esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Spaceborne Target Detection Based on Yolov5 and Image Compression
## 2 System Design  
Figure 1 shows the procedure of the image compression and target detection system on the satellite. The high-data-rate image information is sent to the FPGA for compression using the camera link interface and then the target detection is undertaken on the machine learning platform. Images containing the intended targets are selected and sent to the ground station using the data transmission system. The target detection with the Yolov5  
network is implemented on the Jetson Xavier NX, which is produced by INDIVIA and is a popular platform for machine learning [16]. The image compression is undertaken using ADV212, which is produced by ADI [17; 18]. ADV212 is a single-chip JPEG2000 codec for video and high-bandwidth image compression applications, enabling it to benefit from the enhanced picture quality and capabilities provided by the JPEG2000 image compression standard. When the camera resolution is high, multiple ADV212 chips can be used in parallel to compress the original image. ADV212 can connect directly to a wide variety of host processors and ASICs using an asynchronous SRAM-style interface, DMA accesses, or streaming mode (JDATA) interface. The ADV212 supports 16- and 32-bit buses for control and 8-/16-/32-bit buses for data transfer. The power consumption of one ADV212 is less than 0.5 W which is very suitable for a satellite with limited energy.  
In our design, the original image is compressed before the target detection occurs, which can reduce the complexity and the image size. The original image is first compressed according to the JPEG2000 standard, which can reduce the requirements for the data rate on the interface between the Jetson Xavier NX and the camera. Then, the JP2 image is converted and resized to a JPG image to undergo target detection by the Jetson Xavier NX. An alternative method is detecting the intended targets in the original image data and sending the intended original data to the ADV212 chip to generate the compressed image, which can reduce the task of ADV212, especially in a high-resolution scenario. Although compressed images may affect the detection accuracy, this method can still achieve a high detection rate, which is shown in the simulation results.  
The platform based on FPGA and ASIC is the best solution after considering the speed, hardware resources, development cycle, and other factors. As a highly customized chip, ASIC can meet some specific user needs and solve the most difficult core algorithm problems with minimal cost. Therefore, Xilinx 690T and ADV212 are adopted as hardware platforms for image compression in the proposed system.
## 3 Decomposition and Design of Basic Actions
### Posture Adjustment for Stair-Climbing  
Due to the accumulated error from the motion and sensors during climbing, the robot's orientation would be not perpendicular to the stairs, especially when the staircase has a large number of steps. A posture adjustment is, thus, required [15; 27; 28]. As shown in Figure 5, two cases can be found, which are analyzed below.  
**Case 1:** As shown in Figure 4(a), before the robot starts to climb, the robot's four Mecanum wheels are on the ground; thus, the control model is no different from the model in Section 3.1. In other words, the rotating velocity of each wheel \(v_{am}\) can be obtained by substituting \(v_{x}=v_{y}=0\) and \(\omega=\omega_{ad1}\) into  
Figure 5: The process of posture adjustment during stair-climbing: (**a**) before climbing; (**b**) on stairs.  
Equation (1), where \(\omega_{all}\) is the rotation velocity of the robot when adjusting pose. Then, the robot rotates around the center of the robot's chassis with a radius of \(R_{1}\) until it is perpendicular to the stairs.  
**Case 2:** As shown in Figure 5b, the robot is on the stair; thus, the rear two Mecanum wheels float, and the front two Mecanum wheels and EH C support the robot platform. In order to use a similar control model to Equation (1) to simplify the control, an elegant method is adopted. That is, the wheelbase \(L\) is replaced by \(L^{\prime}\), the distance between the center of the two front Mecanum wheels and the center of the two wheel-legs. Then, the inverse kinematic model of the chassis would be the same as that in Equation (1), where only the former two equations are reserved.  
\[\left\{\begin{array}{l}v_{\omega 1}=v_{y}-v_{x}-(L^{\prime}+d)\omega_{ad2}/2\\ v_{a2}=v_{y}+v_{x}+(L^{\prime}+d)\omega_{ad2}/2\end{array}\right., \tag{2}\]  
where \(L^{\prime}\) is twice the distance between the center of the front wheel and the new rotation center in Figure 5b in the \(y\)-direction, and \(\omega_{ad2}\) is the rotation velocity of the robot when adjusting pose in this case.  
According to Equation (2), by substituting \(v_{x}=v_{y}=0\), the rotation speed of the front two Mecanum wheels can be obtained. In other words, the front two Mecanum wheels are driven by treating the rear two directional wheels as central with a radius \(R_{2}\).
