esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of Antenna Height on 3D Channel:
## 3 Results
### Scenario A: Straight Street in the Center of Paris
\[\sigma_{AS}=\sqrt{\frac{\sum_{i=1}^{L-1}P_{i}\theta_{i}^{2}}{\sum_{i=1}^{L-1}P _{i}}-\left(\frac{\sum_{i=1}^{L-1}P_{i}\theta_{i}}{\sum_{i=1}^{L-1}P_{i}} \right)^{2}} \tag{5}\]  
\[\sigma_{DS}=\sqrt{\frac{\sum_{i=1}^{L-1}P_{i}\tau_{i}^{2}}{\sum_{i=1}^{L-1}P_{ i}}-\left(\frac{\sum_{i=1}^{L-1}P_{i}\tau_{i}}{\sum_{i=1}^{L-1}P_{i}}\right)^{2}} \tag{6}\]  
Figure 4: The statistic results of logarithm exponent values under straight street scenario with various antenna heights (4 m, 6 m, 8 m, 10 m, 12 m, 14 m,16 m, and 18 m), the EAoD down, EAoD up, EAoA down, and EAoA up represent \(\lambda^{{}^{\prime}}_{\text{1 EAoD}}\), \(\lambda^{{}^{\prime}}_{\text{2 EAoD}}\), \(\lambda^{{}^{\prime}}_{\text{1 EAoA}}\), and \(\lambda^{{}^{\prime}}_{\text{2 EAoA}}\), respectively. Figure 3: PDFs of EAoD and EAoA under the straight street scenario. (**a**) The description of the antenna height at 8 m; and (**b**) the description of the antenna height at 12 m.  
where \(P_{i}\) is the ray power, \(\theta_{i}\) and \(\tau_{i}\) are the angle and delay of \(i\)th ray. The statistic results of the AS and the DS are represented in Figures 5 and 6, respectively. Figure 5 indicates that the value of the AS of EAoD is smaller than that of EAoA, and the variation range of the AS of EAoD is also smaller. The AS reaches its maximum value when the antenna height (10 m) is half of the building's height. Figure 6 exhibits that the value of the DS is nearly unchangable when the antenna height is around half of the buildings height and increases conspicuously when the antenna is near the bottom or the top of the buildings. By using the linear least square regression, we can obtain the correlation of the AS and DS with antenna height:  
\[\sigma_{AS,EAOD}=\left\{ \begin{array}{rl}0.1m+9.4,&m\leq 10\\ -0.11m+11.5,&10<\ m\leq 20\end{array} \right. \tag{7}\]  
\[\sigma_{AS,EAOA}=\left\{ \begin{array}{rl}0.383m+23.07,&m\leq 10\\ -0.4625m+31.525,&10<\ m\leq 20\end{array} \right. \tag{8}\]  
\[\sigma_{DS}=\left\{ \begin{array}{rl}-2.905m+53.74,&m<8\\ 30.5,&8\leq m\leq 12\\ 2.3667m+2.1,&12<\ m\leq 20\end{array} \right. \tag{9}\]  
Figure 5: The AS under the straight street scenario with various antenna heights (4 m, 6 m, 8 m, 10 m, 12 m, 14 m, 16 m, and 18 m). (**a**) The description of the AS of EAoD; and (**b**) the description of the AS of EAoA. Figure 6: The DS with various antenna heights (4 m, 6 m, 8 m, 10 m, 12 m 14 m, 16 m, and 18 m). where \(m\) is the antenna height. \(\sigma_{AS,EAOD}\), \(\sigma_{AS,EAOA}\), and \(\sigma_{DS}\) are the AS of EAoD, EAoA, and the DS, respectively.
# Wireless Sensor Network Aided Assembly Line Monitoring According to Expectations of Industry 4.0
###### Abstract  
Striving for excellence during the assembling process through incorporating the expectations of Industry 4.0 requires complex information management on issues of overall system status, especially the physical characteristics and position of the parts being assembled, as well as the assembling units and tools. This research introduces both an overall customized assembling system supervision model, which is based on a modified four-layer control system hierarchy that suits the specific requirements of such systems and the developed wireless sensor network technology for assembling process management with a particular focus on localization. The developed model highlights the localization problems of the system as well as other aspects required for overall system status determination. The localization of assembled parts is based on the fingerprint localization method by using the received signal strength indicator. The proposed localization algorithms are based either on artificial neural networks or on the weighted k-nearest neighbor method. The developed model has been tested both in laboratory conditions and in a simulated industrial environment. The research results offer a general solution to the problem of assembling system supervision, regardless of size and shape, with emphasis on the localization problem solution.  
