esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cytotoxicity of the PsIsG\({}_{h}\)
The PsI\({}_{\rm G}\)\({}_{h}\) released from the Ar_BC exhibited significantly higher inhibiting activity against biofilm development compared to the enzyme released from the unmodified BC, which is consistent with the obtained protein release results. As shown in Figure 9b, for 2.5 \(\upmu\)g and 1.25 \(\upmu\)g of PsI\({}_{\rm G}\)\({}_{h}\) loaded on Ar_BC, the biofilm inhibition rates were 63% and 55%, whereas for protein released from the nonmodified BC, the biofilm inhibition rates were 57% and 53%, respectively. The highest influence of BC modification by LPAP was observed for the concentration of PsI\({}_{\rm G}\)\({}_{h}\) equal to 0.62 \(\upmu\)g, whereas for nonmodified CB, the biofilm inhibition rate was 12%, while for Ar_BC it was 43%. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{6}{c}{**Mathematical Model**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Zero-Order**} & \multicolumn{2}{c}{**First-Order**} & \multicolumn{2}{c}{**Higuchi**} & \multicolumn{2}{c}{**Korsmeyer-Peppas**} \\ \cline{2-9}  & \(\mathbf{K_{0}}\) & \(\mathbf{R_{0}}^{2}\) & \(\mathbf{K_{1}}\) & \(\mathbf{R_{1}}^{2}\) & \(\mathbf{K_{H}}\) & \(\mathbf{R_{H}}^{2}\) & \(\mathbf{K_{K\text{-P}}}\) & \(\mathbf{R_{K\text{-P}}}^{2}\) & \(\mathbf{n}\) \\ \hline PelA\({}_{h}\)—Ar\_BC & \(1.24\times 10^{-3}\) & 0.92 & \(-2.7\times 10^{-3}\) & 0.98 & 0.034 & 0.97 & 0.016 & 0.93 & 0.63 \\ PelA\({}_{h}\)—BC & \(1.13\times 10^{-3}\) & 0.94 & \(-2.7\times 10^{-3}\) & 0.98 & 0.031 & 0.98 & 0.033 & 0.98 & 0.51 \\ PsI\({}_{\rm G}\)\({}_{h}\)—Ar\_BC & \(5.1\times 10^{-4}\) & 0.60 & \(-9.8\times 10^{-4}\) & 0.71 & 0.016 & 0.85 & 0.15 & 0.90 & 0.25 \\ PsI\({}_{\rm G}\)\({}_{h}\)—BC & \(4.9\times 10^{-4}\) & 0.88 & \(-9.1\times 10^{-4}\) & 0.92 & 0.014 & 0.98 & 0.14 & 0.98 & 0.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Release constants and correlation coefficients of the model equation applied to PelA\({}_{\rm h}\) and PsI\({}_{\rm G}\)\({}_{\rm h}\) release from native BC and LPAP-modified BC. Figure 9: The antibiofilm activity of PeAl\({}_{\rm h}\) and PsIG\({}_{\rm h}\) against _P. aeruginosa_ PAO-1 biofilm in soluble form (**a**,**d**), immobilized on BC (**b**,**e**), and immobilized on Ar\({}_{\rm z}\)_BC (**c**,**f**). The means with the same superscript are not significantly different, with \(p\)\(>\) 0.05. Error bars represent standard deviation.
## 2 Analytical Solutions of Pavement Response under Random Moving Loads and Pavement Temperatures
### Definition of Random Moving Loads and Pavement Temperature  
The mechanical responses at a certain point inside the pavement are not constant because of random load pressure, vehicle wandering, different speeds, and various temperatures of the asphalt layer when a vehicle passes over a pavement section.  
In Equation (1), only the random factors of load pressure and vehicle speed could be considered. In order to describe all the random factors in the analytical solutions of an asphalt pavement under a moving load, Equation (1) should be rewritten as follows:  
Figure 1: Viscoelastic three-layered system under a rectangular uniform moving load.  
\[R(x,y,z,t)=\frac{P}{4\pi^{2}}\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}F \left(\widetilde{\widetilde{E}}_{1},E_{2},E_{3},\mu,\rho,h_{1},h_{2},\xi_{1}, \xi_{2},z\right)\cdot e^{i\xi_{1}(x-Vt)}\cdot e^{i\xi_{2}(y-y_{w})}\mathrm{d} \xi_{1}\mathrm{d}\xi_{2} \tag{4}\]  
where, \(y_{w}\) = vehicle wandering; and \(\widetilde{\widetilde{E}}_{1}\) = viscoelastic modulus of the asphalt layer in the complex plane, which is related to \(\xi_{1}\), \(V\), and the temperature of the asphalt layer, and can be written as Equation (5). This equation is an effective model for the consideration of the viscoelastic properties of the asphalt layer.  
