esa-sceva/satcom-chunk-collection huggingface dataset:
# Change-Point Detection in Autoregressive Processes via the Cross-Entropy Method
## 5 Discussion
### Application  
We use a new real data set to demonstrate the practical aspects of our algorithm. The data were downloaded from the Reserve Bank of Australia--the daily AUD/CNY exchange rate from 2 nd January 2018 to 24 th March 2020. Both ARIMA and AR models have been used in the literature to model different exchange rates [48; 49; 50]. If an ARIMA process is used to model an exchange rate, then a change-point is recorded as a change in either the coefficients of AR part, or the MA part, or both of them. Dealing with the parameter change in the MA part of ARIMA process will be a matter of our future research. Before applying the CE.AR method, the stationary test and pre-examination are needed; the first difference of AUD/CNY is non-stationary, and we can get the rough profile of this series by using auto.arima() function which is built in forecast package [51]. If there is no obvious change in autocorrelation coefficient but in the mean level, ARiseg or the similar methods are preferred.  
Figure 7 shows the estimation results obtained by the CE.AR and strucchange method. Since the actual number and the locations of change-points can not be identified in advance, we need to take both methods into account and seek for the agreement between them. The CE.AR method yields four possible change-points in 4 December 2018, 4 March 2019, 2 January 2020, and 9 September 2020, while strucchange\" method presents three change-points that occurred in 3 May 2018, 28 February 2018, and 6 May 2019. Both methods have correctly identified the period from December of 2018 to May of 2019, and it seems that a regime started around 4 March 2019. According to the CE.AR method,  
Figure 6: Frequency of the estimated locations when \(N>0\); the true profile of change-points is at 20, 60, 120.  
the new regime started on 9 March 2020, so it managed to identify a very volatile period related to the COVID-19 pandemic.  
If we bring the views from data itself to the real world, the cause of the changes may be the fallout from the China-United States trade war since 2018 or the uncertain climate in the Eurozone. The external shock has an impact on the Australian market, which has been reflected by the data and the estimated change points.  
Hence, we suggest that the CE.AR algorithm would be a good tool for analyzing the daily AUD/CNY exchange rate; combined with the strucchange method, it will reveal more historical information. Forecasting the exchange rate based on the recent change-points will be done in the future research.
## 3 Results
### Granger Analysis: Results  
Let us start with the results returned by the Granger procedure. They are reported in Table 4. As previously mentioned, we have subjected to our Granger tests the data relative to the following counties of New York State: New York, Kings, Bronx, Queens, Richmond, Nassau, Westchester, Rockland, Suffolk, Monroe, Onondaga, and Oneida. For each county, we have evaluated if the time series of the average daily values of the PM\({}_{2.5}\) particulate (\(X\)) _Granger-causes_ the time series of the new daily COVID-19 infections (\(\gamma\)). As already mentioned, the two time series were staggered by 14 days. This means that, for each  
Figure 3: KNN algorithm at work with data from the Bronx (confusion matrix).  
\(Y_{i}\), the following temporal relation held \(Y_{i}-14=X_{i}\). The time series of the new daily infections started on 4 March. The air pollution time series started consequently fourteen days before, precisely on 19 February. With regard to the end of the infections time series, we considered different alternatives, using 20-22 March as the final days. Consequently, the end of the PM\({}_{2.5}\) time series was set, respectively, on 6-8 March. Therefore, for each county, we subjected three different time series to our Granger procedure. Since we are considering 12 counties, the total number of tests we carried out was 36. For each of these 36 tests, Table 3 shows the corresponding \(p\)-values.  
As previously explained, the null hypothesis can only be rejected if the corresponding \(p\)-value, returned by the statistical test, is lower than 0.05. Only in that case, can we maintain that the PM\({}_{2.5}\) time series Granger-causes the time series of the COVID-19 infections.  
As shown in Table 4, out of 36 tests, the null hypothesis was rejected in as many as 33 cases, yielding 92% of experiments in favor of a hypothesis of an association between the PM\({}_{2.5}\) particulate and the spread of the COVID-19, in the various geographical areas relative to NYC. The few cases when the null hypothesis was not rejected are highlighted in red in Table 4.