assembling process monitoring; wireless sensor network; localization technology 2020
## 2 Materials and Methods
### Electro-Precipitation Materials  
BDD substrates with dimensions of 2 \(\times\) 2 cm\({}^{2}\) were fabricated with thicknesses of 400 to 500 nm. The BDD layers are grown using conventional CH\({}_{4}\) in H\({}_{2}\) mixture in a microwave high-density plasma, grown after dense seeding on Si substrates. The material exhibits a nanocrystalline nature, with grain sizes of the order of a few tens of nm. Such BDD electrodes have been used for a broad range of electrochemical applications aimed at the detection of analytes in complex media, such as chemicals in sea water [37], neurotransmitters in physiological serums [38], biofluids in urine [39], or a broad range of applications [37; 40; 41; 42; 43; 44]. Doping of the layers was tuned at 2 \(\times\) 10\({}^{21}\) at.cm\({}^{-3}\) for optimal electrochemical performance [45]. Bare BDD electrodes are used, i.e., with no particular surface functionalization, as promoted by the ability to clean the surface in complex media [39; 46; 47]. Electrochemical cells investigated are similar to those used and described in [36]. They are composed of a planchet (here either stainless steel or BDD substrates), and put on a stainless steel support on which an acrylic glass cell equipped with a plastic seal is screwed, as depicted in Figure 1. The estimated planchet exposed area of the cathode is of about 1.2 cm\({}^{2}\) and a platinum pin is used as the anode. Electrolyte solutions are homogenized using a mechanical stirring system composed of a mechanical rotator (rotating rate of 1500 rpm) on which a plastic disposable coffee stirrer is connected.  
Figure 1: Electro-precipitation cell: (**1**) plastic coffee stirrer connected to a mechanic rotating system; (**2**) platinum anode; (**3**) glass cell; (**4**) O ring plastic seal; (**5**) stainless steel or BBD planchet cathode; (**6**) stainless steel support with an electrical contact.
## 3 Results and Discussion
### Evaluation of Button Sensor Antenna: Simulated Results
#### 3.1.2 Human Body Phantoms  
A button sensor antenna design is wearable; however, it is on a solid platform that provides better rigidity and stability than a textile. In healthcare applications, this capability is advantageous. Figure 10 depicts various body phantoms and antenna locations that were used in the simulations to characterize the performance of the wearable antenna.  
Figure 10: Set up for the 3-layer on-body cases considered for various testing purposes.  
Figure 9: Radiation patterns of BSA on 3-layer phantom models at lower (**left**) and upper (**right**) bands in various scenarios (Solid black line, H plane; red line, E plane, at a gap of 5 mm).
## 2 Materials and Methods  
### Study Area and Flux Sites  
In this research, a typical ASA area of northwest China with of about 143,000 km\({}^{2}\), the inland Heihe river basin, is selected as the main research region (Figure 1). Distinctive cold and arid landscapes are widely spread around the whole Heihe river basin [30; 31]. In May 2012, the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) was carried out in northwest China [32]. In August of the next year, a hydrometeorological observation network was fully built and put into operation as part of ChinaFlux. A string of flux towers on various vegetation types was installed to monitor the ecosystem scale CO\({}_{2}\) and H\({}_{2}\)O exchanges and these provide basic data for the study. A total of six flux sites were used in this research, namely [33], the Daman superstation (S_Dm), the A'rou superstation (S_Ar), the Zhangye wetland station (S_Zy), the Dashalong station (S_Dsl), the Sidaoqiao superstation (S_Sdq), and the Hunhelin station (S_Hhl). These flux sites cover the main types of ASA ecosystems, including two grasslands, one wetland, two woodlands, and one cultivated land.  
