esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Types of Attacks, Exploited Vulnerabilities, and Countermeasures
#### 2.1.2 Exploited Vulnerabilities  
**Psychological factors** play a significant role in phishing susceptibility as attackers often exploit common cognitive biases and emotional triggers to manipulate users. Key psychological factors include trust and authority, where phishing emails often impersonate authoritative figures or trusted organizations to gain credibility [4]. Fear and urgency are also common tactics, as messages that invoke fear or a sense of urgency can prompt users to act quickly without carefully thinking and evaluating the email's legitimacy. Additionally,greed and curiosity can entice users to interact with such emails by promising financial gain, rewards, or other kind of profit.  
Some other factors that also impact susceptibility to phishing are **behavioral tendencies**. Routine and habit play a key role, as users that are used to clicking on links or opening attachments without scrutiny are more likely to fall for phishing emails. Furthermore, a lack of awareness on phishing techniques and the associated risks makes users more vulnerable [18].  
**Contextual elements** refer to the situational circumstances that affect a user's likelihood of falling for a phishing email. Time pressure is a significant factor, as users under time constraints may not take the time to thoroughly evaluate the legitimacy of an email. Distraction also plays a key role; multitasking or being in a distracting environment without paying the attention needed can reduce a user's ability to detect phishing cues. On the other hand, the type of device used, such as a mobile phone with a smaller screen, can make it harder to notice signs of phishing. Additionally, the organizational environment, including the culture and security practices of an organization, can influence user susceptibility [19].  
**Demographic factors** refer to the statistical characteristics of a population such as age, gender, and education level that can influence a user's susceptibility to phishing attacks. Research indicates that younger and older individuals may be more vulnerable compared to middle-aged adults. Younger users are more familiar with technology, and this might exhibit overconfidence in their ability to detect phishing, leading to riskier online behaviors. Conversely, older adults might lack the digital familiarity required to recognize phishing attempts, making them the main targets for attackers [20]. Gender differences also play a role; studies have shown that women are generally more cautious and may be less likely to fall for phishing email than men, who might take more risks online [21]. Additionally, users with higher levels of education are typically better at identifying phishing emails due to greater exposure to information about cybersecurity threats [22].
## 4 Discussion  
Contrary to expectations, a decrease in noise produced by the handpiece does not mean it lowers the anxiety level of patients, as louder music in other cases was preferred (Figure 6). The results of the interview showed that excessively loud music was not preferred, as it caused anxiety. However, patients preferred to listen to Sound Source C louder than Sound Source B because the notch filter used in sound masking technology removed some frequency bands of music and lowered the sound pressure by about 3.3 dB (Table 3: loudness). Total and average RMS amplitude shows the root mean square values of the selection. RMS values are based on the prevalence of specific amplitudes, often reflecting perceived loudness better than absolute or average amplitudes. Loudness shows the average amplitude, similar to RMS, but based on the LUFS scale, which is optimized for human perception (Table 3). It was confirmed through an interview with P1, such as _\"I picked a louder sound to hear it better because there were parts I couldn't hear well compared to the original song\"_.  
It was found that notch-filtered music could induce positive emotions in the patients. In particular, the fact that the average anxiety values of Sound Sources B and C in Figure 7 are the same means that the proposed sound masking technique maintains the psychological noise reduction effect of music without reducing it. Compared to the noise of the handpiece (Sound Source A), it was observed that when the patients listened to music (Sound Sources B and C), the number of patients who felt the emotion of sadness increased. It was revealed through an interview that this was not a negative effect of sound masking technology, but rather an emotional characteristic of each music genre. In this study, the patients were given an option to freely select their preferred music, and some participants listened to the ballade genre song and evaluated it according to the music (Appendix A). It was confirmed that the proposed technique did not induce negative emotions, such as the interview with P4: _\"I liked ballad music, so I felt sadness, but it was not an unpleasant feeling.\"_  
Like in previous studies [6; 7; 8], it was confirmed that although the patients could be induced to be emotionally stable when they listen to music, we found the volume or genre of music can have some influence on their emotions.
# Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target
## 3 Method
### Methodology
#### 3.2.1 Intrinsic Parameters Calibration of Camera  
[PERSON]'s method [13] finds the corner points correspondence of the chessboard to estimate the intrinsic parameters of the camera. However, it needs too many captures to get a good result. Our method makes some improvements by calibration with only one capture. As shown in Figure 2a, the camera can acquire the image of three chessboards during a single shot. Then, we need to separate the image into three districts roughly by hand, which is rather easy to do. Assuming that the equation of each chessboard plane is  
Figure 2: Sketch map of calibration target and camera-flash system: (**a**) extrinsic parameters calibration model; (**b**) camera is fixed on a precise rotation stage, and flash LiDAR is fixed on a precise motorized linear stage.  
\(z=0\), the corner point of each chessboard can be expressed as \([x^{\prime},y^{\prime},0]^{T}\). According to the coordinates' transformation and intrinsic parameter model in Section 3.1.1, the conversion between the chessboard coordinate and pixel coordinate can be established as follows:  
\[z\left[\begin{array}{c}u\\ v\\ 1\end{array}\right]=\mathbf{M}\left[\begin{array}{c}x\\ y\\ z\end{array}\right]=\mathbf{M}\left[\left[R_{1},R_{2},R_{3}\right]\left[ \begin{array}{c}x^{\prime}\\ y^{\prime}\\ 0\end{array}\right]+T\right]=\mathbf{M}[R_{1},R_{2},T]\left[\begin{array}{c} x^{\prime}\\ y^{\prime}\\ 1\end{array}\right], \tag{4}\]  
where \([x,y,z]^{T}\) and \([x^{\prime},y^{\prime},0]^{T}\) represent the coordinate of corner points in the camera coordinate system and chessboard coordinate system. We define the following:  
\[\mathbf{H}=\mathbf{M}[R_{1},R_{2},T]=[h_{1},h_{2},h_{3}], \tag{5}\]  
where \(\mathbf{H}\) is the homography matrix, figured out by four pairs of feature points according to Equation (4). The rotation vector \(R_{1}\) and \(R_{2}\) are orthogonal to each other and as normal vectors, they satisfy the following:  
\[\left\{\begin{array}{c}R_{1}{}^{T}R_{2}=\left(\mathbf{M}^{-1}h_{1}\right)^{ T}(\mathbf{M}^{-1}h_{2})=0\\ R_{1}{}^{T}R_{1}=\left(\mathbf{M}^{-1}h_{1}\right)^{T}(\mathbf{M}^{-1}h_{1})=R_ {2}{}^{T}R_{2}=\left(\mathbf{M}^{-1}h_{2}\right)^{T}(\mathbf{M}^{-1}h_{2})=1 \end{array}\right.^{,} \tag{6}\]  
Hence, one chessboard can provide two equations of intrinsic matrix \(\mathbf{M}\). The triangular pyramid target has three chessboards on its planes and can provide six equations of \(\mathbf{M}\), sufficient to work it out. Then, three pairs of rotation vectors \(R_{1}\) and \(R_{2}\) can be figured out according to Equation (6), which are used in Section 3.2.2.
## 3 Case Study: Purse Seiner in the Adriatic Sea
### LCA Models for Different Power Systems
#### 3.2.10. The LCA of an All-Electric Ship  
All-electric ships operate solely on battery. There are different types of batteries, but currently, the Li-ion battery stands out with the best performance, low emissions, and high energy density compared to other types [47]. Although fully electrified ships are attractive  
Figure 16: Processes included in the LCA of an ammonia-powered ship (ICE-A).  
Figure 15: Processes included in the LCA of an ammonia-powered ship (FC-A).  
alternatives due to zero emissions during their operation, their application highly depends on battery capacity and trip range [44].  
