esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on Multiple Factors Affecting the Accuracy of Multiclass Skin Disease Classification
###### Abstract  
Diagnosis of skin diseases by human experts is a laborious task prone to subjective judgment. Aided by computer technology and machine learning, it is possible to improve the efficiency and robustness of skin disease classification. Deep transfer learning using off-the-shelf deep convolutional neural networks (CNNs) has huge potential in the automation of skin disease classification tasks. However, complicated architectures seem to be too heavy for the classification of only a few skin disease classes. In this paper, in order to study potential ways to improve the classification accuracy of skin diseases, multiple factors are investigated. First, two different off-the-shelf architectures, namely AlexNet and ResNet50, are evaluated. Then, approaches using either transfer learning or trained from scratch are compared. In order to reduce the complexity of the network, the effects of shortening the depths of deep CNNs are investigated. Furthermore, different data augmentation techniques based on basic image manipulation are compared. Finally, the choice of mini-batch size is studied. Experiments were carried out on the HAM10000 skin disease dataset. The results show that the ResNet50-based model is more accurate than the AlexNet-based model. The transferred knowledge from the ImageNet database helps to improve the accuracy of the model. The reduction in stages of the ResNet50-based model can reduce complexity while maintaining good accuracy. Additionally, the use of different types of data augmentation techniques and the choice of mini-batch size can also affect the classification accuracy of skin diseases.  
image classification; skin diseases; transfer learning +
Footnote †: journal: Medical Sciences  
0000-0001-8811-177929
# Research and Evaluation Method for Echelon Usage of Double-Life Equipment Considering Calendar Life
## 2 Problem Description
### Problem Description  
A unit undertaking certain use tasks has \(N\) sets of double-life equipment. The initial working life of the equipment No.\(i\) is \(\textit{Tvl}_{i}^{0}\), and the initial calendar life is \(\textit{Tvl}_{i}^{0}\), \(i\in[1,N]\). The calendar life (\(\textit{Tvl}_{i}\)) will only decrease by one month with the change of calendar time, and the working life (\(\textit{Tvl}_{i}\)) will decrease according to the use of the unit. During the \(J^{0}\) month period, the unit will perform \(K_{j}\) tasks in the \(j\) month, and will use \(M_{j,K_{j}}\) equipment, consuming \(U_{j,K_{j}}\) hours of equipment working life. The equipment No.\(i\) consumes \(x_{ij}\) hours in the \(j\)th month. In the \(j\)th month, the total working life consumption is \(\textit{Tyl}_{j}\) hours, and there are:  
\[\textit{Tyl}_{j}=\sum_{i=1}^{N}x_{i,j}=\sum_{k=1}^{K_{j}}M_{j,k}\times U_{j,k}\]  
The equipment calendar life is taken as the abscissa of the ladder diagram, and the working life is taken as the ordinate to draw the ladder diagram. After \(J^{0}\) months of use, the following three problems can be solved:  
1. Find the controllable range of life under the current indicators; that is, analyze the relationship between calendar life and working life to keep the equipment in the controllable range and be affected by those indicators.
2. Find the optimal distribution curve of the working life that is least affected by external indicators. That is, if N equipment is in the controllable range, after the changes of external indicators, the calendar life of the equipment that appears outside the controllable range is the largest, and the reserved time to deal with the equipment outside the controllable range is the longest. The longer the time is, the more means can be used to deal with it, which shows that the impact of external indicators is the least.
3. Find the optimal life reserve. When the distribution of working life is least affected by external indicators, how much should the total of the working life be?
