esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Device Structure and Environment Variation
#### 3.3.3 Convection Environment Variation  
In addition to device parameters, changes in the external environment also affect the power density of the device. In the case of a human body application, the thermal resistance between the core of the body and the TEG does not change, but the thermal resistance of TEG to air changes dramatically depending on the convection environment such as wind speed or heatsink structure [20; 27; 41; 42]. It is necessary to investigate whether this external environment change affects the optimum carrier concentration of the power density.  
Figure 7a shows the results of calculating the power density according to the convective heat transfer coefficients \(h_{air}\), which affects the thermal resistance from TEG to air. As \(h_{air}\) increases, the power density increases sharply due to the rapid decrease of the external thermal resistance. At the same time, the carrier concentration of maximum power density also shifts to the right. Because the proportion of the device thermal resistance to the thermal resistance of the entire system is increased, the power density according to the carrier concentration behaves as if the thermal resistance of the material increases.  
In the environment where a temperature difference is forcibly applied, the power density should show the same tendency as the power factor. Since the carrier concentration of the maximum power factor is always higher than that of the ZT [14], the maximum power density can be located at the right of the maximum point of ZT when the external thermal resistance becomes small enough. However, this case is not likely to occur even if \(h_{air}\) is extremely high because the thermal resistance between the body core and the TEG is sufficiently large in the human body application.
# Advancing Biological Research: New Automated Analysis of Immunofluorescence Signals
## 3 Results
### Q-IF Software Flow Description
#### 3.2.1 ROIs Calculation  
As Figure 3 depicts, image analysis starts by obtaining an image containing numerous cells; the ROIs are isolated and, finally, the background is cropped out for each ROI.  
Figure 3: ROIs finding and cropping process. The process starts by analysing the confocal acquired image in.tif format. The subsequent step takes care of the ROIs calculation, then every ROI is cropped, thus removing the background. Notice that the grey background in the last image results from the cropping process.  
Figure 2: Q-IF signal analysis flow: every step takes the outcome of the previous step as input. Steps are enumerated, and the bar plots about experimental sets comparison are the final output.  
Q-IF calculates the ROIs of an image containing cells by performing several image-processing operations. First, it reads the image using the OpenCV library, converts it to grayscale, and then applies a morphological opening operation to remove small points, i.e., dots due to non-specific antibody signals. Next, it applies a morphological closing operation to join larger points in the image. The resulting image is used to create a binary threshold image using a filter to separate the ROIs from the background. Finally, the algorithm finds the contours of the ROIs and saves each region as a separate image. The resulting ROIs are used for further analysis, such as pixel intensity quantification and area measurement, following the cropping step. In order to test the algorithm for calculating ROIs, in this study, we use the images provided in the data availability statement.
## 3 Research Progress of High-Temperature Rock Mechanics in Large-Buried Tunnels
### Thermo-Mechanical Properties of Rock Mass
\[D=1-\frac{E_{t}}{E_{0}}\exp\left[-\frac{1}{m}\left(\frac{\varepsilon}{ \varepsilon_{f}}\right)^{m}\right] \tag{6}\]  
where \(D\) is the damage variable; \(E_{0}\) and \(E_{t}\) are the elastic modulus of rock after treatment at room temperature and high temperature, respectively; m is the parameter characterizing the evolution of rock damage under load; \(\varepsilon\) is strain; and \(\varepsilon_{f}\) is the peak strain of rock. Similarly, [PERSON] et al. [60; 61; 62] combined Drucker-Prager and Hoek-Brown criteria with Weibull distribution function respectively to deduce the thermal-mechanical coupled damage model, as shown in Equations (7) and (8). For Drucker-Prager criterion,  
