esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimization and Validation of the Process  
To optimize the process, preset criteria were used, as mentioned in Section 2.5, and the corresponding desirability functions for \(\mathrm{a_{w}}\) and \(\mathrm{L/L_{0}}\) were solved in pair, using Minitab(r) 17.1.0 (Figure 6). The desirability function approach is one of the most frequently used tools in multi-response optimization techniques in practice. The optimized conditions were found to be 38.7 \({}^{\circ}\)C, 30% glycerol, and immersion time equal to 130 min.  
At those optimum conditions, the theoretical values of all dependent variables were estimated using the second order polynomial equations developed, and they are presented in Table 4. Aiming at verifying these theoretical values, three independent experiments were carried out at the optimal conditions. Average values and standard deviations are reported also in Table 4.  
Figure 4: 3D response surface graphs of the effect of OD time and glycerol concentration at a constant temperature (40 \({}^{\circ}\)C) on (**a**) lightness retention (\(\mathrm{L/L_{0}}\)) and (**b**) total color change (\(\Delta\)E).  
Figure 5: 3D response surface graphs of the effect of OD time and temperature at a constant glycerol concentration (40%) on (**a**) lightness retention (\(\mathrm{L/L_{0}}\)) and (**b**) total color change (\(\Delta\)E).
# Additive Manufactured Waveguide for E-Band Using Ceramic Materials
## 2 Manufacturing Process
### Metallization Process  
After the sintering processes, the surface of the waveguide was coated by a conductive layer. Before, contaminations were removed by a polar agent (e.g., dichloromethane). Since used ceramic material is acid-resistant, conventional metallization approaches, such as electroless silver plating [14; 15], etc., are difficult in application. Therefore a novel, patent pending approach, as described in [40], was used. The ceramic waveguide was dipped in a silver nanoparticle ink and subsequently heat-treated in order to activate the ink. Afterward, an additional copper layer was applied by electroplating. The dipping as well as the electroplating process benefits from the gaps in the waveguides sidewalls.  
An image comparing the fabricated ceramic waveguide with a waveguide of the same dimensions printed by a conventional epoxy resin [14] is shown in Figure 3. Both specimens feature the same metallization process for reasons of comparability. To enable mounting on conventional measurement equipment, both waveguides can be placed in an additional DLP-printed fixture.
## 1 Introduction
### Transition for Secondary Atomization after Drop Impact on Liquid Films
Thus, in this review, we opt for the most general form for this criterion. Lastly, [PERSON] et al. [7] determined an empirical correlation for the spreading/splashing transition of single droplets impinging on dry surfaces and thin liquid films. In the experimental data that they used to fit the correlation, different fluids were included providing a wide range of impact conditions. However, they only tested impacts with thin liquid films (\(\delta=0.1\)). Figure 2 plots the criteria organized in Table 1, showing the region for the [PERSON] et al. [5] correlations depending on the dimensionless liquid film (\(\delta=h_{f}/D_{0}\)) and the region for the Tropea and Roisman [6] correlation depending on \(\eta\). Most correlations have a similar logarithmic slope, except for the correlation proposed by [PERSON] et al. [7]. In scale, [PERSON] et al. 's correlation is close to the dimensionless film thickness condition of \(\delta=0.1\). However, their experiments covered a broader range of Reynolds values, eventually, leading to a lower influence of Re, as the corresponding exponent points to with its lower value. Figure 2 also shows the measurement region of the droplet characteristics used in this work. The region covers fuel mixtures that can be implemented in aero-engines since the American Society for Testing and Materials (ASTM) presently authorizes jet fuel blends with 50% in volume with fuels derived from HEFA (hydroprocessed esters and fatty acids) in aviation turbines [10,11]. HEFA fuels have proven to be able to replace conventional jet fuels [12]. [PERSON] and [PERSON] [13] compared the performance of a Jet A-1/HEFA blend with Jet A-1 on a miniature turbojet engine. The most significant \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Exponent \(\alpha\)** & **Criterion, \(K_{c}\)** & \
\begin{tabular}{c} \(Re\) **Validation** \\ **Range** \\ \end{tabular} &
\begin{tabular}{c} \(\delta\) **Validation** \\ **Range** \\ \end{tabular} \\ \hline [PERSON] and [PERSON] [4] & 0.3584 & 1136.7 & 8000–33,600 & 1 \\ [PERSON] et al. [5] & 0.4 & 2100 + 5880 \(\cdot\delta^{1.44}\) & 428–1828 & 0.1–1 \\ [PERSON] and [PERSON] [6] & 0.4 & 2823.6 + 357.71 \(\ln\left(\frac{\eta}{12-\eta}\right)^{\frac{1}{4}}\) & 150–3184 & - \\ [PERSON] et al. [7] & 0.272 & 756.7 & 988–13,900 & 0.1 \\ \hline \hline \end{tabular}  
* [PERSON] and [PERSON] [4] consider a wetted condition equivalent to the impact on a very rough surface; 1 with \(\eta=m_{s}/m_{0}\) is the mass ratio between the mass of secondary droplets produced by splash and the mass on the initial impinging droplet.  
