esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential Role of Plant Extracts and Phytochemicals Against Foodborne Pathogens
## 5 Plant Extracts and Phytochemicals against Parasites Causing Foodborne Diseases
### Toxoplasma gondii  
Toxoplasmosis is a parasitic infection caused by _Toxoplasma gondii_ and transmitted from infected pregnant women and contaminated food and is a major cause of foodborne hospitalization and death [254]. Meat from various sources including pork, cattle, wild game meat, poultry meat, lamb if not properly cooked and vegetables contaminated withocysts, infected water and feces can cause transmission of toxoplasma infection [255]. Proper cleansing, cooking of meat and washing of fruits and vegetables can significantly reduce the risk of toxoplasmosis. Immunocompromised and organ transplant individuals are at more risk to develop this infection. Several toxoplasmosis outbreaks in Korea are reported to be linked with the use of uncooked pork [256]. Toxoplasmaosis is reported as a major cause of neurologic infections in HIV infected individuals [257]. Toxoplasma is reported as the second major cause of foodborne infections and the fourth major cause of hospitalization and deaths in the United States [4]. In Greece, toxoplasmosis has been reported among the top five contributors of foodborne infections, leading to major disabilities and deaths [258].  
Several studies regarding the efficacy of natural products against toxoplasmosis have been reported. In a study, [PERSON] et al. reported the antiparasitic potentials of traditionally used plant extracts against _Toxoplasma gondii_ and _Neospora caninum_[259]. Solvent extracts of _Sophora flavescens_ Aiton, _Torilis japonica_ ([PERSON].) [PERSON], _Ullmus macrocarpa_ [PERSON], _Sinomenium acutum_ ([PERSON].) Rehder [PERSON] and _Pulsatilla koreana_ (Yabe ex Nakai) Nakai ex T. [PERSON] collected from South Korea were tested. Among the tested herbs, _T. japonica_ exhibited significant inhibitory potential against _T. gondii_ by inhibiting parasite proliferation from 54% to 99% at 19.5-156 ng/mL; S. _flavescens_ extracts restrain _T. gondii_ growth by 27.2-98.7% at a concentration ranging from 39-ng/mL to 156 ng/mL. Extracts from other plants including _Pulsatilla koreana_, _U. macrocarpa_ and _S. acutum_ showed moderate antiparasitic activity against _T. gondii_. [PERSON] et al. tested the effectiveness of isolated compounds from _Simarouba amara_ and _Brucea jaumica_ fruits against _Toxoplasma gondii_, _E. histolytica_ and _Giardia intestinalis_. Among the tested compounds, ailanthione, bruceantin, bruceine B, bruceine C, bruceine D, brusatol, glaucarubinone and quassin exhibited IC\({}_{50}\) values of 0.0251, 0.0115, 0.75, 0.842, 7.56, 0.179, 0.374 and 111 \(\mu\)M, respectively [260].  
In a study, [PERSON] et al. tested methanolic extracts from fifteen traditional herbs against _T. gondii_. Among the tested samples, _Sophora flavescens_, _Zingiber officinal_, Meliae Cortex, _Acorus gramineus_ Aiton, _Dryopteris cassirhizoma_ Nakai and _Glycyrrhiza glabra_ were potent and exhibited EC\({}_{50}\) values of 0.20, 0.18, 0.77, 0.11, 0.15 and 0.13 mg/mL, respectively against _Toxoplasma gondii_[261].
## 4 Experiment and Results
### Robustness to Contour Initializations  
We evaluate the influence of contour initializations on the segmentation results for the LIDLSM and DFALS models. The segmentation results of these two methods initialized by different level set curves are shown in Figure 10. Despite the differences among several initializations, the DFALS model yields accurate segmentation results. In contrast, the LIDLSM is extremely sensitive to the initial contour positions, which can be seen from its unreasonable segmentation results. Therefore, benefiting from the deep feature-based adaptive time step, the DFALS model shows stronger robustness to different contour initializations.  
Figure 8: Auroral oval image and the corresponding benchmark image. (**a**) Auroral oval image. (**b**) Benchmark image.  
