esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology
### Interfacing Protocol
#### 3.10. IEEE 802.11p  
IEEE 802.11p and 802.11b [29] are commonly utilized for wireless connections in vehicle communication. IEEE 802.11p is a PHY/MAC cross-layer standard that incorporates OFDMA [31] and uses a frequency spectrum of 5.9 GHz for V2V communication. The IEEE 802.11p MAC is designed on CSMA/CA, which stands for Carrier Sense Multiple Access with Collision Avoidance. The control channel (CCH) is devoted to broadcasting, while the service channel (SCH) [50] is used for non-safety transmissions, as a component of the multichannel access process. Before the broadcast, a node detects the channel, and, throughout transmission, the node and channel become dormant for a short inter-frame space (SIFS) [51]. Only when the channel is empty does broadcasting restart. In the event of a busy channel, an adjustable back-off scheme is developed using a contention window and only transmits when the back-off timer has expired. Control packets are transmitted across the CCH with no requirement for the receiver to acknowledge them. If no acknowledgement is received within the time interval, the packets are repeated in SCH.  
Figure 8: Edge computing.
## 3 System Model and Problem Statement
### System Model
#### 3.1.2 Traffic Model  
There are four types of traffic patterns in WSNs, which are time-based, event-based, query-based and hybrid traffic pattern [1]. Usually, routing is trigged when a source node has traffic to send. It is worth noting that traffic pattern has a very important impact on routing performance like energy consumption, latency, _etc._  
Time-based traffic pattern is used when each sensor nodes take turns to report their collected data in a time series manner. It is mainly used in applications like temperature and seismic monitoring where long term observation results like mean value or trend are needed for future prediction. Event-based traffic pattern is used for applications like target tracking or intrusion detection _etc._ When a target is entering into the nearby region of a sensor node, the target will be detected and tracked with an increased (or burst) traffic sent by involved sensor nodes to remote sink node. Query-based pattern means remote commander can send query to obtain observations in certain area. Hybrid traffic pattern is also commonly used. For example, during the time-based traffic monitoring period, the remote sink node may send a query to demand for certain information simultaneously. In this paper, we will study the characteristic of energy consumption under different traffic patterns so as to achieve energy balancing and prolong network lifetime.
# A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging
## 3 Explanation Methods for Deep Learning Cancer Detection Models
### Post-Hoc
#### 3.1.3 Breast Cancer  
Deep learning has achieved remarkable accuracy for the detection and segmentation of breast cancer [102; 103; 104; 105; 106; 107; 108; 109; 110; 111]. Detection in Mammography using Deep Convolutional Neural Networks by the author of [112] employee deep convolutional neural networks to localize classifications and masses in mammogram images without training directly on the full images. The authors implement a VGGNet architecture and report an accuracy of 92.53. The network was train by using images cropped around a ROI and then tested on the original, unaltered images. The authors then apply Grad-CAM.  
A Neoadjuvant chemotherapy (NAC) aims to minimize the tumor size before surgery. Predicting response to NAC could reduce toxicity and delays to effective intervention. The authors of [113] implement a CNN to predict response using DCE-MRI images and then produce Grad-CAM heatmaps for post-hoc explanation. They report accuracy ranging from 0.69 to 0.88.
# A New Multivariate Approach for Real Time Detection of Routing Security Attacks in VANETs
## 6 Results and Discussion
### Evaluation of the Proposed Scheme
Based on Figure 7, we note that in the beginning of the simulation, the R-J values were approximatively very close to the critical values for the non-rescaled data as well as with the Min-Max and Decimal Scaling techniques. However, as we progressed in the simulation, these values decreased noticeably and all the points fell under the critical values in both cases: with and without applying the data rescaling techniques. These outcomes reveal that the data traffic gathered at different times consisting of three variables, namely the throughput, dropped packets ratio, and overhead traffic ratio, are not from a multivariate  
Figure 7: Results of the R-J calculations in the black hole scenario (scenario 2). normal distribution. This conclusion leads to the rejection of the multivariate normality assumption and subsequently confirms the presence of malicious behavior. From the above figure, it was also observed that applying the three data rescaling techniques resulted in R-J coefficient values higher than those calculated on the original data. We note that from the 15 th second of the simulation time, the overall values exceed the R-J values computed on the original data. Additionally, the plots show that the Max-Min technique gave better results in terms of sensitivity to the normal distribution followed by the DSN technique, since all the points in their plots are beyond those of the non-rescaled data. In contrast, it was observed on the ZSN curve that not all the points exceeded those of the non-rescaled data. In conclusion, we argue that applying data rescaling techniques can be considered an essential prerequisite for many attack detection tasks but it is often ignored. Further, the question of which technique is more effective still remains. In our study, it is outlined that the Max-Min technique gives better results in comparison with the non-rescaled data and the two remaining techniques. An important conclusion that can be made is that our approach can detect abnormal activities in a simple manner with no updates to the routing protocol operations. Further, all statistical measurements are bounded in time, i.e., correspond to each particular time window, which gives meaningful results when monitoring the network traffic in real time.
