esa-sceva/satcom-chunk-collection huggingface dataset:
# Mycobacterial Biofilm: Mechanisms, Clinical Problems, and Treatments
## 3 Genes and Molecules Involved in Mycobacterial Biofilm Development
### MprB  
External molecules also influence mycobacterial biofilm formation. One of the human stress hormones--epinephrine--not only accelerates Mtb growth but also stimulates the establishment of biofilm. [PERSON] and colleagues found that the biofilm induced by epinephrine shows better tightness and smoothness compared with vehicle-treated ones [31]. The _mprB_ gene knock-down Mtb strain (_mprBKD_) displays slower growth, which indicates that the function of epinephrine is likely achieved by binding with MprB protein, the probable sensor of epinephrine [31].
## 2 Materials and Methods
### The Proposition of RS-DFV  
#### 2.2.1 The Basic Concept of DFV  
To simulate the object description method of the human brain, one study [22] designed a special feature vector that was called dependent feature vector (DFV) for REB fault description. The topology and logical structure of DFV is displayed in Figure 2a. In DFV, there must be at most one leading feature (LF). However, A DFV could include many dependent features (DFs) or no DF. DFV could mining the essential difference among all kinds of faults through its unique nested structure. Moreover, the difference between different faults is magnified by the means of adaptive invalid features of the DFV, and the difference among faults of the same type is reduced at the same time, as illustrated in Figure 2b and Equations (1)-(7). Therefore, DFV greatly improved the accuracy of fault description and fault diagnosis.  
Figure 1: The framework of REB Fault diagnosis based on tree heuristic feature selection (THFS) and the dependent feature vector combined with rough sets (RS-DFV).  
In a DFV, the valid features must be acquired through the analysis of sample data. However, invalid features do not have to be calculated, and it is only necessary to subjectively give an effective value for them. The unified evaluation mechanism of invalid items in a DFV could greatly improve its fault discrimination.  
For the examples in Figure 2b, F1 and F2 are two different fault types, \(xi_{j}\) is the \(j\)th feature item of the fault Fi, \(|fv_{1}-fv_{2}|\) is the Euler distance between F1 and F2 based on the traditional feature vector, and \(|DFV_{1}-DFV_{2}|\) is the Euler distance between F1 and F2 based on the DFV. As long as the value (X) of the invalid features is suitable, Equations (3) and (4) are true. It is obvious that the DFV could magnify the difference between different faults through the value assignment of the invalid features.  
\[|fv_{1}-fv_{2}|^{2}=(x1_{5}-x2_{5})^{2}+(x1_{7}-x2_{7})^{2}+(x1_{11}-x2_{11})^{ 2}+(x1_{73}-x2_{73})^{2}+(x1_{92}-x2_{92})^{2} \tag{1}\]  
\[|DFV_{1}-DFV_{2}|^{2}=(x1_{5}-x2_{5})^{2}+(x1_{7}-x2_{7})^{2}+(x1_{11}-X)^{2}+ (X-x2_{73})^{2}+(X-x2_{92})^{2} \tag{2}\]  
\[\left\{\begin{array}{l}(x1_{11}-X)^{2}\gg(x1_{11}-x2_{11})^{2}\\ (X-x2_{73})^{2}\gg(x1_{73}-x2_{73})^{2}\\ (X-x2_{92})^{2}\gg(x1_{92}-x2_{92})^{2}\end{array}\right. \tag{3}\]  
\[|DFV_{1}-DFV_{2}|\gg|fv_{1}-fv_{2}| \tag{4}\]  
\[|fv_{11}-fv_{12}|^{2}=(x11_{5}-x12_{5})^{2}+(x11_{7}-x12_{7})^{2}+(x11_{11}-x1 2_{11})^{2}+(x11_{73}-x12_{73})^{2}+(x11_{92}-x12_{92})^{2} \tag{5}\]  
\[|DFV_{11}-DFV_{12}|^{2}=(x11_{5}-x12_{5})^{2}+(x11_{7}-x12_{7})^{2}+(x11_{11}- x12_{11})^{2}+(X-X)^{2}+(X-X)^{2} \tag{6}\]  
\(|fv_{11}-fv_{12}|\) is the Euler distance between two faults of type F1 based on the traditional feature vector, and \(|DFV_{11}-DFV_{12}|\) is the Euler distance between two faults of type F1 based on DFV. No matter what is the value (\(X\)) of the invalid features, Equation (7) is true. Obviously, the DFV was able to significantly lessen the discrepancies among the same faults.  
