esa-sceva/satcom-chunk-collection huggingface dataset:
# A Rectangular Notch-Band UWB Antenna with Controllable Notched Bandwidth and Centre Frequency
## 1 Introduction
In [24], many slots and split-ring resonators were used to realize a notch with sharp selectivity with controllable bandwidth; however, because of the complex geometry, the notch-band frequency cannot be shifted. Hence, the design of a compact rectangular notch UWB antenna with controllable notch bandwidth and frequency is needed. This paper presents the design and realization of a compact UWB microstrip patch antenna with a rectangular notch band having controllable notch bandwidth and frequency. Two EBGs are used to realize the rectangular notch at the WLAN band. Moreover, we demonstrate that notch bandwidth and frequency could be controlled according to the application requirements by tuning the parameters of the EBG. Figure 1: Conventional notch and the rectangular notch.
## 2 System Architecture
### System Architecture Design  
The system consists of three modules, which are a node module, a base station module and a data distribution module (Figure 1). The node module is placed inside greenhouses, and the base station module is placed in public areas outside the greenhouses. The base station is equipped with a LCD screen so that the real-time values of temperature and humidity, both inside and outside greenhouses, and soil moisture can be observed.  
The relationship between the nodes and the base station is illustrated by a star topology structure as shown in Figure 2.  
The WEB releasing module is installed in the data center in Beijing; in fact, it can be installed in any computer with a fixed IP. The system adopts two network communication modes: (1) a wireless network formed between the nodes and the base station through the 802.15.4 protocol; (2) a GPRS network between the GPRS transmission module in the base station and the GPRS transmitter module at the WEB releasing module.
# An Explainable Artificial Intelligence Approach for Detecting Empathy in Textual Communication
## 5 Methodology
### Algorithm Comparison  
Evaluation of a classification algorithm refers to measuring the performance of a model when it encounters previously unseen data [25,80]. We required an adequate metric for measuring the performance of the algorithms involved [25]. We wanted to ensure that our contrast pattern approach was the most appropriate method for measuring empathy in textual communications, when compared to other algorithms. For this, we made use of two performance metrics: The Area Under the ROC Curve (AUC) [80,80,81] and the Closeness Evaluation Measure (CEM). The AUC was chosen since it was the most widely used nominal metric for cases with class imbalance [26]. Meanwhile, the CEM is a robust metric for measuring performance of algorithms in ordinal classification tasks. It rewards exact matches, considers ordinal relationships, and does not assume predefined intervals between classes [82]. The use of two metrics was due to the nature of the Empathy Score, which presented an order. We were interested in obtaining the performance of the classifiers when the problem is taken as a nominal [25] and as an ordinal [82,83] classification problem.  
We decided to use 5-fold cross validation for our experiments [15]. The reason for the selection of 5 as our number of folds was that we wanted to ensure that a moderate amount of data found in EC could be considered for the testing set. With a larger number of folds, we ran the risk of shrinking the number of testing instances to the point that the found patterns might not be applicable to the set used for evaluation. Additionally, we applied the same 80/20 principle found in various applications of the Hold-out method [25]. To carry out the cross-validation splits, we decided on the use of the Knowledge Extraction based on an Evolutionary Learning (KEEL) tool [84]. Additionally, the data from one fold could later be used as a data point for statistical analysis.  
The process for classification between the algorithms would be as follows:  
1. Get one of the five pairs of dataset for training and testing.
2. Perform the classification on one of the algorithms.
3. Obtain the evaluation metrics: 1. For the PBC4 cip record, the patterns obtained by the miner; 2. Record the AUC for the classifier; 3. Record the CEM for the classifier.
4. Return to step 2 until all algorithms have been evaluated.
5. Return to step 1 for another pair of datasets, until all five have been used for classification.  
We would carry out the experiment using five different algorithms, alongside PBC4 cip. The results would be analyzed using statistical methods. In this manner, we would obtain the performance using a contrast-pattern algorithm in contrast to algorithms commonly used in NLP applications [25]. The algorithms we would use are:  
