esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Research Gap and Motivation  
Based on the previous works, many deep neural networks (DNN) approaches have been proposed explicitly to address the issues associated with channel state information (CSI), channel estimation, and signal detection. To the best of authors' knowledge, there has been no study that has investigated the combination between Deep learning (DL)-based channel estimation algorithm and the optimal power allocation scheme for multiuser detection in a downlink non-orthogonal multiple access (NOMA) system in fading channels. Most of the works are managing the issues of power optimization and deep learning-based channel estimation separately.  
In addition, many of the proposed DNN approaches for channel estimation require pre-training to obtain the appropriate DNN weights initialization, which may lead to an increase of the number of hidden layers with a huge number of neurons in each layer. In order to enhance the aforementioned schemes, this work proposes a framework that examine the integration between DNN-based channel estimation and the optimum power allocation scheme in a multiuser NOMA system and then inspect the system performance. Furthermore, in our proposed system structure, we manage to eliminate the need for a pre-trainingstage, minimize the number of DNN layers, and minimize the number of pilot symbols, and at the same time, we can realize a notable improvement in network convergence.
## 7 Conclusions  
In conclusion, we demonstrate an ultra-high-speed ICS OTN system utilizing 130 Gbaud PCS-64 QAM 1.2 Tb/s OTN transponders and DP-QPSK 200 Gb/s ICS OTUs with an SOP-based sensing capability over a deployed MCF cable for the first time. A real-time 267.8 Tb/s 2 \(\times\) 70.76 km ICS transmission is experimentally achieved using only C-band signals thanks to the high-performance 1.2 Tb/s OTN transponders. And the ICS system can sense external disturbances to the MCF cable such as shaking, striking, and so on through the 200 Gb/s ICS OTUs by monitoring changes in the polarization state of the received signals. To the best of our knowledge, the transmission capacity of this MCF-based ICS system is the highest among all real-time ICS field trials over a deployed MCF cable, and it can be further enhanced by incorporating L-band signals. This work demonstrates the feasibility of simultaneously achieving ultra-high-speed transmissions and the real-time sensing of environmental disturbances over field-deployed MCF cable, and it is beneficial to next-generation ultra-high-speed highly reliable optical transmission networks.  
Conceptualization, J.C., L.Z., Y.D., and Z.L.; methodology, J.C., Y.D., Y.W., C.Z., B.Y., and Y.C.; software, J.C. and L.Z.; validation, J.C., Y.W., L.Z., and Z.L.; formal analysis, B.H., B.W., and T.Z.; investigation, J.C., L.S., and L.Z.; resources, C.W., B.H., J.L., Y.S., Q.W., and N.G.; data curation, J.C., Y.D., Y.W., and Z.L.; writing--original draft preparation, J.C., L.Z., Y.D., Z.L., and C.W.; writing--review and editing, Y.S., C.C., and B.Y.; visualization, Y.D. and Y.W.; supervision, J.C. and Z.L.; project administration, J.C., Y.W., C.W., T.Z., L.Z., and C.C.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** Authors [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] were employed by the company Department of Networks, China Mobile Communications Group Co., Ltd.; authors [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] were employed by the company Network Management Center, China Mobile Communications Group Shandong Co., Ltd.; authors [PERSON] and [PERSON] were employed by the company State Key Laboratory of Optical Fiber and Cable Manufacture Technology, YOFC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 4 Experiments
### Model Architecture  
This study adopted the neural network architecture outlined in [25], characterized by a simple design aimed at optimizing performance. The architecture is segmented into three primary sections: a batch normalization tail, a robust four-block backbone, and a decision-making head consisting of a linear layer topped with a softmax function for classification.  
Each block within the backbone is meticulously structured to enhance feature extraction and includes two layers of 3D convolution, each followed by ReLU activation to introduce non-linearity and enhance model learning capabilities. Following the convolutions, a 3D MaxPooling layer is applied, serving to progressively reduce the dimensionality of the input arrays while simultaneously increasing the number of feature channels. This systematic reduction and feature enhancement after each block are crucial for efficiently processing the volumetric data typical in medical imaging applications.
# A Benchmark for Water Surface Jet Segmentation with MobileHDC Method
## 5 Experiments
### Ablation Study  
Efficiency of Hybrid Dilated Convolution Layers. To demonstrate the effectiveness of hybrid dilated convolution layers, we conducted a simple comparative experiment. We removed the hybrid dilated convolution layers while keeping the rest of the network unchanged and compared the network performance with the results presented in this paper. Assuming that the dilation rates of the four dilated convolution layers are \(R=(r_{1},r_{2},r_{3},r_{4})\), we conducted network training with different dilation rates to find the optimal dilation rates for the hybrid dilated convolution layers. As shown in Table 4, we can observe that the network achieves the best performance when the four dilated convolutions are concatenated with a dilation rate of \(R=(2,4,4,7)\) in terms of recall and mIoU. However, due to the extremely low average pixel proportion of jets in the Library dataset, which is only 1.4%, the use of dilated convolutions may lead to excessive loss of detailed information, thereby impacting accuracy.  