\[|DFV_{11}-DFV_{12}|<|fv_{11}-fv_{12}| \tag{7}\]
## Abbreviations  
The manuscript uses the following abbreviations:  
CDM Common Data Model  
OHDSI Observational Health Data Sciences and Informatics  
IPFS Inter Planetary File System  
HS Hospital Information System  
DLT Distributed Ledger Technology  
GDPR General Data Protection Regulation  
RTBF Right To Be Forgotten  
IRB Institutional Review Board  
SP Service Provider  
IdP Identity Provider  
IdM Identity Management  
ACL Access Control List  
SSO Single Sign On  
SSI Self-Sovereign Identity  
DID Decentralized Identifiers  
VC Verifiable Credential  
RBFT Redundant Byzantine Fault Tolerance  
ACA-Py Aries Cloud Agent Python
# An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions
## 3 Weather Effects
### Space Weather  
The earth's atmosphere observed in Figure 19 is composed of a few layers, including the troposphere, stratosphere, mesosphere, thermosphere, ionosphere, and exosphere, with most of the planet's weather occurring in the troposphere [50]. The previously discussed weather phenomenon mainly affects RADAR, LiDAR, ultrasonic sensors, and cameras. GNSS is an exception to the group being mainly affected by environmental conditions in our solar system (space weather) [36].  
Figure 19: Earth’s atmospheric layers.  
The ionosphere is abundant in electrons and ionized particles and renders radio communication possible [50]. This layer also can shift in size, depending mainly on solar influence [50]. GNSS operates in this region and can be influenced by interrupting wavelengths and objects coming in from the exosphere. The troposphere, unlike the ionosphere, has the benefit of multiple layer protection, where meteors can be disintegrated in the mesosphere and ultraviolet radiation can be absorbed and dispersed through the stratosphere's ozone layer [50]. Technology utilized in the ionosphere is at a higher disadvantage if something were to permeate the planet's atmosphere, observed in Table 5.  
The L-Band frequencies used by GNSS are severely debilitated by space weather. Space weather, which actually accounts for the sun's atmospheric interactions, does have an effect on range delay and loss of lock [51]. The sun has a direct effect on the ionosphere, whether that may be through ionospheric scintillations, changing the density and distribution of the total electron content (TEC), or the time of day and year. The ionosphere can be broken down into three main layers denoted as the D, E, and F regions. These regions are classified by altitude and each effect signals differently. The D-region attenuates signals inversely proportional to the frequency squared. This being the case, attenuation within the L-Band is negligible. The E and F regions cause little to no attenuation due to low air density. They tend to reflect the signal depending on the frequency and incidence angle of the transmitted wave. The Ionosphere's index of refraction is a function of the transmitted wave frequency and the TEC of the region. At nighttime, the TEC distribution is generally lower within the E region; therefore, lower reflections and range delays occur [51]. Ionospheric scintillations have a direct effect on the E and F regions, which result in an increase in loss of lock for the GPS. Solar flares cause variations relative to the TEC, which also causes loss of lock variations [51]. Additionally, solar radio bursts negatively impact tracking performance.
# Flutter Mechanism of a Thin Plate Considering Attack Angles
## 5 Flutter Characteristics of Thin Plates at 7\({}^{\circ}\) Angle of Attack  
As mentioned in Section 3, although the flutter modes at various angles of attack are all characterized by bending-torsion coupling flutter and are driven by coupled aerodynamic negative damping, the nature of flutter at a 7\({}^{\circ}\) angle of attack differs. This section provides a detailed analysis of this difference. Due to space constraints, the comparison is limited to the 0\({}^{\circ}\) and 7\({}^{\circ}\) angles of attack.  
