esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Active Noise Barrier  
An active noise barrier is composed of a noise barrier and an active control system. The noise barrier is used to control the noise in the middle and high-frequency ranges, and the active control system is used to control the noise in the low-frequency range. The active control system consists of additional sources, control systems, and error sensors, where the additional source is generally set on the same side as the primary source, mostly attached to the facade of the barrier, and the error sensors are set at the region where the noise reduction needs to be focused, usually behind the barrier. The scheme of an active noise barrier is shown in Figure 1. The principle of an active noise barrier in low-frequency noise abatement is to achieve superimposed noise reduction by controlling the additional source to generate a series of sound waves that have the same amplitude and opposite phase with the primary source at the region where error microphones are located.  
Diffraction occurs when a sound wave encounters the top edge of a sound barrier during propagation; sound waves travel in a straight line, but diffraction at the top of the barrier bends the direction of propagation by an amount related to the wavelength of the sound wave. The bending of the propagation direction allows the sound waves to travel to the protected area bypass the barrier. There is a loss of sound energy during the diffraction process, resulting in a reduction in noise. In the case of an active noise barrier, sound waves from both the primary source and the additional source are diffracted at the top of the barrier and continue to propagate to the rear of the barrier. These waves combine to form the sound field behind the barrier. However, controlling the sound field beyond the region where the error microphones are located can be challenging, and this may lead to increased noise pollution in large spaces behind the barrier. This is an unavoidable phenomenon determined by the noise reduction mechanism of an active noise barrier. In practice, there are often many buildings and other protected regions that are taller than the barrier, as shown in Figure 1. Therefore, there is a need for effective noise protection in large spaces. In addition to increasing the number of error sensors, methods to improve the noise reduction effect of a barrier in large spaces can be achieved by optimizing the location of the additional sources to improve the sound field distribution behind the barrier.  
The direction of the waves will be changed after diffraction at the top of the barrier, and the waves in a certain frequency range may propagate mainly in a certain direction: it is beneficial to reduce the noise pollution behind the barrier when the propagation direction of the waves is bent upward. Therefore, the noise reduction effect of the barrier in a large space can be evaluated by analyzing the directivity of the waves around the top of the barrier and the sound field behind the barrier.  
Figure 1: The scheme of an active noise barrier.
# A 12 dBm B1 dB N-Path Notch Filter for Transmitter Leakage Suppression in Wideband Receiver
## 4 Circuit Implementation  
The schematic for the proposed filter is depicted in Figure 5. It features an eight-path notch filter with an in-band impedance flip-flop, driven by an eight-phase 12.5% duty cycle clock to achieve high linearity in blocker power rejection. High blocker power suppression is facilitated by a quarter-wavelength transmission line, which, combined with an N-path BPF in Stage I and an N-path BRF in Stage II, ensures a high rejection depth. Off-chip LC equivalents are utilized for the quarter-wavelength transmission lines to minimize chip area. The filter architecture includes an eight-path N-path BPF, an eight-path N-path BRE, two off-chip LC equivalent models, and an eight-phase 12.5% duty cycle clock generator.
###### Abstract  
This review focuses on studying passive vibration isolation techniques and their applications. Advantages and disadvantages of different vibration isolation techniques will be compared in order to find an innovative method to reduce seating suspension system vibration and improve the ride comfort of vehicles' drivers. Quasi-zero stiffness (QZS) isolators of high-static-low-dynamic stiffness are found to have good application prospects in low-frequency vibration isolation. In order to improve the isolation performance of the seating suspension system, a specially made seat cushion will be studied where a quasi-zero stiffness (QZS) isolator such as a double-diamond isolator is selected as one of the cells/units/elements of the seat cushion mattress. The double-diamond isolator structure does not represent the whole seating suspension system. The transmissibility ratio of the double-diamond isolator is evaluated by analytical and simulation models and compared to that of a conventional linear spring isolator. Although the performance of the passive vibration isolation/control system is usually worse than that of the active or semi-active control system, the cost of the passive vibration isolation/control system is lower than that of the active and semi-active control system, and the relative simplicity of adding a vibration isolation cushion mattress in an existing passive seating suspension system makes it low in cost, easy to implement, and more attractive than the active and semi-active vibration control systems.  
passive suspension; seating suspension system; vibration control; drivers' health; ride comfort; high-static-low-dynamic-stiffness (HSLDS); quasi-zero stiffness (QZS) +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
### System Architecture  
The RABE design features two interfaces; the first one is a digital I/O data, control, and communication interface between the prototype and the SoC, which is controlled by an FPGA, and which is part of the BBP; the connection interface with the RF stage is achieved through an FMC connector; the second one is an RF analog output with an SMA connector as the interface to the RF filtering stages, power amplifier, and antenna. The main core of the RABE is powered by an AD9354 IC.  
