esa-sceva/satcom-chunk-collection huggingface dataset:
_Forests_**2014**, \(5\), 1798-1814; doi:10.3390/f5071798  
_forests_  
**ISSN 1999-4907**  
www.mdpi.com/journal/forests  
_Article_  
**Optical Medium Spatial Resolution Satellite Constellation Data for Monitoring Woodland in the UK**  
**[PERSON] 1, [PERSON] 2,* and [PERSON] 2**  
1 Geography Department, Adekunle Ajasin University Akungba-Akoko, 23401 Ondo-State, Nigeria; E-Mail: [EMAIL_ADDRESS]  
2 School of Environment and Life Sciences, University of Salford, Salford M5 4 WT, UK; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: 44-161-295-5646.  
_Received: 7 March 2014; in revised form: 23 June 2014 / Accepted: 2 July 2014 / Published: 23 July 2014_  
**Abstract:** The aim of this study was to test the potential of a constellation of remote sensing satellites, the Disaster Monitoring Constellation (DMC), for retrieving a temporal record of forest leaf area index (LAI) in the United Kingdom (U.K.). Ground-based LAI measurements were made over a 12-month period in broadleaf woodland at Risley Moss Nature Reserve, Lancashire, U.K. The ground-based LAI varied between zero in January to a maximum of 4.5 in July. Nine DMC images, combining data from UK-DMC and NigeriaSat-1, were acquired, and all images were cross-calibrated and atmospherically corrected. The spectral reflectance of the test site was extracted, and a range of vegetation indices were then computed and correlated with the ground measurements of LAI. The soil adjusted vegetation index (SAVI) had the strongest correlation, and this was used to derive independent estimates of LAI using the \"leave-one-out\" method. The root mean square error of the LAI estimates was 0.47, which was close to that calculated for the ground-measured LAI. This study shows, for the first time, that data from a constellation of high temporal, medium spatial resolution optical satellite sensors may be used to map seasonal variation in woodland canopy leaf area index (LAI) in cloud-prone areas, like the U.K.  
**Keywords:** phenology; leaf area index; broadleaf woodland; high temporal resolution imagery; northwest England; DMC; Landsat ETM
## 3 Multi-Sensor Architecture
### Components and Equipment
#### 3.1.2 Sensors
The sensor includes a heater that is used to heat the tin dioxide, making it sensitive to the target gas. As the concentration of hydrogen sulfide increases, the resistance of the sensor decreases, and this change in resistance is used to determine the gas concentration. The CJMCU-8128 is a sensor module that integrates three sensors: the CCS811 for measuring carbon dioxide (CO\({}_{2}\)) and total volatile organic compounds (TVOC), the Si7021 for measuring humidity and temperature, and the BMP280 for measuring barometric pressure and temperature. This module provides a compact and convenient solution for environmental sensing in various applications. The SDS011 is a widely used dust sensor that measures fine particulate matter (PM2.5 and PM10) concentrations in the air. It is commonly employed in air quality monitoring systems to assess the level of airborne particles which can have health implications. Its compact and lightweight design makes it suitable for portable and stationary air quality monitoring applications. The specification of sensors used for building the multi-sensor system is shown in Table 11 (Appendix A). The integration of multiple sensors is realized to monitor the air quality based on the recognition that air quality depends on diverse pollutants and environmental factors.
## 4 Discussion
### Perturbations in the Pre-Swing Phase  
The Pre-Swing phase occurs at 50-60% of the gait cycle, immediately preceding the swing phase. Perturbation induced in this phase had the most significant impact on increasing step length, artificially accelerating the swing phase, and forcing participants to actively decelerate the limb. However, concerns arise as the gait cycle for perturbations in the PS phase was disrupted even before the perturbation occurred.  
