esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Preparation of the Spiked Culture  
A _Flavobacteria_ sp. isolate was cultured over 7 d at 10 \({}^{\circ}\)C in a 450 mL shaking bottle culture, shaking at 300 rpm. A DVR1 medium was used for cultivation, consisting of 6.0 g malt extract (Sigma-Aldrich, St. Louis, MO, USA), 10.0 g Peptone from casein (Sigma-Aldrich), 6.0 g yeast extract (Sigma-Aldrich), 450 mL filtrated seawater and 450 mL pH\({}_{2}\)O [11]. The media was sterilized by autoclaving at 120 \({}^{\circ}\)C for 30 min under pressure and inoculated using an inoculation loop. After cultivation, the fermentation broth was centrifuged at 4000 rpm for 10 min to remove the cells using a Multifuge 3 S-R equipped with a SORVALL 75,006,445 rotor (Heraeus GmbH & Co. KG, Hanau, Germany).  
An aliquot of 300 mL of the supernatant was spiked with eight natural products--namely, rifampicin (R3501, Sigma-Aldrich), vancomycin (Sigma-Aldrich), colchicine (C3915, Sigma-Aldrich), cyclosporine (32425, Fluka+, Honeywell, Charlotte, NC, USA), paclitaxel (86346, Fluka+, Honeywell), ampicillin (A5354, Sigma-Aldrich) and gentamycin (A2712, Merck, Darmstadt, Germany). The concentrations of the respective natural products are given in Table 1 (structures in Figure 1). After the supernatant was spiked, it was thoroughly mixed and split into two samples of 150 mL each. The two samples were processed in parallel; one was extracted using liquid-liquid extraction and one using liquid-solid extraction.  
\begin{table}
\begin{tabular}{c c c} \hline
**Compound** & **Natural Product Class** & **Concentration (ug/L)** \\ \hline Rifampicin \({}^{\star}\) & polyketide & 2.0 \\ancomycin \({}^{\dagger}\) & glycopeptide & 10.0 \\ Colchicine \({}^{\dagger}\) & alkaloid & 5.0 \\ Tetracycline \({}^{\star}\) & polyketide & 10.0 \\ Cyclosporine \({}^{\star}\) & cyclic peptide & 10.0 \\ Paclitaxel \({}^{\star}\) & terpenide & 2.0 \\ Ampicillin \({}^{\ddagger}\) & \(\beta\)-lactam antibiotic & 10.0 \\ Gentamycin\({\ddagger}\) & aminoglycososide & 10.0 \\ \hline \end{tabular} \({}^{\star}\) dissolved in ethanol; \({}^{\ddagger}\) dissolved in pH\({}_{2}\)O; \({}^{\ddagger}\) readymade solution.  
\end{table}
Table 1: Final concentrations of the natural products in the bacterial culture.
## 1 Introduction
### Relevant Literature  
The path planning problem seeks to determine the optimal sequence of waypoints to visit in order to meet certain mission objectives while minimizing costs, such as the total length of the mission [1; 2]. Path planning problems typically rely on approximating the cost of the mission by the length of the solution to a Euclidean Traveling Salesman Problem (ETSP), where the cost to travel from one waypoint to the next is approximated by the Euclidean distance between the two waypoints. This approximation simplifies the overall optimization but may lead to UAV routes that are far from optimal because the aircraft kinematic constraints are not considered.  
Another area of UAV research is trajectory planning, in which the goal given an initial and final waypoint pair is to determine the optimal control inputs to reach the final waypoint in minimum time given kinematic constraints of the aircraft. In 1957, [PERSON] showed that for an approximate model of aircraft dynamics, the optimal motion between a pair of waypoints can be chosen among six possible paths [3]. Similar results were proven later in [4] using tools from optimal control theory. In [5], the authors propose a means of choosing the optimal Dubins path without computing all six possible Dubins optimal paths.  
A significant amount of research has gone into combining the problems of motion planning and path planning [6; 7; 8; 9; 10]. In these works, the dynamics of the UAV are taken into consideration by using the Dubins model when determining the optimal sequence of waypoints. This problem is typically referred to as the Dubins Traveling Salesman Problem (DTSP).  