Efficiency of Decoder. The decoder plays an important role in integrating global context from multi-scale features for segmentation models. Here, we analyze the impact of the decoder module on network performance. As shown in Table 5, although the U-shape structure of the encoder-decoder network introduces additional parameters, which increases the time complexity to some extent, it obviously enhances the network's feature representation capability and achieves a substantial performance improvement by integrating deep and shallow features within the network.
## 5 Conclusions  
The correlation of the PSC signal with statistical quantities derived from acoustic emission data was studied when cement mortar specimens were subjected to uniaxial compressive loading and left to collapse due to increased internal damage accumulation. The PSC signal was recorded throughout the experiment until the final fracture. The acoustic activity was studied through the \(F\)-function, which expresses an average value of the production rate of AE hits, and the \(P\)-function, which expresses an average value of the energy rate of the hits. Using the cumulative frequency-width distribution of the AE hits, the variability of the \(b\)-values was also studied.  
The experimental results highlight that:  
* Identical qualitative behaviors between the acoustic activity and the PSC signal emerged in all stages of the loading experiment.
* The peaks of the produced AE hit rates and energy rates are concurrent with the PSC peaks and with the local minima of the \(b\)-values, which converge on the value 1.0.
* Especially the appearance of the PSC signal, \(F\)- and \(P\)-function peaks during the constant axial compressive stress application at a level near the total strength of the specimen in combination with the \(b\)-values that descend close to 1.0, is an indication that the final collapse of the specimen is imminent.
* The appearance of above-described behaviors is combined with an increased axial deformation rate.  
Finally, the appearing peak in the PSC signal is in agreement with the motion of charged dislocations model, since it is combined with an obvious increased rate of axial deformation. In conclusion, both the PSC and the AE activity can detect upcoming significant damage and provide pre-failure indicators.  
Conceptualization, D.T. ([PERSON]); methodology, D.T. ([PERSON]); software, A.K.; writing--original draft preparation, A.L.; writing--review and editing, I.S.; project administration, D.T. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 System Functional Modules
### Base Station Module Hardware and Software Design
#### 3.3.1 Design for the JN5139 Module  
When JN5139 is connected to the power supply for the initialization, it starts building the network, and waits for its successful communication with the base station or nodes. After the node logging data are received, the confirmation will be sent back and the latest base station time and operating parameters of the node are added. This node is then added into its own node management list, while the node on-line information is uploaded to ARMLPC2138 in the base station. When JN5139 receives uploaded data through the node, it will transmit the data to the base station ARM. The confirmed information is also sent back to the node; when JN5139 receives any information from the base station, it will save the data and respond to the base station (Figure 10).
## 5 Simulation Setup and Experiments
### V2I and V2V Communications  
In our study, we utilized Postman to generate the results, focusing on critical factors influencing API performance. These factors include API status, API response size, socket initialization, DNS lookup, TCP handshake, transfer rate, and download rate. Less impactful factors were omitted, such as preparation time and response headers. We tested our developed API using five HTTP requests to ensure a comprehensive evaluation. Table 6 presents the API response time for each request, where a status code of 200 indicates a successful response. Although the response size remained consistent at 289 bytes, the response times for other parameters varied across instances. Our APIs consistently provided successful responses, with the server delivering JSON responses to the vehicle (client side) for each request. This novel concept of vehicular communication in 5G VANET utilizing RESTful APIs holds the potential to enhance VANET's overall performance significantly.  
Our algorithms also incorporate security and privacy features, and the introduction of RESTful APIs adds a layer of security to the client-server model deployed for vehicular communication. Harnessing the substantial bandwidth and rapid communication rates offered by 5G technology, our proposed algorithm demonstrates resilient packet delivery and expedited API responses. The streamlined code and compact size of the APIs contribute to their exceptional performance, as the client-side data remain in a stateless form. Furthermore, RESTful APIs facilitate cache storage, resulting in improved response time. We ensured accurate measurement of all attribute values by clearing the Postman cache, which demonstrated the notably faster response rate achieved after implementing cache storage, as shown in Table 10.