###### Abstract
conducted a study [36] to measure social networks in sheep using data loggers which were custom designed GPS devices with the ability to record the phase shift of GPS signals at a rate of 1 Hertz, with an estimated accuracy of 20 cm achieved by applying Real-Time-Kinematic (RTK) corrections to the GPS signal during post processing. They found the GPS device effective at accurately characterising the network structure in a mixing experiment with Merino sheep. Normal Global Navigation Satellite System (GNSS) operation, which utilises multiple satellite constellations, has location errors of a few meters in clear locations but tens of meters under challenging environments. With RTK GNSS which is a normal GNSS operation improved for carrier phase tracking of the satellite signals and differential correction, the errors are reduced to the order of tens of centimeters in open environments with good satellite visibility. The accuracy achieved will depend on the exact technology that is used [37]. Under challenging conditions, the RTK correction signal may be blocked to varying degrees by buildings, dense tree coverage, or other animals and may require additional data collection such as IMU (inertial measurement unit) to improve geo-spatial data accuracy. An RTK device may enable highly accurate geo-spatial tracking of animals for precise detection of social networks. Further practical considerations for long-term deployment of sensor devices include the issue of power consumption. One way to overcome this problem and extend the device's battery life is to decrease the sampling rate, but this would likely result in missing data which could impact the accuracy of the social network analysis [38]. Additionally, a misrepresentation of network properties in a simulated animal social network as a result of using incomplete information (edge sample size) has been reported by [PERSON], 2010 [39]. Thus, the appropriate rate of sampling and its impact on sheep social network analysis requires further investigation. This study validated a novel RTK device that attains accuracy that cannot be achieved by GPS alone, to automatically track individual animals kept in groups. Specifically, the current study aimed to:  
1. Test the accuracy of the RTK devices in terms of consistency and error points first in the laboratory and then later in the field using a group of sheep. 2. Validate the devices for identifying leaders based on sheep movement data during a grazing period. 3. Validate that the generated GNSS positional data could be used to detect social networks in sheep. 4. Determine the optimal sampling rates to extend the battery life but still identify sheep social networks. Validation of these on-animal sensor devices could enable more accurate automated data collection to understand livestock social behaviour in future research.
## 4 Discussion and Conclusions  
We experimentally demonstrated 25 Gbps transmission over an up to 25 km SMF in a 10G-class DML based IM-DD system in C-band. We performed the characterization of our 10G-class DML in terms of measured frequency response at different bias current \(i_{b}\). \(i_{b}=60\) mA was selected to ensure the optimum performance of the DML. In [12; 13],  
Figure 5: Maximum ODN loss as a function of ER over 20 km or 25 km SMF in C-band (1550 nm) and with bias current of the DML \(i_{b}=60\) mA. Solid: FFE; dashed: FFE + DFE. (**a**) EDB, (**b**) PAM-2, and (**c**) PAM-4.  
the channel frequency response of a DML-based IM-DD system was analyzed in O-band through only simulations and theoretical calculations. We analyzed in detail the measured frequency response of different lengths up to 118 km SMF in a DML based IM-DD system in C-band following the ideas from [24]. The theoretical frequency response and measured ones matched well. From the matching procedure we obtained the laser chirp parameters \(\alpha\) and \(\kappa\). We also discussed the decomposition of the frequency response. We theoretically showed that the bandwidth of the DML based IM-DD over SMF system is depends mainly on the frequency of the first notch of the adiabatic chirp. Finally, we experimentally demonstrated that PAM-2 is a promising choice for 25G-PON if operation in C-band is the target, because in combination with DFE it provides the maximum admissible ODN loss among other options and can reach the typical required power budget for 25G-PON (29 dB). Moreover, since the 25G- and 50G-PON standards currently both operate in O-band both in the downstream and upstream directions, very likely, the O-band window well be fully occupied in the future. Then, C-band will be available, representing a good choice to move some services of 25G-PON. Moreover, the fiber losses and the impact of fiber nonlinearities are lower in C-band.  
Conceptualization, R.G., V.F., P.T.-F. and H.W.; methodology, R.G., V.F., P.T.-F., and H.W.; software, H.W.; validation, R.G., V.F., P.T.-F. and H.W.; formal analysis, R.G., V.F., P.T.-F. and H.W.; investigation, R.G., V.F., P.T.-F. and H.W.; resources, R.G., V.F., P.T.-F. and H.W.; data curation, P.T.-F. and H.W.; writing--original draft preparation, H.W.; writing--review and editing, R.G., V.F., P.T.-F. and H.W.; visualization, R.G., V.F., P.T.-F. and H.W.; supervision, R.G., and V.F.; project administration, R.G., and V.F. All authors have read and agreed to the published version of the manuscript.  