1. K-nearest neighbors algorithm (KNN) [85];
2. Random Forest Classifier (RF) [86,87];
3. Gaussian Naive Bayes (GNB) [85,88];
4. Decision Tree Classifier (DT) [15,85];
5. Multilayer perceptron classifier (MLP) [15,85].
## 8 Conclusions  
LDDoS attack is a recent evolution of DDoS attack that is more challenging and difficult to detect. As traditional security schemes proposed for defending against DDoS are not efficient in detecting LDDoS attacks, novel security mechanisms developed for LDDoS are needed. In this paper, we presented an extensive review of several existing LDDoS detection methods for SDN. In particular, we have proposed a taxonomy of LDDoS attacks in all layers of the SDN architecture. Furthermore, integrated analysis and identification of shortcomings or disadvantages of all LDDoS detection approaches were discussed and summarized. Classification-based and deep learning mechanisms for LDDoS detection have received growing attention from researchers because of the nature of security attacks that are dynamically changing and evolving. Our survey shows that mechanisms based on deep learning and incorporating a hybrid model such as CNN-LSTM and CNN-GRU perform better than classification-based mechanisms that rely on only ML classification algorithms. CNN-LSTM and CNN-GRU provided a high detection rate, up to 99.5%, with a lower false rate than other methods. We have also demonstrated a brief survey about LDDoS attacks in IoT networks based on SDN architecture for the industrial domain. As part of future work, we will provide a more detailed issues on IoT networks based on SDN and propose detection mechanisms to protect IoT networks from LDDoS attacks using machine learning approaches. In addition, open issues are also discussed and analyzed which suggest future developments and pave the way for young researchers to develop novel approaches for detecting and defending the SDN against LDDoS attacks.  
Conceptualization and writing original draft preparation, A.A.A.; supervision--methodology and editing, M.S.M.Z.; writing--reviewing and editing, M.A.A.; data collection and analysis, M.Y.D.; reviewing and editing, B.I.; organizing sections of the manuscript, S.A. All authors have read and agreed to the published version of the manuscript.  
This research has been supported by Ministry of Education Malaysia, Fundamental Research Grant No. 015 MA0-047.  
Not applicable.  
The authors acknowledge the support of this research by Ministry of Education Malaysia, Fundamental Research Grant No. 015 MA0-047.  
The authors declare no conflict of interest.
## 3 Results  
### Dynamic web mapping service  
The developed dynamic WMS (WMS access at: [[http://webgrs.wur.nl/cgi/projects/sensorweb/pmapper/pmapper_gpp/map.phtml](http://webgrs.wur.nl/cgi/projects/sensorweb/pmapper/pmapper_gpp/map.phtml)]([http://webgrs.wur.nl/cgi/projects/sensorweb/pmapper/pmapper_gpp/map.phtml](http://webgrs.wur.nl/cgi/projects/sensorweb/pmapper/pmapper_gpp/map.phtml)).) for vegetation productivity (Figure 2) enables end-users to visualize and analyze the spatio-temporal development of GPP over the Netherlands. Standard functionality of the WMS (e.g., zooming and panning, measure distance, make layers transparent, printing and downloading) is provided through the p.mapper framework. Individual vegetation productivity layers for a specific date can be downloaded in GeoTiff format and combined with other relevant spatial data. In addition, some dedicated functionality has been added to the WMS in order to visualize actual changes in vegetation productivity:* Information on most recent vegetation productivity: after selection of a pixel, actual values of vegetation productivity are listed for all opened layers of the WMS (Figure 6: upper left);
* Trajectories of vegetation productivity: after selection of a pixel, the time-series of vegetation productivity of all available dates for this pixel is presented for the most recent year available (Figure 6: lower right).
## 3 Multi-MAF: A Hybrid Adaptive Algorithm for Routing in WSNs
### Forwarding Rule  
In a hybrid operation, the routing strategy chosen by the sink must be propagated to all network nodes and a broadcast control message might be used to switch the source nodes from one algorithm to another. In this work, we simplify this transition by assuming that all network nodes always respond normally to control messages as their original protocols determine, but validity is maintained through a timestamp and a predefined period of time given by the periodicity of the reconstructions and requisitions of EF-Tree and SID, respectively. Thus, we apply an integrated forwarding rule to automatically adjust the network according to the sink decision: every data packet (generated or forwarded) is sent through a routing tree if it is available (EF-Tree behavior); otherwise, data is sent to the neighbor from which a specific and valid requisition was received (SID behavior); otherwise, the data is sent by broadcast (Flooding).