It is assumed that battery capacity needs to be sufficient to power the ship for the whole fishing trip. Therefore, battery capacity \(BC\) (kWh) corresponds to daily energy consumption, and it is calculated by the following equation:  
\[BC=1.5\cdot(P_{ME,ave}\cdot t_{ME}+P_{AE,ave}\cdot t_{AE}). \tag{17}\]  
Battery capacity is increased by 50% due to safety, battery degradation, and maintaining state of charge.  
The processes included in the LCA of an all-electric ship are battery manufacturing and the WTT phase of electricity (Figure 17), while TTW emissions are equal to zero.  
The M phase refers to the manufacturing of the Li-ion battery. By dividing the required \(BC\) and battery energy density, which for Li-ion battery with nickel manganese cobalt oxide chemistry is 0.22 kWh/kg [102], battery weight is calculated. The battery needs to be replaced after 9000 cycles of charging and discharging. The WTT phase of electricity refers to its generation, transmission, and distribution. The emissions released during electricity production depend on the electricity mix, i.e., sources of energy used. In this paper, the European electricity mix from the GREET 2022 database is used.
# An Efficient Parallelization of Microscopic Traffic Simulation
## 3 Results
### Assessment with S.F. Bay Area Scenario  
The full 24 h scenario of the S.F. Bay Area with 18 million trips and a rush hour scenario from 7:00 to 9:00 with 3 million trips were used to compare the ParSim simulator presented in this work with the LPSim parallel microsimlator implementation previously developed in [18]. The ParSim simulator completed the 24 h scenario in 1534.68 s or 25.58 min on a single GPU with 8 GB of memory, see Table 8. The same scenario was simulated with LPSim using 4x Nvidia A100 graphic cards with 40 GB of memory each. Comparing ParSim with LPSim, it can be concluded that ParSim finished the rush hour 7 times faster while occupying a much less memory on the GPU. These two values are related, as communication between multiple GPUs will slow down the overall simulation process.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Indicators**} & \multicolumn{2}{c}{**25\% Travel Demand**} & \multicolumn{2}{c}{**50\% Travel Demand**} \\ \cline{2-5}  & **Off-Peak Hour** & **Peak Hour** & **Off-Peak Hour** & **Peak Hour** \\ \hline Correlation Coefficient (r) & 0.87 ** & 0.88 ** & 0.89 ** & 0.90 ** \\ Mean Absolute Errors (MAE) & 12.95 & 14.11 & 22.26 & 25.44 \\ \hline \end{tabular} Note: ** Correlation is significant at the 0.01 level.  
\end{table}
Table 7: Pearson’s correlation coefficients (r) and MAEs between ParSim and SUMO simulations.  
Figure 9: Spatial distribution of simulated traffic volumes from ParSim (**a**) and SUMO (**b**).
## 4 Data and Results
### Technology Utilization and the Difficulty(ies) Faced during the Project  
#### 4.1.1 Summary of the Key Purposes of Local Construction Project Participators  
The results regarding difficulties were derived from the multiple-choice question about the challenges that the projects encountered, which motivated the companies to adopt BIM. Table 7 shows the primary purposes (difficulties) that our respondents (62 responders) mentioned in 33 cases, with reference to the frequency and the percentage of cases. It can be seen from Table 7 that difficulties in the construction schedule, unexpected safety problems, barriers in the data environment, and low visualization on a project occurred the most (over 70%).  
1.2 Summary of the Key Digital Technologies Related to Solving Difficulty(s) in a Local Construction Project  
The results for technology utilization were derived from the multiple-choice question about utilizing BIM to solve difficulties (their answers in the difficulties section). As Table 8 shows, the highlighted difficulties, including managing construction safety (No. 1), controlling construction quality (No. 1), and planning site layout (No. 3) resulted in a complex situation that required more technologies (see the requirement of technology in the 'Tech & Describe' column of Table 8) or needed a higher digitalization level for an EPC project participant. However, the need for techniques of estimating cost, designing special structures, and building multi-disciplinary technical design (tied for last place) showed minimal importance (the lowest requirement for specific techniques).