\[\sigma_{1}=E\varepsilon_{1}\Bigg{\{}1-\delta+\delta\exp\left[-\left(\frac{ \alpha_{0}I_{1}+\sqrt{J_{2}}}{F}\right)^{m}\right]\Bigg{\}}+2\mu\sigma_{3} \tag{7}\]  
where \(\sigma_{1}\) is axial stress; \(\varepsilon_{1}\) is axial strain; \(E\) is the elastic modulus; \(\delta\) is the damage variable correction factor; m and \(F\) are Weibull parameters; \(\alpha_{0}\) is the material constants; \(I_{1}\) is the first invariant of stress tensor, \(J_{2}\) is the second invariant of stress deviator tensor; \(\mu\) is Poisson's ratio; \(\sigma_{3}\) is confining pressure. For Hoek-Brown criterion,  
\[\sigma_{1}=E\varepsilon_{1}\exp\left\{-\left[\frac{m\sigma_{c}\sigma_{1}E \varepsilon_{1}}{F_{0}(\sigma_{1}-2\mu\sigma_{3})}+\frac{(\sigma_{1}-\sigma_{ 3})^{2}E^{2}\varepsilon_{1}^{2}}{F_{0}(\sigma_{1}-2\mu\sigma_{3})^{2}}\right] ^{n}\right\}+2\mu\sigma_{3} \tag{8}\]  
where \(\sigma_{1}\) is axial stress; \(\varepsilon_{1}\) is axial strain; \(E\) is the elastic modulus; \(n\) and \(F_{0}\) are Weibull parameters; \(m\) is material constants; \(\sigma_{c}\) is uniaxial compressive strength; \(\mu\) is Poisson's ratio; \(\sigma_{3}\) is confining pressure. The above research results indicate that heat conduction in rock mass will not only produce large numbers of cracks from the micro level, but also degrade the macroscopic mechanical properties of rock mass.
## 3 The Proposed Coverage Flight Path Planning Algorithm
### Locating Area of Interest on the Modelled Map and Extracting Free Spaces Geometry
#### 3.2.2 Extracting Free Spaces Geometry
By utilizing the global information about the AOI and following the procedure explained above, the best coverage direction can be selected which yields a smaller number of turns in the path. The proposed method utilizes the cumulative knowledge of the several parts of the AOI to find the best coverage direction which reduces the number of turns significantly.
## 1 Introduction  
Following the detection of gravitational waves by the LIGO (Laser Interferometer Gravitational-wave Observatory) [1], detecting such waves at lower frequencies is the next exciting scientific challenge for the coming decade [2]. The LISA (Laser Interferometer Space Antenna) mission, proposed by ESA (European Space Agency) and with contributions by NASA (National Aeronautics and Space Administration), is the earliest and most mature plan to study gravitational waves in the frequency band between 0.1 mHz and 1 Hz [3; 4]. The LISA mission with its three satellites will use heterodyne interferometry to convert the tiny displacements between test masses into a phase fluctuation in the interferometric signal. The length of the interferometer arms that are formed by LISA's satellites is 2.5 million kilometers [5]. About two months before LIGO announced the first detection of gravitational waves, the LISA pathfinder was launched into space for technical validation [6; 7]. After successful tests of the LISA pathfinder, the LISA mission is scheduled to be launched in the year 2034 [7; 8]. Similar to LISA, the Taiji space gravitational wave detection, the length of the interferometer arm being 3 million kilometers, initiated by Chinese Academic of Sciences in 2008, plans to be launched in 2033 [9]. A pathfinder mission is planned to validate all key technologies prior to the Taiji mission [10; 11; 12].  
In assembling the optical bench of Taiji and its pathfinder, an optical bonding technology is expected to be adopted in joining the optical components with their substrates [13; 14; 15]. The hydroxide-catalysis bonding [13], which has been successfully applied in the Gravity Probe B mission and the LISApathfinder [16], was chosen for joins in the optical bench for the Taiji and its pathfinder mission. Different from the traditional epoxy glue, the hydroxide-catalysis bonding forms covalent bonds between materials through hydration and hydrolysis. There is no additional material layer formed during the bonding process, and therefore satisfies the special requirements of the Taiji pathfinder, such as structural strength, reliability, and extreme thinness of the adhesive layer.  