# A Review of Machine Learning and IoT in Smart Transportation
## 1 Introduction
### Machine Learning in IoT  
With IoT, devices are connected with each other, communicate with each other and collect an enormous amount of data every day. In many applications, IoT devices may also be programmed to trigger some actions based either on some predefined conditions or on some feedback from the collected data. However, in order to analyze the collected data and extract meaningful information and create smart applications, human intervention is required. IoT devices need not only to collect data, and communicate with other devices, but also to be autonomous. They need to be able to take context based decisions and learn from their collected data. This need led to the creation of the term \"Cognitive IoT\" (CIoT) [10]. Also, there is a necessity for Intelligent IoT devices, able to create automated smart applications with automated resource allocation, communication, and network operation. Deploying ML algorithms in an IoT infrastructure can introduce significant improvements in the applications or the infrastructure itself. ML can be applied for network optimization, congestion avoidance, and resource allocation optimization, but also for real-time or offline data analyzing and decision making.  
Moreover, as the number of devices increases, the amount of the collected data increases as well. Having to deal with \"big data\" is very common in IoT applications. Big data cannot be handled properly with conventional databases. Special infrastructure is needed to handle the great volume of structured and unstructured data and special techniques to analyze them [11]. There are many ML algorithms like \"Ensemble\", or Artificial Neural Networks (ANN) that can help dealing efficiently with big data, and they will be discussed in the following sections.
# Design of Cloud-Based Real-Time Eye-Tracking Monitoring and Storage System
## 2 Materials and Methods
### Database Server (SQL)  
The SQL (structured query language) is a fourth-generation declarative programming language for relational DBMSs (database management systems) and it is used to communicate with and manipulate databases [55]. The MySQL database stores and retrieves data via the REST API. The stored procedures and functions are designed as a security layer to perform operations that would receive queries from the API for SQL processing in the database [56].  
There are many parameters to consider when evaluating database performance. In the next section, we will highlight the qualities that made SQL databases more suitable for this system over NoSQL databases. In particular, NoSQL databases outperform SQL databases regarding writing speed and scalability. NoSQL databases perform better when dealing with large scalability requirements and facilitating rapid data updates [57]. However, SQL databases better manage complex relationships and multiple client scenarios [57]. The characteristics of the SQL, structure, and capability to maintain data integrity make them suitable for scenarios involving relational data tables (such as the study carried out). Due to the anticipated availability of multiple user results and relational data in this system, the SQL database was chosen over NoSQL.
## 7 Performance Evaluation of Multicast R-GOOSE over Public WAN
### Test Setup for Latency Measurement  
In this experiment, communication latency is measured for both non-secured and secured (signed) R-GOOSE messages transmitted within P2M architectures over both 4G and 5G networks, as shown in Figure 19. The idea is to use 4G as the backup transmission technology in places with poor 5G coverage. Raspberry Pi and Linux PC are used as the IP-Multicast sender and receiver devices, respectively. Moreover, two network routers act as FHR and LHR within P2M architectures. These routers are cellular routers (cisco IR829 [44] with a built-in 4G module and an external 5G module [45]) and are equipped with 4G and 5G SIM cards so they can connect to public Internet via both 4G and 5G communications. The 4G SIM (300 Mbit/s data-only subscription) and 5G SIM (600 Mbit/s data-only subscription) cards are commercial SIM cards from a Finnish mobile network operator (Elisa). The test location was the Hervanta Campus of Tampere University.  
In order to measure R-GOOSE communication latency, the sender and receiver must be synchronized to a common time reference, which in this case is the GPS reference clock. To this end, Raspberry Pi receives GPS time from the GPS Expansion board [46] (Adafruit Ultimate GPS Hat +antenna) via NMEA [47] protocol. The Linux PC receives GPS time information from the Network Time Server (LANTIME M600 [48]) via the IEEE 1588v2 Precision Time Protocol (PTP) [49].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**P2M Architecture** & **Tunnel Technology** & **Scalability** & \begin{tabular}{c} **Implementation** \\ **Complexity** \\ \end{tabular} & \begin{tabular}{c} **Public WAN** \\ **Communication** \\ \end{tabular} & **Security** \\ \hline GRE & Point-to-Point tunnel & No & Low & Yes & IPsec \\ \hline DMVPN & \begin{tabular}{c} Point-to-Multipoint \\ tunnel \\ \end{tabular} & Yes & High & Yes & \
\begin{tabular}{c} IPsec, NHRP \\ authentication \\ \end{tabular} \\ \hline GETVPN & Tunnel-less & Yes & High & No &
\begin{tabular}{c} GDOI, IPsec with \\ Address Preservation \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparing characteristics of P2M architectures.