# Improving the Performance of Vietnamese-Korean Neural Machine Translation with Contextual Embedding
## 4 Experimentations and Results
### Machine Translation Experiments
#### 4.2.2 BERT-Fused Model  
Our VietBERT+NMT MT system is based on the BERT-fused model for supervised learning [3]. Unlike other free-based embedding models, such as word2 vec, BERT is a context-based embedding model. Therefore, a word will be created by BERT in different contexts, using dynamic vector embedding. In the BERT model, the input sentence is fed into the encoder. The encoder uses a multi-head attention layer to understand the context of each given word and returns the representation of each word, based on context.  
In this research, the structure of NMT with the BERT-fused model is demonstrated in Figure 7.  
The BERT-fused translation model is based on the NMT model. The Vietnamese BERT-based model is used to extract the representations for an input sentence. Through the attention mechanism, the representations are then fused with the layers of both the encoder  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Sentences** & **Average Length** & **Tokens** & **Vocabularies** \\ \hline Korean & Initial & 11.6 & 4,782,063 & 389,752 \\ \hline MA and WSD & 412,317 & 20.1 & 8,287,635 & 68,719 \\ \hline Vietnamese & & 14.5 & 5,958,096 & 39,748 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Vietnamese–Korean bilingual corpus.  
and the decoder in the machine translation model. We used the Vietnamese BERT-based model with the dropout ratio set to 0.3; the embedding dimension, FNN dimension, and the number of layers in the model were set at 512, 1024, and 6, respectively. Before being inserted into MT models, all words in sentences of the parallel corpus were split into sub-words by BPE [42].
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 5 Energy-Efficient Transmission Algorithm  
Based on the predicted large-scale channel states, an energy-efficient transmission algorithm is proposed to schedule the modulation and coding modes.
# Conductive Textiles for Signal Sensing and Technical Applications
## 4 Testing for Conductive Textiles
### Electromagnetic Shielding (EMS) Testing
#### 4.2.1 Open-Field Test Method  
The Open-Field Test method, also known as the free-space method, is more suitable for flexible, thin textile substrates. This method is used on a finished product or a finished electronic assembly. The test set-up involves mounting a device at 30 m from a receiving antenna and recording radiated wave emissions. The test requires a spacious open field (approximately 900 m\({}^{2}\) area) and no metal or conductive object should be present between the sample and antenna [282].  
The measurement procedure of this method contains [282]:  
* Calibration measurement of the transmitted signal without material (P0);
* Measurements of the transmitted signal as a function of incident angle with material (P1);
* The ratio of P1/P0 is the power transmittance required to determine complex permittivity.
# Static Analysis of Underground Ant Nest Structures
## 2 Materials and Methods
### 3D Modelling of Underground Ant Nests  
In order to obtain a more accurate digital model of the nest structure, this paper used Industrial Computerized Tomography to scan a paraffin-cast nest model, because industrial CT is nowadays an important application for 3D digital scanning [15]. Industrial CT is a radiographic detection technology, the basic principle of which is to obtain a two-dimensional tomographic image of the subject through the attenuation law of the radioactive rays passing through the subject. After scanning multiple sections repeatedly to obtain enough tomographic images, a three-dimensional image can be reconstructed according to a certain algorithm. This scan uses the high-resolution YXLON FF35 CT equipment developed by YXLON, as shown in Figure 2. In the laboratory, the high-resolution industrial computed tomography system YXLON FF 35 CT was used for larger soil samples [16].  
The YXLON FF35 CT is a high-precision scanning device with micron-level accuracy [17]. The scanning resolution of the underground ant nest structure was set to 60 \(\upmu\)m, which saves the scanning time while ensuring the resolution. The paraffin model of the ant nest was scanned by CT to obtain the STL file of each ant nest block, as shown in Figure 3a, and the complete model of the ant nest after splicing is shown in Figure 3b,c. STL is a file format that describes the shape of a collection of surfaces of three-dimensional objects. The surfaces of the objects are displayed as unstructured triangular mesh and can only be used to represent closed two-dimensional or three-dimensional shapes.  
Figure 1: (**a**) Soil blocks containing ant nests; (**b**) numbering of model blocks of ant nests.  