## 8 Challenges and Directions for Future Research
### Deep Packet Inspection (DPI)  
DPI plays a crucial role in identification the of malicious activities within cloud environments. Its effectiveness, however, is often hindered by tactics employed by attackers such as payload encryption, protocol encapsulation, and obfuscation. These tactics can mask the presence of malicious commands and communications, including those that involve CPLS platforms used as C&C infrastructure. Furthermore, the complexity of CPLS traffic, combined with the large volume of encrypted data and the legitimate use of these platforms, adds another layer of challenge to the application of DPI. This complexity requires more sophisticated analysis and detection techniques, which can discern subtle patterns of abuse amidst the large volume of normal traffic. In light of these challenges, future research should explore innovative approaches to overcome these obstacles and distinguish between legitimate and malicious uses of CPLS platforms without violating user privacy.
## 5 Challenges, Open Research Problems, and Future of WBANs
### Mobility
#### 5.1.7 Bio-Inspired Sensors  
Some of the most exciting and challenging areas of applications of the future, WBANs are deeply related to areas like bioscience (life sciences), biotechnology and nanoscience (nanotechnology). We can mention for example: (i) point-of-care portable or in-body, easy-to-use, stand-alone systems to perform medical analysis out of clinical laboratories [104]; (ii) _in vivo_ controlled drug release systems, which can be ingested into a human body and which must act to deliver appropriate quantity of drugs or other caring means in a pre-determined, self-regulating or real time-controllable way [105, 106].  
In order to be adapted to these applications, the system should be autonomous in terms of avoiding as much as possible direct contribution to the human operator to their functions, which can be achieved by integrating various devices and circuits for processing data and in terms of power supply. In addition, a key factor for the development will be able to connect them to obtain information and manage them from remote.  
Hardware component for medical and biological applications has to consider bio-compatibility problems and the high specific properties of the interaction at the molecular and atomic level. This can be achieved through development of technologies, such as: (a) advanced synthetic or non-molecular receptors [107]; (b) innovative three-dimensional membranes for highly-molecular controlled release [108]; (c) bio-hybrid systems as bio-coated nanoparticles for a vehicle of drugs or transfection agents inside the cell [109]; (d) bio-inspired systems to simulate specific features of biological systems as adaptation and self-regulation [110]; (e) smart surfaces for organic transduction electronically specific mechanical or optical stimuli [111].  
It is clear that the high bio-nanoscience content involved in the design of new WBANs systems will fuel innovation in many fields as molecular sensing, new transduction techniques of physical signals, new smart, multi-functional materials.  
The conceivable evolution of sensor systems for WBANs could be depicted as developed along two phases. The first one is the application of conventional (micro) and non-conventional (nano) technology to the bio-physical world and, in particular, by patterning and disposing biological matter and by handling fluids in a microscopic, high-controlled way to separate and process small volumes of sample or selectively interact with single cells. The second phase will be dominated by the exploitation of bioscience for the creation of micro and nanoscale systems and materials with unique properties and functions. These systems will be characterized by properties of self-assembly, reversibility, adaptability, self-replication, possibility of interaction at the atomic scale [69, 112].
# Entropy-Based Anomaly Detection for Gaussian Mixture Modeling
## 1 Introduction
### Related Work  
There are several commonly used approaches to accommodate noise and outliers in GMMs, including the following: (i) adding one or more components to the mixture to represent noise and modifying the EM algorithm accordingly to estimate parameters [7; 8; 9]; (ii) relaxing the normality assumption of the components, while preserving elliptical contours of clusters, by using mixtures of heavy-tailed distributions, such as mixtures of \(t\) distributions [1], mixtures of power exponential distributions [10], and mixtures of contaminated normal distributions [11]; (iii) downweighting or completely discarding a proportion of the observations by trimming [12; 13; 14]. The last two approaches aim to make the statistical estimation of mixture models more robust, rather than specifically identifying anomalies. In contrast, our proposal seeks to improve the methodology of the first approach for the specific purpose of identifying anomalies.
