esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on High-Performance Fourier Transform Algorithms Based on the NPU
## 4 Methods
### Acceleration Effect and Discussion
#### 5.2.2 Analysis of the Acceleration Effect of MM-2 DFT  
The experiment results showed the MM-2 DFT algorithm with the NPU cube unit is more suited for small input sizes than large input sizes. In detail, that algorithm is 4-8 times faster than its competitor, given a DFT input size up to 256 \(\times\) 256, and maintains its advantage until the input size exceeds 1024 \(\times\) 1024. This attribute to the powerful computing power of the cube unit makes up for the high complexity of MM-2 DFT, and the scheduling overhead of MM-2 DFT is lower. Consequently, with the small input image size up to 256 \(\times\) 256, the MM-2 DFT achieves high performance by leveraging the cube unit's powerful computational capabilities of NPU with low scheduling overhead. However, as input size increases, time complexity gradually dominates the execution time. That results in the MM-2 DFT's inefficient compared with FFT_CL at input sizes of 2048 \(\times\) 2048 or larger. In conclusion, MM-2 DFT can achieve high performance in input sizes below 1024 \(\times\) 1024 by leveraging the computational power of the cube unit and reducing scheduling overhead. When the input size is not lower than 2048 \(\times\) 2048, the fast Fourier algorithm shows performance advantages due to its lower time complexity.
## 5 Conclusions and Future Work  
In this paper, a preliminary work on the feasibility of using LiDAR measurements for estimating the likelihood of multipath presence is presented. From the estimation of planes obtained by the LiDAR cloud of points and the knowledge of the satellite positions, the multipath on GNSS might be estimated. However, this would only justify the possibility of the multipath, the presence of which is dependent also on the material and the reflection properties of the obstacles. However, the example presented in this paper also shows that this kind of detection would not have been possible by just observing the \(C/N_{0}\); in fact the signal drop is masked by the variance of the measurement of the signal-to-noise ratio in an urban environment. As a side results of this work, it has also been shown that significant drops in the \(C/N_{0}\) in urban environment are more often due to scattered multipath and that such phenomenon can be identified since it is common to all the satellites in view.  
As further work, deeper integration of the information obtained by the LiDAR and the GNSS receiver is foreseen, with the twofold objective of designing an \"environment analyzer\" able to actually report multipath that is relevant for GNSS receivers and applications, by merging the information derived by the two sources.
## 1 Introduction  
Dexamethasone (DEX) is a synthetic glucocorticoid commonly used as an anti-inflammatory and immunosuppressant reagent to treat atopic dermatitis and psoriasis [1]. Despite these beneficial effects of dermatitis, adverse effects, such as skin atrophy and impaired wound healing, are reported [2].  
Wound healing is a dynamic and complex process that requires the involvement of various cell types, including fibroblasts, keratinocytes, endothelial cells, and macrophages [3]. Keratinocytes play an important role in re-epithelialization and contribute to skin barrier formation. Keratinocytes are activated to help repair cutaneous wounds. Activated keratinocytes secrete various cytokines which are involved in the proliferation and re-epithelialization phase of the wound healing process [4]. In general, DEX, when applied topically, impairs or delays the process of skin wound healing by inhibiting the proliferation and migration of Keratinocytes [5]. Hence, enhancing the migration and proliferation rate of keratinocytes may promote wound healing in the DEX-impaired wound healing process.  
