esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Path Planning for Autonomous Ships in Restricted Waters
## 4 Discussion
This method can meet the real-time requirements of CA decision-making systems for autonomous ships. Figure 19: Comparison of dynamic path planning results for different values of \(r_{k}\) with or without the angular bisector method. (**a**) \(r_{k}\) = 0.25 nm without using the angular bisector method; (**b**) \(r_{k}\) = 0.5 nm without using the angular bisector method; and (**c**) \(r_{k}\) = 0.25 nm using the angular bisector method. This method is a deterministic path planning algorithm. It computes the collision-free path in a dynamic environment based on the prior path and the complete theory about the potential field and gradient. The calculation is simple, the parameter setting is clear, and the calculation solution is reliable. Each test was repeated many times, and the solution was unique under the same test conditions. This is an important property in CA decision-making systems for autonomous ships. For some stochastic-based algorithms, the convergence of a solution might not be guaranteed due to the presence of random variables. Lastly, there are still some shortcomings in this method. For instance, the own ship is given a priority to keep out of the way of other ships and fewer obligations to be a stand-on ship. The algorithm does not consider speed changes in CA actions for the own ship as well as some extreme conditions such as stopping or reversing her means of propulsion. Without considering external disturbances of wind, waves, current and other natural conditions, the APF model of static obstacles needs implicit functions that may be acquired by curve fitting based on the data extraction of the ENC.
# The Use of Drones in Spain: Towards a Platform for Controlling UAVs in Urban Environments
## 2 Brief Background  
Over the last few years, the use of UAVs in surveillance and civilian tasks has increased significantly [1]. Moreover, UAVs will have more applications in the future. The rapid advances in technology allow reducing the size of devices and their cost. All of this makes it possible to use UAVs in ways that would have been unimaginable 10 years ago.  
Among their many applications, UAVs can be used for social purposes such as search and rescue operations [2], automatic forest fire monitoring and measurement [3] or disaster management [4]. Commercial tasks also appear within sectors such as agriculture [5], construction [6], mapping and geology [7], surveillance [8] or in the cinema industry [9].  
In comparison to other alternatives, the low cost of new UAVs and the wide range of areas to which they can be applied have cause great interest in this technology. They have become so widespread that it is necessary of legislate the use of UAVs to prevent illegal acts and accidents.  
Some studies in the literature analyzed the legal aspects of using robots and autonomous systems in fields such as video surveillance, image rights and defence. These complex issues are already being put on the agenda to manage the deployment and operation of UAVs as their technical expertise evolves. One of the first serious analysis of these legal and ethical issues was conducted by [PERSON] and [PERSON] [10].  
However, an analysis of the wide range of functionalities provided by UAVs is not sufficient for developing a platform that would monitor UAV flights. It is necessary to consider the legal aspect when developing the platform, since all the rules that regulate the use of these vehicles must be encompassed by the developed platform.
# Multiple Instances QoS Routing in RPL: Application to Smart Grids +
## 4 Experiment Setup
### Battery Level Measurement  
Each node from the FIT IoT-LAB platform is composed of three parts as shown in Figure 6:  
* the gateway that is responsible for flashing the open node and connecting it to the testbed's infrastructure
* the open node that runs the experiment firmware
* the control node that runs radio sniffing and consumption measurement  
Because we needed to run scenarios with varying battery levels on different nodes, it was impractical to rely on actual lithium batteries. Instead, we relied on the real-time consumption measurement performed by the control node. The gateway collects consumption measurements every 140 us, and write Orbit Measurement Framework (OML) files, with a us time stamped value of the power consumption of the open node in Watts.  
