esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline
**Pearson (r, 95\% CI)** & **Proposed vs. Manual** & **Naive vs. Manual** & **Combined. vs.** & **DeepLab vs. Manual** \\  & & & **Manual** & **DeepLab vs. Manual** \\ \hline ine LVMM (g) \({}^{\pi}\) & 0.86 ([0.80, 0.90]) & 0.79 ([0.73, 0.84]) & 0.41 ([0.28, 0.52]) & 0.47 ([0.35, 0.58]) \\ LVEDV (mL) & 0.99 ([0.98, 0.99]) & 0.98 ([0.97, 0.99]) & 0.99 ([0.99, 0.99]) & 0.57 ([0.41, 0.69]) \\ LVESV (mL) & 0.99 ([0.98, 0.99]) & 0.98 ([0.97, 0.99]) & 0.97 ([0.96, 0.98]) & 0.65 ([0.51, 0.75]) \\ LVSV (mL) & 0.92 ([0.88, 0.95]) & 0.84 ([0.76, 0.89]) & 0.83 ([0.75, 0.89]) & 0.13 ([\(-\)0.08, 0.33]) \\ LVEF (\%) & 0.93 ([0.89, 0.95]) & 0.75 ([0.65, 0.83]) & 0.76 ([0.65, 0.83]) & 0.08 ([\(-\)0.13, 0.28]) \\ \hline \end{tabular} LVMM: LV myocardium mass (g), LVEDV: LV end-diastolic volume (mL); LVESV: LV end-systolic volume (mL); LVSSV: LV stroke volume (mL); LVEF: LV ejection fraction (%); \({}^{\
u}\): \(n\) = 180 images from 90 subjects.  
\end{table} 
Table 3: Relationships (Pearson r and []=95% CI) for algorithm vs. manual LV function measurements for \(n\) = 180 images from 90 previously unseen ACDC test subjects. Figure 3: Relationships and agreement between the proposed algorithm vs. manual measurements of LVMM (**A**), LVEDV (**B**), LVESV (**C**), LVSV (**D**), and LVEF (**E**) (\(n\) = 180 images from 90 subjects). Linear regression and Bland-Altman analyses of algorithm vs. manual LV indices are shown in the top and bottom plots, respectively. \(Alg\): algorithm results; \(Man\): manual results. Solid lines (blue) indicate the biases and dotted lines (red) represent the 95% limits of agreement. bias =16.4 \(\pm\) 79.0 g, 95% LOA = [\(-\)138.5, 171.3] g); LVEDV (Naive: bias=10.9 \(\pm\) 14.5 mL, 95% LOA = [\(-\)17.5, 39.2] mL; Combined: bias = 12.0 \(\pm\) 11.2 mL, 95% LOA = [\(-\)10.0, 34.0] mL), LVESV (Naive: bias = 8.8 \(\pm\) 16.7 mL, 95% LOA = [\(-\)24.0, 41.5] mL; Combined: bias = 19.0 \(\pm\) 18.1 mL, 95% LOA = [\(-\)16.4, 54.4] mL), LVSV (Naive: bias = 2.1 \(\pm\) 17.4 mL, 95% LOA = [\(-\)31.9, 36.1] mL; Combined: bias = \(-\)7.0 \(\pm\) 14.7 mL, 95% LOA = [\(-\)35.8, 21.8] mL), and LVEF (Naive: bias = \(-\)3.1 \(\pm\) 15.8%, 95% LOA = [\(-\)34.0%, 27.7%]; Combined: bias = \(-\)9.5 \(\pm\) 13.6%, 95% LOA = [\(-\)36.1%, 17.2%]). For the proposed algorithm and the naive method, U-net training/pre-training was completed in approximately 5 h. The fine-tuning procedure required an additional \(\sim\)12 min, including 10 s to post-process each image using the CKC algorithm and 10 min to update the U-net parameters. The combined method required \(\sim\)15 h for training and DeepLabV3+ required \(\sim\)5 h. For all the trained/fine-tuned models, inference of a new 2D cine image stack required \(\sim\)1 s.