# Time-Sensitive Networking in IEEE 802.11 be: On the Way to Low-Latency WiFi 7
## 1 Introduction
Thus, whereas WiFi networks will never be able to offer bounded delay guarantees due to their own nature and operation in license-exempt bands, the adoption and integration of TSN concepts would keep WiFi as one of the leading wireless access technology in the 6G era, and a key actor to support the increasing needs of the IoT. The remainder of this article is organized as follows. Section 2 overviews the limitations of current WLANs to handle time-sensitive traffic. Section 3 describes the main features of IEEE 802.11 be in terms of PHY and MAC layers. A brief description of TSN and the potential enhancements to support it in WiFi 7 are provided in Section 4. The most representative WiFi 7 use cases that could leverage low-latency communications are reviewed in Section 5. Indeed, the case study of an interactive museum is reviewed in Section 6 to show the benefits of using such a technology. Last, Section 7 presents the obtained conclusions and discusses open challenges.
# A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion
## 3 Deep Learning-Based TKGC Methods
### Language Model-Based Methods  
Pre-trained language models [67; 68] and large language models [69] are making new waves in the field of natural language processing and artificial intelligence due to their emergent ability and generalizability.  
SToKE [48] employs the pre-trained language model BERT [70] to learn joint structural and temporal contextualized knowledge embeddings. Specifically, it first builds an event evolution tree (EET) for each query. TKG can be regarded as a structured event sequence that records the query-related structure and temporal contexts. Then, a temporal embedding and structure matrix are proposed to learn the temporal information and structural dependencies of facts in EET. Finally, TKGC is formulated as a mask prediction problem, and the pre-trained language model BERT is fine-tuned by masking the missing entities of the query.  
[PERSON] et al. [49] propose Llama-2-7b-CoH and Vicuna-7b-CoH, which conceptualize TKGC as a dual process of large language model fine-tuning and generation along a historical chain. During the fine-tuning, the Low-Rank Adaptation [71] method is used, and the data in TKG are divided into an input part and a supervised labeling part, guiding the large language model to adapt to the mapping relationship between the textual information of the specific TKG and the inherent complex logic in temporal events. Additionally, the local information across multiple single-step graphs is used for historical data augmentation to explore the ability of a large language model to perceive graph-modality information.  
Methods in this category use pre-trained language models or large language models for TKGC, which can provide additional text information outside the TKGs to assist the TKGC task, but which have high requirements on the hardware platforms and require a large amount of training data.
# Junction Temperature Optical Sensing Techniques for Power Switching Semiconductors: A Review
## 4 Method of Calibration
### TSOP Sensing Technique  
Proper calibration is essential for implementing TSOP methods, because the estimation of junction temperature by EL is based on intrinsic properties, which are sensitive to differences in fabrication and variations in electrical parameters that affect the operation of the MOSFETs in the third quadrant window [108]. For SiC modules mounted on a ceramic substrate and covered by a transparent silicone gel, the detector could be immersed in the silicone gel to enhance the optical coupling [105]. Otherwise, a photodetector may be mounted above the side wall of the device to measure the light emitted from the surface of the substrate. [PERSON] et al. [19] placed the detector at about 2 mm over a SiC chip covered with transparent silicone gel. Considering that the light wavelength travels through the gel, a dark box shielded the ambient light from the setup desk to avoid disturbance from external noise.  
Typically, SiC MOSFET is biased with a negative voltage within the range of \(-5\) to \(-15\) V to set the device in reverse conduction mode to achieve the third quadrant operation for emission. As illustrated earlier in Figure 7b, the SiC module is placed on a heat controller, which could be a digital hot plate designed to raise the diode's temperature to decouple the influence of forward current and the junction temperature on the emitted light. The required pulse current for the EL process could be generated from a DC source in a typical range of 2 to 50 A. Depending on the desired accuracy and budget, a Si _p-i-n_ photodiode can be used to detect photons with or without external bias. The photodetector generates a current proportional to the emission, depending on the junction temperature of the SiC MOSFET. In addition, the spectrum emitted under different conditions can also be analyzed on a spectrometer for characterization purposes [106].
