esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Conditions and Methods
### Key Indicators and Conditions
#### 2.1.2 Maintenance Measures and Effects  
This paper recommends the selection of typical maintenance technologies suitable for rural roads, given their large maintenance scale, complex road shape, low maintenance funds, and low level of mechanization. The recommended measures include five strength grades: P1 (slurry sealing layer), P2 (thin layer cover), P3 (crushed stone regeneration: using a multi hammer crusher to break the old road surface into smaller particle sizes, compacting it with a roller, and then adding a new road structure), P4 (milling and repaving one layer), and P5 (milling and repaving two layers). By calculating the costs of each different maintenance technology and evaluating previous engineering application effects, this paper provides the cost and improvement in pavement damage (RPCI) and driving quality (RRQI) after implementation, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Evaluating Indicator**} & \multicolumn{5}{c}{**Evaluation Grade**} \\ \cline{2-6}  & **Excellent** & **Good** & **Average** & **Inferior and Poor** \\ \hline \multirow{2}{*}{Pavement damage} & RPCI & \(\geq\)90 & \(\geq\)80 & \(\geq\)70, \(<\)80 & \(<\)70 \\  & CR (\%) & \(\leq\)0.4 & \(\leq\)2.6, \(<\)0.4 & \(>\)2.6, \(\leq\)7.8 & \(>\)7.8 \\ \multirow{2}{*}{Pavement driving quality} & RRQI & \(\geq\)90 & \(\geq\)80, \(<\)90 & \(\geq\)70, \(<\)80 & \(<\)70 \\  & IR (m/km) & \(\leq\)3.0 & \(>\)3.0, \(\leq\)4.7 & \(>\)4.7, \(\leq\)5.8 & \(>\)5.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key evaluation indicators and grading standards for pavement performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Measures**} & **Cost (China RMB)** & **Life/** & \multicolumn{2}{c}{**Implementation Effect (Improvement Value)**} \\ \cline{3-5}  & **\%/m\({}^{2}\)** & **Year** & **RPCI** & **RRQI** \\ \hline \hline P1: slurry sealing layer & 29 & 2–4 & MIN (100, RPCI + 10) & MIN (100, RRQI + 10) \\ \hline P2: thin layer cover & 90 & 4–6 & MIN (100, RPCI + 10) & MIN (100, RRQI + 10) \\ \hline P3: crushed stone regeneration & 72 & 4–6 & MIN (100, RPCI + 20) & MIN (100, RRQI + 20) \\ \hline P4: milling and repaving 1 layer & 134 & 6—8 & 100 & 100 \\ \hline P5: milling and repaving 2 layers & 298 & 7–9 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Maintenance technology cost and effect after implementation.
## 5 Experiments
### Practical Evaluation on Resource-Constrained Devices
#### 5.5.3 Energy Consumption
This architecture would involve the synchronization of resource-constrained devices with multiple public blockchain networks. Such a setup introduces new challenges related energy efficiency. To assess the sustainability of this scenario, future evaluations should incorporate standardized energy consumption metrics widely used in the literature [113] like annual power consumption (MWh) or energy consumed per transaction (Wh/Tx). These indicators enable objective comparisons across different blockchain platforms and consensus mechanisms. For instance, recent advancements, such as the transition of Ethereum to PoS through Ethereum 2.0, have led to a drastic reduction in energy usage, significantly enhancing the energy efficiency of DApps. Nonetheless, existing energy assessment models primarily designed for PoW systems are becoming increasingly inadequate for characterizing modern blockchain architectures. As noted in [43], evaluating the environmental footprint of PoS-based platforms, Layer 2 scaling solutions (e.g., rollups), and sharded architectures requires updated models that account for their reduced computational overhead [114].
