esa-sceva/satcom-chunk-collection huggingface dataset:
# SQL Injection Detection Based on Lightweight Multi-Head Self-Attention
## 3 The Proposed Approach
### Web System with SQL Injection Detection  
For web applications, the SQL is a powerful language for managing and manipulating database. SQL codes are commonly used for remotely add, delete, modify, and query data in a database in web servers. Nevertheless, web applications are also vulnerable to SQL injection attacks, which occur when an attacker sends malicious SQL codes to the databases of the applications through web forms or URL parameters. Some impacts of injection attacks include authentication bypass, compromised data integrity, compromised availability of data, and information disclosure. Figure 1 shows a simple example of SQL injection attacks, where attackers use tautology SQL injection to bypass the authentication. In this example, the administration account is hacked, and the entire database can be retrieved without a password. As shown in Figure 1, the administrator account can be accessed without the correct password (e.g., **root2024**). This is accomplished by injecting a malicious statement (e.g., **OR '1' = 'I'- - '**) after the statement specifying the administration account name (e.g., **username = 'administor)** in the SQL query from the hacker. The command for password check (e.g., **AND password= 'dummy123'**) would then be ignored for authentication. Consequently, even though the password in the hacker's SQL is not correct, the database can still be accessed.  
One way to solve this vulnerability issue is by the employment of a detection system. The system is able to detect and drop malicious SQL statements before they reach the database. A common approach for implementing a detection system is by the accommodation of a large neural network model in a cloud. However, when the system is deployed in a remote cloud, delivery of the SQL codes to the cloud for detection may incur long latency. Moreover, even for the local deployment of the detection system, a large neural  
Figure 1: A simple example of SQL injection. The authentication for database access is bypassed by tautology SQL injection in this example. The hacker is therefore able to retrieve the entire database without the correct password to the administration account.  
network model may impose high computation and storage burdens for the local servers. It is therefore desirable to employ a lightweight neural network model with high accuracy for SQL injection detection.  
Figure 2 shows an example for the deployment of the proposed model in the local server. In the example, the proposed model and its corresponding web server and database are accommodated locally in an edge device, which is a low-cost Personal Computer (PC) with only limited computation and/or storage resources. Because the network size of the proposed model is small, computation overhead for the inference operations would be low even for the low-cost PC. Furthermore, no delivery latency is incurred because the model and its protection targets are in the same edge device. Fast and accurate detection can then be achieved without the utilization of cloud services.
# Design of Multi-Mode Antenna Array for Use in Next-Generation Mobile Handsets
## 9 Conclusions  
The design and characteristics of a new MIMO smartphone antenna with a multi-mode operation is successfully investigated in this paper. The designed handset antenna contains eight modified PIFA elements deployed at four corners of the mainboard. The proposed design operates at 2.6, 3.6, and 5.8 GHz for sub-6 GHz 5G mobile terminals. It offers good characteristics in terms of bandwidth, isolation, and radiation patterns. In addition, quite good characteristics are observed in the presence of the user. Due to the tri-band and polarization diversity, the antenna can be considered for multi-mode future handset applications. Furthermore, due to the available space on the smartphone antenna system, a compact 28 GHz phased array is proposed to be integrated onto the 5G smartphone.  
Writing--original draft preparation, N.O.P., H.J.B., and R.A.A.-A.; resources, N.O.P., and R.A.A.-A.; For other cases, all authors have participated. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement H2020-MSCA-ITN-2016 SECRET-722424.  
The authors wish to express their thanks to the support provided by the innovation program under grant agreement H2020-MSCA-ITN-2016 SECRET-722424.  
The authors declare no conflict of interest.
# Phishing Attacks Survey: Types, Vectors, and Technical Approaches
## 5 Current Anti-Phishing Methodologies and Techniques
### Technical Anti-Phishing Techniques
#### 5.2.4 Machine Learning  
Since the detection of phishing emails, messages, and websites is mainly a matter of classification, most current research focuses on machine learning approaches. A range of different machine learning techniques have been researched and implemented including decision trees, neural networks, and support vector machines (SVM). These have been used for a variety of anti-phishing techniques from phishing email detection [75] to identifying discrepancies between the websites' structures, its HTTP transactions, and the site's supposed identity [76].  
