esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum Circuit-Width Reduction through Parameterisation and Specialisation
## 6 Derivation of Parameterised Subtractor Circuits  
The specialisation of modulo adders used in deriving quantum comparator and subtractor circuits was detailed in Section 5. Here, parameterised subtractors for the example \(n=4\) (used in division by 4-qubit divisor) are detailed. As explained later in Section 7, the present work considers divider designs where the divisor is represented directly in 2's complement. For \(n=4\), the 5-qubit string representing the divisor in 2's complement is then considered as the basis for a parameterised 4-qubit subtractor, where, as a result of the assumed positive divisor, \(|q4d^{\prime}\rangle=|1\rangle\) is guaranteed. The parameterised quantum circuit design is shown in Figure 11. Clearly, the parameterised gate operations acting on the leading qubit of the input register (termed \(|b4\rangle\) here) match the gate operations in the comparator circuit for division by a 4-qubit divisor.  
Figure 10: Parameterised comparator circuit for division by 5-qubit divisor. Parameterisation employs qubits in 2’s complement representation.  
Comparing the quantum circuit in Figure 11 with the quantum circuit implementation of an equivalent ripple-carry adder/subtractor, it is apparent that the complexity of the parameterised quantum circuits is significantly larger in terms of the number of gate operations and, in particular, the number of control qubits used in the majority of gate operations. Therefore, 'complete' parameterised circuits as shown in Figure 11 are there to be introduced temporarily during the quantum-circuit transformation with subsequent reduction steps creating smaller, reduced circuits.
# Object-Environment Fusion of Visual System for Automatic Pear Picking
## 5 Conclusions  
This paper introduces an object-environment fusion visual system that is designed for automatic pear picking. The system involves three key modules: object perception, environmental perception, and picking pose estimation. Specifically, we provided detailed implementations for the three modules. For object perception, we compared Mask R-CNN(Swin Tiny), YOLOv5, YOLOv8, and Mask R-CNN(ResNet-50) to identify pears.  
In terms of environmental perception, voxel-based representation was used to simplify the point cloud. With respect to picking pose estimation, we employed a sampler and evaluator to obtain a set of collision-free picking types for real operation. We evaluated the system in the S1 and S2 datasets, which were acquired in an indoor laboratory and an outdoor orchard, respectively. From the results, Mask R-CNN(Swin Tiny) had the best performance in identifying pears. In the S1 dataset, the AP(Bbox) and AP(Mask) of Mask R-CNN(Swin Tiny) were 0.94 and 0.93. In the S2 dataset, the AP(Bbox) and AP(Mask) of Mask R-CNN(Swin Tiny) were 0.83 and 0.79. Moreover, in the S1 dataset, the SR, MeanE, and MedE of our method were 93.02%, 1.85 cm, and 2.10 cm within the range of 50\(\sim\)90 cm. In the S2 dataset, the SR, MeanE, and MedE of our method were 87.11%, 2.61 cm, and 3.94 cm within the range of 50\(\sim\)90 cm.  
In conclusion, these experiments demonstrate that the collaborative visual perception system is able to perform pear recognition, localization, and picking pose estimation. However, the complexity of the illumination and occlusion remains a significant factor, causing a decline in performance or even failure. Future research will investigate the optimization of the algorithm to improve its robustness and efficiency.  
Conceptualization, Y.L. and C.G.; methodology, Y.L. and C.G.; software, Y.L. and C.G.; validation, Y.L. and C.G.; formal analysis, Y.L. and N.Y.; investigation, Y.L.; resources, C.G. and N.Y.; data curation, Y.L. and C.G.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L., N.Y. and C.G. All authors have read and agreed to the published version of the manuscript. This research is supported by the Key Research and Development Program in Zhejiang (grant number 2022C02055). The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest. The authors declare no conflicts of interest.
# Polyisocyanurate Foam Pyrolysis and Flame Spread Modeling
## 2 Materials and Methods
### Gram-Scale Experiments  
#### 2.2.1 CAPA II Tests  
CAPA II is an experimental apparatus developed to study pyrolysis and gasification of gram-scale, axisymmetric solid samples in a controlled atmosphere. In this apparatus, the sample is exposed to a radiant heat flux and its mass loss rate (MLR), back surface temperature, and cross-sectional profile are tracked simultaneously in time. The sample is placed on a copper foil inside a sample holder that rests on a high-resolution mass balance. The sample's front surface is heated with a nearly uniform radiant heat flux, while its sides are insulated to ensure nearly one-dimensional heating in the axial direction. The sample is supported by a copper foil whose back surface is coated with a black paint of known emissivity. A calibrated infrared camera is focused on this surface to provide an accurate temperature measurement, which does not interfere with the operation of the mass balance. Further details on the CAPA II design, characterization, and diagnostics can be found in an earlier publication [27].  