###### Abstract  
Saponins are pharmacologically active compounds that have been shown to ameliorate abnormal physiological processes and be aptly applied in folklore for the treatment of maladies occasioned by infectious agents. Consequently, saponins from _Vitex doniana_ and _Pentaclettra macrophylla_ were evaluated for antibacterial properties, as these herbs are used in folk medicine. Dried pulverized plant materials were defatted, and solvents with varying polarity were applied at varying ratios for the extraction of saponins. Phyto-chemistry was in accordance with standard methods, while an antibacterial assay was made through the agar well diffusion and micro broth dilution techniques. Phytochemical quantitation showed high concentrations of tannins, 231 \(\pm\) 0.6 CE/g, and saponins, 58% from _V. doniana_. Similarly, _P. macrophylla_ stem bark extract also showed high concentrations of tannins, 309 \(\pm\) 2.42 CE/g, alkaloids, 71% \(\pm\) 0.5%, and saponins, 87% \(\pm\) 3.4%. The ethanol extracts of _V. doniana_ inhibited the growth of _Staphylococcus aureus_ (ATCC 11775) and a clinical strain with inhibition zone ranges of 15.5 \(\pm\) 2.12 to 7.0 \(\pm\) 0.0 (mm) against leaf extracts and 20.0 \(\pm\) 1.41 to 7.0 \(\pm\) 0.0 (mm) against stem bark extracts. Conversely, saponin extract from _V. doniana_ showed a broad spectrum of activity, as it inhibited both Gram-negative and -positive test strains, _E. coli_ clinical strain (20.0 \(\pm\) 1.41 mm), _P. aeruginosa_ clinical strain (18.5 \(\pm\) 0.71 mm), _E. coli_ ATCC 11775 (17.0 \(\pm\) 0 mm), and _S. aureus_ clinical strain (13.0 \(\pm\) 1.41 mm). However, a broad spectrum was similarly achieved with _P. macrophylla_ extracts, as all test bacteria genus was susceptible. Saponin fractions showed a high potency and broad spectrum antibacterial activity and thus a validation of the folklore applications and the potential for use as a drug or drug scaffold.  
Keywords: phyto-constituents; folk medicine; cold maceration; aglycone; steroidal saponins +
Footnote †: journal: Applied Sci. 2016
# Effect of Bond-Slip on Dynamic Response of FRP-Confined RC Columns with Non-Linear Damping
## 1 Introduction
In previous analysis, [PERSON] [31] used nonlinear rotational springs at the end of beam-column elements to include the member end rotation due to strain penetration effects, which cause large computation and low precision problems due to the empirical nature. In order to simplify the calculation, the steel bars' bond-slip effect is often ignored in the structural analysis. However, in subsequent studies neglecting the effects of bond-slip will lead to smaller prediction of lateral deformation and overestimation of energy dissipation under cyclic load and curvature of skeleton curve [32]. Until [PERSON] and [PERSON] [33] introduced a hysteretic model for the reinforcing bar stress versus slip response using a zero-length section element for dealing with the member end rotation in the RC column foot. Nevertheless, as far as the author knows, there is almost no research conducted on the influence of localized steel bond-slip on of FRP-C RC columns based on nonlinear damping theory. Given the FRP confinement effect and the importance of reinforced slip on the structural seismic analysis, it is necessary to consider the influence of bond-slip on energy dissipation and damping performance of FRP-C RC columns. For establishing a nonlinear damping model considering localized steel bond-slip for FRP-C RC column, this paper mainly carries out the following works: (1) A zero-length section element available in the OpenSees [34; 35] is introduced to simulate the steel bars' bond slip of FRP full-confined RC columns; (2) and the regression analysis of unit energy dissipation formula is conducted to redefine the damping ratio of FRP-C RC columns; and (3) finally, the time-history analysis of FRP-C RC columns considering localized steel bond-slip under harmonic loads is conducted.
# AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
## 2 Related Work
### Classical Computer Vision-Based Approaches
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**References** & **Methods/Architectures** & **Dataset** & **Pre-Processing** & **Post-Processing** & **Results** \\ \hline \multicolumn{6}{l}{_Classical computer vision-based_} \\ \hline
[14] & Region growing & PRIVATE & Local contrast \& hole filling & - & 83\% acc \\ \hline
[15] & Active contours & LIDC-IDRI & Thresholding \& morph operations & Markov random field & 69\% IoU \\ \hline
[16] & Level sets & LIDC-IDRI & Statistical intensity & Region condition & 94\% acc \\ \hline
[17] & Graph cuts & PRIVATE & Gaussian smoothing & - & 98.74\% dice \\ \hline
[18] & Adaptive thresholding & LIDC-IDRI & Histogram equalization \& noise filtering & Morph operations & 96\% acc \\ \hline
[19] & GMM fuzzy C-means & LIDC-IDRI \& GHC2 MCPLA & Non-local mean filter \& gaussian pyramid & Random walker & 86\% dice \\ \hline
[20] & Region-based fast marching & LIDC-IDRI & Convex hulls & Mean threshold & 61–93\% dice \\ \hline \multicolumn{6}{l}{_Dvp learning-based_} \\ \hline
[10] & U-Net & LIDC-IDRI & Nodule ROI selection & - & 74\% dice \\ \hline
[7] & iW-Net & LIDC-IDRI & Nodule ROI selection & - & 55\% IoU \\ \hline
[8] & U-Det & LUNA16 & Data aug. & - & 82.82\% dice \\ \hline
[11] & Nodulenet & LIDC-IDRI \& LUNA16 & Nodule ROI selection \& Data aug. & - & 71.85\% IoU \\ \hline
[9] & DB-ResNet & LIDC-IDRI & Nodule ROI selection \& Data aug. & Remove noisy voxel & 82.74\% dice \\ \hline
[13] & MRRN & TCIA \& MSKCC \& LIDC-IDRI & Nodule ROI selection \& Data aug. & - & 74\% dice \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of existing lung nodule segmentation methods. The undeclared information is marked with dashes (-) in the referred literature. Aug., morph, acc and IoU stand for augmentation, morphological, segmentation accuracy and intersection over union, respectively.