This disruption does not seem attributable to mistimed perturbation induction, as [PERSON], et al. [29] reported an average delay of 56 ms in perturbation induction by the GRAIL and D-Flow systems, nor does it appear to stem from changes in percentage intervals due to gait cycle shortening or lengthening. A more plausible explanation seems to be that the perturbation in the PS phase was induced last, prompting participants to adopt a different gait pattern in anticipation, as observed in studies by [PERSON], et al. [41]. The hypothesis that participants adopted a different gait strategy is also supported by the kinematic curves of the knee joint, showing a lack of full extension compared to unperturbed gait. This relationship is particularly visible in the area of [7-35.4]% of the gait cycle (Figure 6E). Significant changes also occur in the hip joint [16-45]% and in the ankle joint [9-17]%, and [43-48]% in areas before the disturbance. The perturbation in the PS phase significantly affected all extreme kinematic values, except for the hip extension angle. The greatest changes compared to unperturbed gait were observed for plantar flexion \(-20.93\)\(\pm\) 8.22 vs. \(-4.96\)\(\pm\) 2.25 deg and knee extension \(3.4\)\(\pm\) 3.81 vs. \(-3.28\)\(\pm\) 1.95 deg.  
As for the analysis of kinetic parameters, changes in the gait pattern appeared in the [5-36]% of the gait cycle for the ankle torque and within [13-21.18]%, and [36.31-52.14]% of the gait cycle for the knee joint torque curves. However, changes in the kinetic parameters of the hip joint are directly related to the moment of perturbation, appearing, respectively, in the areas of [55-60]% and [72.41-95]% of the gait cycle. The most significant alteration in extreme values occurred at the hip joint, with the hip extension torque increasing by 73.02%. This represented the most substantial change in this parameter among all examined perturbations. These results are consistent with [PERSON], et al. [42], who also identified the hip joint as a primary control for this type of disturbance.  
Regarding the perturbation during the Pre-Swing (PS) phase, the most notable changes were noted for the hip extension torque. However, the existence of significant alterations preceding the induced perturbation is intriguing and challenging to explain. These pre-perturbation changes are possibly attributable to anticipatory muscle activation in preparation for the impending perturbation. But this issue requires further research.
# Video Human Action Recognition Based on Motion-Tempo Learning and Feedback Attention
## 4 Experiments
### Ablation Study
#### 4.2.2 The Impact of Motion Tempos at Different Temporal Granularities  
The MgAFM improves the model's ability to model motion information by integrating sub-action motion features with different tempos into the original input features. To verify which granularities of motion features contribute to improving model performance, we conducted ablation experiments by incrementally adding motion features with different frame intervals to the model. As shown in Table 2, when only the fast-tempo motion features extracted from adjacent frames are added to the original input, the Top-1 accuracy is merely 51.5%. As the types of motion features increase, the action-recognition accuracy gradually improves. When motion features with four granularities (frame intervals 0, 1, 2, and 3) are added to the original input, the Top-1 accuracy reaches a maximum of 52.4%.  
When the motion features with frame intervals of 4, 5, and 6 are further added to the original RGB features, the model's performance begins to decline. We analyzed that the correlation of larger temporal granularity cannot provide useful motion change information for subsequent representation learning. Using such motion features as input can negatively impact the model's recognition performance.
