esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of an Autonomous Underwater Helicopter with High Maneuverability
## 3 Dynamic Analysis of the AUH
### Dynamic Model of the AUH  
To describe the motion conveniently, the coordinate system is established as shown in Figure 2. According to the momentum theorem, the linear motion description of AUH can easily be described as:  
\[\mathbf{F}=\frac{d\mathbf{H}}{dt}=\frac{d(m\mathbf{V}_{GE})}{dt} \tag{1}\]  
where \(\mathbf{F}\) is the resultant external force vector. \(\mathbf{H}\) is the momentum of AUH. \(\mathbf{V}_{GE}\) is the velocity of AUH relative to fixed coordinate system E-xyz. Through the transformation between coordinate systems, the equation of motion in moving coordinate system o-pqr can be described as:  
\[\mathbf{F}=m\frac{d(\mathbf{V}+\mathbf{\omega}\times\mathbf{R}_{Go})}{dt}=m\left[ \frac{\partial\mathbf{V}}{\partial t}+\mathbf{\omega}\times\mathbf{V}+\frac{ \partial\mathbf{\omega}}{\partial t}\times\mathbf{R}_{Go}+\mathbf{\omega}\times(\bm {\omega}\times\mathbf{R}_{Go})\right] \tag{2}\]  
Figure 1: AUH’s main working pattern and the differences between AUH and other underwater vehicles.  
According to [PERSON]'s second law, the AUH rotational motion is described as:  
\[\mathbf{T}=\frac{d\mathbf{L}}{dt}=\mathbf{J}\frac{\partial\mathbf{\omega}}{ \partial t}+\mathbf{\omega}\times(\mathbf{J}\mathbf{\omega})+\mathbf{R}_{ \text{{G}o}}\times m\frac{\partial\mathbf{V}_{\text{{G}o}}}{\partial t}+ \mathbf{R}_{\text{{G}o}}\times(\mathbf{\omega}\times m\mathbf{V}_{\text{{G}o}}) \tag{3}\]  
where \(\mathbf{T}\) is the resultant external moment vector relative to the origin of the moving coordinate system. \(\mathbf{L}\) is the Momentum moment of AUH relative to fixed coordinate system E-pqr. \(\mathbf{R}_{\text{{G}o}}\) and \(\mathbf{V}_{\text{{G}o}}\) are distance and velocity of AUH's center of gravity relative to o-pqr, respectively. \(\mathbf{J}\) is the moment of inertia. In Equation (3), the first term represents the moment of inertia of rotating motion; the second term represents the moment of inertia of rotating unbalanced rotation of rotating axis; the latter is benign because the origin is not in the center of gravity; the third term is the moment of inertia of linear motion; and the fourth term is the moment of inertia of centrifugal motion.  
The complete description of AUH motion can be obtained by Equations (2) and (3). Next, we design the propulsion system by force analysis as simply as possible.
# Variational Mode Decomposition Based Time-Varying Force Identification of Stay Cables
## 3 Stay Cable Experiments
### Full-Scale Model Experiment
#### 3.2.1 Recognition Results for Condition 1  
For condition 1, the cable vibrated under the constant tensile force of 2170 kN for 115 s. The original signal and the signal after smoothing are shown in Figure 9. A fast Fourier analysis of the smoothed signal which shows the fundamental frequency information is shown in Figure 10. The frequency of the cable was identified by the peak value of the spectrum, and the fundamental frequency was calculated by the frequency difference method. Specifically, the average frequency of each calculated frequency difference was used to obtain the fundamental frequency of the cable. The fundamental frequency was calculated as 0.83 Hz. However, due to that the low-frequency components were not easy to capture, the multiplication frequency was considered. the low-frequency components were very dense, and it was not easy to select the first-order frequency. Therefore, the multiplication frequency was considered and the first-order frequency was determined by multiplying the average frequency of the intensive phase by the amplification factor, and then gradually select the frequency with prominent subsequent amplitude as the corresponding Frequency order. Since the third-order frequency was 2.49 Hz, the bandpass filtering with the filtering range [2.1 Hz, 2.8 Hz] was selected. The results of VMD decomposition are shown in Figure 11. After performing the Hilbert-Huang transform on IMF1, the time-varying cable forces are shown in Figure 12.  
