esa-sceva/satcom-chunk-collection huggingface dataset:
# Combining Unsupervised Approaches for Near Real-Time Network Traffic Anomaly Detection
## 1 Introduction
Assuming that a large part of the monitored traffic is due to normal network activities, in a first step, a Deep Learning algorithm is used to discriminate normal traffic from suspicious traffic quickly. In the next step, only the suspicious traffic is further analyzed with an Unsupervised Learning algorithm to accurately determine whether it is attributable to abnormal activity or not. In this paper, three approaches are presented, which differ from each other in the Deep Learning algorithm used in the first phase. Three types of algorithms based on the autoencoder architecture have been identified:  
* Deep Autoencoder Gaussian Mixture Model;
* Deep Autoencoder;
* Memory-Augmented Deep Autoencoder. Connections considered normal by the Deep Learning algorithm do not need further analysis; in contrast, for traffic considered suspicious in this approach, the Extended Isolation Forest algorithm is used, which is considered a precise and stable algorithm to be used in an unsupervised Anomaly Detection system. The approach proposed in this paper aims to minimize the prediction time employed by a near real-time Anomaly Detection system adopting an unsupervised approach and, at the same time, maximize the accuracy of an unsupervised Anomaly Detection system. The main contributions of this work are as follows:  
* Proposal of three new algorithms merging unsupervised deep learning with shallow learning using Extended Isolation Forest;
* Analysis of results in terms of precision, recall, F1, AUC ROC, and accuracy;
* Evaluation of training and execution times as expected in the near real-time context;Finally, using the explainable artificial intelligence technique called SHAP, it was shown that the features synthesized by the joint use of MemAE and EIF increase the robustness of the results and improve their performance. In Section 2 of this paper, the state of the art related to Machine Learning and Deep Learning algorithms used to detect anomalous behaviors in a monitored network is described. Subsequently, in Section 3, Deep Autoencoding with GMM and Isolation Forest, Deep Autoencoder with Isolation Forest, and Memory-Augmented Deep Autoencoder with Isolation Forest are proposed. Section 4 describes the setup of the experiments, the results, and reasoning, including also an explainable artificial intelligence in order to understand which are the features that the models identify and discriminate to find a 0-day attack. Section 5 contains conclusions of this work.
# THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management
## 8 Conclusions  
With the rapid development of positioning technology, more and more trajectory data needs to be processed. Extending the existing distributed framework to support spatiotemporal query capabilities will benefit the development of moving objects databases. In the paper, we design a trajectory data management scheme called THBase through HBase coprocessor. THBase proposes a storage and partition model suitable for trajectory data management in HBase, and implements L-index structure based on Observer coprocessor to accelerate spatiotemporal queries. Additionally, it utilizes Observer coprocessor to ensure the reasonable distribution of trajectory data and index data. On these bases, the node-locality-based parallel algorithms are proposed for processing spatiotemporal queries through Endpoint coprocessor, to realize the effective control to data transmission overhead, thus ensuring efficient query performance. We validate the storage and query performance of THBase by experiments on real dataset. Experimental results show that THBase outperforms CTDM and SPDM in terms of query. For future work, we plan to support more query types by utilizing THBase.  
Conceptualization, J.Q.; Data curation, J.Q.; Methodology, J.Q.; Supervision, L.M.; Validation, J.Q.; Writing--original draft, J.Q.; Writing--review & editing, L.M. and J.N. All authors have read and approved the final manuscript.  
Figure 7: Performance of _k_-_NN_ query: (**a**) result of changing data size, (**b**) result of changing \(k\).  
**Funding:** This research was funded by National Natural Science Foundation of China, grant number 61802425.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Experimental Results
### Speedups
#### 6.1.5 Total Speedup  
The total speedup of the algorithm for the four datasets is shown in Table 2 for the examination-threaded approach. The transfers of all of the data to and from the GPU are taken into account. The speedup is much larger in population size, 8192, which can be explained by the fact that in small population sizes, the total execution time is dominated by the memory transfers and not the computation and that not a significant number of threads are launched, so that the GPU can be efficient. From Figures 4-7 and from Table 2, it is shown that the speedup is not increasing much for populations greater than 8192. This can be accredited to the fact that the number of blocks and the number of the total threads increase so much at this point, that the GPU processes them sequentially.
