esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reconfigurable Single-/Dual-Bandstop Filter with Controllable Equal-Ripple Performance
## 2 Design and Analysis of the Proposed Reconfigurable BSF
### State I: S-BSF
#### 2.1.1 Design Equation of State I  
Figure 3 exhibits the topologies of the even-mode and odd-mode equivalent circuits for State I. The step-by-step input impedances and the total input impedances for even-mode and odd-mode are indicated in Figure 3 and can be calculated as follows:  
For the even mode, the input impedances of two step-impedance open stubs are represented by \(Z_{ine(1)}\) and \(Z_{ine(2)}\). \(Z_{ine1}\) is defined as the total input impedance of the even-mode equivalent circuits. The abovementioned input impedances can be obtained by the following:  
\[Z_{ine(1)}=\frac{-jZ_{2}Z_{3}+jZ_{2}(Z_{2}+Z_{3})\sin^{2}\theta}{\cos\theta \sin\theta(Z_{2}+Z_{3})} \tag{1}\]  
Figure 3: Topologies of (**a**) even-mode and (**b**) odd-mode equivalent circuits for State I.  
Figure 2: Scheme of the general response for (**a**) S-BSF state and (**b**) D-BSF state of the proposed topology in Figure 1. (Solid red lines and dashed blue lines represent \(S_{11}\) and \(S_{21}\) respectively).  
\[Z_{ine(2)}=\frac{-2 jZ_{1}Z_{4}+jZ_{1}(Z_{1}+2Z_{4})\sin^{2}\theta}{\cos\theta\sin \theta(Z_{1}+2Z_{4})} \tag{2}\]  
\[Z_{inel}=\frac{1}{j\frac{\cos\theta\sin\theta(Z_{1}+2Z_{4})}{2Z_{1}Z_{4}-Z_{1}(Z _{1}+2Z_{4})\sin^{2}\theta}+j\frac{\cos\theta\sin\theta(Z_{2}+Z_{3})}{Z_{2}Z_{ 2}-Z_{2}(Z_{2}+Z_{3})\sin^{2}\theta}} \tag{3}\]  
Similarly, for the odd mode, \(Z_{ino(1)}\) and \(Z_{ino(2)}\) are defined as the input impedances of the step-impedance open stub and the step impedance short stub. The total input impedance of the odd-mode equivalent circuits is represented by \(Z_{ino1}\). The abovementioned input impedances are expressed by the following:  
\[Z_{ino(1)}=\frac{-jZ_{2}Z_{3}+jZ_{2}(Z_{2}+Z_{3})\sin^{2}\theta}{\cos\theta \sin\theta(Z_{2}+Z_{3})} \tag{4}\]  
\[Z_{ino(2)}=jZ_{1}\tan
## 3 Materials and Methods
### Identification of the EOs and Hydrolate Constituents by GC-MS  
Identification and quantification of the chemical constituents of the essential oil and hydrolates of sea fennel were performed by GC-MS using a gas chromatograph (gas chromatograph model 8890 GC equipped with an automatic liquid injector model 7693A, and a tandem mass spectrometer (MS) model 7000D GC/TQ (Agilent Inc., Santa Clara, CA, USA). Chromatographic separation was performed on the nonpolar HP-5 MS UI column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent Inc., Santa Clara, CA, USA). Helium was used as the carrier gas at a flow rate of 1.0 mL min, and the sample injection volume was 1 \(\upmu\)L. Analyses were performed using MS full scan (33-350 m/z). The ion source temperature was set at 200 \({}^{\circ}\)C, the interface temperature at 250 \({}^{\circ}\)C, and the ion voltage at 70 eV. For EOs analysis, the column temperature program was set for the first 3 min at 60 \({}^{\circ}\)C and then heated to 246 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min and maintained for 25 min isothermally. For hydrate analyses, the initial oven temperature started with a 3 min isotherm at 40 \({}^{\circ}\)C, followed by a temperature increase to 80 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min, followed by another temperature increase at a rate of 10 \({}^{\circ}\)C/min to 220 \({}^{\circ}\)C, where it was maintained for 5 min.  
