esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Principles
### LACGAN-Based Small-Sample Data-Augmentation Method
#### 4.1.2 Network Architecture Design  
The specific design of the LACGAN network is shown in Figure 2. Based on a traditional GAN, mainly the following improvements have been made:  
1. A label-embedding layer is added to the input of the generator. It maps discrete category labels into continuous vectors, which are then concatenated with jamming vectors to form combined inputs. This enables the generator to clearly distinguish the feature distributions of different jamming types in the latent space, achieving precise control over jamming types.
2. Instance normalization (InstanceNorm) is adopted in the discriminator. The discriminator of a traditional GAN only outputs the authenticity probability and uses batch normalization (BatchNormNorm), which leads to instability in small-batch training. Instance normalization performs normalization on the feature dimension of each sample and does not depend on the batch size, improving the fine-grained classification of samples.
3. The LACGAN introduces learnable scaling factors at the output layer of the generator by setting learnable intensity parameters. It can automatically learn the optimal intensity range for different jamming types through backpropagation, dynamically adjusting the jamming intensity. This enhances the adaptability of the generator in different Signal-to-Jamming-plus-Noise Ratio (SJNR) conditions.
# A Vehicle Velocity Prediction Method with Kinematic Segment Recognition
## 2 Data Preprocessing  
In this section, kinematic fragment samples in typical driving conditions were extracted and characteristic parameters were calculated. In order to reduce the amount of calculation and avoid information overlap of multi-dimensional characteristic parameters, principal component analysis was used to reduce the dimension of characteristic parameters with multi-dimensionality.
## 5 Technical Architecture of the Framework
### Advantages of IBBA Framework
* The generated hash value of the data file is pushed to the blockchain to ensure non-repudiation. At the same time, the data file is uploaded to a big data system hosted on the cloud (IBBA Backbone). * Similarly, data from other IoT sensors installed across various process steps of a process line are processed to determine contamination and pushed to big data (IBBA Backbone). * The big data system collates and aggregates data from multiple process lines across geographical regions and acts as a central repository of the 4V data. The analytics deployed on such systems will have the ability to gain more insights into various facets of food safety (5 th V, value). * At the same time, the random samples collected are sent to authorized laboratories for more thorough analysis. The lab results are pushed to the IBBA Backbone module to trigger a product recall decision dissemination if required through the IBBA Portal module. * IoT systems are designed and installed on transportation vehicles carrying bread pellets to distribution centers or retail stores. These devices measure parameters like ambient temperature, humidity, or any relevant parameters required to predict spoilage. The processed data (IBBA Edge) from such onboard devices reach IBBA Backbone over mobile networks. * The delivered food pellets at distribution centers/retail stores are tracked with the mobile app (IBBA Mobile) which in turn communicates with the IBBA Backbone module to address traceability issues. * In the event of a recall, IBBA Backbone in coordination with IBBA Portal and IBBA Mobile modules helps to trace back/trace forward the location of spoiled bread. This capability ensures the implementation of recall decisions quickly, effectively, and accurately.
# CFD-DEM Simulation of the Transport of Manganese Nodules in a Vertical Pipe
## 5 Results and Discussion  
### Effect of Initial Solid Volume Fraction  
#### 5.1.1 Distribution of Instantaneous Particle Velocity  
At \(V_{mix,0}\) = 4.0 m/s and \(d_{s}\) = 15.0 mm, instantaneous particle velocity distributions at different initial SVFs are shown in Figure 5. It is interesting that particles with high velocities tend to concentrate in the central zone of the pipe, while low-velocity particles gather near the wall, which is consistent with the results obtained in [28]. Essentially, liquid velocity in the central zone is higher than that near the wall due to the effect of friction between the liquid and the wall. Furthermore, particles tend to move towards the pipe axis, minimizing the energy loss induced by particle-wall interactions.  
