esa-sceva/satcom-chunk-collection huggingface dataset:
# Quality Analysis of Unmanned Aerial Vehicle Images Using a Resolution Target
## 2 Theoretical Background
### MTF Analysis Using the Slanted Edge Target  
Figure 5a represents the slanted edge target and the boundary of the target is emphasized with a red dotted line. Figure 5b is the content of extracting DN from the target, and the black dotted line means extracting the DN value at the corresponding location. Figure 5c is the ESF graph generated from the extracted DN, Figure 5d is the PSF graph, and Figure 5e is the finally generated MTF graph.  
The first step of the MTF analysis involves using the slanted edge target to find the boundary that is useful for analysis from the slanted edge target, as illustrated in Figure 5a. To find the boundary, a sufficient number of DNs is determined to produce the edge-spread function (ESF) and point-spread function (PSF) graphs stably. If too many DNs are extracted and analyzed, the image noise affects the MTF analysis. Sixteen DNs are typically used, but fifteen to sixteen DNs were used in this study.  
In the second step, the mean of the DNs extracted from each line, as illustrated in Figure 5b, is calculated to generate the ESF illustrated in Figure 5c. Unlike the edge target, which is arrayed perpendicularly, the slanted edge target has slanted boundaries and different pixel array angles; hence, the mean DNs calculated from each line obtains the ESF without aliasing.  
The most important step in an MTF analysis is detecting the boundary of the slanted edge target and extracting the DNs. If the perpendicular edge target is used, a few DNs are generated, as illustrated in Figure 6a. Figure 6b, however, shows that, if the slanted edge target is used to extract the DNs, multiple scan lines can be used, which makes it possible to extract and analyze an appropriate number of DNs across the boundary. In the case of a vertical edge target, the same set of DN values is generated at any location. However, in the case of a slanted edge target, a set of DN values is generated at different locations; thus, performing the MTF analysis is possible by extracting appropriate DN values across the entire boundary. The slanted edge target has an advantage as it obtains the ESF without aliasing from UAV images using the non-metric digital camera, and it enables a more accurate MTF analysis [20]. In the third step, the PSF graph is generated after the ESF graph is generated and differentiated, as illustrated in Figure 5d. Fitting the graph with the Gaussian function while producing the PSF graph reduces the effect of noise on the MTF value. Finally, the Fourier transform is used to generate the MTF graph from the PSF graph, as illustrated in Figure 5e.  
Figure 5: MTF analysis step using slanted edge target. (**a**) represents the slanted edge target and the boundary of the target is emphasized with a red dotted line. (**b**) is the content of extracting DN from the target, and the black dotted line means extracting the DN value at the corresponding location. (**c**) is the ESF graph generated from the extracted DN, (**d**) is the PSF graph, and (**e**) is the finally generated MTF graph.
# An Accuracy-Aware Energy-Efficient Multipath Routing Algorithm for WSNs
## 2 Related Work
### Data Accuracy Guarantee in WSNs  
According to the locations within the network where data accuracy guarantee tasks are performed, existing research can be categorized into two distinct schemes: transmission-based and source node-based schemes.  
For the transmission-based scheme, as exemplified by literature [15], a congestion control algorithm is introduced to address the problem of substantial data accuracy loss in existing congestion control algorithms. This algorithm operates under the premise that nodes transmit data upward to the sink through a routing tree. Given the upbound error at the destination node, the maximum allowable error for each subtree can be calculated based on the tree's topological attributes. When congestion occurs, nodes employ lossy compression to compress data within the maximum tolerable error range, which can mitigate the congestion while preserving data accuracy to a certain extent at the destination node. Similarly, in the literature [33], accuracy loss is reduced by integrating a clustering algorithm with a routing algorithm, therefore diminishing the number of data copies in the network. This algorithm leverages a minimum spanning tree-based routing algorithm to transmit data copies to the destination node. In this scheme, cluster heads aggregate copies within their respective clusters to reduce the number of copies. However, the tree-based routing approach in this context is less reliable.  
In the source node-based scheme, existing algorithms try to ensure accuracy during the data collection. For instance, in the literature [14], spatial-temporal correlations of sampling data are harnessed to reduce the volume of transmitted data and ensure the data accuracy. This approach then integrates multipath routing with a clustering algorithm to ensure accuracy during transmission. However, it cannot address the issue of accuracy loss caused by data aggregation in multipath transmission. In [34], the authors proposed an AUV-aided hybrid data collection scheme based on the Value of Information (VoI). It tries to keep the VoI of urgent information generated at the source node by assigning it a higher transmission priority. Nevertheless, it attenuates the VoI of normal data, potentially compromising the accuracy of all data.  
