esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 4 Orthogonal Multiple Access (OMA)
### eMBB Rate  
The eMBB signals received at the ENs are forwarded over the analog fronthaul to the BBU, where centralized digital signal processing and decoding are performed. In the case of OMA, the eMBB signal is free from URLLC interference, hence signal \(\mathbf{Y}\) received by all ENs over all radio channels in (5) can be written as  
\[\mathbf{Y}=\mathbf{X}\mathbf{H}+\mathbf{Z}. \tag{31}\]  
Figure 7: Block diagram of the operation of the ENs and BBU for Orthogonal Multiple Access (OMA). A/A stands for Analog-to-Analog.  
By substituting (31) in (24), it is possible to compute the expression for the eMBB per-UE rate under OMA, as shown in Lemma 2. Notice that, unlike the case of URLLC packets, the \(n_{F}nt_{T}\) blocklength of eMBB packets allows for the use of standard asymptotic Shannon theory in the computation of eMBB information rate.  
**Lemma 2**: _In the given C-RAN architecture with analog fronthaul links, for a given bandwidth amplification factor \(\eta\geq 1\), the eMBB user rate under OMA is given as_  
\[R_{B}=\mu\frac{1-\frac{L_{U}^{-1}}{M}}{log\left(det\left(\mathbf{I}+\bar{P}_{ B}\mathbf{R}_{eq}^{-1}\mathbf{H}_{eq}\mathbf{H}_{eq}^{T}\right)\right)}\,, \tag{32}\]  
_where \(\bar{P}_{B}=P_{B}\left(1-L_{U}^{-1}\right)^{-1}\) is the transmission power of eMBB users under OMA, \(\mathbf{H}_{eq}=\mathbf{H}\otimes\mathbf{H}_{c}^{\eta}\) is the overall channel matrix comprising both the radio channel \(\mathbf{H}\) and the equivalent cable channel \(\mathbf{H}_{c}^{\eta}\) defined in Lemma 1, and \(\mathbf{R}_{eq}=\mathbf{I}_{M}\otimes\mathbf{H}_{c}^{\eta}\mathbf{H}_{c}^{ \eta}+\frac{1}{\lambda^{2}}\eta\frac{1}{\eta}\) is the overall wireless plus cable noise at the BBU._  
see Appendix B.  
As a first observation, the eMBB rate (32) linearly scales with the normalized bandwidth \(\mu\). This shows that a potential performance degradation in terms of spectral efficiency can be incurred in the presence of fronthaul channels with bandwidth limitations, i.e., with \(\mu<1\). This loss is pronounced in the presence of significant inter-channel interference, i.e., for large \(\gamma\). In fact, a large \(\gamma\) increases the effective noise power as per expression of matrix \(\mathbf{R}_{eq}\). It is also important to point out that in the considered C-RAN system based on the analog relaying of radio signals, the overall noise at the BBU is no longer white as it accounts both for the white cable noise and the wireless noise, where the latter is correlated when there is some bandwidth redundancy, i.e., when \(\mu\geq 1/l_{S}\) or \(\eta>1\).
# Swarm Intelligence-Based Multi-Robotics: A Comprehensive Review
## 3 Applications of Swarm Intelligence in Multi-Robotics
### Agriculture  
SI offers significant potential for transforming agricultural practices by deploying multi-robot systems [38, 59]. By leveraging the collective behavior of simple agents, SI enables efficient, scalable, and adaptive solutions to complex agricultural challenges [40, 41]. This section discusses several applications of SI in multi-robotics for agriculture, highlighting their benefits and impact.  
* Field Coverage and Monitoring [60, 61]: In large agricultural fields, uniform coverage and monitoring are critical for soil analysis, crop health assessment, and pest detection. Multi-robot systems, guided by SI algorithms, can ensure comprehensive field coverage by distributing the robots evenly across the area.  
* Planting and Seeding [39]: Precision planting and seeding are crucial for maximizing crop yield and minimizing waste. SI algorithms enable robots to coordinate their movements and actions to plant seeds at optimal distances and depths.