# A Comparison of Deep Learning Methods for ICD Coding of Clinical Records
## 5 Results
### CodiEsp  
Similar experiments are carried out on the CodiEsp dataset, while only using the top 50 most frequent codes. The same hyperparameter settings are used as in Table 2. Results are visualized in Tables 7 and 8. Results for the full target space are not reported, as 90% of the target categories would only have 5 or less positive training examples. Furthermore, this would lead to a target space of 1767 different categories with only 500 training examples in total, which makes training of a decent model unfeasible.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Micro F1**} & **Macro F1** & **Micro AUC** & **P@5** \\ \hline \multirow{4}{*}{**Dis-50**} & **CNN** & 61.70 & 53.79 & 90.62 & 59.87 \\ \cline{2-6}  & **GRU** & 62.38 & 54.88 & 91.77 & 60.03 \\ \cline{2-6}  & **DR-CAML** & 67.68 & 63.74 & 93.47 & 63.48 \\ \cline{2-6}  & **MVC-LDA** & 65.21 & 60.06 & 92.29 & 62.41 \\ \cline{2-6}  & **MVC-RLDA** & 65.43 & 61.22 & 92.34 & 61.73 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of hierarchical models obtained on the MIMIC-III Dis-50 dataset.  
Figure 8: Ablation study on highest performing model (hierarchical MVC-RLDA) for MIMIC-III dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Micro F1** & **Macro F1** & **Micro AUC** & **P@5** \\ \hline \multirow{4}{*}{**CodiEsp**} & **CNN** & 12.52 & 6.17 & 49.35 & 7.96 \\ \cline{2-6}  & **GRU** & 11.54 & 11.03 & 50.54 & 7.68 \\ \cline{1-1} \cline{2-6}  & **DR-CAML** & 9.58 & 8.24 & 48.63 & 7.96 \\ \cline{1-1} \cline{2-6}  & **MVC-LDA** & 10.84 & 6.23 & 49.26 & 4.17 \\ \cline{1-1} \cline{2-6}  & **MVC-RLDA** & 11.52 & 6.67 & 48.01 & 3.70 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of flat models on top 50 codes obtained on the CodiEsp dataset.  
To assess the importance of the different components of the highest performing model on CodiEsp, an ablation study is conducted. The multi-view, the hierarchical component, and the regularization on long descriptions of the target ICD-codes are each removed while all other components stay the same. The difference in performance is measured and visualized in Figure 9.
# A Real-Time DAO-YOLO Model for Electric Power Operation Violation Recognition
## 4 Experimental Results and Analysis
### Comprehensive Comparison with Other YOLO Models  
To assess the DAO-YOLO model's performance, we conducted a quantitative comparison against mainstream YOLO-series architectures using identical experimental setups, including the same dataset, software, and hardware configurations. The comparative results are systematically summarized in Table 4.  
As demonstrated in Table 4, the proposed model attained a [EMAIL_ADDRESS](%) of 88.2% and [EMAIL_ADDRESS]-0.95 of 63.9%, surpassing all seven established YOLO models in both metrics. The [EMAIL_ADDRESS] and [EMAIL_ADDRESS]-0.95 reached 0.070 and 0.101, respectively, both lower than those of the other seven mainstream YOLO models, indicating that the proposed model has superior robustness. With 2.9 M parameters and 7.9 G FLOPs, the proposed model is more lightweight than YOLOv9t, YOLOv10n, and YOLOv11n, but slightly larger than YOLOv6n, YOLOv7t, and YOLOv8n. The model achieves 86.7 FPS, outperforming YOLOv6n, YOLOv7t, and YOLOv8n, in speed but trailing behind YOLOv5n, YOLOv10n, and YOLOv11n. For power companies, FPS is the most crucial metric, as it indicates real-time image processing capability. Most power company monitoring probes actually operate between 25 and 30 FPS. The proposed model delivers 86.7 FPS, which comfortably meets real-time operation standards. To summarize, our model not only meets real-time operational demands but also achieves the highest detection accuracy among comparable solutions.The proposed model shows significant performance advantages over conventional YOLO models in EPOVR tasks.