The S_Dm site and the S_Zy site are located in the Daman and Yingke irrigated areas near Zhangye city Gansu province, respectively. They are also in the midstream of the Heihe river (artificial oasis  
Figure 1: Distribution of the six flux sites in northwestern China. The land cover classification data are from land use and land cover data of the Heihe River Basin in 2000 ([[http://www.heihedata.org/data/](http://www.heihedata.org/data/)]([http://www.heihedata.org/data/](http://www.heihedata.org/data/))).  
experimental area) having a continental arid climate [32]. The S_Zy site is situated on a wetland with a reed surface in the Zhangye National Wetland Park. The S_Ar and the S_Dsl sites are part of the upstream cold region experimental area. The former is in a valley with an east-west direction and a maximum width of 3 km [15]. The latter is covered with snow most of the time [34]. The S_Sdq and the S_Hhl sites are located on the natural oasis experimental area in the downstream. More information can be found in Table 1 and Figure 1.
# A Blockchain Network Admission Control Mechanism Using Anonymous Identity-Based Cryptography
## 3 Proposed Mechanism
### Ulser Admisson Control
#### 3.4.1 Verify  
When users authenticate with validating nodes, they use their communication identifiers and corresponding private keys to establish their identity.  
Assuming the message to be signed is \(M\) which contains the IP address, anonymous identifier, and timestamp, and \(Y\) represents the set of selected CKGC identifiers, the user obtains the digital signature \((h,\ S,Y)\) for message \(M\) as follows:  
* The user aggregates the communication private key components as \(\sum_{i\in Y}ds_{Bi}=[\sum_{i\in kc_{i}}][m_{1}]^{-1}P_{1}\), which serves as the private key for the signature.
* Compute \(g=e\Big{(}P_{1},P_{pub-y}\Big{)}\), where \(P_{pub-y}\) is the sum of the selected CKGC public keys \(\sum_{i\in Y}P_{pub-ci}\).
* Generate a random number \(r\in[1,N-1]\).
* Calculate \(w=\ g^{\prime}\) and \(h=H_{2}(M||w,\ N)\).
* Compute \(l=(r-h)\textit{ mod }N\).
* Finally, calculate \(S=[l]ds_{B}\). The signature for message \(M\) is \((h,\ S,Y)\).  
To verify the received message \(M^{\prime}\) and its digital signature \((h^{\prime},S^{\prime},Y^{\prime})\), the validating node follows these steps:  
* Combine the public keys of CKGCs in \(Y^{\prime}\) as \(P_{pub-y}=\sum_{i\in Y^{\prime}}P_{pub-ci}\).
* Calculate \(g=e\Big{(}P_{1},P_{pub-y}\Big{)}\).
* Compute \(t=g^{\prime h^{\prime}}\).
* Calculate the integer \(h_{1}=H_{1}(ID_{B}||hid,N)\).
* Then, compute \(P=[h_{1}]P_{2}+P_{pub-v}\).  
* Compute \(u=e(S^{\prime},P)\).
* Calculate \(w^{\prime}=u\cdot t\).
* Compute \(h_{2}=H_{2}(M^{\prime}||w^{\prime},N)\).
* Check if \(h_{2}=h^{\prime}\), the signature verification is successful; otherwise, the verification fails.
## 4 Discussion
In one variant of the performed verification, the optimum value of \(\varrho\) was chosen by searching for the smallest difference between the radius of the reference circle and the mean radius of curvature of the ellipse \(\Delta R\). The performed experiment showed that, for the coefficient \(\rho=1.4\), the smallest difference between the standard radius and the mean radius of ellipse curvature was \(\Delta R\) equal to 2.50 mm. For such a value of the coefficient \(\rho\), a dimension close to the dimension of the reference wheel diameter d was determined analytically; however, only 33.5% of all measured points were included in the plotted ellipse. This small percentage of points included in the ellipse area is due to the characteristics of the performed test. The use of motion capture testing was characterized by a proportional scatter of the measured marker position relative to a circle defined by a reference diameter d to which the marker was attached. In the case of the second verification variant, the number of points contained within the plotted ellipse was adopted as the criterion of optimality. In this case, the best results were obtained for a coefficient \(\varrho\) of 1.60, for which the percentage of points inside the ellipse k was 100%. Nevertheless, the difference between the reference radius and the mean radius of curvature of the ellipse \(\Delta R\) was 40.01 mm in this verification variant. The performed experiment demonstrated that, by controlling the value of the coefficient \(\rho\), the method can be adjusted to different types of distribution, and it is possible to extract various parameters--important for the performed description of the experimental test--from the analyzed set of points. Studies have confirmed the feasibility of analyzing motion capture data over a specific range of speeds [55; 56]. As part of the work, the method was verified for four different experimental studies. On this basis, it was found that the method is the most accurate for the results with a normal distribution. In the case of other distributions, the coefficient \(\rho\) should be selected independently, which may introduce an additional error.