Figure 9: Extract training samples from the auroral oval images.
# Designing an Adaptive Underwater Visible Light Communication System +
## 5 Transmitter Design
### Signal Processing  
A software-defined radio is used to implement digital techniques by replacing traditional hardware like mixers, modulators, and other analog circuits. This makes the SDR more adaptive and flexible to quickly reconfigure in order to handle different signaling techniques. This project uses NI-2920 for software processing. The modulated signal from MATLAB is filtered and upconverted at the daughterboard for pass band transmission [33]. A wide-bandwidth transceiver (WBX) with a frequency range of 50 MHz to 2.2 GHz is the default daughterboard for NI-2920. However, since the desired operating frequency for this specific UVLC system is below 50 MHz, a low-frequency (LF) daughterboard is used instead. The LF daughterboard operates between 0 and 30 MHz. Unlike the WBX, this low-frequency board does not include a local oscillator that could contribute to phase noise.
## 4 Results
### Human Behaviour  
Literature research showed that the human factor is often a failure factor in the successful use of smart features, including occupancy sensing. To look at the added value of occupancy sensing, it is therefore also essential to look at the human factor and especially the influence on people and their behaviour. The sub-question for this topic was, Does occupancy sensing change the behaviour of end users? The users were asked how they experience or have experienced the human influence, and the suppliers were asked how they deal with behaviour and how they can advise the customer about this. The literature review revealed three factors that influence the human side and, therefore, the added value of occupancy sensing. These are behaviour, change management and privacy. This question focuses mainly on the users, but because the suppliers have much practical experience, they can also provide useful insights into people's behaviour and its consequences.  
Conclusions  
Research shows that occupancy sensing can change the behaviour of the end users. Occupancy sensing can improve the use of a building and make it a lot easier for the employees. On the other hand, it has to be used properly. The literature and interviews show that the human factor is a critical success factor. The technology is relatively new, and people find that difficult and even threatening. The moment an organisation involves the employees into the process and removes the uncertainties, even the most reluctant people will be able to make it. On the other hand, there are also organisations for which occupancy sensing does not work. The management must be behind it; if not, it will never succeed. This is also the reason why the non-user interviewed does not choose to use occupancy sensing. They know that they will not be able to convince the organisation and that it is, therefore, a waste of investment. Occupancy sensing may decrease workplace costs or provide a more efficient way of working. However, when employees are entirely dissatisfied and, therefore, less productive, those savings are quickly reversed and can only cost more in the long run.
## 2 The Evolution of Space Situational Awareness Policies
Existing space situational awareness systems primarily detect and perceive unknown targets one by one within their detection range. The development of large-scale spacecraft constellations urgently calls for perception systems capable of addressing clustered targets. Furthermore, to ensure the long-term operation of increasingly sophisticated space payloads on orbit, it is crucial to minimize the probability of collisions between these payloads and space debris. This places higher demands on the sensitivity of space situational awareness systems, necessitating the research and development of equipment and systems capable of detecting extremely small space debris. In order to reduce the detection time of space collision threats, providing more time for spacecraft to generate and execute collision avoidance strategies, space situational awareness systems need to have a longer detection range and higher recognition speed. This enables the early and swift identification of potential close encounters and collision threats.
# Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5
## 3 Materials and Methods
### Improved YOLOv5
#### 3.3.3 Optimize the SPP Module  
While the drone is performing aerial photography, if the altitude is too high, it will cause the target to have small proportions in the image. The size of the input feature map of the SPP module is 512 \(\times\) 19 \(\times\) 19. After the convolution kernel of 256 \(\times\) 512 \(\times\) 1 \(\times\) 1, the number of channels of the feature map changes, and the size of the output feature map is 256 \(\times\) 19 \(\times\) 19. Then, self-sampling this feature map with three parallel max-pooling layers, and then splicing the output feature map into the channel, outputting a feature map with a size of 1024 \(\times\) 19 \(\times\) 19. Finally, a feature graph with an output size of 512 \(\times\) 19 \(\times\) 19 is obtained after the 512 \(\times\) 1024 \(\times\) 1 \(\times\) 1 convolution kernel. To improve the detection accuracy of small and medium targets, a 3 \(\times\) 3 maximum pooling layer is added to the SPP module to improve the receptive field of the model. At the same time, in order to ensure that the number of input channels of the CSP2_1 module is consistent with the number of  
Figure 5: (**a**) The network structure of improved BottleNeckCSP_1 module; (**b**) The network structure of improved BottleNeckCSP_2 module.  
output channels of the SPP module, the weight matrix of the second convolution kernel in the SPP module is then increased by 1/4 of the number of channels. The improved SPP module is shown in Figure 6.
