esa-sceva/satcom-chunk-collection huggingface dataset:
# Single Photon Counting Performance and Noise Analysis of CMOS SPAD-Based Image Sensors
## 6 Spatio-Temporal Oversampling of Photon Counting Pixels
### Multi-Photon Binary Quanta Imaging  
As previously discussed, setting the oversampling threshold greater than a single counted photon provides a benefit to output frame rate assuming the sensor output data rate remains the same. By setting the oversampling threshold at two photons rather than one, half number of field readouts are required to reach a certain oversampled signal level as each binary bit now represents more than one photon. However, for the SPAD-based analogue counting pixel this is at the cost of oversampling greater cumulative noise, FPN or PRNU with each successive field image.  
An experiment is performed on the image sensor [16] recording the \"bit density\" (the number of pixels outputting a logical high indicating the multi-photon counting threshold is reached) against increasing integration time for a fixed light level. The pixel array in configured in analogue counting CTA mode with V\({}_{SOURCE}\) = 0.15 V. Figure 8 highlights the normalized bit density (D) to normalized exposure (H), where 1.0 H = 5 \(\upmu\)s integration time, for an incrementing comparator threshold capturing two to eight photons. The theoretical curves from [9] are plotted alongside for comparison. As no CDS is implemented, the high FPN due to column comparator mismatch and source follower threshold variation will effectively induce a PRNU in the measured data for all pixels which is seen as the discrepancy between ideal and measured data particularly in the plotted line for the four photon threshold. The closest fit in terms of photon number (2 to 8) is listed in the legend alongside.  
Figure 9 is the measured normalized RMS noise which has the characteristic shape from [PERSON]'s theoretical Quanta Image Sensor paper in [9]. A few remarkable characteristics of multi-photon threshold binary imaging that are experimentally verified in this noise plot. The rising slope of each of the noise plots indicates the shot-noise dominant region. The 2-photon line demonstrates the \"soft-knee\" shot noise compression with a smooth roll-off after the peak after H = 1.0 as expected in 1-photon or 2-photon threshold QIS. The subsequent increasing thresholds show a horizontal shift in the exposure \(x\)-axis as a higher number of photons (or equivalent SPAD events) are required to trigger the binary output. This can also be observed in the horizontal shift in the D-LogH plot in Figure 8. The maximum noise in the 8-photon threshold is measured as 1.52 times higher than the  
Figure 8: Multi-photon threshold oversampled binary imaging normalised bit density to exposure. Ideal curves from [9] are presented alongside measured results.  
2-photon threshold where the theory [9] suggests it should be no more than square root of two higher (1.412 times).
## 4 Conclusions  
This paper introduced an innovative neural network architecture for static sound event localization and detection, adapting the detection transformer (DETR) methodology for auditory analysis. Our approach involves a novel application of the ResNet-50 model within a CNN framework, processing stereo audio signals into mel-spectrograms. By forgoing traditional anchor and non-maximum suppression methods common in visual object detection, our system efficiently infers a set number of audio predictions. This method, enhanced by bipartite matching and the Hungarian algorithm, ensures accurate classification, localization, and detection of sound events. Our experimental findings, based on a dataset created with diverse audio samples including drone noises and human speech, validate the proposed model's effectiveness in sound event localization and detection. The research presents a significant contribution to the field, demonstrating a unique adaptation of DETR principles to the auditory domain. The implications of this study extend to various practical applications, notably in emergency response and environmental monitoring, highlighting the model's potential in real-world scenarios.  
In the future, we will develop the proposed method to robustly detect and localize not only static sound events but also dynamically moving sound events and conduct in-depth comparative analysis with various SELD methods. It is implied that our method can be utilized in systems capable of making appropriate responses using microphone signals acquired from drones in disaster or hazardous environments.  
Conceptualization, C.C.; data curation, H.J.P.; funding acquisition, M.B.S.; investigation, H.J.P.; methodology, C.C.; project administration, M.B.S.; software, C.C.; supervision, M.B.S.; validation, H.J.P. and C.C.; visualization, C.C.; writing--original draft preparation, C.C.; writing--review and editing, M.B.S. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (grant 20 UUTI-B157786-02).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**ActualPred.** & **Child** & **Male** & **Female** \\ \hline child & 4023 & 127 & 251 \\ \hline male & 98 & 3949 & 27 \\ \hline female & 87 & 13 & 4028 \\ \hline  & & **(b)** & \\ \hline child & 3914 & 194 & 293 \\ \hline male & 118 & 3930 & 26 \\ \hline female & 162 & 16 & 3950 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix for the classification performance. (**a**) Confusion matrix for the classification performance of the proposed SED method. (**b**) Confusion matrix for the classification performance using GoogLeNet [25].