A third area of UAV related research is a version of path planning that takes into account the communication range of the aircraft or the sensor footprint of the aircraft. This problem is best described as a Traveling Salesman Problem with Neighborhoods (TSPN). Now, not only does one determine a sequence of regions but also an entry point at each region. Many researchers have addressed this problem with various regions, but most have used the Euclidean distance as the cost function [11; 12; 13]. [PERSON] was the first to tackle the TSPN with the Dubins vehicle model in [14] using a genetic algorithm approach, then later in [15] by using a sampling-based roadmap method, which we will call RCM, that is proven to be resolution complete. In the latter method, the DTSPN is transformed into a General Traveling Salesman Problem (GTSP) with non-overlapping node sets, and then to an Asymmetric Traveling Salesmen Problem (ATSP) through a version of the Noon and Bean transformation [16].
# Research on the Compound Jamming Method against FDA-MIMO Beamforming
## 5 Simulation Analysis  
In this section, the proposed compound jamming method is validated. The existing fake targets were named as existing fake target (EFT) and labeled as EFT1 and EFT2, and the newly generated fake targets were named as new fake target (NFT) and labeled as NFT1 and NFT2. The number of transmitting array elements was \(M=10\), and the number of receiving array elements was \(N=10\), the reference carrier frequency was \(f_{0}=9\) GHz, the wavelength was \(\lambda=0.03\) m, and the position information of the desired target was \((\theta_{0},r_{0})=(15^{\circ},20\) km). The parameters were set as shown in Table 1.
## 3 Global Performance of SFTs with Suspension Cables
The acceleration caused by waves has a decisive effect when vehicles, trains, or life passes through the SFT. The smaller this value, the higher the serviceability. Figure 9a exhibits the acceleration in Type 1. It can be seen that the acceleration in the wave load acting direction (y-axis) and the buoyancy acting direction (z-axis) was almost similarly large, and the maximum value was 0.1 m/s\({}^{2}\). Conversely, in Figure 9b, showing Type 2, the acceleration in the y-axis was smaller than the value in the z-axis. However, the value in the z-axis was 0.3 m/s\({}^{2}\), which is larger than that in Type 1. Moreover, the value of the wave acting direction (y-axis) was decreased to 0.05 m/s\({}^{2}\). This is because Type 2 has a middle anchorage, so that the deformation in the z-axis was controlled, as shown in Figure 8b. Next, Figure 10 displays the section force of the wave load on the SFT model. Here, SFx means an axial force, and SFy and SFz indicate the shear forces on the y-axis and z-axis, respectively. Figure 10a shows the generated resistance force on Type 1. It can be found that there was almost no shear force in the z-direction (SFz), and it can be seen that the shear force in the y-direction (SFy) was the largest at the towers due to waves. The maximum axial force acting on the SFT was about 100 MN. Figure 10b shows the section force on Type 2. Since it was supported by a middle anchorage, the axial force (SFx) was decreased to approximately 10 MN, and the generated shear force (SFy, SFz) in the wave load direction (y) and the buoyancy direction (z) was almost the same, but the value was lower than that in Type 1. The shape of the shear force diagram for Type 2 also differs from that for Type 1. Finally, Figure 11 plots the sectional moment on the SFT model. It can be seen that both Type 1 and Type 2 had a large torsional moment. Numerically, the torsional strength of Type 1 was greater than that of Type 2. Figure 10: Section force at each point of the SFT: (**a**) section force at each point of Type 1; (**b**) section force at each point of Type 2.
## 3 Adaptive Inversion Control
### Design of Luenberger Observer  
The Luenberger observer is an observer model based on the mathematical model of PMSM. The estimated state variables are modified according to the error feedback to achieve the purpose of error convergence and state reconstruction. The Luenberger observer has the advantages of fast response and high observation accuracy. We use position error feedback to construct the Luenberger observer:  
\[\left\{\begin{array}{c}\dot{\theta}=\hat{\omega}+l_{1}\big{(}\theta-\hat{ \theta}\big{)}\\ \dot{\omega}=\frac{n_{p}}{J}\phi{i_{q}}-\frac{\gamma_{0}}{J}+l_{2}\big{(} \theta-\hat{\theta}\big{)}\\ \dot{\tau}_{L}=l_{3}\big{(}\theta-\hat{\theta}\big{)}\end{array}\right. \tag{50}\]  
where \(\widetilde{\theta}=\big{(}\theta-\hat{\theta}\big{)}\), \(\widetilde{\omega}=\omega-\hat{\omega}\), \(\widetilde{\tau}_{L}=\tau_{L}-\tau_{L}\).  