It can be seen that the transmission-based scheme struggles to deliver highly reliable transmissions to the destination, while the source node-based scheme faces challenges in eliminating the accuracy loss during transmission or may focus solely on specific data types. In a large-scale sensor network employing multipath routing to ensure data transmission reliability and considering the constraints of limited resources, adopting intersection-based multipath routing appears reasonable. Therefore, data aggregation at intersection nodes is inevitable, and consequently, controlling the number of aggregation nodes becomes crucial to reduce the accuracy loss caused by multiple aggregations.
## 3 Results and Discussion
### Physical Characteristics
After the analysis of comparison of the means in the Tukey test, for total fruit weight in relation to the control treatment, T1, all treatments showed statistically different results. The T2, T3 and T5 showed greater numerical difference. However, the treatment derived from the 1.5% dose showed a decrease in this variable, when compared with treatments T2, T3 and T5. This result may suggest that the fruit weight was a function of the fresh mass of the aerial part (Table 4) which was influenced by the production of photo-assimilates resulting from the treatments. [PERSON] et al. [29], studied continuous plant growth and verified a greater fruit dry matter in pepper hybrid Elisa grown in a greenhouse. These authors stated that the total production of conventionally grown pepper plants in this type of environment, exceeded 90% commercially mature fruits during 32 harvests for a period of 8 months. In the T2 and T3 treatments, the estimated number of boxes per 1000 plants was 29 and 27 (11 kg, each), respectively. This was higher production in terms of number of boxes when Reabilit(r) Algas was used, at any concentration, compared with the control, whose estimate of boxes per 1000 plants was 17. Such results can be explained by the mineral composition of the biostimulant used as a complementary fertilizer. It can be considered that this biostimulant created physiological, photosynthetically and nutritional balance in the plants, promoting an increase of the nutrient concentration in the leaf tissue and directly affecting the fruit production. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Treatment** & **LD Max** & **LD (av)** & **LD Min** & **TD Max** & **TD (av)** & **TD Min** & **L/T (av)** & **FRM (av)** & **TRFM 6** & **AE TFRM** & **Production 1000** & **NB **\({}^{**}\) **1000** \\  & **mm** & **mm** & **mm** & **mm** & **mm** & **mm** & **Ratio** & **(g)** & **Plants (kg)** & **(\%)** & **Plants (kg)** & **Plants (kg)** & **Plants \\ \hline
**T1** & 117.3 & 89.7 & 57.6 & 61.8 & 51.1 & 35.0 & 1.76 & 55.80 & 1.2 \(\pm\) 9.5c & 00 & 186 & 17 \\
**T2** & 115.5 & 96.6 & 73.0 & 72.7 & 60.0 & 44.9 & 1.60 & 89.69 & 1.9 \(\pm\) 6.4a & 69 & 314 & 29 \\
**T3** & 109.3 & 967 & 82.7 & 79.5 & 61.2 & 50.0 & 1.58 & 84.39 & 1.8 \(\pm\) 6.5a & 59 & 295 & 27 \\
**T4** & 99.4 & 82.4 & 61.9 & 73.1 & 58.3 & 42.3 & 1.40 & 70.25 & 1.5 \(\pm\) 7.3b & 32 & 246 & 22 \\
**T5** & 112.3 & 92.5 & 74.1 & 85.3 & 63.2 & 52.4 & 1.46 & 84.43 & 1.6 \(\pm\) 6.0a & 44 & 267 & 24 \\ \hline \end{tabular} \({}^{*}\) Averages followed by the same lowercase letter in the column do not differ by [PERSON]’s test at 5% probability. \({}^{**}\) Proper box = 11 kg.  
\end{table} 
Table 4: Results of longitudinal diameter (LD), transverse diameter (TD), longitudinal vs. transverse ratio (L/T), fruit mass (FRM), average (av.) values per treatment; total fruit mass (TFRM), sum of 6 plants in each treatment; productivity (PROD) and number of boxes (NB), estimate of harvest per 1000 plants and agronomic efficiency index (AEI) of total fruit mass, of 6 pepper plants.