By constraining the vertical displacement (i.e., assuming the vertical direction is rigidly connected with an infinite vertical bending frequency), single-degree-of-freedom flutter calculations were performed for both the 0\({}^{\circ}\) and 7\({}^{\circ}\) angles of attack. The variation curves of aerodynamic damping are shown in Figure 11, and the calculation results are summarized in Table 4.  
From the table and Figure 11, it can be observed that for the 0\({}^{\circ}\) angle of attack, during the single-degree-of-freedom flutter calculation, \(A_{2}^{*}\) remains negative, and the system's non-coupling aerodynamic damping is always positive. As a result, a single-degree-of-freedom torsional flutter cannot be excited in the torsional mode. Thus, the flutter at a 0\({}^{\circ}\) attack angle is essentially a bending-torsional coupling flutter driven by coupling aerodynamic forces. However, for a 7\({}^{\circ}\) attack angle, even when the vertical displacement is constrained and the effects of coupling aerodynamic forces are ignored, single-degree-of-freedom torsional flutter occurs at V = 7.3 m/s and U = 8.94 m/s, which is higher than the 7.76 m/s for coupling flutter. The result indicates that at a 7\({}^{\circ}\) attack angle, flutter of the thin plate occurs before reaching the torsional flutter corresponding to a reduced wind speed of 7.3 (where \(A_{2}^{*}\) transitions from negative to positive), and the flutter form is coupling flutter. At this point, coupling aerodynamic forces provide sufficient aerodynamic negative damping to the system. In contrast, the aerodynamic positive damping is minimal, significantly weakening flutter performance and an earlier critical flutter point. If the vertical degree of freedom is constrained to prevent coupling motion, the flutter wind speed will correspond to the reduced wind speed where \(A_{2}^{*}\) transitions from negative to positive.  
Considering that the test results of flutter characteristics at large angles of attack may be affected by the wind tunnel blockage, CFD can effectively avoid this defect. A more systematic and in-depth study on the flutter characteristics of the thin plate considering attack angles, combined with CFD technology, will be carried out in the future.
## 4 Experiments
### Ablation Experiment  
Ablation experiments were conducted on the PDWT-YOLO model using the VisDrone-2019 dataset, adding the different improvement methods mentioned above, individually or in combination, to verify the effectiveness of each individual method. To ensure accuracy, all the experiments were conducted using the same parameters and environments. This experiment involved adding different improvements to the YOLOv7-tiny model, including adding a decoupled head (B1 in Table 3), continuing to add the P2 layer small-object detection head (B2 in Table 3), and then adding WIoU (PDWT-YOLO in Table 3). The experimental results show that each individual addition improved the [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95, and the three indicators of inference time, params, and FLOPS did not increase significantly. The [EMAIL_ADDRESS], [EMAIL_ADDRESS]:0.95, APS, and APM of the B1 model increased by 1%, 0.7%, 0.9%, and 0.7%, respectively, compared with the basic model. The [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 of the B2 model increased by 4.7% and 4%, respectively, and the APS, APM, and APL also increased compared with the B1 model. The performance of the PDWT-YOLO was better than that of the B2 model; the [EMAIL_ADDRESS], [EMAIL_ADDRESS]:0.95, APS, and APL were, respectively, 1%, 0.3%, 0.4%, and 0.4% higher than those of the B2 model. Although its APL was lower than that of the B2 model, the detection ability of the comprehensive-scale features was better. This indicates that WIoU further improved the accuracy of the target detection model. The ablation experiment shows that our three improvements (independently or jointly) can enhance performance in object detection.  