# Elastoplastic Constitutive Model for Energy Dissipation and Crack Evolution in Rocks
## 5 Validation of the Theoretical Model
### Analysis of Elastic Parameters  
In the absence of elastoplastic coupling, the elastic parameters (\(E,\mu\)) are determined from the secant modulus of the dilatational stress \(\sigma_{d}\) and the corresponding Poisson's ratio, as indicated in Equation (2) and illustrated in the stress-strain curve in Figure 3c. By neglecting the confining pressure effects, the average elastic modulus is calculated as \(\overline{E}=6.51\) GPa, and the average Poisson's ratio is determined to be \(\overline{\mu}=0.244\).
## 2 Principles and Methods
### Low Earth Orbit Constellation Pattern  
Walker constellations are considered for GNSS-R missions as they are able to provide good reflection point distributions [14]. A 2D-LFC can also be considered as a Walker constellation [21], and it has been verified that 2D-LFC outperforms other flower constellations in the spatial and temporal distributions of RO events for the design of LEO constellations in GNSS RO missions [19]. Therefore, in the present study, we adopted the 2D-LFC pattern when seeking the optimal configurations for GNSS-R missions.  
To define a 2D-LFC, nine parameters are needed including three integer parameters and six continuous ones [21]. Among the three integer parameters, \(N_{o}\) denotes the number of inertial orbit planes; \(N_{so}\) denotes the number of satellites per orbit; while \(N_{c}\) denotes the phasing parameter, which governs the phasing distribution of the satellites in the constellation and satisfies \(Nc\in[0,N_{o}]\). The six continuous parameters are the orbit altitude (a), the eccentricity (e), the inclination (i), the argument of perigee (\(w\)), the longitude of the ascending node (\(\Omega_{11}\)), and the initial mean anomaly of the first satellite of the constellation (\(M_{11}\)). With the above nine parameters defined, the constellation configuration for a 2D-LFC can be expressed as follows, considering that all satellites are uniformly distributed in orbits [21]:  
\[\left[\begin{array}{cc}N_{o}&0\\ N_{c}&N_{so}\end{array}\right[\left[\begin{array}{c}\Omega_{ij}-\Omega_{11} \\ M_{ij}-M_{11}\end{array}\right]=2\pi\left[\begin{array}{c}i-1\\ j-1\end{array}\right], \tag{1}\]  
where \(i(i=1,\cdots N_{o})\) denotes the \(i\)-th orbit, and \(j(j=1,\cdots N_{so})\) denotes the \(j\)-th satellite in a certain orbit; \(\Omega_{ij}\) and \(M_{ij}\) denote the longitude of the ascending node and the mean anomaly of the \(j\)-th satellite on the \(i\)-th orbital plane, respectively. In the regular pattern of the 2D-LFC, the satellites moving in the circular orbits might collide with each other. To avoid this problem, the constraint on the minimum approach distance between any two satellites, which is calculated based on the differences in the mean anomalies and the differences in the longitude of the ascending nodes of the two satellites, was considered as explained in [22], and the fitness value is set to maximum when this constraint condition is not satisfied in the optimization process.
## 2 System Model
### Mutualism Scenario
#### 2.1.2 Wiretap Channel for Mutualism Scenario  
For the mutualism scenario, the Shannon channel capacity formula over wiretap channel is given by the following equation:  
\[\begin{split} R_{\text{ED}_{n}}^{1}&=k_{\text{BS}} \ \beta_{1}\log_{2}\Bigg{(}1+\frac{P_{\text{BS}}\big{|}g_{\text{BS-ED}_{n}}\big{|} ^{2}}{\sigma_{\text{ED}_{n}}^{2}+P\big{|}\big{|}g_{\text{j-ED}_{n}}\big{|}^{2} }\Bigg{)}+\beta_{1}\log_{2}\Bigg{(}1+\frac{P_{\text{BS}}\ \big{|}g_{\text{BS-HT}}\ \big{|}g_{\text{HT-ED}_{n}}\big{|}^{2}}{ \sigma_{\text{ED}_{n}}^{2}+P\big{|}\big{|}g_{\text{j-ED}_{n}}\big{|}^{2}} \Bigg{)}\\ &+k_{\text{HT}}\ \gamma\beta_{2}\log_{2}\Bigg{(}1+\frac{P_{\text{ active}}\big{|}g_{\text{HT-ED}_{n}}\big{|}^{2}}{\sigma_{\text{ED}_{n}}^{2}+P \big{|}\big{|}g_{\text{j-ED}_{n}}\big{|}^{2}+P_{\text{BS}}\big{|}g_{\text{BS- ED}_{n}}\big{|}^{2}}\Bigg{)}.\end{split} \tag{9}\]  
