esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper presents a system design that can extract power from an array of MFCs using a time-interleave technique that not only enables a custom MPPT per MFC in the arrange but also allows a proper MFC charge-recovery time to avoid overly depleting the cells in the array, which can lead to an MFC failure or premature end-of-life. An optimal power harvesting process was measured when enabling a maximum power point tracking in the DC-DC power conversion block across four different MFCs with an output characteristic power at MPPT ranging from 6400 uW to 435 uW with an observed 50.7% peak efficiency. The results of the proposed system show a robust performance of the proposed PMS after more than 24-h of MFC EH. The system was able to run autonomously to charge an output supercapacitor of 5 F from 0 V to 3.3 V in 13.6 h and endure a 24-h power extraction with no signs of MFC over-depletion or failure. The system powered an internet-enabled smart node five times, and the information was correctly retrieved from a remote server using a smartphone device. By successfully completing this MFC-powered IoT-sensor node demonstration, EH based on MFC technology has been verified as a promising area that has the capabilities to run power intense systems, such as internet-enabled technologies, which can potentially enable the arrival of a new generation of self-powered smart nodes. Finally, future work in this area includes the development of efficient PMS startup circuits tailored to MFC technology, more efficient low-power data acquisition and processing techniques for remote sensing, in which the addition of new sensors and analytical models in the cloud can give accurate real-time and predictive information about the health state of the MFC. The following are available online at www.mdpi.com/2076-3417/8/12/2404/s1, File S1: source code and circuit schematics of this work, applsci-386823. [PERSON] is the main author and thus responsible for writing the paper. C.E. has contributed to Section 2.1 and by providing MFC devices, equipment, laboratory space and materials, S.C.-B. contributed to Section 3.3 and industry insight in the area of power management systems. [PERSON] and [PERSON] have been responsible for revising and supervising the paper in the MFC side and the electronics circuits aspects, respectively. Conceptualization: A.C.-R. and S.C.-B. ; Data curation, A.C.-R.; Funding acquisition, A.H. and E.S.-S.; Investigation, A.C.-R.; Project administration, E.S.-S.; Resources, A.H. and E.S.-S.; Software, A.C.-R.; Supervision, A.H. and E.S.-S.; Validation, C.E. ; Writing--review and editing, A.C.-R., C.E. and S.C.-B. This research was partially funded by the Qatar National Research Foundation (QNRF), grant number #NPRP 7-1634-2-604
## 5 Discussion
### Legal and Ethical Considerations  
Deploying this technology in traffic management systems entails substantial legal and ethical challenges. The use of automated algorithms for liability determination carries significant legal implications that necessitate transparent operations and strict adherence to existing legal frameworks to gain public trust and regulatory approval. Furthermore, the risk of algorithmic bias, especially in situations involving vulnerable road users, introduces ethical dilemmas that must be addressed diligently. It is imperative to rigorously assess these systems and maintain a level of human oversight to mitigate potential biases and ensure the fairness and reliability of the technology.
###### Abstract  
A set of physicochemical methods, including X-ray photoelectron spectroscopy (XPS), X-ray diffraction, electron microscopy and X-ray absorption spectroscopy, was applied to study Pt/TiO\({}_{2}\) catalysts prepared by impregnation using a commercial TiO\({}_{2}\)-P25 support and a support produced by pulsed laser ablation in liquid (PLA). The Pt/TiO\({}_{2}\)-PLA catalysts showed increased thermal stability due to the localization of the highly dispersed platinum species at the intercrystalline boundaries of the support particles. In contrast, the Pt/TiO\({}_{2}\)-P25 catalysts were characterized by uniform distribution of the Pt species over the support. Analysis of Pt4f XP spectra shows that oxidized Pt\({}^{2+}\) and Pt\({}^{4+}\) species are formed in the Pt/TiO\({}_{2}\)-P25 catalysts, while the platinum oxidation state in the Pt/TiO\({}_{2}\)-PLA catalysts is lower due to stronger interaction of the active component with the support due to strong interaction via Pt-O-Ti bonds. The Pt4f XP spectra of the samples after reaction show Pt\({}^{2+}\) and metallic platinum, which is the catalytically active species. The study of the catalytic properties in ammonia oxidation showed that, unlike the catalysts prepared with a commercial support, the Pt/TiO\({}_{2}\)-PLA samples show higher stability during catalysis and significantly higher selectivity to N\({}_{2}\) in a wide temperature range of 200-400 \({}^{\circ}\)C.  