## 2 Materials and Methods
### ELMo Contextual Word Embedding  
ELMo is a pretrained contextual word embedding model. It is a bidirectional _LSTM_ (biLSTM) language model which can generate context-dependent word embeddings [20]. The prediction process of the biLSTM language model is to maximize the log-likelihood of the probability of token \(i\) from both directions.  
\[\sum_{i=1}^{N}\,(log\,p(t_{i}\,|\,t_{1},t_{2}\ldots,t_{i-1};\Theta_{x},\, \stackrel{{\rightarrow}}{{\Theta}}_{LSTM},\,\,\Theta_{s})+ log\,p(t_{i}\,|\,t_{i+1},t_{i+2}\ldots,t_{N};\Theta_{x},\,\stackrel{{ \leftarrow}}{{\Theta}}_{LSTM},\,\,\Theta_{s})), \tag{1}\]  
where \(t_{i}\,\) is token \(i\), \((t_{1},t_{2}\ldots t_{i-1})\) is the forward context of token \(i\), \((t_{i+1},t_{i+2}\ldots,t_{N})\) is the backward context of token \(i\), \(\Theta_{x}\) represents the parameters of the token's representations, \(\stackrel{{\rightarrow}}{{\Theta}}_{LSTM}\) represents the _LSTM_ parameters in the forward direction, \(\stackrel{{\leftarrow}}{{\Theta}}_{LSTM}\) represents the _LSTM_ parameters in the backward direction, and \(\Theta_{s}\) represents the parameters of the Softmax layer. Then, ELMo combines the representation from each layer of token \(i\) as follows:  
\[R_{i}=\bigg{\{}h_{i,j}^{LSTM}\bigg{|}j=0,1,\ldots,L\bigg{\}}, \tag{2}\]where \(R_{i}\) is the representation of token \(i\), and \(h_{i,j}^{LSTM}\) is the hidden layer which is equal to \(\left[\begin{matrix}\overline{h}_{i,j}^{LSTM}&\overline{h}_{i,j}^{LSTM}\\ \end{matrix}\right]\). ELMo collapses the representations from all layers into one single vector. For a specific task, the ELMo representation of token \(i\) is calculated by  
\[ELMo_{i}^{task}=\ \gamma^{task}\sum_{j=0}^{L}s_{j}^{task}h_{k,j}^{LSTM}, \tag{3}\]  
where \(\gamma^{task}\) is the scalar factor to adjust the scale of vector based on the feature of a specific task, and \(s_{j}^{task}\) is the normalized weight of each layer.  
ELMo showed better performance in several NLP tasks compared to other context-independent embedding models like Word2 vec and GloVe [20]. For some specific domains, a domain-pretrained ELMo model had better performance than generalized ELMo [43,44]. In the clinical NER task, the LSTM-CRF model with ELMo pretrained on the medical corpus Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC III) [45] significantly outperformed the same model with generalized ELMo [24]. In our work, we use the MIMIC III medical corpus pretrained ELMo to produce word embeddings as input variables. Thus, the main comparison is between our work and previous work with clinical ELMo embeddings.
# Heatwave Damage Prediction Using Random Forest Model in Korea
## 1 Introduction
This emphasizes the need to consider various variables as well as weather-related parameters to predict heatwave damage. In this study, a heat-related health prediction model was developed on the basis of a machine learning algorithm for early warning systems. The purpose of this study is to help decision makers to preemptively respond, reducing human and economic losses. This paper is organized as follows: Section 2 describes the architecture of the random forest (RF) architecture and variables that can represent damage caused by heatwaves obtained from a big data collection site operated in South Korea. Experimental results, including variable evaluation, model optimization, and RF's accuracy evaluation in comparison with the tradition regression models is mention on Section 3. A trained model was applied to the site and visualized--this is also specified in Section 3. Section 4 presents the discussion and conclusion of this study.
# Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset
## 4 Employment Scam Detection & Relevant Problems
### Phishing  
Phishing combines social engineering with sophisticated attack vectors to direct users to bogus websites in an attempt to (a) increase website traffic; (b) spread malware; (c) unleash Cross-site scripting (XSS) attacks; and (d) acquire sensitive information. Usability tests proved that participants were unable to differentiate between legitimate and fake web sites while anti-phishing security indicators in major web browsers were almost invisible to the layman [43].  
Phishing content detection can be benefited from technical information attesting unauthorized redirects to other domains, the level of visual or structural similarity among online services as well as from previously reported bad user experience. Similarly to spam filtering, supervised classification algorithms such as regression trees, SVM, Random Forest (RF), and Neural Networks (NN) have been used extensively for phishing detection. The classifiers rely on features extracted from the page URL (punctuation and random tokens in URLs), the page content (spam words detection), the page layout and design (sloppy HTML markup and clumsy stylesheets) and network characteristics (blacklisted domain or IP address, spoofed DNS records). [PERSON] et al. [4] published an in-depth comparison of various anti-phishing machine learning techniques. Moreover, [PERSON] [44], a TF-IDF approach to detect fake web sites, evaluated lexical signatures extracted from the content of the suspicious web page. Lastly, a distinct anti-phishing approach presented by [PERSON] et al. [45], introduced Dynamic Security Skins that depend on the visual similarity between fake and legitimate websites.