# Weight Reduction of a Ship Crane Truss Structure Made of Composites
## 3 Structural Analysis
### Load Cases  
During lifting operations in the port and the sea, the CM is subject to the influence of loads from the weight of the cargo, inertia due to the motion of the vessel, wind loads, etc. The design and strength calculation of offshore cranes must be carried out in accordance with the rules of the maritime classification societies, for example, DNVGL-ST-0377 or EN 13852-1:2004(E). The rules include several basic design cases that take into account the above loads in the form of dynamic factors, heel angles, acceleration values that depend on the ship's basic measurements and wave loads, and wind pressure. In general, more than ten combinations of loads, accelerations and positions of the CM are obtained.  
Dynamic loads were converted to static loads with corresponding dynamic coefficients. An equivalent static load set is defined as a static load set which generates the same displacement field as that from a dynamic load at a certain time for different crane positions.  
DNV rules consider 11 load combinations, which are divided into three groups: regular loads caused by the lifting and movement of cargo on the high seas and in the port, irregular  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Carbon-Fiber Composite** & **Glass-Fiber Composite** \\ \hline Part & Rovings & Frames \\ \(E\) & 112 GPa & 110 GPa \\ \(\
u\) & 0.3 & 0.3 \\ \(\rho\) & 1330 kg/m\({}^{3}\) & 1540 kg/m\({}^{3}\) \\ \(\sigma_{u}\) & 447 MPa & 603 MPa \\ \hline \hline \end{tabular} \(\sigma_{u}\) is the ultimate compressive strength in 0\({}^{\circ}\) direction.  
\end{table}
Table 1: Properties of materials.  
loads caused by wind, temperature and environmental influences, and special loads caused by emergency situations. Ultimate loads are considered in the third group. From 11 load combinations we chose 6 the most dangerous.  
To evaluate strength and stability of the structure, three characteristic positions of the CM (Figure 3) were considered in accordance with regulatory documents [6]. The load combinations include forces \(S_{k}\) and \(P\) (Figure 4) representing the combined action of gravity, cargo weight, wind pressure, inertia forces caused by the ship's motions, loads from the roll and trim, and dynamic loads caused by the operation of drives and mechanism, calculated for a given roll angle \(\theta\). The calculation scheme is shown in Figure 4.
## 3 Spectral Analysis
### 22 March 2020 Zagreb Earthquake  
The acceleration response spectra from the QUHS and QARH records are shown in Figure 8 and summarized in Table 3. The spectral peaks for QUHS station (Figure 6--up) are mainly between 0.1 and 0.2 s, equivalent to a frequency of 5-10 Hz, with the horizontal components having an auxiliary peak at around 0.5 s or 2 Hz. For the QARH station (Figure 6--down) the peak values are again mainly between 0.1 and 0.2 s, equivalent to frequencies of 5-10 Hz, and with the horizontal components having a second peak at around 0.5 s or 2 Hz.  
As mentioned earlier, the authors of [5] modeled the acceleration from the Zagreb March 2020 earthquake at locations within an epicentral distance of 5 km. The corresponding target values of PGA on rock were calculated in the range of 0.159-0.185 g. Using the model for V\({}_{\text{S30}}\) categories defined in the Eurocode-8 [11] they estimated a median site amplification factor of at least 2, with the possibility of 3 at some locations and in the city center which suffered most of the damage. [PERSON] [12] performed semi-empirical estimation of the Zagreb M5.5 ground motion amplification using 1D equivalent-linear site response analysis and computed amplification factors of between 1.7 and 2.3 for the site profile between the epicenter and QUHS and QARH stations.