# A Comparative Study of Weighting Methods for Local Reference Frame
## 3 Evaluation Methodology
### Datasets  
The selected 6 datasets are Retrieval [28], Laser Scanner [29], Kinect [13], Space Time [28], LiDAR Registration [30], and Kinect Registration [13]. These datasets are acquired by different devices, resulting in different data modalities. The Retrieval dataset is obtained by a Cyberware 3030 MS scanner. It contain 6 noise-free models. The Laser Scanner dataset include 5 models and 50 real scenes, forming 188 model-scene pairs. The real scenes are obtained by a Minolta Vivid 910 scanner. The Kinect dataset is acquired by a Microsoft Kinect sensor. It include 6 models and 16 real scenes, providing 43 model-scene pairs. The Space Time dataset is composed of 8 models and 15 real scenes. This dataset is created by the Space Time Stereo technique. It provide 24 model-scene pairs. The LiDAR Registration dataset is obtained by scanning 4 objects from different views using a Minolta Vivid 910 scanner, deriving 22, 16, 16, and 21 point clouds, respectively. The Kinect Registration dataset consists of 15, 16, 20, 13, 16, and 15 point clouds of 6 objects, which are also acquired by a Microsoft Kinect sensor. The Laser Scanner and LiDAR Registration datasets are high-quality, while the Kinect and Kinect Registration datasets are noisy and sparse. The Space Time dataset is contaminated by noise and outliers. The aim is to make our experiments cover various data modalities (i.e., LiDAR, Kinect and Space Time) and application scenarios (i.e., shape retrieval, 3D object recognition and 3D registration), and different kinds of nuisances, including noise, point density variation, clutter, occlusion, shot noise, and partial overlap (i.e., missing regions) are contained in these datasets. The detailed description of the 6 datasets is listed in Table 1, and the examples of some of them are shown in Figure 2. Note that we call 2 input shapes as model and scene for simplicity, though such appellation was originally used for object recognition.
## 5 Conclusions  
Molecular communication is an emerging field that combines nanotechnology, biotechnology, and communication technology, offering efficient and biocompatible communication on a nanoscale, in contrast with traditional electromagnetic methods. In this study, multiple access to concentration-encoded molecular communication using TH-CPSK was proposed and analyzed. From the simulation results, we confirmed that errors due to channel-dependent interactions are not negligible.  
Figure 8: Comparison of symbol error rate according to the number of hops and TN\(-\)RN distances.  
Figure 9: Symbol error rate according to the number of hops and TN-RN distance, with \(r=3\) cm.  
nel complexity tend to increase as the number of interfering users increases. In particular, the error increases rapidly at distances where the channel influence is noticeable, suggesting that it is effective to use fewer pulse positions at certain distances when the channel influence is relatively large. The decline in the achievable data rate as the number of users increases is consistent with this trend of increasing errors, with the data rate being particularly high at distances where the channel has less influence. Therefore, the number of hops must be selected considering the channel-delayed diffusion characteristics, and it is expected that it will be advantageous to apply this scheme in CEMC systems considering the distance between nanomachines. These insights contribute to the advancement of MC applications, marking a step toward more efficient and stable communication system design.  
Conceptualization, S.-Y.J.; methodology, P.S. and S.-Y.J.; software, S.-J.K.; validation, S.-J.K., P.S. and S.-Y.J.; formal analysis, S.-J.K.; investigation, S.-J.K., P.S. and S.-Y.J.; resources, S.-Y.J.; data curation, S.-J.K.; writing--original draft preparation, S.-J.K. and P.S.; writing--review and editing, P.S. and S.-Y.J.; visualization, P.S. and S.-Y.J.; supervision, P.S. and S.-Y.J.; project administration, S.-Y.J.; funding acquisition, P.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the 2023 Yeungnam University Research Grant.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.
# Big Data-Driven Deep Learning Ensembler for DDoS Attack Detection
## 3 Proposed System Model
### Dataset Description
#### 3.2.2 APA-DDoS Dataset  
The article also uses the APA-DDoS dataset [28], which consists of 151,201 entries. Every record contains different characteristics of network connections to aid in investigating and identifying DDoS attacks. The dataset has 23 unique characteristics that provide a comprehensive overview of network traffic. These elements include fundamental network characteristics, including source and destination IP addresses, source and destination TCP ports, and IP protocol numbers. In addition, the dataset provides information on the length of each frame and the presence of several TCP flags (SYN, reset, push, acknowledgement) and IP flags (More Fragments, Don't Fragment, Reserved Bit).  