At low initial SVF, as shown in Figure 5a, the particles are dispersed over the cross-section. Such a pattern has only been observed at the initial SVFs higher than 5% and for small particles with diameters less than 2.3 mm [3]. This particle scattering phenomenon becomes more remarkable with increasing initial SVF. The particles separate from each other due to collision. As the initial SVF increases, the collision between particles is intensified. It is demonstrated in Figure 5 that the particle velocity decreases with increasing initial SVF, which is more evident in Figure 6. The pressure energy serves as a source for the kinetic energy of liquid and particles. With more particles delivered, the sum of the kinetic energy of particles increases, resulting in a high total pressure drop. However, if the sum of the kinetic energy of particles is averaged over all particles, each particle shares less kinetic energy than that associated with small particle number. Therefore, particle velocity decreases with increasing particle number, as shown in Figure 6. Meanwhile, inevitable energy loss during the transport process is caused by friction and collisions. Furthermore, with increasing initial SVF, the reinforced contacts between particles and between particles and the wall incur energy loss. Moreover, collisions between particles are intensified as particle number increases, which intensifies energy consumption and increases in total pressure drop.  
Figure 5: Instantaneous particle velocity distributions at different initial SVFs (\(V_{mix,0}\) = 4.0 m/s, \(d_{s}\) = 15.0 mm).
# The Power System and Microgrid Protection--A Review
## 3 Protection Scheme of Microgrid
### Voltage-Based Scheme  
The voltage-based method is another approach to protect microgrids using voltage measurements [106]. The method uses the voltage level gradient through the power system during faults and is often applied as a backup protection scheme [86; 107]. The authors of Reference [106] implemented the voltage-based protection scheme in combination with directional elements to develop a protection scheme for low fault and low voltage radial microgrids. Several research works have described the abc-dq transformation to detect voltage disturbances. The method converts the signals from abc reference to a dq frame [108; 109]. The output voltages of micro-source are transformed into DC quantities by the dq reference frame. Any disturbance at the micro-source output due to the fault on the grid is reflected as disturbances in the dq values [108]. The authors of Reference [108] suggested a protection scheme based on the abc-dq transformation of the voltage waveforms to detect faults in a microgrid. The proposed method can protect both internal and external faults in a set zone of protection associated with the microgrid.  
[PERSON] and [PERSON] [110] presented the voltage-based protection method in a dq rotating frame in islanded microgrids. Their method presents a variety of faults in a three-phase power system and a voltage-based protection scheme monitoring the voltages seen at the converter's terminals. The principles of this scheme can be applied to both single-phase and three-phase generators. It should also be noted that determining the fault location based on voltage measurements is extremely difficult due to the magnitude of the voltage dip during the faults. This is because the voltage magnitude would be the same at different locations [89]. In addition, the voltage drop may not be large enough in HIFs. Consequently, the under-voltage protection function may not be able to distinguish a faulty condition from a power system overload [55].
# A Hybrid Key Generator Model Based on Multiscale Prime Sieve and Quantum-Inspired Approaches
## 3 Theoretical Background Useful for the Quantum-Inspired Subkey
Quantum mechanics is probabilistic. The measurement of a quantum system described by the Schrodinger equation produces random results that follow the probability distribution of the wavefunction. The use of [PERSON]'s uncertainty principle or measurements of quantum superposition states can provide extremely secure randomness, which can be used for cryptographic key generation. 3. Quantum cryptography and key distribution (QKD). One of the most advanced applications of the Schrodinger equation in cryptography is quantum key distribution (QKD), for example the BB84 protocol. Application: QKD applies the quantum states of photons (e.g., polarisation or phase) to securely exchange cryptographic keys between two parties so that an attacker cannot intercept them without being detected. 4. Computational simulations for complex keys. The numerical simulation of the Schrodinger equation for a multiparticle quantum system leads to chaotic behaviour that is difficult to predict. How it is used: a simulated quantum system can be used to generate sequences of pseudo-random numbers, which can then be used as input for cryptographic algorithms. Quantum hash functions based on wave dynamics. Quantum hash functions can be constructed by exploiting the non-linear and probabilistic solutions of Schrodinger equations. How it is used: an algorithm can transform a quantum state into a deterministic but non-reversible hash, which can be used in quantum digital signatures.
## 5 Experimental Evaluation  
### Benchmark Experiments  
To evaluate the effectiveness of the proposed framework, the LM-CNN-LB method was compared with the following methods:  
* SVM-NBB: A model trained on the source domain was directly applied to predict the target domain without any transfer learning method. The classifier was an SVM using the bag-of-words (BOW) schema and a linear kernel; the source domain and target domain were all from \(D_{b}\). For example, the source domain was \(B_{D_{b}}\) and the target domain was \(D_{D_{b}}\); the cross-domain classification task was \(B_{D_{b}}\)\(\rightarrow\)\(D_{D_{b}}\).
