esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Evaluation of 254-nm Ultraviolet Light on Disinfecting SARS-CoV-2 Surface Contamination  
The effectiveness of the UVC radiation against SARS-CoV-2 has been tested in a biosafety level 3 (BLS-3) containment facility at the ISS. About 1.7 \(\times\) 10\({}^{4}\) TCID\({}_{50}\) were placed upon each material surface with a sterile pipet tip and let it dried. SARS-CoV-2 showed high susceptibility to UVC irradiation on different surfaces as reported in the log-scale graph of Figure 4 where the viral load is reported as function of the exposure time (min). On polystyrene surface, an UVC dose of 24 mJ/cm\({}^{2}\) of 254 nm UVC light (0.01 mJ/cm\({}^{2}\) per second, for 40 min) reduced SARS-CoV-2 viral load to undetectable levels, measured by calculation of 50% endpoint by serial dilution (TCID\({}_{50}\)). On a metal surface, 18 mJ/cm\({}^{2}\) of UVC light (0.01 mJ/cm\({}^{2}\) per second, for 30 min) was enough to reduce the viral load to undetectable levels, while disinfection of the rubber only required 3 mJ/cm\({}^{2}\) (0.01 mJ/cm\({}^{2}\) per second, for 5 min).  
Experimental data for the three materials was fitted using a linear regression model. The goodness of the fit is measured by the R\({}^{2}\) value that is equal to 0.947 (\(p\)-value: 5.7 \(\times\) 10\({}^{-3}\)) and to 0.944 (\(p\)-value: 5.3 \(\times\) 10\({}^{-3}\)) respectively for metal and polystyrene. A lower value of R\({}^{2}\) is measured for the rubber 0.66 (\(p\)-value: 9.3 \(\times\) 10\({}^{-2}\)). Results are summarized in Table 1, reporting the 95% confidence interval for obtaining the log3 reduction. The SARS-CoV-2 TCID\({}_{50}\)/mL values reported in Figure 4 are normalized with respect to the control measurements of the sample carried out at the same time but without exposure to UVC radiation. The control measure was used to evaluate the percentage of reduction in viral concentration due to environmental factors. After 40 min, the reduction of the TCID\({}_{50}\)/mL for polystyrene was of 22%, while on metal and rubber were of 10% and 6% respectively, demonstrating that the viral load on the surfaces slowly reduces over time.  
From the value of the regression model we evaluated the UVC dose need to achieve 90% to 99.99% of virus infectivity reduction on different surfaces (log1 to log4) virus infectivity, as reported in Table 2.  
Figure 4: UVC Susceptibility measured on different surfaces. The mean \(\pm\) SEM values from experiments (\(n=3\)) are indicated for polystyrene, metal and rubber; the lines represent the best-fit linear regressions of experimental data.
## Appendix A  
Since the inner PID control loop is designed as a stable system, convergence of the model error \(e_{m}=y-y_{m}<\varepsilon\in R\) ensures the bounded input and bounded output stability of the ML-MR PID-MIT control structure. In this manner, we can investigate the convergence condition of \(e_{m}\).  
**Lemma A1**.: _For the ML-MR PID-MIT control structure, the model error becomes zero (\(e_{m}=0\)) when the adaptation gain \(\theta\) is equal to 1 and \(T(s)=T_{m}(s)\)._  
Proof.: Let us consider model error \(e_{m}=y-y_{m}=T(s)\theta r-T_{m}(s)r\) for the ML-MR PID-MIT control structure and apply the condition of \(\theta=1\) and \(T(s)=T_{m}(s)\). Accordingly, one can show that:  
\[e_{m}=T(s)\theta r-T_{m}(s)r=T_{m}(s)r-T_{m}(s)r=0\]  
**Theorem A1** (Stability of the ML-MR PID-MIT Control Structure).: _For a stable system model \(T(s)=T_{m}(s)\), the model of the ML-MR PID-MIT control structure is stable only if the model error function is as follow:_  
\[e_{m}=-\frac{1}{\gamma}\bigg{(}s^{2}+\frac{s^{2}}{C(s)G(s)}\bigg{)}, \tag{1}\]  
_where the bounded input and bounded output are stable. For the below equation the model error converges to zero._  
\[\lim_{s\to 0^{+}}\bigg{(}s^{3}+\frac{s^{3}}{C(s)G(s)}\bigg{)}=0, \tag{2}\]  
**Proof A2.** Let us consider the adaptation rule of the ML-MR PID-MIT control structure, which is given by Equation (9) as \(\theta=-\gamma\frac{1}{s}y_{m}e_{m}\). The model can be written as:  
\[e_{m}=\frac{s\theta}{-\gamma y_{m}}\] (A3)  
The output of the reference model \(y_{m}\) can be written as \(y_{m}=T_{m}(s)r(s)\). For set-point control stability, one considers the step response of the system. Therefore, the reference input is assumed to be a step function by using \(r(s)=\frac{1}{s}\) in Equation (A3).  
