esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Recent Phased Arrays for Millimeter-Wave Wireless Communication
## 3 Discussion
To address the increased path loss at mm-wave frequencies and the lower power consumption compared to that of sub-6-GHz communication links, LOS communication using phased-array techniques with an array antenna gain is required. Various antenna array architecture designs for outdoor mm-wave communication with different beamforming techniques, i.e., analog, digital, and hybrid techniques, were analyzed [9]. The antenna array design with circular elements is more robust to vibrations and is therefore the best candidate for outdoor mm-wave communication in terms of beam gain, gain fluctuation, and beam misalignment of phased-array antennas. Analog beamforming compensates for the high path loss by generating high beamforming gains, whereas digital beamforming presents a better performance with an enhanced structural complexity. Therefore, a combination of analog and digital beamforming array architectures on both the transmitter and receiver ends has become the focus of research in recent years days based on the trade-off between the flexibility and performance in outdoor mm-wave communications [55; 73]. Cost is an important factor that needs to be significantly lowered owing to the widespread use of phased arrays in 5G wireless communication. Therefore, ICs that are based on silicon technology (SiGe or BiCMOS) [34; 35; 58; 59; 54] along with low-cost PCB technology [35; 36; 60] should be considered. Scalable, RFIC-based phased arrays that are capable of beam-scanning in both the horizontal and vertical planes currently attract much attention from researchers [60].
## 2 Methodology
### Experimental Data  
Data used for demonstration of the trained VOC-Net model consists of experimental spectra measured in our laboratory for six VOCs: ethanol, methanol, formic acid, acetonitrile, and acetaldehyde. Absorption spectra for these molecules have been previously measured in the 220-330 GHz range using a THz microelectronics spectrometer (Figure 1), as reported by [PERSON] et al. [34; 35; 36; 37]. Measurements were carried out at room temperature (297 K) in a gas cell with an absorption pathlength of 21.6 cm. The spectrometer provides frequency resolution of 0.5-15 MHz, depending on operating parameters.  
For the measurement of absorption spectra, THz radiation is generated using microelectronics based frequency multiplication of a radio frequency source. That radiation is coupled to free space with a diagonal horn antenna that produces a diverging beam of THz radiation. The diverging output is collimated with a Teflon lens and passed through the gas cell. Following passage through the gas cell, the radiation is focused using a second Teflon lens onto a Schottky diode detector, where the signal is captured and sent to a data acquisition system. Absorbance is determined by measuring a baseline reference intensity, \(l_{0}\), with the gas cell under vacuum and then measuring the absorbed intensity, \(I\), when the gas cell is pressurized with a gas of interest. The spectral absorbance A is given by,  
\[A=-ln(\frac{I}{I_{0}})=ecl \tag{2}\]  
where \(\epsilon\) is spectral absorption coefficient, which is a function of the spectroscopic parameters for the probed transitions (i.e., line position, strengths, and shapes), thermodynamic conditions, and gaseous composition; \(c\) is concentration of the absorbing gas; and \(l\) is the pathlength. The acquired experimental spectra are resampled to match the 0.016 cm\({}^{-1}\)resolution of the simulated spectra. The initial frequency resolution of the experimental spectra was 0.000016 cm\({}^{-1}\), except for chloromethane which was 0.00036 cm\({}^{-1}\).
# Monitoring Water Area Dynamics in Kashgar (2003-2023) Using Multi-Source Remote Sensing Data
## 3 Results and Analysis  
### Accuracy Evaluation Analysis  
To validate the accuracy of the five aforementioned extraction methods, this study utilized Landsat and Sentinel-2 imagery acquired from June to September 2023, covering the Kashgar region. The results are presented in Table 1. The Random Forest algorithm demonstrated robust performance across both Landsat and Sentinel-2 imagery, achieving an average overall accuracy of 96.05% and an average water body accuracy of 99.87%, with minimal misclassification or omission errors. Notably, Sentinel-2 imagery exhibited the highest overall accuracy, reaching 97.10%, with a corresponding water body accuracy of 99.79%. Conversely, the lowest overall accuracy (95.00%) and water body accuracy (99.74%) were observed for Landsat 8 imagery. In summary, the Random Forest algorithm exhibits strong robustness, high accuracy, and broad applicability, making it an ideal choice for water area extraction in the Kashgar region.  