If \(k_{\text{BS}}=1\), assuming that the signal coming from BS and HT is coherently combined by ED using maximal ratio combining (MRC), the following equation can be written [56]:  
\[R_{\text{ED}_{n}}^{1}=\beta_{1}\log_{2}\Bigg{(}1+\frac{P_{\text{BS}}\big{|}g_ {\text{BS-ED}_{n}}\big{|}^{2}+P_{\text{BS}}\ \big{|}g_{\text{BS-HT}}\ \delta_{\text{HT-ED}_{n}}\big{|}^{2}}{ \sigma_{\text{ED}_{n}}^{2}+P\big{|}\big{|}g_{\text{j-ED}_{n}}\big{|}^{2}} \Bigg{)}+k_{\text{HT}}\ \gamma\beta_{2}\log_{2}\Bigg{(}1+\frac{P_{\text{active}}^{1}\Big{|}g_{\text{ HT-ED}_{n}}\Big{|}^{2}}{\sigma_{\text{ED}_{n}}^{2}+P_{\text{BS}}\big{|}g_{ \text{JS-ED}_{n}}\big{|}^{2}+P_{\text{BS}}\big{|}g_{\text{JS-ED}_{n}}\big{|}^ {2}}\Bigg{)}. \tag{10}\]  
As can be seen from in Equation (10), the BS ceases broadcasting the signal to the IR at time \(\gamma\beta_{2}\), and sends a interference signal to the ED\({}_{n}\). Thus, the SNR value in ED\({}_{n}\) decreases, and BS contributes to the communication of HT with IR. The total number of bits transmitted to all EDs is given as  
\[R_{\text{ED}}^{1}=\sum_{n=1}^{N}R_{\text{ED}_{n}}^{1}. \tag{11}\]
# A Low Phase Noise Crystal Oscillator with a Fast Start-Up Bandgap Reference for WLAN Applications
## 3 Results and Analysis  
The final implementation of the low phase noise crystal oscillator is presented in Figure 15, featuring five outputs to cater to multiple modules, with one designated for testing purposes. The open cover diagram of this designed crystal oscillator after the flow of the chip is shown in Figure 16. The fabricated chip, which incorporates a bandgap reference circuit, an LDO circuit, and output buffers, occupies a total area of 0.088 mm\({}^{2}\) in a 55 nm CMOS process. During testing, the oscillator demonstrates stable oscillations at a frequency of 40 MHz once a crystal is attached to the printed circuit board.  
Figure 14: Comparison of phase noise of crystal oscillator with or without RC low-pass filter.  
Figure 15: Complete circuit diagram of crystal oscillator.  
The crystal oscillator chip designed in this study is powered by a 3.3 V external power supply, with 1.2 V generated by an LDO circuit inside the chip specifically for the oscillator. The chip consumes a total of 369.8 \(\upmu\)A of power and exhibits a phase noise of \(-164.36\) dBc/Hz at a 100 kHz offset, as illustrated in Figure 14. Additionally, the start-up time test conducted on this crystal chip indicates a value of 0.718 ms, as shown in Figure 17.  
The simulation test results of the crystal oscillator designed in this article are summarized in Table 1.  
Figure 16: Photomicrograph of the XO chip.  
Figure 17: Test Result of start-up time of XO circuit.
## 2 Materials and Methods
### Data Sources and Processing
#### 2.2.2 Data for Verifying the VMFDI  
The SPEI has been widely applied to reflect regional meteorological drought [15; 49]. The GRACE-DSI is a reasonable indicator for hydrological drought [18]. In this study, the SPEI, GRACE-DSI, VPD, SM, SIF, evapotranspiration (ET), PRE, observed drought events, and crop growth status indicators were used to validate the VMFDI (Table 1).  
The SPEI product (SPEI base v2.8), with a 0.5\({}^{\circ}\) spatial resolution, was calculated via the FAO-56 Penman-Monteith model with monthly precipitation and potential evapotranspiration from the CRU TS 4.06 dataset. Data from three temporal scales--SPEI01, SPEI03, and SPEI12--were selected to evaluate the VMFDI's drought monitoring ability.  
The GRACE-DSI, based on standardized anomalies of GRACE terrestrial water storage (GRACE-TWS), was derived from the GTWS-MLrec dataset (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)) for 2003-2020 [50], reconstructed by a machine learning method.  