The error dynamic equation can be obtained from Equation (50):  
\[\left[\begin{array}{c}\dot{\widetilde{\theta}}\\ \dot{\widetilde{\omega}}\\ \dot{\widetilde{\tau}}_{L}\end{array}\right]=\left[\begin{array}{ccc}-l_{1} &1&0\\ -l_{2}&0&-\frac{1}{7}\\ -l_{3}&0&0\end{array}\right]\left[\begin{array}{c}\widetilde{\theta}\\ \widetilde{\omega}\\ \widetilde{\tau}_{L}\end{array}\right] \tag{51}\]  
The characteristic equation of the observer can be obtained from Equation (51):  
\[s^{3}+l_{1}s^{2}+l_{2}s-\frac{l_{3}}{7}=0 \tag{52}\]  
According to the Routh stability criterion, the system (51) is asymptotically stable if and only if \(l_{1}>0\), \(l_{2}>0\). Suppose the pole of the observer is \(S_{p}\big{(}S_{p}<0\big{)}\), then the parameter is \(l_{1}=-3S_{p}\), \(l_{2}=3S_{p}^{2}\), \(l_{3}=J5_{p}^{3}-Jl_{1}l_{2}<l_{3}<0\).
# Privacy-Preserving Data Analytics in Internet of Medical Things
## 5 Proposed Method for Privacy Preservation in Healthcare Data
### Limitations of Implementing Differential Privacy  
* By implementing DP in real-time healthcare systems, computational delays can arises due to the need to add noise and adjust parameters dynamically. This can be crucial in scenarios where immediate data analysis is required, such as in any emergency situation.
* Continuous streams of data are produced by dynamic systems (e.g., from monitoring sensors). In this situation, maintaining differential privacy necessitates frequent modifications to privacy guarantees, which can be difficult and resource-intensive.
* The parameters for differential privacy must be continuously adjusted due to the continuous changes in patient data. This continual adjustment can make the system architecture more difficult and raise the possibility of inconsistent privacy protections.
* The requirement for real-time privacy guarantees in EHRs may encourage the addition of excessive noise, which could reduce the usefulness or accuracy of the data. This distortion in data can negatively impact clinical insights and outcomes.
* Ensuring that DP approaches scale well without appreciable performance deterioration can be challenging in dynamic systems with growing data volumes. When real-time analysis is required, this becomes especially difficult.
* Systems for providing real-time healthcare frequently face severe resource limitations. Maintaining DP comes with a computational cost that can strain system resources, potentially impacting other critical functions.
## 2 Cooling Mechanism  
Figure 1 shows the schematic diagram of an intermediate gearbox. It is mainly composed of spiral bevel gears, double row conical roller bearings, end covers, an air vent, and the casing. The oil is distributed at the bottom of the casing. Gears splash the oil onto the meshing location, the casing, and other solid surfaces. The air vent balances the atmospheric pressure inside and outside the casing. The bearings generate substantial heat and the gears cannot precisely transport oil to the bearing, therefore two oil guide pipes are present along the left and right end covers. The lubricating oil enters the bearing through the oil guide pipes, ensuring the proper lubrication of the bearing.  
At the high rotational speeds of the spiral level gear, oil mixes with air, forming a complex oil-air flow inside the intermediate gearbox. The flow state of the oil and air varies with time, meaning that the convective heat transfer mechanism between the lubricating oil and the solid surface is extremely complicated. Primarily, three heat dissipation mechanisms occur in the intermediate gearbox--conduction, convection, and radiation--as shown in Figure 2. The effect of heat radiation is weak, so it is ignored. Conduction primarily occurs inside the solid and is not affected by the lubrication method. The lubrication and cooling mechanism of the various parts inside the casing using the splash lubrication method is not clear, i.e., the law of convective heat transfer is not clear. To address this shortcoming, this study analyzes the lubrication and temperature characteristics of gears and bearings with splash lubrication.