* SVM-NLB: The model trained on the source domain was directly applied to predict the target domain without any transfer learning method. The classifier was SVM, using BOW and a linear kernel. The source domain was from \(D_{bl}\), and the target domain was from \(D_{b}\). For example, when the source domain was \(B_{D_{bl}}\), the target domain was \(D_{D_{b}}\), and the cross-domain classification task was \(B_{D_{bl}}\)\(\rightarrow\)\(D_{D_{b}}\).
* SCL-MI: [PERSON] et al. [2] applied structural correspondence learning for cross-domain sentiment analysis (SCL). SCL-MI was an improvement of SCL [31]. The source domain and the target domain were both from \(D_{b}\).
* SFA: Spectral feature alignment was proposed by [PERSON] et al. This approach bridged the gap between different source and target domains via word alignments [3]. The source domain and target domain were both from \(D_{b}\).
* SS-FE: The SS-PE approach was used to conduct both labeling adaptation and instance adaptation for domain adaptation as in [5]. The source domain and target domain were each from \(D_{b}\).
* CSC: The authors of [8] proposed a common subspace construction method for cross-domain sentiment classification called CSC. The source domain and target domain were each from \(D_{b}\).
* PJNMF: This method links heterogeneous input features via pivots via joint non-negative matrix factorization [6]. The source domain and target domain were each from \(D_{b}\).
* LM-CNN-NLB: LM-CNN-LB was applied to the source domain using \(D_{bl}\) to train the \(S_{cnn}\). Then, the \(S_{cnn}\) was directly applied to predict the target domain from \(D_{b}\).
* LM-CNN-BB: Like LM-CNN-LB but the \(S_{cnn}\) was trained on the source domain from \(D_{b}\). The training set size was 1600 and the validation set size was 400. Then, the \(T_{cnn}\) was also trained on the target domain from \(D_{b}\). The training set size was 400, the validation set size was 200, and the remaining 1400 data points comprised the test set.  
In the process of training the \(S_{cnn}\) of LM-CNN-LB, the training set size was 90,000, and the validation set size was 12,000. To train the \(T_{cnn}\), the training set size was 4000 (4% of the target domain from \(D_{l}\)) and the validation set size was 1000 (1% of target domain from \(D_{l}\)). Finally, the \(T_{cnn}\) model was used to test 2000 target domain product reviews from \(D_{b}\). The embedding layer weights were pre-trained on Google News.
## 2 Main 3-HP Biosynthesis Pathways for the Construction of Microbial Cell Factories
### \(\beta\)-Alanine Pathway  
\(\beta\)-alanine can be formed by the transamination or decarboxylation of various amino acids, such as arginine, lysine, and aspartic acid, which are widely available metabolites in  
Figure 1: Main pathways and key enzymes for 3-HP biosynthesis from renewable substrates. Blue module: assimilation pathways of different renewable substrates; orange module: various 3-HP biosynthesis pathways. 1,3-PDO, 1,3-propanediol; 3-HPA, 3-hydroxypropionaldehyde; 3-HP-P, 3-hydroxypropionyl-phosphate; ACC, acetyl-CoA carboxylase; ACKA, acetate kinase; ACS, acetyl-CoA synthase; ADH, alcohol dehydrogenase; ALDH, aldehyde dehydrogenase; AraE, major facilitator superfamily transporter; AspA, aspartate ammonia- lyase; AspC, aspartate aminotransferase; BAPAT, \(\beta\)-alanine pyruvate transaminase; DHAP, dihydroxacetone phosphate; DhaT, 1,3-propanediol dehydrogenase; FadD, long-chain acyl-CoA synthetase; FadL, long-chain fatty acid transport protein; GDHt, glycerol dehydratase; GlpD, glycerol-3-phosphate dehydrogenase; GlpF, glycerol uptake facilitator protein; GlpK, glycerol kinase; HPDH, 3-hydroxypropanoic acid dehydrogenase; MCR, malonyl-CoA reductase; MdlC, benzoylformate decarboxylase; MmsB, 3-hydroxyisobutyrate dehydrogenase; PanD, aspartate-\(\alpha\)-decarboxylate; PCx, pyruvate carboxylase; PDHC, pyruvate dehydrogenase; PduL, phosphotransferase; PduP, propionaldehyde dehydrogenase; PduW, propionic acid kinase; PTA, phosphate acetyltransferase; TTS, phosphotransferase system; XylA, xylose isomerase ; XylB, xylulokinase.  
