esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Coupling Dynamic Model of the Advance Support and the Roof  
According to the relationship between the advance support and the roof, the coupling dynamic model of the advance support and the roof is constructed in the light of the theory of overburden rock mass block (Figure 3). The following assumptions are made: \(m_{s}\) is the equivalent mass of the advance support; \(k_{s}\), \(c_{s}\) and \(z_{s}\) are the support stiffness, damping and vertical displacement of the advance support, respectively. The roadway roof is composed of countless overburden rock mass blocks. The mass and vertical displacement of each overburden rock mass block are \(m_{i}(i=1,2,\cdots,n)\) and \(z_{i}(i=1,2,\cdots,n)\), respectively. The rocks, which have a higher stiffness than the soft and loose structures between the rock, are regarded as rigid bodies. In contrast, the soft structures between the rocks are viewed as the viscoelastic Kelvin solid. Moreover, the elastic coefficient is \(k_{j}(j=1,2,\cdots,n-1)\) and the damping coefficient is \(c_{j}(j=1,2,\cdots,n-1)\). \(k^{\prime}\) and \(c^{\prime}\) act as the stiffness and damping of contact between the advance support and the roof. As the rocks exhibit viscoelastic properties, their vertical displacement is \(\delta_{i}(i=1,2,\cdots,n)\)[25].  
Figure 3: Simplified two-dimensional graph of the dynamic model of coupling between the advance support and the roof.  
The coordinate origin of the dynamic model of the coupling system is seen as the static equilibrium position of the rock system. Through analysis of the static equilibrium position, the differential equation of the coupling dynamic response between the advance support and the roof is obtained [26]:  
\[M\ddot{z}(t)+C\dot{z}(t)+K[z(t)+\delta]=Mg \tag{1}\]  
where \(M\), \(C\) and \(K\) are the mass matrix, damping matrix and stiffness matrix of the system, respectively.  
\(\ddot{z}\) is the acceleration vector of rock blocks;  
\(\dot{z}\) is the velocity vector of rock blocks;  
\(z\) and \(\delta\) are the displacement vector of rock blocks and viscoelastic fixing between rock blocks, respectively. \(z=[z_{1},z_{2},\cdots,z_{n},z_{s}]^{T}\), \(\delta=[\delta_{1},\delta_{2},\cdots,\delta_{n},\delta_{s}]^{T}\);  
and \(g\) is the acceleration of gravity.  
The expressions of the mass matrix, damping matrix and stiffness matrix of the system are demonstrated as follows:  
\[M=\left[\begin{array}{ccccc}m_{1}&&&\\ &m_{2}&&&\\ &&\vdots&&\\ &&&m_{n}&\\ &&&m_{s}&\\ \end{array}\right] \tag{2}\]  
\[C=\left[\begin{array}{ccccccccc}c_{1}&-c_{1}&&&&\\ -c_{1}&(c_{1}+c_{2})&-c_{2}&&&&\\ &\ddots&\ddots&\ddots&&\\ &&&-c_{j-1}&(c_{j-1}+c_{j})&-c_{j}&&\\ &&&\ddots&\ddots&\ddots&\\ &&&&-c_{n-1}&(c_{n-1}+c^{\prime})&-c^{\prime}\\ &&&&-c^{\prime}&(c^{\prime}+c_{s})\\ \end{array}\right] \tag{3}\]  
\[\mathbf{K}=\left[\begin{array}{ccccccccc}k_{1}&-k_{1}&&&&\\ -k_{1}&(k_{1}+k_{2})&-k_{2}&&&&\\ &\ddots&\ddots&\ddots&&\\ &&&-k_{j-1}&(k_{j-1}+k_{j})&-k_{j}&&\\ &&&\ddots&\ddots&\ddots&\\ &&&&-k_{n-1}&(k_{n-1}+k^{\prime})&-k^{\prime}\\ &&&&&-k^{\prime}&(k^{\prime}+k_{s})\\ \end{array}\right] \tag{4}\]
## 6 Conclusions  
In this paper, a three-axis ISP based on a cantilever structure was designed which supported a rolling range from \(-85^{\circ}\) to \(+85^{\circ}\) in sweep imaging mode. The techniques were developed for airborne area camera vertical imaging and sweep imaging based on a three-axis platform. The image attitude is regulated in inertial space by rotating three axes to derivated gimbal angles. The area camera image motion calculation model was proposed, and the distribution of image motion on the sensor plane was analyzed. The linear velocity of the image motion may vary considerably at different positions of the sensor plane while the target roll or pitch angle is nonzero, so it is impossible to fully compensate for image motion for every pixel. The linear velocity of the IMC residual was analyzed using the proposed method. The exposure duration was controlled to limit the residual of the image motion compensation. The flight tests implied that the imaging attitude was correctly controlled. The image azimuth or roll deviation was less than \(0.1^{\circ}\). The pitch axis was rotated at the given angular rate to compensate for the image motion while imaging, and the deviation of the image pitch was less than \(0.35^{\circ}\). The along-fight overlap rate in the vertical imaging mode was greater than \(65\%\), which meets the stereo mapping requirement. The sweep imaging technique considerably enlarged the cross-flight angle of view, and gap-free coverage of the survey area was maintained. The image quality was excellent, and the black and white line pairs for evaluation could be clearly distinguished, which verified that the image motion was well compensated for and the residual of the image motion compensation was well constrained. The image attitude control method and the IMC residual model proposed in this paper have promising application prospects in the fields of aerial sweeping surveys and oblique photogrammetry mapping for area cameras.  