## 1 Introduction
While existing research addresses high-gain, beam-steering, and isolation enhancement, many designs are either bulky, complex, or limited in gain or bandwidth. The proposed linear 1 \(\times\) 6 array using the series-fed technique, fabricated on Rogers 3003, addresses these gaps by combining  
* High gain (-9.9 dBi);
* Wide impedance bandwidth (30.21%);
* A simple and compact design;
* Superior cross-polarization levels in both E and H planes. It enhances performance by integrating a two-way power divider, enabling modular planar array formation, which remains underexplored in existing works.
## 5 Discussion  
In this study, the accuracy of the TC center positions estimated by the automatic algorithm was assessed by comparing them with visually located TC center positions. This comparison is detailed in Table 2, where \(D1\), \(D2\), and \(D3\) represent the distances between the visually located TC center positions and the coarsely estimated, precisely estimated, and NRCS-adjusted TC center positions, respectively. For TC Noru, the comparison yielded distances of 21.42 km for \(D1\), 14.39 km for \(D2\), and 8.19 km for \(D3\). In the case of TC Doksuri, the distances were found to be 14.36 km for \(D1\), 20.48 km for \(D2\), and 17.10 km for \(D3\). Interestingly, the initial coarse estimation (\(D1\)) provided a relatively accurate center position. However, the precision decreased slightly in the subsequent stages. For TC Koinu, the distances between the visually located and estimated positions were 47.82 km for \(D1\), 31.59 km for \(D2\), and 5.42 km for \(D3\).  
Figure 6: NRCS-adjusted estimation for centers of **(a)** TC Noru; **(b)** TC Doksuri; **(c)** TC Koinu. The NRCS-adjusted TC centers are denoted by blue points, with the point corresponding to the lowest NRCS value.  
Upon analyzing the data presented in Table 2, an observable trend is that the estimation error for TC centers decreases as the estimation procedure progresses through its stages. These results illustrate a clear improvement in accuracy as the estimation process progresses. The initial coarse estimation provides a broad approximation, which is significantly refined during the precise estimation stage, and further honed by the NRCS adjustment, resulting in the accurate estimation. However, the case of TC Doksuri presents a notable exception to this general trend. Specifically, for Doksuri, the highest estimation accuracy was achieved during the first stage of the algorithm. This anomaly can be attributed to the distinct characteristics of TC Doksuri, particularly its larger eye region compared to TCs Noru and Koinu. The expansive size of Doksuri's eye may have introduced complexities in Stage 2 and the optional stage, potentially complicating the TC-center estimation due to increased variability and structural details within the eye region itself.  
In addition, the three SAR images used in this study, acquired in TOPSAR and ScanSAR modes, consist of multiple sub-swaths. While the splicing noise between sub-swaths and striping noise within sub-swaths could potentially interfere with SSWD retrieval, the algorithm demonstrated robust performance, with the TC-center estimation results maintaining high accuracy despite these challenges.
# Full Characterization of a Molecular Cooper Minimum Using High-Harmonic Spectroscopy
## 2 Materials and Methods  
### Experimental Details  
All measurements presented here were obtained by frequency converting chirped-pulse-amplified Ti:sapphire pulses at a 1-kHz repetition rate into mid-infrared (MIR) pulses, using a commercial optical parametric amplifier (OPA; HE-TOPAS Prime, Light Conversion, Vilnius, Lithuania). The pulses were \(\sim\)65 fs in duration at a central wavelength of 1650 nm. Our motivation for working with MIR wavelengths was twofold. Firstly, with HHG spectra composed of odd harmonic orders of the driving field, the spacing in the XUV frequency comb scales as the inverse of the laser wavelength, and thus, MIR sources allow for a finer energetic sampling. Secondly, because the HHG cut-off energy scales proportionally with the ponderomotive energy (i.e., quadratically with the wavelength), the MIR driving field provides a wider range of experimentally accessible energies relative to Ti:sapphire wavelengths. For intensity scans, a waveplate/polarizer pair was inserted into the beam for flexible control over the pulse energy.  