## 3 Types of Underwater Communication Systems
### Underwater Wired Communication  
Ocean waters host numerous intercontinental fiber-optic and electrical cable projects driven by international collaborations [64; 65]. The subsea industry integrates complex, large-scale subsea equipment and machinery [64]. In addition to simple coaxial and fiber-optic cables, subsea hydraulic control systems utilise massive umbilical cords and extensible pipes that contain both electrical and optical cables [66; 67; 68]. Subsea cables are complex and can have a diameter of up to 320 millimeters (12.6 inches) [54]. UWiC provides a fast and secure connection [5; 54]. However, its implementation presents several challenges that must be carefully considered [54], as shown in Figure 1. The logistical complexities, capital expenditures, and operational overhead associated  
Figure 1: An example of underwater communication cable [63]. with deploying and maintaining large-scale infrastructure in the marine environment are substantial [5; 10; 45; 60; 61; 62; 69; 70]. Table 3 refers to the types of underwater communication systems and their strengths and weaknesses. Subsea materials include submarine cables and dry-mate and wet-mate connectors [45]. The deployment of subsea cables on ROVs or sea rovers [55] requires trained ship operators [45], making the process time-consuming and labour-intensive. To reduce operational costs and address deployment constraints, innovative system design is essential, opening an opportunity for UWC systems as vital alternatives [5; 69; 71]. UWC networks are indispensable tools for exploring the remote and poorly understood regions of the deep ocean, which are a major focus of scientific research and academic interest [71]. Table 4 lists the types, application, and costs of subsea connectors. Table 5 specifies the features of UWiC for coaxial and optical cables and admixtures of both. Underwater cables are designed based on a well-established formula that defines the characteristics necessary to endure harsh underwater conditions, including pressure, temperature, corrosion, tension, lift force, hydrodynamic forces, and water ingress [49; 54; 55]. Material treatment, quality, and insulation (e.g., polyurethane) technologies vary signif \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Type** & **Application** & **Cost (Approximate)** \\ \hline Submersible Pump Connector & Oil and gas production, water supply & USD 10,000 to USD 50,000 \\ \hline ROV Connector & ROVs & USD 5000 to USD 20,000 \\ \hline Subsea Power Cable Connector & Offshore wind farms, subsea power grids & USD 50,000 to USD 200,000 \\ \hline Subsea Fibre-Optic Connector & Telecommunications, data centres, oceanographic research & USD 100 to USD 500 \\ Subsea Optical Power Connector & Subsea power systems, underwater sensors & USD 500 to USD 2000 \\ \hline Subsea Hybrid Power and Data & Subsea systems requiring both power and data transmission & USD 10,000 to USD 50,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Subsea connectors: types, applications, and costs [54].
## 2 Underwater Wireless Sensor Network
### Acoustic Link and Its Constraints  
Applying acoustic waves in underwater environments revealed many challenges such as high propagation delay, high energy consumption, high path loss, and low bandwidth. The use of high radio frequency in UWSNs causes rapid absorption, and the use of low radio frequency requires a large antenna. Therefore, the use of radio frequency as a communication medium in UWSNs is impossible. The use of an acoustic link is the most promising communication medium in UWSNs [17; 18; 19; 20; 21].
## 3 Relay Node Selection Issue  
If all the wireless terminals in houses are used as relay nodes, there will be a lot of routing protocol signals. Referring to the basic model of relay nodes and end nodes in communication networks, we applied a model in which end nodes are accommodated in relay nodes. In the previous study, we assumed that one cluster consists of about 100 houses wireless terminals and the relay function is operated in one node in each cluster, as shown in Figure 4 which are actual area in Fukushima Prefecture, Japan. This assumption of one node per 100 houses was not logically supported.  
If the relay function is operated in all the nodes, the UAV route will become a smooth curve. However, as the number of relay nodes increases, the number of control messages to construct the route increases, and the message forwarding load of each node increases. In the OLSR-based scheme, as the number of relay nodes increases, the processing load on each node increases due to the relay forwarding process for flooding TC messages and the weighted Dijkstra method calculation. In the AODV-based method, the processing load of each node increases in the RREQ message evaluation process and the relay forwarding process due to the increase in the number of received RREQ messages. It is necessary to reduce the number of relay nodes for constructing safe and efficient UAV routes. End nodes with no relay function will be connected to the closest relay node. Figure 5 shows an image of this simplified network.  
Figure 4: Models of each node location.  
Figure 5: Image of simplified network.  