Figure 1 shows the communication model that was implemented, the RF stage (of this work) that was designed, implemented, and tested, and tuning for the RABE in yellow, which was the contribution of the paper.  
The BBP stage consisted of the development and coupling of the Serial Communication Protocol (SPI), as well as the programming of the registers to control the flow of data, synchronization, and processing of the data from the BBP to the RABE through the FMC connector, which is responsible for carrying all the bits for the operation of both devices, including the voltage and ground lines.  
In the RF stage, the SPI was synchronized to program all the necessary registers in the IC, the DACs, the filtering stage, the preamplifiers, and the local oscillator, which were configured to be tuned at the required frequency. It is noteworthy that each of the configured actions corresponds to the programming of register groups in order to obtain the correct result shown in yellow in Figure 2.
## 4 Resource Management in Heterogeneous Network (RM HetNet)
### Mode Selection (MS)
#### 4.3.3 Open Issues and Proposed Solutions  
D2D communication is considered a challenging and dynamic environment; consequently, a comprehensive evaluation of the distributed artificial intelligence solution (DAIS) mode selection algorithm is needed to be investigated. The SE and power consumption of the network have been analyzed and considered various factors. Two parameters, the data rate threshold and the device battery threshold, notably impact the EE and SE. In addition, a blockchain technology-based D2D network that addresses security and privacy concerns in a decentralized manner will be beneficial for safe communication. Under the D2D communication network, blockchain technology is crucial for establishing smart contracts between the D2D relay and the direct D2D.
## 2 Modeling of InGaP/GaAs Tandem Solar Cells
### Physical Models  
It is obvious that the accurate modeling of the tunnel junction within a solar cell's structure plays a crucial role in understanding and optimizing its performance. This complex and challenging task is addressed using the nonlocal band-to-band tunneling (NLBBT) model. Indeed, an electrode is introduced, which is seamlessly adapted to the interlayer's geometry, coupled with an overall resistor. This resistor allows ensuring precise current flow from the anode to the cathode, preventing unwanted leakage through the introduced electrode [24; 25; 26; 27; 28].  
The physical model of the studied solar cell in this paper is implemented in Silvaco's ATLAS TCAD, which is an advanced computer-aided design (CAD) software (version 2018) that was proved for its capability to accurately simulate semiconductor device behavior [29; 30; 31]. This powerful tool integrates a range of models, including Fermi level, concentration-dependent mobility (CONMOB), Shockley-Read-Hall (SRH) recombination, optical recombination (OPTR), band-gap narrowing (BGN), and Auger recombination, enabling precise replication of the intricate physical mechanisms governing solar cell performance under various operating conditions [32; 33; 34].
## 4 State-Feedback Control
### Event-Triggering Rule
To provide some insight into how this time \(\mathcal{T}(\gamma,L)\) is computed in [23], let us define the variable \(\phi\in\mathbb{R}_{\geq 0}\) with the following dynamics:  
\[\dot{\phi}=-2L\phi-\hat{\gamma}^{2}(\phi^{2}+1)\qquad\phi(0)=\lambda^{-1}, \tag{17}\]  
where \(\lambda\in(0,1)\), \(\hat{\gamma}=\sqrt{\gamma+v}\) with \(\gamma\) comes from Assumption 1, with any small \(v>0\) and \(L\), as defined in (14). Hence, the time \(\mathcal{T}(\gamma,L)\) corresponds to the time it takes for \(\phi\) to decrease from \(\lambda^{-1}\) to \(\lambda\). We note that, in view of (15), if we enforce \(\gamma^{2}W^{2}(e)\leq\varepsilon|x|^{2}\), then it holds that  
\[\langle\
abla V(x),\mathcal{A}_{1}x(t)+\mathcal{B}_{1}e(t)+ \mathcal{E}_{1}w(t)\rangle\leq-|x|^{2}+\eta|w|^{2} \tag{18}\]  
which ensures that the closed-loop system is \(\mathcal{L}_{2}\)-stable. Hence, the triggering threshold \(\sigma\) in (12) is defined as \(\sigma=\frac{\sqrt{\varepsilon}}{\gamma}\). To summarize, the ETC (12) is designed as follows. We  
1. Stabilize the plant model (3) in continuous-time;
2. Check the feasibility of Assumption (12) and determine the value of \(\gamma,L\). 3. Compute the MATI bound \(\mathcal{T}(\gamma,L)\) from (16) and take \(T\in\mathcal{T}(\gamma,L)\). 4. Determine the value of the ETC threshold \(\sigma\), as will be specified later.