\begin{table}
\begin{tabular}{l l} \hline
**Conditions** & **Description** \\ \hline
1 & F = 2170 kN in t \(\in\) [0 s, 115 s] \\
2 & F = 1635 kN in t \(\in\) [0 s, 115 s] F = 1903 kN in t \(\in\) [115 s, 230 s] \\
3 & F = 2170 kN in t \(\in\) [0 s, 115 s] F = 1635 kN in t \(\in\) [115 s, 230 s] F = 1903 kN in t \(\in\) [230 s, 290 s] \\ \hline \end{tabular}
\end{table}
Table 2: Designed conditions information of the full-scale experiment.  
Figure 8: Field layout of the stay cable in the full-scale model experiment.  
Figure 9: Condition 1 of the full-scale model experiment: original and smoothed cable vibration signal.
# Bol Looseness Detection Based on Piezoelectric Impedance Frequency Shift
## 4 The Experimental Results and Analysis
### The M16 Bolt Experimental Results  
The M16 bolt with the piezoelectric patch bonded on its head was first placed on the side bolt position, as shown in Figure 1, and the testing machine applied a load from 0 to the maximum 40 kN on the center bolt. Therefore, the side bolt endured a load of 0 to 20 kN. The sweep frequency range is from 3.92 MHz to 3.93 MHz, as shown in Figure 3. Figure 5 shows the conductance frequency changes of M16 bolt under different bolt preloads from 0 to 20 kN in three repeated experiments. It can be seen that the frequency, which can be considered a specified peak frequency of the coupling system of the piezoelectric patch and the bolt, decreases as the bolt preload increases. The frequency change is linear with the change of the bolt preload. Thus, by observing the peak frequency change, the bolt preload can be determined.  
Figure 4: M16 bolt under different axial force: (**a**) Conductance; and (**b**) Susceptance.  
Figure 5: Conductance frequency changes of M16 bolt under different forces.  
The specified peak frequencies in the susceptance signatures also have this tendency, as shown in Figure 6 for three repeated experiments. The specified peak frequencies of the imaginary part of admittance decrease as the bolt preloads increase.  
Then the M16 bolt with the piezoelectric patch on its head was place in the center bolt position as shown in Figure 1, and the maximum force applied by the testing machine was set to 35 kN which covered the bolt's rated preload range. The sweep frequency range was changed from 3.91 to 3.932 MHz. The specified peak frequency changes of both the real and imaginary parts of admittance for M16 bolt under different preloads are shown in Figure 7. From Figure 7, it can be seen that the specified peak frequency decreases as the bolt preload increases and the frequency change is still linear with the change of bolt preload. That means the bolt preload can be determined by the specified peak frequency shift, and this method can fulfill the full range measurement of the bolt preload.
## 4 Discussion  
The results of this research are limited and cannot be extrapolated to cover the structural dynamic performance of steel frames with connection haunch reinforcement because this research considered only four cases of frames with different joint haunch configurations. To further understand the dynamic performance aspects of typical steel frame subjected to blast loading, the following points are recommended to be undertaken:  
* In this research, the blast loadings were applied on the columns of a typical steel frame structure, whereas in actual structures, the blast pressure are also applied onto unmodeled items such as pipelines and equipment located on the structure. Further study is recommended to investigate the effect of blast loading on the pipelines and additional equipment attached to the structures, both with and without haunch reinforcement.
* The Gurson porous model has an important local effect in the beam-column connection. Therefore, further sensitivity study comparing the effects of using material constitutive law of [PERSON] versus Gurson model on material plasticity is suggested to investigate the stresses evolution in joint area. In addition to the material model aspect, a solid element is to be included in the study as element selection plays major impact in the analysis result as shown in the study
* Generally, the analysis results demonstrated an enhanced performance when the haunches with a size greater than the size recommended by AISC [14] was used. To investigate the optimum haunch size, it is recommended to carry out parametric study on haunch sizes and stiffener plate thicknesses. A parametric study on the beam and column sizes are also suggested to understand the effect of a combination of haunches and frame configurations.