microorganisms [19]. At present, \(\beta\)-alanine is usually synthesized from aspartic acid using aspartate decarboxylase (PanD), while aspartic acid can be synthesized from fumarate or oxaloacetate by transamination, which is used for 3-HP synthesis via the \(\beta\)-alanine pathway. The \(\beta\)-alanine pathway was first constructed in _E. coli_ by [PERSON] [20]. In this pathway, \(\beta\)-alanine is converted into MSA by \(\beta\)-alanine pyruvate transaminase (BAPAT), after which MSA is reduced to 3-HP by NADPH-dependent 3-hydroxypropanoic acid dehydrogenase (HPDH) [21]. In subsequent studies, \(\gamma\)-aminobutyric acid transaminase (GABT) was also used for the conversion of \(\beta\)-alanine to MSA [22].
# Improved PR Control Without Load Current Sensors and Phase-Locked Loops for APFs
## 5 Experimental Verification  
Based on the completed simulation verification, a 1 kVA two-level parallel APF experimental platform was established, as shown in Figure 10, with detailed system parameters listed in Table 1. The platform employs the YXSPACE-SP6000 controller and the YXPHM-MMCFB01 series inverter. The three-phase AC voltage source is provided by YXACS15-YZ, while a rectifier bridge and power resistors are used as nonlinear loads. The DC side utilizes capacitors to maintain a stable voltage.
# Re-Optimized Design for the ADR-Based Dual-Loop Controller of 100 m Class Aperture Radio Telescope
## 3 Re-Optimized Design of the Dual-Loop Controller
### Velocity Controller Re-Optimization
The final block diagram of the antenna's velocity-loop control is shown in Figure 3.  
Combining Equations (16) and (18), then the LADRC can be rewritten as a combination of a feedback controller \(G(s)\) and feedforward controller \(H(s)\), as shown in the following equations:  
\[G(s)=\frac{1}{b_{0}}\frac{K_{\rm av}s^{2}+(K_{\rm pv}\beta_{1}+\beta_{2})s+K_{ \rm pv}\beta_{2}}{s[s+(K_{\rm pv}+\beta_{1}-K_{\rm av})]}, \tag{21}\]  
\[H(s)=K_{\rm pv}\frac{s^{2}+\beta_{1}s+\beta_{2}}{K_{\rm ap}s^{2}+(K_{\rm pv} \beta_{1}+\beta_{2})s+K_{\rm pv}\beta_{2}}, \tag{22}\]  
Equation (21) shows that the equivalent feedback controller \(G(s)\) is a PID control cascading a low-pass filter (PID-F), and Equation (22) shows that the equivalent feedforward controller \(H(s)\) is a notch filter. Equation (21) explains the reason for adding disturbance velocity compensation to improve the system's anti-disturbance performance from the PID point of view, i.e., both reducing the low-pass filter cutoff frequency (bandwidth) and introducing the derivative term in the original PI. The \(H(s)\) can also be rewritten as a combination of a unit-magnitude feedforward controller and a bypass lead-lag compensator on the equivalent closed-loop feedback controller output. The transfer function of this lead-lag compensator \(F_{\rm r}\) is  
\[F_{\rm r}=\frac{(K_{\rm pv}-K_{\rm av})s-\beta_{2}}{s+(K_{\rm pv}+\beta_{2}-K_ {\rm av})}, \tag{23}\]  
Equation (23) shows that the equivalent bypass feedforward controller is similar to command-speed compensation. Thus, it can be seen through Equations (21)-(23) that the first-order LADRC can be comparable to a combination of conventional PID-F with command-speed feedforward compensation. It is worth noting that despite this equivalence, the first-order LADRC explains the combination from an anti-disturbance point of view and gives a complete set rather than dispersive parameter-tuning methods. Figure 3: Block diagram of the antenna’s velocity-loop control system with a novel 1 st-order LADRC.