## 3 Results and Discussion
### Method Validation
#### 3.2.4 Precision  
Precision was tested within the same day (intra-day precision) and between three consecutive days (intermediate precision) at three concentration levels (low, intermediate, and high). In all cases, three repetitions were carried out for each sample (Table 4). Intra-day precision was additionally calculated after six repetitions at 100% of the labeled amount for each drug. The results indicate that the analytical method is reproducible, as the RSD % values were <2%.
## 3 Methodology
### Resource Optimization  
Fulfilling the demands of containers for resources can be challenging. The cluster of containers has multiple resource needs (such as CPU and memory) which need to be allocated efficiently. In addition, poor/good container placement on the physical machines (PM) can affect the network latency of container communication. In large container clusters, performance parameters such as scaling out and recovery from failure require real-time analytics [30; 55]. The queue-based tasks and resource scheduler are commonly used in well-known cloud orchestration platforms such as Docker Swarm and Kubernetes. In such schedulers, the task placement request on the cloud enters a queue. From the queue, the scheduler fetches the request and processes one container at a time. Most commonly, the variants of heuristic packing algorithms, such as first-fit decreasing (FFD) and best-fit decreasing (BFD), are used for queue-based schedulers.  
In the proposed framework for resource optimization, the migration service is introduced, which will be responsible for shifting the workload between containers and providing optimal resource utilization of computational resources; refer to Figure 10. There could be many reasons to migrate the containers from one host to another host. A few of these possibilities include:  
* The overall work needs to be distributed across multiple hosts to comply with the service level agreement.
* Consolidating the number of hosts; thus, a smaller number of hosts will result in lower consumption of energy.
* A specific host performs better than the currently running host.
# Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues +
## 5 Inter-Annotator Agreement (IAA)
### IAA and Full Dataset Release  
We release the full version of AnnoMI, which has the following attributes:  
* **Question**: {Open question, Closed question, No question}
* **Input**: {Information, Advice, Options, Negotiation/Goal-Setting, No input}
* **Reflection**: {Simple reflection, Complex reflection, No reflection}
* **(Main) Behaviour**: {Question, Input, Reflection, Other}
* **Talk Type**: {Change, Neutral, Sustain}  
For the 7 IAA transcripts that are annotated multiple times, we release the annotations from each expert, but we take a majority vote over multiple annotations in order to facilitate dataset analysis (Section 6) and experiments (Sections 7 and 8). Compared to the original version [14], which only contains (Main) Behaviour and Talk Type annotations, the full version can enable the development of fine-grained classifiers operating at the label sub-type level, such as simple and complex reflection. While we do not pursue such development in this study due to the lower All(Strict) IAAs of some attributes, future work may explore the treatment of these labels as probabilistic for model training and other research purposes.
## 3 Analysis of Edge Method Accuracy
### Edge Contrast  
Generally, related studies have proved that contrast does not affect the accuracy of MTF measurement by edge method [10]. However, when the edge contrast is too low, that is, it is difficult to distinguish the edge visually, and the stability of MTF measurement results will also be affected. Therefore, in this section, contrast is analyzed again as an influencing factor. The BDE images with four angles of 3, 10, 25, and 40 deg, \(400\times 400\) pixels, and 300 dpi are determined for analysis, and the edge contrast is controlled at 0.04-0.84 by adjusting the gray values on both sides of the edge. The OMNI-sin edge method is used to measure the MTF of the edge images with different angles and different contrasts in Figure 3, and the simulation results are shown in Figure 14.  
Figure 14: MTF for some tilt angle BDE@Con images when adopt different edge contrast using OMNI-sin edge method: (**a**) when edge angle is 3 deg, (**b**) when edge angle is 10 deg, (**c**) when edge angle is 25 deg, (**d**) when edge angle is 40 deg.  
Figure 13: Graphic diagram of ROI extraction at minimum/maximum ROI-H and normal/stretched/improper ROI-W: (**a**) when edge angle is 3 deg, (**b**) when edge angle is 10 deg, (**c**) when edge angle is 25 deg, (**d**) when edge angle is 40 deg.  