_Pueraria montana_ var. _lobata_ (Wildt.) Sanjappa & Pradeep is traditionally used to treat fever, dysenter, cardiovascular diseases, and diabetes mellitus in many countries in Asia [6]. _P. montana_ extract shows the beneficial effects on skin aging by suppressingintracellular reactive oxygen species production and increasing collagen formation in UVB-irradiated human dermal fibroblasts [6]. Moreover, the main compositions of _P. montana_ are isoflavuoids, which are suggested to possess skin regeneration and wound healing activity by enhancing proliferation, migration, and collagen production [7; 8; 9; 10]. Previous studies indicated that Puerarin is the major isoflavue found in the root extract of _P. montana_[11; 12]. Puerarin has attracted considerable attention in recent years, especially for its pharmacological effects such as anti-atopic dermatitis, pro-angiogenesis, and antioxidant effects [13; 14; 15]. In particular, the oral administration of Puerarin promoted the formation of new vessels and supported the wound healing process in diabetic rats [16]. On the other hand, its potential for improving impaired wound healing is not fully elucidated. Therefore, this study examines the effects of Puerarin on keratinocytes migration and proliferation in DEX-treated cells in vitro and assesses the medicinal value of Puerarin in surgically induced skin wounds in mice.
## 7 Conclusions  
With the performed literature review, the principal requirements of an FMCS applied in the context of medium-sized cities (for the \"collective\" transit service, which is common in many countriesof Latin America) were identified, such as interoperability and integration with other transport services, the use of adequate location, tracking, and communication technologies, and the measurement of suitable parameters for analysis and implementation of security protocols that guarantee the correct flow of information.  
The design of the proposed architecture was done through a decentralized work approach, by dividing the operations of the system between different modules and actors, yielding a better distribution of processes and a decrease in the workload.  
LoRa technology, with its network protocol LoRaWAN, was a good option as a communication technology between modules for the designed ITS architecture because it meets the requirements of low cost, good performance, and an adequate level of security, identified as necessary for the implementation of such architecture. Tests performed with LoRa technology (only LoRa packets) showed problems when two or more devices transmitted simultaneously, so the implementation of the LoRaWAN protocol was necessary, thus achieving effective uplink and downlink communication.  
With the results obtained, it is feasible to continue with the development of the FMCS, by making some adjustments to the designed, developed and tested prototype to improve its operation, increase its functionality and verify its operation by scaling some parameters. An important aspect to improve the operation is to evaluate the possibility of changing the employed GNSS module, to improve the connection time to the satellite network and the energy consumption. To increase functionality, it is necessary to advance in the development of operations performed by all types of actors in the system. Finally, to validate the operation by scaling some aspects, tests have to be performed at a larger scale (with more gateways, more nodes, and larger distances), in order to guarantee the correct operation of the proposed architecture in real work environments (using vehicles on the roadways).
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 4 Modulation Recognition Based on Deep Learning in Small Sample Environment
### The Small Sample AMR Algorithm Based on Signal Data Enhancement
#### 4.1.1 Generate Dummy Data  
When the number of labeled samples is small, generating dummy data to expand the dataset is an important method to deal with the problem of small-sample modulation recognition.  
Figure 13: Modulation identification in the case of small samples.  
In 2021, [PERSON] et al. proposed the enhanced depth convolution generation countermeasure network (SDCGAN) to expand radar signals [99]. The convolutional neural network is used for recognition and classification. The experimental results show that when the signal-to-noise ratio is 8 dB, only 40 samples can make the average recognition rate reach more than 99%.  
The authors of [100] introduced a data augmentation method in the small sample case as follows: (i) use GAN to generate pseudo data similar to the original signal, and then use the image quality assessment method PSNR to filter out high-quality pseudo signals, (ii) invert the original signal and the fake signal to further increase the diversity of samples, and (iii) input the enhanced dataset into the designed CNN network to complete the classification and recognition. This method can achieve a recognition rate of more than 80% when the number of signal samples is 10 and a recognition rate of 90% when the number of samples is close to 400.