\end{table} 
Table 1: Transition criteria for \(K_{c}\). differences in terms of operating parameters concern the fuel consumption, which is smaller for the blend. Moreover, the emission indices of CO, CO\({}_{2}\), and NO\({}_{x}\) are smaller in comparison to Jet A-1.
## 4 Experimental Results
### Training Results of the Proposed Models With or Without Pruning (Techniique)
#### 4.1.2 Experimental Results of Deep Neural Network Model
Figure 5: Spectrogram visualization comparing the proposed methods (w and w/o the pruning) and the conventional method [6]: (**a**) Input CW signal collected by the sonobuoy; (**b**) Input LFM signal collected by the sonobuoy; (**c**) CW signal reconstruction using the conventional method; (**d**) LFM signal reconstruction using the conventional method; (**e**) CW signal reconstruction using the proposed method; (**f**) LFM signal reconstruction using the highweight proposed method; (**h**) LFM signal reconstruction using the lightweight proposed method. Figure 6: Overlapped frequency analysis for the time segments 1.0 s–1.5 s of the CW signal and 2.0 s–2.5 s of the LFM signal. “Power (dB)” represents the squared magnitude of each spectrum expressed in decibels. To compare power spectra, normalization was performed using the maximum values of the clean, noisy, and reconstructed signals. (**a**) Frequency analysis of the CW signal; (**b**) Frequency analysis of the LFM signal.
# Micro-Satellite Systems Design, Integration, and Flight
## 3 ChipSat RTOS and RF Development
### Implementing RF Communications on TI Launchpad Using CCS
In approximately the middle of each of these periods, it reads the frequency of the transmission and translates it into a binary 1 or 0. GFSK allows for gradual frequency changes, lowering the cost of the equipment and decreasing the RF leakage. There are several levels of GFSK. At the base case, a 2 GFSK will oscillate between a higher and lower frequency. 2 GFSK is what is implemented in the ChipSat modulator. See Figure 16. The transmitter records the frequency changes at each period, as noted below by the gray circles. 2 GFSK gives the transmitter the ability to gradually change its frequency. One way to make the transmission faster is to use a 4 GFSK. The 4 GFSK divides the input signal into sequences of 2 bits. In binary, there are four possible sequences like this:  
Figure 16: Dual Gaussian frequency shift keying (2 DFSK). Note that the \(y\) axis in this instance displays frequency. Image Credit: “802.11 Wireless Networks: The Definitive Guide” [36]. Figure 15: Binary frequency shift keying (BFSK). 00, 01, 11, and 10. Each one of these cases has its own carrier frequency. Figure 17 below shows the different separate frequencies. 4 GFSK can send and receive data at double the speed of 2 GFSK because it can switch between two bits for every frequency change. The next improvement would be to use 8 GFSK. Figure 18 below shows an example of an 8-bit carrier signal. 8 GFSK is another way to send three bits. But sending three bits only makes the transmission speed 50% faster than 4 GFSK. With eight separate frequencies, it is harder, more costly, and more likely to make mistakes because of the many different frequencies to demodulate. For Alpha, neither 4 GFSK nor 8 GFSK were used on the ChipSats because they made the signal less reliable over long distances. Dual GFSK was used.
## 5 Simulation Results
### Simulation Parameters  
In our simulations, we consider a unidirectional road, where UAVs are randomly located along the road. Each vehicle has a divisible computing task that can be processed on both the MEC server on the UAV and the vehicle. The vehicle and the UAV adopt the IEEE 802.11p wireless communication techniques [62]. We compare it with three algorithms: the vehicle local computing (\(\lambda_{i,j}=0\)), the average task offloading (\(\lambda_{i,j}=0.5\)), and the vehicle-RSU LBTO algorithm. In the vehicle-RSU LBTO algorithm, the MEC server is installed on the RSU. In order to adapt to the statistical significance of the results, we adopt the Monte Carlo method to calculate the average of results. The detailed simulation parameters are summarized in Table 1[50]. In the following, the simulations are performed by Matlab.  