Figure 14a-d show that the accuracy of the edge method is improved with the increase of edge contrast, but the improvement effect is not obvious. The consistency of MTF curves with edge contrast greater than 0.15 is relatively high, which indicates that the contrast has little influence on the accuracy of the edge method. It is consistent with the conclusion in reference [10] and the consensus in the field of MTF measurement. However, when edge contrast is lower than 0.1, MTF in the middle and high frequency ranges is prone to aliasing. This paper only determines that too low edge contrast will have a certain impact on the stability of MTF measurement. It is necessary to further analyze and verify to what extent the contrast reduction will seriously affect the accuracy of the edge method.
## 3 Results
### PDW Spectroscopy Signal Correlation  
For an analysis of the correlation between the PDW spectroscopy signals \(\mu_{\mathrm{s}}\)' and \(\mu_{\mathrm{a}}\) at 638 nm, respectively, with process relevant characteristics, the experiments described above were analyzed. The reduced scattering coefficient \(\mu_{\mathrm{s}}\)' followed the course of the CDW, while the absorption coefficient \(\mu_{\mathrm{a}}\) increased with the rise of RCDW. The correlation of the respective values from all five cultivations (i.e., batch and pulse-based fed-batch cultures) are shown in Figure 4. A root mean squared error of 0.96 for the linear correlation of \(\mu_{\mathrm{s}}\)' and the CDW was obtained, whereas the linear correlation of \(\mu_{\mathrm{a}}\) and the RCDW resulted in a R\({}^{2}\) of 0.90. Equations (A1)-(A3) in Appendix A show the obtained formulas for calculating the CDW, RCDW, and subsequently the PHB concentration using the linear relationships of the _in-line_ PDW spectroscopy signals with the corresponding _off-line_ values. Due to disproportionally large PDW spectroscopy signal intensification after 48 h of the pulse feeding experiments (Figure 3), the last four data points of each replicate were not included in the correlation analysis.
# Overview of Distributed Machine Learning Techniques for 6G Networks
## 4 Distributed Machine Learning Algorithms
### Multi-Agent Reinforcement Learning (MARL)  
Various wireless communication problems can be modeled as sequential decision-making problems and can be effectively solved through the RL-based approaches. Most wireless environments require multiple agent settings where agents can interact with each other and can impact each other's decision-making processes. Therefore, naturally, the multi-agent reinforcement earning (MARL) is a potential candidate for solving various sequential decision-making problems in diverse wireless environments. In particular, mainly due to the developments in the deep learning-based techniques for functional approximations, operations research, and multi-agent systems, MARL has gained lots of attention in the last decade [16; 29]. The MARL settings can be classified into three major groups based on the agents' interactions with each other [29]. In the first case of fully cooperative settings, all agents can work together toward optimizing the common goal or reward signals. In the case of such collaborative settings, usually agents can have similar rewards. In another case of fully competitive MARL settings, agents can compete with each other; each agent selfishly tries to maximize its reward. Therefore, the total sum of reward values for all the agents involved in the training process can be zero. There are also hybrid MARL systems where both cooperative and competitive agents can be present.  
In Figure 4, we show an example of a MARL system involving the \(N\) agents. At a given time step, from a given state \(s\in\mathcal{S}\), with \(\mathcal{S}\) being a possible state space of the environment, each \(n\)th agent with \(n\in N\) can take action \(a\in\mathcal{A}_{n}\). Here \(\mathcal{A}_{n}\) is the available action space of the \(n\)th agent. Effectively, the total set of actions \(A=A_{1}\cup A_{2}\cdots\cup A_{n}\) can grow exponentially and become a bottleneck for the implementation of the MARL process. Based upon the combined action from all agents \(a=a_{1}\cup a_{2}\cdots\cup a_{n}\), each agent can receive the new observation set including the possible new state and rewards. Implementing such a centralized training process can be challenging, especially in dynamic wireless environments where each agent can have limited observability, making the problem non-stationary.  
Figure 4: Multi-agent reinforcement learning framework.  
Figure 3: Federated learning framework.
# Poly(lactic acid)-Based Electrospun Fibrous Structures for Biomedical Applications
## 8 Biomedical Applications of PLA-Based Nanofibrous Structures
### Tissue Engineering  
PLA nanofibrous scaffolds have a good potential for tissue engineering, as a substrate for bio-functionalization, suitable topographical surface for cell adhesion, and as a reservoir for drug delivery. The small diameter of PLA nanofiber closely matches that of ECM fibers, and the relatively large surface area is beneficial for cell attachment and bioactive factor loading. Electrospinning is a versatile technique for the production of nanofibrous scaffolds, and recent advancements in this process aided the creation of particular scaffolds addressing the needs of specific tissues, such as loading different bioactive molecules and having fiber alignment. Many studies have been investigated PLA nanofibrous scaffolds in vitro, but the application of these scaffolds into the human body will require more testing in suitable animal models. The perspective of PLA would likely be its combination with other biomaterials in order to improve its strengths and reduce its weaknesses, which would be a promising strategy for the engineering of complex tissues [72].