Conceptualization, Y.Y.; software, Y.Y., C.Y. and N.H.; validation, Y.Y., C.Y., Y.W., N.H. and H.K.; writing--original draft preparation, Y.Y. and C.Y.; writing--review and editing, Y.Y., Y.W. and C.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Science and Technology Major Project of China (No. 2016 YC0803000) and the Key Laboratory of Airborne Optical Imaging and Measurement at the Chinese Academy of Sciences.  
The authors declare no conflict of interest.
## 3 Results  
Results of gas permeability of investigated samples are shown in Table 2. For the ease of comparison with other data, results are given in different units: permeability coefficient for hydrogen (P\({}_{\rm H2}\))(cm\({}^{3}\)STP\(\cdot\)cm\(\cdot\)cm\({}^{-2}\cdot\)s\({}^{-1}\cdot\)cmHg\({}^{-1}\) (Barrer)); diffusion coefficient (m\({}^{2}\cdot\)s\({}^{-1}\)); and filtration coefficient (m\({}^{2}\) and mD (milidarcy)). Blend of hydrogen (10%) in methane was used for the permeability tests. Results refer to this particular type of gas. For impermeable samples (10\({}^{-11}\) cm\({}^{3}\)STP\(\cdot\)cm\(\cdot\)cm\({}^{-2}\cdot\)s\({}^{-1}\cdot\)cmHg\({}^{-1}\) or lower), only hydrogen was permeating the sample, so these results refer to the pure hydrogen. As expected, tests showed a wide range of measured permeability values. The highest gas permeability coefficient has multi-grain materials, like concrete, polymer-concrete, and geopolymer. Filtration coefficient \"k\" of investigated concrete samples is 10\({}^{-17}\) m\({}^{2}\) or higher. Lower gas permeability was observed for mudstone and salt rock before creep process. Both rocks have the filtration coefficient of 2.13 \(\times\) 10\({}^{-19}\) m\({}^{2}\) to 2.99 \(\times\) 10\({}^{-19}\) m\({}^{2}\), respectively. However, these rocks are still permeable for gases. In general, the lowest gas permeability has plain-structured materials, like epoxy resins and salt rock, after the creep process. Range of the filtration coefficient of these materials is of 10\({}^{-24}\) m\({}^{2}\). Salt rock samples became impermeable after about 12 days of exposure to 2.0 MPa of confining pressure of water. Permeability dropped from 2.99 \(\times\) 10\({}^{-19}\) m\({}^{2}\) to 5.80 \(\times\) 10\({}^{-24}\) m\({}^{2}\). Hydrogen permeability of epoxy resin samples varied, depending on the additives in the resin. Admixture of
# An Efficient Network Resource Management in SDN for Cloud Services
## 5 Performance Evaluation
In contrast, the proposed algorithm reallocates the  
Figure 11: Experimental results for Workload-E: (**a**) OSPF algorithm; (**b**) proposed algorithm. Figure 12: Experimental results for Workload-F: (**a**) OSPF algorithm; (**b**) proposed algorithm. optimal paths assigned to the earlier web flows to the new multimedia flows. As a result, the proposed algorithm provides much better performance than the OSPF. The situation is slightly different on Workload-C. On that workload, the number of multimedia flows is initially 100 and does not change over time. Whereas, the number of web flows starts at 20 and grows to 180 over time. In this situation, both the OSPF and the proposed algorithm assign the optimal paths to the multimedia flows and do not replace them even though the number of web flows increases. Therefore, there is little difference in network utilization on Workload-C. In summary, the proposed algorithm shows higher performance than the OSPF on all workloads. As a result, the network resource management method proposed in this paper is clearly more effective than the OSPF.