Pt/TiO\({}_{2}\); NH\({}_{3}\) oxidation; pulsed laser ablation; XPS +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussion
### The Developed FRep CAD/CAM System  
The proposed FRep CAD/CAM system [28] is written in C++ using the Qt Creator environment. 3D solids in the system are defined by implementations of C++ template function with two groups of parameters. The first group consists of current coordinates X, Y, Z. The second is an array used for parametrization of the FRep model. The template engine uses not only built-in data types (float, double), but also allows the use of interval [29] and affine arithmetic [30] to optimize computations [18]. Moreover, the developed system is equipped with the HyperFun interpreter [31] to provide modeling capabilities. Some primitives are implemented as built-in functions, for example, a free-form object used for topology optimization. The performance of the system when working with built-in functions is much higher.  
The developed system uses the contouring algorithm from [18] both for visualization and direct slicing. This algorithm uses MS approach on regular and adaptive grids. Users can choose interval or affine arithmetic for grid adaptation, and use finer grids at the boundary. The effect of adaptive grids is significant when a task requires high accuracy. According to [18] the system performs contouring up to six times faster on adaptive grids than on regular ones. This can be observed on grids with cells 500 times smaller than model sizes.  
The system translates generated contours into G-codes, or exports them into STL files. The support of STL format is necessary as some 3D printing equipment has proprietary G-code protocols. Therefore, two manufacturing scenarios can be implemented--direct slicing and STL-based slicing. The second option is slower. According to [19] on some models the direct slicing runs up to 300 times faster than STL-based one.  
The developed system has other advantages of FRep. Models created in the system are fully parameterized and have no cracks, overhangs and inverted faces. Moreover, the size of files with models is significantly smaller. For example, the model shown in Figure 4 occupies near 30 KB as an FRep defining function. The same model in STL format occupies approximately 100 MB, which is 3000 times more. This difference increases dramatically with increasing complexity of topology or in the case of porous models.  
This system has a broad set of applications. FRep is more attractive in applications where models have microstructures. These models require high precision when they have a small feature size. Complex models with such qualities appear in natural gas separation. Therefore, our system can be applied in this field [32]. Moreover, similar models exist in the implantation [33]. The use of our software for the design of topologically optimal implants seems attractive.
# Small Satellite Tools for High-Resolution Infrared Fire Monitoring
## 3 The FireBIRD Main Payload
### Bi-spectral Sensors of TET-1 and BIROS

Table 3: Technical specifications of the BIRD MIR and TIR CMT detectors that were used in the IR sensors of TET-1 and BIROS [5].