\[e_{m}=\frac{s^{2}\theta}{-\gamma T_{m}(s)}\] (A4)  
According to Lemma 1, the convergence of the model error to zero (\(e_{m}=0\)) is possible for \(\theta=1\) in the case of \(T_{m}(s)=T(s)\). By applying this condition, the model error is written as:  
\[e_{m}=\frac{s^{2}}{-\gamma T(s)}\] (A5)  
The function \(T(s)\) is the transfer function of the inner loop, which is stable due to optimal tuning of the PID control system. When the \(T(s)\) function given by Equation (2) is used in Equation (A5), one obtains  
\[e_{m}=-\frac{1}{\gamma}\bigg{(}s^{2}+\frac{s^{2}}{C(s)G(s)}\bigg{)}\] (A6)  
The inner loop is configured to be stable, therefore the output of the inner loop is bounded for \(y<\xi\in R\) for a step reference input. When the model error \(e_{m}\) of the system given by Equation (A6) is shown to be stable, the bounded input and bounded output of the ML-MR PID-MIT control structure become stable, so that \(e_{m}=y-y_{m}<\varepsilon\in R\) is satisfied. In order to ensure convergence of the model error to zero, a final value theorem can be applied as:  
\[\lim_{s\to 0^{+}}se_{m}=0\] (A7)  
Then, one obtains the convergence condition as follows:  
\[\lim_{s\to 0^{+}}\bigg{(}s^{3}+\frac{s^{3}}{C(s)G(s)}\bigg{)}=0.\] (A8)
## 3 Results and Discussion  
### Pulsed-Light Treatment: Energy Irradiation Received on Samples  
The binomial treatment with an electric potential difference ranging from 1828 to 3000 V and distance to the source UV lamp (ranging from 2.6 to 5 cm) had different energy irradiation rates on the samples, as described by other authors [26], measured as the radiant energy that is received on sample surfaces per unit area and named fluence (J/cm\({}^{2}\)) (Figure 1). This means that the energy dose (fluence J/cm\({}^{2}\)) given to the poultry samples varied from 2.82 \(\pm\) 0.06 (J/cm\({}^{2}\)) to 9.68 \(\pm\) 0.15 (J/cm\({}^{2}\)). According to the model, with the same number of pulses (five) a higher voltage gives higher fluence values, while lower fluence values were observed for higher distances from the source light. Additionally, [PERSON] and [PERSON] [26] have stated that fluence decays exponentially with increasing vertical distance from the lamp.  
Based on the dose of energy received by the poultry meat samples, it was possible to observe an increase in their temperature from 2.5 \({}^{\circ}\)C to 10 \({}^{\circ}\)C according to increased doses of fluence (Figure 2). Other authors also reported this significant temperature increase when fluence increased [16; 27].  
Figure 1: Response surface of binomial factorial electric potential difference (volt, V) and distance (D, cm) of the light source over fluence (J/cm\({}^{2}\)).
## 3 Drone Swarms as Networked Control Systems
### Non-Interactive Deployment Strategy
#### 3.2.1 Network Topology  
In a non-interactive deployment strategy, drones are controlled by the ground controller, meaning that this strategy relies on the existence of efficient uplinks and downlinks, as shown in Figure 1, allowing the ground controlled to transfer flight status to the drones and to collect data from them. In scenarios with multiple drones, drones are not capable of communicating directly nor even through the ground controller. Figure 1 illustrates a case with multiple drones.  
In this type of simple deployment strategy there is a bidirectional communication link from each drone to the ground control system allowing for direct transmission of application specific data and control commands. This strategy is suitable for small size applicability scenarios with lower coverage range. The main limitation of this strategy is that the ground control station is a single point of failure on which the entire network can have a shutdown, together with transmission delays culminating from the long wireless links aiming to cover a wider area. From a communication point of view a non-interactive deployment strategy relies on a precise air-to-ground channel model, antenna design, and mobility model.
