esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain-Assisted Security Protocol for Group Handover of MTC Devices in 5G Wireless Networks
## 5 The Proposed BSPGH
### Group Handover Authentication  
\(MTCD_{1}\) initiates an aggregated signature request to other group members in the group, broadcasting the temporary identity \(TID_{\text{t}}\) of the t-gNB. \(MTCD_{1}\) also sends its  
Figure 4: First MTCD handover authentication.  
temporary identity \(TID_{1}\) and the group key material \(GID_{1}\). Other members within the group verify the identity of \(MTCD_{1}\) by validating \(GID=GID_{1}+H(TID_{1})\).  
\[Step\text{-}1:MTCD_{1}\to MTCD_{i}:\{GID_{1},TID_{t},TID_{1},TS_{1}\}\]  
\[Step\text{-}2:MTCD_{i}\to MTCD_{1}:\{TID_{i},TS_{i},n_{i}\cdot P, Sig_{i}\}\]  
The group member \(MTCD_{i}\) receives the aggregated signature request, verifies the identity of \(MTCD_{1}\), selects a random number \(n_{i}\in Z_{q}\) generates its respective signatures, and sends its signature \(Sig_{i}=h_{i3}msk_{i}+n_{i}+h_{i2}psk_{i}\).  
\[Step\text{-}3:MTCD_{1}\to t\text{-}gNB:\{(TID_{2}||\ldots||TID_{i}),(n_{2} \cdot P||\ldots||n_{i}\cdot P),Sig,TS_{i}\}\]  
After receiving the signatures from all group members, \(MTCD_{1}\) calculates the aggregated signature \(Sig=\sum_{i=2}^{n}Sig_{i}\) for the group and sends the aggregated signature to the t-gNB for performing group signature verification using the equation \(\sum_{i=2}^{n}Sig_{i}\cdot P=\sum_{i=2}^{n}h_{i3}\cdot mpk_{i}+\sum_{i=2}^{n}n _{i}\cdot P+\sum_{i=2}^{n}h_{i2}\cdot R_{i}+\sum_{i=2}^{n}h_{i1}\cdot h_{i2} \cdot T_{pub}\) to pre-authenticate all group members. If the equation holds, the t-gNB verifies all MTCDs. Subsequently, t-gNB generates the session key \(K_{it}=n_{i}\cdot n_{t}\cdot P\) between \(MTCD_{1}\) and the t-gNB.  
\[Step\text{-}4:t\text{-}gNB\to MTCD_{1}:\{TID_{t},TS_{t},n_{t}\cdot P, Sig_{t}\}\]  
The t-gNB verifies the signatures of the group members to authenticate their identities. If the identity authentication is successful, the t-gNB sends the timestamp \(TS_{t}\), random number \(n_{t}\cdot P\), and its own signature \(Sig_{t}=H_{0}(GID,TID_{t},TS_{t},n_{t}\cdot P)\) to the \(MTCD_{1}\), because it has passed the first MTCD handover authentication already.  
\[Step\text{-}5:MTCD_{1}\to MTCD_{i}:\{TID_{t},TS_{1},TS_{t},n_{t}\cdot P, Sig_{t}\}\]  
After receiving the message from \(MTCD_{1}\), \(MTCD_{1}\) generates the signature \(Sig_{t}^{\prime}=H_{0}(GID,TID_{t},TS_{t},n_{t}\cdot P)\). If \(Sig_{t}^{\prime}=Sig_{t}\), the identity verification of t-gNB is successful. If the verification is successful, \(MTCD_{1}\) generates the session key \(K_{it}=n_{i}\cdot n_{t}\cdot P\) between \(MTCD_{1}\) and the t-gNB. At this point, mutual authentication and key negotiation between \(MTCD_{1}\) and t-gNB are complete. This phase is shown in Figure 5.  
Figure 5: Group handover authentication.
## 6 Uncertainties Associated with Distances Derived from Point Clouds
### Distance Measurement: Uncertainty Contribution from Spatially Correlated Location Effects
Example: comparison of two gauge blocksSuppose two gauge blocks of nominally the sample length are measured side by side, parallel to each other, with \(\mathbf{x}_{1}\) and \(\mathbf{x}_{2}\) as the measured points on the end faces of the first gauge block and \(\mathbf{x}_{3}\) and \(\mathbf{x}_{4}\) those corresponding to the second gauge block. For this case, \(d_{12}\approx d_{34}=D\), say, \(d_{13}\approx d_{24}=d\), say, and \(n_{12}\approx n_{34}\) so that \(c\approx 1\). If \(d\) is much smaller than \(D\), then \(d_{14}\approx d_{23}\approx d_{12}\), and  
\[u^{2}(d_{12}-d_{34})\approx 4\sigma_{ET}^{2}\Big{(}1-e^{-d^{2}/\lambda_{ET}^{ 2}}\Big{)}.\]  
This uncertainty can be thought of as a quantification of the Abbe contribution to the uncertainty due to the fact that the measuring lines associated with the two gauge blocks are displaced by \(d\) from each other. For \(d\) much smaller than \(\lambda_{ET}\), this contribution is negligible.