# High-Quality Data from Crowdsourcing towards the Creation of a Mexican Anti-Immigrant Speech Corpus
## 1 Introduction  
Nowadays, high-quality data are a valuable resource because modern artificial intelligence (AI) depends entirely on them. Numerous AI applications use machine learning methods that must be adjusted by processing large amounts of good-quality data. However, accessing high-quality data is one of the greatest challenges in using machine learning algorithms, in cases such as the study of online anti-immigrant speech in Mexico, a growing issue repeatedly pointed out by the Mexican government and international organizations [1, 2]. Despite having a vast amount of HS resources available from public data repositories, there are no Mexican anti-immigrant speech resources for natural language processing. The closest works that we have found are not related to Mexican Spanish anti-immigrant speech (see Section 1.2).  
In this scenario, crowdsourcing is an alternative when there are no data to use in specific machine learning tasks. However, although crowdsourcing is an interesting solution, it presents serious challenges. It is difficult to control the quality of the data collected and annotated by a large group of non-experts. The algorithm's accuracy can be significantly affected by human errors and spammers, which can lead to poor data quality. The latter can ultimately cause useless models for in-production technology. According to the HarvardBusiness Review, \"_poor data quality is enemy number one to the widespread, profitable use of machine learning_\" [3].  
In this article, we construct a high-quality training corpus using a mixed approach combining expert annotation and non-expert annotation obtained with crowdsourcing to study Mexican anti-immigrant speech on Twitter. We implement crowdsourcing through a custom annotation platform where we can control many aspects of the annotation, including a quality control mechanism and the continuous improvement of a classification model.  
Our main contributions are as follows: (1) the first labeled corpus with a total of 11,582 labeled texts around Mexican anti-immigrant speech (3326 classified as positive instances and 8256 classified as negative); (2) the proposal of a methodology to control the quality from non-expert annotators; (3) a set of refined guidelines crafted from the observed phenomena in real texts; (4) a better understanding of Mexican anti-immigrant speech; (5) a baseline machine learning model to automatically obtain high-volume data for further investigation in a specific geographical, temporal, and cultural context.  
We start by describing the context of the study as well as the principal objectives (Section 1.1) and the relevant related work (Section 1.2). We point out the research purpose and research questions in Section 2 Then, we detail the novel methodology to guarantee high-quality data despite the source of the data in Section 3. In Section 4, we present and discuss the study's key results. Lastly, we present some final remarks in Section 5 and present the conclusions and future work in Section 6.
# Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm
## 2 Background
### TinyML  
TinyML is strictly connected to EdgeML. The TinyML community aims at promoting the development of algorithms, software, and hardware suitable to run ML models on low-cost and resource-constrained IoT devices. Prospectively, by leveraging TinyML the analysis and interpretation of data will become viable on IoT devices and, if needed, replying in real time will also become viable. Both academia and industry agree that TinyML will play a fundamental role in the future in providing intelligent IoT solutions, with the least possible access to the Cloud. TinyML, as a service project ongoing at Ericsson Research, is just an example of this believe ([[https://www.ericsson.com/en/blog/2019/12/tinyml-as-a-service](https://www.ericsson.com/en/blog/2019/12/tinyml-as-a-service)]([https://www.ericsson.com/en/blog/2019/12/tinyml-as-a-service](https://www.ericsson.com/en/blog/2019/12/tinyml-as-a-service)) (accessed on 16 November 2024)).  