# A Spatial Reconstruction Method of Ionospheric foF2 Based on High Accuracy Surface Modeling Theory
###### Abstract  
The ionospheric F2 critical frequency (foF2) is one of the most crucial application parameters in high-frequency communication, detection, and electronic warfare. To improve the accuracy of spatial reconstruction of the ionospheric foF2, we propose a high-accuracy surface (HAS) modeling method. This method converts difficult-to-solve differential equations into more manageable algebraic equations using direct difference approximation, significantly reducing algorithm complexity and computational load while exhibiting excellent convergence properties. We used seven stations in Brisbane, Canberra, Darwin, Hobart, Learmonth, Perth, and Townsville, with one station as a validation station and six as training stations (e.g., Brisbane as a validation station and the other stations--Canberra, Darwin, Hobart, Learmonth, Perth, and Townsville--as training stations). The training stations and the HAS method were used to train and reconstruct the validation stations at different solar activity periods, seasons, and local times. The predicted values of the validation stations were compared with the measured values, and the proposed method was analyzed and validated. The reconstruction results show the following. (1) The relative root mean square errors (RRMSEs) of HAS method prediction in different solar activity epochs were 13.67%, 7.74%, and 9.19%, respectively, which are 13.57%, 7.41%, and 6.41% higher than the prediction accuracy of the Kriging method, respectively. (2) In the four seasons, the RRMSEs of the HAS method prediction are 9.27%, 13.1%, 8.81%, and 8.09%, respectively, which are 10.83%, 11.73%, 4.25%, and 12.00% higher than the prediction accuracy of the Kriging method. (c) During the daytime and nighttime, the RRMSEs of HAS method prediction were 9.23% and 11.17%, which were 5.92% and 11.99% higher than the prediction accuracy of the Kriging method, respectively. (d) Under the validation dataset, the average predictive RRMSE of the HAS method was 10.29%, and the average predictive RRMSE of the IRI prediction model was 12.35%, with a 2.06% improvement in the predictive accuracy of the HAS method. In general, the prediction effect of the HAS method was better than that of the Kriging method, thus verifying the effectiveness and reliability of the proposed method. In summary, the proposed reconstruction method is of great significance for improving usable frequency prediction and enhancing communication performance.  
H 2024 16 3247. [[https://doi.org/10.3390/rs16173247](https://doi.org/10.3390/rs16173247)]([https://doi.org/10.3390/rs16173247](https://doi.org/10.3390/rs16173247))  
1  
A Spatial Reconstruction Method of Ionospheric foF2 Based on High Accuracy Surface Modeling Theory.
## 3 The Motion State Estimation Method  
### Overview  
The workflow of the proposed method is given in Figure 1. Concretely, for a multiple-frame point cloud (\(P_{t},P_{t+1},\ldots,P_{t+i},i\in[1,\ldots,9]\)), the ego-motion parameters (rotation matrix \(\mathbf{R}\) and translation vector \(\mathbf{T}\)) are estimated to match the point cloud \(P_{t+i}\) scanned at time \(t+i\) with \(P_{t}\) for ego-motion compensation. Then, the ego-motion compensated point cloud is projected to the range image, and the differential image of the range image is obtained. The concatenated range image and differential image are utilized for motion object segmentation using a CNN backbone network. Finally, the motion vector estimation module associates motion objects at different times through spatio-temporal fusion and clustering to realize the estimation of motion vectors. The details of each module will be illustrated in the following sections.