The PIR foam samples used for the CAPA II experiments were 1.27 \(\times\) 10\({}^{-2}\) m thick, 7.00 \(\times\) 10\({}^{-2}\) m diameter disks, which were cut from the core of the Tuff-R insulation panels. The samples were dried for at least 24 h in a desiccator in the presence of Drierite prior to testing. The initial mass and room temperature density of the samples were 1.6 \(\times\) 10\({}^{-3}\) kg and 32 kg m\({}^{-3}\), respectively. The samples were pyrolyzed in nitrogen atmosphere with oxygen content below 0.6 vol.% O\({}_{2}\) in the vicinity of the front sample surface. Two tests were performed at a set radiant heat flux of 40 kW m\({}^{-2}\) and three at 70 kW m\({}^{-2}\) to collect the necessary data statistics. One additional test was performed at 70 kW m\({}^{-2}\) on a sample whose top surface was coated with a high emissivity paint to help determine the emissivity of the surface of the unpainted sample.
## 2 Methods and Results
### Optimal Frequency Detection for EFM Image  
Evaluations for image quality were performed using calculation of signal-to-noise ratio (SNR). The root mean square (RMS) noise can be defined as the square root of the mean variances from the pedestal region. Hence, the SNR can be written as follows:  
\[\text{SNR}=\text{signal}/\text{RMS}\text{ noise} \tag{5}\]  
Subsequently, we investigated various side-band frequencies for the optimization of the recipe using the DDS method. In this experiment, a commercial Pt/Ir-coated EFM tip with a force constant of 2.8 N/m and resonance frequency of 75 kHz (PPP-EFM, NANOSENSORS(tm), Neuchatel, Switzerland) was used. As shown in Figure 2, we obtained various EFM images of the TGS surface using both of typical and modified methods. All measurements were carried out at the same position to directly compare the image qualities. Each image was drawn using SPIP software (version 6.3.2, Image Metrology, Hersholm, Denmark) and the SNR was analyzed using Matlab software. The frequency-shift of side-band was varied from 1 to 5 kHz. When we used a spectrum analyzer (HP 8596E, Hewlett-Packard, Palo Alto, CA, USA), it was possible to obtain the maximum SNR at a 2 kHz side-band difference; this value was approximately 2.6 times higher than the result of the lowest SNR value, as shown in Figure 2e.
## 4 Simulation Testing and Analysis
### Algorithm Performance Evaluation
Although the MOWPA algorithm has a slower convergence rate compared to the NSGA-II algorithm, it provides better solution accuracy. Figure 9b represents the population iteration diagram for total energy consumption. The red solid line and dotted line correspond to the optimal and average values, respectively, obtained by the MOWPA algorithm. Similarly, the blue solid line and dotted line represent the optimal and average values, respectively, obtained by the NSGA-II algorithm. The MOWPA algorithm maintains a stable optimal solution around 52 generations, whereas the NSGA-II algorithm exhibits more fluctuation. Overall, the MOWPA algorithm demonstrates superior speed and precision compared to the NSGA-II algorithm in terms of both maximum completion time and total energy consumption. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Data** & \(C\) **(MOWPA, NSGA-II)** & \(IGD\) **(MOWPA)** & \(IGD\) **(NSGA-II)** \\ \hline MK01 & 1.00 & 1.2070 & 4.8498 \\ MK02 & 1.00 & 1.3956 & 1.2903 \\ MK03 & 0.60 & 1.1363 & 2.3480 \\ MK04 & 1.00 & 3.9281 & 4.5960 \\ MK05 & 1.00 & 0.5362 & 3.0486 \\ MK06 & 1.00 & 3.6433 & 8.4704 \\ MK07 & 1.00 & 1.5487 & 0.8967 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison results of calculation examples. Figure 9: Iterative curves with different objectives; (**a**) Maximum completion time; (**b**) Total energy consumption.