# An RF-MEMS-Based Digitally Tunable SIW Filter in X-Band for Communication Satellite Applications
## 5 Conclusions  
In this paper, we highlighted the possibility of achieving four-state digital tuning of SIW filters with the possibility to finely tune the frequencies.  
Feasibility was demonstrated through the design and realization of a two-state tunable filter using commercial RF-MEMS switches. One of their remarkable advantages is the simplicity of the biasing network.  
Furthermore, one major difficulty when designing tunable filter arises when attempting to master the tuning range.  
In this work, the specified tuning range of the filter was set to about 2.3%, but it is possible to widen it to 24.3% by locating the tuning posts in the center of the cavities and by correctly using RF-MEMS switches and wire bondings.  
Indeed, this paper also highlighted that the position of the tuning posts defined the tuning range. In the tuning range, frequency shifts between states were controlled by the number and type of switching elements.  
Finally, it is possible to extend the number of states electronically addressed. For example, one solution would be to use a four-pole RF-MEMS (SP4T RF-MEMS) for each cavity, which would control the electrical connectivity in each direction (N, E, S, W) of one properly located tuning post. Thus, a specification of four targeted sub-bands could be properly addressed.  
[PERSON], [PERSON] ([PERSON]), [PERSON]-[PERSON], [PERSON], [PERSON] ([PERSON]), [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] contributed to the design, the implementation, the realization, the measurements of the circuits and the analysis of the results.  
Figure 8: High-frequency structure simulation (HFSS) and measured S-parameters (S21 top and S11 down) of the tunable filter.  
Funding: This study was supported by \"Direction Generale de l'Armement: DGA\" as part of the FENDER RAPID project. Conflicts of Interest: The authors declare no conflict of interest.
# RHS-YOLOv8: A Lightweight Underwater Small Object Detection Algorithm Based on Improved YOLOv8
## 6 Conclusions  
This work presents RHS-YOLOv8, a lightweight detection framework that balances computational efficiency and accuracy for underwater small object detection. By optimizing the network architecture through multi-scale feature extraction, redundancy reduction, and attention mechanisms, the model achieves a 9.95% reduction in computational load while improving detection accuracy. Its efficiency and performance make it suitable for deployment on underwater platforms (e.g., unmanned vehicles and observation systems) for real-time marine organism monitoring.  
Future research will focus on three directions:  
1. Dataset enhancement: expanding sample diversity, particularly for small objects and complex underwater environments, and incorporating data from varied sea conditions (depth, visibility, and lighting) to improve robustness.
2. Image preprocessing: integrating underwater-specific enhancement techniques (e.g., CLAHE and color correction based on optical physics models) to address image quality challenges.
3. Deployment optimization: accelerating model inference via tools like TensorRT or ONNX Runtime, and enabling cross-platform compatibility (GPU/ARM architectures) for real-time applications in resource-constrained underwater devices.  
Conceptualization, Y.W. and J.T.; methodology, Y.W.; software, W.W.; validation, Y.W., J.T. and W.W.; formal analysis, D.Y.; investigation, D.Y.; resources, S.H.; data curation, S.H.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W.; visualization, Y.W.; supervision, Y.W.; project administration, J.T.; funding acquisition, J.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Postgraduate Cultivating Fund of Jianghan University (301004310001) and Innovation Graduate Research Fund of Jianghan University (KYCXJJ)202320).  
Not applicable.  
