esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Online Log Parsing with Log Punctuations Signature
## 1 Introduction
In this work, we abandon the tree structure and propose a hash-like method, which really improves robustness and efficiency. Unlike previous work, we focus on meaningless punctuations in log messages rather than meaningful words. Because we believe that words processing is the reason why the previous approaches are inefficient. Our intuition is that punctuation marks of log messages from the same template tend to be the same, which means simple punctuations imply template information. There are fixed types of punctuations, and they are easy to process. This paper proposes LogPunk, a robust and efficient log parser based on our novel log signature method. LogPunk is designed as a general-purpose online log parsing method, which is system and log type agnostic. We use log punctuations and length information (cf. Section 2) to generate log signatures. Each log signature corresponds to a signature group. Obviously, log messages with the same event type will have the same log signature and get into the same group. However, a signature group may also contain log messages from different event types with the same signature. We call this _signature collision_ easy to  
Figure 1: An illustrative example of log parsing. solve by further calculating the similarity between the log message and event templates. Fortunately, we found in our experiments that each signature group only corresponds to one or two event templates in most cases. We evaluate LogPunk on 16 datasets from the LogHub [28] comparing with five other log parsers. Experiments demonstrate that LogPunk is efficient but without loss of accuracy. LogPunk achieves the highest accuracy on 14 datasets and the best average PA of 0.919. More importantly, LogPunk performs consistently across different datasets, which means it is robust. In summary, our paper makes the following contributions:  
* We propose a novel and efficient log signature method based on log punctuations and length information applied for log parsing;
* We present an online log parser based on our log signature method, named LogPunk, which is better than the previous log parsers in robustness and efficiency;
* We conduct extensive experiments on 16 datasets and comparing LogPunk with five other log parsers. The results show that LogPunk is accurate, robust, and efficient. The paper is organized as follows. Section 2 introduces the background of log parsing. Section 3 describes our log signature method and the implementation of LogPunk. Section 4 shows the results of evaluating LogPunk on the LogHub datasets. Section 5 compares LogPunk with tree-based methods and discusses the validity. Section 6 introduces the related work of three categories of data-driven log parsing approaches. Finally, Section 7 concludes the paper.
# Virtualized MME Design for IoT Support in 5G Systems
## 2 Background
### Characteristics of M2M Traffic in Cellular Networks  
The penetration of connected machines in the near future is expected to outnumber human connections, reaching values up to one order of magnitude above [27]. This number and the traffic characteristics of M2M devices make difficult their wide deployment in current cellular networks, such as LTE [28].  
M2M communications have different characteristics compared to conventional human-based communications. M2M average traffic volume is small, about two orders of magnitude lower than MBB traffic volume. The M2M ratio of uplink traffic volume is higher than downlink traffic volume. Furthermore, synchronized communication from a large number of devices causes traffic peaks, e.g., in periods of 1 h, 30 min or 15 min, as shown in the time series analysis of M2M traffic [29]. Besides report transmission synchronization events, there will be alarms caused by unpredictable events. Together, all of these events could imply peaks of signaling load two- or four-times higher than the average load [30]; see Figure 2.  
From the network point of view, the scalability of the control procedures triggered by these transmissions is one of the most important challenges for M2M support. As with the CPCEO, the MME is to convey these data packets; it has to be designed to be able to deal with this traffic increase and to avoid any associated congestion. Dynamic provisioning of processing resources is of little use for the vMME to carry the traffic caused by these traffic peaks. First, the billing models of cloud providers (such as Amazon EC2) typically charge per hour, and traffic peaks usually occur at shorter time intervals. Second, there will be M2M traffic peaks caused by alarm events that cannot be predicted, and therefore, they would cause congestion during the tens of seconds or minutes needed to boot new server instances. For these reasons, we will assume that the amount of resources required to serve these traffic peaks has to be dimensioned in advance.
