esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Background of Sensing Technique for Developing Thermal Models in Office Buildings  
Besides different methods of thermal models, the development of an advanced sensing technique has also promoted the development of adaptive thermal comfort. The current sensing technique used for thermal comfort inference can be mainly divided into two categories--wearable sensing devices and contactless sensing devices. For wearable devices, wrist band has been the most popular one [11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. Moreover, [PERSON] et al. proposed a wearable infrared eyeglass frame to measure skin temperature at different points of the skin, including nose, front face, back of ear and cheek bone so as to infer thermal preferences [18].  
Even if wearable devices can directly measure variables like skin temperature to indicate thermal comfort, the major disadvantage is intrusiveness. Therefore, contactless measurement methods have drawn much more attention nowadays, especially infrared (IR) thermography. IR thermography utilizes the emitted infrared radiation to measure surface temperature of an object. In order to measure surface temperature with IR thermography correctly, emissivity of the object has to be known since most of actual objects are grey bodies. Nowadays, IR thermography has been widespread and applied into various functions [21], such as building diagnostics [22], fault detection as well as thermal comfort analysis [23; 24; 25; 26]. [PERSON] and [PERSON] [24] have used IR cameras to dynamically detect and predict thermal comfort. They classified thermal preferences based on skin temperature of forehead, cheek, jaws, upper neck, lower neck, palm core, palm and back of hand, and found that the face outperformed other body regions. [PERSON] et al. [25] utilized infrared imaging to measure skin temperature and control the indoor environment with self-learning algorithms. As a result, 98% of the occupants' feedback demonstrated the control system was able to achieve satisfactory thermal environments. Additionally, an empirical study utilized an infrared (IR) sensor called Lepton to estimate occupant thermal comfort level by measuring skin temperature measured from different face regions. The results have shown that ears, nose and cheeks are most indicative to thermal comfort [26].  
To summarize, contactless sensing technique can solve the issue of intrusiveness while maintain high performance of thermal comfort inference. Moreover, the adaptive thermal comfort is more realistic to the actual indoor environment than static thermal comfort and the performance can be enhanced with machine learning algorithms. Therefore, developing adaptive thermal models with contactless sensing techniques have become an interesting research area recently.
## 6 Visual Data Transmission in WVSNs
Second, network coding is adopted to split the captured video stream into multiple sub-streams via multiple routes to the sink nodes. A general energy model is proposed to capture these two types (i.e., video coding and multi-path routing) to maximize the network lifetime and in the same time to approximately reconstruct the visual field at the sink nodes. In Table 5, we summarize the existing works on visual data transmission in WVSNs. Here we observe that none of the existing works address the delay, visual data quality and power consumption at the same time. In general, smaller transmission power could save battery power, but it also sacrifices the visual data quality at the receiver. End-to-end delay composes of queuing delay, node processing delay and retransmission delay at the MAC layer. Node processing delay in WVSNs comes mostly from the combined FoV calculation. Hidden node problem in the wireless network introduces additional retransmission power loss and delays due to the collisions at the aggregated nodes. This makes the delay-constrained routing algorithms in WVSNs more challenging than in WSNs or other wireless networks. Tradeoff between these three criteria in making the routing decision poses interesting questions for researchers. Figure 3: Network coding in WVSN.
## 1 Introduction  
With years of data traffic exponential growth in wireless networks, energy costs and their contribution to global carbon dioxide emission are emerging as major concerns, which is a severe problem for cellular networks [1]. To meet the traffic exponential growth, the network capacity improvement is constrained by the limited spectral resources and severe energy consumption. How to improve the spectral efficiency while reducing the network energy consumption becomes of high importance for the wireless networks. Therefore, the network Energy Efficiency (EE) that considers both spectral efficiency and energy consumption has been valued not only as an important network performance indicator for modern wireless networks, but also for the operational expenditure reduction and sustainable development [2]. Heterogeneous networks (HetNets) consisting of a conventional macro cell deployment and overlaying pico cells have been investigated in the 3 rd Generation Partnership Project (3 GPP) Long Term Evolution (LTE)-Advanced as a promising paradigm shift to support the deluge of data traffic with higher spectral efficiency and EE [3; 4; 5; 6]. With pico cells deployment in HetNets, wireless links to end users become shorter, which turns out to improve the link quality in terms of spectrum efficiency as well as the network EE [7]. It is estimated that HetNets will contribute 56-fold enhancement to the 1000-fold increase in traffic demand [8]. Consequently, HetNets is emerging as one of the core features for 5G cellular networks.
