esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proposed Fleet Management System
### Mapping and Interconnection of Robots  
The tests involving the AGV and AMRs were conducted in the Mechanical Engineering Department of the Coimbra Institute of Engineering, within a laboratory covering an area of approximately 500 m\({}^{2}\), which has machines and furniture of different sizes and with different forms of organization, as well as some wide movement corridors. Figure 10 shows the laboratory in question, where it is possible to see the circuit created with the magnetic strip to move the AGV. This space is the most suitable for carrying out experimental tests, as it roughly simulates the conditions found in the factory where the final prototype is to be installed.  
To allow all robots to be viewed in a single monitoring window, an AMR with SLAM technology was used to create a map of the entire operating area.  
Figure 10: System architecture—runtime environment and Industrial IOT integration.  
Figure 11 shows the layout of the laboratory where the yellow shaded area represents the space where the mobile robot with SLAM technology moved to carry out the mapping. Figure 12 shows the map created where you can see the location of the machines and furniture in the laboratory.  
After the map is created using SLAM, the fleet management system makes a request the AMR to obtain the map and present it to the operator, as shown in Figure 13.  
Figure 11: The laboratory where the AGV and AMRs conducted experimental tests.  
Figure 12: The area of the laboratory where the mapping was carried out.  
With this process, it is possible to view, in real-time, all the robots moving along their respective routes through a single web page. The AMRs, by utilizing SLAM technology, are capable of creating their maps, which gives them greater flexibility and autonomy in navigation. On the other hand, the AGVs, which operate with technology based on magnetic strips and tags placed on the ground, lack mapping capabilities.  
Therefore, by overlaying the tags on the map created by SLAM, as shown in Figure 14, it becomes possible to visualize the predefined routes of the AGVs within the same dynamically mapped environment created by the AMRs. This approach enables a centralized view of all robots in operation, regardless of the navigation technology used, facilitating the monitoring and supervision by the operator.  
Figure 14: The mapping of the work area.  
Figure 13: The layout of the laboratory obtained after mapping with SLAM technology.  
Figure 15 presents a flowchart developed to improve the understanding of the fleet management software functionality.  
The SLAM-based map used in the system was generated through the native capabilities of the AMR. The map is created during an initial exploration phase and remains static during routine operations, being accessed by the fleet manager for visualization purposes only. Real-time map updates are not currently implemented, and the robots navigate using onboard localization relative to this static SLAM map.
## 3 Results and Discussion
### Printing Optimal Parameter Results  
According to the conclusions in the previous two subsections, and from Equation (9), the most important factors affecting the line width (\(c\)) are the inner radius of the glass nozzle outlet (\(r_{2}\)), the feed pressure of the material supply (P), and substrate moving speed (v). The minimum line width can be consistently and accurately obtained under the current experimental conditions while printing faster and more efficiently. Finally, a uniform and neat line shape with a line width of approximately 32 \(\upmu\)m was successfully printed using a 43 \(\upmu\)m inner diameter glass nozzle, a substrate moving speed of 200 mm/s, a feed pressure of 0.6 MPa, and a print height of 150 \(\upmu\)m, as shown in Figure 9. From the results of Equation (9), the predicted line width was 33.1 \(\upmu\)m and the error range was within 5%. The error was larger because other errors, such as measurement errors at low scales, were perhaps more likely to arise, and the overall predicted results were within acceptable limits. Meanwhile, the geometry of the paste formation could be clearly observed under SEM, and the overall structure was compact and free of obvious defects.
# Bluetooth Low Energy Mesh Networks: A Survey
## 6 Discussion and Open Issues
### Open Issues
#### 6.2.4 Interoperability  
The reviewed academic and proprietary solutions are not conceived to be interoperable. In the first case, the intent is to demonstrate the feasibility of a paradigm for research purposes. In the second one, the purpose is to provide a commercial product, regardless of compatibility with the developments offered by other companies. However, interoperability is crucial to allow products of different manufacturers to communicate with each other, and standardization is required to enable this paradigm. The completion of the two standardization items in progress described in Section 3 will be fundamental to achieve interoperability for BLE mesh networks. However, the interoperability of existing BLE mesh network solutions is an open issue as of the writing.  
