esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Ad-hoc sensor networks have been paid attention in various applications, such as environment monitoring and smart home [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]. Today, heterogeneous Wireless Sensor Networks (WSNs) also gain enormous attention due to the diversification of applications, such as medical applications and environmental monitoring systems [17, 18]. In heterogeneous WSNs, various components are heterogeneous, such as sensor nodes' traffic sources (low/middle/high) and network topology (star/tree). Therefore, it is difficult to control heterogeneous WSNs nodes' operations because of their complexities.  
The IEEE 802.15.4 is the standard of WSN. The devices communicate with superframe structure in the IEEE 802.15.4 standard [19]. In the superframe structure, the communication of each node is managed by the active and sleep cycles (Duty-Cycle). The energy consumption of WSN nodes are saved by controlling DC. The Quality of Service (QoS), such as Packet Delivery Ratio (PDR) and delay, is controlled by tuning the MAC (Medium Access Control) parameters [18, 20, 21, 22].  
[PERSON] et al. proposed a scheduling protocol for star topology WSNs [20]. The protocol proposed in Reference [20] controls active and sleep periods taking into account the buffer occupancy of sensor nodes. However, the protocol proposed in Reference [20] is not suitable for controlling heterogeneous WSNs nodes' operations since it is proposed under the assumption of the homogeneous networks.  
[PERSON] et al. proposed a beacon signal avoidance protocol for homogeneous tree topology WSNs in order to achieve high PDR and low energy consumption [22]. Although the collision avoidance protocol proposed in Reference [22] can avoid beacon signal collisions, it does not guarantee collision avoidance of outgoing packets of sensor nodes.  
The Tele-Medicine Protocol (TMP) [18] is an IEEE 802.15.4-based MAC protocol for heterogeneous WSNs. The TMP tunes DC and MAC layer parameters to meet the QoS requirements in the medical field. The TMP controls the active-period length of a superframeto be shorter in order to reduce energy consumption. However, for high data rate nodes, the shorter the active period, the more packets are lost, and the lower the PDR [23; 24; 25; 26].  
This paper proposes a duty-cycle control method in order to improve the PDR for IEEE 802.15.4-compliant heterogeneous WSNs. The proposed method tunes the DC so that the buffer occupancy of sensor nodes is less than one and assigns DC to each sub-network (sub-network means a network consisting of a router node and its subordinate nodes). The appropriate DC of each sub-network can be used to obtain the high PDR; thus, this paper gives analytical expressions of the buffer occupancy. The proposed method can control DC heterogeneous star and/or tree-topology wireless sensor networks. Simulation results show the effectiveness of the proposed method.
# On the Intersection of Computational Geometry Algorithms with Mobile Robot Path Planning
## 3 Future Directions  
CG has excellent potential for optimally solving robotic path-planning problems, as evidenced by the approaches above. However, there is still space to work on exploiting CG for multi-robotic systems. Future studies should concentrate on the following areas:  
**Optimality and time complexity tradeoff:** Optimal methods, such as DFS, A*, and D*, have a high computational cost in achieving optimal answers; randomized and sampling-based algorithms, on the other hand, produce sub-optimal and approximation solutions in less time. To achieve overall path-planning efficiency, the correct balance between optimality and execution time is essential;  
**Robot adaptability and cost efficiency:** The dynamic environmental parameters of the robot may impact its movements and cause unwanted performance reduction. Robots may lack adaptability and become stuck in classic deadlock scenarios. To seek a collision-free path in an unknown environment with uncertain obstacles, a robot with the ability to change operating behavior over time is required. In addition to increasing the number of onboard sensors when dealing with complicated surroundings, the computation cost may be considerable, due to their high memory needs and processing expense. As a result, the computation cost considerations must be addressed when creating a proper CPP environment model. The hybrid algorithm is an intriguing invention for managing environmental change at the lowest possible cost;  
**Path Smoothness:** In networked multi-robotic systems, communication dependability and connectivity coverage are critical. Because of its limited communication and sensory capabilities, the robot cannot regenerate the optimum path if the unexpected occurs, lowering the effective planning ratio. One of the issues that must be handled is a kinematic constraint of the robot, such as path curvature. Trajectory smoothing on a quick turn enables fast-moving robots such as drones to deliver an effective inertia motion transfer to decrease power consumption and prevent premature mechanical damage. As a result, a smooth path must be projected while adhering to the planned route;  
**Open Questions:** There are open questions that can be addressed through the exploitation of CG in robotics. For example, we can use CG to determine if it is appropriate to employ an optimizing sampling-based planner or a non-optimizing sampling-based planner. Further, CG-based optimization techniques can be applied to optimize the resulting path for low computationally powered robots. We can also analyze convergence rates for sampling-based algorithms and give a defined length of time. Finally, CG can be helpful in solving the problems related to path planning for controlling non-holonomic and under-controlled robots.