## 2 Optical Lattice Clock Based on \({}^{87}\)Sr
### Fiber Phase Noise Cancellation  
The 698-nm grating-stabilized diode laser is stabilized to an ultra-low-expansion (ULE) cavity, having a finesse of about 4 \(\times\) 10\({}^{5}\) with the Pound-Drever-Hall (PDH) technique. The length and orientation of the ULE cavity at 698 nm placed in vacuum are 10 cm and horizontal, respectively. The linewidth of the clock laser is about 1 Hz and the power is 5 mW. The clock laser beam is sent to the cold atoms for probing using a 5-m-long polarization-maintaining optical fiber with a transmission efficiency of about 80%. The fiber introduces a lot of phase noise, which translates into frequency noise and eventually a loss of spectral purity. To maintain the optical coherence of the clock laser source with the strontium cold atoms, we applied an efficient fiber-noise-cancellation (FNC) system. We introduce noise compensation directly at the input of the fiber with an acousto-optic modulator (AOM), where a part of the light comes from the reflected light that is inputted and reflected back from the output end of the fiber; the rest part of the light is used as a reference. It is possible to obtain a  
Figure 1: Energy level scheme of the \({}^{87}\)Sr atom.  
beat signal introduced by the fiber phase noise from the beat signal of the two parts but has a high frequency [21; 22; 23].  
The error signal at low frequency can be obtained directly by mixing the beat signal with a local reference oscillator. The error signal is then used to modulate a voltage-controlled-oscillator (VCO) that drives the AOM around 80 MHz.  
Figure 2a shows the frequency fluctuation of the beat signal with and without the FNC system. With the FNC active, the frequency fluctuation caused by the residual phase noise is reduced below 40 MHz and is sufficiently low for transferring a 1-Hz linewidth laser beam. Figure 2b shows the optical field spectral density of the beat signal with and without FNC, measured with a spectrum analyzer. Figure 2c shows the frequency instability caused by fiber noise when the FNC system is active, obtained by measuring directly the Allan deviation for our phase-locked system. Affected by white phase noise, the Allan deviation of the phase noise cancellation system follows a \(\tau^{-1}\) behavior.
# Analyzing the Economic Performance of a TLS-Based Structural Safety Diagnosis Process
## 3 Results
### ROI Evaluation
#### 3.5.2 Sensitivity and Further Considerations  
Sensitivity analysis of various factors is essential in investment decision making. First, since the initial investment cost varies according to market conditions or functional specifications, it was confirmed that, even when assuming an increase of up to 60% in equipment prices, the company-level ROI would decrease but was still likely to remain above 100%. Figure 2 shows the company-level ROI according to changes in equipment prices.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multicolumn{3}{c}{**Project Level**} & \multicolumn{1}{c}{**Company Level**} \\ \cline{2-5}  & **Project A** & **Project B** & **Project C** & **(A + B + C)** \\ \hline Total cost (_a_) & 13,432,793 & 121,942,416 & 105,459,050 & 240,834,259 \\ Service fee (_b_) & 30,000,000 & 285,000,000 & 120,000,000 & 435,000,000 \\ Revenue (_b \(-\)a_) & 16,567,207 & 163,057,584 & 14,540,950 & 194,165,741 \\ Investment & 120,000,000 & 120,000,000 & 120,000,000 & 120,000,000 \\ ROI (\%) & 13.81 & 135.88 & 12.12 & 161.80 \\ \hline \hline \end{tabular}
\end{table}
Table 8: ROI analysis at the project level and company level.  
Figure 2: Changes in ROI with increasing investment price appreciation.  
Furthermore, reduced service fees for projects could negatively affect ROI. Conversely, larger project scales lead to greater productivity improvements through adopting digital methods, thereby significantly increasing ROI. However, it is important to note a limitation--if only extremely small-scale diagnostic tasks are available, recovering the initial investment could become challenging. Additionally, as this study is based on only three cases, caution should be exercised in generalizing the results.  
Moreover, costs related to training and operational personnel cannot be overlooked in actual business environments. Although this study assumed supplier-provided complimentary training, cultivating internal expert personnel might require additional training expenses. Furthermore, practical factors, such as equipment depreciation and software license renewal costs, must also be considered. These expenses can potentially be distributed and reduced by simultaneously or sequentially performing multiple projects. Lastly, this research focused exclusively on three specific cases (Projects A, B, and C), but actual construction and engineering firms undertake projects of considerably greater diversity in scale and characteristics. Therefore, comprehensive evaluations incorporating various internal datasets and historical performances are recommended to arrive at more accurate and informed investment decisions.