* Pest and Weed Control [9]: Managing pests and weeds is vital for healthy crop growth. Swarm robotics can precisely deploy robots to identify and target pests and weeds, reducing the need for blanket pesticide applications.
* Harvesting [62]: Harvesting crops is labor-intensive and time-sensitive. Swarm robots can collaborate to harvest crops efficiently, ensuring minimal damage and maximizing yield.  
To design a multi-robot system using SI algorithms for efficient agricultural tasks, the primary goals are to ensure optimal field coverage, effective pest control, efficient planting and harvesting, and real-time adaptability to environmental changes. For example, a large agricultural field needs to be monitored and maintained. The tasks include planting seeds, applying pesticides, monitoring crop health, and harvesting crops. The environment is dynamic, with varying weather conditions and possible obstacles like irrigation systems. A swarm of robots will be deployed to perform these tasks efficiently.  
* Field Coverage: The agricultural field must be covered without significant overlaps or gaps.
* Obstacle Avoidance: Robots must navigate around irrigation systems, fences, and other obstacles.
* Battery Life: Each robot has limited battery life, requiring efficient path planning.
* Task Efficiency: Robots must perform tasks such as planting, spraying, and harvesting efficiently.
* Data Reliability: Data regarding crop health and environmental conditions must be accurate.  
The objective is to maximize the coverage and efficiency of agricultural tasks while minimizing energy consumption and ensuring robust performance. The objective function \(F\) can be formulated as:  
\[F=\alpha\left(\frac{\sum_{i=1}^{N}F_{i}}{A}\right)+\beta\left(\frac{\sum_{i=1} ^{N}C_{i}}{N}\right)-\gamma\left(\frac{\sum_{i=1}^{N}T_{i}}{N}\right)-\delta \left(\frac{\sum_{i=1}^{N}E_{i}}{N}\right)-\epsilon\left(\frac{\sum_{i=1}^{N} O_{i}}{N}\right) \tag{24}\]  
where all variables are shown in Table 3.
# Non-Destructive Testing Applications for Steel Bridges
## 5 Complementary to NDT Methods
### Implementation of Robotic Technology
#### 5.1.2 Ground Robots  
Ground-based inspection robots represent the majority of the current advanced equipment used for the NDE and SHM of bridges. They can be further classified based on the type of locomotion they are implementing into a wheel-based robotic platform, climbing robotic platform, and bipedal as well as quadrupedal legged robotic platform [101; 104; 105; 106; 110; 123; 124; 125; 126; 127]. Different types of ground and climbing robots can be adapted to handle different surfaces while still providing accurate navigation. Their mobility consisting of moving speed and traversing abilities should be taken into account for implementation. The pace of a ground inspection robot depends on the dimensions and areas of the structural element to be inspected. The traversing ability implies its steering and the capability to avoid any obstacles along its path, including internal and external corners, steps, welds, walls, or bolts. Furthermore, the battery life of the robotic platform should be considered when designing the path and required inspection areas due to the complexity of retrieving the robot after losing its power. Figure 35 presents several types of ground and climbing robots that can efficiently be used with NDT methods.  
Figure 35: (**a**) robot capable of adhesion with steel bridge components [123]; (**b**) steel climbing robotic system, equipped with a camera and eddy current sensors for the NDE of the steel bridge structure [123]; (**c**) A robotic system collaborating with operator for bridge inspection [31].  
According to a review of robotic platforms in the NDE of civil engineering infrastructures [128], different sensors can be implemented with robotic systems. These sensors can be classified as radars (GPR sensors with EM waves of different wavelengths and frequencies), vision (visual representation with different types and quality of cameras), acoustic (sensors for sound with microphones, ultrasonic sensors, or different IE methods), and electric (sensors for variations in voltage and current, such as eddy current sensors). An example of a robotic system for the inspection of a steel bridge is shown in Figure 36. This climbing robot can move freely on a steel surface while carrying the sensors, collecting the data, and sending it to a ground station for processing [110].  