# Device Management and Data Transport in IoT Networks Based on Visible Light Communication
## 4 Experimentation and Discussion
### Experimentation Results  
#### 4.2.1 Device Initialization  
Figure 15 shows the packet capturing results for the AA initialization process, as shown in Figure 5. In this experiment, PS is equipped with a local DHCPv6 server so that AA gets an IPv6 address (see the packets no. 13-17). After the configuration of the IPv6 address, AA creates and subscribes to a  
Figure 14: VLC frame structure used in experimentation.  
Figure 13: Protocol stack for implementation of VLC-based IoT.  
container named _CNT-AA-SERVICE-DATA-RESP_, which is used for receiving service data from PS during data transport operation (see the packets no. 33-40). Then, AA registers to PS by exchanging Common Service Entity (CSE) information (see packets no. 42, 44, 46, and 47). After registration of AA to PS, PS sends the AA Network Configuration message to the container (_CNT-AA-CONTROL_) of AA (see packets no. 43 and 45).  
The current version of VT is installed as pre-installed firmware of VLC functions without IP communication. Therefore, the location-specific data is included in the firmware. An enhanced version of VT will be made for further study, which will act as an ADN-AE device of a oneM2M platform. Figure 16 shows the information contained in a VLC frame transmitted by VT, following the VLC frame format in Figure 14.  
Figure 17 shows the packets captured during VR initialization. In the figure, VR finds a local router (AA) and configures its IPv6 address. AA has its own local DHCPv6 server. After configuring an IPv6 address, VR is registered to AA. Because VR is an ADN-AE device, all of the information on oneM2M should be recorded into a local CSE. Therefore, VR creates AE and a container, and subscribes to AA (see the packets no. from 46 to 83), in which AE creation is shown in packets 46 to 47,  
Figure 16: Example of VLC frame used in experimentation.  
Figure 15: Packets captured during AA initialization.  
the container is from 48 to 63, and the subscription is from 64 to 83. The subscription container named _CNT-VR-HEARTBEAT_-REQ is for device monitoring, _CNT-VR-SERVICE-DATA_-RESP is for receiving service data during data transport operation, and _CNT-VR-HANDOVER_-RESP is for support VR handover.
# Handover Scheme in LEO Satellite Networks Based on QoE for Streaming Media Services
## 3 Simulation System Design for Handover and Access of Streaming
### Handover Scheme Design Based on Streaming Services
 \tag{24}\]  
where \(CHO_{i,j}(P_{i,j},S_{i,j})\) is the utility function when the user \(u_{i}\) accesses the satellite \(S_{i,j}\) with the power \(P_{i,j}\) at the time of handover. \(\gamma_{Pow}\) is the power loading loss function, which exceeds 1, meaning that the current satellite is underloaded and cannot provide the service; \(Poc_{i,j}\) is the occupied power of the satellite, and \(P_{tot}\) is the total power loading of the satellite. \(\gamma_{Ch}\) is the channel loading loss function. \(Choc_{i,j}\) is the number of occupied channels of the satellite, and \(Ch_{tot}\) is the total number of available channels of the satellite. \(\alpha_{1}\) and \(\alpha_{2}\) are the respective weighting coefficients. The user \(u_{i}\) traverses all the satellites of the set \(S_{i}\) and then selects the satellite with the largest utility function among them for handover.