## 2 Data and Method
### Drifter Current Velocity  
In addition to the HF radars, we also included the current velocity measured by the surface drifters obtained within the framework of the global drifter program (GDP). Details can be found on the website [[https://www.aoml.noaa.gov/global-drifter-program/](https://www.aoml.noaa.gov/global-drifter-program/)]([https://www.aoml.noaa.gov/global-drifter-program/](https://www.aoml.noaa.gov/global-drifter-program/)) (accessed on 10 November 2023) and products are introduced in [21]. Drifters measure surface current velocity through the principle of Lagrangian drift. As the drifter moves with the ocean currents, its trajectory and speed are tracked via satellite-based positioning systems. By analyzing the drifter position changes over time, the surface current velocity and direction are then deduced. This Lagrangian approach offers a unique perspective, directly measuring the water movement rather than inferring it from Eulerian measurements at fixed locations. In this study, we use the 1-hourly interpolated ocean current velocity provided by the GDP products. The collocation criteria between SAR observations and the drifter measurements are a distance of 1 km in space and a temporal duration of 3 h.
# HP-LSTM: Hawkes Process-LSTM-Based Detection of DDoS Attack for In-Vehicle Network
## 3 Preliminary  
### SOME/IP Protocol  
#### 3.1.1 Specification of Message Format  
SOME/IP is an emerging communication middleware proposed by BMW AG (Munich, Germany) in 2011 and incorporated into the AUTOSAR 4.1 specification in November 2013 [12; 27], which supports remote procedure calls (RPCs), event notifications, and the underlying serialization/wire format [28].  
An RPC represents a method call from one Electronic Control Unit (ECU) to another, transmitted through messages. Serialization involves converting data into a byte stream for network transmission. Event notifications encapsulate a general Publish/Subscribe concept. Typically, a client sends subscription event requests to the server. In certain scenarios, the server publishes events to the client, which usually consist of updated values or previously occurred events. Importantly, the subscription and publishing mechanisms are implemented using SOME/IP-SD rather than SOME/IP [13; 29].  
The header frame format of the SOME/IP protocol consists of the following:  
* Message ID (Service ID/Method ID) [32 Bits];
* Length [32 Bits];
* Request ID (Client ID/Session ID) [32 Bits];  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Literature** & **Type** & **ML** & **Che** & **Ea** & **Wb** & **Oa** \\ \hline [PERSON], [PERSON], et al. [15] & AnovaDot and RBFDot SVM & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ [PERSON], [PERSON], and [PERSON] & \multirow{2}{*}{KNN and SVM} & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ [PERSON] [16] & & KNN and SVM & ✓ & \(\times\) & \(\times\) & \(\times\) \\ [PERSON], [PERSON], et al. [22] & Reinforcement learning-based & ✓ & ✓ & \(\times\) & \(\times\) & ✓ \\ [PERSON], [PERSON], et al. [17] & MOHIDS & \(\times\) & ✓ & \(\times\) & ✓ & \(\times\) \\ [PERSON], [PERSON], et al. [19] & MLPC & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ [PERSON], [PERSON], et al. [18] & GANs and DBN & ✓ & \(\times\) & \(\times\) & \(\times\) & ✓ \\ [PERSON], [PERSON], et al. [21] & LSTM & ✓ & ✓ & \(\times\) & ✓ & ✓ \\ \hline [PERSON], [PERSON], et al. [23] & Hawkes Process & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) \\ [PERSON], [PERSON], et al. [24] & Multi-Stimuli Hawkes Process & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) \\ [PERSON] et al. [25] & Multivariate Hawkes process & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) \\ [PERSON], et al. [26] & Multi-Stage Hawkes Process & ✓ & ✓ & \(\times\) & ✓ & \(\times\) \\ \hline
**HP-LSTM** & **Hawkes Process + LSTM** & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}  
* ML: Machine Learning Approach; Che: Consider the impact of historical events; Ea: For Ethernet data; Wb: window-based; Oa: Open access  
\end{table}
Table 1: Comparison of HP-LSTM with existing works.  
* Protocol Version [8 Bits];
* Interface Version [8 Bits];
* Message Type [8 Bits];
* Return Code [8 Bits].  
The entire message comprises the header and payload sections, as depicted in Figure 1. Table 2 elaborates on the content represented by each field in the SOME/IP message.
## 4 Proposed Protocol  
This section describes our proposed EEGT protocol in detail, inspired by the LEACH-C protocol [21]. The operation of EEGT is separated into some rounds; each round consists of three periods: set-up period, path establishment period, and data transmission period. During the set-up period, the sink device divides the network region into logical grid cells that balance the number of sensors. Then the sink device chooses CHNs and super-CHN for grid cells based on the current energy level, the density of the node, and the Euclidean distance between nodes and the sink. In the path establishment period, the Kruskal and ant colony algorithm is combined to construct multi-hop data transmission routes to minimize energy consumption, an MST for intra-cells, and a chain for inter-cells toward the sink device. Finally, sensor nodes collect, aggregate data, and transmit them to the sink device. The overall operation of EEGT in a single round is shown in Figure 3.  