Compounds were identified by comparing their retention indices with the series of \(n\)-hydrocarbons (C\({}_{7}\)-C\({}_{30}\), Supelco Inc., Sigma Aldrich) analysed under the same conditions as described above for both groups of samples, EOs, and hydrolates, respectively. The identification of each component was based on a software comparison of mass spectra with library entries from two commercial databases, Wiley 7 MS library (Wiley, NY, USA) and NIST02 (Gaithersburg, MD, USA), as well as the comparison of their mass spectra and linear retention indices with published data [34]. Relative percentages of the oil components (%) were calculated based on the peak areas on HP-5 MS UI column and are reported in tables according to their elution order, while Linear Retention Indexes (LRIs) were computed using [PERSON] and [PERSON]'s equation [35]. Analyses were performed in duplicate, and the percentages shown in Tables 2 and 3 were calculated as the mean value \(\pm\) standard deviation.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Hydro-Distillation** & **Microwave-Assisted Hydro-Distillation** \\ \hline
**Equipment** & Clevenger-type apparatus & MAE Ethos X, (Milestone SrI, Italy) \\
**Heating time**\({}^{*}\) & 15 min & 3 min \\
**Temperature** & 100 \({}^{\circ}\)C & 98 \({}^{\circ}\)C \\
**Duration** & 3 h & 30 min \\
**Soaking** & Yes & Yes \\
**Other** & & Microwave power 500 Hz \\ \hline \end{tabular} \({}^{*}\) time needed to obtain the distillation of the essential oil first droplet.  
\end{table}
Table 4: Parameters of the used essential oil isolation methods.
## 3 Dual Sparsity Pruning Federated Learning Framework
### Sparse Federated Training for Object Detection
#### 3.2.2 Local Sparse Pre-Training  
Each client employs a data-driven approach to prune its local model. The pruning process starts by analyzing the local dataset to compute the KL divergence, which quantifies the similarity between the local data distribution \(\mathbf{P}\) and a standard distribution \(\mathbf{Q}\). Assuming that there are \(J\) categories, the sample probabilities for each category are equal to their frequencies of occurrence. We characterize the probability of each category by a vector \(\mathbf{P}\) and assume that \(\mathbf{Q}\) is a uniform distribution, i.e., \(\mathbf{Q}(j)=1/J\) for all \(j\). The KL divergence \(k_{i}\) at device \(i\) can be calculated by the following equation:  
\[k_{i}=D_{KL}(\mathbf{P}_{i}\|\mathbf{Q})=\sum_{j=1}^{J}\mathbf{P}_{i}(j)\log \frac{\mathbf{P}_{i}(j)}{\mathbf{Q}(j)}, \tag{5}\]  
\[k_{i}=\frac{k_{i}-\min(k)}{\max(k)-\min(k)}, \tag{6}\]  
\[p_{i}=\frac{1}{1+k_{i}}. \tag{7}\]  
We then normalize \(k_{i}\) according to Equation (6), where \(\min(k)\) and \(\max(k)\) represent the minimum and maximum values of the \(k_{i}\), respectively. Since a larger normalized \(k_{i}\) indicates a greater distribution disparity, requiring a higher sparsity ratio, we can determine the feature \(p_{i}\) of the Bernoulli distribution using Equation (7). Following this, we generate a binary mask matrix \(\mathbf{M}_{i}\) using the _Bernoulli_ (\(p_{i}\)) distribution. For each channel, a binary value (0 or 1) is generated with probability \(p_{i}\). The generated binary mask is then applied to the local gradients during a sparse pre-training phase, leading to a pruned sub-model with a reduced set of parameters. The process is given as follows:  
\[g_{i,l}=\
abla_{\mathbf{\theta}}(L_{i})=backward(L_{i}), \tag{8}\]  
\[\widetilde{g}_{i,l}=g_{i,l}\odot\mathbf{M}_{i}, \tag{9}\]  
\[\mathbf{\theta}=\mathbf{\theta}-\eta\widetilde{g}_{i,l}, \tag{10}\]  
where \(l\) represents the local training step and \(\eta\) refers to the learning rate.