To ensure the consistency and accuracy of water body extraction, this study utilized Landsat imagery from 2003 to 2018 and Sentinel-2 imagery from 2019 to 2023 for water body delineation in the Kashgar region, referencing previous research findings. This approach was adopted based on the observation that Sentinel-2 imagery demonstrated superior overall accuracy in water body extraction for the Kashgar region from 2019 to 2023 compared to Landsat 8 imagery.
# Plant-Inspired Soft Growing Robots: A Control Approach Using Nonlinear Model Predictive Techniques
## 2 Model of Vine Robots
#### Kinematics of Vine Robots  
To obtain the vine-robot's forward kinematic model, the constant-curvature assumption is commonly used [20]. The vine-like robot is considered a one-section extensible continuum-like robot where two degrees of freedom are responsible for the robot's curvature and bending, while another is for lengthening the robot. The pose \(\mathbf{T}_{r}^{b}\) of the robot's distant tip to its base is determined in terms of the robot's configurations \(\mathbf{q}\in\mathbb{R}^{3}\); this includes the robot's length \(s\), the curvature angle \(\theta\) and the angle of curvature plan \(\phi\) as illustrated in Figure 2. Thus, \(\mathbf{T}_{r}^{b}\) is obtained as follows:  
\[\mathbf{T}_{r}^{b}=\begin{bmatrix}C_{\phi}^{2}(C_{\theta}-1)+1&S_{\phi}C_{\phi }(C_{\theta}-1)&-C_{\phi}S_{\theta}&\big{(}sC_{\phi}(C_{\theta}-1)\big{)}/ \theta\\ S_{\phi}C_{\phi}(C_{\theta}-1)&C_{\phi}^{2}(1-C_{\theta})+C_{\theta}-S_{\phi}S_ {\theta}&\big{(}sS_{\phi}(C_{\theta}-1)\big{)}/\theta\\ C_{\phi}S_{\theta}&S_{\phi}S_{\theta}&C_{\theta}&sS_{\theta}/\theta\\ 0&0&0&1\end{bmatrix} \tag{1}\]  
where \(S\), and \(C\) represent the trigonometric sine and cosine of an angle. The robot's tip position \(\mathbf{p}=[x,y,z]^{T}\in\mathbb{R}^{3}\) in Cartesian space can be stated from Equation (1) as  
\[\begin{split} x&=\big{(}sC_{\phi}(C_{\theta}-1)\big{)}/ \theta,\\ y&=\big{(}sS_{\phi}(C_{\theta}-1)\big{)}/\theta,\\ z&=sS_{\theta}/\theta\end{split} \tag{2}\]  
The sPAMs lengths \(I=[s,l_{1},l_{2},l_{3}]\) are the actual actuation space of the vine-robot. However, using the shape parameters generalizes the control problem to accommodate any continuum-like robot with the constant-curvature model mentioned earlier.  
The velocity \(\dot{\mathbf{p}}\in\mathbb{R}^{3}\) of the vine robot's tip relies on the time derivatives of the robot's configuration \(\dot{\mathbf{q}}\) as follows:  
\[\dot{\mathbf{p}}=\mathbf{J}(\mathbf{q})\ \dot{\mathbf{q}} \tag{3}\]  
Figure 2: Schematic of vine-like growing robot and its configuration parameters.  
where \(\mathbf{J}(\mathbf{q})\in\mathbb{R}^{3\times 3}\), representing the Jacobian matrix, is computed analytically as follows:  
\[\mathbf{J}_{q}(\mathbf{q})=\frac{\partial\mathbf{p}}{\partial\mathbf{q}}=\frac{\partial\mathbf{p}}{ \partial(s,\kappa,\phi)} \tag{4}\]  
\(\mathbf{p}\) represents the Cartesian position of the robot's tip mentioned in Equation (2).
## 2 Methodology
### Artificial Neural Network (ANN)
#### 2.2.3 BP-ANN Model of Reservoir Sedimentation  
When combining reservoir sedimentation calculation and reservoir operation, the computational process and constraints are complex, and the time scale is difficult to match. The nonlinear mapping ability of BP-ANN can reflect such complicated relationships between multiple independent and dependent variables without requirements of time-consuming computations.  
The sediment flushing efficiency shown in Equation (1) is selected as the output of the BP-ANN model. Based on the analysis in Section 2.2.1, its main influencing factors in real time include: inflow upstream \(Q_{\text{in}}\), sediment concentration upstream \(S\), release flow downstream \(Q_{\text{out}}\), and reservoir water level \(L\), which are the inputs of the BP-ANN model. Taking the TGR reservoir as an example, a BP-ANN model to simulate and predict the reservoir sediment flushing is established with the observed data from 2003, when the TGP was put into operation, to 2011.