# Design and Implementation of a Six-Degrees-of-Freedom Underwater Remotely Operated Vehicle
## 3 Mechanical Design Structure
### Gripper and Camera Enclosure  
A pneumatically actuated manipulator was designed to perform underwater tasks successfully. The actuator is a pneumatic cylinder (bore 30 mm and stroke 60 mm). It depends on the mechanism of the parallel link to convert the linear motion of the cylinder to a gripping action. The gripper's jaws were designed especially for the missions as shown in Figure 5a.  
Figure 4: Thruster configuration: (**a**) Motor Positioning [26], (**b**) Motion of ROV in 6-DOF. The red triangle represents the direction of motion.  
To perform the tasks appropriately, we set up two cameras to allow the pilot to control the vehicle. The main camera is set in the electronic enclosure and faces the front side of the ROV. The cameras were also used for image processing at the request of our software tasks. The second camera was installed to capture photos of the gripper. The enclosure was made of artelon, with a gland at its end to pass the camera's cable through. An acrylic drum was added to allow the light to pass through for a better vision. The camera enclosure is shown in Figure 5b.
# Object Detection with Low Capacity GPU Systems Using Improved Faster R-CNN
## 4 Experiments and Results  
### Experiments Environment and Evaluation Criteria  
Experiments were performed using the MMDetection toolkit [15] on a desktop PC with Intel(r) Core(tm) i5 2.4 GHz CPU, 6 GB RAM (Intel(r), Santa Clara, CA, USA), single Geforce GTX 1080 graphics  
Figure 6: Network structure of the proposed method.  
card (NVIDIA, Santa Clara, CA, USA) and Ubuntu 16.04 LTS operating system (Canonical, London, United Kingdom). Program codes were written in Python [16] using the PyTorch deep learning library [17].  
We used the precision-recall curve (PRC) and average precision (AP) criteria to evaluate the performance of our proposed model. These two criteria have been standardized in the field of study and have been used in many object detection studies [18; 19; 20; 21].  
**(1)** Precision-Recall Curve (PRC): _Precision_ determines the accuracy of true positive detections and _Recall_ determines the proportion of true positives identified as true. _TP_, _FP_, and _FN_ are used to indicate the number of true positives, the number of false positives, and false negatives, respectively. So, the PRC value is calculated as follows:  
\[Precision=\ \frac{TP}{(TP+FP)} \tag{6}\]  
\[Recall=\ \frac{TP}{(TP+FN)} \tag{7}\]  
If the area overlap ratio between the predicted limit box and the ground reality limit box exceeds 0.5, the detection is considered true positive. Otherwise, the detection is considered false positive. In addition, if more than one detection coincides with the same basic accuracy limiting box, only one is considered true positive, others are considered false positive.  
**(2)** Mean Average Precision (_m_AP): _Recall_ = 0 to _Recall_ = 1 that is, calculating the average value of _Precision_ over the range in the area under PRC, therefore, the higher the _m_AP value, the better the performance.