We will establish a method to select as few relay nodes as possible while keeping the connectivity of all node pairs in the network multi-hop. In the network resulting from the above method, the UAV routes constructed by the optimal route construction method become safe and efficient, with a shorter \"population-dense regions distance\" and a \"total distance\" that is not too long. On the other hand, the smaller the number of relay nodes and the simpler the network, the fewer links are available for routes. As UAV home deliveries become popular, the number of overlapping routes (hereafter, \"route conflicts\") will increase as more UAVs fly over the same region at the same time and routes, which may cause collisions among UAVs. We evaluate the incidence of \"route conflicts\" for different relay node densities and numbers of concurrent UAVs flights. From the perspective of a sufficient number of relay nodes to avoid too many \"route conflicts\", we clarify the minimum relay node density in the network required to apply the proposed method.
## 7 Future Research Directions  
While the proposed FCM-OR protocol demonstrates superior performance compared to existing approaches, certain limitations require further investigation:  
* Longer sender waiting time: One limitation of FCM-OR is the longer sender waiting time when choosing forwarders from low-density areas. As explained in Section 5.2.9 and shown in the \"Sent Data\" metric in Figure 17, nodes in sparse regions usually wake up later in duty-cycled networks, which causes delays and increases the sender's energy use. To overcome this, future work will focus on improving the forwarder selection process by finding a better balance between reducing redundant transmissions and keeping delays low. This could include combining both density and expected delay when picking forwarders or using predictive wake-up techniques to help the sender avoid waiting too long.
* Beacon-based link quality estimation: To further enhance energy efficiency, future work will incorporate beacon packet probing for link quality estimation, as implemented in ORW [3]. This will enable precise link quality assessment, facilitating more energy-aware forwarder selection and enabling a comprehensive evaluation of energy consumption trade-offs in asynchronous duty-cycled WSNs.
## 2 Dataset-Based Learning in the Literature
### Locating Studies, Selection, and Evaluation  
Once the RQs were formulated, the keywords selected were the following: \"data\" and \"active learning\". The bibliographical materials that were used were English language articles, review papers, proceeding papers, books, and book chapters, collected in the databases of WOS and Scopus, from 1990 to September 2023. The search settings were the following:  
* Keywords in the title: data AND (learning OR education).
* Category/topic: education.
* Search within all fields: \"active learning\".  
This search provided a total of 232 results in Scopus and 23 results in WOS. They were transferred to a spreadsheet, merged, and deduplicated, resulting in 236 unique results. Then, a first revision involving the reading of the titles resulted in the retention of 98 records. In a second revision, which involved reading the abstract of the remaining records, 37 records were retained. In a third revision, the remaining records were read, and only 16 were retained for further analysis. The screening process was conducted manually and independently by the first author. The systematic literature review process overview is shown in Figure 1. The records that were removed in these revisions fit into these categories:  
* Records about active learning activities that use datasets in primary and secondary education. Only two articles fell into this category, one of them contextualized in primary school, and the other one, in secondary education; the latter is cited in Section 1 [4].
* Records about the topics of machine learning or active learning algorithms.
* Records about the use of data to evaluate the effectiveness of learning methodologies, not related to learning using datasets (e.g., [27; 28]).
* Records about learning activities in the field of data science not focused on the students' work with datasets (e.g., [29; 30]).
* Records that were not accessible to the authors: four documents in total.
# Blockchain Technology for Secure Communication and Formation Control in Smart Drone Swarms
## 5 Results
### Evaluation
#### 5.2.2 Scalability  
The scalability of these structures is another factor to consider. The addition of extra nodes to the network ensures that it is more robust and efficient against attacks. Nonetheless, a major drawback is the increased delays, since the consensus-reaching process of participants requires broadcasting the message throughout the network, thus performing more hops in this P2P structure. It is possible to avoid major scalability-related delays by using selected drones as nodes in the blockchain (rather than all of them). Another solution is to use tangled-based blockchain models, which tend to be more scalable. Also, the investigation of different consensus mechanisms could potentially lead to better outcomes.  
The complexity of the architecture, along with the manner in which these systems are implemented, is also influenced by the increasing number of drones in the swarm. In a UAV swarm, the addition (or removal) of nodes, along with their continuous movement, location, and speed change, could lead to unstable communications. A real-world application of the suggested model needs to be implemented in order to test the proposed model and produce realistic findings. Further investigation into this matter remains an open question.