Another example of the implementation of robotics with NDT methods is Automated Ultrasonic Testing (AUT). The effectiveness and accuracy of AUT systems have been investigated by several researchers for the detection of defects on steel components [129; 130; 131; 132; 133]. Figure 37 shows a prototype AUT system used for a steel plate and for the in-service bridge.
# A 0.18 um CMOS Millimeter Wave Antenna-on-Chip with Artificial Magnetic Conductor Design
## 4 Discussion  
From the above parameter analysis, it can be seen that the design of the antenna slot, external branch, and AMC can help improving the impedance bandwidth and achieve a fractional bandwidth of up to 103.4%. Table 3 provides various parameters of chip antennas in recent research. The frequency, bandwidth, gain, and size are listed. From the measurement results and references of the chip antenna designed in this paper [8; 19; 20; 21; 22; 23; 24; 25; 26; 27], it can be seen that the chip antenna proposed in this paper has advantages in bandwidth percentage and has more bandwidth for application. In addition, the chip antenna size proposed in this paper is 1.2 \(\times\) 1.2 (mm\({}^{2}\)), which is smaller than in References [20; 22; 23; 24].  
Furthermore, compared with Reference [21], the chip antenna proposed in this paper had a better gain. To sum up, the chip antenna proposed in this paper was not outstanding in gain performance, but could be seen to be competitive in bandwidth and size. Therefore, the characteristics of broadband and small size are very suitable for integrated applications in millimeter wave systems and are easy to integrate with back-end microwave circuits due to broadband characteristics.
## 5 Discussion
### The Effects of CSP and EH in Reserve Services
#### 5.2.2 Chance-Constrained Reserve Scheduling  
Figure 9a,b show the total system reserves in Strategy 3 and Strategy 6, including CSP reserves and thermal units. It is clear that in Strategy 6, the optimized solution to the reserve scheduling demonstrates that during the valley of load demand, the reduced amount of scheduled reserves could decrease the system costs, and more reserves offered in the peak load hours could help reduce the probability of load shedding. The reduced requirement of system reserves in chance-constrained reserve planning increases the operating range of units and promotes the generation efficiency of the power system.
# A CMOS Multiplied Input Differential Difference Amplifier: A New Active Device and Its Applications
## 3 Application Examples  
The main intention of this paper is to introduce a MIDDA as a new active device, suitable for basic electronically controllable applications (amplifiers, integrators, and other building blocks required in analogue signal processing and circuit synthesis), and also as a building block for providing nonlinear operations. The following text focuses on the simple examples of MIDDA-based applications (controllable voltage amplifiers, controllable lossless integrators, controllable first-order high-pass filters, and double-sideband amplitude modulators). It should be highlighted that the presented building blocks, based on linear signal processing operations (amplifiers and integrators), possess features of electronic controllability in both polarities (they can be simply used in both the noninverting and inverting variant, by a simple change in the polarity of the DC control voltage).
## 4 Discussion
This could streamline EEG protocols, making them more patient-friendly and cost-effective. The electrodes identified as most significant in our analysis exhibited a strong correlation with those reported in prior studies. [PERSON] et al. (2023) [PERSON] et al. (2023) conducted a study where EEG signals were collected during TEAS at PC3, PC5, PC7, and PC8 on the pericardial meridian in 21 healthy subjects. Their analysis using standard low-resolution electromagnetic tomography (sLORETA), phase-locking value (PLV), and complex network methods revealed activation in several cortical areas, including the prefrontal cortex (reflected in the signals recorded from the FP1 and FP2 electrodes), the orbitofrontal cortex (reflected in FP1, FP2, and Fz), the temporal gyrus and temporal pole (reflected in T3 and T4), and the triangular part and inferior frontal cortex (reflected in F7 and F8). These results demonstrate the broad cortical effects of TEAS, particularly in regions associated with higher cognitive functions. Additionally, the study by [PERSON] et al. (2015) [PERSON] et al. (2015) investigated neural activity during TEAS in 36 patients with left Bell's palsy. The study used a single-block fMRI design to detect neural activation across different phases of TEAS at acupoints LI4, Jiache (ST6), and a sham point. Their findings indicated increased activation in the pre-motor cortex and supplementary motor area (reflected in Fz) and in the prefrontal cortex (reflected in FP1 and FP2). The consistency of these findings with our electrode selection lends further credence to the notion that these brain areas are more susceptible to the influences of acupuncture and should be a focus in EEG-based studies in this domain. Furthermore, the preferential influence of different TEAS frequencies on specific EEG frequency bands, as reported by [PERSON] et al. (2019) and [PERSON] et al. (2016), has been validated by our study, with the gamma and beta bands exhibiting the highest responsiveness to TEAS. This finding underlines the potential of TEAS in modulating higher-frequency brain functions, which could have significant implications for therapeutic applications.
