esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Exposure Scenarios  
The considered wearable devices may be worn by a worker in a way comparable to a watch or some kind of band, or built-in or attached to various items of work clothes or personal protective equipment such as a helmet. In the same applications, it is necessary to use two cooperating wearable devices simultaneously, i.e., located on the wrist and at the head (for example, as a headband or at helmet) [17]. Among these locations, the worst-case of exposure to EMF emitted by the RF module of wearables is related to their location at the head (the limits of SAR10g for head exposure are half that of the limits for limb exposure, 2 W/kg vs. 4 W/kg for the general public), and further research was focused on this.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Tissue** & **Dimensions, (1) mm** & \begin{tabular}{c} **Layer** \\ **Thickness, mm** \\ \end{tabular} & **Density, kg/m3** & \
\begin{tabular}{c} **Electric** \\ **Conductivity, S/m** \\ \end{tabular} &
\begin{tabular}{c} **Relative** \\ **Permitivity** \\ \end{tabular} \\ \hline skin & \(238\times 159\times 190\) & 4 & 1209 & 1.464 & 38.0 \\ fat & \(230\times 151\times 182\) & 2 & 911 & 0.268 & 10.8 \\ bone & \(226\times 147\times 178\) & 9 & 1908 & 0.394 & 11.4 \\ brain & \(208\times 129\times 160\) & inner volume & 1045 & 1.808 & 48.9 \\ \hline \hline \end{tabular}  
* Dimensions—outer dimensions of particular layers in numerical model of head expressed as dimensions along long axis \(\times\) transverse axis \(\times\) sagittal axis, in mm.  
\end{table}
Table 1: Dimensions and parameters at 2.45 Hz of tissues considered in the numerical model of the head (a multi-layer ellipsoid).  
Figure 5: The laboratory measurements of the S11 parameter: (**a**) the sleeve balun choke used to suppress unwanted surface currents propagating onto the outer shield of the feeding cable and (**b**) equipment used during the S11 measurements.  
The investigations covered exposure scenarios with the MIFA located at the side of the head: (1) on the headband with the antenna plane at a distance of 2 mm from the head, or (2) attached to a helmet of the type required, for example, in an industrial environment, with an antenna plane at a distance of 20 mm from the head (Figure 6). High-density polyethylene (HDPE) with a thickness of 2 mm, an electric conductivity of \(5.0\times 10^{-4}\) S/m and a relative permittivity of 2.25 was used for the helmet.
# Steel Surface Defect Detection Algorithm Based on Improved YOLOv8n
## 3 Results
### Analysis of Experimental Results
#### 3.4.2 Comparative Experiments
To sum up, the improved algorithm presented in this essay not only performed better in terms of precision of measurements, accuracy, and detection speed, but also reduced the amount of arithmetic involved, something which, in turn, improved the detection efficiency. Therefore, the improved algorithm proposed in this work has a rather high generalization value and practicality. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **P\%** & **R\%** & **mAP\%** & **Params/M** & **GFLOPs/G** & **FPS/(Frames/s)** \\ \hline SSD & 75.50 & 61.12 & 73.03 & 21.5 & 31.5 & 42 \\ YOLOv3-tiny & 61.8 & 70.7 & 69.0 & 12.13 & 18.9 & 95 \\ YOLOv5n & 72.3 & 73.9 & 77.7 & 2.50 & 7.1 & 111 \\ YOLOv6n & 71.8 & 70.7 & 76.4 & 4.23 & 11.8 & 99 \\ YOLOv7-tiny & 70.5 & 61.2 & 66.8 & 6.03 & 13.2 & 89 \\ YOLOv8n & 69.2 & 77.4 & 75.9 & 3.01 & 8.1 & 99 \\ RT-DETR & 61.4 & 63.2 & 66.5 & 28.5 & 100.6 & 44 \\ DDI-YOLO (ours) & 72.5 & 71.4 & 78.3 & 2.66 & 7.5 & 158 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative experimental results. Figure 6: Comparison of different loss functions.