## 5 Discussion
### Limitations of the Study
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**ISO 3382-1 Section** & **Recommendation** & **Implemented** & **Notes** \\ \hline  & Temperature and Relative & & \\ Humidity: these quantities should be measured with an accuracy of \(\pm\)1 \({}^{\circ}\)C and 5\%, respectively. & X & & \\ \hline Equipment: omnidirectional sources and receivers. Maximum deviations of directionality for an omnidirectional source are indicated. & X & The deviation of directivity of the used sound source respected the maximum values indicated by the standards [30; 54; 55]. & \\ \hline Number of source positions: minimum 2, located where the natural sound source would take position. Height of sources: 1.5 m. & X & \\ \hline Number of microphone positions: Microphone positions should be at positions representative of positions where listeners would normally be located. For reverberation time measurements, it is important that the measurement positions sample the entire space; for the room acoustic parameters, they should also be selected to provide information on possible systematic variations with position in the room. Height of the receivers: 1.2 m. & X \\ \hline  & Integrated Impulse Response & & \\  & method: any source is allowed provided that its spectrum is broad enough to cover from 125 Hz to 4 kHz. The peak sound pressure level has to ensure a decay curve starting at least 35 dB above the BNL. & X & \\ \hline Time averaging: it is necessary to verify that the averaging process does not alter the measured impulse responses. & & \\ \hline  & Regression analysis: a least-squares fit line shall be computed for the decay curve. If the curves are wavy or bent, this may indicate a mixture of modes with different reverberation times and thus the result may be unreliable. & The open-air condition is characterised by a cliff-decay curve [54] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ISO 3382-1 recommendations and their applications in the measurement campaign (X).
# A Review of Worldwide Strategies for Promoting High-Temperature Heat Pumps
## 4 The Promotion of IHPs over the World
### Eu
Table 6 offers a summary of the EU initiatives enhancing the development and the deployment of HPs both in the building and in the industry sector. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Promotion Programs** & **Characteristics** & **Investment** & **Reference** \\ \hline Energy Performance of Building Directive (EPBD) & It offers a long-term energy renovation strategy for buildings, targeting a 80\% to 95\% reduction in CO2 emissions by 2050, with HPs identified as a high-efficiency alternative to fossil fuel-based heating systems. It also supports initiatives to increase awareness and knowledge about HPs among consumers, professionals, and policymakers. & - & [94] \\ \hline Revised Renewable Energy Directive (RED) & It indicates HPs as a pivotal technology for renewable heating and cooling. & - & [95] \\ \hline Energy Efficiency Directive (EED) & It states that when calculating the share of renewable energy within a district heating system, all heat provided to the network by HPs qualifies as renewable energy, and it establishes that the evaluation of waste heat utilization must consider HPs together with other heat-to-power technologies. & - & [96] \\ \hline Revised Energy Taxation Directive & It offers guidelines to align between gas and electricity prices, by reducing the VAT for energy–efficient technology while taxing fossil fuels. & - & [97] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Regulations and initiatives to boost the development of decarbonizing technologies both in the residential and in the industrial sector in the EU. Self-elaboration based on [93; 94; 95; 96; 97; 98; 99; 105].
# PDDQN-HHVBF Routing Protocol Based on Empirical Priority DDQN to Improve HHVBF
## 4 PDDQN-HHVBF Protocol  
In the large-scale networking of M-UWSNs, researchers often set nodes with an appropriate density in the water to meet the mission requirements. However, due to the movement of ocean currents, the nodes drift, and the density of nodes in some areas is low, and the nodes in some areas are too dense. Routing holes may arise if the AUV sends data to a less dense area. In addition, if data are forwarded to an area with high density, the selected relay node may appear to be the same relay node of multiple nodes, resulting in the problem of high delay caused by data collision or multiple backoffs when sending data at the same time. In addition, the more relay nodes that can be selected, the slower the algorithm converges. In view of the above problems, this paper proposes PDDQN-HHVBF, which combines the HHVBF protocol virtual pipeline method to transmit data in the pipeline with the distance vector as the parameter and the DDQN algorithm based on empirical priority to find relay nodes. The DDQN algorithm based on empirical priority can try to avoid the problem of routing holes, and find the route that can be successfully transmitted to the Sink node. The virtual pipeline makes the data packet propagate to the Sink node with the shortest distance, and the short-distance routing protocol greatly reduces the collision probability of the data packet in large-scale M-UWSNs. In addition, PDDQN-HHVBF will fully consider the energy problem of candidate relay nodes, so that the node energy load is balanced. In addtion, in this way, the M-UWSNs network lifetime is longer. Finally, this paper proposes a \"Store-Carry-Forward\" mechanism. This mechanism makes AUV not forward data temporarily when it cannot find a suitable relay node, but continue to carry and store the data and continue to collect new data packets according to the track until it finds the suitable relay node. Then, the AUV forwards all stored data at the suitable relay node.  
This section will introduce the specific process of the PDDQN-HHVBF routing protocol, from the establishment of the virtual pipeline, the setting of candidate relay nodes, the DDQN algorithm based on empirical priority, the selection of the optimal relay node, and the \"Store-Carry-Forward\" mechanism of AUV. These five aspects describe in detail the working principle of the PDDQN-HHVBF routing protocol.