Machine learning methods of phishing detection are much more versatile and able to detect changes in phishing sites that could cause other detection methods to bypass the site. Machine learning can provide the ability to prevent against even zero-day phishing attacks if provided with enough training data. This methodology, although powerful, is highly dependent on the size and quality of the training dataset and the fine tuning of hyperparameters to obtain optimal accuracy [77].
## 2 Materials and Methods
### Assembly of Clean Reads and Functional Annotation  
The quality parameters of raw and filtered read sets were checked for each sample using FastQC [28]. The resulting raw reads were filtered with SOAPnuke (v1.4.0) [29] to remove the sequencing adapter, low-quality base, and unknown (N) base. Trinity [30] and Tgicl [31] software programs were applied to assemble the clean reads, carry out clustering, eliminate redundant data, and obtain unique genes. Trinity was carried out under the default settings (25 K-mer). The completeness of the transcriptome assembly of garlic was assessed using BUSCO v3.0.2 [32]. Functional annotation of unigenes was performed by comparing genes with NT ([[http://ftp.ncbi.nlm.nih.gov/blast/db](http://ftp.ncbi.nlm.nih.gov/blast/db)]([http://ftp.ncbi.nlm.nih.gov/blast/db](http://ftp.ncbi.nlm.nih.gov/blast/db)) (accessed on 3 July 2019)), NR ([[http://ftp.ncbi.nlm.nih.gov/blast/db](http://ftp.ncbi.nlm.nih.gov/blast/db)]([http://ftp.ncbi.nlm.nih.gov/blast/db](http://ftp.ncbi.nlm.nih.gov/blast/db)) (accessed on 3 July 2019)), KOG [33], KEGG [34], and SwissProt ([[http://ftp.ebi.ac.uk/pub/databases/swissprot](http://ftp.ebi.ac.uk/pub/databases/swissprot)]([http://ftp.ebi.ac.uk/pub/databases/swissprot](http://ftp.ebi.ac.uk/pub/databases/swissprot)) (accessed on 3 July 2019)) databases using the software BLAST [35], whereas GO [36] annotation was conducted by Blast2 GO [37] software.
## 3 Results and Discussion
### Advantages of the Proposed Algorithm
Furthermore, it is necessary to investigate the hyper-parameters of shallow layer perceptron to show the existence of several local minima and saddle points [46]. Again, this problem can be resolved with the help of choosing the right kind of hyper-parameter settings or some heuristics. To summarise, the issue with BP and GD is that they are slowly converging and may get stuck in local minima and saddle points indefinitely. Furthermore, it is computationally very expensive (calculate derivatives) being its inherent disadvantages, which tie back to its above-discussed limitation to deploying on-the-edge equipment. Therefore, we have proposed a lightweight method with a minimum adjustments of parameters, and we can obtain better results given the above-specified limitations. * In addition, the computing resource requirement for the proposed approach is relatively less stringent as for manual involvement during the feature extraction process; only CPUs are sufficient to do the inferencing. In contrast, higher computing resources are needed in terms of training and inferring from deep learning experiments [47; 48; 49]. Training a deep neural network is very time-consuming. We need dedicated hardware (high-powered GPUs, high RAM, SSD, etc.) [50] to train the latest state-of-the-art image classification models in a day and on top of it in case undesirable results imagine retaining the model Imagine this issue with our attempt to create a robotic instrument using computing on the edge which can make real-time inference for pores and fractures with some empirical methods specific accuracy to the domain. We found that the accuracy levels drastically reduced the inference of the full RCNN and YOLO-based object detection model after we quantised [49] the model. Therefore, there is a limit on productionising deep learning models, whereas we found that our proposed model can easily make inferences on edge (IoT) hardware such as Jetson Nano and even Raspberry Pi.