# A 40 Mb/s VLC System Reusing an Existing Large LED Panel in an Indoor Office Environment
## 4 Conclusions  
The feasibility of upgrading the existing indoor LED-based lighting infrastructure with add-on VLC functionality was validated by our demonstration system. With a bias-tee, a minimum modification was applied to the large LED panel that was installed in the ceiling to enable intensity modulation for simultaneous communication and illumination. A transmission rate of 40 Mb/s was supported up to 2 m with a measured illuminance level of \(\sim\)300 lux. The key factors preventing our system from achieving higher data rates are the limited bandwidth of the LED panel and the flicker that is caused by the low-cost LED driver. To combat those issues, we utilised the low-complexity MultiCAP modulation to exploit the spectrum out of the 3-dB bandwidth and proposed a DSP-based solution to remove the baseline wander effect that is caused by the flicker.  
The following are available at [[https://www.mdpi.com/1424-8220/21/5/1697/s1](https://www.mdpi.com/1424-8220/21/5/1697/s1)]([https://www.mdpi.com/1424-8220/21/5/1697/s1](https://www.mdpi.com/1424-8220/21/5/1697/s1)), Video S1: vlc-panel-demo-multicap-last-carrier.  
Conceptualisation, X.L. and Z.G.; methodology, X.L.; software, X.L. and P.A.H.; validation, X.L.; writing--original draft preparation, X.L.; writing--review and editing, Z.G., S.Z. and P.A.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the European Union COST Action NEWFOCUS CA19111 and Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement no 764461 (VisloN).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 What Is Big Data
### Volume, Velocity and Variety  
McKinsey adds that the definition is contextually contemporaneous and relative, in other words what constitutes big data will change as the technology advances, \"big data in many sectors today will range from a few dozen terabytes to multiple petabytes\" [3]. [PERSON] recollects that in the 1980s, the 100 GB hard disk enclosed in the IBM 3850 MSS (Mass Storage System) was used to provide researchers with ready access to the entire 1980 U.S. Census database, at the time it was considered to be a big data problem--although the term might not have been used in that context at the time [1].  
The big data problem therefore comprises of three dimensions:  
* **Volume** The size of data is too large (either or both in terms of number of items or size) to be processed effectively and efficiently.
* **Velocity** It takes too long to extract meaningful data from the dataset. This is a feature of volume and variety but additionally refers to unstructured data.
* **Variety** The dataset comprises of numerous complex structures of data and includes for instance: computer access logs, imagery, financial transactions, and website navigation trees.  
For a big data problem to exist, it needs to present a problem across any of the three dimensions outlined above. In other words, data could be significant in size, but comprise of a single format which is easily understood and processed, in such a case we would not refer to it as big data.  
Quite often, the problems of velocity and variety are a function of volume. Enterprises are collecting increasingly voluminous sizes of data. This is exemplified by a number of well-known cases. HP's Neoview data warehouse processes around 267 million transactions generated daily by 4000 US Wal-Mart stores. This is in addition to the four petabytes of customer transaction data held by Wal-Mart [4]. The Large Hadron Collider produces around 15 petabytes of data per year. The data is accessed by thousands of scientists around the world and is stored in eleven \"tier 1\" computer centres each of which stores a large fraction of the data. The data is then made available to 160 \"tier-2\" centres for analysis [5]. CardioDX analyses clinical data from thousands of patients to build diagnostic algorithms used by physicians to determine the likelihood that patients had obstructive coronary artery disease [6].