# Wideband Eight-Antenna Array Designs for 5G Smartphone Applications
## 3 Findings and Analysis
Figure 11: _Cont._  
To further validate the findings presented in Figure 11, the antenna field pattern is plotted in a planar expansion diagram using HFSS, where theta angles (0 to 180 degrees along the y-axis) and phi angles (0 to 360 degrees along the x-axis) are represented, as depicted in Figure 12. Across the three frequencies, Antennas 1 to 4 exhibit a consistent dominant component (gain phi/gain theta) as depicted in the diagram. Specifically, the planar expansions at 3.5 GHz and 4 GHz appear quite similar for Antennas 1 to 4. However, at 5.1 GHz, Antennas 1 to 4 show a different dominant component, indicating a shift to a conventional loop mode, distinct from the modes observed at 3.5 GHz and 4 GHz (refer to Figure 8). The investigation of the envelope correlation coefficient (ECC) is crucial for assessing the performance of MIMO antennas in diverse applications. Generally, a lower ECC indicates a higher CC, and for mobile phone MIMO antennas, an ECC below 0.3 is considered the design standard. Assuming an isotropic distribution of incident signals, the ECC can be calculated from far-field patterns using the following formula stated in [1]:  
Figure 4:0  
In Equation (1), \(E_{1}\) and \(E_{2}\) represent the far-field radiation patterns produced by exciting ports 1 and 2, respectively. By applying this formula, the correlation between these two radiation patterns can be determined. The calculated ECC for the array, based on both simulation and measurement, is illustrated in Figure 13a,b. The simulation results display ECC values below 0.05, while the measurement outcomes show values under 0.1, significantly lower than the standard threshold of 0.3. These results confirm a low  
Figure 4:1 Normalized radiation patterns of proposed array (Ant.1–Ant.4 only) at (**a**) 3.6 GHz (**b**) 4.0 GHz (**c**) 5.1 GHz. correlation between adjacent elements, indicating that the proposed array is highly effective in multipath fading environments. Figure 14 shows the calculated CC of the proposed array in an 8 \(\times\) 8 MIMO system. The results indicate a desirable CC of 37-40 bps\(/\)Hz, slightly lower than the calculated upper limit of 46 bps\(/\)Hz. Figure 4:2 Antenna field pattern planar expansion diagram at 3.6, 4.0, and 5.1 GHz. Figure 4:3 _Cont._
# Selecting Target Range with Accurate Vital Sign Using Spatial Phase Coherency of FMCW Radar
###### Abstract  
Respiration and heartbeat are basic indicators of the physiological state of human beings. Frequency-modulated continuous wave (FMCW) radar can sense micro-displacement in the human body surface without contact, and is used for vital-sign (respiration and heartbeat) monitoring. For the extraction of vital-sign, it is essential to select the target range containing vital-sign information. In this paper, we exploit the coherency of phase in different range-bins of FMCW radar to effectively select the range-bins that contain accurate signals for remote monitoring of human respiration and heartbeat. To quantify coherency, the spatial phase coherency (SPC) index is introduced. The experimental results show that the SPC can select a range-bin containing more accurate vital-sign signals than conventional methods. This result demonstrates that the proposed method is accurate for monitoring of vital signs by using FMCW radar.  