# Design of Nanosecond Pulse Laser Diode Array Driver Circuit for LiDAR
## 2 Circuit Principle
### Pulse LD Driving Circuit Model
\tag{3}\]  
When \(L=4R^{2}C\), solving the second-order differential equations represented by Equations (1) and (2), the characteristic roots of these two second-order differential equations are two identical real roots. Expressions for \(v_{ON}\) and \(v_{OFF}\) can be obtained, as shown in Equation (4). \[\left\{ \begin{array}{ll}v_{ON}=U\cdot\left[1-\exp\left(-\frac{1}{2 RC}\cdot t \right)\right]\\ v_{OFF}=U\cdot\exp\left(-\frac{1}{2 RC}\cdot t\right)\end{array} \right. \tag{4}\]  
When \(L<4R^{2}C\), solving the second-order differential equations represented by Equations (1) and (2), the characteristic roots of these two second-order differential equations are two distinct complex roots. Expressions for \(v_{ON}\) and \(v_{OFF}\) can be obtained, as shown in Equation (5). Figure 2: Pulse LD and its equivalent circuit. (**a**) Pulse LD. (**b**) Equivalent circuit. Figure 3 shows the simulation curves of the pulse responses of \(v_{ON}\) and \(v_{OFF}\) at both ends of the LD when different values of \(R\), \(L\), and \(C\) are set. The switch conduction time is set to 5 ns, and the power supply voltage is set to 20 V. In Figure 3a, \(R=1\)\(\Omega\), \(C=500\) pF, and \(L=4000\) pH are set, which means \(L>4R^{2}C\). It can be seen that the response pulse has one peak, the peak value of the pulse is less than 20 V, indicating a large output pulse attenuation, and that the voltage decay duration after the switch is turned off is relatively long. In Figure 3b, \(R=1\)\(\Omega\), \(C=500\) pF, and \(L=2000\) pH are set, which means \(L=4R^{2}C\). It can be seen that the response pulse has one peak, with a similar waveform shape to that in Figure 3a, but the peak value of the pulse is close to 20 V, and the voltage decay duration after the switch is turned off is relatively short, showing a better effect than Figure 3a. In Figure 3c, \(R=1\)\(\Omega\), \(C=500\) pF, and \(L=100\) pH are set, which means \(L<4R^{2}C\). It can be seen that the response pulse oscillates, and the peak value of the pulse exceeds the power supply voltage of 20 V. The output pulse width is less than 5 ns, and the voltage rapidly drops and oscillates after the switch is turned off. By reasonably controlling the values of \(R\), \(C\), and \(L\), we can control the oscillation amplitude and decay time. To obtain higher output pulse voltages and narrower output pulse widths, we need to operate the LD in the mode shown in Figure 3c. We can adjust the \(R\), \(C\), and \(L\) parameters by connecting an inductor in series or a capacitor in parallel next to the LD.