Figure 3: Flowchart of the EEGT protocol in the operation of one round.
# Semantic-Enhanced Foundation Model for Coastal Land Use Recognition from Optical Satellite Images
## 3 Foundation Model-Enhanced Coastal Land Cover Recognition
### Image Gridding-Based Land Use Classification on Non-Water Areas  
#### 3.4.1 Foundation Model-Enhanced Classification  
We employ a state-of-the-art foundation model regarding remote sensing classification called ViTAE-RVSA [30] to classify the land use visualized in the satellite image into different types of land use/land cover. The backbone of ViTAE-RVSA is ViT, which has shown remarkable performance in various classification tasks. Based on the architecture of ViT, ViTAE-RVSA incorporates residual visual attention mechanisms to enhance the capability of recognizing complex remote sensing land uses.  
The architecture of ViTAE-RVSA includes a ViT encoder, a residual visual attention, and a feature decoder. The ViT encoder focuses on extracting features from the input remote sensing image. The residual visual attention, which includes a number of blocks, aims to process the results generated from the ViT encoder. Each residual visual attention block includes a self-attention layer and a residual connection, which are used to obtain long-range dependencies from feature maps and preserve the original features, respectively. The feature decoder mainly utilizes a number of transposed convolution layers to process the outputs of residual visual attention for generating classification results.  
Considering the powerful capability of few-shot learning conducted by ViTAE-RVSA, we utilize the selected labeled datasets, which are mentioned in Section 3.2, to fine-tune ViTAE-RVSA, and then classify each satellite image into multiple land cover types.
# Coverage and Rate Analysis for Location-Aware Cross-Tier Cooperation in Two-Tier HetNets
## 3 Analysis of the Coverage Probability and Average Rate
### Statistical Distance to Serving Base Station  
We denote \(X_{M,in}\), \(X_{M,out}\), \(X_{F}\) and \(\mathbf{X}_{C}=\left(X_{1},X_{2}\right)\) to be the distances between the typical user and their serving BSs.  
**Lemma 2**.: _The probability density functions (PDFs) of \(X_{M,in}\), \(X_{M,out}\), \(X_{F}\) and \(\mathbf{X}_{C}=\left(X_{1},X_{2}\right)\) are given as follows:_  
\[f_{X_{M,in}}\left(x\right)=\frac{2\pi\lambda_{1}x}{\mathcal{Q}_{M,in}}\exp \left(-\pi\lambda_{1}x^{2}\right),x\in\left(0,D\right] \tag{11}\]  
\[f_{X_{M,out}}\left(x\right)\approx\frac{2\pi\lambda_{1}x}{  
\[\Omega_{C}=\left\{(x_{1},x_{2}):x_{1}>D\;\;\text{and}\;\;\left(\frac{P_{\text{F}}}{P _{1}}\right)^{1/a_{2}}x_{1}^{\
icefrac{{a_{1}}}{{a_{2}}}}<x_{2}<\left(\frac{\beta P _{\text{F}}}{P_{1}}\right)^{1/a_{2}}x_{1}^{\
icefrac{{a_{1}}}{{a_{2}}}}\right\}.\]  
Proof.: See Appendix B.