## 5 Conclusions  
By comparing the images obtained with the FNLM denoising algorithm and other conventional denoising filters, we demonstrated that the FNLM denoising algorithm achieved the best denoising performance and similarity with the original image. In addition, we demonstrated that the FNLM denoising algorithm improved the time resolution significantly as compared with the NLM denoising algorithm.  
In conclusion, we expect that the FNLM denoising algorithm can contribute to reducing the ratio of misdiagnosis of thoracic disease and the exposure dose because the noise, which is the advantage of the low-dose examination, can be suppressed efficiently by the FNLM denoising algorithm. Future studies will focus on further improving the proposed algorithm to elicit better image characteristics.  
Conceptualization, B.-G.K., H.-W.J., and Y.L.; Data curation, B.-G.K., S.-H.K., and C.R.P.; Formal analysis, B.-G.K., S.-H.K., and Y.L.; Funding acquisition, H.-W.L.; Software, S.-H.K. and Y.L.; Writing--original draft, B.-G.K., S.-H.K., and C.R.P.; Writing--review and editing, H.-W.L. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Technology Development Program (S2827843) funded by the Ministry of SMEs and Startups (MSS, Korea).  
The authors declare no conflict of interest.
## 3 Results
### Comparative Analysis
#### 3.1.3 Level Walking  
The COM trajectory graphs (Figure 2) show less vertical excursion (Figure 2a), less anterior-posterior (Figure 2b) excursion, and less mediolateral (Figure 2c) excursion in LC compared to HC. However, the comparison of average peak-to-peak values for LC and HC did not demonstrate a statistically significant difference.  
The visualization of the COM trajectory in three-dimensional space (Figure 3a) illustrated a more evident difference between the two groups; moreover, the calculated volume encompassed by this trajectory (Figure 3b) was observed to be significantly smaller in LC than in HC. The mean values of these volumes are listed in Table 3.  
The peak excursions in the mediolateral trajectory graph (Figure 2c) occurred later in the gait cycle of the LC in comparison to HC, exhibiting a difference in phase durations. To ascertain the significance of this observation, the following parameters were examined: percentage of gait cycle the participants spent in each of the four phases of stance (loading response (LR), mid-stance (MS), terminal stance (TS), and pre-swing (PS)), single limb support (SLS) percentage, double limb support (DLS) percentage, and the swing phase (SW) portion of the gait cycle. The average phase duration, represented as the percentage of the gait cycle, for LC and HC, and their respective \(p\)-values, are shown in Table 3.  
Figure 2: Average COM trajectory excursion graphs for LC and HC: (**a**) vertical trajectory graph, (**b**) anterior-posterior trajectory graph, and (**c**) mediolateral trajectory graph (the continuous and dotted vertical lines mark the peak occurrences for HC and LC).
## 2 Problem Description and Assumptions  
The takeaway delivery discussed in this paper refers to the process in which consumers place orders through online ordering platforms, and the goods are prepared offline and delivered to the consumers by the rider after the merchant accepts the orders. The rider receives the goods from a fixed address for merchant self-operated takeaway delivery. Its route optimization problem is a TSP to minimize the distance traveled.  
When establishing the model for merchant self-operated takeaway delivery, it was assumed that:  
1. Merchants have sufficient delivery capacity.
2. Most takeaway deliveries are goods with regular outer packaging, small volume, and lightweight. The capacity limit of the delivery vehicle is no longer considered.
3. The delivery distance is generally within 5 km. Most delivery vehicles are electric vehicles, and their batteries can be replaced quickly. Therefore, this paper does not consider the battery life of the delivery vehicles.
4. The number of riders \(K\) is assumed to be less than or equal to the number of orders \(N\). When \(K>N\), only the first \(N\) riders are each assigned one order, and the remaining riders remain untasked.
## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### Collective Contribution to Reliable AI in Healthcare  
Integrating blockchain technology with advanced data validation algorithms marks a significant advancement in ensuring data integrity within healthcare AI. This combination comprehensively addresses the multifaceted challenges of data management and security in healthcare settings. Blockchain technology provides a groundbreaking platform for secure and immutable data storage, ensuring that patient records and clinical trial information are protected from tampering and unauthorized access. The decentralized and transparent nature of blockchain ensures that data modifications are recorded and traceable, enhancing trust and reliability in the data used by AI systems. Concurrently, advanced data validation algorithms act as a second defense layer, continuously ensuring the accuracy and consistency of healthcare data. These algorithms are crucial in identifying and correcting any anomalies or inaccuracies in patient data before AI systems use them. The collective impact of integrating these technologies is substantial. These technologies foster trust among healthcare professionals and patients by securing data integrity and maintaining quality. They pave the way for AI to augment healthcare delivery with the utmost reliability and trustworthiness, addressing national efficiency, patient safety, and treatment outcomes. This synergistic approach represents a commitment to harnessing the power of AI in healthcare in a manner that respects the integrity of data and, by extension, the integrity of healthcare services and patient care [29].  
The combined impact of technologies like blockchain and advanced data validation algorithms profoundly ensures reliable AI in healthcare. These technologies provide a robust data integrity, security, and privacy framework. The evolution of privacy-preserving techniques, including PPML, HE, and SMPC, highlights the ongoing efforts to enhance data security in healthcare AI. These advancements point towards future challenges and potential developments in balancing privacy with data utility and managing communication costs in systems like Federated Learning. Integrating various privacy-preserving strategies through hybrid techniques offers promising solutions to complex privacy concerns in AI healthcare systems [9].
# A Low-Complexity Security Scheme for Drone Communication Based on PUF and LDPC
## 3 Security Architecture
### Error Correction Schemes
#### 3.4.2 GS Perturbation Selection  
In the proposed scheme of this paper, the response of the identity PUF of the drone changes by several bits, and the GS adds perturbations on top of it. This is performed to maximize the number of error-correcting bits in the ECC, which not only allows each response of the identity PUF to be controlled by the GS but also greatly increases the difficulty of an intrusive attack (described in detail in Section 4.3). As can be seen in the previous section, the power-up perturbation \(\varepsilon\) and the GS perturbation \(\delta\) are symmetric in the information part and the parity-check part, so the location of the GS to add the perturbation needs to be constrained by the following. The symbol bit position of \(\delta\) is as different as possible from the symbol bits supervised by the syndromes affected by the unstable bits. That is the symbol bit of \(\delta\), \(\delta_{j^{\prime}}\
otin\{N_{i^{\prime}},s_{i}\in K_{j^{\prime}},m_{j}\in S _{\text{plus}}\}\). We take the base matrix \(A\) as an example; as shown in Figure 4, if \(S_{\text{plus}}=\{m_{5}\}\), then \(\delta_{j^{\prime}}\in\{m_{1}\}\). Based on the above, if the parity matrix \(P^{T}\) is considered as the adjacency matrix of the check  
Figure 3: Three parts are arranged in parallel.  
bits and information bits, the disturbance \(\delta_{j}\) needs to be outside the two-hop range of all the unstable bits in \(S_{pus}\). Depending on the matrix performance and the length of the codeword, this study selects \(n_{\delta}=2\) according to \(P_{e}\), where \(n_{\delta}\) is the number of symbol bits in sequence \(\delta\).
# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 2 Proposed Method
### Input Feature  
In self-supervised ASD systems, Log-Mel features are used in many studies, such as [13; 14; 15; 25; 29; 30; 39]. The extraction of Log-Mel spectrograms involves three key stages: Short-Time Fourier Transform (STFT) [40], Mel filter banks, and logarithmic compression.  
The frequency and time resolutions of the STFT are given by the following:  
\[\Delta f=\frac{f_{s}}{N},\quad\Delta t=\frac{N}{f_{s}}, \tag{3}\]  
where \(f_{s}\) is the sampling rate, and \(N\) is the window length.  