## 4 Experiment
### Depth Estimation Efficiency Analysis  
The efficiency experiment for depth estimation using light field cameras aims to evaluate the performance and efficiency of light field cameras in performing depth estimation for three-dimensional scenes.  
To assess the efficiency of depth estimation, the experiment employs the computational speed metric, which reflects the rate at which algorithms process light field images and generate depth maps. During the experimental process, various factors that may influence the efficiency of depth estimation are considered. For instance, the complexity of the algorithms, and the performance of the computing devices can all potentially impact the efficiency of depth estimation. Therefore, the experiment adopts identical parameter settings and hardware configurations to evaluate the efficiency of depth estimation.  
As shown in Table 8, the algorithm proposed in this paper performs well on the three types of datasets in the efficiency experiment, requiring less time for depth estimation and demonstrating significant efficiency improvements compared to the algorithms of [PERSON] and [PERSON]. The main reason for the efficiency enhancement of the method in this paper is the use of GPU and parallel acceleration in the dense matching phase.
# Policy-Based Smart Contracts Management for IoT Privacy Preservation
## 3 Proposed Method: Policy-Based Smart Contract Management for IoT Privacy Preservation
### Model Ingredients
#### 3.1.2 Service Provider Side  
The service provider is responsible for the private blockchain network hosted in a cloud environment, which is composed of the following elements:  
* Private blockchain network: any private blockchain network can be used to process smart contracts and respond to their queries. This blockchain technology was employed because it provides immutable record storage and can hold executable software that authenticates users, checks authorizations, and enables efficient and secure data exchange in a peer-to-peer network. Smart contracts are expected to play a major role in managing, controlling, and, most importantly, securing IoT devices. It is a computerized transaction protocol that enforced the terms of the contract.
* Gateway: this is an interface that allows networks to communicate with each others and is in charge of collecting user authorizations and sending the data to the user's mobile application. It assigns a pair of public and private keys for signing and addressing transactions, which validates the user's identity and cannot be altered by any party. Anyone can send transactions to the public key; however, it requires the private key to unlock them. The approved nodes can then safely exchange the secret key using their public keys to encrypt it. The gateway is owned by the blockchain provider and is considered as an external entity.