## 3 Security Implications and Fundamental Approaches of Blockchain-Based Data Storage Methods
### The Role of Cryptographic Methods in Blockchain-Based Data Storage Systems
#### 3.2.5 Blockchain Hashing  
Hashing is a fundamental method for ensuring data integrity and authenticity in blockchain systems [16; 101]. This technique is primarily used in on-chain systems for validating transactions and ensuring data immutability [24]. Hashing enhances security and transparency, preventing unauthorized data modifications [25]. However, managing  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference Number** & **Cryptographic Method** & **Storage Type** & **Application Domain** & **Security Requirement** & **Sectoral Applications** & **Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{[120; 125; 133; 134; 135; 136]} & Homomorphic Encryption & Off-chain, Hybrid & Performing operations on encrypted data & High & Healthcare, IoT & Protects data privacy, & Implementation complexity \\ \hline \multirow{3}{*}{[16; 23; 24; 25]} & Blockchain Hashing & On-chain & Data integrity and validation & Medium & Education, healthcare & Provides immutability & Inefficient on large datasets \\ \hline \multirow{3}{*}{[58; 59]} & Threshold Cryptography & On-chain, Off-chain, Hybrid & Access control, key management & High & Healthcare, IoT & Provides decentralized management & Implementation complexity \\ \hline \multirow{3}{*}{[137; 138; 139; 140]} & Multi-Party Computation (MPC) & Off-chain, Off-chain, Hybrid & Private data sharing, access control & High & Health, finance, IoT & Enables secure computation with data privacy & Computational complexity, high transaction cost \\ \hline \multirow{3}{*}{[121; 141; 143]} & Post-Quantum Cryptography & On-chain, Off-chain & Quantum-resistant data security & High & IoT, public sector, health & Resistant to quantum threats & Limited application standardization \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._large datasets using hashing alone can be inefficient, making it less effective for large-scale data storage [101]. Hashing is directly related to on-chain reference storage and is commonly used for on-chain data verification solutions [23].
# The Use of Radio and Telemedicine by TMAS Centers in
###### Abstract
A literature review of telemedical assistance in the marine industry domain has never explored the previous objectives in detail. Therefore, we will be able to provide a broader understanding of the area and facilitate future technological applications. Only two reports gave an overview of patient safety and legal impacts [12, 13]. Another study gave an overview of European remote medical care services at sea from a variety of communication channels and generational perspectives [14]. As far as we know, no comprehensive study has focused on contextualizing data collection and medical operations between ships and TMAS centers using radio communications and telemedical devices. The purpose of this study was to explore the evolution of medical assistance at sea, from primitive radio-based methods to modern telemedicine. A discussion of existing medical assistance case studies has also been provided, along with the most important aspects, including techniques and communication tools.
# Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios
## 3 Results and Discussion
Eventual actuators for weed control would only have a few seconds to detect weeds by processing images and delivering the treatments. The obtained results revealed disparities in the required inference time among different models. Specifically, YOLOv5m exhibited efficient inference time, taking less than 20 ms for all the datasets except for the Baseball Field, where it required approximately 24 ms. Similarly, YOLOv6l and YOLOv7 showed a similar trend, achieving detection within less than 30 ms for the public 'Weeds', Home Lawn and Manila grass datasets, while requiring longer inference time for the Baseball Filed dataset. For YOLOv5m, YOLOv6l and YOLOv7, the average mAP_0.5 values were 0.72, 0,73 and 0.74, respectively, with corresponding average mAP_0.5:0.95 values of 0.55, 0.55 and 0.54. Notably, YOLOv5m and YOLOv7 consistently achieved detections in less than 30 ms, while YOLOv6l resulted with an average inference time of 33 ms (Figure 5). Conversely, YOLOv8l exhibited a contrasting trend, requiring more than 30 ms for inference on all test datasets, except for the Baseball Field dataset, where it required less than 20 ms. YOLOv8l resulted with an average mAP_0.5 of 0.76 and an average mAP_0.5:0.95 of 0.56, with an average inference time of 32 ms. On the other hand, EfficientDet exhibited the lowest average mAP_0.5 and mAP_0.5:0.95 (0.67 and 0.49, respectively) and the highest average inference time (50 ms). The situation among inference time, models and test datasets is not straightforward and warrants further investigation for clarification. Thus, additional studies should be conducted to explore and elucidate the intricate dynamics among these factors.