# A Survey of Analog-to-Digital Converters for Operation under Radiation Environments
## 2 Performance Evaluation
### Parameters Shared with COTS  
ADC data-sheets usually include standard parameters--input signal bandwidth, digital output rate, resolution and power dissipation--accompanied with FOMs in quasi-static--DNL (differential non-linearity), INL (integral non-linearity), gain and offset errors--and dynamic--DR (dynamic range), ENOB (effective number of bits), SFDR (spurious-free dynamic range), SINAD (signal-to-noise and distortion), SNDR (signal-to-noise and distortion ratio), SNR (signal-to-noise ratio) and THD (total harmonic distortion)--operations. Other parameters of interest provided are the operational temperature range, the input full-scale and the required clock frequency and jitter. Physical implementation details (such as manufacturing process or silicon area) are usually hard to find due to their commercial sensitive nature.  
Parameters presented in the data-sheet of a radenv-ADC could depend on the target application. For example, linearity is evaluated with ENOB in telecommunications while INL and DNL are preferred in video acquisition. In Section 3.2 we explain how we have overcome this inconvenience to established a common playground to compare radenv-ADCs.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 1 Introduction
### Duty Cycle  
Applying the duty cycle for sensors [5; 6] means alternating between active and sleep states while favoring the sleep state as much as possible. Obviously, such a design saves energy by avoiding idle listening, collisions and traffic overhead generation [7; 8]. The duty cycle can be either synchronous or asynchronous. The first category implies clock synchronization and coordination between nodes to set their schedule. An example of framework for synchronous duty cycle is the work of the 6 TiSCH (IPv6 over the Time Synchronized Channel Hopping mode of IEEE 802.15.4e) working group [9; 10] from the IETF (Internet Engineering Task Force). According to 6 TiSCH, communications are coordinated based on a slotted time division multiple access (TDMA) with frequency hopping. In contrast, in asynchronous design, sensor nodes wake up independently and perform \"online\" sender-receiver coordination.  
To avoid synchronization overhead, ODYSSE is based on asynchronous sender-initiated coordination (like B-MAC [11]). To further improve delays, two adaptive variants are proposed; the duty cycle is tuned depending on the traffic (infrequent events or bulk transmissions).
## 3 Key Techniques of near Real-Time Remote Sensing
### Near Real-Time Edge Computing Technology  
Near real-time edge computing consists of satellite in-orbit processing and ground processing of user terminals [34; 35]. In-orbit processing compresses the redundant data to reduce the amount of down-link data. With the reduction in satellite manufacturing and launching costs, satellites can be equipped with higher-performance computers. The in-orbit processing technology is mainly clarified into three directions: (i) in-orbit remote sensing task planning and near real-time data distribution; (ii) image restoration tasks, including radiometric correction, geometric correction, etc., which are typically completed on the ground; (iii) tasks that are closely related to remote sensing users, including image segmentation, target detection, identification, tracking, etc. For the scenarios with limited satellite onboard processing capability, satellite Internet terminals or portable remote sensing terminals carry out near real-time processing after receiving remote sensing data, including geometric correction, radiation correction, information extraction, etc.
## 2 Materials and Methods  
### Methodological Framework for UAS--4G Communications  
4G communications make sense when the aircraft cannot maintain the radio link with the GCS, typically when a limit distance is exceeded or when there are topographic occlusions between them. Therefore, we consider BVLOS conditions when it is necessary to use such cellular network. The presented solution can transmit imagery (both First Person View (FPV) orientation or zenithal captured) and telemetry data from the aircraft via the 4G network. The imagery, which can be real-time video, is intended to be viewed in the GCS o in any device with internet connection.  
The hardware and system architecture inside the drone include a set of navigation sensors (GNSS, IMU, barometer, compass, and pitot tube) that feed data to the FC, and a telemetry module with an antenna to radio-link the FC with the GCS. In order to add 4G capabilities, an extra microcomputer, a modem and a SIM card are needed to connect the drone to Internet. Even though the extra hardware could be powered through the FC, it is worth noting that we power it with an independent power circuit using a power module and a regulator in between. This avoids the supply through the FC and helps to keep the independence between functions, for safety reasons. Regarding the video cameras, these must be compatible with the microcomputer ports. In the case of FPV video cameras, they must comply with specific regulation features such as the Field of View (FOV) and frames per second (fps), while for downwards Earth Observation (EO) cameras the fit for purpose spatial and spectral resolution and geometric quality are the critical issue.  