The research received no external funding.  
Not applicable.  
We thank the support of the PhotoNext initiative at Politecnico di Torino. This work was carried out in the framework of a research contract with Telecom Italia Lab (the TIM company research laboratory).  
The authors declare no conflict of interest.
# Wheat Powdery Mildew Detection with YOLOv8 Object Detection Model
###### Abstract  
Wheat powdery mildew is a fungal disease that significantly impacts wheat yield and quality. Controlling this disease requires the use of resistant varieties, fungicides, crop rotation, and proper sanitation. Precision agriculture focuses on the strategic use of agricultural inputs to maximize benefits while minimizing environmental and human health effects. Object detection using computer vision enables selective spraying of pesticides, allowing for targeted application. Traditional detection methods rely on manually crafted features, while deep learning-based methods use deep neural networks to learn features autonomously from the data. You Look Only Once (YOLO) and other one-stage detectors are advantageous due to their speed and competition. This research aimed to design a model to detect powdery mildew in wheat using digital images. Multiple YOLOv8 models were trained with a custom dataset of images collected from trial areas at Tekirdag Namik Kemal University. The YOLOv8m model demonstrated the highest precision, recall, F1, and average precision values of 0.79, 0.74, 0.770, 0.76, and 0.35, respectively.  
YOLO; object detection; computer vision; deep learning; powdery mildew
## 4 Experiments and Discussions
### Real Data Experiments
#### 4.2.3 Nifecgdb  
In order to further verify the effectiveness of FECG extraction at different pregnancy stages and channels by the proposed method, eight records were selected for experiments. These were ecgca473 Channel 5, ecgca585 Channel 6, ecgca649 Channel 3, ecgca748 Channel 5, ecgca771 Channel 3, ecgca840 Channel 4, ecgca886 Channel 3, and ecgca902 Channel 5. The experimental results are shown in Figure 17.  
Figure 17: Extraction results for eight records in the NIFECGDB database.
## 3 Evaluation
### Datasets  
Existing public datasets such as MOT [51; 54; 55] and UT-interaction [56] were chosen to evaluate the performance of the individual components of the system. However, there are no existing datasets to perform a holistic analysis. Thus, in order to analyze this, a new dataset was created from the University of Peradeniya premises, which is referred to as the UOP dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Set** & **Notation** & **Description** \\ \hline \multirow{2}{*}{\(\mathbb{P}\)} & \(p_{d}\) & Distance between people \\  & \(p_{h}\) & Handshake interactions between people \\ \hline \multirow{2}{*}{\(\mathbb{Q}\)} & \(q_{g}\) & People belonging to the same group \\  & \(q_{m}\) & People wearing masks \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in threat quantification.  
The **multiple object tracking (MOT)** datasets are a set of image sequences with annotations for people localization and people IDs. Three datasets [51; 54; 55] were used to evaluate the capability of an algorithm to uniquely identify and track a person through a video.  
The **University of Texas-Interaction (UTI)**[56; 57] dataset comprises twenty video sequences of human interactions in two or four-people settings. The actions in the dataset include handshake, punch, point, kick, push and hug where each video spans roughly 1 min.  
The **UOP dataset**[39] is a collection of ten video sequences that were collected from the University of Peradeniya premises by enacting a scene with human interactions such as handshakes, close contacts, and grouped conversations. These videos were recorded by a wall-mounted CCTV camera in the university corridor and waiting area. The video consists of either four or five persons, with each video spanning 1 min. The ground truth for this dataset was annotated manually for training and evaluation.
## 3 Results
### Simulation Results  
Simulate the reading and writing process of the communication module using Model-sim simulation software, and the simulation results are shown in Figure 13. Among them, USB_ADDR is an addressing signal for FIFO. When the values of this signal are 00 and 10,  
Figure 12: Measurement value sequence.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement Data Number** & \(\overline{x}\) & \(\sigma_{x}\) & **SNR (dB)** \\ \hline
60 & 898 & 0 & / \\
182 & 410.98 & 0.0  
the communication module performs read and write functions, respectively. The results of the simulation experiment indicate the feasibility of the USB2.0 communication used in this simulation.  