For principle verification for the Taiji pathfinder mission, an optical bench was built employing the hydroxide-catalysis bonding and tested in our laboratory. In this paper, the optical layout of the interferometer is given in Section 2. Next, the adjustment strategy of the optical bench assembly is presented in Section 3. Details of the hydroxide-catalysis bonding and assembly process are given in Sections 4 and 5. Finally, the noise performance of the optical bench is given in Section 6.
## 3 Key Technologies in Trust Management for WSNs
### Trust Evaluation Methods
#### Intrusion Detection System  
At the controller level, [PERSON] et al. [46] used an intrusion detection system (IDS) module to analyze network statistics collected from each node to complete indirect trust evaluation. When the IDS detects network anomalies from the statistical data and identifies a node associated with these anomalies, the identified node is considered vulnerable or compromised. Consequently, the untrustworthiness of such a node increases by one, and its packet forwarding activities are temporarily halted.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 5 Methodology  
Significant advancements have been made in the field of IoT networking concepts due to the extensive array of applications and use cases; IoT networking protocols have evolved into a vibrant research area influenced by their diverse characteristics [101]. Consequently, considerable studies have been dedicated to evaluating and comparing the effectiveness of various IoT and related protocols. This study has adopted a simple but effective methodology by conducting a literature survey to carefully select publications related to networking protocols for constrained devices. To gather pertinent literature within this study domain, our literature screening and selection were focused on protocols at the application, transport, and Internet layers of the IoT protocol suite. The selection process prioritised publications that directly compared the performance of different protocols, as these studies provide valuable insights into the strengths and weaknesses of each protocol. Additionally, publications that examined the fundamental functions and design purposes of the protocols were included in the survey.  
DQ is integral to the success and broad adoption of the IoT paradigm [6]. Recognising this paramount importance, the second phase of this study focuses on identifying the protocols capable of satisfying our study-specific DQ dimensions. This was achieved by performing an exhaustive evaluation and analysis of the performances of each of the selected protocols [102]. In following this course, we hoped to distil the sets of protocols that not only meet but optimally satisfy our study-specific DQ dimensions for V2I applications and use cases. To achieve this, we conducted a mapping exercise, linking each of the protocols' design objectives, performance evaluations and inherent characteristics to our study-specific DQ dimensions. This crucial step was carried out to assess the strengths, suitability, and limitations of the protocols and to establish a clear correlation between the capabilities of each protocol and their potential to fulfil the communication requirements in IP-based V2I interactions, thereby laying the groundwork for a use-case -driving approach in V2I.
# Q8S: Emulation of Heterogeneous Kubernetes Clusters Using QEMU
## 1 Introduction
### Background
These operations include filtering, network address translation (NAT), and package manipulation (mangle) applied at varying stages of package processing such as at arrival (prerouting), upon local reception (input), at the time of forwarding (forward), and before sending (postrouting) as well as for any locally created packages (output). The behavior of a system can be replicated using emulation or simulation. The two techniques form distinct approaches, with simulation utilizing a model to resemble a given system and its behavior through mathematical or logical abstractions [30]. Emulation, on the other hand, attempts to replicate the exact behavior of the target system by mimicking its hardware and software environment. Simulations can be highly flexible, but because the actual system involved is abstracted into a model, accuracy may be reduced. In emulations, the target system components are mimicked as close as possible, resulting in high accuracy at the cost of reduced flexibility and higher computational cost. QEMU and Libvirt  
QEMU is a generic open-source emulator with support for a large range of hardware platforms and architectures such that developers can test their software against multiple platforms without actual access to the hardware. QEMU can operate as an inner hypervisor to emulate a VM with a specified hardware configuration, for example, ARM, on top of an x86 node. However, when doing so, hardware acceleration features that would reduce the performance overhead of the virtualization are not available, as the hardware emulated in the VM must be identical to that of the physical host for hardware acceleration. Libvirt provides an API, a daemon, and management tools for working with QEMU and other virtualization systems including command line tools Virsh and Virt-install. The interaction of libvirt and QEMU is shown in Figure 1, with libvirt providing its tools in user space such that QEMU can be managed to create the requested VMs while interacting with the underlying system kernel. It should be noted that in Figure 1, QEMU would be the only hypervisor and would directly work with the hypervisor Kernel-based Virtual Machine (KVM) to create VMs. For QEMU to be an inner hypervisor, the physical hardware would need to be an emulation of the outer hypervisor, and KVM would not be used. Ubuntu Cloud-Images are Ubuntu Operating System (OS) images that include a feature called cloud-init, which can be configured to apply settings and execute scripts on a fresh image during the first boot. These configurations consist of multiple parts including user-data and meta-data files. The user-data file contains user creation settings, ssh configurations, the packages to install, and the scripts to run. The meta-data file can add further network configuration and set the hostname. Figure 1: Interactions of libvirt and QEMU on a Linux host.