# Slotted ALOHA on LoRaWAN-Design, Analysis, and Deployment
## 4 LoRaWAN MAC Model
(**a**) \(T\) is greater than RX1 Delay, a collision must occur; (**b**) vulnerability period needed to perform a successful link between an end device and the gateway. The effective LoRaWAN throughput decreases drastically with respect to the classic P-ALOHA, with a maximum of 8% at G equal to 0.25. This result shows that the LoRaWAN throughput (for each channel) is poor when half-duplex communication is required. To confirm this conclusion, we developed a simulator in MATLAB to evaluate the channel collision rate in a WSN with different time-of-air configurations with the uplink and RX1 windows. In details, the simulator generates traffic on a variable number of nodes and each one transmits following a Poisson uniform distribution. When a simulation starts, the number of nodes grows from 1 and stops as soon as the desired (G) is achieved. The maximum number allowed is 1000, and each element tries to access into the channel with a variable duty cycle and a maximum of 40 attempts. The simulator checks every 1 ms if two end devices try to communicate simultaneously, with an overall simulation time of two hours. Figure 5 shows the \(S(G)\) for unconfirmed and confirmed LoRaWAN single channel link; indeed, in Figure 6a,b the results of two different simulations are presented. In Figure 6a the packet time is greater than the RX1 Delay, in Figure 6b the time of air is <<1 s, so the probability for a transmission successfully performed is not equal to zero, and the network performance slightly increases. Figures 5 and 6 show that the proposed model is valid and that the effective channel throughput in LoRaWAN networks is tightly constrained. In S-ALOHA MAC protocol all the components of a LoRaWAN link can be fitted into a single slot (Figure 1a), uplink and downlink windows as well as the RX1 Delay, keeping the channel throughput of Slotted LoRaWAN (S-LoRaWAN) equal to standard S-ALOHA. As expressed for the P-ALOHA, if we consider \(T\) the channel time used to transfer the payload, we can define 2.22\(T\) the total slot time used, taking into account the LoRaWAN overhead. With these considerations, the maximum channel throughput in S-ALOHA is 16%, doubled respect to the classic half-duplex LoRaWAN. Figure 5: Unconfirmed and confirmed LoRaWAN single channel throughput. Figure 6: Confirmed LoRaWAN throughput and collision rate for one channel acquired by MATLAB simulation. (**a**) SF = 12, BW = 125 KHz, CR = 4 Maximum throughput is 8%; (**b**) SF = 6, BW = 125 KHz, CR = 4 Maximum throughput is 11%.
# Minimizing the In-Cloud Bandwidth for On-Demand Reactive and Proactive Streaming Applications
## 4 Optimizing Cloud-Based Streaming Internal Bandwidth: Case of Popular Video
### NOPP Deployment  
To deploy NOPP, the following tasks should be performed::  
* The administrator of the streaming system should set (1) the number of segments of the video and (2) the streaming time horizon. in the system ggraphical interface If he wants to use a periodic scheduling, then he should indicate that instead of the time horizon.
* The system solves either the first version (with fixed time horizon) or the second version (periodic) of the LP. A manifest file containing the optimal scheduling is then generated and stored in the storage node with video segments.
* The SN starts by requesting the manifest (of the session description) file from the storage node to know how to reorder the arriving segments. Then, it starts the download of video segments, the reordering and the streaming to the Internet users' devices. The storage node could use RTP or HTTP/3 to push the video segments.  
