The chemo-sensitization of these cells is mediated by the suppression of c-FOS protein expression [89].  
Although not many nanoformulations have been synthesized to improve the bioavailability of kaempfer in the treatment of CRC, [PERSON] et al. designed PEGylated gold nanoparticles that efficiently co-encapsulated DOX and the natural compound, improving the cytotoxic effect of both drugs separately and inducing apoptosis in the HT-29 line,without having high toxicity in the HTB-38 non-tumorous colorectal line. Finally, an in vivo experiment that was performed on mice showed a significant reduction in tumor volume without observing side effects [90].  
Data on the use of kaempfer in vivo are still scarce, so it is imperative to continue its research to better understand its anticancer potential.
# Moving towards Smart Cities: A Selection of Middleware for Fog-to-Cloud Services +
## 3 Preliminary Foglet-Middleware Design
### General Description of the Proposed Foglet  
In this section, we propose a foglet as a middleware that aims at performing the interaction between the customers and fog-to-cloud providers (Figure 1). When a user delegates a task to fog-to-cloud platforms, it sends a request to the foglet, where, the handler module ensures the communication between the end-client and the foglet by intercepting a request and sending back the results. Then, the request is processed by the selection service manager that is responsible for the management of data in this architecture. In fact, this module will first check the available services that belong to centralized and decentralized platforms. To do that, it sends a notification to the service repository that contains a list of available QoSs related to the edge and cloud services. Then, a list of edge-cloud candidates is selected according to the user's request and comes back to the selection service manager. Following this, the menu of selected services is sent to the module of rank services for evaluation of the quality of services according to defined criteria. At that point, the Fuzzy-TOPSIS MCDA methods will be applied to choose one alternative (or service) as an optimal solution. To do that, the MCDA methods will evaluate the alternatives in terms of criteria. Then, they will select a more appropriate service according to the request, which will be redirected by the scheduler to a selected fog/cloud service. After sending back the results, the scheduler, which ensures the communication between the foglet and services, will intercept the response and redirect it to the handler module that will return it to the end-user.
## 3 Approaches for Quantifying the Precision, Stability, Accuracy, and Uncertainty of COSMIC-2 Data
### COSMIC-2 Precision
The variation of standard deviation (Figure 5a-c) may be associated with atmospheric change within 300 km apart, especially in the troposphere (from the surface to 10 km). The residual errors of ionospheric correction mainly cause an increase in the standard deviation above 25 km. This result gives us confidence in the performance of the COSMIC-2 receiver. The precision for COSMIC-2 for temperature, water vapor, and refractivity are compatible with those from COSMIC, which are similar in magnitude to simulated errors presented by [43]. Note that the compared precision of any combination of COSMIC-2 receiver pairs is very close to that of the FM1-FM4 results (not shown).
# P-Distill: Efficient and Effective Prompt Tuning Using Knowledge Distillation
## 3 Methodology
### Prompt-Enhanced Distillation (P-Distill)  
We initiated the training of a student model which employs shorter continuous prompts, rather than the teacher model, using the same prompt attachment methodology. During the initial training phase, we initialized the continuous prompts of the student model, \(P^{S}_{k}\) and \(P^{S}_{v}\in R^{n_{k}\times d}\), based on the teacher model's prompts, \(P^{T}_{k}\) and \(P^{T}_{v}\). Subsequently, student prompts were also attached to the key and value vectors across all layers to compute the attention heads. The length of the student model prompts, represented by \(n_{s}\), is shorter than that of the teacher model prompts \(n_{t}\). The student model, denoted by \(f^{S}\), takes the text embedding \(\overline{x}\) as input and generates the output logits \(y^{S}\). The teacher and student models share the same underlying language model architecture, differing only in the length and content of their respective prompts. In this context, we focused on distilling the knowledge from the more extensive teacher model prompts into the shorter student model prompts. To enhance the effectiveness of knowledge transfer, we propose two novel methods for knowledge distillation.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 5 Challenges in Smart Grids
### Challenges in Attack Detection  
Implementing accurate and robust detection methods across different scales of power systems presents a daunting task, compounded by the need for cost and resource efficiency. Moreover, these systems must be straightforward to integrate, maintain, and upgrade with the existing infrastructure, ensuring minimal disruption and maximum compatibility.  
Additionally, the increasing sophistication of cyber attacks, fueled by the advancements in AI and machine learning, necessitates the continuous adaptation and improvement of detection technologies. This calls for a collaborative effort between researchers, industry experts, and policymakers to develop standards and practices that enhance the security and resilience of smart grids against the evolving threats. In [119], the author points out that game theory and reinforcement learning approaches may be the future research directions because they use minimum data to analyse complex power system models.
# A New Hybrid Prime Code for OCDMA Network Multimedia Applications
## 8 EVM Analysis  
The error vector magnitude (EVM) is a new important metric for measuring the quality of the optical signal with BER and throughput performances. For N randomly transmitted symbols, EVM is defined by the root mean square value of the difference between the received error vector and the ideal transmitted vector [55]. For M-ary modulation the BER can be expressed as a function of the EVM as [56; 57]:  
\[EVM=\frac{1-\frac{1}{\sqrt{M}}}{0.5\ log_{2}(M)}erfc\left(\sqrt{\frac{3/2}{(M-1) EVM^{2}}}\right) \tag{10}\]  
For more simplicity, EVM can also be expressed as:  
\[EVM\%=\frac{BER_{max}-BER}{BER}\times 100\% \tag{11}\]