After compiling the Verilog program and burning it into FPGA, the EZ-USB Interface was used as a communication tool for data communication. The results showed that USB2.0 communication based on CY68013A could be successfully applied to existing EIT devices.  
The communication speed of the communication module is the most concerning issue. To test the actual communication speed of the communication module in this study, the upper computer program Streamer provided by Cypress company was used to test the USB transmission speed. After connecting the main control circuit board of the system to the computer using a USB transmission cable, open the Streamer to establish the connection. After selecting the corresponding read and write ports, the operator can initiate data transmission and monitor the transmission speed in real-time through the Streamer. The test results are shown in Figure 14, with a transmission rate of 40,500 KB/s, which is 316.4 Mbps; The receiving rate is 45,000 KB/s, 351.6 Mbps. Based on the calculation of the frame rate in Section 2, it can be seen that the optimized communication method can allow data transmission over 10,000 frames per second, greatly improving the system's data acquisition rate.
# Container Volume Prediction Using Time-Series Decomposition with a Long Short-Term Memory Models
## 1 Introduction
After validating variables by the VAR model, we apply into ordinary deep learning models with external variables and without external variables to validate the prediction performance improvements in various aspects. From our study, the contributions of this study are as follows: (1) We propose a time-series decomposition method to predict the container volume in detail considering the performance and the trend followability at the same time; (2) we utilized various external variables used from previous studies in order to confirm the effectiveness of each variable by VAR model; (3) we provide the advantages of a time-series decomposition method that can effectively mitigate the errors from one another and ultimately improves the performance of the prediction. The remainder of this paper is organized as follows. Section 2 provides an overview of time-series analysis, deep learning prediction, and deep learning in container volume prediction. Section 3 describes the data analysis of the container volume and explores external variables for the implications, which is validated by a statistical model called the VAR model; this is followed by a description of the proposed method of time-series decomposition. Section 4 provides the results derived from our experiments and compares them with the results of various other time-series prediction models. Finally, Section 5 summarizes the discussion based on our experiments and Section 6 presents the conclusions along with future research ideas.
## 3 Feature Extraction
### Wavelet Transform  
In the PQD classification research, several signal processing techniques have been used for characterizing the characteristics of signals, i.e., wavelet transform, DWT, S-transform, VMD, etc. [18; 42; 43; 46; 47]. In this work, we used the DWT for extracting the signal characteristics due mainly to its flexible time-scale and high conservation of information without reduced resolution [17; 48]. This method has also been used extensively in many applications for power systems, such as fault detection and localization, classification of the shunt compensated transmission line, islanding detection technique, and PQD classification [49; 50; 51; 48]. The DWT is transformed from the WT based on a discrete wavelet scales. The expression of DWT is given as following equation;  
\[DWT(m,n)=\frac{1}{\sqrt{a_{0}^{m}}}{\sum_{k}f(k)\psi\bigg{(}\frac{(n-kb_{0}a_{0 }^{m})}{a_{0}^{m}}\bigg{)}} \tag{1}\]  
where the parameter \(m\) indicates the frequency localization, \(n\) is the time localization, \(a_{0}^{m}\) represents the scaling parameter that repeats the length of wavelet as a function of \(m\), \(b_{0}\) is the translation parameter, \(f(k)\) is the discrete point sequence of signal. The \(\psi\) represents the mother wavelet based on the fourth-order Daubechies, since its suitability for PQD classification was claimed in several previous studies [52; 53].  
Figure 1: PQD classification scheme.
# Review of Renewable Energy-Based Charging Infrastructure for Electric Vehicles
## 2 Technological Infrastructure
### Converters  
When it comes to a solar converter, the PV arrays are connected to a DC/DC converter that allows for full power point tracking control. The AC/DC converter is in charge of converting DC/AC power in a bidirectional fashion. The power used from the grid is primarily AC. It must be converted into DC to charge the electric vehicles. The conversion of power occurs before the charging begins or relays the power from the grid to electricity networks.  