## 5 Database Content and Analysis Examples
### Data Analysis Example Using Standard Samples  
A comprehensive suite of analytical techniques (XAFS, HAXPES, XRD, PDF, and SAXS) was applied to standard Pt and Pt-Co catalyst nanoparticles supported on various carbon materials (Table 5). By leveraging these complementary approaches, a multifaceted view of the structural evolution and electronic state modifications of the catalyst emerges. This section highlights the interplay between the particle size, crystallinity, alloy composition, electronic properties, and support interactions, ultimately guiding future catalyst design strategies.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Sample Name** & **Pt (wt\%)** & **Co (wt\%)** & **C (wt\%)** & **Carbon Support** \\ \hline TEC10V30E & 28.9 & 0.0 & 71.1 & Vulcan XC72 \\ TEC10V50E & 46.5 & 0.0 & 53.5 & Vulcan XC72 \\ TEC10E30E & 30.0 & 0.0 & 70.0 & Vulcan XC72 \\ TEC10E50E & 46.3 & 0.0 & 53.7 & Ketelphack \\ TEC10E250E & 46.8 & 0.0 & 53.2 & Graphitized Refenblack \\ TEC10F30E & 27.2 & 0.0 & 72.8 & Acetylene black \\ TEC10F50E & 46.3 & 0.0 & 53.7 & Acetylene black \\ TEC35V31E & 29.6 & 2.2 & 68.2 & Vulcan XC72 \\ TEC36E52 & 46.2 & 5.2 & 48.6 & Ketelphack \\ TEC36F52 & 47.4 & 4.9 & 47.7 & Acetylene black \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overview of datasets in the FC-BENTEN database, showcasing its comprehensive data coverage and research utility. This table summarizes the number of analysis-requested samples, corresponding datasets, and total datasets collected using various measurement techniques, including XAFS, HAXPES, XRD, PDF, SAXS, and CV. The “Total” for “Number of Analysis-Requested Samples” (567) includes overlapping samples that were analyzed using multiple measurement techniques. When excluding such overlaps, the number of unique samples amounts to 202. The data exemplify the database’s systematic approach to capturing diverse analytical parameters, supporting advanced research into the structural and electronic properties of PEFC materials.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Measurement Technique** & **Number of Analysis-Requested Samples** & **Number of Datasets for Analysis-Requested Samples** & **Total Number of Datasets** \\ \hline XAFS & 79 & 196 & 272 \\ HAXPES & 85 & 873 & 1298 \\ XRD & 144 & 234 & 358 \\ PDF & 110 & 178 & 235 \\ SAXS & 128 & 205 & 366 \\ CV (Electrochemical) & 21 & 21 & 43 \\ Total & 567 & 1707 & 2572 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sample list showing Pt (wt%), Co (wt%), C (wt%), and carbon support types for the analyzed materials. All samples were obtained from TANAKA Precious Metals. Samples TEC35V31E, TEC36E52, and TEC36F52 were Pt-Co-based, whereas the other samples were Pt-based. The Carbon supports were classified as solid spheres (Vulcan XC72, Acetylene black) or hollow spheres (Ketelphack, Graphitized Ketelphack).
# A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems
## 4 Simulation Results
### Ber  
We can observe form (14) and (32) that the difference in BER performance lies in the difference between \(\|\bar{\mathbf{H}}_{k}^{n}\|^{2}\) and \(c^{2}\). For the conventional system, both subcarrier selection and MRT precoding can bring the diversity gain and array gain [33]. For the proposed scheme, \(c\) is the scaling coefficient of the ZF precoder. We know that when ZF is used as receiver equalizer, it has the drawback of noise enhancement. This drawback turns out to be the transmit power penalty when it is used as a transmit precoder. However, \(c\) is involved with all channel coefficients on all subcarriers and therefore, we could expect a larger diversity gain. In Figure 3, we present 100 random realizations of \(\|\bar{\mathbf{H}}_{k}^{n}\|^{2}\) (blue asterisks) and \(c^{2}\) (brown circles). \(\|\bar{\mathbf{H}}_{k}^{n}\|^{2}\) was in general larger than \(c^{2}\), indicating a larger array gain; while \(c^{2}\) was less fluctuating, indicating a larger diversity gain.  
Figure 4 compares the simulated BER performance of the proposed system to the OFDM system. As expected, in the high SNR regime, the BER of the proposed scheme decayed much faster than the conventional OFDM system due to the larger diversity gain; while in the low to medium SNR regime, OFDM had better performance due to the array gain. At BER = \(10^{-4}\), both systems produced a similar performance.  
The array gain of the OFDM system comes from the subcarrier selection diversity, which is shown in Figure 5. The performance of the OFDM system without subcarrier allocation was worse than that of the proposed scheme.  
Figure 4: The comparison of BER performance for uncoded QPSK modulation. (**a**) \(M=2\), \(N=64\); (**b**) \(M=4\), \(N=64\); (**c**) \(M=2\), \(N=256\); (**d**) \(M=4\), \(N=256\).  
Figure 5: The effect of subcarrier allocation on BER performance for uncoded QPSK modulation with \(M=4\) and \(N=64\).