The system network architecture is based on a Virtual Private Network (VPN) composed of three main IP address endpoints: 1/The on-board drone location. 2/The GCS pilot location. 3/The remote server location. Each of these three VPN endpoints have access to internet and are securely linked between them, with an internal fixed identifier, as shown (Figure 2).  
Figure 2: Virtual Private Network (VPN) architecture scheme.  
A dedicated software is installed in an on-board microcomputer, which reads the telemetry from the Flight Controller (FC), the video from a camera, and is accessible to the VPN endpoints for its visualization in a web interface. In addition, it can stream both telemetry and video data to the VPN endpoints, which can then locally reproduce the data by reading directly from the network ports. The microcomputer acts both as a hub between the drone input data to be sent (FC and cameras), and as a bridge between the drone and the VPN.  
Regarding the streaming protocols, TCP/IP is the recommended protocol to securely transmit the telemetry without losing data packages, while Real Time Streaming Protocol (RTSP) through UDP is the best option to transmit the video and assure a combined low latency. The telemetry data encoding and packaging is done by following the Micro Air Vehicle Link (MAVlink) protocol, which can be decoded and plotted in widely used GCS software.
###### Abstract  
The rapid proliferation of IoT devices necessitates robust security measures to protect against malicious traffic. Anomaly detection, primarily through traffic classification supported by artificial intelligence and machine learning techniques, has emerged as a practical approach to enhancing IoT network security. Effective traffic classification requires efficient feature selection, which is critical for resource-constrained IoT devices with limited computational power, memory, and energy. This study proposes Statistical Moments Difference Thresholding, a feature selection method leveraging statistical central moments to identify significant features distinguishing between legitimate and malicious traffic. The aim is to reduce feature dimensionality while maintaining high detection accuracy. Validated on the MQTTset dataset through binary and multiclass classification using seven ML algorithms, the results highlight its ability to enhance computational efficiency without compromising performance, showcasing its potential in real-world IoT security applications.  
ao _et al._ Article
# Secure and Privacy-Protected Bioinformation Implementation in Air Passenger Transport Based on DLT
## 5 Scenario II: iAPIS Based on Corda
### Background  
APIS is an electronic data interchange system that allows air carriers to transmit travel data to border control agencies. It includes passenger information on the face of a passport like full name, gender, and nationality. APIS can help enhance border security by providing offers with pre-arrival and departure manifest data on all passengers and crew members [35].  
The APIS has two different types, non-interactive batch style APIS and interactive APIS. The comparison between them is shown in Table 1.  
Figure 4: Baggage ownership information sharing CorDapp.  
Figure 3: Baggage mismatch avoidance sequence diagram.  
iAPIS provides passenger information to the destination country's border control agency through a real-time connection, and such real-time communication enables the agency to respond instantly to the carrier based on the results of the vetting process. In contrast, a batch-style API system collects passenger information during the check-in process and then consolidates the information into a single message that is submitted to the destination country's border control agency in a centralized batch process. This can lead to delays and inaccurate information. In summary, the iAPIS system enhances border security by providing pre-arrival and departure manifest data for all passengers and crew members to officers in a more timely and efficient manner.  
The conventional iAPIS system transmits personal information in passports and flight information. However, this type of information does not contain the real-time bioinformation of the passengers, which poses a risk of fraud and forgery. In this research, facial information from biometric information is added to iAPIS for information sharing to improve the work efficiency and vetting accuracy of border control agencies.
# Vulnerability of Satellite Quantum Key Distribution to Disruption from Ground-Based Lasers
## 4 Discussion
### Countermeasures
#### 4.2.2 Spectral Filtering  
Another improvement likely to be incorporated into QKD systems is narrower spectral filtering of the incoming light, since this is also necessary to enable QKD receivers to operate in daylight. Narrower optical filters used to reject light not at the exact QKD transmission frequency will mean either better knowledge of the QKD transmission frequency will be required for effective disruption, or the laser terminal will have to emit light over a broader spectrum, increasing the average power consumption of the laser.