## 4 Conclusions  
In this paper, we proposed a three-stream 3D convolutional network called NIRExpNet to recognize NIR facial expression from video sequences. The NIRExpNet considers the motion information of facial expression and extracts spatio-temporal features from the video. We divided the whole network into two components: global network and local network to consider both global and local information of facial expressions. For the global network, we input the complete facial video sequences and employed VGG-M-2048 model to extract global dynamic information. For the local network, the upper and lower regions of facial video sequences were segmented as two data flows input into the local network. We fused two streams by using the concatenation fusion followed by a fully connected layer with 2048 neurons. We further fused the global and local networks to extract facial expression features in motion. Experimental results on the Oulu-CASIA NIR facial expression database showed that the NIRExpNet can achieve an average recognition rate of 78.42%, and therefore, outperform some state-of-the-art methods, such as LBP-TOP [11], 3D HOG [6], DTAGN [38], and 3D CNN DAP [31].  
**Acknowledgments:** This work was partially funded by the National Natural Science Foundation of China (Grant No. 61301297), and the Southwest University Doctoral Foundation (No. SWU115093).  
**Author Contributions:** [PERSON] conceived the research, [PERSON] performed the data analysis, and [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy** \\ \hline LBP-TOP [11] & 72.33\% \\
3D HOG [6] & 60.00\% \\ DTAGN [38] & 66.67\% \\
3D CNN DAP [31] & 72.12\% \\ NIRExpNet & 78.42\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons between our approach and state-of-the-art methods.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{8}{c}{**Expression**} \\ \cline{2-7}  & **Anger** & **Disgust** & **Fear** & **Happiness** & **Sadness** & **Surprise** & **Average** \\ \hline NIRExpNet & 71.01\% & 79.44\% & 62.44\% & 96.01\% & 75.12\% & 86.55\% & 78.42\% \\ LBP-TOP [11] & 65.67\% & 56.60\% & 68.18\% & 78.75\% & 71.21\% & 86.25\% & 72.33\% \\ DTAGN [38] & 69.25\% & 70.32\% & 59.32\% & 71.13\% & 60.21\% & 71.12\% & 66.67\% \\
3D CNN DAP [31] & 69.82\% & 73.41\% & 60.21\% & 83.23\% & 64.30\% & 81.55\% & 72.12\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons of different methods on the Oulu-CASIA NIR database.
# Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure
## 2 Background on Architectures for Remote Sensing Data Integration
### Distributed Integration of Remote Sensing Data  
The most widely used data integration models include: [11]  
1. The data warehouse (DW)-based integration model, which copies all data sources of each heterogeneous database system into a new and public database system, so as to provide users with a unified data access interface. However, due to the heterogeneity of each independent database system, vast data redundancy is generated, and a larger storage space is also required.
2. The federated database system (FDBS)-based integration model, which maintains the autonomy of each database system and establishes an association between each independent databasesystem to form a database federation, then providing data retrieval services to users. However, this pattern can not solve the problems of database heterogeneity or system scalability [12].
3. The middleware-based integration model, which establishes middleware between the data layer and the application layer, providing a unified data access interface for the upper layer users and realizing the centralized management for the lower layer database system. The middleware not only shields the heterogeneity of each database system, providing a unified data access mechanism, but also effectively improves the query concurrency, reducing the response time. Therefore, in this paper, we will adopt the middleware-based integration mode to realize the distributed remote sensing data integration.