# Design of Outer Membrane Vesicles as Cancer Vaccines: A New Toolkit for Cancer Therapy
## 3 New OMV Applications: Tumor Vaccines
### Mechanisms of OMVs Design as Cancer Vaccines
Following entry into host cells, OMVs are also detected by nucleotide-binding oligomerization domain-containing protein 1 (NOD1). Detection and degradation of intracellular OMVs results in the recruitment and activation of DCs to facilitate the development of T cell immunity (Th2 and Th17). Through the various mechanisms of entry into host cells described above, OMVs not only interact with mucosal epithelial cells, resulting in the production of cytokines and chemokines and the generation of a pro-inflammatory response, but also gain access to the submuc beyond the host's epithelial cell barrier, and directly interact with various immune cell populations, including neutrophils, macrophages, and DCs. The detailed mechanisms through which OMVs induce inflammation and modulate the host immune system have been reviewed [17]. We restrict our discussion to which OMVs modulate presentation of heterologous antigens targeting cancer cells or other immune cells to facilitate the induction of innate and adaptive immunity and thus to achieve the goal of eradicating tumor cells. Although there are relatively few studies directly investigating the anti-tumor immune response induced by OMVs delivering immunogenic antigens at present, we can reveal the mechanisms of OMV-dependent antitumor immune responses by referring to the interaction between OMVs and the host immune system (Figure 1C). OMVs contain numerous PAMPs, including DNA, RNA, lipoproteins, LPS, and peptidoglycan [49]. These PAMPs of OMVs enable them to engage with host pattern recognition receptors (PRRs) such as Toll-like receptor 4 (TLR4) to initiate pro-inflammatory signaling cascades that lead to the production of cytokines and chemokines [82]. Therefore, OMV-delivered antigens as adjuvants could stimulate APCs through the up-regulated expression of receptors and co-stimulatory molecules, and thus, enhanced T helper cells production and fully amplified cellular and humoral immune responses [83; 84]. Moreover, OMVs that have entered host cells are detected by intracellular host pattern recognition receptors, like nucleotide-binding oligomerization domain-containing protein 1 (NOD1), which seems to be the key intracellular host pattern recognition receptor that is responsible for the establishment of OMV-dependent immune responses. OMVs can mediate inflammatory signaling via the NOD1 receptor, ultimately resulting in the recruitment and activation of DCs to facilitate the development of T cell immunity [85; 86]. Furthermore, OMV delivered antigens are presented by APCs to CD4+ T cells, which leads to the generation of antigen-specific B cell responses [66; 87]. Taken together, OMVs as the adjuvant or delivery vector can be easily phagocytized and processed by APCs, thereby promoting adaptive immune responses including cytotoxic T-cell lymphocyte responses that are crucial for tumor and metastasis eradication.
# Efficient Removal of Heavy Metal Ions in Wastewater by Using a Novel Alginate-EDTA Hybrid Aerogel
## 3 Results and Discussion
### Effect of Environmental Temperature  
The influence of environmental temperature on the adsorption performance has also been investigated and illustrated in Figure 7. The adsorption performance of the Alg-EDTA is slightly affected by the variance in ambient temperature as shown in the figure. The adsorption rate of Cd\({}^{2+}\) and Pb\({}^{2+}\) by Alg-EDTA adsorbents remains between 90-95% in a wide ambient temperature window from 5 \({}^{\circ}\)C to 40 \({}^{\circ}\)C. Based on these experimental data, the value of Gibbs free energy (\(\Delta\)G) can be calculated from Equation (1) [14; 15], the enthalpy (\(\Delta\)H) and entropy (\(\Delta\)S) values can be obtained from the slope and intercept of linear Equation (2), respectively (Figure 8). As listed in Table 2, the negative \(\Delta\)G at all temperatures indicates that the Alg-EDTA bind to Cd\({}^{2+}\) spontaneously, the positive \(\Delta\)H shows that the Cd\({}^{2+}\) adsorption process is endothermic.  