## 3 Results and Discussion  
### Characterization of the IERs  
IERs, including AERs and CERs, were based on a co-polymer of styrene and divinylbenzene (DVB) as a crosslinking agent with functional groups: One of tertiary amine and the other a sulfonate group, respectively (Scheme 1). The chemical structures of the IERs were confirmed by FT-IR spectroscopy (Figure 1). The characteristic bands of the aromatic C=C stretching and aliphatic C-H vibrations are shown at 1634 cm\({}^{-1}\), and at 2923 cm\({}^{-1}\), respectively. The characteristic band of the aromatic C-H stretching vibrations of the AERs and CERs appeared at 3019, and 3066 cm\({}^{-1}\), respectively. For the AERs, the characteristic bands located at 889, 859, and 826 cm\({}^{-1}\) were ascribed to R-N\({}^{+}\)-(CH\({}_{3}\))\({}_{3}\) stretching vibrations, and the characteristic band of the C-N vibration of R-N\({}^{+}\)-(CH\({}_{3}\))\({}_{3}\) appeared at 1479 cm\({}^{-1}\)[30]. For CERs, the stretching vibrations of the sulfonate group are shown at 1173, 1126, and 1038 cm\({}^{-1}\), and the C-S vibration appears at 775 cm\({}^{-1}\). The IR assignments of IERs confirmed that the AERs and CERs had different functional groups. SEM images of both IERs showed spherical shapes with diameters in the order of hundreds of micrometers, and the surfaces were smooth, indicative of gel-type resins (Figure 2). The zeta potentials of the IERs were determined to be +75.9 mV (for AERs) and \(-\)42.5 mV (for CERs) because of their oppositely charged ionic groups, also verified from FT-IR. Because the two IERs had oppositely charged functional groups, they should show different adsorption behaviors toward the negatively charged EDTA.
# Research on Lifespan Prediction Methods Using Ultrasonic Microimaging for Electronic Packaging
## 4 Solder Joint Failure Modeling and Lifespan Prediction  
### Failure Modeling Based on Cumulative Failure Probability  
The curves in Figure 11 represent the failure process of solder joint number 107 on the second flip chip of sample board number 1. To enable a more detailed quantitative analysis and lifespan prediction, it is necessary to convert these data into a mathematical expression. This study employed the Anderson-Darling (AD) test to determine the most suitable probability distribution that best describes the failure curve of the solder joint. Table 1 presents the results of the Anderson-Darling test. The results showed that with the exception of the two-parameter Weibull distribution, the \(p\)-values for the other distributions were less than 0.05 and the AD values were higher, indicating insufficient fit. The two-parameter Weibull distribution exhibits the highest degree of agreement with the experimental data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Distribution Function** & **AD** & \(\boldsymbol{p}\) \\ \hline Normal & 3.416 & \textless{}0.005 \\ Exponential normal & 6.402 & \textless{}0.005 \\ Index & 7.043 & \textless{}0.003 \\ Weibull & 5.212 & \textless{}0.010 \\
3-parameter Weibull & 2.790 & \textless{}0.005 \\ Gamma & 5.451 & \textless{}0.005 \\ Logistic & 3.087 & \textless{}0.005 \\
2-parameter Weibull cumulative & 0.987 & 0.526 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of Anderson–Darling test.  
Figure 11: Failure curve of solder joint 107 on flip chip 2 of experimental sample 1 using proposed method.  
Based on the conclusions derived from Table 1, a failure model for solder joint number 107 on the second flip chip of sample board number 1 has been developed, as illustrated in Figure 12. The probability density function (PDF) and cumulative distribution function (CDF) for the two-parameter Weibull distribution are defined as follows:  
\[f(t)=\frac{k}{\lambda}(\frac{l_{i}-\gamma}{\lambda})^{k-1}exp[-(\frac{l_{i}- \gamma}{\lambda})^{k}] \tag{3}\]  
\[F(t)=1-exp[-(\frac{l_{i}-\gamma}{\lambda})^{k}] \tag{4}\]  
where \(i\) represents the failure time, \(k\) denotes the shape parameter, \(\lambda\) is the scale parameter, and \(\gamma\) represents the location parameter.  
The results indicate that the parameter values for the model of solder joint number 107 on the second flip chip of sample board number 1 are \(k=1.24,\ \lambda=74.61\) and \(\gamma=0\). Consequently, the cumulative failure probability model for this solder joint based on the two-parameter Weibull distribution is:  
\[F(t)=1-exp[-(\frac{t_{i}}{74.61})^{1.24}]\]  
The model provides a precise mathematical description of the entire service lifespan-to-failure process of solder joint number 107 on the second flip chip of sample board number 1. However, it currently applies only to this specific solder joint, and therefore further validation of its generalizability is required to extend its applicability.