A software running inside the testbed's user area was then collecting these consumption files for each node in the experiments, and numerically integrating the values through a basic rectangle sum. At the beginning of each experiment, the battery capacity of each node was decided randomly between two different values. During the experiment, when a node's consumed virtual battery exceeded the virtual battery capacity, the node was electrically shutdown by the gateway. The network must then reorganize without the missing peer. The experiment was stopped when at least 20% of the nodes ran out of battery. The integrated total consumed energy in Joules, as well as the battery percentage,  
Figure 5: Topology of the deployment on FIT IoT-LAB Lille’s site ([[https://www.iot-lab.info/lille-new-physical-topology-released](https://www.iot-lab.info/lille-new-physical-topology-released)]([https://www.iot-lab.info/lille-new-physical-topology-released](https://www.iot-lab.info/lille-new-physical-topology-released)).).  
Figure 6: Hardware of an IoT-LAB node [36].  
were sent to each node through its serial port using the gateway's tooling that replicates the open node serial port on an accessible TCP socket. A Contiki process received this information on the node, which is used afterwards in the metric computation and route calculation. For real-life application of this paper in an actual sensor network, devices would be fitted with an adequate interface to their battery controller subsystem, which would be queried by the Contiki's application through an I2C, SPI or similar link. We note that the physical environment conditions that may influence the discharge and lifetime of the batteries [38; 39] are out of scope of this paper.
## 2 Materials and Methods
### Effect of He-CAPP on Mono Species Biofilm of P. gingivalis HW24D-1 (XTT Assay)  
_P. gingivalis_ HW24D-1 strain, which is a fimbried and most virulent strain of _P. gingivalis_, was able to form a thick monospecies biofilm and chosen to be tested through XTT assay. XTT Reagent (2,3-bis-(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanilide), which is a tetrazolium-based compound, is sensitive to cellular redox potential and in the presence of actively respiring cells converts from a water-soluble compound to an orange-colored formazan product. Biofilm of _P. gingivalis_ (HW24D-1) was performed as follows; a suspension of _P. gingivalis_ (\(\lambda\) = 560, DO = 0.5 \(\pm\) 0.02) in Brain Heart Infusion (BHI) broth supplemented with hemin (5 ug/mL), menadione (1 ug/mL) and 1% yeast extract, was obtained through spectrophotometry. Biofilms were grown for 48 h in anaerbiosis jar at 37 \({}^{\circ}\)C, washed with physiologic solution and treated with He-CAPP for 1, 3, 5 and 7 min, or not treated. Then, 40 L of XTT (1 mg/mL) solution in PBS, 2 uL of 0.7 mg/mL menadione solution in ethanol, and 158 uL of phosphate buffered saline (PBS) were added per well in a 96-well plate. Plates were incubated in aluminum foil in a 5% CO\({}_{2}\) chamber for 3 h, and then they were read by spectrophotometer at 494 nm wavelengths, in a plate reader (Bio-Tek, Synergy HT). Experiments were performed in triplicate for three times (n = 9). Data was analyzed By Kolmogorov-Smirnov normalization test, one-way ANOVA, and [PERSON]'s post-test using GraphPad Prism software. Significance level was set at 5%.  
Figure 1: (**A**) Schematic setup of the plasma jet with flexible plastic long tube. (**B**) Scheme of plasma application in a 96-well plate during the experiments.
# The Traffic Adaptive Data Dissemination (TrAD) Protocol for both Urban and Highway Scenarios
###### Abstract  
The worldwide economic cost of road crashes and injuries is estimated to be US$518 billion per year and the annual congestion cost in France is estimated to be $5.9 billion. Vehicular Ad hoc Networks (VANETs) are one solution to improve transport features such as traffic safety, traffic jam and infotainment on wheels, where a great number of event-driven messages need to be disseminated in a timely way in a region of interest. In comparison with traditional wireless networks, VANETs have to consider the highly dynamic network topology and lossy links due to node mobility. Inter-Vehicle Communication (IVC) protocols are the keystone of VANETs. According to our survey, most of the proposed IVC protocols focus on either highway or urban scenarios, but not on both. Furthermore, too few protocols, considering both scenarios, can achieve high performance. In this paper, an infrastructure-less Traffic Adaptive data Dissemination (TrAD) protocol which takes into account road traffic and network traffic status for both highway and urban scenarios will be presented. TrAD has double broadcast suppression techniques and is designed to adapt efficiently to the irregular road topology. The performance of the TrAD protocol was evaluated quantitatively by means of realistic simulations taking into account different real road maps, traffic routes and vehicular densities. The obtained simulation results show that TrAD is more efficient in terms of packet delivery ratio, number of transmissions and delay in comparison with the performance of three well-known reference protocols. Moreover, TrAD can also tolerate a reasonable degree of GPS drift and still achieve efficient data dissemination.  