## 3 Results
### Spatial Resolution with the LaGEMPix  
#### 3.2.1 Edge Spread Function (ESF)  
To evaluate the spatial resolution using the edge response method, we placed a lead block of 10 \(\times\) 20 \(\times\) 2.5 cm\({}^{3}\) size in front of the detector covering a portion of its active area, as shown in Figure 5a. This detector prototype was developed as a proof of concept; the readout matrix has some defects, resulting from a yield issue in the fan-out area toward the active area of the detector. In particular, some lines in the backplane are disconnected and can therefore not be read out. These appear as horizontal and vertical non-functional black lines in the readout image in Figure 5b.  
Figure 4: Current at the anode as a function of the drift field strength. Initial configuration (red): transfer field 1 = 3.0 kV/cm; transfer field 2 = 3.0 kV/cm; induction field = 5.0 kV/cm. Final configuration (blue): transfer field 1 = 2.0 kV/cm; transfer field 2 = 1.75 kV/cm; induction field = 5.0 kV/cm. A drift field of 1 kV/cm was selected as it yielded the maximum signal. The uncertainties are smaller than the data points.  
Figure 5: (**a**) The LaGEMPix with a 2.5 cm thick lead block placed in front of the detector: (1) gas outlet; (2) the 100 \(\times\) 100 mm\({}^{2}\) triple-GEM stack coupled to the optical readout with the thin Mylar window on top; (3) 3D-printed braces to hold together the triple-GEM detector and the image sensor; (4) lead block; (5) cables to connect the detector to the readout system; (6) FPGA module; (7) custom-made support. (**b**) Heat map of the imager after irradiation with 40 kV X-rays.  
A ROI indicated by a green rectangle was defined around an area of the readout with a minimum number of defects. Figure 6 shows the edge response obtained by averaging all pixel values in the same column from the averaged background-subtracted image. This figure also shows a fit with a logistic function according to Equation (1). The FWHM obtained by this method is 9.70 \(\pm\) 0.09 mm.
## 3 ANN Architectures and Training Algorithms
### Radial Basis Function Network  
Due to adding hidden nodes and layer(s) and the nonlinearity of the sigmoid activation function of each hidden nodes and/or output nodes in MLPs, the BP algorithm has the possibility to produce complex error surfaces which contain many minima. Since some minima are deeper than others, it is possible that the algorithm will not find a global minima. Instead, the network may fall into local minima, which represent suboptimal solutions. The RBF networks [46] were introduced with the centers of data clustering means, and then the linear solution for the connection weights to the network output directly to a unique local minimum, the global minimum, which shortens network training process greatly.  
Figure 2 is a diagram of a one-hidden-layered RBF network structure. The RBF networks also are MFNs. With sufficient larger number of hidden nodes, they are universal approximators as well. The RBF network training also is supervised, involving determination of data clustering centers, c\({}_{k}\) (k = 1, 2,..., h\({}_{c}\)), where h\({}_{c}\) also is the number of the centers to be determined, and network connection weights from the hidden layer to the network outputs, w\({}_{kj}\) (k = 1, 2,..., h\({}_{c}\); j = 1, 2,..., m). Similar to BP training, the RBF training is to establish the map between process inputs and desired outputs by minimizing the errors between the desired outputs and the calculated outputs driven from the inputs and network learning. The objective function of error minimization is defined as:  
\[E=\sum_{i=1}^{N}\sum_{j=1}^{m}(d_{j_{t}}-y_{j_{t}})^{2} \tag{6}\]  
and:  
\[y_{j_{t}}=\sum_{k=1}^{h_{c}}w_{\mathrm{k_{j}}}R(\left\|\sum_{i=1}^{n}x_{i_{t }}-c_{k}\right\|) \tag{7}\]  
where R( ) is a RBF function.  
A RBF function is any function R that satisfies R(x) = R(\(\left\|x\right\|\)). It is a real-valued function that depends on the distance from the origin as R(x) = R(\(\left\|x\right\|\)) or from other point or center, c, as R(x - c) = R(\(\left\|x\text{ - c}\right\|\)). The norm \(\left\|\cdot\right\|\) is usually Euclidean distance.  
The linear solution of output layer weights in equation (7) guarantees the objective function (6) to get the global minima and greatly speed up the training process.  
RBF networks integrate nonlinear RBF activation functions in hidden layer and linear combination of weights in output layer. In input data clustering, different techniques can be used, such as k-means clustering [60] and fuzzy c-means [61]. There are choices of RBF functions, such as Gaussian RBF, multiquadric RBF, polyharmonic spline, and thin plate spline. Some of the RBFs will bring more parameters to determine in training process, such as Gaussian RBF, R(x - c) = exp(-\(\beta\|\)x - c\(\|^{2}\)), which brings one more parameter, \(\beta\), to determine.