## 3 Security and Privacy-Preserving Solutions for IoE
### Integration of Different Security Layers
#### 3.5.3 Proposed Integrated Layered Architecture
```
1:LocalModelUpdates[]//Updated model parameters from each IoE device group
2:PublicKey_QR//Public key for Quantum-Resilient Encryption
3:SmartContract_SC//Access control and logging smart contract
4://running on the blockchain
5:BlockchainRecord//Secure and immutable record of the updated model
6:AggregatedModel//Securely aggregated (global) model
7:Initialize AggregatedModel\(\leftarrow\)ZeroModel
8:foreach LocalModel in LocalModelUpdates[]do
9:AggregatedModel\(\leftarrow\)AggregatedModel + Weighted(LocalModel)
10:endforeach
11:EncryptedModel\(\leftarrow\)Encrypted_QR(AggregatedModel, PublicKey_QR)
12:TX\(\leftarrow\)FormatTransaction(EncryptedModel, Metadata)
13:ifValidateSmartContract(SC, TX) == True then
14:BlockchainRecord\(\leftarrow\)CommitToBlockchain(TX)
15:else
16:Raise Error(\"Smart Contract Rejected the Aggregation Record\")
17:endif
18:returnBlockchainRecord, AggregatedModel
```  
**Algorithm 3**_Secure global aggregation and blockchain logging_  
Each local model trained by the devices of each IoE group is aggregated using a weighted average. Then, the aggregated model is encrypted with Quantum-Resilient Encryption (e.g., lattice-based cryptography). After encryption, the data is written to the blockchain in a format compatible with the smart contract. If the input is not verified (e.g., access control fails), the system returns an error. In the final step, both the log record and the new global model are returned as outputs. _Algorithm 4. Quantum AI-based Threat Detection and Alerting_: The purpose of this algorithm is to analyze the data received from the blockchain to detect potential threats and trigger an early warning system. This algorithm represents the final analysis layer of the system and is executed by advanced Quantum AI models. The model updates and event data obtained from the blockchain are first decrypted and transformed into meaningful input. Then, this data is compared with historical threat patterns, anomaly scores, and system behavior logs. The Quantum AI module has superior capabilities compared to classical AI algorithms, especially in recognizing high-dimensional and complex data patterns. Using methods such as Quantum Annealing or Quantum Support Vector Machines (Q-SVM), risky behaviors and vulnerabilities are detected much faster. Based on this analysis, the algorithm produces real-time threat predictions and sends proactive alerts to system administrators. These alerts allow security teams to take action minutes or even hours before a potential attack. Quantum-resilient encryption is first decrypted (Decrypt_QR), and access is granted only to the authorized analysis engine. The data is then preprocessed for analysis, including log normalization and vectorization. After that, the Quantum AI model scores potential threats, and for threats exceeding the defined threshold, the system generates alerts. These alerts help initiate defensive actions proactively.
## 2 Results and Discussion
### Determination of Amox and Dox in Commercial Drugs  
Quantitative determination plays a crucial role in the pharmaceutical industry due to the direct effect of active pharmaceutical ingredients on human health. A precise and accurate analytical method permits quantitative determination at trace levels without any interference effects. The high extraction efficiency of Amox and Dox from pharmaceutical formulations to extraction solutions is also a key factor.  
Three different brands of Amox (250, 500 and 1000 mg/capsule) and Dox (100, 250 and 500 mg/capsule) tablets were extracted and analyzed using the validated HPLC-DAD method. The removal of excipients using an extraction step before the HPLC-DAD analysis was appraised as unneeded. When comparing the experimental data with the label information, both of the antibiotics found in all of the commercial formulations matched with the label information, with recoveries in the range of 98.2-101% for Amox and 98.5-98.9% for Dox. Moreover, the obtained results indicated that using about 50 mg of each sample or tablet was sufficient for precisely quantifying both antibiotics (Amox and Dox). Consequently, the proposed HPLC-DAD method requires no sophisticated software and is applicable for the quality control and routine analysis of the studied antibiotics. A higher recovery (110.07%) was stated by [PERSON] et al. for the determination of Dox in pharmaceutical formulations (tablets) [33].
## 3 Results  
### Total Viable Count (TVC)  
Due to the occurrence of bacterial growth on specifically enriched media, along with the cultural and morphological characteristics of the colonies observed, the following bacterial pathogens: _E. coli_, _Salmonella_ sp., _Y. enterocolitica_, _Campylobacter_ sp., and _L. monocytogenes_, were clearly identified and confirmed in the wastes under investigation. The initial counts were as follows: 2.0 \(\times\) 10\({}^{6}\), 7.0 \(\times\) 10\({}^{4}\), 3 \(\times\) 10\({}^{5}\), 9.0 \(\times\) 10\({}^{5}\), and 1.0 \(\times\) 10\({}^{4}\) with logarithmic values of 6.3, 4.8, 5.48, 5.9, and 4, respectively. The aforementioned counts of the respective bacterial pathogens were reduced to undetectable levels (<detection limit, 10\({}^{2}\) cfu/g) following the psychrophilic anaerobic digestion. An overall 1 log bacterial count reduction (T\({}_{90}\)) was demonstrated by all the bacterial pathogens, although over a different number of days. Taking into consideration the time (in days) over which the bacteria survived, the decimation occurred as follows: _E. coli_ (77 days) _< Salmonella_ (84 days) _< Y. enterocolitica_ (98 days) _< Campylobacter_ (112 days) _< L. monocytogenes_ (175 days). The decay rates/survival patterns of the viable bacteria cells were as displayed in Figure 2. _L. monocytogenes_ displayed the longest survival time of 175 days, unlike _E. coli_, which experienced the least survivability (77 days) in the hostile environment of the biodigester.  
