esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A UAV Linear Model  
The transformation between the body-fixed frame and the inertial frame is achieved with a rotation matrix \(R\) for inverse mapping and a transitional matrix \(T\)[8; 9; 32; 51] of the form  
\[R=\begin{bmatrix}1&0&-s(\theta)\\ 0&c(\phi)&c(\theta)s(\phi)\\ 0&-sin(\phi)&cos(\phi)c(\theta)\end{bmatrix},\quad T=\begin{bmatrix}1&s(\phi)t (\theta)&c(\phi)t(\theta)\\ 0&c(\phi)&-s(\phi)\\ 0&s(\phi)/c(\theta)&c(\phi)/c(\theta)\end{bmatrix}. \tag{1}\]In this study, the heading angle of the UAV--yaw (\(\psi\))--is fixed and is equal to \(\psi=0^{\circ}\). For this reason, the defined rotation matrix form is \(R=R_{\phi}R_{\theta}\).  
Unlike the nonlinear model of the UAV, the definition of the linear model is represented with simpler equations. As a consequence, \(U\) shows the control input values of the system of Equation (A2):  
\[U=[F_{thrust}^{B}\ \ \tau_{\phi}\ \ \tau_{\theta}\ \ \tau_{\psi}].\] (A2)  
Equation (A3) is obtained from the equation corresponding to each \(U\) input value:  
\[U=\begin{bmatrix}F_{thrust}^{B}\\ \tau_{\phi}\\ \tau_{\phi}\\ \tau_{\phi}\\ \end{bmatrix}=\begin{bmatrix}b&b&b&b\\ -bl&-bl&bl&bl\\ -bl&bl&bl&-bl\\ -d&d&-d&d\\ \end{bmatrix}\begin{bmatrix}\Omega_{1}^{2}\\ \Omega_{2}^{2}\\ \Omega_{4}^{2}\\ \end{bmatrix}.\] (A3)  
\[\psi^{B}=I_{B}^{-1}\tau_{rotor}^{B}=\begin{bmatrix}\dfrac{1}{I_{x}}l\tau_{ \phi}/\sqrt{2}\\ \dfrac{1}{I_{y}}bl\tau_{\phi}/\sqrt{2}\\ \dfrac{1}{I_{z}}d\tau_{\phi}\\ \end{bmatrix}.\] (A4)  
In the above equations, \(\Omega_{i}\) denotes the angular velocity of the UAV propeller, \(d\) is the drag coefficient of rotors, and \(l\) is the distance between the rotor and CoM. When writing the inertia matrix (\(I_{B}\)) for each torque, they are defined as inertia tensors (\(I_{x}=0.679\times 10^{-2}\), \(I_{y}=0.679\times 10^{-2}\), \(I_{z}=1.313\times 10^{-2}\)[24; 34]).  
To linearise the equation above by using the Taylor approximation, the following equation is used:  
\[U_{i}=U_{i}^{0}+\dfrac{dU_{i}}{d\Omega_{i}}\Omega_{i}^{0}(\Omega-\Omega_{i}^{0 }).\] (A5)  
In the equation above, the subscript of \((\cdot)^{0}\) means the hovering mode condition of the UAV. Therefore, the UAV hovering operation point is given by the following equation:  
\[U^{0}=[mg\ \ 0\ \ 0\ \ 0]^{T},\] \[\Omega_{i}^{0}=\sqrt{\dfrac{mg}{4b}}.\] (A6)  
\[\begin{bmatrix}\ddot{x}\\ \ddot{y}\\ \ddot{z}\end{bmatrix}=\begin{bmatrix}g(s(\psi_{0})\phi+c(\psi_{0})\theta)\\ g(s(\psi_{0})\theta+c(\psi_{0})\phi)\\ F_{thrust}^{B}\\ \end{bmatrix}.\] (A7)
# SDNPS: A Load-Balanced Topic-Based Publish/Subscribe System in Software-Defined Networking
## 6 Conclusions  
In this paper, we attempt to construct a topic-based publish/subscribe system in an SDN environment.  
The improvements provided by SDNPS constitute the basis for a highly reliable event diffusion infrastructure able to efficiently balance the load of links and avoid imprudent forwarding for events. By encoding topics and embedding them into packet headers as IPV6 multicast addresses, we can directly perform filtering and forwarding operations on SDN switches without detours to brokers. As a result, this significantly reduces the end-to-end latency.  