## 3 Results and Discussion
These NDF, CP, and MC calibration model statistics, obtained using the NIRS measurement system, are like those acquired via commercial portable instruments, using a wavelength range like that evaluated in this work [22; 23]. The SEC values are in accordance with laboratory results, and the effect of the sampling procedure has been studied comparatively in this work. As a summary of the obtained results, Table 7 selects the best models obtained for each sampling procedure (raw or ground alfalfa) and parameter. As can be seen, the second derivative is the best of the assayed pretreatments that provide satisfactory results for the nutritive value quantification. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Raw alfalfa**} \\ \hline Wavelength range: & \multicolumn{2}{c}{901–1600 nm} & \multicolumn{2}{c}{901–1700 nm} \\ Mathematical & \(R^{2}\) & \(SEC\) & \(R^{2}\) & \(SEC\) \\ \hline
1 4 4 SG & 0.524 & 0.503 & 0.211 & 0.572 \\
2 4 4 SG & 0.619 & 0.492 & 0.129 & 0.861 \\ SNV 1 4 4 SG & 0.783 & 0.374 & 0.734 & 0.464 \\
1 4 4 SG SNV & 0.502 & 0.579 & 0.312 & 0.491 \\ SNV 2 4 4 SG & 0.675 & 0.409 & 0.679 & 0.434 \\
2 4 4 SG SNV & 0.861 & 0.219 & 0.687 & 0.444 \\ \hline \hline \multicolumn{5}{c}{**Ground alfalfa**} \\ \hline Wavelength range: & \multicolumn{2}{c}{901–1600 nm} & \multicolumn{2}{c}{901–1700 nm} \\ Mathematical & \(R^{2}\) & \(SEC\) & \(R^{2}\) & \(SEC\) \\ pretreatment & \multicolumn{2}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline
1 4 4 SG & 0.650 & 0.530 & 0.652 & 0.506 \\
2 4 4 SG & 0.770 & 0.435 & 0.243 & 0.819 \\ SNV 1 4 4 SG & 0.570 & 0.586 & 0.670 & 0.519 \\
1 4 4 SG SNV & 0.867 & 0.318 & 0.347 & 0.723 \\ SNV 2 4 4 SG & 0.604 & 0.566 & 0.591 & 0.579 \\
2 4 4 SG SNV & 0.781 & 0.424 & 0.301 & 0.625 \\ \hline \hline \end{tabular} NIN2N3: derivative order, number of left smoothing points, number of right smoothing points; SG: Savitzky–Golay Derivative; SNV: standard normal variate; R\({}^{2}\); coefficient of determination for calibration, SEC: standard error of calibration.  
\end{table} 
Table 6: The calibration statistics of the mineral content multivariate models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Sampling** & **Mathematical Pretreatment** & **Range (nm)** & \(\mathbf{R^{2}}\) & \(SEC\) \\ \hline NDF & Raw & SNV 2 4 4 SG & 900–1600 & 0.955 & 1.066 \\  & Ground & SNV 2 4 4 SG & 900–1600 & 0.892 & 1.861 \\  & Raw & SNV 2 4 4 SG & 900–1600 & 0.885 & 0.377 \\  & Ground & 2 4 4 SG & 900–1600 & 0.906 & 0.530 \\  & Raw & 2 4 4 SG SNV & 900–1600 & 0.861 & 0.219 \\  & & 1 4 4 SG SNV & 900–1600 & 0.867 & 0.318 \\ \hline \hline \end{tabular} NIN2N3: derivative order, number of left smoothing points, number of right smoothing points; SG: Savitzky–Golay Derivative; SNV: standard normal variate; R\({}^{2}\); coefficient of determination for calibration, SEC: standard error of calibration.  
\end{table} 
Table 7: Statistical analysis of alfalfa nutritive values (N = 57).