The formulas above demonstrate that there is a trade-off between frequency and time resolution: larger window sizes improve frequency resolution but reduce time resolution, thereby limiting the ability to effectively capture transient low-frequency components. In contrast, spectral features provide higher frequency resolution and are more effective in capturing both high-frequency and low-frequency components. The non-uniform filter design of Log-Mel spectrograms reduces sensitivity to high-frequency information, while the windowing used in STFT limits the representation of low-frequency components. Spectral features, with their fine-grained frequency representation, offer a more comprehensive reflection of the spectral structure of sound signals. As a single input feature, spectral features avoid the redundancies associated with multi-feature fusion and provide high-quality, discriminative input data, thereby enhancing anomaly detection performance.  
Recognizing these advantages, researchers have shifted their focus toward processing methods that emphasize frequency domain information. [PERSON] [30] and [PERSON] et al. [41], for instance, introduced temporal marginalization techniques to aggregate temporal information in time-frequency representations, thereby prioritizing frequency feature extraction. By discarding temporal dynamics, their approach maximized the potential of spectral features, thereby highlighting their value in anomaly detection tasks.  
Inspired by this, our study directly focuses on frequency domain information by employing spectral features as the primary input, enabling more effective capture of critical frequency components. Specifically, we apply the Discrete Fourier Transform (DFT) to convert audio signals into frequency domain representations. For an audio signal \(x(n)\) of length \(L\), the DFT is computed as follows:  
\[X(k)=\sum_{n=0}^{L-1}x(n)\cdot e^{-j2\pi\Omega n/L},\quad k=0,1,\dots,L-1. \tag{4}\]By leveraging spectral features, our proposed approach captures anomalous patterns more effectively, thereby significantly enhancing detection performance while maintaining computational efficiency.
###### Abstract  
5G NR (new radio) introduces the concept of massive MIMO (multiple-input multiple-output) technology, in which a larger number of antenna arrays are installed on the transceiver. Due to the increased number of antenna elements allocated close to each other (approximately at a distance of half a wavelength), mutual coupling becomes a serious problem leading to performance degradation of the MIMO communication system. In this communication, two different configurations of closely spaced antenna array elements are studied. In order to reduce the mutual coupling, a combination of a metamaterial-based frequency-selective surface (FSS), a metallic strip, and a slot element in the ground plane is examined. It is found that the proposed technique significantly suppresses mutual coupling from \(-12\) dB to \(-25\) dB. Both designs are fabricated and experimentally validated. The simulation results are in good agreement with the measurements. The proposed mutual coupling reduction technique may be suitable for massive MIMO systems in fifth-generation (5G) new radio applications.  
5G NR; FSS; isolation; massive MIMO; mutual coupling; metamaterial; transceiver Article A Study of Mutual Coupling Suppression between Two Closely Spaced Planar Monopole Antenna Elements for 5G Massive MIMO System Applications  
[PERSON], 1,*(c), [PERSON], 1,2c, [PERSON], 3 and [PERSON] 2
###### Abstract  
In this study, the resource blocks (RB) are allocated to user equipment (UE) according to the evolutional algorithms for long-term evolution (LTE) systems. Particle Swarm Optimization (PSO) algorithm is one of these evolutionary algorithms, which imitates the foraging behavior of a flock of birds through learning and grouping the best experience. In previous works, the Simple Particle Swarm Optimization (SPSO) algorithm was proposed for RB allocation to enhance the throughput of Device-to-Device (D2D) communications and improve the system capacity performance. Genetic algorithm (GA) is another evolutionary algorithm, which is based on the Darwinian models of natural selection and evolution. Therefore, we further proposed a Refined PSO (RPSO) and a novel GA to enhance the throughput of UEs and to improve the system capacity performance. Simulation results show that the proposed GA with 100 populations can converge to suboptimal solutions in 200 generations. The proposed GA and RPSO can improve system capacity performance compared to SPSO by 2.0 and 0.6 UEs, respectively.  
device-to-device; LTE systems; resource allocation; particle swarm optimization algorithm; genetic algorithm; system capacity +
Footnote †: _Appl. Sci._**2018**, 8, 1271; doi:10.3390/app8081271