GD measurements were performed using the reconstruction of attosecond beating via interference of two-photon transitions (RABBITT) method [22; 23]. The RABBITT apparatus is based on a Mach-Zehnder interferometer, in which a majority of the MIR driving beam is delivered to a pulsed gas valve (Even-Lavie, 200-\(\upmu\)m nozzle diameter) in a 400-mm reflective focal geometry to create HHG,and a small amount of the MIR is retained in the other arm of the interferometer. The molecular density in the gas pulse was estimated to be \(\sim\)10\({}^{18}\) cm\({}^{-3}\). In all cases, the molecules were probed in unaligned conditions, so any effects due to the permanent dipole [24] of methyl chloride were neglected due to angle averaging. The XUV light was propagated through a 200-nm aluminum filter to remove the remaining MIR, before being refocused into a home-built magnetic-bottle electron spectrometer (MBES). The MIR arm was recombined and focused, along with the XUV, in a gas jet in the MBES with variable delay, and the RABBITT interaction retrieved information about the XUV spectral phase from oscillations in the population of photoelectron sidebands at the energies of even harmonics. Neon was chosen as the detection gas due to its relatively uniform photoionization cross-section, and its GD contribution across the energetic range of interest (\(\sim\)30-70 eV) [25].
## 3 Outage Probability of RIS-ONOMA
### DL Sum Capacity Maximization through Optimal Power Allocation  
This section discusses in detail the optimal power fractions for RIS-SR-ONOMA and RIS-AP-ONOMA systems for maximizing DL sum capacity. The sum capacity of the RIS-SR-ONOMA system having two users at DL is given as  
\[C_{T}=C_{SU}+C_{WI}=log_{2}\big{(}1+\Upsilon_{1}^{x_{1}}\big{)}+log_{2}\big{(} 1+\Upsilon_{2}^{x_{2}}\big{)} \tag{35}\]  
Here \(C_{SU}\) and \(C_{WI}\) represents capacities of \(SU\) and \(WI\) respectively. Both \(SU\) and \(WI\) power allocation factors \(\alpha_{2}\) and \(\alpha_{1}\) need to be optimized to maximize their sum capacity, while satisfying their QoS constraints listed in (2), (36) and (37)  
\[log_{2}\big{(}1+\Upsilon_{1}^{x_{1}}\big{)}\geq\dot{\mathrm{D}}_{WI} \tag{36}\]  
\[log_{2}\big{(}1+\Upsilon_{2}^{x_{2}}\big{)}\geq\dot{\mathrm{D}}_{SU} \tag{37}\]  
Figure 2: Pictorial representation of RIS-AP-ONOMA system.  
Substituting (5) and (11) in (35) and simplifying,  
\[C_{T}=log_{2}\bigg{(}(1+\beta_{1}\varrho_{s})\bigg{(}\frac{1+\beta_{2}\alpha_{2} \varrho_{s}}{1+\beta_{1}\alpha_{2}\varrho_{s}}\bigg{)}\bigg{)} \tag{38}\]  
The term \((1+\beta_{1}\varrho_{s})\) is constant. The sum capacity is increasing function of \(\alpha_{2}\), which lies in the interval:  
\[\underbrace{\frac{D_{SU}}{\beta_{2}\varrho_{s}}}_{\alpha_{2}^{min}}\leq\alpha _{2}\leq\underbrace{\frac{\beta_{1}\varrho_{s}-D_{WU}}{\beta_{1}\varrho_{s}(1 +D_{WU})}}_{\alpha_{2}^{max}} \tag{39}\]  
The condition in (39) is derived in detail in Appendix C.  