Figure 6: Dataset detection effect comparison. (**a**) YOLOv7-tiny, and (**b**) PDWT-YOLO.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{\
\begin{tabular}{c} **Decoupled** \\ **Head** \\ \end{tabular} } & \multirow{2}{*}{**P2**} & \multirow{2}{*}{**WIoU**} & \multirow{2}{*}{**[EMAIL_ADDRESS]**} & \multirow{2}{*}{
\begin{tabular}{c} **mAP@** \\ **0.50.95** \\ \end{tabular} } & \multirow{2}{*}{**Inference**} & \multirow{2}{*}{**Params**} & \multirow{2}{*}{**GFLOPS**} & \multirow{2}{*}{**APS**} & \multirow{2}{*}{**APM**} & \multirow{2}{*}{**APL**} \\  & & & & & & & & & & \\ \hline YOLOv7-tiny & & & & 34.5 & 17.5 & **2.9** & 6.2 & **13.1** & 10.4 & 26.5 & 36.5 \\ B1 & \(\surd\) & & & 35.5 & 18.2 & 3.2 & **5.8** & 18.9 & 11.3 & 27.2 & 34.2 \\ B2 & \(\surd\) & \(\surd\) & & 40.2 & 22.2 & 4.3 & 6.44 & 24.2 & 14.7 & 31.4 & **38** \\ PDWT-YOLO & \(\surd\) & \(\surd\) & \(\surd\) & **41.2** & **22.5** & 4.4 & 6.44 & 24.2 & **15.1** & **31.8** & 36.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiments with VisDrone-2019 test dataset.  
The entire change processes of [EMAIL_ADDRESS] and [EMAIL_ADDRESS] are plotted in Figure 7, and each curve can converge at about 200 epochs. Evidently, each of the additions enhances the performance of the model compared with YOLOv7-tiny.
## 1 Introduction
Based on this, we derive the formulation of EE for a two-tier HCN with respect to MU density, BS densities, target signal-to-interference ratio (SIR), and power consumption of BSs. Then, we use a one-dimensional search algorithm to find the optimal density and transmit power, respectively. Finally, we propose a joint optimization strategy for picocell BS density and transmit power that can maximize EE. The remainder of this paper is organized as follows. Section 2 introduces the system model. Section 3 analyzes the EE. The proposed EE optimization scheme is presented in Section 4. Simulation results are discussed in Section 5. Section 6 concludes the paper.
## 3 Numerical Scheme
### Vertical Polarisation
#### 3.3.2 Collision Normal Forces  
The force vector \(\mathbf{f}_{\mathbf{w}}^{n}\) can be written as:  
\[\mathbf{f}_{\mathbf{w}}^{n}=\frac{\delta_{t}\mathbf{\Phi}^{n}}{\delta_{t} \mathbf{\Delta}^{n}}+\left(\delta_{t}\mathbf{\Delta}^{n}\right)\odot\mathbf{ \Psi}^{n} \tag{49}\]  
where the division is pointwise, and \(\odot\) is the pointwise product. This expression for \(\mathbf{f}_{\mathbf{w}}^{n}\) is written as a function of the vector penetration \(\mathbf{\Delta}^{n}\), defined as:  
\[\mathbf{\Delta}^{n}=\left[\begin{array}{c}\vdots\\ \mathbf{\Delta}_{N}^{n}\\ \vdots\\ \hline\frac{\Delta_{F}^{n}}{\Delta_{B}^{n}}\end{array}\right]\qquad\qquad\qquad \qquad\qquad\mathbf{\Delta}_{N}^{n}=\varepsilon-\mathbf{w}^{n} \tag{50b}\] \[\Delta_{F}^{n}=\mathbf{\mu}_{F}^{n}\mathbf{\Upsilon}^{n}-w_{F}^{n}\] (50c) \[\Delta_{B}^{n}=\mathbf{\mu}_{B}^{n}\mathbf{\Upsilon}^{n}-w_{B}^{n} \tag{50d}\]  
where the elements of vector \(\varepsilon\) are \(\varepsilon_{l}=\varepsilon(lh)\), and \(w_{F}^{n}\) and \(w_{B}^{n}\) are the respective vertical positions of the finger and bow.  