\[\Delta\mathrm{G} =-\mathrm{RTlnK_{o}} \tag{1}\] \[\mathrm{lnK_{o}} =\frac{\Delta\mathrm{S}}{\mathrm{R}}-\frac{\Delta\mathrm{H}}{ \mathrm{RT}} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Kinetic model** & **Formula** & **Parameters** & **Cd\({}^{2+}\)** & **Pb\({}^{2+}\)** & **Cu\({}^{2+}\)** & **Cr\({}^{3+}\)** & **Co\({}^{2+}\)** \\ \hline pseudo-first-order & \(\begin{array}{c}\mathrm{q}_{\mathrm{t}}=\mathrm{q}_{\mathrm{t}}(1-\\ \mathrm{exp}(-\mathrm{k}_{\mathrm{t}}(1))\end{array}\) & \(\mathrm{q}_{\mathrm{t}}\) (mg/g) & 79.932 & 147.926 & 44.947 & 34.328 & 38.946 \\  & \(\mathrm{k}_{\mathrm{t}}(1-\mathrm{min})\) & 0.009 & 0.008 & 0.008 & 0.007 & 0.007 \\  & \(\mathrm{R}^{2}\) & 0.984 & 0.979 & 0.987 & 0.992 & 0.992 \\ \hline pseudo-second-order & \(\begin{array}{c}\mathrm{q}_{\mathrm{t}}=\mathrm{q}_{\mathrm{t}}(1-\\ 1/(1+\mathrm{q}_{\mathrm{t}}\mathrm{k}_{\mathrm{t}}\mathrm{k}_{\mathrm{t}}) \end{array})\) & \(\mathrm{q}_{\mathrm{t}}\) (mg/g) & 97.141 & 181.306 & 55.578 & 43.500 & 49.693 \\  & \(\mathrm{R}^{2}\) & \(\mathrm{R}^{2}\) & \(\mathrm{R}^{2}\) & \(\mathrm{R}^{2}\) & \(\mathrm{R}^{2}\) & \(\mathrm{R}^{2}\) & \(\mathrm{R}^{2}\) \\ \hline - & - & \(\mathrm{q}_{\mathrm{t}}\) (experiment, mg/g) & 82.027 & 148.56 & 45.222 & 33.459 & 37.526 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Kinetic parameters for heavy metal ions sorption onto the Alg-EDTA.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**T (K)** & \(\Delta\)**G (KJ/mol)** & \(\Delta\)**H (KJ/mol)** & \(\Delta\)**S (KJ/(mol\(\cdot\)K)** \\ \hline
288 & \(-\)2.084 & - & - \\
293 & \(-\)2.285 & - & - \\
298 & \(-\)2.507 & 9.300 & 0.040 \\
303 & \(-\)2.668 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermodynamic parameters for Cd\({}^{2+}\) sorption onto Alg-EDTA.  
Figure 6: Effect of adsorption time on (**A**) Cd\({}^{2+}\), (**B**) Pb\({}^{2+}\), (**C**) Cu\({}^{2+}\), (**D**) Cr\({}^{3+}\) and (**E**) Co\({}^{2+}\) sorption by Alg-EDTA.
## Appendix A Optimization Method  
The MZI performance is measured mainly by velocity matching, impedance matching and RF losses. Velocity mismatch decides the response of electro-optic modulator in terms of the cosine of mismatch factor and is given by Equation (A1). Impedance mismatch causes the reflection of an RF wave from the load and thus degrades the power delivery to the phase shifter and is given by Equation (A2). RF loss is decided by the imaginary part of the RF effective refractive index, which defines the decay of power towards the end of the electrode, and is given by Equation (A3).  
\[\alpha_{vel.mismatch}=cos\left(\frac{2\pi\left(n_{eff}-n_{op}\right)}{ \lambda_{rf}}L\right)\] (A1)  
\[R=\frac{Z_{in}-Z_{L}}{Z_{in}+Z_{L}}\] (A2)  
\[\alpha_{rf}=exp\left(-\frac{2\pi imag\{n_{eff}\}}{\lambda_{rf}}L\right)\] (A3)  
The results for the first equation are squared and subtracted from the maximum value and added to the square of the second and third equations for defining the target function given by  
\[Target=\left(\left(1-\alpha_{vel.mismatch}^{2}\right)^{2}+|R|^{2}+(\alpha_{ rf}^{2})\right)\] (A4)  
To optimize the value of \(\mathrm{w_{c}}\) and \(\mathrm{w_{g}}\), optimization is run across values obtained in Figure 2 to find the minimum value of the target function.
# Unveiling LoRa's Oceanic Reach: Assessing the Coverage of the Azores LoRaWAN Network from an Island
## 1 Introduction
LoRa is one of the main LPWAN communication technologies, and its use has been studied for maritime applications, ranging from monitoring fishing vessels [5] and passengers [8] to water quality monitoring in fish farms and coastal areas [6; 11]. LoRa technology provides large coverage distances, low deployment costs, and low energy consumption [12]. However, it also has limitations such as low bandwidth, high vulnerability to interference, and low transmission rate [13].
## 3 Results
### ISL Range Bias Estimation against Weighting Approach and Ground Station Sets
#### 3.2.3 Comparison of Ground Station Sets with GNSS Only
When using a regional set of station results, SISRE values are characterized with very small repeatability in subsequent simulations, even with the support of ISLs. For simulation scenarios using 16 stations located in Europe, the connectivity scheme and geometrical properties can strongly impact the orbit accuracy. Thus, regional ground station sets are not recommended for precise orbit determination when it is possible to use globally located ground stations, even when ISLs are available. Figure 8: Mean RMS estimation errors for satellite and station clocks considering the station set and VCE approach with respect to connectivity schemes: (**a**) sequential dial one-way (SDOW), (**b**) sequential one-way (SOW), (**c**) intra-plane closed (IPC), and (**d**) intra-plane open (IPO). The simulation scenario names are given in Table 7.