# Evaluation of HY-2 Series Satellites Mapping Capability on Mesoscale Eddies
## 2 Materials and Methods  
### Materials  
#### 2.1.1 Along-Track Altimeter Data  
To assess the performance of HY-2 series satellites, we set the Jason-3+Sentinel-3A/B configuration as a reference group and the Jason-3+Sentinel-3A/B+HY-2B/C/D configuration as an evaluation group for comparison. It is worth noting that several satellites were not chosen because Cryostat-2 is designed for cryosphere sounding [27], SARAL/AltiKa is in the drifting orbit and is about to retire [28], and Sentinel-6 had just been launched without data distribution at the time of our experiment [29].  
The HY-2B, HY-2C and HY-2D, oceanic dynamic environment series satellites of China are handled by the National Satellite Ocean Application Service (NSOAS, [[https://osdds.nsoas.org.cn/](https://osdds.nsoas.org.cn/)]([https://osdds.nsoas.org.cn/](https://osdds.nsoas.org.cn/)) (accessed on 18 February 2022)). Jason-3 [30] is a cooperative satellite launched by the National Oceanic and Atmospheric Administration (NOAA); National Aeronautics and Space Administration (NASA); Center National d'Etudes Spatiales (CNES); and the European Organization for Exploitation of Meteorological Satellites (EUMETSAT), with data distribution obtained by the Copernicus Marine and Environment Monitoring Service (CMEMS, [[https://marine.copernicus.eu/](https://marine.copernicus.eu/)]([https://marine.copernicus.eu/](https://marine.copernicus.eu/)) (accessed on 18 February 2022)). Sentinel-3A/B [31] are joint missions conducted by the European Space Agency (ESA) and EUMETSAT, which are distributed by CMEMS. The main characteristics of each mission are summarized in Table 1.
# SNR-Based Receiver-Type Decision Using Deep Learning for Multiple-Input Multiple-Output Detection
## 3 Proposed Deep Learning-Based MIMO Receiver  
Thanks to the optimality of the ML decision rule, the ML receiver can achieve optimal error performance [21]. However, as explained in Section 2, it demands significantly high computational complexity, which can be critical in scenarios where the receiver must operate with limited battery power [22]. The linear receiver can be straightforwardly exploited in MIMO detection with much lower computational complexity, even though it suffers from performance loss, particularly in poorly conditioned channels [23, 24]. To obtain the advantages of both receivers, we propose a MIMO receiver that adopts the two types of receivers alternatively and present the operation method of the proposed receiver. Especially, deep learning is exploited for the proposed receiver to identify the receiver type according to the characteristics of the MIMO channels.
# Automated Design of CubeSats using Evolutionary Algorithm for Trade Space Selection
## 3 Knapsack Problem in System Design
By applying the principle of survival of the fittest, EAs progressively produce a better solution over generations by operating on a population of potential solutions. By mimicking the process of natural evolution, EAs perform evolutionary operations borrowed from natural genetics such as selection, crossover, and mutation. Theoretically, this method of evolving a population of individuals would progressively generate individuals that are better suited to their environment, just as in natural adaptation [28; 29]. Figure 2b shows the structure of a simple evolutionary algorithm (EA). When successful, this approach will produce near-optimal solutions. These solutions when compared to solutions obtained through point design by a team of experts potentially presents major improvements. However, these solutions should be used as a starting point by the design team and would need further study to determine their implementation feasibility.