The collection contains TCP sequence, acknowledgement numbers, and aggregated metrics such as the overall count of packets and bytes in each connection. It keeps a separate record of sent and received packets and bytes to differentiate traffic more effectively. Every record is marked to show if the connection is harmless or linked to a particular kind of DDoS assault, such as DDoS-ACK or DDoS-PSH-ACK. The dataset sample is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Duration** & **orig\_jp\_bytes** & **proto\_udp** & **resp\_bytes** & **proto\_kp** & **missed\_bytes** & **orig\_bytes** & **orig\_pkts** & **conn\_state\_SF** &... & **Label** \\ \hline
3 & 180 & 0 & 0 & 1 & 0 & 0 & 3 & 0 &... & POHPS \\ \hline
0 & 60 & 0 & 0 & 1 & 0 & 0 & 1 & 0 &... & C&C \\ \hline
0 & 40 & 1 & 0 & 0 & 0 & 0 & 1 & 0 &... & Benign \\ \hline
0 & 60 & 0 & 0 & 1 & 0 & 0 & 1 & 0 &... & DDoS \\ \hline
0 & 40 & 1 & 0 & 0 & 0 & 0 & 1 & 0 &... & OKIRU \\ \hline
3 & 180 & 0 & 0 & 1 & 0 & 0 & 3 & 0 &... & Tori \\ \hline
0 & 60 & 0 & 0 & 1 & 0 & 0 & 1 & 0 &... & C&C \\ \hline \hline \end{tabular}
\end{table}
Table 2: IOT-23 dataset (dataset sample).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**ip\_sec** & **tcp\_srcport** & **tcp\_flag\_syn** & **tcp\_flag\_ack** & **ip\_proto** & **Bytes** & **Packets** & **tcp\_dstport** & **frame\_len** &... & **Label** & **ip\_dst** \\ \hline
192.1681.1 & 2412 & 0 & 1 & 6 & 432 & 8 & 8000 & 54 &... & DDoS-ACK & 192.168.23.2 \\ \hline
192.1681.1 & 2413 & 0 & 1 & 6 & 540 & 10 & 8000 & 54 &...
# Data Quality Analysis and Improvement: A Case Study of a Bus Transportation System
## 4 Data-Cleaning Methods
### Cleaning Method for Missing Data
(2) The Average Relative Error (ARE) is a metric to measure the repairing accuracy of time dimension. The value of this metric is between 0 and 1. The smaller the value, the closer the repaired value is to the actual value. For example, a value of 0 means the repaired timestamp is equal to the original timestamp. ARE is given in Formula (3):  
\[\text{ARE}=\frac{\sum_{k=1}^{N}\Big{|}\ T_{k}^{repair}-T_{k}^{original}\Big{|} }{\sum_{k=1}^{N}T_{k}^{original}} \tag{3}\]  
where \(T_{k}^{original}\) is the actual arrival or departure timestamp at station \(S_{k}\), and \(T_{k}^{repair}\) is the corresponding repaired timestamp of the missing data. Note that the time is converted to seconds relative to a reference time of \"00:00:00\". (3) The correlation coefficient (R) is a metric to measure the relationship between a sequence of the repaired values and a sequence of the original values. The value of R is between 0 and 1, and the repairing accuracy is not affected by the number of missing stations if R is equal to 1. R is defined by Formulas (4)-(6):  
\[\text{R}=\frac{\sum_{k=1}^{N}\Big{(}\ T_{k}^{repair}-\overline{T^{repair}} \Big{)}\Big{(}T_{k}^{original}-\overline{T^{original}}\Big{)}}{\sqrt{\sum_{k=1 }^{N}\Big{(}\ T_{k}^{repair}-\overline{T^{repair}}\Big{)}^{2}}\ \sqrt{\sum_{k=1}^{N}\Big{(}\ T_{k}^{ original}-\overline{T^{original}}\Big{)}^{2}}} \tag{4}\]  
\[\overline{T^{repair}}=\frac{1}{N}\ \sum_{k=1}^{N}T_{k}^{repair} \tag{5}\]  
\[\overline{T^{original}}=\frac{1}{N}\ \sum_{k=1}^{N}T_{k}^{original} \tag{6}\]  
Similarly, where \(T_{k}^{original}\) is the actual arrival or departure timestamp at station \(S_{k}\), and \(T_{k}^{repair}\) is the timestamp that was repaired for the missing data of station \(S_{k}\).