# Comparative Analysis of Current Approaches to Quality Estimation for Neural Machine Translation
## 3 Multilingual Pretrained Language Models for QE
### XLM-RoBERTa  
Because XLM learns using Wikipedia, there is a limitation in that data on low resource language is insufficient. In XLM-R [9], the data are expanded to a much larger scale. XLM-R is a multilingual masked language model that adopts large-scale pretraining by utilizing CommonCrawl data [39], which comprises 100 languages. XLM-R gains state-of-the-art performance for cross-lingual classification, question answering, and sequence labeling. Among the three pretraining schemes for XLM, only MLM is utilized for XLM-R training, and MLM proceeds in the same way as XLM. By expanding the model capacity and leveraging larger data sizes than permitted for XLM, XLM-R alleviates the performance degradation caused by the curse of multilinguality.  
The curse of multilinguality represents a trade-off between the number of languages in the training data and the model performance at a fixed model capacity. Increasing the number of languages in training data can encourage an improved performance for monolingual and cross-lingual benchmarks to a certain extent because the understanding of low-resource languages is supported by similar high-resource languages. However, if the model capacity is fixed, an excessive number of languages will lead to the overall performance degradation of this method because of the decrease in the per-language capacity. XLM-R alleviates this problem by extending the number of model parameters.  
XLM-R adopts a multinomial distribution (5) for applying different sampling ratios to each language. Unlike XLM, XLM-R sets \(\alpha\) to 0.3 to strengthen the sampling ratio of low-resource languages. The training process for the XLM-R-based QE model is similar to that of Section 3.1.
# From 5G to 6G--Challenges, Technologies, and Applications
## 2 Fifth-Generation Network's Shortcomings
### A. Communication Speed and Scalability  
It is projected that by 2030, global mobile traffic will be 670-times what it was in 2010, mainly due to machine-to-machine (M2M) communications [11]. This is an unprecedented exponential growth that motivates researchers worldwide to achieve technological breakthroughs in many network aspects, especially in spectral and energy efficiency techniques. The fifth-generation network is portrayed to bring to the network enhanced mobile broadband (eMBB), i.e., offer speeds up to 20 Gbps [12], and massive machine-type communication (mMTC) support, as shown in Figure 3. However, this will not be able to keep up with the near future demands, as it is expected by 2030 that 5G will reach it is limits [13]. The demand-driven nature of communication speeds dictates that in less that 10 years from now, the data transfer rates will have to experience substantial improvements to be well beyond 1 Tbps (up to 10 Tbps) [14]. Thus, looking beyond 5G incorporates researching techniques that can offer such speeds. Moreover, 5G is designed to utilize the _millimeter_ wave range of 20-100 GHz [15]. However, it is not possible in this range to achieve such high speeds due to current transceiver designs and digital modulation techniques' limitations, such as non-linear power amplifiers, phase noise, and poor analog-to-digital converter (ADC) resolution [16]. Consequently, the next leap in communication will consider looking at frequencies beyond 100 GHz, possibly up to a few \(THz\)[17], as this spectrum is available in abundance to achieve high data rates. It was shown in a comparison [18] that beyond 100 Gbps speeds can be achieved in the 300 GHz range compared to 4 Gbps in the 60 GHz range.  
The extremely high data rates are justified by the kind of services that are emerging or expected to be widely adopted in the near future. Services such as _augmented reality_ (AR), human nano-chip implants, connected robotics, autonomous systems, and telemedicine [19] are currently being under development and enhancement to be deployed on a wide scale in the near future. Additionally, with the envisioned growth in M2M communications, it is expected that there will be hundreds of billions of devices connected to the Internet [20]. However, 5G is expected to offer the best performance tradeoffs only up to the scale of a billion devices [21]. Therefore, the next major mobile network upgrade will be scaled to accommodate such a huge number of device connections and a more-than-ever condensed network.