\(\mathbf{\Phi}^{n}(\mathbf{\Delta}^{n})\), \(\mathbf{\Psi}^{n}(\mathbf{\Delta}^{n})\) are now vector functions of \(\mathbf{\Delta}^{n}\):  
\[\mathbf{\Phi}^{n}(\mathbf{\Delta}^{n})=\frac{K}{\mathbf{\alpha}+1}\odot[ \mathbf{\Delta}^{n}]_{+}^{\mathbf{\alpha}+1}\qquad\qquad\qquad\mathbf{\Psi}^ {n}(\mathbf{\Delta}^{n})=K\odot\boldsymbol{\beta}\odot[\mathbf{\Delta}^{n}]_{+}^ {\mathbf{\alpha}} \tag{51b}\]  
where the exponentiation operation is also element-wise, and the nonlinearity parameters \(K\), \(\mathbf{\alpha}\), and \(\boldsymbol{\beta}\) are themselves in vector form:\[\mathbf{K}=\left[\begin{array}{c}\vdots\\ K_{N}\\ \vdots\\ \hline K_{F}\\ \hline K_{B}\end{array}\right] \tag{52a}\] \[\mathbf{\alpha}=\left[\begin{array}{c}\vdots\\ \alpha_{N}\\ \vdots\\ \hline\dfrac{\alpha_{F}}{\alpha_{B}}\end{array}\right]\] (52b) \[\mathbf{\beta}=\left[\begin{array}{c}\vdots\\ \beta_{N}\\ \vdots\\ \hline\dfrac{\beta_{F}}{\beta_{B}}\end{array}\right] \tag{52c}\]  
Finally, the discrete equations for the evolution of the vertical positions of the finger and bow can be written as:  
\[\mathbf{M}_{FB}\delta_{tt}\mathbf{w}_{FB}^{n}=\mathbf{f}_{\mathbf{w}FB}^{n}+\mathbf{f}_{ \mathbf{ext}\;w,FB}^{n} \tag{53a}\] \[\mathbf{M}_{FB}=\left[\begin{array}{cc}M_{F}&0\\ 0&M_{B}\end{array}\right]\] (53b) \[\mathbf{w}_{FB}^{n}=\left[\begin{array}{c}w_{F}^{n}\\ w_{B}^{n}\end{array}\right]\] (53c) \[\mathbf{f}_{wFB}^{n}=\left[\begin{array}{c}f_{F}^{n}\\ f_{B}^{n}\end{array}\right]\] (53d) \[\mathbf{f}_{\mathbf{ext},FB}^{n}=\left[\begin{array}{c}\rho_{\mathbf{ext},F}^{n}\\ \rho_{\mathbf{ext},B}^{n}\end{array}\right] \tag{53e}\]
# Artificial Intelligence-Enhanced Colorimetric Assessment of Self-Cleaning Road Marking Points
## 3 Results and Discussion
### CIELAB Color Coordinates Comparative Analysis  
Figure 4 presents the CIELAB color coordinates (L*, a*, b*) of the specimens obtained via spectrophotometric versus AI-enhanced colorimetry methods. The detailed results concerning the color coordinates values are properly identified in the Supplementary Materials in Tables S1 and S2. The comparison of L values from both techniques revealed a coefficient of determination (R2) of 0.77798, indicating a positive correlation. Besides there being a general linear trend between the values, the graph indicates potential discrepancies in lightness measurements between the two methods. Factors contributing to this discrepancy may include the lighting inside the photobox and the non-uniform spreading of the pollutant over the samples. Another relevant factor is that the spectrophotometer measures lightness in a specific area of the specimen, whereas the digital image processing considers the average lightness of all pixels in an image, which encompasses the entire surface of the specimen.  
The scatter plots for the a* and b* coordinates reveal an R2 of 0.98717 and 0.92296, respectively, indicating a strong correlation between the colorimetric methods. These values suggest that image processing for color measurements with AI is highly consistent with spectrophotometric measurements. For the a* coordinate, AI-enhanced colorimetry accurately captured the green-to-red variation, similarly reflecting the spectrophotometric readings. For the b* coordinate, there was also a strong correlation; however, minor discrepancies may exist due to factors such as the internal lighting of the photobox, which may impart a residual coloration to the specimens. Therefore, the analysis of the self-cleaning capability of the functionalized RM paint using colorimetry could be performed using both techniques and compared.