Among the two standardization items, the IETF one is based on using IP, while no evidence has suggested so far that Bluetooth SIG's Bluetooth Smart Mesh will also be. Nowadays, constrained devices are being connected to the Internet by means of two main approaches; (i) IP-based and (ii) non-IP-based (via protocol translation gateways). While the latter was the main approach more than a decade ago, in the last years the market has changed towards IP convergence. It is reasonable to consider that the market presence of IP-based technology for constrained devices will continue to increase, since this facilitates interoperability, scalability, and application development.  
As stated in 6.1.5, IPv6-based BLE mesh solutions require the use of data channels for the transmission of both routing and data messages. Among these, static solutions do not appear to be solid candidates given the intrinsic dynamics of mesh networking. Therefore, solutions based on dynamic routing that use data channels for the transmission of both routing and data messages, e.g., [16; 17], are currently the most promising ones to unleash the potential of BLE mesh networks.
# Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning
## 4 TTR Using Reinforcement Learning
### State Definition  
Figure 6 provides an illustration of the state definition utilized in this study, which involves periodically examining whether train arrivals or departures at physical stations are affected by delays. When trains adhere to the scheduled timetable, the situation is similar to that of physical station 2/3 of Train 1 in Figure 6. However, in the case of a delay, the situation of trains would align with physical station 4 of Train 1, and physical station 3 of Train 2. The state (\(S\)) is represented as a vector, where the length of the vector is determined by the number of physical stations. Each element of the vector \(S_{i}(k)\) represents the status of the nearest train \(k\) arrival at station \(i\), as shown in Equation (11). The determination of the nearest train \(k\) can be achieved using the operator \(k=\operatorname*{argmin}_{k}\left|\sigma_{time}-\overline{G_{k,i}^{arr}}\right|\), where \(\operatorname*{argmin}_{k}f(k)\) represents taking the value of \(k\) that minimizes the function \(f(k)\).  
Figure 6: State definition with train delays.  
The state value is defined by Equations (11) and (12), where \(G_{time}\) represents the current checking time.  
\[S=\Big{\{}S_{i}(k)=D_{k,i}^{delay}\Big{\}},\quad k=\underset{k}{\text{argmax}} \Big{|}G_{time}-\overline{G_{k,i}^{arr}}\Big{|},\forall i\in I^{\prime} \tag{11}\]  
\[\left\{\begin{aligned} D_{k,i}^{delay}=0\quad if\overline{G}_{k,i}^ {dep}=G_{k,i}^{dep}\\ D_{k,i}^{delay}=\overline{G}_{  
According to importance sampling, it is necessary to update the weights via multiplying the reward by the importance sampling ratio \(\Omega\). When updating the reward for strategy \(B\), the reward update calculation can be expressed using Equation (18):  
\[R(S_{i}(k),a_{i}(k))\gets R(S_{i}(k),a_{i}(k))\times\Omega \tag{18}\]  
By utilizing the adjusted reward values to update the reward value for strategy \(B\), experiences from strategies \(A\) and \(C\) are incorporated. The weight adjustments, taking into account the importance sampling ratio, are considered in this process. A similar approach can be applied when updating the reward value for strategies \(A\) and \(C\).
## 5 Outlook
### Evaluation and Reference Data  
Despite the development of different methods and indices, minimal changes have been applied to accuracy assessment methods over the past several years. The confusion matrix remains as the most common accuracy assessment method. However, validating a time series is still a challenging task, and it is unclear whether all results within a time series should be validated, or a robust single year validation is adequate. Collecting reference data for Landsat change detection is another challenge, due to spatial and temporal roadblocks. Because reference data are used for training and evaluation, there is a need to have a suitable spatial and temporal reference data distribution.  
Although reference data can be collected for studies covering large areas using office-based techniques and through sampling from high-resolution images or land cover for a single timeframe, collecting reference data for long period studies remains a problem. Reference data from Google Earth imagery are only available since 2000 and historical aerial photos and/or land cover maps are not usually available. However, there are alternative approaches to address the reference data problem, such as cross-validation [99], bootstrapping [100], interpretation-guided classification with nested segmentation [101], crowdsourced land cover [102], open land use map [103], volunteered geographic information [104], and deep learning network to filter and classify volunteered photographs [105]. A potential solution to the reference data issue would be the generation of a freely available global reference dataset, collected by governments and other users sharing data from previous studies or historical maps and photos. Comparison between office-based techniques and field visits shows that change target and magnitude are the best indicators of whether a field validation is required or not. Most change types derived from Landsat data can be validated adequately using office interpretation [94].