# A Systematic Literature Review on Image Captioning
## 4 Discussion
### Model Architecture and Computational Resources
#### 4.1.3 Attention Mechanism  
The attention model was established with an intention to replicate natural human behavior--before summarizing an image, people tend to pay attention to specific regions of that image and then form a good explanation of the relationship of objects in those regions. The same approach is used in the attention model. There are several ways in which researchers have tried to duplicate it, which are widely known as hard or soft attention mechanisms [5]. Some other scientists have highlighted top-down and bottom-up attention models. Ref. [89] recently confirmed that the better approach is still top-down attention mechanisms as the results from experiments with humans and with machines  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multicolumn{3}{c}{**CONVOLUTIONAL NEURAL NETWORKS ARCHITECTURES**} & \multirow{2}{*}{**Year**} \\  & **\#Params** & **\#Multiply-Adds** & & **Top-1 Accuracy** & **Top-5 Accuracy** & **Year** \\ \hline
**Alexnet** & 61M & 724M & 57.1 & 80.2 & 2012 \\
**VGG** & 138M & 15.5B & 70.5 & 91.2 & 2013 \\
**Inception-V1** & 7M & 1.43B & 69.8 & 89.3 & 2013 \\
**Resnet-50** & 25.5M & 3.9B & 75.2 & 93 & 2015 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Table of comparison for CNN architectures (from ref. [88]).  
showed similar results. In the top down model, the process starts from a given image as input and then converts it into words. Moreover, a new multi-modal dataset is created with the highest number of new instances from human fixations and scene descriptions.
# An Overview of Emerging Nuclear Sensor Technologies: Challenges, Advancements and Applications
## 3 Applications of Nuclear Sensors
### Medical Imaging  
In medical imaging, techniques like Positron Emission Tomography (PET) and Single-Photon Emission Computed Tomography (SPECT) require incredibly sensitive, accurate, and fast radiation detectors [79; 80]. Advanced sensor technologies can improve SPECTimaging by improving both spatial resolution and energy discrimination. For example, graphene-based sensors, with their extraordinary intrinsic electron mobility (often exceeding \(200,000\) cm\({}^{2}\)/Vs in optimized CVD-grown monolayers [9]) and exceptional thermal conductivity (up to 5000 W/mK [16]), provide rapid charge collection and high sensitivity to gamma photons. Graphene's atomic-scale thickness and large specific surface area (approximately 2630 m\({}^{2}\)/g) facilitate efficient interactions with gamma emissions in the 70-300 keV energy range [81]. However, sustained exposure to ionizing radiation (e.g., cumulative doses in the range of 1-2 kGy) may induce defect formation [82]. This is evidenced by an increased I\({}_{D}\)/I\({}_{G}\) ratio in the Raman spectra [83].
## 4 Discussion
In the present study, because the 3D-based VT alone did not improve visual acuity, we assumed that it was not effective enough, and that applying the multicomponent intervention of VT-FFR-ES is better for controlling the progression of myopia. Some links between auditory-induced FFR and the visual system can be found. FFR is a footprint of early auditory nerve processing and is related to the optic nerve [20]. The interactive effect of binaural tones of different intensities was reported on visual perception [33]. The current study provided MRI evidence of visual cortex activation through FFR generated by various difference auditory binaural frequency (8-14 Hz) entrainment regimes. This is the first report to demonstrate an activated visual cortex by FFR or binaural beats. In our study, the significant effect of visual acuity is the first evidence of a single intervention of a 3D-based VT program with auditory frequency entrainment. Unexpectedly, we found that the single 3D-audiovisual intervention, a 3D-based VT program with auditory 8-14 Hz frequency entrainment, could improve visual acuity in schoolchildren with myopia. ES is a promising, safe, and effective therapeutic modality for improving vision [21]. Electrical acupuncture is commonly used to control myopia in children and adolescents [22; 23]. Several studies have evaluated the effectiveness of electrical acupuncture stimulation at the Taiyang acupoint for myopia in an animal model with lens-induced myopia [16; 24], and reported that ES improved neuromuscular modulation (such as retinal GABA) and increased resting-state functional connectivity in visual cortices in lens-induced myopic eyes, but did not influence refractive error or axial length [16]. The Taiyang acupoint is often selected for treating myopia in traditional Chinese medicine, because it is located around the bilateral orbital area. Some mixed acupoint stimulations for myopia therapy may clear the meridians, improve chororiretinal microcirculation, enhance cerebral circulation [34; 35], and activate the visual cortex [36]. This is the first report to demonstrate that a single 30-min session of VT-FFR-ES can effectively improve visual acuity, and that a 12-session program of VT-FFR-ES over 1 month can effectively prevent myopia among schoolchildren. The main strength of the present study is the use of a randomized crossover design to identify the best intervention for improving visual acuity in children with myopia. However, this study has some limitations. First, our participants were schoolchildren from a single school in central Taiwan and may be susceptible to the development of myopia at their age, thereby precluding the generalizability of our findings to other target populations. Second, the 1-month follow-up period precluded insights into the long-term effects of the multicomponent intervention. Finally, we cannot confirm the intervention effects on the elongation of the eye (i.e., measurements of axial length) and did not assess the rebound effects during the short follow-up period.