* In the absence of actual blast loading data, it is also recommended that a parametric study on pressure time histories is carried out.
## 3 Results and Discussions
### In Situ Treatments  
Following the well plate experiments, three areas of Room 93 covered by phototrophic biofilms were treated in situ by spraying with liquorice and benzalkonium. Data obtained comparing the effects of the two biocides are reported in Figures 6 and 7.  
The photosynthetic yields (Figure 6), measured 5 min after the application of benzalkonium, were significantly lower than the control and the liquorice treated biofilms (\(p<0.05\)). However,  
Figure 6: In situ treatment of biofilms growing in Room 93 of the Domus Aurea, Rome. Three treatments (CTRL (black), BENZ (white), LIQ (grey)); two applications, the first on day 0 and the second on day 14. PAM fluorometer measurements of yield were made immediately before both treatment applications (0 and 14 day), 5 min after (+5 min), then on days 7 and 14 after the first application, and then on days 35 and 76 (n = 3).  
Figure 7: In situ Digital Microscope pictures of the treated surfaces at different time intervals acquired with Handheld Digital Microscope (USB) Dino-Lite Premier 1.3 MP (AnMo Electronics Corporation, Taiwan). The effect of LIQ and BENZ on the discolouration of the biofilms is evident.  
yield recovery in the biofilms treated with the liquorice extract was almost complete by day 14, when compared to the control (\(p<0.05\)), but after the second treatment yield remained 57% lower than the control by day 76. This suggests a possible chronic effect of the treatments or that a higher concentration of the active ingredients is needed to have a lasting effect.  
Images of the treated areas taken weekly until 21 days are shown in Figure 7.  
The biofilms collected 14 days after the first application were transferred to the lab and observed using light and stereo microscopes (Figure 8). As already observed with PAM measurements, both in situ and lab images showed that BENZ had a stronger effect on the photosynthetic cells, particularly evident in the filaments of _S. julianum_ that turned from green to white. The liquorice extract also had a visible biocidal effect, albeit at a lower level, where photosynthetic cells turned from green to light yellow, although this was possibly due to the staining by the liquorice extract itself on the carbonaceous sheath of _S. julianum_. While it was not possible to continuously follow the biofilms, it would be intriguing to know if the presence of some green filaments in the liquorice-treated area were a sign of recovery by some resilient cells that could support the PAM fluorimetry data.  
To date, there are still no in situ studies containing detailed biofilm monitoring after treatments on a daily basis. This remains an intriguing challenge for future studies that should have a great impact on effective protocol development for biocide application for conservation purposes.
# Incoherent Digital Holography: A Review
## 2 Principle of OSH  
### Setup of OSH  
Figure 1 illustrates the schematic of a typical OSH system. A laser of wavelength \(\lambda\) and angular frequency \(\omega\) is used as the source and the laser beam is separated by a polarizing beamsplitter (PBS). The transmitting beam is collimated by the beam expander BE1 and focused by the lens L1; the reflected beam from the PBS is collimated by the beam expander BE2. In the path of the reflected beam, a pupil, \(p(x,y)\), located in the front focal plane of lens L2, is inserted to modify the properties of the recorded hologram. The two beams are recombined by the beamsplitter (BS) and projected toward the object \(T(x,y,z)\) through lens L2. The object is located at a distance \(z\) away from the back focal plane of the lens. The interference pattern from the two beams is formed on the object. In the present case, we choose \(p(x,y)=1\), and we have the interference of a spherical wave and a plane wave, which is a FZP on the object. A frequency shift between the two beams is introduced by a frequency shifter, such as an electro-optic modulator (EOM), as shown in Figure 1. Therefore, the interference fringe  
in the object space oscillates at the modulation angular frequency \(\Omega\) of the EOM and thus is called a time-dependent Fresnel zone plate (TDFZP). The object is mounted on an \(x\)-\(y\) translational stage and is raster scanned by the TDFZP; the light scattered from the object is detected by a single-pixel detector, that is, a photodetector, PD1, as shown in the figure, to give a signal (Sig) as an input of the lock-in amplifier. Meanwhile, a reference signal is detected by photodetector PD2 at the non-scanning channel of the interferometer to give a reference signal as another signal to the lock-in amplifier. Finally, the outputs of the lock-in amplifier, which contain the amplitude and phase information of the scanned object, are digitized and arranged as a complex hologram in a computer.  