# Experimental Study on the Impact of CO2 Treatment on Different Lithofacies in Shale Oil Reservoirs
## 4 Conclusions  
In this research, the effects of CO\({}_{2}\) treatment on different lithofacial types of shale have been systematically studied through core observation and XRF analysis, mineral composition analysis, micromorphology analysis and core soaking experiments. The conclusions are as follows:  
The shale in the Qingshankou Formation can be divided into three main lithofacial types: foliaceous shale, laminated feisic shale and laminated diamictic shale. These three main types of shale account for more than 90% of the Qingshankou formation and are the main target area in exploration and development thereof. There are significant differences in micromorphologies and pore structures between them. There are relatively more pores with some natural microfractures in foliaceous shale, a few micropores in laminated feisic shale and hardly any micropores in laminated diamictic shale.  
After CO\({}_{2}\) treatment, there are significant differences between the changes in micromorphologies and pore structures among the three main types of shale. For foliaceous shale, many new, small dissolution pores are generated, accompanied by the precipitation and migration of minerals. For laminated feisic shale, large-scale dissolution occurs and results in many large-sized pores (10-30 \(\upmu\)m in diameter), which improves the porosity and pore connectivity of the shale samples. For laminated diamic shale, the dissolution of biogenic calcium carbonate minerals results in many large dissolution pores and dissolution fractures, which greatly improve the porosity and pore connectivity of the shale samples.  
Under the effects of mineral composition and micromorphology, CO\({}_{2}\) treatment has both improving and damaging effects on porosity and permeability for foliaceous shale. When the target formation mainly consists of foliaceous shale, serious consideration should be given to CO\({}_{2}\) technology to prevent potential reservoir damage. Meanwhile, for laminated feisic shale and laminated diamictic shale, both porosity and permeability improve significantly after CO\({}_{2}\) treatment. When the target formation mainly consists of laminated shale, the application of CO\({}_{2}\) technology is recommended.  
Conceptualization, H.L. and J.T.; methodology, S.M. and Y.G.; investigation, D.L. and G.C.; writing--original draft preparation, J.T.; writing--review and editing, S.M.; supervision, H.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Central Program of Basic Science of the National Natural Science Foundation of China (No. 72088101), the National Key Research and Development Programof China (No. 2018 YFE0196000), and the Foundation Program for Directly Affiliated Institutions of CNPC (No. 2019D-500808).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Numerical Methods Comparison of Shock-Induced Separation on Transonic Axisymmetric Hump
## 2 Methodology
### DES Method and DDES Method  
DES is a type of method blending RANS and LES. Based on the DES97 method with one-equation S-A turbulence model proposed by [PERSON] [27], [PERSON] [28] and [PERSON] [29] modified the length contained in the two-equation SST turbulence model and developed the DES method with \(k-\omega\) SST model. The hybrid length \(l_{hyb}\) is defined as follows:  
\[l_{hyb}=l_{DES}=min(l_{SST},C_{DES}\Delta) \tag{3}\]  
where \(\Delta\) represents the sub-grid scale; it can be determined by:  
\[\Delta=\Delta_{max}=max(\Delta x,\Delta y,\Delta z,) \tag{4}\]  
The \(\Delta_{max}\) means the maximum scale of three directions of grid units. \(C_{DES}\) is defined as:  
\[C_{DES}=(1-F_{1})C_{DES}^{k-\varepsilon}+F_{1}C_{DES}^{k-\omega} \tag{5}\]  
\(C_{DES}^{k-\varepsilon}\) and \(C_{DES}^{k-\omega}\) are constants and, respectively, are equal to 0.61 and 0.78. The parameter \(l_{SST}\) in Formula (3) represents the length scale of RANS, as follows:  
\[l_{SST}=\frac{k^{1/2}}{(\beta^{*}\omega)} \tag{6}\]  
Changing \(l_{SST}\) to \(l_{hyb}\) and bringing it to Formula (6) and \(k\) equation, Formula (2) can be rewritten as DES form:  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho ujk)}{\partial x_{j}} = P-\frac{\rho k^{3/2}}{l_{hyb}}+\frac{\partial}{\partial x_{j}}((\mu+ \sigma_{k}\mu_{t})\frac{\partial k}{\partial x_{j}}) \tag{7}\]  
According to the above formulas, the specific form of \(k\) equation is determined by the scale of turbulence length.  