\[\widetilde{\widetilde{E}}_{1}(\xi_{1}V,T)=E_{\infty}\left(1-\sum_{j=1}^{J} \frac{g_{j}}{1-i\xi_{1}Va_{T}\tau_{j}}\right) \tag{5}\]  
where, \(T\) = temperature of the asphalt layer; and \(a_{T}\) = time-temperature shift factor, which is related to temperature \(T\), and can be expressed by the following Williams-Landel-Ferry (WLF) equation:  
\[\log_{10}(a_{T})=\frac{-C_{1}\cdot\left(T-T_{ref}\right)}{C_{2}+\left(T-T_{ref }\right)} \tag{6}\]  
where, \(C_{1}\) and \(C_{2}\) = model coefficients; and \(T_{ref}\) = reference temperature.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 9 Joint Routing, Power and Bandwidth Allocation in FDMA WSNs  
### System Model and Design Objectives  
In [4] a frequency division multiple access (FDMA) based single-immobile-sink WSN was considered. The objective was to jointly allocate data flows and bandwidth for the network links in order to minimize the total transmission power in the WSN. Flat fading was assumed which makes the link rates dependent only on the power levels and the bandwidth of the link irrespective of frequency dependent gains. Each sensor \(i\) has a limited total power \(P\) and a total preallocated bandwidth \(W_{i}\). The power and bandwidth allocated to the sensor's links \(l\in O\left(i\right)\) should satisfy \(\sum_{l\in O\left(i\right)}p_{l}\leq P\) and \(\sum_{l\in O\left(i\right)}w_{l}\leq W_{i}\ \forall i\in S\), where \(S\) is the set of nodes and \(O\left(i\right)\) is the set of the outgoing links of node \(i\).  
Each node allocates disjoint and continuous frequency bands to its outgoing links. All nodes are assumed to have a maximum communication range, therefore a link between two nodes exists only if both are within the communication range of each other. The flow on a link cannot exceed Shannon's capacity of the link which was compactly represented as \(c_{l}\left(p_{l},w_{l}\right)=w_{l}log\left(1+\gamma_{l}\frac{p_{l}}{w_{l}}\right)\) where \(\gamma_{l}\) is a constant related to the link \(l\).
# A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels
## 4 Design of PCEB-MS Protocol
### UCEBA Algorithm
#### 4.2.1 Selection of the Candidate Cluster Head  
After power adjustment, the obtained coverage areas, all the sinks that broadcast the sink ID data packet in their coverage areas, and the sensor nodes that receive the same sink ID are considered to be in the same region. Clustering is then conducted in this region. Some of the sensor nodes located in the overlapping region will obtain a two sink ID; these sensor nodes will then transmit data directly to the closer sink without needing to join a cluster.  
In selecting the candidate cluster head, a threshold value, \(T\), is firstly preset. A \(u_{0}\) value is then randomly generated in each sensor node in [0, 1]. If this value is smaller than \(T\), then the sensor node becomes the candidate cluster head. The calculation formula of \(u_{0}\) is shown as follows:  
\[u_{0}=u\frac{E_{0}}{E_{cu(i)}}, \tag{13}\]  
where \(E_{0}\) refers to the initial energy of the sensor node, and \(E_{cu(i)}\) refers to the residual energy of \(i\) sensor node. \(u_{0}\) is then calculated using Equation (13) after generating \(u\) randomly. A sensor node is selected as the candidate cluster head if \(u_{0}\leq T\). Moreover, a sensor node with larger residual energy has a smaller \(u_{0}\) after calculation and is thus more likely to become the candidate cluster head. A sensor node with \(u_{0}>T\) become a non-candidate cluster head and it then enters a sleeping state.
# Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion
## 4 Experimental Verification
### Case Study 1: Predicting the RUL of Bearings Using the PHM 2012 Dataset  
#### 4.2.1 Introduction to the Dataset  
The bearing dataset for the IEEE PHM 2012 Data Challenge was acquired from the PRONOSTIA test platform, and the data acquisition platform is shown in Figure 9. The vibration acceleration data of rolling bearings during their whole life cycle, from normal operation to failure, under various operating situations, are obtained by the accelerated life degradation test, and the test is halted when the magnitude of the vibration signal surpasses 20 g. The vibration signals obtained are segregated into horizontal and vertical orientations. The data is sampled at a frequency of 25.6 kHz, with recordings taken every 10 s; the time duration of the collection is 0.1 s; and there are 2560 pieces of vibration data collected each time. Table 2 displays the data set's operational circumstances.
# Analysis of the Development Status of eLoran Time Service System in China
## 7 Summary  
eLoran is an internationally standardized positioning, navigation, and time service system. It is mainly based on the traditional Loran-C and combines technologies, such as all solid-state transmitters, data links, integrity monitoring, differential enhancement, and digital reception. It utilizes the characteristics of the stable phase and the precise prediction of ground wave propagation in long wave signals to achieve high reliability, accuracy, completeness, and the continuity of services. It has the ability to form a mutual backup with the GNSS system such that they can complement and enhance each other.  
The combination of eLoran and GNSS has become a relatively safe time service model that has been widely adopted internationally. The inherent shortcomings and fragility of GNSS time service system restrict the availability and robustness of its time service, posing security risks and application limitations. Therefore, it is necessary to build multiple integrated time service resources to ensure the reliability of the time service. The eLoran time service system is a radio technology that China completely independently controls. In addition, eLoran and GNSS have many different physical characteristics, including that they are, respectively, ground- and satellite-based, had a low and high frequencies, higher and lower signal levels, perform regional and national control, etc. It is unlikely that the two will experience the same mode of failure at the same time. The application of satellite  
Figure 12: The 151.875 \(\upmu\)s delay.  
Figure 13: UN152B receiver status.  
ground integration, mutual supplementation, and backup will leverage their respective advantages, providing more secure and reliable time services.  
The eLoran system is an important national time service system in China, which can effectively improve the safety and reliability of major projects and important infrastructure, and enhance China's economic operation and national security guarantee capabilities.  
Conceptualization, C.Y. and S.L.; investigation, C.Y.; resources, S.L.; data curation, Z.H.; writing--review and editing, C.Y.; funding acquisition, C.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Youth Innovation Promotion Association CAS, grant number: 2021409.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the eLoran system is still under construction.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Methods
#### 2.2.3 THz-TDS Measurements  
THz-TDS measurements were carried out on a commercial system (TeraPulse Lx, Teraiwew), with a frequency (\(\
u\)) resolution of 0.04 THz. All measurements were performed in a nitrogen-purged chamber. Sample thickness (\(L\)) was measured using a micrometer (\(\pm\)0.005 mm) prior to the TDS measurement and was used for the calculation of the frequency-dependent refractive index (\(n(\
u)\)) and loss coefficient (\(\alpha(\
u)\)). The frequency-domain field amplitude (\(E(\
u)\)) and phase (\(\phi(\
u)\)) for the sample (s) and reference (\(r\)) pulse were obtained from the time-domain data by applying zero padding (next power of 2), apodisation (Hamming approximation) and fast Fourier transform [13; 24; 25]. Then, (\(n(\
u)\)) and (\(\alpha(\
u)\)) of each sample were calculated using the standard equations [13]:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Material** & **Avg. Particle Size (\(\mu\)m)** & **Manufacturer True Density (g/cm\({}^{-3}\))** \\ \hline PTFE & PTFE & 1 & 2.15 \\ HDPE1 & PE & 42 & 0.94 \\ HDPE2 & PE & 125 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Three polymer materials used for pellet fabrication, with particle size and solid true density at 25 °C provided by the material supplier.  