## 4 Relay Based QoS-Aware Routing Protocol for I2O WBAN
### QoS Metric Modeling
#### 4.3.3 Delay  
As analyzed in (17) and (20), maximizing the network lifetime will increase the delay. In addition, the I2O WBAN links suffer from high energy attention leading to transmission link instability causing higher data transmission delay. Propagation delay is an important factor in dealing with high data rate transmission scenarios. The mathematical model of the end to end delay can be expressed as:  
\[\textit{Objective:Min}\ \tau_{SC}=\tau_{S}+\tau_{RC} \tag{25}\]  
where \(\tau_{SC}\) is the delay for the in-body node \(S\) transmits to the coordinator \(C\). \(\tau_{S}\) and \(\tau_{RC}\) represent nodal delay at \(S\) and delay for data transmission between \(R\) and \(C\), respectively.  
Subject to:  
\[\tau_{S}\geq\tau_{S}^{Tx}+\tau_{S}^{queue}+\tau_{S}^{Proc}+\tau_{S}^{CC},\ \forall S\in N \tag{26}\]  
\[x\geq N\geq 0,\ \forall x\in Z^{+} \tag{27}\]  
\[P_{SR}\geq P_{S} \tag{28}\]  
\[\gamma_{S}^{dep}\geq\gamma_{S}^{arr} \tag{29}\]  
\[BER_{i}\geq BER_{pre} \tag{30}\]  
\[Min\ d_{SR}\to d_{min} \tag{31}\]  
Constraint (26) illustrates that the nodal delay \(\tau_{S}\) consists of the propagation delay \(\tau_{S}^{Tx}\), queuing delay \(\tau_{S}^{queue}\), data processing delay \(\tau_{S}^{Proc}\) and channel capture delay \(\tau_{S}^{CC}\)[21]. Constraint (27) provides the upper and lower bounds for \(N\). Considering a dense I2O WBAN network, (number of \(N\) is very large), the channel access delay \(\tau_{S}^{CC}\) will increase due to more sensor nodes contending the channel access. Constraint (28) regulates the number of data packets that need optimizing due to the limited packet handing capacity at the receiver. Similarly, Constraint (29) demonstrates that the packet arrive rate \(\gamma_{S}^{arr}\) should be less than the packet transmit rate \(\gamma_{S}^{dep}\) and this can reduce the queuing delay \(\tau_{S}^{queue}\). Constraint (30) points out that BER should be higher than a predetermined BER threshold, otherwise the rate of dropped packets will increase \(\tau_{S}^{Proc}\). Constraint (31) illustrates that minimizing the transmission distance is an effective method to decrease the propagation delay \(\tau_{S}^{Tx}\), which can be defined as:  
\[\tau_{S}^{Tx}=d\left(S\right)/c \tag{32}\]  
where \(d\left(S\right)\) is the distance between the in-body sensor node to relay in each round and \(c\) is the speed of electromagnetic wave, respectively. Since there is no agreement or standard for superframe structures in I2O WBAN devices, we follow [37] and estimate the delay as stated in (32).
## 5 Conclusions  
This paper examined the performance of a drone-assisted downlink FSO system. Furthermore, the effect of AoA fluctuations due to the orientation deviation of the hovering drone on the FSO link was investigated in detail. The unified closed-form expressions for the PDF and the CDF were obtained using the IM/DD and the heterodyne detection techniques, respectively, where the FSO link followed Malaga fading distribution. After that, this paper derived closed-form expressions for the average BER and the ergodic capacity. In addition, the asymptotic formula for the average BER under heterodyne detection was obtained with convergence at high optical power. The derived analytical results were proved by Monte Carlo simulation results and demonstrated that the performance of such systems is dependent on the receiver FoV and the ratio of beam radius to receiver aperture radius. Moreover, the system with heterodyne detection performed better than that with IM/DD technique. Eventually, the results demonstrated that the performance of the drone-assisted communication system severely degraded when the turbulence conditions worsened and the link distance increased. All these fundamental investigations provide helpful guidance for the application of the drone-assisted FSO communication system.  