## Abbreviations  
RES parameters  
\(\mu_{nDR}\)DSO reward coefficient without demand response  
\(\mu_{sol}\)DSO reward coefficient due to energy purchased from solar farm  
\(\mu_{BESS}\)DSO reward coefficient due to energy available from battery energy storage system  
\(\omega_{t}\)SoC of battery at current time step  
\(\omega_{t+1}\)SoC of battery at next time step  
\(\eta_{c}\)Battery charging efficiency  
\(\eta_{d}\)Battery discharging efficiency  
\(\beta_{d}\)Battery self-discharge rate  
\(\phi_{sd}\)Self-discharging time step  
\(\phi_{t_{c}}\)Battery charging time step  
\(\alpha_{c}\)Battery charging rate  
\(p_{c,t}\)Battery charging power  
\(p_{d,t}\)Battery discharging power  
\(\phi_{u}\)Battery discharging time step  
\(p_{c,t,max}\)Battery maximum allowed charging power level  
\(p_{d,t,max}\)Battery maximum allowed discharging power level  
\(\delta_{c,t}\)Battery charging status (0 = discharging, 1 = charging)  
\(\delta_{d,t}\)Battery discharging status (0 = charging, 1 = discharging)  
\(p_{sol}\)PV module supplied power  
\(V_{T_{s}}\)PV module thermal voltage  
\(I_{S_{sat}}\)PV module output current  
\(n_{c}\)Number of PV module cascaded cells  
\(K\)Boltzmann constant  
\(T\)PV module irradiated temperature  
\(\varphi_{e}\)PV module electronic charge  
\(V_{o}\)PV module output voltage  
\(I_{S}\)Solar cell series current  
\(R_{S}\)Solar cell series resistance  
\(\theta_{d}\)Diode quality factor  
\(R_{r}\)Solar cell parallel resistance  
\(p_{real,m_{j}}^{min}(t)\)Minimum real power requirement for EV \(m\) in aggregator \(j\) at time \(t\)  
\(t_{i}\)Time slot duration  
\(a\)Optimal power charging coefficients  
\(b_{1},b_{2}\)Nominal power charging coefficients  
\(Ch_{m,j}(t)\)Charging status of EV \(m\) in aggregator cluster \(j\) at time \(t\) (0 = commuting, 1 = plug-in)  
\(S_{m,j}(t)\)SoC of battery for EV \(m\) in aggregator cluster \(j\) at time
###### Abstract  
Drone surveys are gaining popularity for many construction applications, including in the fields of civil engineering, such as road construction, earthwork, structure monitoring, and coastal topography analysis. Drone surveying has a high potential for periodical long-term ground settlement measurement in the field of geotechnical engineering. Traditionally, manual measurement has been performed for limited points with controlled surface measurement points, but drone surveying may enable automated and periodical measurement for a wide and remote site. However, the accuracy of the elevation measurement and the surface settlement prediction has not been investigated, and the use of drone surveying has thus been limited. Therefore, an experiment was carried out to apply drone LiDAR (Light Detection and Ranging) surveying for soft ground settlement measurement at a large land reclamation site showing a very large settlement up to 10 m. Periodic drone LiDAR surveying was conducted, and the data were processed with direct georeferencing and with outlier removals (such as trees and construction vehicles) in order to generate a clean surface point cloud. We then compared the processed elevation data with ground control data to check the vertical accuracy and to predict the settlement as well as for consolidation. The drone LiDAR survey showed 13 cm, 42.9 cm, and 6.23% differences in RMSE (Root Mean Square Error) in terms of vertical accuracy, predicted long-term settlement, and consolidation, respectively. The drone LiDAR accuracy seems very useful for monitoring settlement over a large and remote land reclamation site of soft ground, showing settlement up to several meters where, without a surface measurement, installment is limited.  
soft ground; land fill; ground settlement; drone; LiDAR Article