There are several various versions of OSH systems. By using three lasers with different wavelengths and three EOMs, a color-scanned hologram has been recorded in single scanning [49]. The EOM can be replaced by acousto-optic modulators (AOMs) to generate the frequency shift [50]. Homodyne scanning is also an alternative choice to record a scanned hologram without a frequency shifter [51]. However, in homodyne scanning holography, multiple scannings are required to remove the zeroth-order beam and twin image resulting from the hologram. The object-scanning scheme as illustrated in Figure 1 can be replaced by a beam-scanning scheme for high-speed applications [17]. In addition, the proposed dual-beam scanning, as shown in Figure 1, can be replaced by single-beam scanning, which is superior for the coherent mode of operation of OSH [52]. Finally, instead of using a lock-in amplifier for the amplitude and phase extraction of the signal, demodulation can be performed digitally by the spatial-temporal demodulation technique [29].
## 4 Analysis and Findings of Research Questions
### Research Question 2: What Are the Detection Mechanisms of APT Attacks on Mobile Phones?  
An APT is a sophisticated attack involving infecting a system and remaining there for a lengthy period to hack personal data. When APT assaults are launched against a dynamic and complex infrastructure, typical detection approaches can be extremely challenging to adopt [116]. Defending against an APT attack using only a single tool is impossible. Adopting the \"defense-in-depth\" approach establishes a system that can identify and stop an APT attack at every stage, irrespective of location or network layer.  
The correlation of events generated by these various protection methods is critical for defending an organization or entity against APT assaults. This research question pinpoints the mechanism to identify APT attacks on smartphones, as presented in Table 10. Notably, different cyber attack types target specific platforms, such as personal computers, the IoT, cloud computing [117], and smartphones. Various detection mechanisms to identify advanced persistent threat (APT) attacks have been explored. However, these studies have only partially solved this problem, as they often fail to detect all attack stages. In addition, some approaches have proven ineffective at efficiently detecting the attack in its early stages, resulting in substantial damage being inflicted before detection occurs. This is primarily attributed to APT attacks' stealthy, sophisticated, and evolving nature. Moreover, the attack's behavior is intentionally designed to resemble normal activities, employing a \"low and slow\" approach, which further complicates the detection process [118].  
As illustrated in Figure 10, APT defensive strategies have been classified into three broad categories: monitoring, detection, and mitigation. Each category or class can be classified further.
# Acoustic Emission Based Fault Detection of Substation Power Transformer
## 3 Methods
### Machine Learning Algorithms
#### 3.3.2 kNN  
k-nearest neighbor (kNN)--A nonparametric method used for classification and regression [28]. kNN is a classical and relatively simple classifier. It classifies an unknown sample x by assigning it the label most frequently represented among the k nearest samples, which means a decision can be made by taking a vote for the labeled k nearest neighbors.  
Usually only two parameters are needed in this method, they are Number of neighbors and Distance metric. In this work, we selected these parameters as follows:  
Number of neighbors: k = 5  
Distance metric: Minkowski  
The input of the kNN model is the 9 \(\times\) 39 feature vectors (Figure 2).
## 2 Materials and Methods
### Inclusion and Exclusion Criteria  
To ensure the validity and safety of our study on recreating the hunter-gatherer lifestyle, specific inclusion and exclusion criteria were established to select appropriate participants. These criteria aimed to capture a representative sample while mitigating potential confounding factors and risks.  
Inclusion Criteria:  
* Age: Male participants aged between 18-50 years, reflecting an age range likely to engage in hunting-gathering activities during ancestral times.
* Physical Fitness: Participants with a moderate-to-high level of physical fitness, able to withstand the demands of a 4-day walking event.
* Medical History: Absence of chronic diseases, cardiovascular issues, or any other medical condition that might compromise the safety or results of the study.