## 3 Results
### Accuracy Measures  
Accuracy was found to be 61.0% for LIWC, 63.6% for QDAP, 62.6% for SN and 67.4% for COMB. Accuracies are significantly higher than 50% according to one-sided binomial tests (\(p<0.001\) for all four methods). A total of 43.4% of tweets for LIWC (25.0% for QDAP, 7.8% for SN) included neither an evaluable positive nor an evaluable negative word, which is further evidence for the limited informative value of analyzing single tweets. Figure 1 illustrates the set accuracy for various proportions p, various test set sizes n and m = 10,000 test sets each. Results show that for sets of 50 or 100 tweets, even tweet sets with a clear imbalance in sentiments (75% of one polarity) are not sufficient to enable set accuracies to exceed 95% for any method. However, for the best-performing methods and sets of 500 tweets, a proportion of 65% from the same polarity is sufficient to correctly classify more than 95% of sets for both methods. For sets of 1000 tweets, even a proportion of 60% of one polarity is sufficient.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Tweet** \\ \hline \multirow{4}{*}{Positive} & “[PERSON] is anything but human. \#FCBLIV” \\ \cline{2-3}  & “What a save @[PERSON] @MUNLIV \#pl \#ffc” \\ \cline{2-3}  & “C’mon United. \#GGMU \#MUNLIV” \\ \hline \multirow{4}{*}{Negative} & “Stupidity from [PERSON] for putting [PERSON] instead of [PERSON] \#MUNLIV” \\ \cline{2-3}  & “Never a pen, minimal contact outside box on [PERSON], [PERSON] lucky to get a free kick \#AtletiJuve \#ucl” \\ \cline{2-3}  & “Liverpool BATTERED \#fic \#FCBLIV @btsportfootball” \\ \hline \multirow{4}{*}{Neutral} & “VAR overrules ref. Freekick rather a pen \#AtletiJuve” \\ \cline{2-3}  & “Bit surprised [PERSON] has broken up the [PERSON]/[PERSON]/[PERSON] midfield that’s played in most of the big games this season if it is [PERSON] at right back today \#MUNLIV” \\ \cline{2-3}  & “Barcelona vs. Liverpool: [PERSON] scores two goals in seven minutes \#FCBLIV [[https://t.co/sZWqueNll](https://t.co/sZWqueNll)]([https://t.co/sZWqueNll](https://t.co/sZWqueNll)).” \\ \hline \multirow{4}{*}{Nonsense} & “Looking for professional business flyer designer? Please contact following link. [PERSON] \#MUNLIV \#AtikNinning HOLD THE DATE \#SundayMorning \#FelizDoming [[https://t.co/jvMrrxjN4D](https://t.co/jvMrrxjN4D)]([https://t.co/jvMrrxjN4D](https://t.co/jvMrrxjN4D))” \\ \cline{2-3}  & “INVIOLABLE LE BUT [PERSON] \#FCBLIV” \\ \cline{2-3}  & “@TheSportsman The Savior has emerged... [PERSON][PERSON] (as). The Messenger from [PERSON] (as). The Mahdi (as) that is born during the end of time... \\ \cline{2-3}  & The Messenger from [PERSON] (as). The Messenger from [PERSON] (as) \#AtletiJuve \#skamfrance \\ \cline{2-3}  & https://tco/Qq8 eA54 ESE https://tco/6 bGIObNEw” \\ \hline \hline \end{tabular}
\end{table}
Table 3: Example tweet in each category of the manual annotation.