###### Abstract  
Astronaut behavioral and mental health are key factors for space mission success. NASA's Cognition test battery is often preceded by a brief 18-item Alertness and Mood Survey (AMS) adding subjective assessments to Cognition's 10 objective neuropsychological tests. Therefore, the main objective of this study was to generate a single AMS summary score for short (<6 months) and long (>6 months) space missions based on the relevance of individual items. With the paired comparison (PC) method, 19 subject matter experts (SMEs) were asked to rate the relevance of 14 AMS items for astronaut behavioral health. Boredom (22.1% of comparisons), monotony (23.1%), and sleepiness (35.6%) were considered the least relevant, and health (74.3%), depression (76.5%), and crew conflicts (77.9%) were considered the most relevant by SMEs. Six of the fourteen items differed statistically significantly between PCs for short- and long-duration missions: sleepiness, tiredness, energy level, and mental status were considered more relevant for short-duration missions, while monotony and loneliness were considered more relevant for long-duration missions (all adjusted \(p<0.05\)). We also demonstrated systematic changes in AMS summary scores during a 60-day 6\({}^{\circ}\) head-down tilt bed rest (HDBR) study, with increased alertness and mood disturbance during and after the HDBR period relative to pre-HDBR levels. This analysis identified the AMS domains considered most relevant for space mission success by SMEs, and highlighted differences between long- and short-duration missions. The resulting AMS summary scores were based on item relevance and will be useful for monitoring astronaut behavioral health on short- and long-duration space missions.  
spaceflight; cognition; mood; alertness; paired comparison; bed rest; isolation; confinement +
Footnote †: journal: Medical  
So far, only four astronauts have spent more than one year consecutively in space [8]. Thus, we have limited knowledge about how humans will react to the spaceflight environment on prolonged exploration missions such as a \(>\)1000-day future mission to Mars on which astronauts will have to endure unprecedented levels of radiation exposure outside Earth's protective magnetic shield, confinement, and isolation aggravated by communication delays and threat to life.
## 4 Embedded Deployment  
In order to address the potential safety concerns associated with real-time meter readings, we implement the model presented in this paper on an intelligent car, as depicted in Figure 12. The intelligent car integrates the Jetson TX1 embedded development board. It contains a 256-core NVIDIA Maxwell GPU, 64-bit ARM Cortex-A57 CPU, 4 GB LPDDR4 memory, 16 GB flash memory, Bluetooth, an 802.11 ac Wi-Fi module, and a gigabit ethernet card. It operates on the Linux for Tegra operating system, leveraging its substantial computational power and image-processing capabilities to achieve intelligent functionalities, such as obstacle avoidance, path planning, and autonomous driving.  
Simultaneously, the intelligent vehicle is furnished with an array of sensors, including an Astra Pro camera, LSLiDAR N10 lidar, an MPU9250 gyroscope, and an Actions ATS3605D smart voice chip. These sensors collect data on the car's surroundings, enable the vehicle to detect obstructions, devise driving routes, and manage its movement.  
We intend to utilize the Jetson TX1 as the primary control unit for our intelligent vehicle. By executing machine learning algorithms and deep learning models, it will be capable of processing and analyzing sensor data, ultimately achieving intelligent functionalities.  
During the inference stage, we implemented the detection and recognition network model onto the Jetson TX1 intelligent car, seamlessly integrating the input and output of both network components. As illustrated in Figure 13, the intelligent car's inference pipeline with the meter reading recognition network primarily comprises five stages: (i) input images captured from the camera, (ii) meter reading area detection, (iii) image cropping from the detected area, (iv) meter reading recognition, and (v) meter reading aggregation.  
Figure 11: The schematic diagram of channel pruning; channels with lower scaling factors are cut out according to the set threshold.  
Figure 12: The entire body of the Jetson TX1 intelligent car, which uses our lightweight network.  
After capturing an image from the camera using OpenNI, we resized the input image to 640 \(\times\) 640 pixels. We then utilized the proposed model in this paper to identify the meter reading area within the image. Based on the confidence level of the detection, the detected meter reading areas are classified as either clear and operable or illegible and faulty. If an area is deemed illegible, it is discarded, and the image is recaptured. The operable area is then cropped and input into our recognition network to obtain the meter readings. Once again, based on the confidence level, the recognized characters are classified as either correct or false. False readings are rejected, and the meter reading area detection process is repeated. The correct readings are sorted and combined according to their positions, and the final output results are saved and displayed. Figure 14 shows the detailed flow chart.  