\(\alpha_{2}^{min}\) should be lower than \(\alpha_{2}^{max}\). Hence,  
\[\frac{D_{SU}}{\beta_{2}\varrho_{s}}\leq\frac{\beta_{1}\varrho_{s}-D_{WU}}{ \beta_{1}\varrho_{s}(1+D_{WU})} \tag{40}\]  
To attain the QoS constraints, the overall SNR requirement of RIS-SR-ONOMA system is given by  
\[\varrho_{s}\geq\frac{D_{SU}}{\beta_{2}}+\frac{D_{SU}D_{WU}}{\beta_{2}}+\frac {D_{WU}}{\beta_{1}} \tag{41}\]  
The optimal value of \(\alpha_{2}\) is the largest value, which maximize the overall capacity. The optimal value of \(\alpha_{2}\) in RIS-SR-ONOMA is given by  
\[\alpha_{2}^{opt}=\frac{\beta_{1}\varrho_{s}-D_{WU}}{\beta_{1}\varrho_{s}(1+D_ {WU})} \tag{42}\]  
The optimal value of \(\alpha_{1}\) in RIS-SR-ONOMA is given by  
\[\alpha_{1}^{opt}=1-\alpha_{2}^{opt} \tag{43}\]  
In the same fashion, we can obtain the optimal power fractions for RIS-AP-ONOMA system.
###### Abstract  
To handle the exponential growth of data-intensive network edge services and automatically solve new challenges in routing management, machine learning is steadily being incorporated into software-defined networking solutions. In this line, the article presents the design of a piecewise-stationary Bayesian multi-armed bandit approach for the online optimum end-to-end dynamic routing of data flows in the context of programmable networking systems. This learning-based approach has been analyzed with simulated and emulated data, showing the proposal's ability to sequentially and proactively self-discover the end-to-end routing path with minimal delay among a considerable number of alternatives, even when facing abrupt changes in transmission delay distributions due to both variable congestion levels on path network devices and dynamic delays to transmission links.  
networks; routing; congestion; variable link delay; SDN; algorithm design; multi-armed bandits +
Footnote †: journal:  
Article  
A Bayesian Multi-Armed Bandit Algorithm for Dynamic End-to-End Routing in SDN-Based Networks with Piecewise-Stationary Rewards.
## 5 Results
### Comparison of SL and FL  
SL and FL were compared in terms of the performance of local models using the test data from the client data in Table 3, as shown in Figure 2. Since there were 50 client local models, their performance was averaged for evaluation. The performance comparison between SL and FL is shown in Table 5. The accuracy-based LMECS outperformed both SL and other FL algorithms in overall performance. In terms of accuracy, our algorithm (81.42 \(\pm\) 2.33) showed a 3% improvement over SL (78.17 \(\pm\) 0.42) and up to a 9% improvement over existing FL algorithms (62.81 \(\pm\) 14.8). Sensitivity, an indicator of critical care prediction, improved by 3% (83.44 \(\pm\) 0.63) compared to SL (80.70 \(\pm\) 1.55) and also outperformed the existing algorithms. Specificity, PPV, and NPV also showed better performance than both SL and the existing FL models. In terms of AUC performance, the AUC-based LMECS was the highest, but not significantly different from other LMECS algorithms. This LMECS showed a 2% improvement over SL (79.68 \(\pm\) 0.74) and 6% and 23% improvements over FedAvg (75.72 \(\pm\) 1.69) and FedProx (75.72 \(\pm\) 1.69), respectively. These experimental comparisons demonstrate that applying various metrics within the LMECS algorithm can provide adaptability and excellent performance suitable for user-specific tasks. Our experimental results indicate that LMECS can achieve performance levels similar to centralized learning, highlighting its potential to reach the capabilities of a centralized approach while maintaining the benefits of FL. Additionally, when compared to standalone  
Figure 4: Sensitivity performance comparison between LMECS and existing FL algorithms.  
Figure 5: AUC performance comparison between LMECS and existing FL algorithms.  
learning, the use of LMECS-based FL positively impacted the performance of client local models, showcasing an enhancement in functionality over existing FL algorithms.