## 3 Materials and Methods
### Volatile Analysis
#### 3.5.2 GC-MS Analysis  
For the volatile analysis, a previously described method was used [9]. The GC-MS unit consisted of a Perkin Elmer system (GC Clarus(r) 580 GC module and Clarus(r) SQ 8 S MS module) and gas chromatograph equipped with a DB-5 MS fused-silica column (30 m \(\times\) 0.25 mm i.d., film thickness 0.25 \(\upmu\)m; J & W Scientific, Inc., Folsom, CA, USA), and the unit was interfaced with a Perkin-Elmer Turbomass mass spectrometer (software version 6.1, Perkin Elmer, Shelton, CT, USA). The SPME fibre was desorbed at 250 \({}^{\circ}\)C for 5 min. The oven temperature was programmed at 40-170 \({}^{\circ}\)C, at 3 \({}^{\circ}\)C/min, then subsequently at 25 \({}^{\circ}\)C/min up to 290 \({}^{\circ}\)C, followed by isothermal for 15 min. The transfer line temperature was 280 \({}^{\circ}\)C, the ion source temperature was 230 \({}^{\circ}\)C, and the carrier gas and helium were adjusted to a linear velocity of 40 cm/s\({}^{-1}\). The ionization energy was 70 eV, the scan range was 40-300 u, and the scan time was 1 s. The compound identifications were based on a comparison of the obtained spectra with those of the NIST mass spectral library, and they were confirmed using linear retention indices, which were determined using the retention times of an n-alkane (C\({}_{7}\)-C\({}_{36}\)) (Supelco, Bellefonte, PA, USA) mixture that was analysed under identical conditions. Moreover, they were compared against published data, and when possible, with commercial standard compounds. Quantitation (average value for three replicates per sample) was conducted using relative values obtained directly from the peak total ion current (TIC).
# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 4 Landing Site Detection
### Place Recognition
#### 4.2.1 Cluster of Alternative Landing Sites  
The scene map was constructed from data streams; however, the vast area covered by the UAV made performing a semantic analysis of all locations unrealistic. Therefore, the local maximum of the costmap was computed to identify alternative landing sites to reduce the UAV's reaction time for making landing decisions. The costmap was projected onto the horizontal plane, which was divided into scaling grids. Each grid stored the average cost value of all projected points. The present study applied a Quick Shift algorithm to segment a set of clusters suitable for landing whose center was a local maximum in the costmap corresponding to each cluster. The Quick Shift algorithm finds the modes of each cost \(C_{sum}\) by iteratively shifting each grid \(g_{xi}\) to the nearest neighboring grid \(g_{yi}\) with a lower cost value as follows:  
\[\begin{split} g_{yi}&=\operatorname*{arg\,min}_{xj: D_{yi}<D_{xi}}d(g_{xi},g_{xi}),\\ P_{xi}&=\frac{1}{N}\sum_{xj=1}^{N}\theta(d(g_{xi},g_{xi})),\end{split} \tag{19}\]  
where \(d(g_{xi},g_{xi})\) is the distance between \(g_{xi}\) and \(g_{xi}\); and \(P_{xi}\) is the density of grid \(g_{xi}\) with the kernel function, \(\theta\). The iterative procedure yields numerous clusters, which can be regarded as alternative landing sites.
# A Stackelberg Trust-Based Human-Robot Collaboration Framework for Warehouse Picking
## 5 Experiment
### Human Strategy and Robot Strategy  
In this subsection, the strategies for both humans and robots are generated, with the primary focus on robot strategies based on human trust. To support the proposed framework, three types of robot strategy models are considered.  
For human strategies, the strategy for tasks is given by Equation (11). As a follower in the collaborative system, humans first observe and consider the actions of the robot leader. If the robot chooses action \(a_{t}^{r}\to w_{quit}^{r}\) (not executing the task), the human strategy becomes a pure strategy, \(\pi_{h}=[1,0]\). If the robot chooses action \(a_{t}^{r}\to w_{exec}^{r}\) (executing the task), the human strategy is influenced by trust and depends on the task type. The human strategy is expressed as a set of probabilities for executing actions, as shown in Figure 8. The intervention probabilities under different trust states are provided for various task types. The blue line indicates the intervention probability for regular tasks, while the red line represents the probability for complex tasks.  
Figure 8: Human intervention probability for tasks.  
Figure 7: Trust transition matrix.  