Figure 11: \(B^{*}\) as a function of \(N\) for circular and fixed time scheduling.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(N\) & \(B^{*}\) & \(t\) (s) & \(N\) & \(B^{*}\) & \(t\) (s) & \(N\) & \(B^{*}\) & \(t\) (s) & \(N\) & \(B^{*}\) & \(t\) (s) \\ \hline
2 & 2 & 0.00024 & 14 & 4 & 0.00671 & 26 & 5 & 0.54185 & 38 & 5 & 0.17875 \\ \hline
3 & 2 & 0.00260 & 15 & 4 & 0.00784 & 27 & 5 & 0.57136 & 39 & 5 & 0.12900 \\ \hline
4 & 3 & 0.00023 & 16 & 4 & 0.01246 & 28 & 5 & 0.51263 & 40 & 5 & 0.22479 \\ \hline
5 & 3 & 0.00128 & 17 & 4 & 0.01099 & 29 & 5 & 0.44969 & 41 & 5 & 0.62120 \\ \hline
6 & 3 & 0.00144 & 18 & 4 & 0.01755 & 30 & 5 & 0.72769 & 42 & 5 & 0.20406 \\ \hline
7 & 3 & 0.00214 & 19 & 5 & 149.31110 & 31 & 5 & 0.04119 & 43 & 5 & 0.96535 \\ \hline
8 & 3 & 0.00242 & 20 & 4 & 0.07884 & 32 & 5 & 0.04682 & 44 & 5 & 0.90229 \\ \hline
9 & 4 & 0.03365 & 21 & 5 & 0.81072 & 33 & 5 & 0.05502 & 45 & 5 & 1.16545 \\ \hline
10 & 4 & 0.02758 & 22 & 5 & 0.92566 & 34 & 5 & 0.05346 & 46 & 5 & 1.16282 \\ \hline
11 & 4 & 0.00388 & 23 & 5 & 0.92439 & 35 & 5 & 0.06545 & 47 & 5 & 1.46099 \\ \hline
12 & 4 & 0.00496 & 24 & 5 & 1.01415 & 36 & 5 & 0.08506 & 48 & 5 & 1.68586 \\ \hline
13 & 4 & 0.00744 & 25 & 5 & 0.38786 & 37 & 5 & 0.09712 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal bandwidth \(B^{*}\) for 47 instances of \(N=\mathbb{C}\).
# Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography
## 4 Experimental Results  
### ChestX-Ray14 Dataset and Preprocessing  
To verify the efficacy of our proposed approach, we conduct experiments on the publicly available ChestX-ray14 dataset recently introduced in [4]. With a total of 112,120 X-ray images acquired from 30,805 unique patients with 14 disease labels, it is the largest collection of front-view chest radiographs to date. Each image is marked by a single or multiple labels based on the radiology reports, with 90% accuracy. Furthermore, 984 labeled bounding boxes (B-Box) are provided by board-certified radiologists. Thus, we select these 984 images as \"annotated\" for testing CAM visualization and the remaining 111,240 \"unannotated\" images for training the DenseNet-121 model. We show the complex and diverse distribution of 10,000 sampled images by plotting t-distributed stochastic neighboring entities (t-SNE) [59] and conducting a principle component analysis (PCA) [60] (Figure 3).  
Before inputting images into the DensNet-121 model (Figure 2), we downscale the original \(1024\times 1024\) PNG images to \(224\times 224\) PNG, and we normalize them into the range [\(-\)1,1] based on the mean and standard deviations of the images. We also augment the training and validation data with batch augmentation and random horizontal flipping methods. In contrast, to extract features from different perspectives based on our proposed shallow descriptors (Figure 4), we keep the original size of the images and do not apply any data augmentation techniques.  
We used Python 3.6 for (i) both handcrafted and deep feature extractions and (ii) implementation of the deep pretrained DenseNet-121 model for CAM visualization and classification tasks of 14 thoracic diseases with TensorFlow 1.8.0 deep learning framework of CUDA 9 and cuDNN 7.5 dependencies. In the latter part, we adopted the different classifiers implemented in Matlab 2018b. 10-fold cross-validation was also applied for the classifiers. The total computation time costs for our proposed massive experiments took 143.9 h on a system with an i7-4770K 4-core CPU, 32G of memory, and a GPU, GeForce GTX 1070.  
Figure 4: _Cont._  
Figure 3: t-distributed stochastic neighboring entities (t-SNE) dimensions colored by digits with the usage of PCA.