## 3 Novel Human Machine Team Design
### HMT Architecture Design  
Constructing a generalized system requires a few primary components or building blocks. In our previous work, we identified six major components of an HMT, with emphasis on architectures, interfaces, and metrics based on various literature [36; 49; 50; 51]. These are defined in detail in our previous work [4] and include Architectures, Interfaces, Metrics, Roles & Rules, Team building, and Verification & Validation. These components may be used to construct a generalized HMT architecture. However, the processes of constructing and designing HMT architecture starts with establishing team requirements. The basic requirements of HMT can be defined as follows [5]:  
* Machine task; e.g., a VBSA might be required to \"assist ECP with error detection and recommend steps to resolve errors\".
* Human-related task; e.g., a Human ECP may need to \"provide emergency care to the patient and simultaneously report to SA\".
* Interface mode; e.g., The communication mode may \"prevent any hindrances to ECP's motor skills while treating the patient\".  
Figure 4: Modes of operations of Voice-Based Synthetic Assistant (VBSA) (reproduced from [5]).  
Figure 5: Unified Modeling Language (UML) diagram depicting the Amazon Web Services (AWS) architecture of VBSA (reproduced from [5]).  
While the foundations of HMT were laid at Defense Advanced Research Projects Agency (DARPA) in 2001 [54], it took five years more for the research community to identify a set of metrics that facilitates a well-organized structure of human robot interactions (HRI). For many metrics, we found close but different descriptions of the same metric, primarily for HMI in human-robot or robot-only swarms [55; 62]. Identifying common metrics will allow benchmarking of HMT designs, comparison of findings, and development of evaluation tools. The primary difficulty in defining common metrics is the diverse range of HMT applications. Here, we focus on metrics for all three agents of HMT, i.e., human, machine, and team. The goals of this section are (1) identification and classification of metrics, (2) evaluation of the identified metrics to find common metrics, and (3) proposal of common metrics that can be used in future HMT benchmarking.  
Once the HMT is developed, we also need to measure its effectiveness in accomplishing the assigned task. To this end, we compiled a list of metrics through the analysis of 188 works and thorough review of 77 articles among these. We further categorized them into Human, Machine, and Team metrics as part of our survey on common metrics [4].
# Damage Detection Method Based on Continuous Wavelet Transformation of Lamb Wave Signals
## 6 Experimental Evaluation
For detection cells near the physical boundary, there may be both the damage-scattered and edge-scattered fluctuations, which can be identified separately as shown in Figure 27a for sensing pair A1B1. In a rare case, when distances between damage location and receiver and plate's edge and receiver are same for one of the sensing pairs, the damage-scattered and edge-scattered fluctuation may overlap, as shown in Figure 27b for sensing pair A2B2. The possibilities of damage existence from such sensing paths where the damage scatterings are weak and overlapped with incident waves or edge scatterings can be ignored in the aggregate damage imaging. Alternatively, the baseline can be used for cells near the physical boundary of the plate in practical applications.
# A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics
## 2 Related Work  
### Sentiment Analysis and Emotion Recognition in Social Networks  
With advances in machine learning and the emergence of big data sets, the computational detection of opinions, emotions, and subjectivities in unstructured UGC (from free-formatted texts, reviews, and blogs) has been applied to research over the past decade [21]. The linguistic and machine learning approaches are two of the main methods used to detect sentiments or emotions. The linguistic method uses dictionaries or lexicons that contain pre-determined affective words to calculate the frequency of a word and determine the emotional attributes of text [22]. The machine learning method uses computer algorithms to automatically learn text sentiment, given the trained data set [23]. This study adopted a lexicon-based method to construct the sentiment-related features and a lexicon of mood state was constructed.  
In contrast to emotion, which reflects short-term affects and is connected with occurred experience, the mood state reflects medium-term affects [24]. In NLP research, affects, feelings, emotions and sentiments are often considered similarly and used interchangeably [25]. In general, moods differ from emotions, feelings, or sentiments in that they are less specific, less intense, and less likely to be triggered by a particular stimulus or event [24; 26]. They are different in many ways, such as duration and time mode, and the comparison is shown in Table 1. Thus, in general, the mood state is a relative stable variable that may influence one's behavior over a long time and could be reflected in online social behavior.