## 2 Glycerol-Based Biomanufacturing Using _Yarrowia_
### Bioconversion of Glycerol to Various Bio-Products
lipolytica_ Wratislavia K1, was performed by [PERSON] et al., Ammonium sulfate, monopotassium phosphate, and sodium chloride were identified as critical medium components that affect the ratio of polyols produced, with optimized concentrations being 2.25, 0.22, and 26.4 g L\({}^{-1}\), respectively. The optimum C/N ratio was found to be 81:1. In the optimized medium with 100 g L\({}^{-1}\) crude glycerol, the strain produced 39.3 g L\({}^{-1}\) erythroid, which corresponded to 0.40 g g\({}^{-1}\) yield [76]. For fodder yeast production, [PERSON] et al., similarly compared the biomass production of 21 _Y. lipolytica_ strains isolated from different environments grown on pure and crude glycerol. After analysis of technological process parameters and biomass chemical composition, they identified a strain as most suitable for biomass production that gave 11.7 and 12.3 g L\({}^{-1}\) biomass with 1.30 and 1.37 g L\({}^{-1}\) h\({}^{-1}\) productivity, respectively, when pure and crude glycerol (25 g L\({}^{-1}\)) was used. Surprisingly, higher biomass production was achieved on biodiesel-derived crude glycerol even at low pH (under pH 4.0), thus, additionally preventing bacterial contamination [77]. Biodiesel-derived crude glycerol was tested also for the synthesis of pyruvate acid that is widely used in the production of polymers, pharmaceuticals, and food additives. The selected strain _Y. lipolytica_ VKM Y-2378 synthesized pyruvate acid, accompanied by _a_-ketoglut acid as by-product that was minimized by medium engineering (i.e., growth limitation by thiamine, ammonium sulfate as nitrogen source, addition of glycerol, 28 \({}^{\circ}\)C, pH 4.5, and aeration between 55 and 60% saturation). The selected strain, cultivated under optimal conditions in a bioreactor with crude glycerol, synthesized 41 g L\({}^{-1}\) pyruvate acid with a yield of 0.82 g g\({}^{-1}\)[78]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Strain**} & **Carbon** & **Product** & **Engineering 2** & **Strategy** & **Set-Up 3** & **Data from** **[Ref. ]** \\ \hline \multirow{3}{*}{W29} & \multirow{3}{*}{CG} & 81.11 g L\({}^{-1}\) citric acid; 24.90 g L\({}^{-1}\) malic acid & ME & Optimization of the carbon and nitrogen source; pH 4.0 & \multirow{3}{*}{B} & \multirow{3}{*}{[72]} \\  & & & & & & \\ \cline{1-1} \cline{6-6} \multirow{3}{*}{A-101-B56-5} & \multirow{3}{*}{PG} & 57.15 g L\({}^{-1}\) citric acid; invertase & SE; ME & & & \\  & & & & & & \\  &Finally, the exploitation of glycerol and its superiority compared to glucose as a carbon source for the production of isoprenoids (e.g., \(\beta\)-carotene and limonene) is highlighted by other studies [79]. For readers interested in this topic, the recent reviews by [PERSON] et al., 2019, [PERSON] et al., 2020, and [PERSON] et al., 2022 are highly recommended [80; 81; 82].
# Enhancement of PV Panel Power Production by Passive Cooling Using Heat Sinks with Perforated Fins
## 4 Validation of the Model  
### NOCT Calibration  
The PV panel tilted at 45\({}^{\circ}\) was initially exposed to a direct solar radiation of 800 W/m\({}^{2}\), air temperature of 20 \({}^{\circ}\)C and wind velocity of 1 m/s from behind, similar to the NOCT conditions. According to the PV panel data sheet [43], in this scenario, the \(t_{NOCT}\) should be 45 \(\pm\) 2 \({}^{\circ}\)C, while the operating temperature from simulation was 47.16 \({}^{\circ}\)C. Taking into account the good matching between results and expectation, further validation was conducted. Therefore, by comparing the results from modelling with those obtained by using the acknowledged Equation (1) [14; 47], the following variation of the operating temperature was obtained (Figure 6).  
\[t_{PV}=t_{air}+\frac{t_{NOCT}-20}{800}\cdot G \tag{1}\]  
The results showed that the values computed using the current work differed from those of Equation (1) within the range of 0.28-1.98%.