# A Stackelberg Game Approach to Model Reference Adaptive Control for Spacecraft Pursuit-Evasion
## 3 Stackelberg Equilibrium-Based Model Reference Adaptive Control Algorithm
### Stackelberg Equilibrium-Based Model Reference Adaptive Control Algorithm
The Lyapunov difference must be meticulously expanded as follows:  
\[\mathbf{L}_{p}^{*}=-\Big{(}\mathbf{W}_{1,p}+\mathbf{Y}_{p}^{\mathrm{T}}\mathbf{\hat{P}}_{p}^{* }\mathbf{Y}_{p}\Big{)}^{-1}\mathbf{Y}_{p}^{\mathrm{T}}\mathbf{\hat{P}}_{p}^{*}\big{(}\mathbf{X} _{d}+\mathbf{Y}_{e}\mathbf{L}_{e}^{*}\big{)} \tag{13}\]1 \[\mathbf{L}_{p}^{*}=-\Big{(}\mathbf{W}_{1,p}+\mathbf{Y}_{p}^{\mathrm{T}}\mathbf{\hat{P}}_{p}^{* }\mathbf{Y}_{p}\Big{)}^{-1}\mathbf{Y}_{p}^{\mathrm{T}}\mathbf{\hat{P}}_{p}^{*}\big{(}\mathbf{X} _{d}+\mathbf{Y}_{e}\mathbf{L}_{e}^{*}\big{)} \tag{13}\]2 \[\mathbf{L}_{p}^{*}=-\Big{(}\mathbf{W}_{1,p}+\mathbf{Y}_{p}^{\mathrm{T}}\mathbf{\hat{P}}_{p}^{* }\mathbf{Y}_{p}\Big{)}^{-1}\mathbf{Y}_{p}^{\mathrm{T}}\mathbf{\hat{P}}_{p}^{*}\big{(}\mathbf{X} _{d}+\mathbf{Y}_{e}\mathbf{L}_{e}^{*}\big{)} \tag{13}\]3  
Substituting the error dynamics (25), we derive:  
\[\mathbf{L}_{p}^{*}=-\Big{(}\mathbf{W}_{1,p}+\mathbf{Y}_{p}^{\mathrm{T}}\mathbf{\hat{P}}_{p}^{* }\mathbf{Y}_{p}\Big{)}^{-1}\mathbf{Y}_{p}^{\mathrm{T}}\mathbf{\hat{P}}_{p}^{*}\big{(}\mathbf{X} _{d}+\mathbf{Y}_{e}\mathbf{L}_{e}^{*}\big{)} \tag{13}\]4  
Using the DARE, this simplifies to:  
\[\mathbf{L}_{p}^{*}=-\Big{(}\mathbf{W}_{1,p}+\mathbf{Y}_{p}^{\mathrm{T}}\mathbf{\hat{P}}_{p}^{* }\mathbf{Y}_{p}\Big{)}^{-1}\mathbf{Y}_{p}^{\mathrm{T}}\mathbf{\hat{P}}_{p}^{*}\big{(}\mathbf{X} _{d}+\mathbf{Y}_{e}\mathbf{L}_{e}^{*}\big{)} \tag{13}\]5  
Assuming the parameter update laws:  
\[ \begin{split}\mathbf{\tilde{L}}_{p,t+1}&=\mathbf{\tilde{L} }_{p,t}+\mathbf{\Gamma}_{p}\mathbf{Y}_{d}^{\mathrm{T}}\mathbf{P}\mathbf{e}_{t}\mathbf{\tilde{f}}_{t }^{\mathrm{T}}\\ \mathbf{\tilde{L}}_{e,t+1}&=\mathbf{\tilde{L}}_{e,t}-\mathbf{ \Gamma}_{e}\mathbf{Y}_{e}^{\mathrm{T}}\mathbf{P}\mathbf{e}_{t}\mathbf{\tilde{f}}_{t}^{\mathrm{ T}}\end{split}  \tag{31}\]  
the parameter error differences are:  
\[\Delta\mathbf{V}_{2,t}+\Delta\mathbf{V}_{3,t}=2\text{tr}\Big{(}\mathbf{\tilde{L}}_{p,t}^{ \mathrm{T}}\mathbf{Y}_{d}^{\mathrm{T}}\mathbf{P}\mathbf{e}_{t}\mathbf{\tilde{f}}_{t}^{\mathrm{ T}}\Big{)}-2\text{tr}\Big{(}\mathbf{\tilde{L}}_{e,t}^{\mathrm{T}}\mathbf{Y}_{e}^{ \mathrm{T}}\mathbf{P}\mathbf{e}_{t}\mathbf{\tilde{f}}_{t}^{\mathrm{T}}\Big{)}+\mathcal{O}( \mathbf{\Gamma}^{2}) \tag{32}\]  