The hybrid length defined in the original DES method is overly simplistic; as a result, the LES mode can intrude into the boundary layer, reducing vortex viscosity and causing nonphysical phenomena such as grid-induced separation (GIS). To address this issue,Menter and [PERSON] [30] developed the Delayed DES (DDES) model based on the two-equation \(k-\omega\) SST model. The model optimized the hybrid length \(l_{hy\phi}\) as:  
\[l_{hyb}=l_{DDES}=min[l_{SST},\frac{C_{DES}\Delta_{max}}{1-F_{SST}}] \tag{8}\]  
The primary difference lies in the introduction of the blending formulation \(F_{SST}\), which significantly determines the transition between RANS and LES. It is easy to know, when \(F_{SST}\) is equal to 0, Formula (8) will change to the standard DES model (Formula (3)). In this paper, the \(F_{SST}\) is described as follows:  
\[\begin{split} F_{2}=tanh(arg_{2}^{2})\\ arg_{2}=max\Big{(}2\frac{\sqrt{k}}{\beta\omega d},\frac{500\omega }{d^{2}\omega}\Big{)}\end{split} \tag{9}\]
## 4 Discussion
### The Impact of pH on the Degradation of Pesticide Molecules  
Taking the discharge voltage of 20 kV in the Figure 4 below as an example, it can be seen that the pH value of the solution gradually decreases with increasing treatment time because the plasma generates ions such as NO\({}^{3-}\), NO\({}^{2-}\), and H\({}^{+}\) during the discharge process, causing the solution to become acidic [38]. [PERSON] et al. [39] found that the pH value of imidacoprid solution treated with nonthermal plasma gradually decreased during the degradation process, and [PERSON] [40] found that the pH value of abamectin solution decreased by 37.38% after treatment with nonthermal plasma for 240 s.  
Figure 4: pH changes in the solution during the plasma discharge process.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Molecular Weight** & **Molecular Formula** & **Chemical Structure** \\  & & **Depiction** & **XLogP3-AA** \\ \hline Avermectin & 873.1 & C\({}_{48}\)H\({}_{72}\)O\({}_{14}\) & 3.8 \\ Bifenazate & 300.35 & C\({}_{17}\)H\({}_{20}\)N\({}_{2}\)O\({}_{3}\) & 4.2 \\ Spiromesifen & 411.3 & C\({}_{21}\)H\({}_{24}\)C\({}_{2}\)O\({}_{4}\) & 0 \\ Etoaxazole & 359.4 & C\({}_{21}\)H\({}_{23}\)F\({}_{2}\)NO\({}_{2}\) & 5.4 \\ Lufenuron & 511.1 & C\({}_{17}\)H\({}_{8}\)C\({}_{2}\)F\({}_{8}\)N\({}_{2}\)O\({}_{3}\) & 6.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computed properties of pesticides [37].  
The degradation behavior of different types of pesticide molecules can be influenced by pH, which may be one of the factors causing different degradation rates of different pesticide molecules. The degradation rate of avermectin is different under different pH conditions. Under acidic conditions, the stability of avermectin is lower and the degradation rate is faster, leading to a more significant degradation effect. When the pH value is within the neutral range, the stability of avermectin is the highest, and the degradation rate is the slowest. Under alkaline conditions, the degradation rate of avermectin also increases. Bifenazate is relatively stable under neutral or weak acidic conditions but is prone to degradation under alkaline conditions, resulting in a slightly inferior degradation effect compared with avermectin. Spiromesifen is relatively stable under neutral and weak acidic conditions but is easily decomposed under alkaline conditions. The degradation of etoxazole is relatively less affected by pH value, but its degradation rate may be slowed down under acidic conditions. Therefore, the degradation effect on spiromesifen and etoxazole that is inferior to that on bifenazate. Lufenuron degrades faster under neutral or weak alkaline conditions but slower under acidic conditions, resulting in the poorest degradation effect among the four pesticides.