# An Energy Efficient MAC Protocol for Wireless Passive Sensor Networks
## 4 Experiments and Analysis
### Network Model and Parameters Configuring
#### 4.1.2 Network Model Establishment and Parameters Configuring  
To quantitatively evaluate the performance of the IS-MAC protocol, the IS-MAC protocol is compared against the S-MAC protocol under a random network scenario. There are five nodes deployed in a square area of 1000 * 1000 m and there are no hidden terminal and exposed terminal problems. The communication range of each node is 250 m. The topology of nodes is a star topology. In a star topology, each node in the network is connected to a central node through the point-to-point mode and information is transmitted to the destination node by the central node. The central node performs centralized communication control strategy, so the central node is very complex. The central node is 200 m away from each node. The topology of the network is shown in Figure 3.  
In this experiment, the nodes are classified into three types: sending nodes, receiving nodes, and forwarding nodes (central nodes). When communication starts, sending nodes will send data flow to their fixed destination (receiving node) by forwarding nodes. Forwarding nodes can forward data from sending nodes as well as sending their own data. Events happen randomly within the field and nodes can generate one unit of data packet when one event happens within their sensing range. Table 2 lists the key network parameters used in this simulation.  
Figure 3: Topology of Nodes in the Network.  
Figure 2: (a) Energy Rate of S-MAC protocol and IS-MAC protocol under different numbers of nodes; (b) throughput of S-MAC protocol and IS-MAC protocol under different numbers of nodes.  
The size of per data packet used in these simulations was 512 bytes and the max packets of each node's interface queue was 50. In order to generate comparable results, all parameters were the same for all three protocols. The initial energy of each node was 1000 J in order to provide unlimited energy. Compared with classic WSNs, the energy provided for each node in unit time is reduced by half in order to simulate WPSNs.  
The duty cycle was set 30% because the proposed algorithm performs best when the duty cycle is 30%. When the duty cycle was changed from 30% to 80%, the S-MAC protocol and the IS-MAC protocol were compared in energy consumption and throughput. Energy consumption is the total energy consumption of all nodes during a time period in the network. The vertical ordinate represents energy consumption and throughput. The horizontal ordinate represents duty cycle. The results are shown in Figure 4.  
It is obvious that the gap of energy consumption and throughput between the IS-MAC protocol and the S-MAC protocol is biggest when duty cycle is 30%. Moreover, to simplify the experiments, the influence of routing traffic was negrlected and it was assumed that there was no obstacle and interference between any two nodes. The energy consumption of MAC protocol was considered to be the most significant indicator.
## 2 Architecture of the Proposed SDAMs
### Software Architecture
#### 2.2.3 DSP Process  
The processing flow of the DSP process is shown in Figure 8. After initialization, the DSP process waits for the call from the ARM process. According to the demand from the ARM process, the DSP  
Figure 7: The processing flow of the ARM-DSP interaction process.  
process may call the receiving subprocess or the transmitting subprocess and turn into the receiving or transmitting state.  
The receiving subprocess is a process of cyclically sampling ADC data and receiving ARM messages. The receiving loop ends only when the DSP process receives the receiving end command from the ARM process. The transmitting subprocess is not a loop; it ends the transmitting state after the data is transferred and returns to the main function. At the end of the receiving loop or the transmitting process, a corresponding process end message is sent to the ARM process.
### Related Works
We explored an approach to LL-ReTx and achieved some promising results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Solution** & **Method** & **Application** & **Platform** & **Optimization** \\ \hline Wharf [12] & FEC & DCN 1 & FPGA & Throughput \\ LinkGuardian [16] & LL-ReTx & DCN & P4 & FCT \\ SQR [17] & LL-ReTx & DCN & P4 & FCT \\ SatGuard [18] & LL-ReTx & LSN 2 & Simulation & FCT \\ UEC [19] & LL-ReTx & AI and HPC & Unknown & FCT \\ S-FRER [14] & FRER & FlexE & Simulation & Bandwidth \\ \hline \hline \end{tabular}  
* Data Center Network (DCN). 2 Low-Earth-Orbit (LEO) Satellite Network (LSN).  
\end{table} 
Table 1: Comparison of link-layer packet loss recovery strategies.