The paper pays most of its attention to the inter-cluster routing mechanism incidentally with several different aspects of the event dissemination mechanism and proposes algorithms to construct topic-connected overlay networks, which in fact are a set of minimal cost Steiner trees induced by relevant publishers and subscribers. Particularly, our algorithms are interrelated with the topic tree. In this way, we can restrict the event dissemination scope to those pertinent nodes only, which greatly saves the bandwidth. Simultaneously, we also take the residual bandwidth into account. By maximizing the residual bandwidth as the optimization objective, we realized a load-balanced routing schema.  
For all that, there are still some rough edges. These can be ameliorated later. In the future, we are going to work on two aspects of the system. The first fundamental aspect that must be pursued is the real-time requirement for some events, e.g., real-time alarm event for the Internet of Things (IoT). End-to-end latency can be cut down further by manipulating and fully capitalizing on the programmable characteristic of SDNs. Second, we will extend our experiments and test the system with reference to [35].  
This research is supported by the National Natural Science Foundation of China under Grant Nos. 61372115, 61132001; the \"973\" program of the National Basic Research Program of China Grant No. 2012 CB315802; the National High-tech R & D Program of China (863 Program) under Grant No. 2013 AA102301.  
[PERSON] and [PERSON] conceived of the whole paper. [PERSON] and [PERSON] designed and performed the experiments. [PERSON] analyzed the data. [PERSON] wrote the paper. [PERSON] and [PERSON] revised the whole paper.  
The authors declare no conflict of interest.
# Effect of the Inter-Ring Delay Time on Rock Fragmentation: Field Tests at the Underground Mine
## 3 Theoretical Analysis of IRDT in Tunnel Excavation Blasting  
### Energy of Rock Fragmentation in Tunnel Excavation Blasting  
The energy of rock fragmentation in blasting engineering mainly comes from the energy consumption in drilling and blasting, but according to relevant studies, these two types of energy are not entirely used for rock fragmentation [15; 16]. It is estimated that only about 10% of the input energy is used for rock fracturing in rock drilling, while most of the input energy is wasted as heat or other forms of energy [17]. In blasting, the energy used for rock fracture and fragmentation ranges from 5% to 15% [18]. Therefore, it can be concluded that:  
\[E=\eta_{a}E_{a}+\eta_{b}E_{b} \tag{1}\]  
where \(E\) is the energy of rock fragmentation; \(E_{a}\) and \(E_{b}\) is the energy consumption in drilling and blasting, respectively; and \(\eta_{a}\) and \(\eta_{b}\) is the energy efficiency in drilling and blasting for rock fragmentation, respectively.  
As shown in Equation (1), if the energy efficiency in drilling \(\eta_{a}\) remains constant, to effectively reduce the blasting cost (number of drill holes and amount of explosives) while maintaining a certain degree of rock fragmentation, it is necessary to increase the energy efficiency in blasting \(\eta_{b}\). Additionally, if the energy efficiency in drilling \(\eta_{a}\), the energy consumption in drilling and blasting \(E_{a}\) and \(E_{b}\) remains constant, an increase in the energy efficiency in blasting \(\eta_{b}\), will result in a corresponding increase in the energy of rock fragmentation \(E\), getting better rock fragmentation results.  
Figure 2: Original blasting pattern.
# An Empirical Study on Software Defect Prediction Using CodeBERT Model
## 3 Motivation
### The Influence of Prediction Patterns in Software Defect Prediction  
The traditional software defect prediction pattern is to predict whether there are software defects in the source code. Specifically, given the features generated from machine learning models, software defect prediction aims to predict 0 for clean code and 1 for buggy code. During the process, a prediction model will not be aware that it performs defect prediction. Assume that we tell the model that it is performing software defect prediction tasks by some means; the model is expected to directly link the textual concept of defects to the semantic parts expressed by natural languages in the source code. Unlike the traditional prediction pattern that takes only code semantics into account, the new prediction pattern also considers textual semantics. For example, if a function is named \"Workaround\" and the function is unnaturally complex, a model could judge from textual semantics that a historical bug may not be fully solved and may reoccur in the future, and judge from code semantics that a function with very high complexity may be more likely to be buggy. It is worth noting that the new prediction patterns require that the model is a programming language model that understands both natural language and programming language, e.g., CodeBERT.