## 3 Methodology
### Entity Attention Network
#### 3.4.3 Entity Gated Mechanism  
The Entity Attention Mechanism is primarily used to attenuate the noise interference of words with weak associations to the head entity. However, in the text, there may exist some words that have strong associations with the head entity but are almost irrelevant to the relation extraction task. This is also a form of noise.  
In order to attenuate the text word noise that is weakly associated with the relation extraction task, we propose the Entity Gated Mechanism. Still using the head entity \(k\) as an example, the operation is as follows:  
\[g_{i}^{k}=\sigma((W_{1}S_{g}+b_{1})\oplus(W_{2}s_{i}^{k}+b_{2})) \tag{19}\]  
\[u_{i}^{k}=g_{i}^{k}\odot tanh(W_{3}s_{i}^{k}+b_{3}) \tag{20}\]  
\[U^{k}=\{u_{1}^{k},u_{2}^{k},\dots,u_{n}^{k}\} \tag{21}\]  
where \(W_{1}\), \(W_{2}\), \(W_{3}\), \(b_{1}\), \(b_{2}\), and \(b_{3}\) are trainable weights, \(\oplus\) is the cascade operation, \(\odot\) is the dot-product operation, and \(\sigma\) denotes the shape activation function, which returns a value from 0 to 1.  
The above formula compares \(S_{g}\) and \(s_{i}^{k}\) to calculate the degree of association \(g_{i}^{k}\) between the \(i_{th}\) word and the relation extraction task. Subsequently, based on the degree of association, the text noise is attenuated to obtain the associated feature \(u_{i}^{k}\).
# High-Performance QC-LDPC Code Co-Processing Approach and VLSI Architecture for Wi-Fi 6
## 2 Decoding Process and Parameters Calculation of the Decoder  
### Decoding Process  
The DF received by the decoder is shown in Figure 1, consisting of a series of codewords. Each codeword includes the actual information field Data Bits (DBs), the check field Parity Bits (PBs) obtained from the parity check matrices, and the replica field Repeated Bits (RBs) that may exist. Each bit of these fields is typically populated by a log-likelihood ratio (LLR) soft information of the intrinsic channel observations.  
The decoding process can be divided into three stages: pre-processing, decoding, and post-processing. According to the calculated parameters, the preprocessor will stitch the received fields into a complete codeword required by the decoding core; the output fields are shown in Figure 2. First, the preprocessor needs to append shortened bits that the encoding phase may use to populate the specified length to the standard information field (SIF). If the repeated bits exist, the preprocessor must remove the replica field that the encoder added after the codeword. Furthermore, if the punctured bits are present, the codeword needs to be padded at the end of the check field, and since the deleted bits are indeterminate, the LLRs value of the padding is zero.  
The decoding stage decodes the fields in Figure 2 into the soft information field. A reliable soft information field is obtained through the iterative propagation of the message. Many scholars have worked to achieve the best trade-offs between latency, resource overhead, throughput, and power consumption [9; 10]. The post-processing phase removes the possible SBs and only retains the valid information field DBs needed by the medium access control (MAC).  
Figure 1: Data received by the decoder.  
Figure 2: Pre-processed fields.