Figure 2: Reduction in bacterial counts of the different species over different retention times.
## 2 Resistance Capacity and Performance Degradation Analysis
### Resistance Capacity Evaluation of the Sample Bridge
In contrast, if the depth of the concrete stress distribution block is greater than the thickness of the compression flange \((a>h_{f})\), the flexural strength equation for an I-type or T-type cross-section is used, as shown in Equations (3) and (4), respectively:  
\[M_{n}=A_{ps}f_{ps}\Big{(}d_{p}-\frac{a}{2}\Big{)}+A_{s}f_{s}\Big{(}d_{s}-\frac {a}{2}\Big{)}-A_{s}^{\prime}f_{s}^{\prime}\Big{(}d_{s}^{\prime}-\frac{a}{2} \Big{)}+0.85f_{c}^{\prime}\big{(}b_{2}-b_{w}\big{)}h_{f}\bigg{(}\frac{a}{2}- \frac{h_{f}}{2}\bigg{)}, \tag{3}\]  
\[\text{c}=\frac{A_{ps}f_{ps}+A_{s}f_{s}-A_{s}^{\prime}f_{s}^{\prime}-0.85f_{c}^ {\prime}(b_{2}-b_{w})h_{f}}{0.85f_{c}^{\prime}\beta_{1}b_{w}+kA_{ps}\frac{f_{ ps}}{d_{p}}}, \tag{4}\]  
where \(f_{c}^{\prime}\) (MPa) is the compressive strength of the concrete, \(b_{2}\ (\text{mm})\) is the width of the compression face of the member, \(b_{w}\ (\text{mm})\) is the web width, and \(h_{f}\ (\text{mm})\) is the compression flange depth. Based on the concrete compressive strength (i.e., 28 MPa) of the stress block factor (\(\beta_{1}\)), the flexural strength was calculated to be in the range of 0.65-0.85. According to American Association of State Highway and Transportation Officials (AASHTO) [7], the stress block factor (\(\beta_{1}\)) is calculated by considering the ductility of concrete as follows:  
\[\beta_{1}=0.85\ \big{(}f_{c}^{\prime}\leq 28\ \text{MPa}\big{)}, \tag{5}\]  
\[\beta_{1}=0.85-0.007\big{(}\ f_{c}^{\prime}-28\big{)}\geq 0.65\ \big{(}f_{c}^{ \prime}>28\ \text{MPa}\big{)}. \tag{6}\]  
In addition, when the flexural strength of a PSC-I girder is calculated, the stress of a PS tendon (\(f_{ps}\)) generated when a girder is destroyed is used instead of the yield strength (\(f_{py}\)) of the stress of the PS tendon. For the convenience of hands-on workers, AASHTO [7] presented the following approximation:  
\[f_{ps}=f_{py}\bigg{(}1-k\frac{c}{d_{p}}\bigg{)}, \tag{7}\]  
where \(f_{py}\) (MPa) is the yield strength in the PS tendon, \(k\) is a type of PS tendon correction factor, \(c\ (\text{mm})\) is the distance from the center of the longitudinal reinforcement to the nearest face of the concrete-encased shape along the plane of bending, and \(d_{p}\) (mm) is the distance from the extreme compression fiber to the centroid of the PS tendons.
# Towards a Reference Architecture for Cargo Ports
## 4 Patterns in a Cargo Port
### Container Terminal Physical Structure
#### 4.3.6 Implementation  
This pattern can be implemented for all existing and new container terminals. There are a variety of possible implementations to take advantage of technological advances.  
Remote crane operations are becoming more popular in yard operations and in ship-to-shore cranes. The cranes are being fitted with advanced automation and remote control.