In our simulations, we consider the following two different metrics to demonstrate the performance.  
(1) Task Processing Delay: The task processing delay includes two parts, namely, the vehicle local processing delay and the MEC server processing delay, which can be expressed as Formula (12).  
(2) Successful Task Processing Ratio: The ratio of number of tasks that have been successfully processed to the number of total tasks. The successful task processing ratio \(R_{suc}\) can be defined as  
\[R_{suc}=\frac{m}{n}*100\%, \tag{52}\]  
where \(m\) represents the tasks have been successfully processed in the task maximum allowed latency and \(n\) represents number of total tasks.
## 2 Control Fundamentals
### Control Type for VSI  
A VC-VSI requires, at least, disposing of an LC-type (inductive-capacitive) filter at its output; see Figure 1a. The AC capacitor voltage, \(U_{C}\), is managed by a voltage control loop. Its control action regulates the reference of the inner current loop, \(I_{L_{1}}^{*}\), ensuring the converter current during operation. Usually, a second inductance is also considered, \(L_{2}\), which represents the aggregation of the leakage inductance of a transformer and the line impedance between the capacitor and the point of common coupling (PCC). This impedance makes possible a third master control loop corresponding to the AC droop control in GSCI. This allows handling the power exchange with the mains. Its control action determines the amplitude and phase for the voltage reference \(U_{C}^{*}\). Accordingly, the aim of using an AC droop control strategy is to allow the operation of several VC-VSI in parallel, mainly without the use of communications [8; 9; 10]. AC droop control is usually considered as a non-ideal voltage source because its apparent power regulation is achieved by managing the difference of the frequency and voltage amplitude between \(U_{C}\) and \(U_{PCC}\).  
On the other hand, a CC-VSI only strictly requires an inductive output filter. Apparent power can also be controlled, but it is directly regulated according to a translation from the apparent power reference to amplitude and phase in terms of current, as shown in Figure 1b. The active or reactive power amount can be simply translated to current by the ratio of the reference over the instant voltage or the instantaneous voltage lagging a grid quarter period, respectively.
# Evaluating the Metabolomic Profile and Anti-Pathogenic Properties of _Cannabis_ Species
## 3 Results and Discussion
### Insecicidal Activities of Cannabis
Despite the literature showing positive activity against some insects, the ability of other essential oils obtained from the stem wood and the fresh and dry bark of _Cannabis_ is often ignored in modern research. Based on this observation, it shows that exhaustive profiling of bioactive compounds and the chemical characterization and analysis of _Cannabis_ compounds for their insecticidal activity has not been fully achieved. For instance, _Cannabis_ has been observed to produce volatile sulfur compounds known to contribute significantly to the pungent aroma [173]. Although it is reported that this compound is produced in much lower concentrations than the major terpen and sesquiterpenes [174], no study has broadly explored its insecticidal potential. This is even though some other plants such as garlic (_Allium sativum_), which is known to possess significant amounts of organosulfur compounds, have proven to contain strong insecticidal activity against _Aedes aegypti_ and _Tribolium castaneum_[175]. Furthermore, research has proved that the effectiveness of plant-derived compounds, such as saponin, steroids, isoflavoids, essential oils, alkaloids, and tannins, has the potential to exert insecticidal activities on various insects, notably mosquito larvicides [176]. Numerous investigations from various medicinal plants have shown the efficacy of these active compounds and essential oils as insect larvicides, repellents, over deterrents, and growth inhibitors without causing any harm to humans [177; 178]. Surprisingly, details relating to their insecticidal properties on _Cannabis_ have thus far not been sufficiently researched nor documented. Some previous reports suggest that the repellent connects with the female receptors, hindering the impression of smell and thus keeping the mosquitoes from perceiving the host [179]. In addition, researchers on mechanisms assume that these phytochemicals have the potential to change the physiology of insects in a variety of ways by affecting biochemical processes in insects, especially disrupting the endocrine balance [180]. Therefore, more in-depth in silico research studies focusing on the mechanisms should be carried out to produce concrete empirical evidence as most studies were conducted under in vitro conditions. Moreover, bioactive compounds are yet to be tested for their insecticidal activity. Their components must be included in silico analyses to know their absorption, distribution, metabolism, and excretion properties and possible human health risks [181]. According to [PERSON] et al. [182], who used computational analysis, the possible mechanism of action could be predicted for each essential oil or plant extract according to their components, which will significantly help to support its insecticide activity when in vivo experiments are validated. Tables 1-3 below summarize the different uses of _Cannabis_ as anti-bacterial activity, antifungal, and anti-viral.