# Provisioning of Fog Computing over Named-Data Networking in Dynamic Wireless Mesh Systems
## 3 The Proposed Solutions
### Dynamic Face Management
#### 3.1.2 Implementation Details  
In Named-Data Networking (NDN), the concept of a Face (implemented as the nfd:Face class) is instrumental in facilitating best-effort delivery services across various underlying communication mechanisms, such as sockets. This abstraction is key in abstracting the complexities and specific details of the underlying protocols, ensuring seamless integration with the forwarding layer of the NDN network [26]. Faces include two main parts--a link service and a transport service (NetDeviceTransport). Faces are created through callbacks by going through each network interface associated with the node (called NetDevices, ND), such as Ethernet port, WiFi board, etc. Configuration of the transport service includes a remoteUri attribute representing the remote endpoint of communication (for example, the MAC address of the neighbor node). This attribute is read-only (immutable during the Face lifetime). The process begins with identifying the scheme corresponding to the underlying protocol, such as Ethernet, in channel-level applications. This is followed by a representation specific to the scheme, which details the underlying address. An example of such an address in the context of Ethernet is _01:00:5e:00:17:aa_[26].  
In our manuscript, we primarily focus on the application of our modifications within the ndnSIM environment, utilizing NetDevice and WifiNetDevice. However, it is important to note that in a practical, real-world deployment, the NFD interacts with Ethernet adapters through the libpcap library. Although the current scope of our study is centered around simulation, we acknowledge the potential and feasibility of adapting our proposed modifications for real-world implementation, see Figure 4.
# A Comprehensive Review on Network Protocol Design for Autonomic Internet of Things
## 3 Review of Protocol Designs
### [PERSON] et al. in [13] proposed the energy management based on Sensor MAC (SMAC) protocol for WSN networks that perform energy harvesting from solar power. In SMAC, the nodes are periodically turned to sleep state to reduce the energy consumption. The wake up state and the active period are determined by the contention window at the MAC layer, which is assumed as a fixed duration so that the duty cycle depends only on the sleep period. Each active period is divided into two phases. The first phase is for synchronization among nodes, while the second phase is to process RTS/CTS contention.  
In the SMAC protocol mechanism, the node that is chosen as the winner in the contention process can start to send data after the active period ends. All other nodes must enter the sleep state as they are not managed as the winner or they experience an RTS collision. The winner of contention process will not enter the sleep state until all data packets sent obtain acknowledgement.  
The concept of the SMAC protocol is validated through analytical model. The model can show that the throughput will be higher with the increase of duty cycle, but it will also lead to the increase in energy consumption. The S-MAC weakness is that the network performance can be degraded due to the increase of packet collisions, because of the high population of nodes in the network.
## 3 Results
### Correlation Analysis  
Correlation analysis was performed between the parameters L1, L2, l1, l2, MM1, MM2, PP1, and PP2. The results are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Group** & **PA2-PA1** & **aP2-aP1** & **l2-l1** & **L2-L1** & **MM2-MM1** & **PP2-PP1** \\ \hline \hline RME & & & & & & \\ Average value & +3.99 & +2.29 & \(-\)0.54 & \(-\)0.52 & +4.41 & +3.3 \\ \hline Std.Deviation & 2.841 & 1.792 & 0.773 & 1.180 & 1.632 & 1.178 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reported changes in parameters of the dental arch during the treatment phase with RME.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Variables** & **L2** & **l1** & **l2** & **MM1** & **MM2** & **PP1** & **PP2** \\ \hline L1 & 0.860 *** & 0.739 *** & 0.635 *** & 0.055 & 0.108 & \(-\)0.092 & 0.238 \\ L2 & & 0.701 *** & 0.765 *** & \(-\)0.056 & \(-\)0.054 & \(-\)0.083 & 0.280 \\ l1 & & & 0.907 *** & 0.299 & 0.179 & 0.181 & 0.243 \\ l2 & & & & 0.215 & 0.014 & 0.082 & 0.143 \\ MM1 & & & & & 0.736 *** & 0.293 & 0.304 \\ MM2 & & & & & & 0.261 & 0.506 ** \\ PP1 & & & & & & 0.836 *** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correlation analysis between variables L1, L2, l1, l2, MM1, MM2, PP1, and PP2 in the RME appliance group.  
It can be seen from Table 3 that:  
* All statistically reliable correlations are positive;
* L1 correlates strongly with L2, l1, and with l2;
* L2 correlates very strongly with l1 and l2;
* I1 correlates very strongly with l2;
* MM1 correlates strongly with MM2;
* PP1 correlates strongly only with PP2;
* MM2 correlates strongly with PP2.  