## 7 Challenges and Limitations
### Ethical and Regulatory Issues  
The deployment of AI in communication networks raises various ethical and regulatory issues. On the ethical front, the bias embedded in AI models can lead to unfair outcomes. For example, if an AI system used for network management is trained on biased data, it may lead to improper prioritization of network traffic, unfair resource allocation, or even discriminatory treatment of certain user groups. Ensuring fairness and accountability in AI systems is vital, particularly as AI decisions increasingly impact human lives [140].  
One approach to mitigate bias is through rigorous dataset auditing and preprocessing. Techniques such as adversarial debiasing and fairness-aware training have shown promise in reducing bias during model development. Additionally, explainable AI (XAI) methods can provide insights into model decision-making, allowing operators to detect and address potential biases before deployment [140].  
From a regulatory perspective, ensuring compliance with regulations such as the General Data Protection Regulation (GDPR) is a critical consideration in the development and deployment of AI systems for communication networks [141]. While this report briefly mentions GDPR, a deeper integration of its principles into the design of AI models can enhance trustworthiness and user adoption. Below, I outline specific strategies to align AI systems with ethical and regulatory standards, followed by real-world examples demonstrating these approaches in practice.
## 3 Dynamic Response of Systems with Viscoelastic Elements
Considering steady-state harmonic responses, the harmonic external forces can be described as follows:  
\[\mathbf{P}(t)=\widetilde{\mathbf{P}}(\lambda)\text{exp}(\mathrm{i}\lambda t) \tag{10}\]  
where \(\widetilde{\mathbf{P}}\) is the vector of excitation force amplitudes, and \(\lambda\) is the frequency of excitation. The displacement of the structure can then be expressed as follows:  
\[\mathbf{q}(\mathbf{t})=\widetilde{\mathbf{q}}(\lambda)\text{exp}(\mathrm{i}\lambda t) \tag{11}\]  
Substituting (10) and (11) into Equation (4), we obtain the following formula:  
\[u(t)=cD_{t}^{\alpha}x(t) \tag{5}\]0  
where \(\mathbf{D}(\lambda)\) is the dynamic stiffness matrix, which can also be obtained by substituting into Equation (9) \(s=\mathrm{i}\lambda\). \[u(t)=cD_{t}^{\alpha}x(t) \tag{5}\]1  
We assume that all design parameters can vary within certain specified bounds; therefore, we can represent them as interval numbers. The equation of motion in general form can be written as follows:  
\[u(t)=cD_{t}^{\alpha}x(t) \tag{5}\]2  
or  
\[u(t)=cD_{t}^{\alpha}x(t) \tag{5}\]3  
where  
\[u(t)=cD_{t}^{\alpha}x(t) \tag{5}\]4  
The solution to Equation (15) can be expressed as follows:  
\[u(t)=cD_{t}^{\alpha}x(t) \tag{5}\]5  
where \(\mathbf{H}^{I}(\lambda)\) denotes the interval frequency response function and can be determined as follows:  
\[u(t)=cD_{t}^{\alpha}x(t) \tag{5}\]6  
The individual elements of the matrix \(\mathbf{H}^{I}(\lambda)\) with indices \(i\) and \(j\) can be interpreted as the frequency response function determined for the degree of freedom with index \(i\) when unit harmonic excitation acts in the direction of the dynamic degree of freedom of the structure with index \(j\). Determining the FRF matrix from Equation (18) requires the direct application of interval arithmetic. This method will later be referred to as the direct interval method (DIM). However, as demonstrated in the examples presented, this approach leads to significant overestimations, as described by [PERSON] et al. in in [44] and [PERSON] and [PERSON] in [13]. To mitigate the overestimation, a method will be applied where the calculation of the FRF matrix will be divided into steps, computing each column of the matrix \(\mathbf{H}^{I}(\lambda)\) sequentially. Since the dynamic response of the structure, understood as vector \(\widetilde{\mathbf{q}}^{I}(\lambda)\), can be obtained using Equation (17), it is worth noting that if we represent the vector \(\widetilde{\mathbf{P}}^{I}(\lambda)\) as  
\[\mathbf{P}= \begin{bmatrix}1&0&\ldots&0\end{bmatrix} ^{T} \tag{19}\]  
the first column of the matrix \(\mathbf{H}^{I}(\lambda)\) will be determined. Subsequent columns can be obtained by substituting value 1 at successive positions in vector \(\mathbf{P}\). This approach involves solving interval equation systems to compute the FRF matrix. Various iterative methods that can be applied for this purpose are presented by [PERSON] in [47; 56] and [PERSON] in [57]. The specific method used in this work will be described in the next section.