# Efficient Small-Object Detection in Underwater Images Using the Enhanced YOLOv8 Network
## 3 Approach
### Prediction Head for Small Objects  
We explored the underwater dataset UTDAC2020 and identified numerous instances of extremely small objects. [PERSON] et al. [7] speculated that shallower features might be effective when the targets are small and the features of the targets are not obvious. Consequently, we introduced an additional prediction head specifically for detecting small objects with shallower features. This four-head structure, in conjunction with the other three prediction heads, mitigates the adverse impact of significant variations in object scale. As illustrated in Figure 2, our added prediction head derives from a high-resolution feature map, making it more sensitive to small objects. Through experimentation, we increased the number of channels in the high-resolution feature map detection head while reducing the channels in the low-resolution feature map detection head, maintaining model size and memory usage. This resulted in a significant improvement in small-object detection performance, increasing the AP by 1.2% overall and by 2.1% for small targets like scallops on the UTDAC2020 underwater dataset.
## 5 Discussion  
This research aims to design an object-detection model integrated with an alarm system to reduce human-wildlife conflict in agricultural environments. The primary objective of our study is to implement conservation strategies for endangered animal species while concurrently mitigating the negative impact of wildlife invasions on crop fields. The significance of this study lies in the concern observed among farmers, who are increasingly resorting to killing endangered animals to prevent their intrusion. But, to maintain the ecological balance, the role of these animals is beyond description. Consequently, this research emphasizes formulating a proficient solution to yield advantages for farmers and endangered wildlife. Existing research has discussed the challenges associated with human-wildlife conflict, reinforcing the necessity for safe approaches. However, none of them emphasized the significance of conserving endangered animals.  
Moreover, previous methodologies have frequently proven inadequate because of constraints in detecting and generating real-time alarms. This research overcomes all these limitations substantially. We compiled a dataset of eight species of animals by acquiring images of the animals on the verge of extinction and commonly harming crops. To effectively train the model, we meticulously annotated all the images. The YOLOv8 model  
Figure 13: Comparison with state-of-the-art models.  
was employed for object detection in our study. The model's parameters were meticulously tuned to maximize its performance. The outcome of this optimized model demonstrates its efficacy and stability by obtaining an impressive mean average precision (mAP) of 92.44%. Various performance metrics such as precision-confidence, recall-confidence and F1-confidence figures are illustrated to assess the model's ability to detect objects properly. The aforementioned findings provide evidence of the model's robustness and applicability in real-world implementations. The optimal weight of the model was stored to implement the hardware component. The hardware component involves a variety of devices such as cameras, buzzers and sensors and makes a proper alarm system. The images are subsequently subjected to processing by our object-detection model in order to ascertain the presence of any potential threats. In the case in which a positive detection occurs, an alarm is activated to notify farmers or necessary authorities, facilitating prompt response and intervention. The deployment of a cloud-based approach for storing alarm logs provides several benefits, including improved data accessibility and collaboration.  
Additionally, our system enables real-time updates and offers cost-efficiency. Integrating hardware components with the object-detection system facilitates a comprehensive and practical strategy for addressing the issue of human-wildlife conflict while concurrently reducing the adverse impact on endangered animals and crop fields. In summary, this study allows real-time monitoring investigation and aims to safeguard endangered animals and protect crops.