# A Multi-Task Learning and Multi-Branch Network for DR and DME Joint Grading
## 4 Experiment and Results
### Experimental Results and Analysis
#### 4.2.4 Comparison Experiments  
To verify the effectiveness of MaMNet for joint grading of DME and DR, the proposed method MaMNet is compared with other eleven methods, namely, LzyUNCC [40], VRT [40], Mammoth [40], HarangiM1 [40], AVSASVA [40], VGG16 [41], ResNet50 [42], InceptionV3 [43], Xception [44], and DenseNet121 [45]. The specific experimental results are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Joint Ac** & **DR Ac** & **DME Ac** \\ \hline LzyUNCC [40] & **0.631** & **0.748** & **0.806** \\ VRT [40] & 0.553 & 0.592 & 0.816 \\ Mammoth [40] & 0.515 & 0.544 & 0.835 \\ HarangiM1 [40] & 0.476 & 0.553 & 0.748 \\ AVSASVA [40] & 0.476 & 0.553 & 0.806 \\ HarangiM2 [40] & 0.408 & 0.476 & 0.728 \\ VGG16 [41] & 0.524 & 0.583 & 0.767 \\ ResNet50 [42] & 0.524 & 0.592 & 0.757 \\ InceptionV3 [43] & 0.437 & 0.563 & 0.767 \\ DenseNet121 [44] & 0.456 & 0.485 & 0.699 \\ Xception [45] & 0.467 & 0.515 & 0.738 \\ Proposed Approach & **0.612** & **0.641** & **0.796** \\ \hline \hline \end{tabular}  
* The bold in the table represent the best and second-best results.  
\end{table}
Table 6: Comparison of results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Methods** & _Joint Ac_ & **DR Ac** & **DME _Ac_** \\ \hline
1 & DR: MbN + SFEN & 0.583 & 0.602 & 0.767 \\  & DME: MbN + SFEN & & 0.583 & 0.612 & 0.796 \\
2 & DME: MbN + SFEN + CFEN & & & \\
3 & DR: MbN + SFEN + CFEN & & & \\  & DME: MbN + SFEN + CFEN & & & \\
4 & DME: MbN + SFEN + CFEN & & & \\
5 & DR: MbN + SFEN + CFEN & & & \\  & DME: MbN + SFEN + CFEN & & & \\
5 & DME: MbN + SFEN + CFEN & & & \\ \hline \hline \end{tabular}  
* \end{table}
Table 5: Ablation experiments.  
The first to sixth row shows the results of the methods used by the top five ranked teams in challenge-2 of the ISBI 2018 IDRiD competition (which is ranked based on joint accuracy). The seventh to eleventh row lists the results of the five classical neural networks in deep learning, which are trained with migration learning.  
It can be seen from Table 6 that compared with the 11 advanced methods MaMNet can achieve the second-best results. Although MaMNet doesn't achieve the best results, it still has the following advantages: (1) MaMNet is an end-to-end network that can achieve simultaneously DR and DME grading. The LzyUNCC team trains DR and DME grading networks separately; (2) The best training model of MaMNet takes only 15 min to finish the training and achieves better results. The model [46] proposed by the LzyUNCC team has a large amount of computation and requires more time to obtain the classification results; (3) MaMNet only uses the IDRiD dataset provided by the competition for training. The LzyUNCC team first used 35,000 retinal images from the Kaggle dataset to train the initial model and then fine-tuned it with the IDRiD dataset. In summary, it can be demonstrated that MaMNet performs well on DR and DME joint classification.
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 5 Transition Modes Process Monitoring Method
### Artificial Intelligence Method
#### 5.3.3 Dirichlet Process-Based Method  
To effectively adapt to the new mode, some researchers have selected Gaussian mixture data following the Dirichlet process as a sample to calculate the distribution. A Dirichlet process-Gaussian mixture model (DP-GMM) was constructed after obtaining the distribution mean and covariance matrix and iterating the sample parameters for cluster analysis of non-linear data [90; 91; 92]. This method is essentially an unsupervised clustering algorithm. A disadvantage lies in that its distribution initialization stage may cause parameter errors, and it is sensitive to the monitoring of modes in the transition period, which may lead to misjudgment.