## 3 Design
### Critical Branch Selection
#### 3.2.3 Complexity  
Fuzzer runs very fast to generate inputs getting a shallow path coverage, while concolic execution is better at solving complicated branch constraints to reach deeper code in the program. In theory, we would like to make the fuzzer focus on the easy-to-mutate inputs that can get less complicated branches, while concolic execution focuses on solving the complicated branches. The question is how to define complexity.  
We took the idea from the way fuzzers function. Normally, fuzzers mutate the selected input by different heuristics to get more coverage. The more input offsets a fuzzer needs to mutate to get to a certain point, the bigger the search space is; therefore, the harder this branch is to solve. For example, if a particular branch is related to five different offsets in the input, then the search space will be \(2^{40}\) (five bytes, each byte with eight bits). The intuition is obvious: the more offsets are related to a branch, the harder the branch is to solve. If we can get the information on how many offsets will influence each branch, then we will get an approximate estimation of how hard the mutation is for each branch. That is to say, we should choose branches with the most related bytes in the inputs.  
We use dynamic taint analysis to trace which offsets of the input can influence a branch. By running the taint analysis, we taint the input and then trace all compare instructions to see which offsets in the input will influence each compare instruction. After running the taint analysis tool, we can get results as shown in Figure 4. The first three columns show information about the operands of compare instructions. The fourth column shows the address of each compare instruction. The fifth to twentieth columns show which offsets of the input will influence each byte of operand. Therefore, the union set of these offsets will influence the result of this compare instruction.  
Figure 4: Example of the results by dynamic taint analysis.  
Our method is to get all tainted bytes that will influence each compare instruction and calculate how many offsets there are related to each compare instruction. We consider the number of tainted offsets as the complexity value of each branch. In the _get_complexity()_ function, we do taint analysis on the input and get the number of tainted offsets related to each branch, then assign this number as complexity value \(C\) for each branch. The output of this function is a dictionary of branches and related complexity values. We then sort this \(C\) dictionary by the inverted order, choose the top 50% of the branches, and return a final branch list. Therefore, we will have a branch list related to one input, containing the hardest to solve branches from this input.
## 4 Simulation Results
### Discussion and Limitations  
The existing literature on regulating mixed-automated traffic flow at intersections has focused on introducing traffic lights for right-of-way allocation, e.g., [23; 24]. Our study attempts to optimize the behavior of AVs by predicting the trajectory of AVs and MVs, which leads all vehicles to pass through the signal-free intersection without collision and results in a satisfactory traffic performance. Compared to the strategies of jointly optimizing vehicle trajectories and signal timing, our scheme theoretically makes full use of right-of-way without idling caused by traffic lights. However, the increasing number of non-optimized MVs affects the performance of the proposed scheme. In other words, our scheme is sensitive to the PRA and traffic demand. Through the comparison simulation with [24], we find that our scheme performed better under a high PRA and low traffic demand. The results also imply that the application of signal lights and timing optimization for regulating mixed-automated traffic at intersections are more necessary and superior under a low PRA and high traffic demand. In addition, in terms of computational complexity, the proposed scheme in this paper has a certain computational demand since the optimization problem for each AV is solved at each step. Nevertheless, the proposed scheme still has significant advantages in computation compared MILPs jointly optimizing the vehicle trajectory and signal timing.