VANETs; data dissemination; TrAD; broadcast storm suppression technique; store-carry-forward mechanism +
Footnote †: journal: Sensors
# Virtual Machine Migration Strategy Based on Multi-Agent Deep Reinforcement Learning
## 5 Experimental Results and Analysis
As the number of servers increases, the complexity of the network increases exponentially, increasing the time it takes to generate a migration strategy. Figure 5 shows the number of VMs migrated per hour within 24 h. As can be seen from the figure, the number of NN is the least, followed by NN, DIS_AC, MADRL and  
Figure 4: Time needed to obtain migration strategy in different number of servers. Figure 3: Convergence of different strategy. CON_AC, which are the most effective. This is because the migration strategies between servers coordinate with each other to avoid centralized migration to the same server, which can speed up the processing speed of the system. Compared to Random, NN, and DIS_AC, MADRL migrated 93.57%, 25.59%, and 21.61% more VMs per hour on average, respectively. In this paper, the average energy consumption and migration success rate of the five migration strategies are compared. The number of servers set in the experiment is 10, and there are 20 VMs to be migrated in each time period, with a total of 50 iterations. The experimental results are shown in Figure 6.  
As can be seen from Figure 6, Random has the highest energy consumption and the lowest migration success rate because it migrates randomly and does not optimize energy consumption. NN has the lowest energy consumption, but the migration success rate is 45.4% lower than that of MADRL. The main reason is that the NN strategy always migrates VMs to the nearest server, so the total energy consumption is the lowest. However, the nearest server may not meet the migration conditions, resulting in low success rate of the migration. The migration result of MADRL is similar to that of CON_AC. While optimizing the energy consumption of migration, we can take into account the status of other servers, reduce the probability of migration failure, and obtain better migration performance.
## 4 Conclusions
  
* MFB—multiple feedback topology; LAD—ladder topology. 2 LPF.B——B——B——Butterworth; LPF-C-B—cascaded Butterworth. 3 Upper numbers with input tone configuration: first at 10%, second at 19% of bandwidth, both tone amplitude \(-10\) dBm; lower numbers with input tone configuration: first at 40%, second at 70% of bandwidth, both tone amplitude \(-10\) dBm. 4 Input tone configuration: first at 150%, second at 250% of bandwidth, both toneof the LPF, in-band IIP3 varies between 32.7 dBm and 45.8 dBm. Similarly, THD value varies between \(-85.63\) dB and \(-64.55\) dB when amplitude of the input tone is 4 dBm. One percentage THD is reached when the level of the input signal is 11.3 dBm for 2 nd filter order and 7.2 dBm for 4 th and 6 th filter orders. The large signal performance of the LPF is stable across its bandwidth tuning range--P\({}_{\rm 1 dB}\) is equal to 14 dBm when the LPF is configured for 2 nd order and 10 dBm when LPF implements a 4 th and 6 th order functions. Depending on the filter's order and bandwidth configuration, power dissipation can vary between 1.2 mW and 73.4 mW. Power management and performance optimization is achieved by utilizing two biasing controls of the operational amplifier for different sets of filter configuration. The best small signal linearity performance is achieved, when the LPF is configured for 2 nd order multiple feedback topology, where in-band IIP3 and THD mean values across the entire range of bandwidth selection are respectively 39 dBm and \(-79\) dB. Due to small variations in NF and P\({}_{\rm 1 dB}\), large signal dynamic range per hertz of the proposed filter is nearly invariant to different sets of bandwidth and order configurations. As a future step, the designed LPF will be used as part of an analog baseband chain of SDR transceiver in both transmit and receive paths. The use cases for aforementioned SDR transceiver will be primarily targeted at Intelligent Transport Systems, which utilise a variety of different wireless standards, including 5G NR. Conceptualization, K.K. ; investigation, K.K., M.J., and V.M. ; resources, M.J.; writing--original draft preparation, K.K. ; writing--review and editing, M.J., V.M., and R.N. ; visualization, K.K., M.J., and V.M. ; project administration, R.N. ; funding acquisition, R.N. All authors have read and agreed to the published version of the manuscript. This project has received funding from the European Regional Development Fund (project No 01.2.2-LMT-K-718-01-0054) under grant agreement with the Research Council of Lithuania (LMTLT). The team of authors wishes to express their gratitude to European Regional Development Fund and the Research Council of Lithuania for financially supporting this research as a part of \"Design and Research of Internet of Things (IoT) Framework Model and Tools for Intelligent Transport Systems\" project, grant number DOTSUT-235, No. 01.2.2-LMT-K-718-01-0054. The authors declare no conflict of interest.
# Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
## 2 Literature Review
### Digital Twin Technique  
The concept of the DT, first proposed by [PERSON], was initially applied in product lifecycle management (PLM) [20]. NASA pioneered the application of DT technology in the aerospace field in 2010. As technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data advance, DT technology has gradually expanded into more industries [21].  
DT technology has become widespread across sectors such as manufacturing, healthcare, aerospace, and construction infrastructure [22]. In manufacturing, the adoption of DTs has been key in facilitating cyber-physical integration, optimizing production efficiency, and enabling predictive maintenance through real-time data analysis [23]. Leveraging data-driven approaches, DTs have transformed smart manufacturing, allowing for enhanced decision-making and streamlined operations across the product lifecycle [24]. In healthcare, it supports personalized treatment and public safety management [20]. In construction and infrastructure, especially in bridge SHM, DT technology facilitates real-time bridge monitoring and predictive maintenance [25]. Digital twins rely on technologies such as IoT, big data analytics, cloud computing, and AI [26]. Modern DTM not only encompasses physical and virtual entities but also integrates data, services, and networks, enhancing interaction between the physical and virtual [27]. Furthermore, machine learning algorithms and big data analytics optimize system performance and predict future trends and failures [28]. DTM is used throughout the infrastructure's lifecycle, offering a dynamic and holistic view of its performance and condition. Studies such as that by [PERSON] et al. (2020) demonstrate the potential of DT-based SHM systems for large infrastructure, highlighting the importance of real-time data integration in bridge management [29]. In bridge monitoring, technologies like machine vision, edge computing, and big data fusion enhance real-time monitoring and data processing efficiency for bridges. Machine vision and multi-source data fusion technologies enable effective monitoring of bridge traffic loads and structural health, facilitating timely detection of potential issues and preventive maintenance [30]. It enables multi-dimensional monitoring of bridge structures, allowing timely, data-driven maintenance decisions [31].  
Despite significant advances in many fields, DT technology still faces numerous challenges in practical applications. Constructing and maintaining DTM requires extensive data support, with high costs for data collection, processing, and storage, particularly in the monitoring of large infrastructure such as bridges [28]. Moreover, as scenarios grow more complex, creating a multi-dimensional, multi-scenario DTM presents substantial technical and resource challenges [27]. For infrastructure such as bridges, achieving multi-source data fusion and processing, along with building a high-precision, automated DTM, remains a central focus of current research.