Figure 14: The flowchart of the Jetson TX1 intelligent car for meter reading recognition.  
Figure 13: The pipeline of the Jetson TX1 intelligent car for meter reading recognition.
## Results
### Relationships between Vegetation Indices and Environmental Stresses
Figure 7 summarizes the R\({}^{2}\) values for the relationships between the VIs and the water stress scalar across sites for each biome. Compared with the temperature stress scalar (R\({}^{2}\) = 0.45-0.64, p \(<\) 0.0001), the water stress scalar had weaker correlation with VIs (R\({}^{2}\) = 0.26-0.34, p \(<\) 0.0001). The correlation of the VIs to water stress was relatively higher for COSH (R\({}^{2}\) = 0.13-0.41, p \(<\) 0.0001), WSA (R\({}^{2}\) = 0.33-0.6, p \(<\) 0.0001), SAV (R\({}^{2}\) = 0.37-0.52, p \(<\) 0.0001), and CRO (R\({}^{2}\) = 0.40-0.49, p \(<\) 0.0001) and relatively lower for other biomes: ENF (R\({}^{2}\) = 0.23-0.38, p \(<\) 0.0001), MF (R\({}^{2}\) = 0.28-0.44, p \(<\) 0.0001) and WET (R\({}^{2}\) = 0.25-0.39, p \(<\) 0.0001), EBF (R\({}^{2}\) = 0.18-0.33, p \(<\) 0.0001), DBF (R\({}^{2}\) = 0.24-0.29, p \(<\) 0.0001) and GRA (R\({}^{2}\) = 0.20-0.23, p \(<\) 0.0001). For most biomes, VIs generally captured water stress as well as MODIS GPP. We further chose five sites: DK-Sor (DBF), DE-Hai (DBF), BE-Vie (MF), US-Ne1 (CRO), and CH-Oe1 (GRA) to examine how VIs respond to the low temperature and drought. At CH-Oe1, a grassland site in Switzerland, there was much less precipitation from June to September in 2003 than in 2002, leading to a drought accompanied with high air temperatures during this period in 2003 (Figure 8). During this drought, f\({}_{\rm VPD}\) decreased with lack of rain while f\({}_{\rm Topt}\) did not decrease with high temperature; meanwhile, VIs immediately decreased when the drought and high temperature occurred (Figure 8). At US-Ne1, a cropland site in US, there was much less precipitation from June to July in 2003 than in 2004 but relative more rain from August to September in 2003 than in 2004. US-Ne1 and CH-Oe1 exhibited similar responses in f\({}_{\rm VPD}\), f\({}_{\rm Topt}\), and VIs to drought (Figure S2). At DK-Sor, a DBF site in Denmark, air temperature during the growing season was lower in 2007 than in 2014, and at a result, f\({}_{\rm Topt}\) and VIs during the growing season were also lower in 2007 than in 2014 (Figure 9). Similarly, at both BE-Vie (a MF site in Germany) (Figure S3) and DE-Hai (a DBF site in Germany), f\({}_{\rm Topt}\) and VIs during the growing season decreased due to the relatively low temperature (Figure S4). Figure 7: The boxplots of the R\({}^{2}\) for the relationships of monthly averaged VIs and MODIS GPP with water stress (f\({}_{\rm VPD}\)) for each biome with the total of 121 sites. The letters on the x axis stand for: A - NDVI, B - EVI, C - EVI2, D - NDVIIBRDF, E - EVIBRDF, F - EVI2 BRDF, G - NIRV, H - NIRV, BRDF, and I - MODIS GPP. The biome types are as follows: evergreen needleleaf forest (ENF); evergreen broadleaf forest (EBF); deciduous needleleaf forest (DNF); mixed forest (MF); closed and open shrublands (COSH); woody savannas (WSA); savannas (SAV); grasslands (GRA); wetlands (WET); croplands (CRO).
## 2 Overview
### Common WSN Architecture  
Figure 1 shows a common wireless sensor network architecture for monitoring marine environments, which consists of sensor nodes, sink nodes, a base station, a server and user terminals. Sensor nodes can sense and monitor the _in-situ_ environmental parameters such as water temperature, salinity, turbidity, pH, oxygen density and chlorophyll levels, and transmit the collected data to sink nodes via wireless communication using ZigBee or some other communication protocol. Communication between sensor nodes and a sink node is usually point-to-point. A sink node collects data from a group of sensor nodes, and transmits the collected data to the base station via the GPRS network. The server stores and processes the received data from the base station. The user terminals connect the server over the Internet.  