# Dam Deformation Monitoring Model Based on Deep Learning and Split Conformal Quantile Prediction
## 2 Methodologies  
### Bi-Directional Long Short-Term Memory Network  
RNN was once the best AI algorithm for solving time-series prediction problems because of its ability to convey hidden layer state information [29]. However, researchers have gradually found that the single structural framework of RNN with unreasonable algorithmic solution logic easily leads to problems such as the loss of prior information and gradient explosion when dealing with complex long-term sequences [29,30]. To overcome the drawbacks of RNN, LSTM was proposed [31]. Figure 2 shows the internal unit structure of RNN and LSTM. Compared with RNN, LSTM replaces the recurrent hidden layer inside the RNN with a memory block. This memory block contains an input gate, an output gate, a forget gate, and a memory cell, and it controls the inflow and outflow of information at the hidden level through the synergy of these gate structures with the memory cell [32]. The main computational flow of LSTM is as follows:  
\[\text{Input gate calculation}:\;i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{1}\]  
\[\widetilde{C}_{t}=tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{c}) \tag{2}\]  
\[\text{Forget gate calculation}:\;f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}) \tag{3}\]  
\[\text{Update cell state}:\;C_{t}=f_{t}\cdot C_{t-1}+i_{t}\cdot\widetilde{C}_{t} \tag{4}\]  
\[\text{Output gate calculation}:\;O_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t-1}]+b_{o}) \tag{5}\]  
\[h_{t}=O_{t}\cdot tanh(C_{t}) \tag{6}\]  
In Equations (1)-(6), \(\widetilde{C}_{t}\) and \(i_{t}\) together form the input gate structure; the inflow of information controlled by the forget gate depends mainly on the relationship between the input variables \(x_{t}\) at the current moment and the hidden state \(h_{t-1}\) at the previous moment; the amount of information contained in the latest cell state is determined by \(\widetilde{C}_{t}\), \(i_{t}\), and \(f_{t}\); finally, the hidden state of the current moment \(h_{t}\) is obtained by the calculation of the output gate; \(b_{c}\), \(b_{i}\), \(b_{f}\), and \(b_{o}\) represent the bias vectors of input gate, forget gate, latest cell state, and output gate, respectively; \(W_{i}\), \(W_{c}\), \(W_{f}\) and \(W_{o}\) are the corresponding weight matrices.  
As a special variant of LSTM, the proposed Bi-LSTM has two LSTM layers with different orientations in the memory block to process information from the past and the future [33]. Figure 3 illustrates the structure of the Bi-LSTM. The two LSTM layers contain a pair of anisotropic temporal hidden states, i.e., forward \(\overset{\rightarrow}{h}_{t}\) and backward \(\overset{\leftarrow}{h}_{t}\),  
\[\overset{\rightarrow}{h}_{t}=LSTM(h_{t-1},x_{t},c_{t-1}),t\in[1,T] \tag{7}\]  
\[\overset{\leftarrow}{h}_{t}=LSTM(h_{t+1},x_{t},c_{t+1}),t\in[1,T] \tag{8}\]  
\[H_{t}=[\overset{\rightarrow}{h}_{t},\overset{\leftarrow}{h}_{t}] \tag{9}\]  
where \(T\) is the span of the time sequences.  
Figure 2: Internal infrastructure comparison of (a) RNN cell, (b) LSTM cell.
# Urban Free-Space Optical Network Optimization
## 1 Introduction  
Free-space optical (FSO) communication is an optical point-to-point communication technology. This type of model is based on light propagating in free space (air, outer space, or vacuum) for wireless transmission of data.  
The FSO communication involves the use of modulated optical or laser beams to send telecommunication information through the atmosphere. The concept of light communication is not new; [PERSON] first introduced it in 1880. He demonstrated the use of an intensity-modulated optical beam to transmit telephone signals through the air to a distant receiver. Modern FSO commonly makes use of narrow laser beams to maximize the distance and bandwidth; therefore, it also is referred to as \"laser-com\". For a general survey on FSO, see in [1].  