###### Abstract
Integrating metallic dampers into structural systems has demonstrated a significant capacity for dissipating seismic energy, thereby mitigating earthquake-induced damage. Researchers and practitioners have increasingly adopted full-scale implementation of metallic dampers based on experimental evidence validating their performance. For instance, [PERSON] conducted an extensive experimental program at the University of California, Berkeley, to evaluate the performance of an X-shaped plate damper, known as Added Damping And Stiffness (ADAS) elements [9]. Extensive analytical and experimental investigations have been conducted on the dynamic response of viscoelastic dampers and the seismic response of structures incorporating these dampers. The experimental studies utilized a 2/5-scale prototype structure subjected to various controlled ambient temperatures and recorded ground motions indicating that viscoelastic dampers significantly enhance seismic response reduction in structures [10; 11]. [PERSON] replaced coupling beams with viscoelastic dampers to avoid consuming precious architectural space while addressing issues related to building occupants' perception of vibrations and diminishing both wind and earthquake effects on the structure [12]. Consequently, the building's structural efficacy is notably enhanced with viscoelastic damper implementation compared to conventional design methods. Several researchers have proposed the DBDM for buildings with energy dissipation dampers; however, the amount of research is relatively limited. Therefore, this research examines the effectiveness of the DBDM for CSW structures with energy dissipation dampers. To refine the DBDM, a simplified approach was developed, involving the construction of a DBDM graph in which a capacity curve is derived from pushover analysis and a target performance line is calculated based on the structure's equivalent SDOF model. Structural design compatibility is confirmed when the target line touches the capacity curve. The analysis focused on CSWs at different heights with steel coupling beams and metallic and viscoelastic dampers. Using the proposed method, the necessary damping can be estimated to confirm adequate structural capacity. The efficacy of the DBDM is evaluated by comparing the story drifts obtained by the THA with the design target drift.
Next, the base station collects all the messages transmitted to it (step 15). After this step is completed, the initialization phase is ended and the steady state phase begins. Hence, the base station determines the new cluster heads by using the data of the received messages. More specifically, the node having the highest residual energy in each cluster is elected to be the new cluster head. Moreover, the new values of soft and hard thresholds are defined (step16). After that, all nodes switch their receivers on (step 17). Finally, the base station broadcasts the ids of the new cluster heads along with the values of the thresholds (step 18). Subsequently, each non cluster head node chooses the cluster it intends to belong to. The steady state phase is further carried on as a continuously recurrent execution of the procedures described above from step 6 to step 18. A synoptic overview of the SHPER protocol algorithm is graphically depicted in Figure 2.  
Figure 2: Synoptic overview of the algorithm chart of SHPER protocol.
## 2 Principle
### The Impact of CP on the AO-OFDM System
\tag{10}\]  
\[s_{mk}(t)_{(m\
eq k)}=\left\{ \begin{array}{cc}\frac{\exp[2\pi(\frac{k-m}{T_ {b}})(t+\frac{T}{2})]-\exp[-2\pi(\frac{k-m}{T_{b}})(\frac{T}{2})]}{2\pi(T_{s} )T_{s}}&(\frac{-T_{s}-T_{b}}{2}\leq t<\frac{-T_{s}+T_{b}}{2})\\ 0&(\frac{-T_{s}+T_{b}}{2}\leq t<\frac{T_{s}-T_{b}}{2})\\ \frac{\exp[2\pi(\frac{k-m}{T_{b}})\frac{T_{b}}{2}]-\exp[2\pi(\frac{k-m}{T_{b}} )(t-\frac{T}{2})]}{2\pi(\frac{k-m}{T_{b}})T_{s}}&(\frac{T_{s}-T_{b}}{2}\leq t< \frac{T_{s}+T_{b}}{2})\\ 0&(other)\end{array} \right. \tag{11}\]  
where \(H_{m}(f)\) represents the frequency domain function of the Sinc-shaped filter bank, \(f_{bm}\) represents the center frequency of the \(m\)th Sinc-shaped filter at the receiving end, and \(h_{m}(t)\) represents the corresponding time domain function. It is worth noting that the bandwidth of the filter at the receiving end is the same as the symbol rate. \(r_{k}(t)\) and \(s_{mk}(t)\) are the waveform and pulse-shaping function of the \(k\)th subcarrier. When \(m\) is equal to \(k\), that is, when the target subcarrier passes through the corresponding Sinc-shaped filter with the same center frequency, the pulse-shaping function is \(s_{mk}(t)_{(m=k)}\). The adjacent orthogonal sub-carriers will also pass through the filter, and the pulse-shaping function is \(s_{mk}(t)_{(m\
eq k)}\) after filtering. Combining Equations (10) and (11), we can clearly see that, when the AO-OFDM system inserts CP, there is a flat range for each symbol period after filtering at the receiving end, and the flat range lasts for \(T_{s}-T_{b}=1/\!f_{s}-1/\!f_{b}\). This approach will not destroy the orthogonality due to CP insertion and is not affected by other sub-carriers. The larger the filtering bandwidth of the Sinc-shaped filter at the transmitting end, the more CP is inserted. Although the larger the CP, the lower the spectrum utilization rate of the AO-OFDM system and the larger the flat interval in each symbol period, this interval can allow more sampling time errors.