## 2 Place- and Person-Based Accessibility Measures
### Limits of STA in Describing the Role of the Transport System

Table 1: Main place- and person-based accessibility measures and their key features. assume two people who both have one fixed activity during their day, departing and headed to the same locations simultaneously, and using the same transport system with the same performances. The former works full-time while the latter part-time. According to the _STA_ concept, the different time constraints of the two individuals would lead to an accessibility difference since the part-time worker has more occasions to engage in discretionary activities than the full-time worker. However, the performance of the transport system does not play a role in such accessibility differences. * **Unsuitability of the FOS to represent accessibility differences:** As stressed by [PERSON] et al. [40; 41], the choice of the accessibility measure may significantly influence the outcomes of the analysis. Therefore, it is crucial to deploy a measure that is as suitable as possible to discuss accessibility distribution. Specifically, the estimate should represent an optimisation factor for the observed individuals, i.e., a good they generally aim to increase [42]. This is the case, e.g., with income, which is one of the critical indicators for distributional analyses in socio-economic sciences [43]. _STA_ cannot be easily labelled as an optimisation factor since it is not straightforward to state that individuals aim to maximise the number of discretionary opportunities they could reach on a daily basis. For instance, a person could have a small FOS because (s)he has a tight schedule of fixed activities and no room to engage in discretionary ones. Nevertheless, (s)he could be not much interested in further activities. At the same time, the transport system could be efficient in allowing them to reach all the fixed activities with a reasonable effort [5]. Based on these limits, we introduce the so-called Space-Time Transport Performance measure (_STTP_) in order to complement _STA_ by isolating the role of the transport system in individual accessibility differences.
# WCET Analysis Based on Micro-Architecture Modeling for Embedded System Security
## 2 Related Work
Loop iteration upper limits include the maximum and minimum execution count of multi-exit loops, dependency count of inner loops in nested loops, and others constraints. Common methods for loop bound analysis are abstract interpretation [15] and symbolic execution [16]. Infeasible path constraints are used to exclude redundant control flow in the program that will never be reached, thereby improving the efficiency of the analysis. The detection of an Infeasible path is necessary to analyse reliable hard, critical real-time systems [17]. Ref. [18] proposed an abstract interpretation technique to analyze Infeasible paths in programs, and the technique requires manual intervention in test cases and cannot be fully automated yet. After obtaining the preliminary WCET target equation and a series of constraints from the control flow of the program, the execution time of each basic block along the longest path is calculated by modelling the micro-architecture of the processor. This is a difficult task because most modern processors use multi-level cache, dynamic branch predictor, pipeline, etc., to improve CPU throughput and the complex behavior and interaction of these units make the execution time very unpredictable [5; 19]. To analyse the influence of an instruction cache on program execution time, ref. [20] proposes a method based on IPET, and the basic idea is to estimate whether the cache access is hit or not by analyzing potential cache conflicts within the program and using Cache Conflict Graph (CCG) to model the cache line. Dynamic branch prediction used by modern processors is based on the History State (HS) branch prediction algorithm. The idea is to predict a branch based on the execution history. The implementation uses the Branch History Register (BHR) to save the address of the Branch History Table (BHT). When a branch instruction is executed, the branch prediction for this instruction is looked up and updated in BHT. Ref. [21] considers the impact of branch prediction on WCET analysis by adding HS nodes in CFG. Superscalar out-of-order CPUs can achieve higher performance than in-order CPUs, but it is difficult to guarantee the WCET of the software. Ref. [22] modeled basic blocks and performed an analysis of the five-stage pipeline, considering cache effects by using an execution graph, and avoided enumeration of all possible instruction execution times in the analysis by using intervals to represent the execution time of the instructions. Their analysis of pipelines can also be expressed in the form of linear constraints, meaning it can be easily integrated into IPET. Their methods have been adopted by the static WCET analysis tool Chronos.