# Recent Progress in Hybrid Additive Manufacturing of Metallic Materials
## 2 Hybrid Techniques Based on the Use of Other Manufacturing Techniques with AM
### Laser Metal Deposition + Computer Numerical Control  
[PERSON] et al. [29] reviewed the Laser Metal Deposition (LMD) AM technique in conjunction with a subtractive process in a single-step procedure. The authors proposed a combination of LMD and CNC milling centers for the aerospace industry by using functional lightweight metals followed by a smooth surface finishing (Figure 3). This technique provides a solution to maximize the production volume whilst eliminating changeover cycles. The LMD process produces moderate thermal gradients and defect-free components. The possibility of the laser alloying of tool alloys was explored to reduce the abrasive and adhesive tool wear. According to reports, the chipping volume was significantly reduced, albeit that this was not quantified. Similar to laser case hardening, the application of laser energy for heating hypothetically proves advantageous in surface hardening of components [29]. Laser metal deposition can facilitate the production of several materials in a single process, and the addition of ceramic inclusions on the surface of the metals was proposed to improve surface wear. In another study, the integration of a direct energy deposition technique with CNC machining was achieved in a process termed the \"ArcHLM process\", proposed to be suited for LMD processes [30]. The uniqueness of this technique is that machining is performed after every successive layer of weld-deposited material. As a result of the process, oxide coatings are formed, leading to a rough surface finish. The surface irregularities were estimated to be less than 2 mm. Hence, to avoid  
Figure 2: The classification of additive manufacturing technologies. Reprinted with permission from [28]. Copyright 2015 Elsevier B.V.  
a loss of accuracy along the build direction, each layer was face-milled to the required thickness. This study showed that fast deposition rates are achievable; however, there are issues such as the need for controlled heat gradients and atmosphere to minimize oxidation and phase stability. In the case of maraging steels, the cooling to martensite start is crucial to minimize the amount of retained and reverted austenite, which can increase the ductility at the expense of strength. Instead, the use of the SLM process in conjunction with CNC machining has been proposed to be a better alternative [20]. Small melt pools are produced, which allow for faster cooling and better surface quality. The process demonstrated significantly higher hardness values of about 56 HRC for the hybrid additive technique in comparison to 40 HRC for the material produced solely through printing without heat treatment. This study showed the importance of the surface finish for the mechanical properties. The major setback of this technique is that the time interval between additive manufacturing and machining is not optimized, resulting in long lag times.
## 4 Experimental Results and Security Analysis
### Security Analysis
#### 4.2.5 Differential Cryptanalysis  
Differential cryptanalysis is mainly applied to the general form of cryptanalysis of block ciphers, but also to stream ciphers and cryptographic hash functions. Generally speaking, it is the study of how differences in an input can affect the resultant difference at the output. In the case of a block cipher, it refers to a set of technologies for tracing differences through the network of transformations, discovering where the cipher exhibits non-random behavior, and utilizing such properties to recover the secret key [15].  
For the requirement of keystream generators, it needs strong key sensitivity for the diffusion effect. The sensitivity analysis of our keystream generator was carried out by the comparison between the original secret key and altering a portion of the secret key. We calculated its keystream difference rate (\(kdr\)) according to the following formula [15].  
\[kdr(k)=\frac{\text{Diff}(k,k_{1})+\text{Diff}(k,k_{2})}{2\times N}\times 100\% \tag{14}\]  
where \(N\) is the binary length of the output keystream, and Diff(\(k\), \(k_{1}\)) and Diff(\(k\), \(k_{2}\)) are the different bits numbers between the output keystreams from secret keys \(k\), \(k_{1}\) and \(k\), \(k_{2}\) of size \(N\). In our simulation, \(N=10^{7}\), and the \(k\), \(k_{1}\) and \(k_{2}\) are given by 0x000000000000000000, 0x000000000000001, and 0x0000000000000010, respectively. The secret keys are the input to generate the initial values and system parameters.  
Table 2 shows the keystream difference rates (\(kdr\)) of the proposed chaotic stream cipher. The \(kdr\) values of the original CMLs are 16.49% and 4.27%. The \(kdr\) values of the improved CML based on the logistic map, tent map, and the compound CMLs are 46.97%, 46.23%, 48.68%, and 48.82%, respectively. From the table, we can find that the difference rates of the improved CMLs are better than that of the original CMLs. In addition, the improved compound systems are better than the others.