# Capacity-Operation Collaborative Optimization for Wind-Solar-Hydrogen Multi-Energy Supply System
## 3 Bi-Level Capacity-Operation Collaborative Optimization Method
### Source-Load Uncertainty Treatment  
In order to fully take into account the source load uncertainty during the actual operation of the WSH-MES system, meteorological data and user load data based on long- and short-term memory network (LSTM) forecasts are used as inputs to the system. As LSTM can solve the problem of medium- and long-term time series forecasting [39], it has been widely used for time series forecasting, e.g., in the energy sector [40, 41].  
As shown in Figure 4, the connection of LSTM network modules is a chain structure, where the state of the cell \(C_{t}\) is the key to LSTM. It consists of three gates (forgetting gate \(f_{t}\), input gate \(i_{t}\), and output gate \(o_{t}\)) and internal memory state \(C_{t}\). The main calculation process can be found in reference [42].  
Figure 3: Flow chart of the algorithm for the bi-level optimization problem.
## 3 Proposed Method  
Our proposed method is a three-step process. It was originally described in a past work for daily anomaly detection on DNS data, and we have adapted it for the purposes of analyzing other types of data (e.g., HTTP traffic) within shorter timeframes [11].  
We first process PCAP data hour-by-hour into netflow files. This step is described in Section 3.1. Using each netflow file, we represent each network-hour as a graph with nodes and valued edges and run a graph-based machine learning model known as an Exponential Random Graph Model (ERGM) to produce a log-odds coefficient. This produces an hourly snapshot of network features as coefficients. The ERGM is further discussed in Section 3.2. Autoregressive Moving Average (ARMA) is trained on most of the data (aside from reserved testing data), and for the remaining few hours, it gives a prediction window for each hour representing what it expects to be a range of non-anomalous coefficient values. If our observed data point falls outside the prediction window, ARMA considers this anomalous. The prediction window is of a variable size and can be adjusted easily to optimize detection. ARMA is discussed in more detail in Section 3.3.
## 1 Introduction  
### Green Wireless Sensor Networks  
A Wireless Sensor Network (WSN) consists of several embedded nodes with sensing, processing, and wireless communications capabilities, distributed over an area of interest to monitor physical or environmental conditions [1]. The sensor nodes in WSNs serve as the backbone of the Internet of Things (IoT), where they act as data collection modules (perception layer) of IoT applications. They are the low-end devices of IoT [2]. Being spatially distributed systems, WSNs exploit wireless communication as the means of communication between nodes. This makes them effective for a myriad of applications such as geographical monitoring, habitat monitoring, transportation, military systems, business processes, structural health monitoring, microclimate research, medical care and many others [3]. [PERSON] et al. [4], in an up-to-date survey on the applications of WSN, classified the applications of WSN into six main categories (military, environmental, health, flora and fauna, industrial, and urban) based on the nature of their use. One of the urban applications of WSN is its use in monitoring water distribution networks, resulting in systems referred to as wireless sensor network-based water pipeline monitoring (WWPM) systems [5]. WWPM systems are greatly needed, especially in developing countries where the amount of non revenue water (NRW), mostly caused by leakages on the Water Distribution Network (WDN), sometimes exceed more than 70% of the total NRW [6; 7].  
In large-scale WSN applications exemplified by WWPM systems, a vital requirement prominently emerges: the attainment of low energy consumption. This requirement arises from the fundamental necessity to ensure the sustained operability of sensor nodes over extended durations, all without the requirement for frequent battery replacement. It is worth noting that the sensor nodes employed in such applications typically rely on battery power and are frequently situated in locations that are physically challenging to access [8]. This gives rise to the exigency for what is commonly referred to as \"Green Wireless Sensor Networks\" (GWSNs) [9], where the major concern is to maximize energy efficiency by reducing unnecessary energy consumption and minimizing environmental impact. A green WSN, by definition, is one that adeptly reduces its aggregate energy utilization, thereby prolonging the operational lifespan and enhancing the performance of the network, all while concomitantly mitigating its carbon footprint.