Conceptualization, B.S. and G.X.; methodology, B.S. and G.X.; software, B.S.; validation, B.S., J.C. and G.X.; formal analysis, B.S. and G.X.; investigation, B.S. and G.X.; resources, B.S. and G.X.; data curation, B.S., J.C. and G.X.; writing--original draft preparation, B.S. and G.X.; writing--review and editing, B.S., J.C., G.X., Q.C. and J.W.; visualization, B.S. and G.X.; supervision, J.C., G.X., Q.C. and J.W.; project administration, J.C., G.X., Q.C. and J.W.; funding acquisition, J.C., G.X., Q.C. and J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China of grant number 62271202, 62027802, and 61831008, the Key Research and Development Program of Zhejiang Province of grant number 2023C01003, the Major Key Project of PCL, and in part by the Open Foundation of State Key Laboratory of Integrated Services Networks Xidian University of grant number ISN23-01.  
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflicts of interest.
###### Abstract  
Owing to the challenges faced by conventional therapeutics, novel peptide antibiotics against multidrug-resistant (MDR) gram-negative bacteria need to be urgently developed. We had previously designed Pro9-3 and Pro9-3D from the defensin of beetle _Protaeta brevitarsis_; they showed high antimicrobial activity with cytotoxicity. Here, we aimed to develop peptide antibiotics with bacterial cell selectivity and potent antibacterial activity against gram-negative bacteria. We designed 10-meric peptides with increased cationicity by adding Arg to the N-terminus of Pro9-3 (Pro10-1) and its D-enantiomeric alteration (Pro10-1D). Among all tested peptides, the newly designed Pro10-1D showed the strongest antibacterial activity against _Escherichia coli_, _Acintobacter baumannii_, and MDR strains with resistance against protease digestion. Pro10-1D can act as a novel potent peptide antibiotic owing to its outstanding inhibitory activities against bacterial film formation with high bacterial cell selectivity. Dye leakage and scanning electron microscopy revealed that Pro10-1D targets the bacterial membrane. Pro10-1D inhibited inflammation via Toll Like Receptor 4 (TLR4)/Nuclear factor-xB (NF-xB) signaling pathways in lipopolysaccharideacharide (LPS)-stimulated RAW264.7 cells. Furthermore, Pro10-1D ameliorated multiple-organ damage and attenuated systemic infection-associated inflammation in an _E. coli_ K1-induced sepsis mouse model. Overall, our results suggest that Pro10-1D can potentially serve as a novel peptide antibiotic for the treatment of gram-negative sepsis.  
antimicrobial peptide; gram-negative infection; peptide antibiotics; biofilm; sepsis Article
## 3 Case Study: Measurement Campaign Goal and Setup
### Target Alignment  
The corner reflectors were realigned together with the transponder for each upcoming overpass, so that all corners could be used for all overpasses during analysis. The alignment angles were computed based on the predicted RADARSAT-2 orbit for the respective overpass and the point target location (latitude, longitude).  
An accurate corner alignment is necessary because the peak RCS (_i.e._, at perfect alignment) is taken as the reference value for the computation of the transponder ERCS. The corner reflector RCS is comparably insusceptible to misalignments. On the other hand, the transponder's antenna pattern falls of more rapidly if a deviation from the main beam direction occurs, so more care needed to be taken when a transponder was aligned.  
The alignment of the corner reflectors (made out of aluminum) was performed manually with an inclinometer and a compass. A local magnetic declination of 2.5\({}^{\circ}\) was accounted for when using a compass during azimuth alignment. The alignment standard uncertainty for both axes was estimated to be not above 0.5\({}^{\circ}\).  
The transponder was mounted on a positioner unit, which allows a motorized two-axis alignment in azimuth and elevation with high mechanical repeatability (better 0.1\({}^{\circ}\)). The alignment was zeroed in elevation with a water-level. In azimuth, a compass could not be used like for the corners because the positioner's ferromagnetic components result in a misreading. Instead, the true North direction was determined by measuring a reference azimuth direction with a GPS device. For this, two reference points where placed several dozens of meters away from the transponder, one in front, one behind, and both on the line of the current alignment. By measuring the accurate GPS positions, the azimuth orientation could be computed by determining the angle between the connecting line and true North. The standard uncertainty of this method was estimated to be 0.1\({}^{\circ}\) through cross-validation.