FMCW radar; respiration; heartbeat; range selection; spatial phase coherency +
Footnote †: journal: Applied Sciences  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Selecting Target Range with Accurate Vital Sign Using Spatial Phase Coherency of FMCW Radar. _Appl. Sci._**2021**, _14_, 4514. [[https://doi.org/10.3309/app1110514](https://doi.org/10.3309/app1110514)]([https://doi.org/10.3309/app1110514](https://doi.org/10.3309/app1110514))
## 2 Materials and Methods
### Cytotoxicity and Biocompatibility of CMSC  
Human cervical carcinoma (HeLa) cells were used in a cell suspension containing 1 \(\times\) 10\({}^{5}\) cells mL\({}^{-1}\). The cells were seeded into each well of 96-well tissue culture plates and incubated for 18 h followed by addition of CMSC at concentrations of 100-1000 \(\upmu\)g/mL and further incubated for 48 h at 37 \({}^{\circ}\)C. Then, yellow tetrazolium MTT (3-(4,5-dimethylthiazolyl-2)-2)-2,5-diphenyltetrazolzolium bromide) (10 \(\upmu\)L; 5 mg\(\cdot\)mL\({}^{-1}\)) (MTT Assay kit, Nacalai Tesque, Japan) was added and the optical density (OD) value at 570 nm of each well, with background subtraction at 690 nm, was measured by means of a microtitre plate reader. The following formula was used to calculate the inhibition of cell growth; all assay experiments were performed in triplicate for each sample was calculated using Equation (6) as follows:  
\[\text{Cell viability }(\%)=\frac{\text{mean of abs. value of treatment group}}{\text{mean absorbance value of control}}\times 100 \tag{6}\]  
Direct contact tests were conducted to investigate the biocompatibility of CMSC samples according to ASTM F-813-83 (ASTM, 1983) and ISO 10993-5 (ISO/EN, 1992) with slight modification. The cells were examined by light microscopy (EVOS, AMG, Washington, WA, USA) [32].
# MEBN-RM: A Mapping between Multi-Entity Bayesian Network and Relational Model
## 2 Background
### Relational Model
**Definition 13** (**Relationship Relation Schema**). : _A relationship relation schema, RRS, is a relation schema containing a PK consisting of attributes which are foreign keys pointing to ERSs._  
Therefore, a relationship relation represents a relationship for one entity type (i.e., a unary relation) or a relationship between entities of more than two entity types. In Figure 2, the relation schema _VehicleLocation_ is a relationship relation schema, if we assume that there is a _Time_ ERS and the attribute _LocatingTimeID_ points to the _Time_ ERS. **Definition 14** (**Relation Instance**). : _A relation instance, RI, of a relation schema, RS[\(A_{1}\), \(A_{2}\),..., \(A_{n}\)], is specified by a table with \(n\) columns and \(m\) rows, [\(d_{11}\), \(d_{21}\),..., \(d_{n1}\)],... [\(d_{1m}\), \(d_{2m}\),..., \(d_{mn}\)], where \(A_{i}\) is an attribute of RS, \(d_{ij}\in\text{Dom}(A_{i})\). The relation instance represents a set of \(m\) specific entities of the class represented by the relation._For example, in the relation _Vehicle_, there are six rows (i.e., {v1, wheeled}, {v2, tracked}, {v3, tracked}, {v4, tracked}, {v5, wheeled}, {v6, tracked}]). The instance of the relation _Vehicle_ refers to these six rows or tuples. **Definition 15** (**Relational Database Schema**). : _A relational database schema, \(RDBS[RS_{1},RS_{2},...,RS_{n}]\), is a set of relation schemas._  
For example, the table headers of Figure 2 describe the relational database schema, RDBS[_Vehicle_, _Region_, _VehicleLocation_]. **Definition 16** (**Relational Database**). : _A relational database, \(RDB[RI_{1}\):\(RS_{1},RI_{2}\):\(RS_{2},\)..., \(RI_{n}\):\(RS_{n}]\), is a set of pairs \(RI_{i}\):\(RS_{i}\), where \(RS_{i}\) denotes a relation schema and \(RI_{i}\) denotes a relation instance of \(RS_{i}\)._  
For example, the tables of Figure 2 describe the relational database \(\text{RDB}[\{\)v1, wheeled\(\}...\)[v6, tracked]\(\}\):_Vehicle_, {{r1, offroad, null}...{r2\(1\)1, road, r2_1}\(\}\):_Region_, {{v1, t1, r1}...{v2, t3, r2_1}\(\}\):_VehicleLocation_]. In the relational model, _normalization_ is an operation performed on an RDB to make it more manageable by minimizing redundancy of elements and reducing dependency between attributes [41]. Several normal forms have been suggested such as First \(\sim\) Fifth normal form and Boyce-Codd Normal Form (BCNF) [41; 42; 43; 49; 50; 51].