The results of the correlation analysis between the variables MM2-MM1, PP2-PP, L2-L1, l2-l1, aP2-aP1 and PA2-PA1 in the studied group of patients, proved that (Table 4):  
* The change in width MM2-MM1--correlates positively and strongly with PP2-PP1, aP2-aP1 and PA2-PA1;
* PP2-PP1 correlates with aP2-aP1 positively and strongly, and with PA2-PA1. PP2-PP1 correlates with l2-l1 and L2-L1--negatively;
* L2-L1 correlates only with l2-l1--positively and strongly;
* l2-l1 correlates with aP2-aP1 positively and moderately;
* aP2-aP1 correlates with PA2-PA1 positively and strongly.  
All relationships established so far, as well as the results of the correlation analysis, show that there is a high degree of positive correlation between the variables PP2-PP1 and MM2-MM1 (Table 5). MM2-MM1 correlates significantly with PP2-PP1 and with PA2-PA1, which allows us to look for the presence of a linear regression model and evaluate the average statistical change in the dependent variables from MM2-MM1. A similar correlational relationship was also sought between PP2-PP1 and PA2-PA1. The established relationship is presented by mathematical equations and illustrated with a scatter diagram.
## 2 The Proposed Method
### EKF-Based Sensor Fusion  
The 6 DOF pose of the mobile robot can be expressed as \(x=[p^{T},\Omega^{T},\
u^{T}]^{T}\) where \(p=(p_{x},p_{y},p_{z})^{T}\) denotes the position information of the mobile robot, \(\Omega=(\phi,\theta,\psi)T\) denotes orientation in roll, pitch, and yaw, \(\
u=(v_{x},v_{y},v_{z})^{T}\) denotes the velocity. The output information of IMU is expressed as \(u=[\alpha^{T},\)\(a^{T}]^{T}\), as \(\omega\)= \((\alpha_{x},\)\(\alpha_{y},\)\(\alpha_{z})^{T}\) is the angular velocity, and \(a=(a_{x},\)\(a_{y},\)\(a_{z})^{T}\) is the acceleration. Then, the state transition of the 6-DOF motion estimation system can be expressed as:  
\[\dot{p}=\
u \tag{12}\]  
\[\dot{\Omega}=E_{\Omega}\cdot\omega \tag{13}\]  
\[\dot{v}=R_{\Omega}\cdot a+g \tag{14}\]  
where \(R_{\Omega}\) denotes cosine matrix vector from mobile robot to global coordinate, \(E_{\Omega}\) transform angle velocity into the derivative of Euler angle derivative, and \(g\) is gravity constant. Since a low-cost IMU sensor is used, the effect of the earth rotation is not considered.  
The prediction model of EKF is provided by the output of the IMU sensor. In order to alleviate the error caused by the IMU drift, additional preprocessing or correction is necessary. The observation model of the EKF-based system is the pose information of the 3-DOF in the plane provided by the 2D SLAM system. The error of the IMU is corrected using the 3-DOF pose information, and the pose of the 6-DOF is updated. The process of prediction and update of EKF is shown as follows:  
\[\left(P^{+}\right)^{-1}=(1-k)\cdot P^{-1}+k\cdot C^{T}R^{-1}C \tag{15}\]  
\[\hat{x}^{+}=P^{+}[(1-k)\cdot P^{-1}\hat{x}+k\cdot C^{T}R^{-1}\xi^{*}]^{-1} \tag{16}\]\[\mathrm{K}=\mathrm{PC}^{T}\bigg{(}\frac{1-k}{k}\cdot\mathrm{R}+\mathrm{C}^{T} \mathrm{PC}\bigg{)}^{-1} \tag{17}\]  
\[\mathrm{P}^{+}=\mathrm{P}-(1-k)^{-1}\cdot\mathrm{KCP} \tag{18}\]  
\[\hat{\mathrm{x}}^{+}=\hat{\mathrm{x}}+\mathrm{K}(\xi^{*}-\mathrm{C}\hat{ \mathrm{x}}) \tag{19}\]  
where \(\xi^{*}\) represents 3-DOF pose information provided by the 2D SLAM system, \(k\) represents weight coefficient of updating pose, the 6-DOF pose information generated by EKF is reduced dimension by projection matrix \(\mathrm{C}\), and the result is used as the initial pose of the 2D SLAM system.