Figure 2: YXLON FF35 CT.  
After high-precision CT scanning, some irregular shapes on the paraffin ant nest surface caused by grass roots, soil particles, small stones and other debris were truly reflected, as shown in Figure 3a. Such a structure is difficult to use for finite element simulation, and the STL file must be idealised to remove impurities on the surface, so that the structure of the ant nest model will not change much compared with the paraffin model, and it can be used for finite element simulation. Firstly, the STL file of CT scan was imported into Rhino software, and the individual ant channels that were broken due to excavation were stitched together through Boolean operations to form a complete ant nest structure, then imported to the reverse engineering software Geomagic Studio to simplify and repair the model. Using Geomagic Studio to first remove the nails on the surface of the model, we repaired some defects such as bulges, sharp corners, holes, etc. by manual removal or sewing to obtain a smooth ant nest mesh model. Finally, the idealised ant nest structure as obtained by inverse modelling with Geomagic Studio, fitting the smooth ant nest structure to the surface, as shown in Figure 4, and saving it in STP format for easy import into the finite element software ABAQUS.
# A Method for the Calibration of a LiDAR and Fisheye Camera System
## 1 Introduction
### State of the Art
#### 1.1.2 Target-Based Calibration  
Target-based methods use a specific calibration object to establish a precise correspondence between the camera and the LiDAR point cloud (such as a standard calibration pattern or an ArUco mark-based pattern [26]). Some of these examples use the PnP algorithm [12] to calibrate the LiDAR camera system [27, 28], while others employ ICP-based methods [29]. Other authors approach calibration through iterative random sampling and intersection-line-based quality evaluation [30] or through point-to-plane and point-to-line constraints [31]. These methods have been developed for pinhole cameras, so they are not directly applicable to fisheye cameras. Thanks to the use of a calibration object in the scene, these methods are generally accurate and reliable. Such calibration objects must be easily detectable in both the image and the point cloud and permit establishing a correspondence between the points in both sensor models.  
Two different methods for the calibration of a LiDAR system and fisheye camera are presented in [PERSON] et al. [32] and [PERSON] et al. [33]. Both methods propose an initial removal of image distortion from the fisheye model, converting the image to a pinhole model. As a result, the pinhole image facilitates the automatic detection of the calibration patterns. In these cases, the modification of the image model may not be perfectly accurate due to the non-linearity of fisheye lenses and the difficulty in obtaining the calibration parameters. The calibration of a fisheye lens is more complex than that of a pinhole camera since the fisheye lens introduces non-linear distortions in the image, and its projection model can obey different models, such as equidistant, equiangular, stereographic, or orthographic [34]. Moreover, an extra step with its corresponding added computational time would be included.  
In this paper, a target-based method for the calibration of a LiDAR-fisheye system is proposed. By using a calibration pattern (a rectangular polygon with known measurements) the translation and rotation between the two systems is calculated. The target-based method facilitates the calculation of features and permits solving the problem in a simpler way than the other methods. In addition, the implementation of methods based on deep learning would require large sets of labeled data and knowing the ground truth, and in many cases such information is not available or the acquisition is costly.  
The proposed calibration approach includes the following contributions:  
* It provides a method to compute the transformation between a LiDAR and a fish-eye camera.
* The points detected on the fisheye image are projected to the unit sphere without the need to reproject the points to a pinhole model.
* The Kabsch algorithm is used to calculate the rotation and translation between the two systems.
## 6 Characteristics of the Smart Broker
### Real-World Applications  
The smart broker-based PS architecture can be used in any context where current PS applications work. The proposed method simply provides an opinion about every topic based on the recent performance of the individual publishing entities, that is, the IoT devices. Let us consider a smart city application, where the smart broker can be used to monitor various data streams from different sources to ensure the accuracy and reliability of the data used for critical decision-making processes. The possible publishers are as follows:  
* Traffic Monitoring Sensors, including traffic cameras, speed sensors, and vehicle counters. These publishers can provide topics such as traffic flow, vehicle speed, and vehicle count.
* Environmental Sensors, including air quality monitors and weather stations, can provide information on topics such as the air quality index, temperature, humidity, and pollution levels.
* Public Transit Systems, including buses, trains, and trams, can cover topics such as transit schedules, vehicle locations, and passenger counts.
* Emergency Services, including police, fire departments, and ambulance services, can provide topics such as incident reports, response times, and emergency vehicle locations.
* Infrastructure Sensors, including roadway sensors and bridge monitors, can offer topics like road conditions, structural integrity, and maintenance schedules.  