# Market-Oriented Procurement Planning Leading to a Higher Service Level and Cost Optimization
## Materials and Methods
### Methodological Approach
The application of system dynamics for the integral management of supplies makes sense as the cause-effect relationships show the interrelationships between the elements of the system and help to evaluate the influences of the different decisions in the global system. The first application of system dynamics to a supply chain was made by [PERSON] and consisted of a theoretical supply chain made up of four levels: retail, wholesaler, distributor, and manufacturing. [PERSON] observed how a small change in the pattern of a customer's demand amplified the demand across the SC. This amplification of demand, which further increases the demand of the end customer, is known as the bullwhip or Forrester effect and can be used to measure the efficiency of the supply chain [22]. In the literature, the common assumptions are demand backlogging, linear cost functions, infinite capacity, constant delivery times, and a stationary stochastic demand. Logically, the common goals to reach were to minimize inventory retention costs under the condition to achieve a certain level of service for end customers [28]. The use of system dynamics for modeling supply chain management has reemerged in recent years after a long, stagnant period. The current research is focused on inventory management, supply chain process design and integration, time compression, demand amplification, and international supply chain management [29]. [PERSON] and [PERSON] [1] suggested that the identification of what we do and do not know over the product lifecycle will determine the focus of future research for inventory planning. In this paper, we focus on inventory planning, procurement policies, and demand planning using different elements from a literature review of the previous topics as well as cybernetics and system dynamics:  
* Literature research on supply chain management, demand planning, inventory planning, procurement strategies, and economic analysis considering investments and costs. * The development of a conceptual model as a management framework for decision-making for managerial positions within existing companies. * The design of a case study for the automotive industry to test the conceptual model. * The use of Vensim is a simulation program to apply the case study. Simulation allows companies to assess how and when it is better to make decisions. Therefore, these analyses can help to discover if certain decisions are wise or not acting as a decision-supporting tool [30]. * A comparison of the conceptual model versus other inventory planning models in different demand scenarios based on demand lifecycle patterns extracted from the literature with elements for lifecycle consideration.
# Kiwifruit Detection Method in Orchard via an Improved Light-Weight YOLOv4
## 1 Introduction
[18] presented an improved YOLOv3 network using DenseNet as its feature extraction layer for detecting apples at different growth stages, but lacked fruit recognition in large-view scenes. [PERSON] et al. [19] proposed a lightweight neural network based on an improved YOLOv3-LITE using MobileNetv2 as the backbone of the model, which has an average accuracy of 91.13% and a recognition speed of 16.9 ms for a single image on a computer workstation. [PERSON] et al. [20] proposed an application for kiwi picking by improving YOLOv3-tiny lightweight neural network for robot object detection, which has a model weight of 27 MB and an average detection speed of 34 ms per image on a robotic workstation, but YOLOv4 has a better balance of detection accuracy and detection speed than YOLOv3. Based on the kiwi occlusion, [PERSON] et al. [21] used YOLOv3 and YOLOv4 to classify the target fruits into multiple classes for detection, and the results showed that the highest mAP of 91.9% was achieved by YOLOv4, which cost 25.5 ms on average to process an image, but no improvement was made to the original YOLOv4 network. The original YOLOv4 network structure is too large, with high computational complexity and huge model size, which is not suitable for deployment in picking robots for real-time detection [22; 23; 24]. To achieve fast and precise identification of kiwifruit picking robots in the case of scaffolding cultivation of kiwifruit with insufficient light, overlapping, and clustering, an improved lightweight GhostNet-YOLOv4 neural network is proposed. A feature fusion layer is introduced which is favorable for small object detection, and a combination of \(1\times 1\) convolution and depth-separable convolution is introduced to achieve the function of ordinary convolution by borrowing the Ghost Module structure, compressing the number of network parameters and improving the detection speed of the network. To verify the effectiveness of the improved object detection algorithm in detection, the experiments of the improved algorithm in different scenarios and the results are compared with four classic object detection algorithms: SSD, YOLOv3, YOLOv4, and MobileNetV3-YOLOv4.
# Design of a Compact Planar Magnetic Levitation System with Wrench-Current Decoupling Enhancement
## 2 System Design Procedure
### Compact Planar Maglev System Structure and Permanent Magnet Mover  
Figure 6 represents an overview of the proposed compact planar maglev prototype with system specifications in Table 4. The overall dimensions of the planar stator module were 320 mm wide, 410 mm long, and 290 mm high, with a total weight of 36 kg. The top level was an array of ten coils arranged in the zigzag configuration as designed. The prototype was covered and surrounded by acrylic sheets for mechanical and electrical safety.  
The bottom level had two current amplifier boards. Each amplifier board had five current amplifier units to control five coils individually. The current amplifier (AZB20A8, ADVANCED Motion Controls, Camarillo, United States) received the \(\pm\)10 V analog current command signal from the controller with a supply voltage of 36 VDC.  