# Internet of Things (IoT)-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8
## 3 Methodology  
### System Architecture Overview  
One of the most pressing global issues is road accidents, primarily attributed to inconsistent road surface detection, which affects vehicle stability. Our proposed system employs a dual approach to improve vehicle stability and safety. This section delves into road surface detection, safety, and vehicle stability maintenance. The overall functioning of our proposed system is depicted in Figure 1. The design follows an IoT-based architecture where edge devices (e.g., ESP32 with GY-521 and camera modules) process data locally for real-time stabilization. These devices connect to a cloud server for non-real-time tasks such as telemetry collection, remote diagnostics, and over-the-air (OTA) model or policy updates. This structure ensures low-latency stabilization while retaining IoT deployments' scalability, visibility, and remote control benefits.  
From Figure 1, the proposed system demonstrates a comprehensive data collection, processing, and implementation process to address the challenges of uneven road surfaces. Data is collected using a combination of vehicle-mounted sensors and cameras, capturing real-time information about road conditions. Initially, we used real-time data to test and measure the accuracy of the proposed system. This collected data undergoes processing to detect irregularities such as potholes, bumps, or inclines, ensuring a detailed analysis. There are two different methods for detecting uneven road surfaces: sensors and YOLOv8. We achieve better accuracy with YOLOv8, the successor of the popular computer vision framework YOLO [41]. The detection process is used to stabilize vehicles afterward.  
**ESP32:** The ESP32 microcontroller acts as the central processing unit, facilitating seamless communication between sensors, actuators, and machine learning algorithms. The GY-521 sensor, which features an accelerometer and gyroscope, continuously monitors changes in the vehicle's motion and orientation, providing critical data about road irregularities, such as potholes and bumps. This information is processed by the ESP32, which calculates the necessary adjustments to stabilize the vehicle. There are two distinct detection methods for stabilizing vehicles.
## 4 Experiments and Analysis
### Comparison of PAM Pseudorandom Sequence Estimation Under Different SNRs
Figure 20: Time-domain diagram of several signals under an SNR of \(-\)25 dB when \(\omega_{0}\) = 100.03 MHz: (**a**) Output signal of the Duffing oscillator \(y(t)\); (**b**) Binarized signal \(Sdf(t)\); (**c**) Estimated pseudorandom code; (**d**) Estimated PAM pseudorandom sequence. As shown in Table 4, with the reduction in the SNR, the Pearson coefficient between the estimated pseudorandom sequence or PAM pseudorandom sequence and the actual signals gradually decreases, but it can still exceed 0.75 at \(-\)25 dB, so there is a high correlation, indicating a good estimation result. Parameters such as the code width, duty cycle, and evaluation indicator \(z_{\rm m}\) are found to be similar under different SNRs. However, at \(-\)30 dB, the Pearson coefficients decrease substantially, and there is also a large deviation between the values of the estimated and actual duty cycles. The evaluation indicator \(z_{\rm m}\) is lower than that of other SNRs because the impact of noise leads to insufficient accuracy in three-valued processing, resulting in a large discrimination error. In addition, the pseudorandom sequence estimation performance of different related methods is compared to verify the superiority of the proposed method. These methods include the search method in reference [10], the third-order correlation function (TOCF) method in references [11; 12], the SPWVD proposed in reference [14], the WRFFFT method proposed in [15], and the PCFCRD method proposed in reference [17]. Figure 18 shows the estimation results of the PAM pseudorandom sequence under different SNRs. Both the Duffing oscillator method and the search method initially estimate the carrier frequency accurately and then estimate the PAM pseudorandom sequence accordingly. From Figures 16 and 22, the precision of the carrier frequency estimation based on the Duffing oscillator method at 0 dB is less than that of the search method, but the PAM pseudorandom sequence estimation precision based on the Duffing method is still substantially greater than that of the search method and the carrier frequency known by the third-order correlation function method as well as the other methods, which has a more obvious advantage under low SNRs. In addition, the bit error rate for the PAM pseudorandom sequence of the proposed method is only 11.67% under an SNR of \(-\)30 dB, which is far less than that of the other methods and verifies its effectiveness at a very low SNR. In conclusion, the comparison results show that the proposed AZV and CSR methods based on the Duffing oscillator can acquire an estimation precision of 0.03%, and can realize a high-precision estimation of the element width of the pseudorandom code, pseudorandom sequence, duty cycle of the pulse signal, and PAM pseudorandom sequence. At an SNR of \(-\)30 dB, the carrier frequency and PAM pseudorandom sequence estimation results still have good performance.