Therefore, the converters have unique roles in photovoltaic systems based on balanced energy conversion [41]. Different forms and requirements have been examined in detail, for example, string inverters, in which panels are installed in combination with a micro-inverter, and central inverters, where panels are installed with separate inverters and micro-inverter power optimizers that require further monitoring. These power optimizers are used to track photovoltaic panel arrays' overall performance to constantly alter and change the attached load that keeps the system at maximum operational capability [42].  
Figure 1: EV Charging Infrastructure with a Solar PV charger.
# A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking
## 3 Comprehensive Survey of Deep Learning-Based Image Watermarking
### Methods Using Deep Networks for Feature Transformation
[54] illustrated this by implementing pre-trained CNN and adaptively modifying the input cover image through gradient descent. The dual-faceted loss function encompasses a term that minimizes the perceptual discrepancy between the cover and marked images and another that ensures watermark detectability via a dot product operation, expressed as \(\varphi(m)^{T}\cdot k\). Here, \(\varphi(m)\) denotes the marked image's feature extraction through the deep network and \(k\) is a predetermined key, facilitating the detection of watermark presence. Expanding on this, [PERSON] et al. [55] introduced the capability of multi-bit extraction by assigning distinct keys to each bit of the binary watermark sequence. Contrary to the utilization of convolutional networks pre-trained for classification, they employed networks that had undergone self-supervised learning. This strategic adoption confers a distinct advantage, as the feature spaces derived from self-supervised learning are characterized by augmented robustness, thereby enhancing the effectiveness and reliability of the watermark extraction process. The utilization of deep networks for feature transformation represents a nascent avenue in the domain of image watermarking. This approach deviates from the more intuitive embedder-extractor joint training model, which seamlessly aligns with traditional image watermarking paradigms by encapsulating both embedding and extraction processes. Consequently, the academic literature on this innovative method is relatively sparse. Nonetheless, this emerging methodology paves the way for captivating research trajectories, offering fresh perspectives and approaches in the field. To provide a consolidated overview, we have collated the prevailing challenges and representative solutions in Table 4.
# Hierarchical Classification of Botnet Using Lightweight CNN
## 4 Experimental Results
### Discussion  
The performance of the suggested model closely matches that of the state-of-the-art approach in both two-class and five-class classifications. The classification of a single instance is quick, taking only 9.4 us, tested on an Apple MacBook Pro M1. Notably, the proposed model stands out for its lightweight design, demanding a mere 6.97 KB in memory. In the case of the third-level 11-class classification within the hierarchy, our model continues to outperform existing works in terms of both performance and computational resource demands.  
Comparing our suggested methodology with current state-of-the-art methods allowed for a thorough assessment of its effectiveness, provided in Tables 4-6. In particular, our method was thoroughly tested against prominent techniques [14;  
Developing a detection model tailored for BotNet attack detection within the context of SDN-enabled IoT architecture, as depicted in Figure 11, entails consideration of four primary layers: device, network, controller, and application layers [11; 30]. At the device layer, sensors and actuators detect and interact with the environment. The network layer includes SDN gateways and routers responsible for data forwarding, controlled by the SDN controller. The controller layer encompasses the SDN controller, which developers program to create IoT services. Typically, the controller layer is utilized to enable high-security protocols.  
Implementing lightweight machine learning models for botnet attack detection in IoT devices requires careful selection of efficient algorithms and optimization of their architecture and parameters to minimize resource usage while preserving detection accuracy. As shown in Figure 11 the proposed ML model is deployed on the device layer for analyzing data streams in real-time so that instant decisions are made without relying on centralized processing. The deployment architecture coupled with a very small size of the mode, 6.97 KB, leads to faster response time and improved network performance. Alternatively, the model can be deployed on the data plane, encompassing both network and device layers, as discussed in [31; 32; 33], allowing for attack detection on devices like gateways and routers.