# A High-Robust Sensor Activity Control Algorithm for Wireless Sensor Networks
## 2 QoS Control Algorithms  
### Gur Game  
In 2003, the Gur Game algorithm was first applied to control the number of active sensors [5]. In the Gur Game algorithm, it is assumed that the base station and sensor communicate in a signal hop and that they are time synchronized. Each sensor has a state machine. Before the Gur Game algorithm starts to operate, the sensor selects a state from the state machine as its initial state, and states 2 and 1 represent the active state, while states \(-2\) and \(-1\) are sleep states. As shown in Figure 1, the sensor must receive a data packet from the base station regardless of whether it is in an active or sleep state.  
In each round, each active sensor sends a packet to the base station. The base station counts the total number of received packets and then computes the probability value \(r(t)\) of the state transition using the reward function, as shown in (1), where \(0\leq r(t)\leq 1\). \(Q_{1}\) is the total number of packets received at time \(t\), and \(Q_{0}\) is the target value. After the base station calculates \(r(t)\), it broadcasts the feedback packets to all sensor nodes. After receiving the feedback packets, the sensor randomly generates a random value \(R\) of 0-1. When the random value \(R\) is less than \(r(t)\), the sensors move to the 2 or \(-2\) state of the state machine. Otherwise, the sensors move to the 1 or \(-1\) state in the state machine. The sensor then determines the state of the next round based on the current position in the state machine. After several rounds of operation of the algorithm, the number of active sensors reaches (converges to) the target value.  
\[r(t)=0.2+0.8\text{exp}(-0.002(Q_{1}-Q_{0})^{2}) \tag{1}\]  
Figure 2 shows the results corresponding to the reward function of the Gur Game approach, where the total number of sensors \(m\) is 100 and the target value \(Q_{0}\) is 35. After approximately 1200 epochs, the system achieved the target value. When the value of \(r(t)\) is close to one, the sensor is more likely to remain in its original state. After the Gur Game algorithm runs many times, the number of nodes in the operating state gradually reaches the target value, the calculated probability value \(r(t)\) approaches 1, and the state of the sensor nodes no longer changes.  
Figure 1: State machine used by the sensor in the Gur Game algorithm.  
Figure 2: Execution results of the Gur Game approach when the target value \(Q_{0}\) is set as 35.
## 3 Engineering Application
### Research Object  
The researched FTU is located in the upper reaches of the Dadu River, Sichuan province, west of China. It is a large-capacity unit with a medium-high water head. Its essential performance parameters are listed in Table 1, and the basic structure is shown in Figure 6. The working state of the FTU is closely related to the operating parameters. Therefore, operating conditions must be considered. The operating condition of the FTU can be described by the water head (\(H\)) and the active power (\(P\)). The top cover is located between the turbine and the generator. It seals the runner chamber and connects the main shaft. As a critical component, its vibration amplitude (\(V\)) can reflect the working state of the FTU. The position of the monitoring point is illustrated in Figure 7. Therefore, the raw sample set \(\Psi\) including both operating parameters and monitoring data is formed by (\(H\), \(P\), \(V\)).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Values** & **Units** \\ \hline Type & HLD416A-LJ-696 & \textbackslash{} \\ Rated power & 600 & MW \\ Rated water head & 156.7 & m \\ Rated flow rate & 435 & m\({}^{3}\)/s \\ Rated rotate speed & 125 & r/min \\ Rated efficiency & 96.43 & \% \\ Inlet diameter of the runner & 6.964 & m \\ Number of runner blades & 15 & \textbackslash{} \\ Guide vane distribution diameter & 8.0 & m \\ Number of guide vanes & 24 & \textbackslash{} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic performance parameters of the FTU.  
Figure 6: The basic structure of the FTU.  
The operating parameters of the FTU are monitored by the supervisory control system of the hydropower station. The vibration signals of critical components are acquired by a PSTA-2100 state monitoring system. They are transmitted to the computer monitoring system of the hydropower station through the TCP/IP protocol and Modbus 485 protocol, respectively. The physical distance of the transmission link is usually above several thousand meters, including multiple switches, routers, and different transceiver devices. In addition, these monitoring and communication systems work in the extreme environment of high humidity, strong vibration, and high electromagnetic interference, which may result in short-term failures. Consequently, the raw data directly exported from the computer monitoring system are often low quality, which manifests as data anomalies and data loss.