# Efficacy and Tolerability of a Microneedling Device Plus Exosomes for Treating Melasma
## 1 Introduction
### Pathophysiology of Melasma  
Melasma is a multifactorial disorder resulting from external factors such as ultraviolet radiation (UVR) exposure, oxidative status, and female hormone stimulation, in genetically predisposed individuals [1]. [PERSON]'s lamp examination classifies melasma into epidermal, dermal, or mixed types based on pigment location. However, laser confocal microscopy studies reveal that all melasma types are mixed, suggesting a shared pathophysiology [2]. Although it was initially believed that melasma affected only melanocytes, subsequent histopathological studies of the affected skin have revealed structural and functional alterations across all skin layers [3]. Evidence of solar elastosis, thinning of the stratum corneum (SC), disruption of the basal membrane (BM), and heightened dermal blood vessels suggest that melasma may be a skin condition resulting from \"photoaging\". Pendulous melanocytes associated with basal membrane abnormality were identified as a distinctivehistological feature in melasma [4]. These changes synergistically contribute to hypermelanogenesis [5]. Transcriptomic analyses performed in melasma lesions highlighted the involvement of at least 300 genes, impacting melanocytes and dermal components [6]. Genes involved in melanogenesis, such as tyrosinase (TYR), tyrosinase-related protein 1 (TYRPI), melanocortin 1 receptor (MC1R), and PDZ domain-containing protein 1 (PDZK1), seem to be upregulated. Downregulation of H19 gene impacts melanogenesis and melanin transfer, along with reduced expression of miR-675, which targets microphthalmia-associated transcription factor (MITF), a critical regulator of melanocytic cells. The decreased expression of miR-675 appears to influence cadherin-11 (CDH11), potentially contributing to BM damage [3; 5]. UVR and visible light (VL) act as direct stimulants for melanogenesis. Prolonged sun exposure induces fibroblasts to secrete melanogenic and proinflammatory factors, including stem cell factor (SCF), a ligand for the tyrosine kinase receptor c-kit, which contributes to the increase in melanogenesis [7]. UVB has a significant impact on the epidermis and basal membrane; it induces the upregulation of proopiomelanocortin (POMC) in the epidermis, causing melanogenesis and the translocation of melanosomes to keratinocytes. Furthermore, UVB induces the release of inflammatory mediators such as prostaglandamins and vascular endothelial growth factor (VEGF), fostering endothelial proliferation and elevating the levels of matrix metalloproteinases, which degrade type IV and VI collagen in the skin. UVA, despite being less erythemogenic, exerts a more significant impact on the superficial dermis through the generation of reactive species, proving to be more efficient in inducing pigment darkening and delayed tanning, especially in individuals with darker phenotypes. Also, VL penetrates the deep dermis and subcutis, but only shorter wavelengths (420-470 nm) can induce pigmentation in darker phototypes by activating opsin 3 (OPN3) receptors in melanocytes [3].
# Wireless Communication Technologies for Smart Grid Distribution Networks +
## 2 Available Wireless Technologies for Smart Grid Communications
### Fifth-Generation Wireless Networks  
The amalgamation of 5G architecture and smart grids presents significant opportunities in Transmission and Distribution. The introduction of 5G slicing adds intricate layers across user and utility domains, impacting energy dynamics and load balancing. The dynamic deployment paradigm aligns network functions with service needs. With end-to-end agreements covering communication service administration and network management for efficiency [9], 5G slicing ensures minimal latency.  
The 5G grid segregates networks, which are shared with telecom systems. Industrial control achieves precision through 5G. Adaptable slicing enhances agility, reinforcing security. This approach reduces expenses and manages network velocity [9].  
Technical 5G network services unfold through multi-layered slicing. Feeder automation requires intelligent orchestration for latency and bandwidth. Grid integration involves virtualized infrastructure. Wireless communication and power transmission promise progress, but costs and weather vulnerabilities pose challenges. Mobile radio and microwave transmission are alternatives. Cognitive radio supports smart grid wireless communication. The integration of 5G meets energy needs, aiding in forecasting and maintaining balance. Tailored strategies empower cities, and observability ensures distribution stability.
# 5G Beyond 3 GPP Release 15 for Connected Automated Mobility in Cross-Border Contexts
## 4 Cross-Border Use Case Implementations
### Remote Driving  
In this use case the volume of information sent to the operator is big; the latency of remote actions must be low; the accuracy of positioning is helpful, especially for high speeds; and the reliability and stability of communications are essential to ensure continuous control. To this end, the multi-modal/multi-radio communications [14] are key to operate through an appropriate radio access when the coverage of some of them falls, as presented in Figure 7. Furthermore, the QoS slicing mechanisms [29] can ensure a minimal QoS to allow a coarse-grained environmental view yet a practical one. This use case brings main aspects from mission-critical communications, where the availability of a network slice isolates traffic avoiding bottlenecks or transitory outages potentially introduced by other services or applications. In any case, quick and seamless network handover and roaming are mandatory.  
From the latency perspective, the utilisation of multiple radio technologies is crucial to providing substitute connectivity when cellular radio technology violates the latency threshold. This manner, cellular coverage is augmented through alternatives and 5G vision aims to embrace heterogeneous technologies and contexts [14] with a roadmap to merge technologies in public and private networks and bands. In this regard, network management tools to operate network slicing are also essential to ensure that critical operations are prioritised to other traffic [29]. The harmonisation of the provided slice in a multi-domain infrastructure is especially complicated in cross-border contexts, where each operator deploys its own policies and technology stack to monitor and control the network setup.  
In this case, sidelink communications are not required as the communication is between the cloud service and the controlled vehicle. The broadcast and multicast communications have no use for this pure unicast data flow. However, in this V2N scenario, a multi-SIM approach would be very valuable [17]. If one PLMN fails, the other one can be used in order to ensure the continuity of the remote driving service. In contrast to the rest of use cases, this use case has no V2V or vehicle-to-infrastructure (V2I) element and depends only on V2N, so the whole use case would be disabled in cases wherein the link with the network fails. Edge computing does not play a key role, as no processing tasks need to be offloaded from vehicles or cloud services to the edge.  