# Code Smell Detection Using Ensemble Machine Learning Algorithms
###### Abstract  
Code smells are the result of not following software engineering principles during software development, especially in the design and coding phase. It leads to low maintainability. To evaluate the quality of software and its maintainability, code smell detection can be helpful. Many machine learning algorithms are being used to detect code smells. In this study, we applied five ensemble machine learning and two deep learning algorithms to detect code smells. Four code smell datasets were analyzed: the Data class, the God class, the Feature-envy, and the Long-method datasets. In previous works, machine learning and stacking ensemble learning algorithms were applied to this dataset and the results found were acceptable, but there is scope of improvement. A class balancing technique (SMOTE) was applied to handle the class imbalance problem in the datasets. The Chi-square feature extraction technique was applied to select the more relevant features in each dataset. All five algorithms obtained the highest accuracy--100% for the Long-method dataset with the different selected sets of metrics, and the poorest accuracy, 91.45%, was achieved by the Max voting method for the Feature-envy dataset for the selected twelve sets of metrics.  
code smell; code smell detection; ensemble method; deep learning; Chi-square feature extraction technique; SMOTE class balancing technique +
Footnote †: journal:  
[PERSON] et al. [17] analyzed the impact of code smells upon software design by studying the number of modifications required, in future, within the software. They also checked whether classes affected with code smells need to be altered more frequently and need too much looking after. [PERSON], [PERSON] et al. [18] considered the influence of bad smells on class failure possibility in the future. Their research found that affectedsoftware modules that have code smells have a greater rate of failure in comparison with other modules. [PERSON] et al. [19] investigated the negative impact of the God class (GC) on utilization and discovered that removing the GC reduces the cyclic complexity of the software. [PERSON] et al. [20] worked on multi-label code smell datasets. [PERSON] et al. [21] carried out a systematic literature review to see how far there is reproducible research on code smell detection.  
This research focuses on detecting code smells using ensemble machine learning and deep learning approaches with the software metrics. Metrics have an important role in code smell detection by determining the source code's characteristics.
## 3 Method  
### Motivation  
The domain names in normal DNS traffic can be regarded as semantical sequences, while the AGDs generated by the pseudorandom method often exhibit a certain degree of randomness. A domain name can be defined as \"subdomain.2 LD.TLD\", meaning the top-level domain (TLD) [48] is managed by the root nameserver, and the 2 LD is the next-level label of TLD and is usually registered by a company or a group below a certain TLD. Therefore, the random string of an AGD usually appears in 2 LD, and common AGDs do not contain subdomain parts. However, since some domain names also support the registration of subdomains, such as \"github.io\", the random string of an AGD may also occur in the subdomain. Therefore, we mainly use the \"subdomain.2 LD\" as the main detection unit. In addition, some TLDs named country code TLDs (ccTLDs) often have subdomains such as \"com.cn\", \"net.uk\", \"gov.au\", and \"edu.co\". Such 2 LD.TLDs are not registered by a company or a group and are also regarded as TLDs. In this situation, these 2 LD.TLDs are considered TLDs, and the 3 rd level label of the domain name is considered 2 LD. The 2 LD.TLDs that can be regarded as TLDs are listed in [49].  
The AGDs generated by character-based and dictionary-based methods are quite different and features for detection must be designed accordingly. Our method mainly considers two types of features: meaningful word segmentation features and n-gram sequence features. The overall design of our method is shown in Figure 1. For meaningful word segmentation features, since the components of a dictionary-based AGD are English words, the component units (i.e., words) of a dictionary-based AGD can be restored by meaningful word segmentation. However, the number of words obtained is relatively small (usually less than 10), making it difficult to extract the word sequence features; therefore, after the context-sensitive word embedding feature extraction, the word features are summarized to construct word distribution features. In addition, based on the domain names and the segmented words, we additionally extract 11-dimensional statistical features, including word part-of-speech distribution, to improve the detection ability for dictionary-based AGDs.  