Unlike radio frequency (RF) communication, FSO requires line of sight (LOS) and therefore is used rarely in urban regions, which typically have limited visibility. However, with the ever-growing demand for bandwidth, FSO technology is gaining increasingly more interest as an alternative to RF communication due to its high bandwidth, high security, flexible networking, and exemption from spectrum licensing [2]. Whereas fixed FSO links between buildings have long been established and form a separate commercial product segment in local and metropolitan area networks, the mobile and long-range applications of this technology have yet to be used. The causes for that lack of use are the vulnerability of FSO links to the fading effect, dispersion and signal obstruction due to weather and atmospheric conditions, and narrow beam width [3].  
Therefore, FSO is often addressed as a \"best effort\" means of communications allowing only 99-99.9% connectivity [4]. Yet, recent FSO devices with 1550 nm technology are less vulnerable to weather and can cope with extreme rain, heavy snow, and significant fog. In fact, modern hi-end FSO devices support 10 Gbps links with very high \"up-time\" of 99.99_99.999% at 150-500 m range [5,6]. Thus, FSO technology can be used as a complementary backbone infrastructure for urban networks.
## 5 Intelligent Fault Diagnosis of Sensor Faults  
The purpose of sensor fault diagnosis is to detect the fault data in the sensor, and restore or isolate the faulty sensor so that the sensor can finally provide normal data to the user. Traditional fault diagnosis techniques mainly rely on manual judgment, and the experience and expertise of engineers determine the accuracy of judgment. However, with advances in science and technology, agriculture has entered the 4.0 era, and the data collected by sensors are multi-dimensional and large-scale, and the monitoring objects often have coupling relationships and affect each other. Relying only on manual machine fault diagnosis struggles to meet the current needs in terms of accuracy and real-time. In factory farming scenarios, users want an automated way to reduce labor costs and improve diagnostic accuracy. The introduction of intelligent fault diagnosis (IFD) technology is expected to achieve this goal. IFD refers to the application of machine learning theories (such as artificial neural networks (ANN), support vector machines (SVMs), and deep neural networks (DNNs)) in machine fault diagnosis [83], adaptively learning machine fault diagnosis knowledge from the collected data, automatically establishing a relationship model between the collected data and the health state of the machine, and realizing the automation and intelligence of sensor fault diagnosis technology. This section divides the sensor fault diagnosis technology into three methods: model-based, artificial intelligence-based, and deep learning diagnosis-based, as shown in Figure 4. Relevant sensor fault diagnosis approaches are shown in Table 3.  
Figure 4: Classification of intelligent fault diagnosis methods.
## 5 Case Study and Discussion
### Comparison Method  
The coordination of vehicles requires a series of calculations and feedback, along with consideration of subsequent schedules, which makes manual rescheduling a highly challenging task. Therefore, the most common approach to rescheduling during actual operations is rule-based decentralized control [4]. Consequently, in the absence of backup vehicles, the most common practice is to cancel the remaining trip schedules of the disabled buses when a breakdown occurs. This method does not require reassignments and can be carried out manually with ease.  
In this section, our experimental analysis focuses on comparing the effectiveness of different modeling approaches and algorithms. The central consideration in constructing the ISCR model is whether delays are introduced for trips. We further examine the following two scenarios:  
(a) With time delay (WT): Time delay is introduced during the construction of the connection network to expand the solution space, and the ISCR model is developed based on this connection network with delayed trips.  
(b) Without time delay (WOT): Only trips from the original timetable are taken into consideration, and the ISCR model is constructed based on the connection network without delayed trips.  
Furthermore, the following optimization algorithms are compared. Algorithms II-IV solve the problem after dividing it into periods, but they differ in their sub-stage solution approaches and how they create comprehensive scheduling schemes.  
(I) Manual approach (MA): Recovers public transit operations using the above manual optimization methods.  
(II) Greedy algorithm (Greedy): According to problem (23), the solution of each sub-stage is obtained directly using the Gurobi solver, and the final optimal solution is output by the greedy algorithm.  
(III) Greedy with LNS (Greedy-LNS): According to problem (8), the solution of each sub-stage is found by the LNS algorithm, and the final optimal solution is output by the greedy algorithm.  
(IV) DP with LNS (DP-LNS): According to problem (8), the solution of each sub-stage is found by the LNS algorithm, and the final optimal solution is output by the Dijkstra algorithm.