\[n(\
u)=\frac{\left(\phi_{\mathrm{s}}(\
u)-\phi_{\mathrm{r}}(\
u)\right)\,c}{2\pi\,fL} \tag{2}\]  
\[\alpha(\
u)=-\frac{2}{L}\ln\left[\frac{(n+1)^{2}}{4n}\frac{E_{\mathrm{s}}(\
u)} {E_{r}(\
u)}\right] \tag{3}\]
###### Abstract  
Intelligent reflecting surfaces (IRSs) have emerged as a promising technology to enhance link reliability in a cost-effective manner, especially for line-of-sight (LOS) link blocking caused by obstacles. In this paper, we investigate an IRS-assisted single-cell photon-counting communication system in the presence of building shadows, where one IRS is deployed to assist the communication between a multi-antenna base station (BS) and multiple single-antenna users. Photon counting has been widely adopted in sixth-generation (6G) optical communications due to its exceptional detection capability for low-power optical signals. However, the correlation between signal and noise complicates analyses. To this end, we first derive the channel gain of the IRS-assisted MIMO system, followed by the derivation of the mean square error (MSE) of the system using probabilistic methods. Given the constraints of the transmit power and IRS configuration, we propose an optimization problem aimed at minimizing the MSE of the system. Next, we present an alternating optimization (AO) algorithm that transforms the original problem into two convex subproblems and analyze its convergence and complexity. Finally, numerical results demonstrate that the IRS-assisted scheme significantly reduces the MSE and bit error rate (BER) of the system, outperforming other baseline schemes.  
intelligent reflecting surface; precoding; multi-user; Poisson shot noise +
Footnote †: journal: applied sciences
## 2 Literature Review
### Progress in Alleviating Limitations of the Centralized Network  
The existing construction dust monitoring system for government inspection agencies is managed using a central database system [22]. It is difficult to access the centralized system to read important information without permission from the system manager; all stakeholders are not fairly treated from the point of view of transparency [23].  
According to the study conducted by [PERSON] et al., security problems such as data falsification exist, as the IoT data are recorded in a central network. Moreover, free browsing of the recorded IoT information on a single server is difficult. Therefore, we introduced a new distributed IoT network to solve these problems. One approach to increase trust in accessing IoT data is through distributed services trusted by all the participants, because it guarantees the immutability of the data [24].  
[PERSON] et al. explored the application of blockchain to the IoT in Industry 4.0 using its security tools and technology. The employed sensor network has a client/server structure in which all data are managed using a central server. This structure has security and privacy problems because it is always connected to the network. To solve these problems, the importance of decentralization must be emphasized. Blockchain was suggested as an appropriate technology for the development of the IoT network [25].  
[PERSON] et al. explained that information asymmetry between information providers, owners, and viewers occurs because centralizing organizations can monopolize the information. Their study suggested the application of a blockchain information management structure. The basic idea is to safely store the data produced by personal IoT deviceson a distributed system whose design authorizes the public (the real data owners) to decide what to share and with whom [26].  
In centralized networks, sharing information is systematically difficult because it is concentrated on a single server. This structure causes information asymmetry, and a trust protocol cannot be primarily formed between network participants [27]. To solve the problems of such a centralized network, some studies have emphasized the importance of decentralization. In addition, many studies are being conducted to secure the reliability and accessibility of the network using the blockchain as a representative technology of decentralization.
# A Novel Video Transmission Latency Measurement Method for Intelligent Cloud Computing
## 4 Evaluation  
Combined with the requirements of the above three latency measurement methods on the latency measurement device, we designed a latency measurement device that can meet the requirements of the three measurement methods and implemented the hardware implementation. The design framework and physical drawing are shown in Figure 8, and the device is named CC3030. The device framework is mainly composed of heterogeneous systems based on Xilinx ARTIX-7 series FPGA and STM32F7 series MCU. Among  
Figure 7: Hash packet format.  
them, FPGA is mainly responsible for image information processing and STM32 is mainly responsible for data processing and external interaction. The information processed by FPGA is transmitted to the STM32 microcontroller and the microcontroller calculates the latency. The device provides two HDSDI ports and two HDMI ports for video input and one HDSDI and one HDMI for OSD (On Screen Display) menu display. It can receive external GPS signals and the device's configuration and measurement results can be seen on other devices' web pages through the network port.  
The following is an example of three latency measurement methods using different configurations of this self-made CC3030. The camera model used in the measurement is SHD60 and the two codec systems are H.264 codec and H.265 codec. H.264 codec was selected from Sculpture Networks. The encoder model was Snenc1000 (for San Diego, CA, USA), and the decoder model was Sncupid1000 (for San Diego, CA, USA). H.265 codec is the codec used by Huawei Hislicon HI3519 video codec solution.