Figure 4 shows the TinyML workflow. It starts from data collection by sensors located in the environment; then, the ML model is built in the Cloud or in a data center, compressed to be deployed in MCUs where, eventually, the inference step will take place. The steps in the figure are detailed in [31], where the available open source software frameworks that support the advancement in ML research in MCUs are also listed. An introduction to TinyML, followed by a discussion of hardware and software tools supporting it, and state-of-art applications of TinyML may be found in ref. [32]. [PERSON] et al. [33] provide a holistic perspective on the challenging and rapidly evolving TinyML research field. Ref. [34] is another useful study to obtain an overview on how TinyML-based systems are built. It also focuses on the techniques suitable for reducing the complexity of the ML/DL algorithms so that they can be run on MCUs.
# Defect Detection and Error Source Tracing in Laser Marking of Silicon Wafers with Machine Learning
## 2 Materials and Methods
The scanned picture is set as 8 bits grayscale in 1920 \(\times\) 1080 pixels; this setting implies the brightness at each pixel could have 256 difference levels to reflect the marking status. Firstly, the digital number (DN) of pixels in the upper left and lower right of the image was identified and defined as the region of interest (ROI) by the Canny edge detection approach since it offers sharper and more continuous edges than other approaches [17]. The OpenCV python code was modified to perform this task with the lower threshold set as 95 and the higher threshold as 215 for this kind of imagery with a sharp edge. The locations of these two pixels define the orientation and location of each scanned image, and the resampled DN of pixels within the ROI and imagery coordinate axis are illustrated in Figure 3. The row and column number of the DN in the matrix that is larger than the background (dark color of wafer) is the criterion. We applied this approach to overcome the problem of mis-location or orientation while placing wafers for inspection. After resampling 800 \(\times\) 600 DN from the ROI, three checkpoints were employed to ensure the alignment accuracy. In this case, it is the last pixel of @ and the two dots of \(\mathbf{i}\) in the image. Only the imagery-passed alignment checking can be submitted for further handling steps. The first arrangement of this process is to scan an image of \"the marker sample\", which is an image of the qualified laser marking on the wafer. Then, the height of the microscope lens and on-site illumination remain unchanged throughout the routine, and wafers can be placed by hand or conveyor into the FOV of the microscope. The image of the inspected wafer was then compared to the marker sample by pixel overlapping, and the cause of the defect was identified in near-real time if it was not a qualified product. Figure 3: Definition of the image axis and resampled DN in the ROI. A square frame denotes the FOV of the microscope. Figure 2: Images of the argus microscope (**left**) and MR. CARVE M1 Laser Marking Machine (**right**).
# Underwater Degraded Image Restoration by Joint Evaluation and Polarization Partition Fusion
## 2 Underwater Degraded Image Restoration Methods
### Analysis and Extraction of Low and High Polarization Regions  
The traditional method is the global estimate of polarization; however, targets in the water often have complex polarization characteristics, and the use of the overall global estimation is likely to lead to poor restoration, and some areas are even degraded. In reality, the recovered target does not have only one polarization characteristic, so there will be defects when using the traditional method to recover the image. For the low polarization region, due to the similarity of its polarization and the backscattered light polarization, it is not possible to differentiate between the target and the background when recovering using the traditional method, which often results in the recovered image becoming darker, or even the same as the background, resulting in the low polarization region and the background being indistinguishable, as shown in Figure 3.  
There is a certain gap between the polarization of high polarization objects and low polarization regions in the target, for example, the metal region has high polarization value, while the plastic and other regions have low polarization value. According to this characteristic, the high and low polarization regions in the target are screened out by the polarization partition, and the polarization image is obtained by the Stokes vector, according to Equation (8). Each pixel point of this image represents the polarization value of the corresponding point, and according to different polarization degrees, a reasonable polarization degree partition is established to facilitate the successful extraction of the high and low polarization regions of the target. Figure 3b shows a pseudo-color map of polarization, which shows that in this pseudo-color map, it is easy to filter and distinguish the two according to the differences in the polarization values of different regions, which is convenient for the extraction of different regions.