Combining all terms yields:  
\[ \begin{split}\Delta\mathbf{V}_{t}\leqslant-\mathbf{e}_{t}^{\mathrm{T}} \mathbf{Q}\mathbf{e}_{t}+2\mathbf{e}_{t}^{\mathrm{T}}(A_{d}^{\mathrm{T}}\mathbf{P}+\mathbf{P})(\mathbf{ Y}_{d}\mathbf{\tilde{L}}_{p,t}-\mathbf{Y}_{e}\mathbf{\tilde{L}}_{e,t})\mathbf{\hat{f}}_{t}+\\ \gamma\Big{\|}\mathbf{\tilde{f}}_{t}\Big{\|}^{2}\big{(}\big{\|}\mathbf{ \tilde{L}}_{p,t}\big{\|}^{2}+\big{\|}\mathbf{\tilde{L}}_{e,t}\big{\|}^{2}\big{)} \end{split}
# A Secure Opportunistic Network with Efficient Routing for Enhanced Efficiency and Sustainability
## 6 Our Approach: Proposed Model  
In our approach, we introduce a proposed model that operates on a two-layer protocol framework, each layer serving a distinct yet interconnected purpose. At its foundational stratum, the first layer is intricately crafted for the establishment of a secure protocol. This layer not only capitalizes on the decentralized and tamper-proof nature of blockchain but also acts as the bedrock for elevating the overall reliability of the proposed model. Concurrently, the second layer of the framework is tailored for the seamless execution and implementation of the novel routing protocol. By encapsulating these two layers, the model aspires to create a harmonious synergy, blending the security advantages of blockchain with the dynamic functionality of the routing protocol to address the unique challenges and requirements of OppNets.
# Smart Clothing Framework for Health Monitoring Applications
## 4 Challenges and Future Development
### Social Acceptability
#### 4.3.3 Unavailability of the Internet  
The internet has a great impact on smart clothing in communicating physiological information for monitoring health of individuals. Low-income, rural, and homebound populations without internet facilities will lag behind and not receive the benefits of using such wearable technologies. The International Telecommunication Union, the United Nations and global information and communications technology statistics have reported that only 53.6% of people have access to the internet worldwide due to the high cost of internet service, whereas 84% of households access the internet from home in developed countries [153]. Efforts should be made to improve the accessibility and affordability of the internet.