## 5 Noise
### Voltage Noise  
Both the thermal electrical Johnson-Nyquist noise of the piezoelectric material acting as the capacitor's dielectric \(E_{0}(f)=\sqrt{4k_{\text{B}}T_{0}R_{0}}\) and the thermal mechanical noise of the micromechanical resonator \(E_{\text{m}}(f)=\sqrt{4k_{\text{B}}T_{0}R_{\text{m}}}\) can be accurately predicted [38; 47; 49] based on the sensor's electrical impedance. For the sensor readout system as depicted in Figure 3, the thermal voltage noise contributions related to the output of the differential input transimpedance amplifier are given by  
\[E_{\text{d0}}(f)=\frac{|T(f_{\text{res}})|}{R_{0}(f_{\text{res}})}\sqrt{4k_{\text{ B}}T_{0}R_{0}(f_{\text{res}})}=\frac{1}{\pi f_{\text{res}}C_{\text{f}}}\;\sqrt{ \frac{k_{\text{B}}T_{0}}{R_{0}(f_{\text{res}})}}=\text{const.} \tag{39}\]  
and  
\[E_{\text{dm}}(f)=|T(f_{\text{res}})\tilde{Y}_{\text{m}}(f)|\sqrt{4k_{\text{B}}T _{0}R_{\text{m}}}=\frac{|\tilde{G}(f)|}{\pi f_{\text{res}}C_{\text{f}}\;Q}\; \sqrt{\frac{k_{\text{B}}T_{0}}{R_{\text{m}}}}\
eq\text{const.} \tag{40}\]  
if, as above, \(T(f)\) and \(R_{0}(f)\) are assumed as constant in the vicinity of the resonance frequency, where \(k_{\text{B}}\approx 1.381\cdot 10^{-23}\,\text{J}/\text{K}\) is the Boltzmann constant and \(T_{0}=290\,\text{K}\) the room temperature [29]. As expected, the thermal mechanical noise \(E_{\text{dm}}\) of the resonator is weighted by the magnitude of the frequency response of the damped harmonic oscillator, whereas the thermal electrical noise of the piezoelectric material is independent of the frequency [49]. Both statistically independent voltage noise densities of the sensor add up to  
\[E_{\text{d}}(f)=\sqrt{E_{\text{d0}}^{2}(f)+E_{\text{dm}}^{2}(f)} \tag{41}\]  
and are depicted in Figure 6 for the exemplary sensor without any excitation signal (\(\tilde{V}_{\text{ex}}=0\)).
# An Improved PSO Algorithm and Its Application in GNSS Ambiguity Resolution
## 2 Materials and Methods
### IPSO-AR Method
#### 2.2.2 IPSO Algorithm  
Inertia weight is important for the performance of the PSO algorithm and balances the global exploration and local exploitation abilities of the swarm. In the early evolution stage, the value of the particle velocity is expected to be large to enhance global exploration ability. Meanwhile, given that the strong local exploration ability of the PSO algorithm is desired during the late evolution stage, the velocity value is expected to be small [35].  
In this study, we introduce the concept of population maturity [36] for the adaptive adjustment of inertia weight on the assumption that \(X_{id}=\left[\begin{array}{cc}x_{id}&p_{id}\end{array}\right]\). Population maturity can be described as  
\[\begin{array}{l}m_{k}=\frac{2}{n(n-1)}\sum\limits_{i=1}^{n-1}\sum\limits_{j= i+1}^{n}\left(1-\frac{1}{2N}\sum\limits_{d=1}^{2N}\left|x_{id}-x_{jd}\right| \right)\\ w=\frac{(1-m_{0})w_{1}w_{2}}{w_{2}(1-m_{k})+w_{1}(m_{k}-m_{0})}\end{array}, \tag{11}\]  
where \(w_{1}\) and \(w_{2}\) denote the adjustment range of inertia weight, and \(m_{0}\) is the initial population maturity. During the particle evolution stage, particle distribution becomes increasingly concentrated. This behavior implies that population maturity continuously increases, whereas the corresponding inertia weight continuously decreases to a certain value.  
Although the SPSO algorithm has fast convergence speed, it can easily and rapidly decrease population diversity. Thus, the SPSO algorithm is prone to prematurity and to falling into a local optimum. To avoid these defects, we divide the population into three groups, namely, optimal, suboptimal, and poor populations, in accordance with the fitness function value. These populations are expressed as \((S_{1},S_{2},S_{3})\), and their corresponding population size is \(s\). We construct a Gauss mutation [37] for the optimal population (\(S_{1}\)) during particle evolution.  
\[x_{id}^{k+1}=x_{id}^{k}+Gauss(0,1)\cdot x_{id}^{k}, \tag{12}\]  
where \(d=1,\ \ 2,\ \ \cdots\ N\) ; and \(Gauss(0,1)\) denotes the random number that follows Gauss distribution.  
We can calculate the average fitness value (\(F_{ave}\)) of the optimal population (\(S_{1}\)). The condition for the termination of the IPSO algorithm can be described as  
\[F_{\max}-F_{ave}\leq afv\_out, \tag{13}\]  
where \(F_{\max}\) denotes the individual fitness value of the optimal particle, and \(afv\_out\) denotes the iterative convergence threshold.