## 2 Related Work
The tiny aggregation service (TAG) algorithm [21] is based on a tree structure network, where the number of transmitted packets are decreased by data aggregation. Cluster-based aggregation is used to overcome the problem of transmission delay and the loss of data due to node failures in the route to the sink. The CH aggregates the information from CMs and performs the average, or minimum, or maximum aggregation function. Different cluster-based protocols are explored in [22]. In cluster-wide correlated grouping (CWCG) [23] hybrid structure for data aggregation is proposed, which uses the concept of the temporal and spatial grouping of nodes. It provides reduced transmission cost, but increases the latency. In adaptive data aggregation (ADA) [24], the temporal aggregation degree is controlled by the reporting frequency of the events at the sensor nodes, and the spatial degree of aggregation is set by the aggregation ratio at the CH. The sink has the central control for aggregating the data by sending the temporal and special degree. It provides improvement in the desired reliability compared to that observed. In [25], a grid-based spatial correlation clustering algorithm (GSCC) forms the sensor nodes with high similarity of data values in the same clusters. After the cluster formation, the cluster head node, which is located nearer to the center of the grid, approximates the data of the cluster members according to historical data, and then, only the cluster head sends the aggregated data to the sink. This algorithm is based on data with a Gaussian distribution. In this paper, we consider all three areas of clustering, MAC and data aggregation. We propose a CCBE protocol that has three phases. In the setup phase, it calculates the optimum size of the hexagonal structure and divides the entire network nodes using this structure into different clusters. Then, it selects the CH based on the optimum CH distance and residual energy. In the slot allocation phase, the sink node assigns time slots to the CHs, and CHs assign time slots to the CMs based on the energy capacity. Thirdly is the steady transmission phase, where the CMs send the messages to CHs and CHs gather and aggregate the data using various decision schemes. After aggregation of information, it then forwards to the sink node.
## 2 Materials and Methods
### Urban Environmental Parameters
#### 2.3.2 Normalized Difference Water Index  
The NDWI quantifies water and humidity levels for vegetation and soil. In this case, Sentinel-2 BOA reflectance data from bands 8 (near infrared, _NIR_, band) and 3 (_Green_) is used, through Equation (2):  
\[NDWI=\frac{R_{BOA}Green-R_{BOA}NIR}{R_{BOA}Green+R_{BOA}NIR} \tag{2}\]  
The average summer _NDWI_ at 20 m resolution is computed using Google Earth Engine (r) [34], providing insights into water bodies and humidity levels. The average NDWI value within the same 20 m buffer as described in Section 2.2.2 is computed and assigned to each 50 m street/road segment.
# Thermo-Responsive Shape-Memory Effect and Surface Features in Polycarbonate (PC)
## 3 Characterization of Heating-Responsive SME
### Influence of Programming Temperature and Strain (High Temperature Programming)  
#### 3.2.1 Shape Fixity Ratio  
A few series of tests were conducted at different high programming temperatures to different programming strains. Typical strain versus stress relationships of these tests are presented in Figure 8 for programming strains of 30% and 100%. Note that in each test, upon reaching the prescribed maximum programming strain, cooling to room temperature was followed before unloading at the same speed as in loading.  
Figure 6: Typical stress versus strain relationships in uni-axial tension to five different programming strains at room temperature, and then followed by unloading.  
Figure 7: Shape fixity ratio and shape recovery ratio upon heating to 180 \({}^{\circ}\)C versus programming strain relationships for samples programmed at room temperature.  
As shown in Figure 8, despite some discrepancy, the stress vs. strain relationships of the samples with both 30% and 100% maximum programming strains share the same trend. It is obvious that with the increase in programming temperature, the material shows less resistance to deformation. The yield strength is about 16 MPa in samples programmed to both strains of 30% and 100% at 128 \({}^{\circ}\)C (which is a temperature slightly below the glass transition start temperature, 130 \({}^{\circ}\)C, according to Figure 3), while for the samples programmed at 145 \({}^{\circ}\)C (slightly below the glass transition finish temperature, 150 \({}^{\circ}\)C) and at 160 \({}^{\circ}\)C (above the glass transition finish temperature, 150 \({}^{\circ}\)C), there is no clear yield point and the corresponding strength is much lower.  
When compared with the yield stress of around 90 MPa at room temperature in Figure 6, the yield stress of about 16 MPa at 128 \({}^{\circ}\)C (Figure 8) is significantly lower. Hence, it can be concluded that part of the material is already softened at 128 \({}^{\circ}\)C.  
Figure 9 presents the relationship of shape fixity ratio (\(R_{\beta}\)) versus programming temperature for 20%, 30% and 100% maximum programming strains. Same as those in many other engineering polymers reported in the literature [17; 18; 19], \(R_{\beta}\) of this PC increases with not only the increase in the maximum programming strain, but also the increase in programming temperature.