## 4 Experiments
### Results of Experiments
In Figure 11 which shows the motor torque of each experiment, the tilt motion of the motion platform causes additional torque to maintain the balance of the loading box. Therefore, to optimize the controller in real-time in response to sudden movement or other disturbance, the PID gains also change during each experiment using luggage of fixed properties. As shown in Figure 9c, the PID gains for the experiment with the 3 kg luggage is larger than the experiment with the 5 kg luggage. The reason can be explained in Figure 11a, which shows the motor torque of each experiment for the frame of the pitch axis. As shown in Figure 11a, the absolute value of the motor torque of the experiment with 3 kg is larger than the 5 kg experiment. This is because the 3 kg luggage is positioned in the offset location, and it causes the additional torque to maintain the balance of the loading box. Therefore, the PID gains for 3 kg luggage positioned at the back of the loading box should be larger. Likewise, gains of the experiment with the empty loading device are the lowest. It is because the demanded torque of the empty loading box is much lower than the cases of loading the 3 kg and 5 kg luggage. In conclusion, the proposed adaptive control strategy can adapt the controller to the system of the loading device. Figure 11: Motor torque of the frames of each axis; (**a**) the pitch axis; (**b**) the roll axis. Table 2 shows the largest overshoot and the longest settling time of each experiment in Figures 9a and 10a. The error rate of settling time is 5%. The starting point of the measurement of settling time is from when the motion platform starts the tilting. In Figures 9 and 10, and Table 2, the frame of the roll axis seems to be worse controlled than the frame of the pitch axis with vibration and longer convergence times to 0. The cause is considered that the gimbal structure is interpreted as two independent pendulum systems in the system identification process. Also, the frame of the roll axis is the inner frame of the gimbal structure, and it is relatively more affected by the motion of the outer frame. It may cause more instability. However, considering that there are two rapid angular acceleration changes in one tilting motion of the motion platform, the balance maintenance control of the frame of the roll axis works well by the adaptive control strategy.
# Developments on Perovskite Solar Cells (PSCs): A Critical Review
## 5 Fabrication Methods
### Two-Step Method  
The two-step fabrication or sequential fabrication method involves the spin coating of individual organic (MAI/FAI) and inorganic (PbX\({}_{2}\)/SnX\({}_{2}\)) precursor solutions. This method offers the advantage of being able to control the surface morphology and film quality by optimizing precursor concentrations, dip-coating or spin-coating time, and spinning rates during the deposition of either organic or inorganic halide precursor. In the two-step method, the first step of deposition is spin coating the inorganic halide (PbX\({}_{2}\)) on a substrate; then, the second step of deposition follows by either (i) dipping the PbX\({}_{2}\)-coated substrate into the organic MAI solution or (ii) spin coating the organic MAI solution over the PbX\({}_{2}\) (see Figure 10b) [64]. [PERSON] (2013) used the two-step method to fabricate a PSC with PCE = 15.0%. The study confirms a better control of device morphology from the XRD analysis and SEM cross-sectional micrographs of the device [65]. Firstly, the reported cross-sectional SEM images show the introduction of PbI\({}_{2}\) into the nanoporous TiO\({}_{2}\) film. This is confirmed by the formation of a single layer of TiO\({}_{2}\)/PbI\({}_{2}\) with complete PbI\({}_{2}\) crystal absorption, indicating successful PbI\({}_{2}\) deposition onto TiO\({}_{2}\) nanopores. The XRD pattern shows a characteristic perovskite peak at 14\({}^{\circ}\) diffraction angles, confirming the formation of the perovskite absorber layer upon the second step deposition of MAI by (i) dip coating TiO\({}_{2}\)/PbI\({}_{2}\) bearing substrate.  
The presence of PbI\({}_{2}\) residuals reduces the solar cell light-harvesting ability and charge carrier transport; hence, it reduces the overall PSC performance [66; 67]. Similar to the one-step deposition method above, solvent engineering and the use of additives may be applied to overcome this fabrication drawback in the two-step method. [PERSON] (2015) reported a solvent mixture based on DMF and DMSO in PbI\({}_{2}\) powder dissolution. The DMSO in the solvent mixture prevents early PbI\({}_{2}\) crystallization and further acts as an intermediate exchange agent with MAI during MAPbI\({}_{3}\) film formation. Low temperature and low coating time of MAI during the second step of deposition results in perovskite crystal formation only at the PbI\({}_{2}\) surface which blocks further MAI infiltration into the PbI\({}_{2}\) lattice. Such mass infiltration forms a rough surface structure with the presence of voids and pinholes [68; 69].  
Figure 11: (**a**) X-ray diffraction patterns; (**b**) SEM micrograph; (**c**–**e**) AFM images [39; 62].