The PM mover in this work was a 101.60 mm (4-inch) diameter \(\times\) 12.70 mm (1/2-inch) thickness neodymium disc magnet grade N50. The magnet was mounted to the acrylic frame equipped with reflective markers on the top with different heights for motion tracking as shown in Figure 7. The total weight of the mover was approximately 900 g.  
Figure 5: Magnetic flux density on the area of 300 mm \(\times\) 300 mm at 20 mm above the 9-coil array with (**a**) straight configuration, (**b**) zigzag configuration.
# Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach
## 3 Proposed Approach
### Autoencoder Reconstruction Error  
An AE model is trained using data \(X\). After the training process, the model is able to encode and output \(X^{\prime}=g(H)\), a reconstructed version of \(X\). If the model receives an input that differs from the training data, it cannot successfully reconstruct the input, leading to a high reconstruction error. Thus, AEs can be used to detect anomalies [26; 27] by contrasting the reconstruction error against a predetermined threshold. The Mean Absolute Error (MAE) can be utilized as a loss function L to calculate the reconstruction error, as shown below:  
Figure 2: Stages of feature extraction: Tokening, Padding, and Embedding.  
\[\begin{split} L(\theta)&=\min_{\theta}\frac{1}{2N}\|X-X^{ \prime}\|\\ &=\min_{\theta}\frac{1}{2N}\|X-g(f(X))\|\end{split} \tag{3}\]  
where \(N\) in Equation (3) is the number of training samples. This approach aims to discriminate SIP messages that contain SPIT attacks from normal ones.
## 4 Results and Discussion
### Kalman Filtered Forecasts of Solar Radiation  
Having confirmed the forecasting ability of the WRF model for the solar radiation prediction, we further apply the Kalman filter to improve the raw prediction. This section is concluded with an analysis of the advantages of KF_Kt with respect to KF_TS.
## 2 Preliminaries
### One-Time-Use Accounts  
One-time-use accounts are implemented based on one-time addresses. One-time addresses work on the principle that the user has a unique public-private key pair. The points on the elliptic curve have homomorphic additivity, and a random noise term is added to the key pair to obtain a one-time public and private key for real transactions. In the context of a one-time-use account, each initiation of a transaction prompts the sender to stochastically generate a provisional public key, predicated on the recipient's address, for the purpose of transaction reception. In the Unspent Transaction Output (UTXO) model, each UTXO transaction typically carries a tag that identifies the owner of the UTXO, represented by an address. The recipient who has the private key corresponding to the temporary public key can use the temporary private key to consume the UTXO in the future.  
The user randomly generates the private key \(k\) and public key \(K=kG\), where \(G\) is the elliptic curve parameter. Since the points on the elliptic curve have homomorphic additivity, a random noise term is added to the key pair to obtain a one-time public and private key for real transactions.  
The recipient generates a public-private key pair \(k,K\). The long-term public key \(K\) is published in the outside world. The sender selects a random number \(r\) when sending a transaction and sets the one-time public key \(K^{\prime}=rG+K\). The transaction is sent to \(K^{\prime}\) while passing the random number \(r\) to the recipient.  
To transfer \(r\) to the recipient in a private manner, the Diffie-Hellman secret exchange protocol is used. The sender uses the public key \(K\) disclosed by the recipient to hide \(r\). The hidden result is passed to the recipient; other users only see \(R\) and cannot obtain the hidden \(r\). The Hash function \(H\) maps points on an elliptic curve to scalars. The sender selects a random number \(r\), calculates \(K^{\prime}=H(rK)G+K,R=rG\), and sends \(R\) publicly to the recipient. The recipient receives \(R\), calculates \(K^{\prime}=H(kR)G+K\), and compares the \(R\) and \(K\) obtained. If they are consistent, it means that the one-time address belongs to the recipient and the one-time private key is recovered \(k^{\prime}=H(kR)G+k\).