ET data (2003-2020) were obtained from the MOD16A2 product (500 m resolution) based on MODIS Terra net evapotranspiration data processed with the Google Earth Engine.  
Precipitation data (2003-2020) were obtained from the National Tibetan Plateau/Third Pole Environment Data Center [51], using a 1 km monthly dataset generated by downscaling global climate data [52].  
Observed drought event information from the Bulletin of Flood and Drought Disasters in China (2003-2020) was used to test the monitoring capacity of the VMFDI and other drought indicators. During this period, the Yellow River Basin experienced droughts in 64 months.
## 6 System Evaluation  
The proposed system was evaluated; the evaluation was conducted with Amazon Web Services (AWS) EC2 (2.2 xlarge) on a computer with an e core CPU, 32 GB RAM, running Ubuntu 22.04 LTS using the Hyperledger Caliper tool version 0.3.2. The performance  
Figure 17: Researcher payment.  
Figure 19: Physician enters data.  
Figure 18: Physician page.  
metrics test was threefold: transaction throughput, latency, and send rate. The success rate and throughput were measured in TPS and the latency in seconds. The test benchmark was to execute a chaincode to query access to a PHR. The system was tested in five test rounds submitted at fixed rates of 100, 200,300,400, and 500. There are two types of transactions specified by Caliper: query and open transactions [72]. Query transactions perform a simple read from the state CouchDB, while open transactions perform one read and one write operation per transaction. The result of the evaluation was successful, with a 100% success rate and 0 fails.  
Figure 20 presents a summary of the system's latency. The average latencies were 0.02 s for all rounds. This resulted in an overall average latency of 0.01 s, a maximum overall latency of 0.03, and a minimum overall latency of 0.01 s. Thus, the amount of time for the transaction initialization and actual execution was less than 1 s, which is a good latency metric.  
Figure 21 presents a summary of the system throughput. As mentioned earlier, five rounds were run from 100 to 500 transactions. The overall number of successful TPS was approximately the same for each round, and it slightly decreased as more transactions were sent. The overall average throughput was 262.56 per second.  
Figure 21: Summary of the query throughput.  
Figure 20: Summary of the query latency.  
Figure 22 presents a summary of the open latency. The average latencies were 0.74 s for round 1; 1.56 for round 2; and 2.21, 3.01, and 3.43 for rounds 3, 4, and 5, respectively. This resulted in an overall average latency of 2.19 s, a maximum overall latency of 4.082, and a minimum overall latency of 0.832 s. Figure 23 illustrates a summary of the open throughput. It can be seen that the throughput on the open function was lower than on the query function. The throughputs on all rounds were almost equal for all rounds at approximately 36 TPS. It can be seen that both the throughput and the latency increase with the increasing number of synchronized transactions. Moreover, this also indicates that the system reached its maximum ability to handle and queue the remaining transactions for the following processing [72]. Overall, the constant stability of the throughput indicates Hyperledger's reliability and availability [73].  
In summary, all the transactions were successful, with a 100% success rate. It is important to note that the hardware configuration, blockchain network design, consensus algorithms, complexity, and operation of the chaincode affect the throughput, latency, and send rate of a blockchain-based system's performance [74]. Thus, these findings could vary depending on the test environment [54], as shown below in the comparison.
# Push/Pull Parts Production Policy Optimization in the ATO Environment
## 3 Bi-Objective Model for Push/Pull Policy Selection in an ATO Environment
### Determination of the Parts Parameters  
Within the model formulation, two main parameters are defined for each part \(i\), i.e., \(a_{i}\) and \(EPQ_{i}\), used in the Push/Pull policy optimization procedure. The first one is the agility \(a_{i}\) assessed as follows:  
\[a_{i}=\frac{3600*3600}{T_{STOT_{i}}*T_{CTOT_{i}}}\quad\left[\frac{pieces}{hour^ {2}}\right] \tag{1}\]  
where:  
\[T_{CTOT_{i}}=\sum_{j}T_{C_{ij}}\quad\quad[s] \tag{2}\]  
\[T_{STOT_{i}}=\sum_{j}T_{S_{ij}}\quad\quad[s] \tag{3}\]  
According to [PERSON] et al. (2017), the used agility metric measures the ability of the production system to accelerate to meet production changes. In particular, the agility increases when the total setup time and the total production time through the production processes j decrease. According to those definitions, the agility parameter expresses theability of the production system to respond rapidly to change demands in terms of volume and variety. The utilization of this agility formulation permits consideration of potential candidates for an MTO production policy only the parts with the lowest impact on the production capacity and lowest production lead times, justifying the assumptions about the production capacity and planning.  