## 4 Results and Discussion  
### Plunge and Duell Stages  
Figure 5 shows the temperature distribution in both the plunge and dwell stages at the top surface and weld cross section. As there is no traverse movement, the temperature in the plunge and dwell stages was uniformly distributed, as demonstrated in the top and cross section views. As indicated previously, the tool dwells during the dwell stage to heat the tool and workpiece material. As a result, the peak temperature in the dwell stage is higher than that in the plunge stage. The temperature spreads over a wide area in the dwell stage. The peak temperature in the plunge stage is about 850 \({}^{\circ}\)C and lies under the tool shoulder, while the peak temperature during the dwell stage reaches 1120 \({}^{\circ}\)C and lies near the shoulder-pin intersection point.  
Figure 4: Velocity boundary conditions.  
Figure 5: Temperature distribution in \({}^{\circ}\)C at plunge and dwell stages. (**a**) Top view at plunge, (**b**) top view at dwell, (**c**) cross section view at plunge, and (**d**) cross section view at dwell.  
A uniform flash is formed at the external edges of the joint. It is obvious from Figure 5c,d that the flash in the dwell stage is thicker due to the higher peak temperature and plastic deformation. [PERSON] et al. [46] reported that increasing the welding peak temperature resulted in flash formation. In the same way, [PERSON] et al. [47] showed that the higher heat generated under the tool shoulder softens the material and expels it out by the tool pressure in the form of flash.  
Figure 6 presents the equivalent plastic strain, showing the top and cross section views, in the plunge and dwell stages. As exhibited in Figure 6c,d, the plastic strain distribution in both stages is symmetrical around the weld center. However, the equivalent plastic strain in the dwell stage is higher. The higher equivalent plastic strain value can be found near the shoulder-pin intersection point, as in the temperature distribution, in both stages; it decreases toward the tool shoulder and pin tip. This may be attributed to the severe plastic deformation near the shoulder-pin intersection point, caused by both the tool pin and shoulder.
## 5 Discussion  
Consolidation of our findings and analysis enabled the iNEIS(tm) framework to be developed as shown in Figure 3. The findings reveal that the responses provided by the teachers during the interview were similar to the comments that were written by other respondents who participated in the questionnaire. This created difficulty for the users (especially teachers) as it created double the amount of work for them, which might have affected their time for teaching. However, the respondents also believed that the system did have potential to be good and beneficial if only adequate necessity for its development was provided. Otherwise, the system would not work effectively and would increase the workload for the teachers as a result. This is in line with the authors of [6,7], who believe that MIS can provide administrators and teachers with the information required for informed planning, policy making, evaluation and workload reduction. It is claimed that MIS have changed school management in the areas of leadership, decision-making, workload, human resources management, communication, responsibility and planning.  
Nevertheless, the main issue with the implementation of iNEIS(tm) was people's attitudes towards the system as most of them were reluctant to change. Some of the respondents from the questionnaire also mentioned that most users were very \"closed-minded\" about the implementation of the system. Meanwhile, according to the participants from the interview, some of the users were reluctant because some of them were computer illiterate. Perhaps, if more proper training was carried out, this would help the users to understand better how to use the system and thus more positive feedback from teachers could be obtained. This can be reflected by the theoretical grounding for TAM mentioned in the literature review. As mentioned in the literature review, TAM proposed two important determinants to analyze the factors which caused individuals to accept or reject information technology, namely perceived usefulness and perceived ease of use. The definition of \"perceived usefulness\" is the degree to which a person believes that using a particular system would enhance his/her job. This determinant may be ambiguous for this particular research. It seems that there were not many participants who considered that the use of the system could enhance his or her job performance, but they rather clarified that the system would usually create more work for them. However, \"perceived ease of use\" is the degree to which a person believes that using a particular system would be free of effort. This may be true considering that most participants in this research believed that the introduction of iNEIS(tm) could minimize any issues when it came to completing certain tasks--they considered that the system could be useful as they believed that by using it, workload could be minimized. However, this did not occur in reality, as many issues arose once it was implemented. For example, the system became unstable and data were not synchronized, which made many users feel reluctant to use the system.  
Figure 3: E-iNEIS(tm) framework.