## 4 Results and Discussion
### Artificial Neural Networks Modelling Results
#### 4.1.2 Standard Approach Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Model Performance**} & \multirow{3}{*}{**Statistical Indicators**} & \multicolumn{8}{c}{**Numbers of Neurons in Each Hidden Layer**} \\ \cline{3-11}  & & \multicolumn{2}{c}{**6**} & \multicolumn{2}{c}{**8**} & \multicolumn{2}{c}{**10**} & \multicolumn{2}{c}{**12**} & \multicolumn{2}{c}{**14**} \\ \cline{3-11}  & & \multicolumn{2}{c}{**Test**} & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Test**} & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Test**} & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Test**} & \multicolumn{2}{c}{**Train**} \\ \hline \multirow{3}{*}{Best} & MSE & 0.677 & 0.442 & 0.712 & 0.422 & 0.804 & 0.491 & 0.427 & 0.547 & 0.560 & 0.510 \\  & R & 0.857 & 0.918 & 0.868 & 0.925 & 0.854 & 0.905 & 0.923 & 0.895 & 0.905 & 0.900 \\ \hline \multirow{3}{*}{Worst} & MSE & 1.714 & 0.305 & 1.751 & 0.307 & 1.909 & 0.248 & 3.607 & 0.254 & 2.023 & 0.396 \\  & R & 0.549 & 0.944 & 0.775 & 0.941 & 0.754 & 0.953 & 0.438 & 0.949 & 0.469 & 0.926 \\ \hline \multirow{3}{*}{Test MSE standard deviation} & \multirow{3}{*}{0.371} & \multirow{3}{*}{0.395} & \multirow{3}{*}{0.364} & \multirow{3}{*}{0.872} & \multirow{3}{*}{0.513} & \multirow{3}{*}{
## 3 Results and Discussion  
### Capsule Morphology  
Figure 4 presents the optical images of the capsule with different A/R ratios. Without rejuvenator, the calcium alginate capsule has a transparent brown color and ellipsoid shape.  
With encapsulated rejuvenator, the calcium alginate capsules become black, and increasing the amount of rejuvenator results in a more spherical morphology. Table 2 summarizes the diameter and morphology information of capsules fabricated with different A/R ratios. At least 12 capsules were measured for each A/R ratio type and since the well-controlled manufacture process, the capsules had the same diameters for each A/R ratio type. The results indicate that the diameter of capsules increases with rejuvenator content. When the A/R ratio is beyond 40%, the fabricated capsules become ellipsoids. This change in morphology might be due to dehydration of the hydrated alginate. During the drying process, higher alginate content would result in more dehydration and greater deformation.  
Figure 4: Microscopic images of capsules and the trend with alginate/rejuvenator (A/R) ratio.
## 3 Results  
The performance of the proposed R and QRS detection algorithm is evaluated on a standard MIT-BIH DB. The DB contains two-channel ECG recordings in digital format from 47 subjects comprising 25 men and 22 women of age groups of 32-89 and 23-89, respectively. Approximately 60% of the total subjects were inpatients. The DB contains an 11 bit resolution, 10 mV range, 360 Hz sampling frequency, and complete annotations [2]. The verification of the proposed method consists of R-point detection performance, processing time, and detection performance analysis for noise.
# Adaptive Downward/Upward Routing Protocol for Mobile-Sensor Networks
## 4 Simulation Environment and Performance Evaluation
### Simulation Results
#### 4.2.4 Number of Control Packets  
The number of control packets is the sum of control packets that are sent during the simulation by the routing protocol. ADUP only contains one type of control message that is broadcast by the sink node and propagated by other nodes until it reaches the leaf nodes. AODV generates three types of control messages: Route Request (RREQ) messages, Route Reply (RREP) messages, and Route Error (RERR) messages. RREQ is used in route-discovery processes in order to build a route to reach the destination node. RREP is sent once the destination node receives a RREQ or an intermediate node has an active route to the destination. RERR is sent whenever a node detects a link failure or does not have an active route to the destination. Flooding only uses broadcasting as traffic patten, and it does not need any additional control packets. Thus, in Figure 9 we only show a comparison between ADUP and AODV.  
Figure 9 shows that ADUP has very low overhead compared to AODV, especially when the number of nodes increases. This is due to two reasons. First, ADUP uses dynamic control message management to reduce the number of control packets used by upstream traffic, which helps to save overhead for upstream traffic. Second, ADUP embeds next-hop information in data packets destined for the sink. This allows the sink to build routes for downstream traffic without generating additional  
Figure 8: Dropped-packets ratio.  
control traffic. Moreover, AODV broadcasts RREQ to ask for a route, which adds more overhead to the network, especially when the number of nodes increases.