* Dietary Habits: No strict dietary restrictions or conditions like veganism, vegetarianism, or specific allergies.
* Willingness: Consent to potential caloric restriction and the physical demands of the 4-day walking event.
* Previous Experience: No prior engagement in prolonged fasting or extreme physical activities similar to the one under study within the last three months.  
Exclusion Criteria:
* Medical Concerns: Individuals with any medical condition or on medication that might interfere with metabolic rates or the body's physiological response to physical activity and caloric restriction.
* Recent Injuries: Any muscle, bone, or joint injury in the past six months.
* Dietary Conditions: Individuals on strict diets due to medical conditions or personal choices.
* Addictions: Active smokers or individuals with a history of drug or alcohol abuse.
* Psychological Constraints: Individuals with known psychological disorders or conditions that might influence stress responses or any other parameter being studied.
* Prior Engagements: Participation in any other related research study in the past three months.  
The inclusion and exclusion criteria aim to ensure that the selected participants are representative of a typical, healthy individual from ancestral times while also ensuring that the results are not confounded by external factors.
# Improving Water Efficiency in a Municipal Indoor Swimming-Pool Complex: A Case Study
## 3 Results
### Water Use Characterization
#### 3.1.3 Taps  
* Single lever tap (N = 15): these taps had an average flow of 17.38 L/min;
* Mixer (N = 10): these taps had an average flow of 3.33 L/min, which is considered highly efficient. However, this value could be misleading because we observed that users experienced difficulties in turning off the taps; often, after their use, they remained turned on, inducing large amounts of water wastage;
* Lever tap (N = 5): the average flow was 20.40 L/min;
* Medical tap (N = 3): the average flow was 8.90 L/min;
* Kitchen tap (N = 1): the average flow was 15.00 L/min.  
According to the survey, each user used the tap once for 3 minutes, and the kitchen tap was used six times per day for 5 minutes; considering the annual number of users, the average flow of each tap model, and the frequency of utilization, the current average water consumption is 1950.2 m\({}^{3}\). Therefore, a reduction to 758.4 m\({}^{3}\) (61.1%) is expected with tap replacement. The taps could be replaced by more efficient and similar taps with an average flow of 5 L/min, and of 8 L/min in the case of the kitchen tap. Since these taps supply cold and hot water, the application of calibrated reducers was not considered because the opening time (between 6 and 8 s) was not long enough for hot water to arrive at the tap, leading to successive actuations and, consequently, wasting of water. As a precaution, it is recommended that the medical taps, even those labeled in the inefficient category, are kept because the specifications cover only washbasins and kitchen taps. Furthermore, it is expected that their use will be low [29].
# Detecting Suicidal Ideations in Online Forums with Textual and Psycholinguistic Features
## 3 Materials and Methods
### Feature Extraction
#### 3.3.2 Linguistic Inquiry and Word Count  
We applied LIWC to extract psycholinguistic features from Reddit posts. LIWC-v.22 is a software tool that allows us to gain insight into a person's psychological state based on language use [33]. At the core of this tool is a dictionary of over 12,000 word stems, words, phrases, and select emoticons. The dictionary is divided into 118 categories, each intended to capture various psychosocial constructs. LIWC was chosen to generate psycholinguistic features due to its ability to capture the psychological properties of language (e.g., negative affect), making it particularly suitable for suicidal ideation detection. The effectiveness of this feature extraction method has been validated in prior research [12; 19], further supporting its use in this study.  
LIWC compares each term in the post against the list of dictionary words and calculates the percentage of total terms that match the specific category. For example, if LIWC analyzes a single Reddit post containing 100 words and finds that 5 of those words are related to sadness, the post scores 5% in the _emo_sad_ category. Most LIWC features are calculated as percentages, except for four summary features, including analytical thinking, clout, authenticity, and emotional tone. They are calculated using proprietary algorithms not disclosed in LIWC documentation. In our work, we used the LIWC-22 (Pennebaker Conglomerates Inc., Austin, TX, USA) version of the tool [34]. We extracted 108 features, as we excluded features related to punctuation and emoticons.