Figure 7: Remote driving scenario of the proposed solution.
# A Trajectory Generation Method for Multi-Rotor UAV Based on Adaptive Adjustment Strategy
## 2 Background
### Differential Flatness Principle and UAV Trajectory Representation  
The multi-rotor UAV can be approximated as a 6 DoF rigid body. Hence, its state includes three-axis positions: \(x\), \(y\), \(z\), and three-axis angles: pitch, row, yaw, and 18-dimensional vectors of velocity and acceleration in six states. Reference [5] proved that the multi-rotor has differential flatness. Therefore, its 18-dimensional state can be represented by the following four-dimensional vector  
\[\sigma=[x,y,z,\varphi]^{T} \tag{1}\]  
where \(r=[x,y,z]^{T}\) is the position of the multi-rotor UAV centroid and \(\varphi\) is the yaw angle. The yaw angle needs to be planned according to the task so that the UAV trajectory can be expressed as a three-axis polynomial trajectory  
\[p(t)=\left[p_{x}(t),p_{y}(t),p_{z}(t)\right]^{T},\ p_{\mu}(t)=\sum_{k=0}^{K}a_ {t}t^{k} \tag{2}\]  
where  
\[p_{\mu}(t)=\sum_{k=0}^{K}a_{\mu,k}t^{k},\mu=x,y,z \tag{3}\]
## 5 System Design and Testing
### System Testing  
The testing and calibration system mainly includes a multi-degree-of-freedom high-precision coil spatial pose automatic positioning platform, a strong magnetic structure, strong uniform magnetic field magnetization, an integrated assembly testing platform, a multi-channel ultra-high-precision real-time driving current measurement system, a multi-directional large-span ultra-high-precision force performance measurement system, and a testing control and data analysis processing unit. The overall configuration scheme is shown in Figure 7.  
The least-squares method is applied to fit the current output curve, and the relative output error is calculated. For high-precision coils and high-force coils, the performance data for the high-precision current and high current are shown in Tables 1 and 2.  
According to the test data, the driver has strong linearity in both low- and high-current ranges. The relative output error in the low-current range does not exceed 0.1 mA, and the relative output error in the high-current range does not exceed 2 mA. After fitting and calibration, it meets the design requirements. The sensor circuit box and controller also participated in the joint debugging and testing of the platform system, and the electromagnetic interference between electronic products was verified.  
Figure 6: Maglev vibration isolation control system controller.  
Figure 7: Integrated testing and calibration system.  
The current and output electromagnetic force reached their maximum values in approximately 5 ms. Therefore, the electromagnetic actuation part meets a response output of 200 Hz [1], and the frequency response is higher than that in typical space mission requirements (e.g., <100 Hz for telescopes).
# SE-Lightweight YOLO: Higher Accuracy in YOLO Detection for Vehicle Inspection
###### Abstract  
Against the backdrop of ongoing urbanization, issues such as traffic congestion and accidents are assuming heightened prominence, necessitating urgent and practical interventions to enhance the efficiency and safety of transportation systems. A paramount challenge lies in realizing real-time vehicle monitoring, flow management, and traffic safety control within the transportation infrastructure to mitigate congestion, optimize road utilization, and curb traffic accidents. In response to this challenge, the present study leverages advanced computer vision technology for vehicle detection and tracking, employing deep learning algorithms. The resultant recognition outcomes provide the traffic management domain with actionable insights for optimizing traffic flow management and signal light control through real-time data analysis. The study demonstrates the applicability of the SE-Lightweight YOLO algorithm, as presented herein, showcasing a noteworthy 95.7% accuracy in vehicle recognition. As a prospective trajectory, this research stands poised to serve as a pivotal reference for urban traffic management, laying the groundwork for a more efficient, secure, and streamlined transportation system in the future. To solve the existing vehicle detection problems in vehicle type recognition, recognition and detection accuracy need to be improved, alongside resolving the issues of slow detection speed, and others. In this paper, we made innovative changes based on the YOLOv7 framework: we added the SE attention transfer mechanism in the backbone module, and the model achieved better results, with a 1.2% improvement compared with the original YOLOv7. Meanwhile, we replaced the SPPCSPC module with the SPPFCSPC module, which enhanced the trait extraction of the model. After that, we applied the SE-Lightweight YOLO to the field of traffic monitoring. This can assist transportation-related personnel in traffic monitoring and aid in creating big data on transportation. Therefore, this research has a good application prospect.  
vehicle inspection; YOLOv7; deep learning; attention mechanism; SPPFCSPC module +
Footnote †: journal: Applied Sciences