# Integrated Photonics for IoT, RoF, and Distributed Fog-Cloud Computing: A Comprehensive Review
## 6 Key Enablers of High-Speed Networks Technology
### Compactness and Scalability  
PICs also enhance compactness and scalability, offering key advantages for Fog and Cloud infrastructures. Traditional transceivers with discrete lasers, modulators, and photodetectors require substantial space and complicate deployment in dense environments [46]. PICs, by integrating all optical components on a single chip, dramatically reduce footprint and simplify assembly. This enables higher transmission densities and supports real-time analytics at the edge [47]. In addition, PICs support extensive wavelength division multiplexing (WDM), allowing infrastructures to scale optical bandwidth efficiently and meet growing data demands [48].
## 5 Experimental Results
### Security and Privacy Analysis
#### 5.2.2 Blockchain  
The probability of the attacks to occur in the blockchain paradigm is  
\[P(BC)=P(A)+P(B)+P(C) \tag{7}\]  
\(P(A)\) represents attack-1 and can be performed by a successful attack over all nodes \(\alpha\kappa\) and acquiring nodes' credentials represented as \(\theta\kappa\). The probability can be calculated by  
\[P(A)=\frac{1}{4}\prod_{\kappa=1}^{n}\alpha\kappa\times\frac{1}{4}\prod_{\kappa =1}^{n}\theta\kappa \tag{8}\]  
\(P(B1)\) represents attack-2 and can be performed by a successful attack over nodes and acquiring nodes' credentials represented as \(\theta\kappa\). There is \(\frac{n\times(n-1)}{2}=a\), which is the number of transmissions channels represented as \(\beta\kappa\), which can be created by pairs of nodes for \(n\) nodes [16]. The probability can be calculated by  
\[P(B1)=\frac{1}{4}\prod_{\kappa=1}^{a}\beta\kappa\times\frac{1}{4}\prod_{\kappa =1}^{n}\theta\kappa \tag{9}\]  
The corresponding transmission channels are also considered in the formula which can be calculated similar to \(P(B1)\) and denoted as \(P(B2)\).  
\[P(B2)=\frac{1}{4}\prod_{\kappa=1}^{a}\beta\kappa\times\frac{1}{4}\prod_{\kappa =1}^{n}\theta\kappa \tag{10}\]  
\(P(C)\) represents attack-3 and can be performed by a successful attack over all edge nodes \(\rho\kappa\) and acquiring nodes' credentials represented as \(\theta\kappa\). There are \(\frac{n}{2}=v\), which is the number of mining nodes or validators (edges) that an attacker should control to compromise the ledger. The probability can be calculated by  
\[P(C)=\frac{1}{4}\prod_{\kappa=1}^{v}\rho\kappa\times\frac{1}{4}\prod_{\kappa=1 }^{n}\theta\kappa \tag{11}\]  
Figure 10: Results for All Three Categories of Attacks in the Traditional Central-Based V2V Scenario.
## 3 M2M Information and Services
### Autonomic M2M Services
Many applications that require continuous performance improvement and resource management make use of control theory approach demonstrated in the Lotus Notes application [94], learning based self-optimization techniques such as LEO [95], and active learning based approach [96] based on building statistical predictive models. Self-healing is the property that involves problem detection, diagnosis and repair. CBR [93] performs problem detection in the analysis phase, finds a solution in planning phase and performs problem repair in execution phase. Hybrid approach [97] to create a survivability model provides more robust survivability services. Active probing and Bayesian network [98] is a problem diagnosis technique that allows probes to be selected and sent on demand. The heartbeat failure detection algorithm [99] enables detecting problem when the monitor experiences a delay in receiving of message. Another technique to detect problems in distributed systems is to use tools like Pip [100] that expose structural errors and performance problems by comparing actual system behavior and expected system behavior. Performance Query Systems (PQS) [101] can be used to enable user space monitoring of servers. Component level rebooting is a technique that recovers from defects, without disturbing the rest of the application as shown in Microreboot [102]. Another safe technique to quickly recover programs from deterministic and non-deterministic bugs is demonstrated in Rx [103]. It rolls back the program to a recent checkpoint upon a software failure, and then re-executes the program in a modified environment. Some notable techniques that enable systems to enable self-protection make use of component models such as Jade [104] and self-certifying alerts as demonstrated in Vigilante [105].