# Characterization of a COTS-Based RF Receiver for Cubesat Applications +
## 4 Test Method
### Component-Level Test Description
The length of such a microstrip has been assumed to be equal to the average electrical length of all components placed on the board. The schematic of the calibration kit, illustrated in Figure 4, also shows that the return signal is not connected to the same receiver ground to prevent all possible eventual errors in calibration caused by the rest of the circuit. The calibration of the network analyzer was always performed with the receiver off. Besides the aspects related to the calibration, the implementation of the RF test points required attention for the encumbrance and mounting aspects. Regarding the encumbrance aspect, to save space on the board, a 3D analysis has been performed to search for the minimum spacing to place two near to the RF test points (estimated as equal to 0.4 mm) in order to avoid the mate-connector touching the nearby RF test point, preventing the insertion. The 3D analysis has also helped to estimate the right height of the metallic shields (5 mm). Regarding the mounting aspect, since the RF test points have a small size and the mounting has been done by machine, the footprint design of the connectors has been customized in order to provide the correct guidelines for the operator who physically mounted the components, orienting it correctly. An error in the component orientation would have compromised the whole test approach. Figure 4: Custom calibration kit for the mate-connector of the RF test points. Top image shows the schematic and bottom image shows a photograph of the its implementation on the prototype board.
## 5 Scenarios and Illustrative Applications
### Rural Setting: Harrisburg, Pennsylvania, USA
The system architecture would be modified to operate with intermittent connectivity, incorporating store-and-forward data transmission protocols and enhanced local processing capabilities to maintain functionality even during communication outages. Solar or other self-sustaining energy solutions would be integrated to overcome the limitations of the power infrastructure common in rural areas. HAR components would be calibrated to detect activities relevant to rural settings, such as agricultural machinery operation, resource extraction work, and outdoor recreational activities near industrial zones. Additionally, the framework would incorporate longer-range monitoring technologies, such as drone-based sensors and satellite imagery integration, to cost-effectively cover expansive geographical areas while maintaining situational awareness of scattered population centers.
## 7 Experimental Section
### Network Lifetime
#### 7.2.2 The Number of CHs and Network Lifetime  
To compare with other clustering algorithms, we run LEACH, EADC and LECP-CP in different scenarios. Since CHs send the collected data to the BS directly in LEACH, we also run the cluster head election algorithms of EADC and LECP-CP, taking no account of inter-cluster communication routing, named EADC-single and LECP-CP-single, respectively. As shown in Figure 12, the network lifetimes of LEACH, EADC-single and LECP-CP-single all decrease with the increase of the node number in these networks. There are two reasons for this. On the one hand, the number of CHs generated by these algorithms is in proportion to the number of nodes in the networks; with the increase of the node number, the number of CHs, which communicate with the BS directly, also increases. On the other hand, since the control message complexity of these algorithms is \(O(N)\), where \(N\) is the number of nodes in these networks, thus combining these two facts together, the network lifetimes of these algorithms are definitely reduced. However, compared with LEACH and EADC-single, the network lifetime of LECP-CP-single has a less rapid decline. The reason is that LECP-CP-single can select CHs with higher residual energy and lower energy consumption; thus, CHs can save more energy for transmitting data to the BS.  
Taking inter-cluster communication routing into consideration is quite helpful for increasing the network lifetime, just as EADC and LECP-CP show in Figure 12. Furthermore, due to the introduction  
Figure 11: Comparison of network lifetime under heterogeneous and homogeneous scenarios.  
Figure 12: Comparison of the network lifetimes of different algorithms.  
of the local energy consumption ratio, the performance of LECP-CP in the cluster setup phase and data transmission phase outperforms EADC, since the CHs generated by LECP-CP are more reasonable and the energy consumption ratio in the network is lower. Another reason is that we set the inter-cluster transmission range \(R_{inter-CH}\) and the intra-cluster transmission range \(R_{intra-CH}\) as \(R_{\mathrm{inter-}CH}\geq 3R_{\mathrm{intra-}CH}\); thus, even if the number of dead nodes becomes more and more, since \(R_{inter-CH}\) is large enough, each connected subgraph, _i.e._, each cluster, can still maintain a connection. CHs farther away from BS still can send their data to the BS by other forwarding CHs, instead of sending the data to the BS directly due to the lost connectivity among CHs. From this, we can conclude that LECP-CP has good scalability, and it is suitable for varying network sizes. Figure 13 shows a concrete comparison of LEACH, EADC-single, EADC, LECP-CP-single and LECP-CP in terms of network lifetime in Scenario 2 and 4, respectively. LECP-CP and EADC perform far better than LEACH, EADC-single and LECP-CP-single in prolonging network lifetime attributed to the better cluster head election algorithm and the design of the inter-cluster communication routing tree.