For n-gram sequence features, we use the n-gram algorithm with the LSTM model to characterize the morpheme correlation in domain names. The randomness of the character sequence is weak in dictionary-based AGDs and strong in character-based AGDs. Therefore, for sequence feature extraction of dictionary-based AGDs, we use the LSTM model of a 3-gram sequence that contains more characters in an n-gram. For sequence feature extraction of character-based AGDs, we use the LSTM model of a 1-gram sequence (i.e., character LSTM model) that contains fewer characters in an n-gram.  
Finally, we fuse the 4 kinds of obtained features based on the fully-connected layers and perform classification to realize an end-to-end detection method for the detection of both character-based and dictionary-based AGDs.
## 3 Results and Discussion
### Bioactivity of the Kiwi Peel Extract Obtained under Optimized UAE Conditions  
#### 3.6.1 Antioxidant Activity  
The kiwi peel extract obtained under the global UAE conditions (Table 3) was tested for its capacity to inhibit the formation of thiobarbituric acid reactive substances (TBARS) and the oxidative hemolysis (OxHLIA). The results were expressed in IC\({}_{50}\) values, which correspond to the extract concentration that provides 50% of antioxidant activity in the TBARS assay or required to protect 50% of the erythrocyte population from the free radicals generated by AAPH for a \(\Delta\)t of 60 min. Therefore, the lower the IC\({}_{50}\) values, the greater the antioxidant activity [14]. The extract showed IC\({}_{50}\) values of 0.37 \(\pm\) 0.02 mg/mL and 1.01 \(\pm\) 0.01 mg/mL for TBARS and OxHLIA, respectively, while the trolox presented a considerably higher activity (IC\({}_{50}\) values of 5.4 \(\pm\) 0.3 \(\upmu\)g/mL and 21.8 \(\pm\) 0.3 \(\upmu\)g/mL, respectively). However, trolox is a pure antioxidant compound whereas the extract may contain other constituents without bioactivity, which justify the observed differences.  
Kiwifruit has been described as having a high antioxidant capacity [8], but some studies on its peel have also been carried out. [PERSON] et al. [25] compared the bioactive potential of the pulp and peel of two kiwi varieties and highlight the green kiwi peel as the sample with greater antioxidant activity than the others, including the pulps. These results are in agreement with those of [PERSON] et al. [26] and [PERSON] et al. [27], who described higher values of antioxidant activity for kiwi peel than for pulp. The same conclusionwas reached by [PERSON] [28], who compared flours obtained from by-products (epicarp and bagasse) of two kiwi varieties, which observed a greater antioxidant activity in the flours of the peel than in those of bagasse. The difference in the IC\({}_{50}\) values described in these studies can be justified by the different kiwi varieties or by the different extraction methodologies and solvents applied, as well as by the treatment performed to obtain the plant sample. Considering that the extract evaluated in the present work was obtained under optimized conditions that aimed at maximizing the flavonoids content, a better performance was expected in the in vitro assays.
# Multi-Agent-Supported Tracking Based on Hidden Markov Model with Weighted Entropy
## 4 Experiment  
### Setting  
#### 4.1.1 Datasets  
Experimental evaluations were conducted on the MOT17 [17] and MOT20 [18] benchmark datasets under the \"private detection\" protocol. MOT17, a well-established MOT benchmark, comprises 7 training sequences with 5316 frames and 7 testing sequences with 5919 frames, and is widely used. MOT20, specifically designed for densely crowded environments, includes 4 training sequences with 8931 frames and 4 testing sequences with 4479 frames, providing a rigorous challenge for tracking in highly occluded and cluttered scenes.
## 4 Protocol Constraints
### Length of the Beacon Interval
On the one hand, the beacon interval should be set to a value close to the shortest message period (longest beacon interval), enhancing the network lifetime due to the reduction of energy consumption [42]. Considering this configuration mode, messages streams will have longer end-to-end communication delays (Figure 6). On the other hand, the beacon interval could be set to a value close to the sum of superframe durations, considering the shortest beacon interval that still satisfies the protocol constraint (Figure 7). As a consequence, message streams would have shorter end-to-end communication delays, but with higher energy consumption of nodes. Figure 6: Longest Beacon interval.