The second proposed metric is the Equivalent Pallet Quantity \(EPQ_{i}\), representing the used volume of the part \(i\) as number of parts that can be stacked on a Euro Pallet (EPAL). EPAL is the most used stock keeping unit, with a base of 1200 mm \(\times\) 800 mm, as in Equation (5). In Figure 1, right side, \(EPQ_{i}=12\). \(EPQ_{i}\) is calculated as:  
\[EPQ_{i}=EPBQ_{i}*EPHQ_{i}\quad\left[\frac{\text{{pieces}}}{\text{{pallet}}}\right] \tag{4}\]  
\(EPBQ_{i}\) is assessed according to the part shape (U shape, L shape, and I shape for the flat parts), as follows:  
\[EPBQi=\left\{\begin{array}{l}max\left\{\begin{array}{l}\left[\frac{1200}{ \Lambda_{2}+T_{i}}\right]*\left[\frac{800}{\Lambda_{1}+T_{i}}\right]\\ \left[\frac{1200}{\Lambda_{1}+T_{i}}\right]*\left[\frac{800}{\Lambda_{2}+T_{i} }\right]\\ \left[\frac{1200}{\Lambda_{1}}\right]*\left[\frac{800}{\Lambda_{2}+T_{i}} \right]\\ \left[\frac{1200}{\Lambda_{1}}\right]*\left[\frac{800}{\Lambda_{2}+T_{i}} \right]\\ \left[\frac{1200}{\Lambda_{1}}\right]*\left[\frac{800}{\Lambda_{2}+T_{i}} \right]\\ \left[\frac{1200}{\Lambda_{1}}\right]*\left[\frac{800}{\Lambda_{2}}\right]\\ \end{array}\right.\right.\end{array}\right.\right. \tag{5}\]
# Fake Base Station Detection and Link Routing Defense +
## 6 The Incorporation of Our Scheme in 5G Mobile Networking  
We build our overall defense scheme based on the current 3 GPP standard that is illustrated in Figure 4 as a flowchart. We show the current 3 GPP standardized connection procedures in black-colored blocks in the flowchart whereas our scheme additions are in blue-colored blocks. Our detection scheme does not require any protocol changes but implements the scheme only on the user equipment, which allows efficient deployment.  
As shown in Figure 4, in the initial state during the device powering on, if the user equipment does not detect any fake base stations, i.e., \(D=0\), then it proceeds with the RRC connection setup with a base station as described in Section 3.2. Our detection algorithm begins after setting up the RRC connection with the base station. We show the step-by-step procedure for the fake base station detection in Algorithm 1. After completing the RRC connection with the base station, the user equipment measures the elapsed time duration, \(T\), and the number of transmitted registration request messages, \(N\), before receiving the authentication request or reject message (the expected next message as illustrated in Figure 2a,c). If the measured time duration and the number of messages exceed its corresponding thresholds, \(\tau_{T}\) and \(\tau_{N}\) (discussed in Section 5.1), then our scheme detects the base station as fake. After detection, the user equipment disconnects from the base station, blacklists the base station as fake, and reinitiates the base station selection procedure. However, in current practice, the user equipment again establishes an RRC connection with the fake base station if the base station takes a new identity and transmits a system information message with a higher signal strength because of the deterministic base station selection procedure described in Section 3.2. To address this problem, we use link routing for the user equipment to switch its connection to a base station that has a previous connection history with it (known legitimate base stations). Link routing is in effect only if the user equipment detects any fake base stations; otherwise, it uses the current base station selection procedure. The connection history is stored in the reputation vector (\(\overrightarrow{R}\)), which is generated as a part of a legitimate base station connection and contains the legitimate base station identities. When the user equipment does not detect a fake base station, it continues with the base station with a NAS connection setup and stores the base station identity in \(\overrightarrow{R}\). However, if the user equipment receives an authentication reject message, then it does not continue with NAS connection setup but reinitiates the base station scanning procedure, which we omitted in the flowchart for simplicity (Figure 4).
# A Review of Electrochemical Sensors for the Detection of Glyated Hemoglobin
## 2 Direct Type Electrochemical HbA1c Sensors  
Direct type sensors determine HbA1c by detecting the changes in electrical signals including current, potential, and impedance before and after HbA1c is bound to biological affinity molecules pre-fixed on the electrode surface. Direct sensors are divided into amperometric sensors, potentiometric sensors, and impedimetric sensors.