###### Abstract  
This study introduces a novel framework with the use of extended reality (XR) systems in hydrology, particularly focusing on immersive visualization of hydrologic data for enhanced environmental planning and decision making. The study details the shift from traditional 2D data visualization methods in hydrology to more advanced XR technologies, including virtual and augmented reality. Unlike static 2D maps or charts that require cross-referencing disparate data sources, this system consolidates real-time, multivariate datasets, such as streamflow, precipitation, and terrain, into a single interactive, spatially contextualized 3D environment. Immersive information systems facilitate dynamic interaction with real-time hydrological and meteorological datasets for various stakeholders and use cases, and pave the way for metaverse and digital twin systems. This system, accessible via web browsers and XR devices, allows users to navigate a 3D representation of the continental United States. The paper addresses the current limitations in hydrological visualization, methodology, and system architecture while discussing the challenges, limitations, and future directions to extend its applicability to a wider range of environmental management and disaster response scenarios. Future application potential includes climate resilience planning, immersive disaster preparedness training, and public education, where stakeholders can explore scenario-based outcomes within a virtual space to support real-time or anticipatory decision making.  
hydrology; data visualization; immersive environments; digital twin; metaverse
# Link Budget Analysis with Laser Energy for Time Transfer Using the Ajisai Satellite
###### Abstract
Even though there are some difficulties to realize this concept, it can provide a more accurate and stable time transfer compared to that of the T2L2 technique because there are no error sources, such as modeling error of onboard time comparison unit and the long-term variation of transponder delay. This approach has received considerable attention as a potential technology to allow very high-precision and accuracy of 100 picoseconds or even better. The TLTT concept using the Ajisai satellite was re-formulated as a kilohertz (kHz) SLR application by [PERSON] et al. [10]. The high repetition rate of laser pulses based on kHz laser technology provides full capability for the laser pulse to hit the Ajisai's reflective mirror at the right time, and the event timer at the kHz SLR station can record multiple stop events for TLTT; these were the key problems involved in the TLTT approach using the Ajisai satellite. However, the low signal strength of received photons at the receiving station is still a serious issue for real application. In general, the repetition rate and the pulse energy of a laser system have a reciprocal relationship. This means that a high repetition rate usually decreases the pulse energy for a fixed average power. The low pulse energy of the kHz laser system makes the expected number of photons less than 1 photon/footprint passage of the Ajisai satellite. To overcome this restriction, several methods to improve system performance were proposed, which include (i) increasing the laser energy, (ii) enhancing the optical efficiency, and (iii) using a modified algorithm allowing a single signal transfer [10]. For TLTT realization based on kHz laser technology, there are two critical elements (i.e., geometric configuration and laser pulse energy) to determine the signal strength of received photons. Therefore, the effects of geometric configuration are analyzed in this paper for the TLTT application in terms of the laser link budget, which plays a large role in the variation of the TLTT link budget. Additionally, an analytical approach to find the optimal laser energy for the kHz laser system is investigated based on the threshold of detection probability and the operation concept of the network for TLTT application.
## 2 Materials and Methods
### GPU Computation for Feature Extraction  
The computation of the features for the SVM classification needs to be able to process the raw data matrix \(M_{i}\) containing the data for second \(i\) in time and all the channels along the optical fiber. As mentioned above, for a typical setup of 40 km monitoring length and a sampling rate of 2000 Hz this leads to a data matrix of dimensions in the order of 2000 \(\times\) 60,000. In the case of a Helios DAS interrogator device, the data format is unsigned int 16, which leads to a data rate of 228 MB/s. We then have to cast these data to a float datatype because the first step involves a Fourier transform that doubles the data rate as we use 32 bit float values. This Fourier transform computation has to be done for all 60,000 cable segments in parallel. The highly parallelizable nature of these computations makes general purpose GPUs (GPGPU) highly suitable for the task. The same holds true for the PCA feature reduction, which has to be applied for each cable segment individually. The SVM classifier is not available with GPU acceleration; therefore, the reduced features are downloaded to the CPU before classification. A flow chart of the complete algorithm is depicted in Figure 1.  
Figure 1: Flow chart of the algorithm. The top box describes the steps of the vibration detection (steps on the GPU are in blue); the bottom box describes the steps of the train tracking. FFT = Fast Fourier Transform; PCA = Principal Component Analysis; SVM = Support Vector Machine.