## 3 Results and Discussion
### Tools for Estimating the Volume of Spheroids Used for Multicellular Aggregates  
The tools for estimating the volume of _spheroids_ assume an approximate spherical symmetry of the multicellular _aggregate_ to be measured. Most of the time, maximum diameter \(M\) and its orthogonal diameter \(P\), or the average diameter \(D\) are estimated by using ellipsoid's (Equation (1)) [26] or sphere's (Equation (2)) [27] formula to approximate the mass volume:  
\[V_{ELLIPSOID}=\frac{\pi}{6}\cdot M\cdot P^{2}, \tag{1}\]  
\[V_{SPHERE}=\frac{\pi}{6}\cdot D^{3}\cdot. \tag{2}\]  
Recently, _ReViSP_ was introduced to estimate the volume just assuming a local circular symmetry of the _spheroid_ instead of a global spherical symmetry. However, none of these methods allow for accurate 3D representation and reliable volume data collection in the case of multicellular _aggregates_, where it is not possible to infer the 3D shape of the object. For instance, Figure 3f reports the 3D surface obtained by _ReViSP_ when analysing MA3, a multicellular _aggregate_ with irregular shape. It results in being quite different from the original surface of MA3, strongly elongated along the \(z\)-axis (Figure 2).  
In order to quantitatively compare the volume estimations of _ReViSP_ and _ReViMS_, we first computed a binary version of the maximum intensity projection of MA1, MA2 and MA3 with _Image_[13] and we reconstructed the volumes by _ReViSP_. Then, by computing AD%, we compared the volume instances obtained with _ReViMS_ and _ReViMS_ with Ground Truth. In particular, the new implemented _Chan-Vese model_ was used for segmenting the images with _ReViMS_. Figure 3g shows the 3D surface obtained by _ReViMS_ for MA3. Table 1 reports the volume measurements obtained using _ReViSP_ and _ReViMS_, together with Ground Truth volume and AD%. The average AD% for _ReViSP_ (i.e., 36,6) resulted 40 times higher than the average AD% for _ReViMS_ (i.e., 0,8). Furthermore, it is worth  
Figure 3: Segmentations and 3D surfaces. (**a**) apical (green) and equatorial (blue) planes of Multicellular Aggregate 1 (MA1); (**b**,**d**) original images coming from different \(z\)-planes of MA1. The blurriness of out-of-focus regions is strongly evident in (**b**), the image coming from the apical plane represented in green in (**a**); (**c**,**e**) contours of MA1 (reported in yellow and red), created by the same operator in different times. Very slight variations are visible between the yellow and red contours reported in (**e**), the image coming from the equatorial plane represented in blue in (**a**); (**f**–**h**) 3D surface of MA3, reconstructed by _ReViSP_, _ReViMS_, _MTK_, and _OpenSegSPIM_, respectively.  
noting that _ReViSP_ provided acceptable performance only for MA2 (i.e., 6,0), which is a multicellular _aggregate_ with spherical shape, practically, a _spheroid_[2].
# A Novel Logo Identification Technique for Logo-Based Phishing Detection in Cyber-Physical Systems
## 3 Logo-Based Phishing Detection  
The visual-similarity-based approach is the most challenging approach, yet the most effective one to detect phishing. This is because an attacker needs to achieve visual perfection in its trap in order to convince a victim. That is why they have to place all user trusted elements in the right place. Since the logo is the most visually verified element to authenticate a popular brand, most of the researchers in the field of visual-similarity-based approach for phishing detection focus on checking for a genuine logo of a popular brand on a suspicious page.  
Another reason is that reputed firms also embed unique pattern in their logo to differentiate it from other brands in the same domain. Currently, the use of logo features along with one or more ML algorithm to test and train the features is the topmost approach practiced in this category. Figure 2 shows an example of a phishing form and a genuine real form for Google login. The attacker is using the old version logo of Google to make the page similar to original one. In this case, a logo-based detection mechanism can efficiently identify the use of the Google logo in the page and help to detect the phishing page.  
Figure 3 shows a generic approach of a logo-based phishing-detection mechanism. Initially, the logo is identified and extracted using various boundary-detection mechanisms [16]. Then, various features of the logo, such as the color value, width and length, are extracted. Using the underlying detection algorithm, the logo-based phishing mechanism would respond whether the page is a phishing or genuine page. If the page is found to be a phishing page, it is blocked and added to the blacklist.  
Figure 1: Categorization of phishing attack detection [2].  
Figure 2: Example of phishing of the Google login page (source: [[https://images.google.com/](https://images.google.com/)]([https://images.google.com/](https://images.google.com/)), accessed on 10 August 2022).