## 2 Materials and Methods  
### Paddy Field Test  
A three-level terraced field in the Iitate village (latitude: 37.64444; longitude: 140.797711) was leased from a farmer for the field test. The top terraced field was solely used for the test paddy. This was due to the fact that there was only one pump to pump up cultivation water from a nearby pond and, as such, the spent water from the test paddy field should be used for the reference paddy field if it was located on the other terrain surfaces. The reference paddy field was thus moved to another paddy field, approximately 50 m apart from the test paddy field. The farmer tendered the rice cultivation from 7 May to 10 October 2011. The area of the test paddy field used was approximately 480 m\({}^{2}\). It was mixed with 600 kg of PSC using a tractor prior to the planting of the rice (_Oryza sativa_ L., cv: Akita komachi). No fertilizer was used during the course of the rice cultivation. Mountain water collected in a pond was used in the rice cultivation and contained no \({}^{131}\)I, \({}^{134}\)Cs, or \({}^{137}\)Cs. Similar analytical results were obtained for the rainwater residue on the surface of the paddy fields.  
No PSC pretreatment was used for the reference paddy field. It was planted with the same rice variety and farmed in a similar way as of the test paddy field. The reference paddy field was as large as 500 m\({}^{2}\). Contrary to the test paddy field from which approximately 150 kg of white rice were harvested, the reference paddy field was scarce in rice plants. Attempts were then made to collect and harvest enough rice to make approximately 250 g of polished rice for the subsequent analysis.  
During the rice cultivations above, the atmospheric radioactivity at a height of 1.2 m from the soil surface was 7 to 10 \(\upmu\)Sv-h\({}^{-1}\) at both the test and reference paddy fields.  
Samples of the harvested rice were air-dried to a moisture content of approximately 12%. Thereafter, the hulls were removed from the raw grains using a manual hulling machine. The brown rice was milled further to remove the bran layer resulting in white rice.  
The activity concentrations of \({}^{134}\)Cs and \({}^{137}\)Cs in each component of the rice harvested from both test and reference paddies were determined. In order to compare with the governmental safeguard value, the determined values were not corrected for the moisture content of each rice component.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 8 Results
### Domain Gap in Multispectral Data  
We trained the cloud detector in Table 4 on the source dataset and evaluated their performance on the target dataset, _without_ domain adaptation. For cloud detectors trained on three bands, we selected (i) bands 1, 2, 8a of Sentinel-2 and (ii) bands 1, 2, 5 of Landsat 9. Otherwise, we used the eight shared bands of Sentinel-2 and Landsat 9. The different combinations of model settings are indicated by the naming convention  
\[\text{ARCH-NUMBANDS-SOURCE}\]  
where  
* ARCH is the architecture of either CloudScout or ResNet50,
* NUMBANDS is either 3 or 8,
* SOURCE is either S2 for Sentinel-2 or L9 for Landsat 9.  
For example, CloudScout-8-L9 refers to training the CloudScout architecture with eight bands on Landsat 9 defined as the source domain. The different datasets used to evaluate the source cloud detectors are indicated by the naming convention:  
SAT-SET  
where  
* SAT is either S2 for Sentinel-2 or L9 for Landsat 9,
* SET is either TRAIN for training set or TEST for testing set.  
For example, S2-TEST means the testing set consisting of Sentinel-2 data. Note that the training and testing sets have ground-truth labels obtained by applying a 70% threshold to the cloud masks. The major findings that can be observed from the results in Table 5 are as follows:  
* The domain gap is more prominent in cloud detectors trained on eight bands compared to their three-band counterparts; observe the difference in performance between CloudScout-3-S2 (ACC/FP of 66.40%/0.80%) and CloudScout-8-S2 (ACC/FP of 52.00%/ 48.00%) evaluated on the L9-TEST.
* The domain gap appears to be smaller in ResNet50 than CloudScout; observe the difference in performance between CloudScout-3-S2 (ACC/FP of 66.40%/0.80%) and ResNet50-3-S2 (ACC/FP of 90.80%/1.60%) evaluated on L9-TEST.
* Overall, the effects of the domain gap are significant, since it prevents machine learning models from performing as required by EO mission standards, e.g., CloudScout [5] requires a minimum ACC of 85% and maximum FP of 1.2%.  
The results were obtained by training and evaluating cloud detectors with FP32 weights on a Linux desktop since quantising the weights to FP16 was found to have negligible effects on performance when evaluated on the XE1. The results also confirm the necessity of domain adaptation.