# Length-Bounded Hybrid CPU/GPU Pattern Matching Algorithm for Deep Packet Inspection
## 4 Experiments
### Experimental Setup  
The prefilter and full pattern matching algorithms were implemented respectively based on the platform of Open Multi-Processing (OpenMP) [31] and NVIDIA's (Nvidia Corporation, Santa Clara, CA, USA) Compute Unified Device Architecture (CUDA) [32; 33; 34; 35] programmed with multithreading for parallel processing. The hardware for this experiment is shown in Table 1. The CPU and GPU used here were respectively Intel Core i7-3770 (Intel Corporation, Santa Clara, CA, USA) with four processor cores and NVIDIA GeForce GTX680 (NVIDIA Corporation, Santa Clara, CA, USA) with 1536 processor cores. For the parameters, the number of blocks and the number of threads per block were, respectively, 128 and 64 for the CUDA setting. In addition, the sizes of full matching buffer and pre-filter buffer were, respectively, 256 MB and 1 MB for the configuration.  
We took the Snort rules 2008 [19], from which we extracted and generated the pattern set for the experiment. The pattern lengths range from 1 to 208 bytes with an average of 13.8 bytes. The lookup tables for prefilter and full pattern matching algorithms were generated with the generated pattern set and loaded to our system. A real packet trace was used as the input of incoming packets, which is the web traffic extracted from the portal websites of Google, Amazon and Yahoo. Table 2 lists the payload length statistics of the used packet trace, showing that this trace consists of various-length packets.  
When the system started, those packets from the trace were loaded, allocated and inspected. The execution times of LHPMA and other methods being compared were measured and recorded by the system during the experiment; finally, the throughput results were calculated. In addition, the system also recorded other information such as the number of total incoming packets, full-matched packets and their lengths, in order to observe the distribution and relationship between them.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Device** & **Specification** \\ \hline \multirow{3}{*}{CPU} & Intel Core i7-3770 (3.40 GHz) \\  & Number of cores: 4 \\  & Host memory: 8 GB DDR3 \\ \hline \multirow{3}{*}{GPU} & NVIDIA GeForce GTX680 (1058 MHz) \\  & Number of cores: 1536 \\ \cline{1-1}  & Device memory: 2 GB GDDR5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hardware specification in the experiments.
## 3 Results and Discussion
### Scenario 1 (S1)
#### 3.1.4 NOx-N  
In the calculation of NOx-N environmental capacity, after the implementation of scheme B (HZ1), the EC reduced by 3.84% (Table 7). This impact was not much different from the change of COD environmental capacity (1.48%), and the impact was small. The implementation of schemes C, D, E and F had a great impact on the change of NOx-N environmental capacity, all of which exceeded 18%. Different from COD and NH4-N environmental capacity, the first two pollutants were the most sensitive to scheme D, and the EC changed the most, while NOx-N was most sensitive to scheme E, with a change of 25.38%.  
The feasibility assessment results of NOx-N environmental capacity are shown in Table 6. The results indicated that the evaluation index R3 of scheme B was 10, so scheme B was feasible; the evaluation index R3 of schemes C, D, E and F was 0, so schemes C, D, E and F were not feasible, that is, working conditions HZ2, HZ3, HZ4 and HZ5 were prohibited.
# Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis
## 4 Dataset Description and Properties
### Data Pre-Processing  
Data pre-processing enhances efficiency and minimizes prediction errors in software cost estimation. It is imperative to meticulously choose machine learning methods and datasets based on their respective characteristics [33]. The data pre-processing has been performed on the UNSW_NB15 dataset by combining data from all four CSV files. The imbalanced dataset has more categorical data points than the attack category. The training dataset has 83.77% of class 0 and 12.66% of class 1. The null values from the attributes ct_flw_http_mthd, is_ftp_login, attack_cat have been removed for this study to attain further accuracies. Hence, the dataset has a total of 9 columns with datatype object: namely,'state','service', 'ct_ftp_cmd', 'attack_cat','scip','sport', 'dstip', 'dsport' and 'proto'. According to [17], the 'ct_ftp_cmd' column is supposed to be of numeric type. However, upon closer examination, it was found that this column contained blank entries which have been replaced by '0' to aid the learning process and the data type was changed from object to int. 'is_ftp_login' is a binary attribute having values except 1 and 0 are replaced by 0. The 'attack_cat' attribute has been found with few non-unique values like 'backdoors' and is replaced by the correct value 'backdoor'.