For the robot strategy model, the main contribution of this study is the proposed strategy algorithm, where robots consider long-term rewards based on human trust dynamics. To validate the effectiveness of our framework, three types of robot strategy models are considered:  
* No-Trust Model: In this model, the robot ignores human trust during the collaborative process. While human strategies are influenced by their own trust levels, the robot does not consider these trust factors. The robot is fixed to execute regular tasks and opts out of complex tasks, with no adjustments based on the progression of events in the human-robot collaboration.
* Single-Round Trust Strategy Model: In this model, the robot evaluates human trust using the belief space and assesses human strategies based on the evaluated trust. For each task, the robot applies the single-round Stackelberg strategy to achieve optimal benefits within that round: \[\left\{\begin{aligned} E_{P}\leq g(\pi_{r},\pi_{h}^{pre},\tau_{t}) \\ \pi_{sgl}^{*}=arg\max_{\pi_{r}}\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!  
For the strategy spaces generated by the three robot strategy models, we subsequently constructed corresponding formal models in PRISM to validate their effectiveness and applicability in human-robot collaborative picking tasks.
## 2 Materials and Methods
### Data Generation  
Choba Park at the University of Port Harcourt serves as the study location. The university comprises of three campuses, Choba Park, Abuja Park, and Delta Park, all within 1.2 km of one another, and each has its separate water supply system. Choba Park, on the other hand, has a 2500 m perimeter, 121 acres of land, four huge hostel blocks, engineering, educational, and agricultural scientific institutions, and business centers with banks, canteens, and photocopying facilities. Choba Park is a five-sided polygon because of the five coordinates that define its perimeter. It suffices to enter any one of the five locations, such as 40\({}^{\circ}\)53\({}^{\prime}\)44\({}^{\prime\prime}\) N, 60\({}^{\circ}\)54\({}^{\prime}\)24.65\({}^{\prime\prime}\) E, to access the study area's map on Google. The existing water distribution network of Choba Park can be found in [32]. In contrast, the EPANET hydraulic model of the University of Port Harcourt Choba campus is shown in Figure 1.  
Figure 1: EPANET hydraulic model of the University of Port Harcourt Choba campus with 17 pipes and 15 node junctions.  
The water distribution network was split into two zones: Zone 1 (LZ1), which contains eight candidate nodes, and Zone 2 (LZ2), which contains seven candidate nodes [15]. The emitter coefficient used in the simulation of leak occurrence in the nodes is based on the classical Torricelli equation for flow through an orifice.  
\[q=C*A*p^{pexp} \tag{1}\]  
where \(C\) represents a coefficient, \(A\) represents the orifice aperture area, \(p\) is the fluid pressure, and \(p^{exp}\) is the pressure exponent. In the Epanet software, the pressure exponent, by default, is set to 0.5. The Epanet software applies a simple definition for the emitter function:  
\[eC=q/p^{pexp} \tag{2}\]  
Data were generated by modeling the leak occurrence in the water network according to the number of candidate leak nodes, and 15 leaks are summarized in Table 1. Zone 1 contains 8 candidate leak nodes, while Zone 2 has 7 candidate leak nodes. The dataset generated comprised 8 features of the network, such as pipe roughness, estimated demand, cinematic viscosity, fluid density, piezometric head, gravity acceleration, and valve coefficient and pipe length. The dataset sample used in the training and testing of the model were 480 samples on hourly average. The uncertainty conditions in the dataset used in training and testing the model were affected by a noise level of an amplitude within the range of [\(-4\), 5%]. The WDN simulation results are directly impacted by the model's pipe roughness parameter uncertainty. Since pipe deterioration causes the roughness to diminish with time, it is difficult to accurately identify in WDNs, since it cannot be measured directly. It is unnecessary and can be removed. Therefore, the coefficient of Hazen-Williams (CHW) is simulated, such that CHW \(\in\)[125; 130]. Water demand uncertainty is considered for each node in the water distribution network \(\hat{\text{d}}\in\) [\(-10\)%, \(10\)%].