# A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm
## 3 Remote Sensing Image Retrieval Algorithms Based on Scene Graph Similarity
### Problem Description
The scene graph feature extraction module utilized an edge-enhanced graph convolutional network (EGCN) to extract features, generating node features X and edge features E. The feature similarity calculation module integrated node and edge features into graph features H, using a neural tensor network to compute feature similarity.
# Review Rating Prediction Based on User Context and Product Context
## 4 Experiments and Evaluations
### Datasets and Evaluation Metric  
In order to evaluate the performance of our methods, we respectively implement our experiments on English data sets and Chinese data sets. We have selected two public English data sets Yelp 2013 and Yelp 2014, which is a large-scale dataset consisting of restaurant reviews ([[https://www.yelp.com/dataset/challenge](https://www.yelp.com/dataset/challenge)]([https://www.yelp.com/dataset/challenge](https://www.yelp.com/dataset/challenge))). On the Chinese dataset, we constructed two Douban movie review datasets because there is no suitable public dataset in Chinese.  
Douban ([[http://www.douban.com/](http://www.douban.com/)]([http://www.douban.com/](http://www.douban.com/))) is a popular Chinese review site where users can post comments on movies, books and music and give a 1 to 5 star rating. Through the API interface that is provided by Douban, we download the Douban movie user information and then sort the Douban movie users according to the number of published reviews. We selected film users who have published more than 50 movie reviews as seeds. Focusing on the movie users, through the Douban API interface, grab the movie reviews published by Douban movie users. Based on the captured Douban data, two movie review data sets were constructed. The statistics of the four datasets are shown in Table 1.  
We use mean absolute error (MAE) and root mean square error (RMSE) as evaluation indicators to evaluate the performance of different RRP methods. MAE and RMSE are defined, as follows:  
\[\text{MAE}=\frac{\sum\limits_{\text{testsets}}\left|\hat{v}_{ui}-v_{ui}\right|} {N_{\text{total}}} \tag{15}\]  
\[\text{RMSE}=\sqrt{\frac{\sum\limits_{\text{testsets}}\left(\hat{v}_{ui}-v_{ui }\right)^{2}}{N_{\text{total}}}} \tag{16}\]  
Here, \(\hat{v}_{ui}\) is the review rating predicted by various methods, \(v_{ui}\) is the true rating of the review in the test set, and \(N_{\text{total}}\) is the number of reviews in the test set.
# High-Resolution Multi-Scale Feature Fusion Network for Running Posture Estimation
###### Abstract
Although there have been significant advancements in the research on human pose estimation in recent years, there are still some challenges and limitations, including occlusion, viewpoint variations, and difficulties in handling complex scenarios with multiple individuals. Concurrently, we observed that there is a relatively scant amount of research on pose estimation for running movements, mainly due to the lack of corresponding datasets. In response to this problem, the authors of this study have constructed a benchmark dataset specifically designed for running movements called \"Running Human\". This dataset provides comprehensive annotations encompassing bounding boxes and human body keypoints. It comprises over 1K original images and 1288 instances of running individuals. Additionally, in response to the common issue of joint occlusion in running sports images, we have devised a Receptive Field Spatial Pooling (RFSP) module and used it to reconstruct the HRNet model. The precision of running posture estimation is improved by expanding the model's receptive field and leveraging the multi-branch network's capacity to extract features at different scales. Our method attains performance equivalent to the current state of the art on the Running Human dataset. Additionally, we conducted experiments on two widely applicable benchmark datasets (COCO and MPII), the results of which affirm the effectiveness and exceptional generalization ability of our approach. Specifically, in Section 2, we provide an overview of the related work in this article. In Section 3, we delve into the methodology employed in detail and then introduce the \"Running Human\" dataset that was developed in this study, along with two additional publicly available datasets. In Section 4, we conduct comprehensive experiments on the proposed model across the three datasets. Finally, Section 5 concludes with a summary and prospects for future work on the subject.