The design and deployment of a lasting and scalable WSN for marine environment monitoring should carefully take into account the following factors: the hostile environment, the network  
Figure 1: Common architecture of WSN-based marine monitoring systems.  
topology, communication protocols, the number of nodes, buoys, mooring systems, oceanographic sensors, energy supply, and so on.
## 4 Results
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **LVEF < 40\%** & **LVEF 40–49\%** & **\(p\)-Value** \\  & **(n = 862)** & **(n = 226)** & **\(p\)-Value** \\ \hline
**Laboratory results** & & & \\ NT-proBNP (ng/L) & 685 (216; 1846) & 197 (76; 640) & **<0.001** \\ Hemoglobin (g/L) & 143 (131; 152) & 145 (133; 153) & NS \\ Sodium (mmol/L) & 140 (138; 142) & 140 (138; 143) & NS \\ Urea (mmol/L) & 6.3 (50; 8.6) & 5.9 (4.6; 7.5) & **0.010** \\ Uric acid (\(\mu\)mol/L) & 408 (344; 479) & 370 (301; 429) & **<0.001** \\ Creatinine (\(\mu\)mol/L) & 97 (82; 118) & 92 (79; 107) & NS \\ eGFR (mL/min/1.73 m\({}^{2}\)) & 70 (51; 87) & 70 (58; 84) & NS \\ \hline
**Medication** & & & \\ ACEI/ARB & 761 (88.3\%) & 200 (88.5\%) & NS \\ Beta-blockers & 819 (95.0\%) & 201 (88.9\%) & **0.047** \\ Furosemide & 753 (87.4\%) & 125 (55.3\%) & **<0.001** \\ Hydrochlorothiazzide & 64 (7.4\%) & 42 (18.6\%) & **<0.001** \\ MRA & 638 (74.0\%) & 101 (44.7\%) & **<0.001** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Heart failure is a serious disease with high morbidity and mortality. What can be seen from the above results is that the prognosis of patients in the HFrEF group is worse than in the HFrnrEF group. Of the 1088 patients during the 2-year follow-up, 132 (12.1%) died, 141 (13.0%) were hospitalized for acute heart failure, and 34 (3.1%) underwent OHT or LVAD implantation. The 2-year survival without reaching the primary endpoint (all-cause death, OHT, LVAD implantation) was 82.0% in the HFrEF group and 89.6% in the HFmrEF group (according to the Log-rank test, \(p\) = 0.006; Figure 1).
# Protocol-Specific and Sensor Network-Inherited Attack Detection in IoT Using Machine Learning
## 3 Methodology
### Modeling Phase in SEMMA  
This section introduces the development of the proposed ML-based model, ProSenAD, for protocol-specific and SN-inherited attack detection in RPL-based IoT networks. From the learning paradigms, the type and characteristics of the collected data identified through statistical analysis, research objectives, and consideration for the nature of the RPL-based IoT network, multiclass classification is adopted for attack detection through the ProSenAD model. ML-LGBM [12] is leveraged and optimized for multiclass classification tasks in the proposed ProSenAD model. The optimization elements are discussed later in this section. The benchmark research and the ML model variants are used for comparative analysis of the performance using carefully selected evaluation metrics. To make sure that both categories of attacks are covered, ML-LGBM [12], GAN-C [14], GRU-DL [13] are used for benchmarking. Model variants including gradient boosting (GB), extreme gradient boosting (XGBoost), and baseline light gradient boosting machine (LGBM) are also considered for comparison. The authors of [12] have only focused on protocol-specific version attack, whereas [13] have focused on SN-inherited hello flood attacks. The authors of [14] have addressed two protocol-specific and one SN-inherited attacks. However, wormhole is not considered in [14]. Therefore, these research studies have been used as benchmarks along with the models mentioned earlier in this section.  
The most important constituents of an ML-based model are its parameters and hyperparameters. In the next subsections, different types of parameter categories and metrics are discussed for ProSenAD development. They include core parameters, learning control parameters, input and output workflow parameters, objective parameters, and metric parameters.