## 2 Methodology
### Data and Data Quality Requirements (Sources and Geography)
#### 2.2.4 End-Of-Life Stages
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Description** & **Qty** & **Unit** & **Data Sources/References** \\ \hline
**Biocomposite manufacturing** & & & & \\ _Inputs from technosphere—Electricity/Hetal_ & & & & \\ Electricity & Compounding—1 kg of SPF-reinforced Sago biocomposite & 5.59 & kWh & Assume similar equipment used in [25], Ecoinvent 3 database \\ Electricity & Drying of biocomposite & 0.67 & kWh & Assume similar equipment used in [25], Ecoinvent 3 database \\ Electricity & Extrusion and sheet forming & 1.35 & kWh & Ecoinvent 3 database \\ \hline
**Container forming** & & & \\ _Inputs from technosphere—Electricity/Hetal_ & & & & \\ Electricity—Takedout Food container forming & Thermoforming & 2.9014 & kWh & Ecoinvent 3 database \\ \hline
**End off life** & & & & \\ _Inputs from technosphere—Materials/Fuels_ & & & & \\ _Diesel_ & Used for the equipment in the composting process, & & & \\  & End-of-life: 17\% to composting + 83\% to landfill & 0.00257 & L & [29] and USLCI database \\ \hline _Direct Emissions_ & & & & \\ Ammonia, NH\({}_{3}\) & End-of-life: 17\% to composting + 83\% to landfill & 0.00922 & g & [29], Ecoinvent 3 database \\ Carbon dioxide, CO\({}_{2}\) & End-of-life: 17\% to composting + 83\% to landfill & 0.02007 & kg & [29], Ecoinvent 3 database \\ Methane, CH\({}_{4}\) & End-of-life: 17\% to composting + 83\% to landfill & 0.04953 & kg & [29], Ecoinvent 3 database \\ Nitrogen Oxides, NO\({}_{x}\) & End-of-life: 17\% to composting + 83\% to landfill & 0.25225 & g & [29], Ecoinvent 3 database \\ Particulates, PM & End-of-life: 17\% to composting + 83\% to landfill & 0.00094 & kg & [29], Ecoinvent 3 database \\ Sulfur Oxides, SO\({}_{x}\) & End-of-life: 17\% to composting + 83\% to landfill & 0.00003 & g & [29], Ecoinvent 3 database \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 3 Results and Discussions
### Flow Simulation Model  
#### 3.2.1 Steady-State Model  
Figure 5a shows the calibrated map in the steady-state regime. The value of the simulated groundwater head was comparable with observed data with a difference less than or equal to 1 meter, except in the zone located at the outlet, where the difference was more than 1 meter. However, the aquifer budget balanced (Figure 5b).  
Figure 6 shows a scatter diagram of the observed heads and calculated heads in 17 monitoring wells. Clearly, in the steady state, the measured groundwater levels of the observation wells matched very closely with the calculated values (with a coefficient of determination R\({}^{2}\) = 0.96).
# Poly(lactic acid)-Based Electrospun Fibrous Structures for Biomedical Applications
## 4 Electrospinning of PLA Fibers
### Concentration
#### 4.1.2 Processing Parameters  
The electrospinningning and ultimate properties of the PLA fibers are significantly determined by the processing parameters, such as solution feed rate, applied voltage, and the distance between the nozzle and the collector [36; 38].  
The applied voltage actually assesses the number of electrical charges carried by the jet [35]. A high voltage generally favors the formation of fibers with small diameters, while it can also cause more solution ejection from the nozzle, resulting in thicker fibers than those obtained at a low voltage under the same processing conditions [35; 38]. The experimental works on electrospinningning of PLA fibers have revealed an interdependence of concentration and applied voltage on fiber diameter; however, the evaluation of diameter data shows that the solution concentration is the most important parameter, the applied voltage can change some factors, such as volume of solution ejected from the nozzle, stretching of the jet by the electrostatic force, and also the morphology of the electrospun fibers. To regulate the morphology and diameter of the fibers, an equivalency between applied voltage and concentration should be made [38].  
The distance between the tip of the nozzle and the grounded collector gives the amount of instability to the jet before deposition on the collector. Enough distance is required to ensure complete elongation and solidification of the jet. In a certain range, generally, by increasing the distance, fibers with a smaller diameter will be formed. Regarding the flow rate of the solution, any increase will usually lead to the formation of fibers with higher diameters [35; 38].  
In general, the interdependence of all the processing parameters tunes the morphology and diameter of the electrospun fibers. For example, with the increase of flow rate, it is necessary to enhance the applied voltage, and also the working distance between the nozzle and the collector to ensure complete extension and solidification of the jet and fabricate the uniform fibers. Therefore, it is required to optimize all the processing parameters to control an electrospinningning process [35; 36; 38].