All these entities will have a semi-permanent relationship with each other. These relationships can be captured in the relationship matrix, either manually or automatically, by analyzing historical information. Once the normal relationship is identified, any abnormality can be identified in real time. Based on the reputation scores, the smart broker recommends the most reliable information to city officials, optimizing city operations and enhancing citizen safety and well-being.
# A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime +
## 3 Routing on Wireless Sensor Networks
### Clustering Characteristics  
Clustering characteristics entail how certain aspects of the clustering procedure can affect the effectiveness of hierarchical routing. Cluster qualities have two major issues: cluster characteristics and sensor capacities [4; 5; 6].
## 3 Proposed Model and its Phases
### Network Transmission Phase (NTP)
As mentioned in Figure 2, most of the energy is consumed in data transmission which can overburden when the distance between CH and BS is longer. Thus, a variety of data compression techniques are used in WSNs and IoT. The CHs use a pre-configured application-oriented compression technique to reduce the size and quantity of data packets before transmission to BS. After compressing the aggregated data, as shown in Figure 8, cluster heads send this meaningful and updated information to the BS directly (single-hop) or indirectly via another intermediate node (multi-hop). The flow control of selecting a sensor, data sensing, and data transmission using our proposed model is shown in Figure 8.
# Feature Transformation-Based Few-Shot Class-Incremental Learning
## 4 Simulation Experiments
### Ablation Experiments  
#### 4.3.1 Effect of the Dimensionality of the Linear Mapping  
This section further investigates how mapping features to different dimensions affects model performance. The ablation experiments are conducted on the miniImageNet dataset. Before transformation, the features extracted by the feature extractor are 512-dimensional. In the main experiments above, we adopted a 512-to-512 dimensional linear mapping. Here, we additionally evaluate two commonly used alternative dimensions: 512 to 256 and 512 to 128. As shown in Figure 6, the performance across different mapping dimensions is nearly identical. The trends and values of the three curves are very close. After analysis, we select the 512-to-512 mapping scheme due to its lower performance degradation over time.  
Although the 512-dimensional mapping performs slightly worse than the 256-dimensional mapping in the first session, it shows a slower decline in accuracy as the incremental sessions progress, resulting in the lowest overall performance degradation.
# AI in Gravitational Wave Analysis, an Overview
## 3 Glitch Cancellation/GW Denoising  
The secondary reference problem in our review consists of glitch cancellation tasks and gravitational wave signal denoising. The main goals are to differentiate the signal from noise and remove the identified glitches, avoiding systematic errors in the signal detection and parameter estimation of the GW. Noise in the signal can come from a variety of sources, including seismic activity, thermal fluctuations, and electronic noise in the detectors themselves.  
The task might be very complex due to the nature of noise: it could be stationary and additive with uniform temporal behavior or non-stationary, exhibiting a mutable probability distribution. The non-stationary case is obviously more difficult to identify and remove. The literature offers numerous works employing supervised learning for gravitational wave denoising. In the case of non-stationary noise distribution, accurately estimating probability distribution parameters is crucial to better estimate the profile of disturbing signals. Ref. [28] first applied deep neural networks, specifically recurrent neural networks based on gated recurrent units (GRUs) [29], and then turned to machine learning algorithms, obtaining better results. Regarding the analysis and approach of the problem as a single block, ref. [30] used 1D convolutional neural networks fed by signals from different input channels; the model outputs the prediction of the noise. In this case, the approach to the denoising problem does not consider the repartition into the statistical characterization of noise, but it involves the analysis of the signal as a whole to identify noise interference. Numerous studies have incorporated CNNs into their pipelines, outperforming other denoising models, as seen in ref. [16]. The research shows that the dilated causal convolutions pipeline outperforms existing pipelines, even those based on classic convolution, and provides reliable results in real time. The paper also demonstrates the potential of deep learning in improving the sensitivity of gravitational wave detectors, which can enable the detection of weaker signals from distant sources.  
Also, ref. [14] employed CNN, but the scope was quite different; the authors focused on the reconstruction of the signal combined with simulated noise data.  
A common problem for, roughly, most research areas, is the availability of high-quality data; this issue forced artificial intelligence research teams to adopt unsupervised or semi-supervised learning algorithms. Ref. [15] proposed an unsupervised learning model based on denoising autoencoders with RNNs. The use of recurrent autoencoders allows the network to capture temporal dependencies in the signal, further improving the denoising performance. The results were evaluated based on the mean squared error and overlap, as referenced in ref. [31]. Some works focused on defined signal frequency bands, e.g., ref. [32] used CNN to potentially mitigate the angular noise in aLIGO [33; 34], which is the limiting noise source of the current sensitivity in the 30 Hz band.