The dataset used in the paper can be downloaded here: [[https://github.com/chongweiliu/DUO](https://github.com/chongweiliu/DUO)]([https://github.com/chongweiliu/DUO](https://github.com/chongweiliu/DUO)) (accessed on 16 October 2021).  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Transient Performance under High Heat Flux Millisecond Pulses
#### 3.2.2 The Effects of the Thickness of the Metallic PCM Layer
It can also be seen that the phase change does not reach the periphery boundary even at the maximum \(\gamma\), indicating that PCM with the same area of the heat source can work efficiently. The smaller melting area of PCM is due to the heat spreading from the heat source to the base of the heat sink, causing a lower heat flux and temperature in the marginal area. Figure 10: The variations in \(T_{\rm max}\) and \(\gamma\) with time when \(\delta\) = 0.1 mm, 0.05 mm. Figure 12 shows the transient change in heat flux on the upper side of the metallic PCM during a heat pulse cycle (corresponds to 43\(\sim\)47 ms in Figure 10). Before the arrival of the heat pulse (43-44 ms as shown in Figure 12a), the heat flux on the upper side of the metallic PCM is maintained at approximately 20 W/cm\({}^{2}\), while the heat flux of the chip is 0. When the heat pulse hits (44-44.2 ms), the heat flux on the upper side of the metallic PCM rises rapidly, but the maximum heat flux is much less than the peak heat flux (600 W/cm\({}^{2}\)) of the chip. During the heating period (44.2-45 ms as shown in Figure 12a), the heat flux decreases slightly due to the increase in liquid fraction. The heat flux in the central region is lower than the periphery region due to phase transition and the sudden drop in heat flux near the periphery boundary is due to the heat spreading from the heat source to the base of the heat sink. After the heat pulse (45-47 ms as shown in Figure 12b), the heat flux on the upper side of the metallic PCM decreases, and the area with high heat flux shrinks gradually. When the liquid fraction \(\theta\) reduces to 0, the heat flux above metallic PCM also recovers to the value before the heat pulse. The change processes of heat flux above metallic PCM during a heat pulse cycle demonstrate the thermal buffer performance of metallic PCM. Figure 11: The phase change processes of metallic PCM during a heat pulse cycle.
# Target Localization via Integrated and Segregated Ranging Based on RSS and TOA Measurements
## 6 Conclusions and Future Work  
In this work, the problem of target localization in adverse NLOS environments using integrated RSS and TOA measurements was addressed. It was shown that the considered estimators do not profit from the hybrid measurements by default, and that there is a _gray_ area that needs to be taken into consideration, which depends on inter-sensor distances. Moreover, a simple heuristic approach was presented in order to choose _the best_ measurement for each link, which is then used for performing the localization. It was shown that such an estimator can match or even better the performance of the estimator using one fixed measurement option. Furthermore, the simulation results indicated that the employed TOA model suffers slightly at relatively short inter-sensor distances, but the employed RSS model cannot seem to handle long ones. This does not come as a surprise, but it is a confirmation that the employed RSS model is over-simplistic for long ranges. Finally, note that all estimators considered here are linear ones. Perhaps employing different tools (e.g., convex optimization techniques or multidimensional scaling) which might require higher computational complexity, but could combat better with the negative NLOS bias effect, and thus result in higher localization accuracy might also be worthy of exploring.  
Although our general findings are somewhat similar with the ones presented in [20] for the case of LOS links, it becomes obvious that more work is required when the considered environment contains NLOS links. The negative influence of NLOS biases additionally complicate the problem at hand, since they can have a huge impact when estimating the distance of a link. Moreover, even though our procedure implicitly incorporates inter-sensor distance and does not depend on the knowledge of the noise powers (unlike the existing theoretical formula for calculating the critical distance) there are still many open questions to be considered in order to optimize the mechanism for opting between the available measurements and possibly integrating them in a different (_better_) fashion. These are all open questions that deserve more attention and are left for future work. Likewise, simultaneous localization of multiple targets able to cooperate with each other is of interest for future work. Finally, employing Bayesian theory and filters for real-time tracking of a moving target might be an interesting direction for further research.  
Funding: This work was partially supported by Fundacao para a Ciencia e a Tecnologia under Projects UID/MULTI/04111/0213 and UID/MULTI/04111/0216 (COPELAB--Cognitive and People-centric computing), UID/EEA/00066/2013 and foRESTER PCIF/SSI/0102/2017, and Program Investigador FCT under Grant IF/00325/2015.
# Micro-FL: A Fault-Tolerant Scalable Microservice-Based Platform for Federated Learning
## 2 Background
### Microservices  
Monolithic and microservice-based architectures currently dominate the realm of business application development ([PERSON] et al., 2017). Monolithic architecture, a traditional approach,  
Figure 1: An example of a federated learning architecture: client-server model.  
constructs an application as a single extensive codebase or repository that encompasses various services that are not executable independently [31]. This tightly coupled architecture operates as a singular process in the application server's environment during request handling, with all internal communications managed by an intra-process mechanism. However, as new features are continually integrated in today's fast-paced development cycle, the growing codebase and complexity make code understanding and modification more challenging [32; 33], leading to slower deployment. Another issue with the monolithic architecture is its lack of fault tolerance. In other words, there is no provision for a system component to function independently when another component fails [34], which is possible with the microservices-based architecture.  
In contrast, microservices, a rising trend in software architecture, emphasize the design and development of highly maintainable and scalable software [35]. Thus, by functionally breaking down large systems into a collection of independent smaller systems, microservices help manage the growing complexity of software systems.  
Microservices typically employ containerization technologies, such as Docker, which encapsulate each service and run it within a container. This structure allows for effortless scalability with minimal latency and hardware resource footprints. Docker containers, which are lightweight, efficient, and can scale quickly based on needs [36], prove particularly beneficial for a microservices architecture. For this reason, Docker containers have been selected for the federated learning platform proposed in this study.  
Kubernetes (K8s) is an open-source system for automating deployment, scaling, and management of containerized applications [37]. It offers scalability by dynamically adjusting containers based on resource demands. It ensures high availability through the automatic restart or rescheduling of failed containers, promotes portability, and prevents vendor lock-in through infrastructure abstraction. Furthermore, it optimizes resource utilization by efficiently scheduling containers.  
Hence, integrating a federated learning framework that uses a microservices architecture can facilitate the creation of a performant federated learning system that is both scalable and fault-tolerant.
## 3 Results
### Microbiological Analysis  
The results revealed that the initial microbial counts of HMB for _P. umbilicalis_ and _U. lactuca_ were relatively high, ranging from 3.335 \(\pm\) 0.275 log CFU\(\cdot\)g\({}^{-1}\) for UL to 4.775 \(\pm\) 0.148 log CFU\(\cdot\)g\({}^{-1}\) for PU, as shown in Figure 5. Throughout the storage period, the microbial counts varied depending on the preservation treatment and the type of seaweed.  
Figure 5: Microbial counts for heterotrophic marine bacteria throughout storage days for (**a**) _P. umbilicalis_ and (**b**) _U. lactuca_, preserved by different treatments. The results are expressed as the logarithm of colony-forming unit per gram of sample (log CFU\(\cdot\)g\({}^{-1}\)). Notes: Days 0, 3, 6, 9, 12, and 15 (n = 2 Petri dishes \(\times\) 2 dilutions \(\times\) 1 repetition = 4, for each treatment); LOQ: limit of quantification; LOQ \(<\) 1 log CFU\(\cdot\)g\({}^{-1}\) for heterotrophic marine bacteria.  
For PU (Figure 5a), the MAP and VAC treatments resulted in a decrease in the microbial count of HMB throughout the storage period, while the opposite occurred for CTRL. On day 6, the microbial count for CTRL achieved the value of 5.477 log CFU-g\({}^{-1}\), which would make the seaweed unfit for consumption, and the values were even higher (above the limit of quantification) on day 9. From day 9 onwards, it was possible to notice a sharp drop in the microbial load for both the MAP and VAC treatments, particularly in the case of the VAC samples. On day 15, the microbial load for HMB was below the limit of detection for both the MAP and VAC treatments.  
For UL-CTRL (Figure 5b), the HMB microbial counts were generally lower than those for PU. The VAC treatment showed the lowest microbial counts throughout the storage period, with values ranging from 3.176 log CFU-g\({}^{-1}\) on day 0 to 3.699 log CFU-g\({}^{-1}\) on day 15, while for the MAP treatment, the microbial counts showed the highest variation during the storage period (from 3.176 log CFU-g\({}^{-1}\) on day 0 to 4.124 log CFU-g\({}^{-1}\) on day 6). However, the microbial counts on day 15 were remarkably similar for CTRL and both treatments. Therefore, it can be inferred that the microbial counts for UL remained relatively constant across all treatments during the storage period.  
The samples were also analyzed for pathogenic bacteria such as coliforms, _Escherichia coli_, coagulase-positive Staphylococcus, and _Vibrio_ spp. (quantification), _Salmonella_ spp. and _Listeria monocytogenes_ (identification). All analyses were below detection limits (<LOD) or not detected (N/D), except for coagulase-positive Staphylococcus where non-characteristic black colonies without halos grew in the incubation medium of PU. These results suggested that the seaweeds collected were suitable for human consumption. For more details, see Supplementary Material Table S1.