# Inactivation of Contaminated Fungi in Rice Grains by Dielectric Heating
###### Abstract  
The quality of rice is decreased when contaminated with fungi. _Aspergillus_ species are the most frequently found in rice. This research proposes using a dielectric heating method for fungal inactivation in rice grains by radio frequency (RF) energy. In order to understand the interaction between the fungi contaminating rice and electrical energy, dielectric properties comparison between _Aspergillus_ sp. BP17 and rice powder were measured using an open-ended coaxial probe with a vector network analyzer (VNA) to develop dielectric heating equipment. The effect of RF energy on the dielectric heating system (9 kW, 40.68 MHz) is investigated based on different electric field intensities (150, 190, 225, 300, and 450 kV/m) with different temperatures (70, 80, 90, 100, and 110 \({}^{\circ}\)C). The growth of fungi contaminating rice was determined using a direct count method and reported as a percentage of inactivation. The result showed that the fungal inactivation of 100% was obtained at the electric field intensity value \(\geq\) 225 kV/m at the lowest temperature of 90 \({}^{\circ}\)C. The combination of temperature and electric field intensity significantly (\(p\leq\) 0.05) increased the percentage of fungal inactivation in rice grains. The optimal conditions of dielectric heating are suitable for fungal inactivation in rice industries. These results indicate that the proposed dielectric heating system is useful for inactivation of _Aspergillus_ species.  
dielectric heating; fungal inactivation; electric field intensity; energy consumption Article
## 3 Proposed Architecture
### Merlin
#### 3.2.2 MERLIN Deliberative System  
The deliberative system has to control the behavior a robot needs to achieve certain goals. It is composed of two layers:  
**1: Mission layer.** This layer has to manage the goals a robot needs to achieve. It can be implemented as a normal ROS node or as a FSM, as it is presented in Figure 3. Each state has several goals that are sent to the Executor in the next layer, the Planning layer. As it is shown in Figure 1, this component is called Goal Dispatcher.  
Its main software component is MerlinExecutorClient. It is a Python class that uses a ROS actionlib client to communicate this layer with the Planning layer. Goals must be written in PDDL. This class provides a new functionality that allows canceling the current robot behavior. This is useful in assistive robotics where there are goals with different priorities, mainly oriented to user services.  
**2: Planning Layer.** This layer is responsible for creating the plans, which are the sequences of actions needed to achieve certain goals. Besides, it manages the execution and monitoring of these actions.  
From the software perspective, there are two main components: ROSPlan and the Executor. ROSPlan manages the knowledge about the environment and provides the engine for managing planning. The Executor is a ROS node implemented as a FSM. It is responsible for executing ROSPlan. It has a state to execute every ROSPlan component using ROS services. In addition, the Executor deletes the goals used and replans if ROSPlan fails.  
The Executor is the element that communicates the Mission layer and the Planning layer. To do this, the Executor has an actionlib server that receives goals. When a goal is received, the Executor adds it to the knowledge base and starts ROSPlan.
# Handover Management in 5G Vehicular Networks
## 5 Simulation Setup and Results
Besides, the simulated network architecture uses real-world data, including the positions of the access networks and the mobility pattern of the vehicles. Hence, the proposed scheme can be easily adopted by telecommunication providers that leverage 5G key enabling technologies, such as Cloud/Fog computing and SDN. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Simulation duration & 86,400 s (24 h) \\ \hline \multirow{8}{*}{Network count} & WAVE RSUs: 28 \\  & LTE-A Pro FD-MIMO Macrocell BSs: 5 \\  & LTE-A Macrocell BSs: 8 \\  & LTE-A Femtocell BSs: 70 \\  & WiMAX Macrocell BSs: 8 \\  & WiMAX Femtocell BSs: 41 \\  & Total: 160 \\ \hline \multirow{4}{*}{Cell radius} & WAVE RSUs: 150 m \\  & LTE-A Pro FD-MIMO Macrocells: 320 m \\  & LTE-A/WiMAX Macrocells: 400 m \\  & LTE/WiMAX Femtocells: 30 m \\ \hline \multirow{2}{*}{Networks positions} & According to the Hellenic Telecommunications and Post Commission (EETT) [50] \\  & data (see Appendix A) \\ \hline \multirow{2}{*}{Networks frequencies} & See Appendix A \\ \hline Service Layer Agreement & 3 \\ (SLA) count & \(S_{th,SLA1}\): 0.81843 \\  & (\(Q_{SLA1}\): 0.9, \(SINR_{SLA1}\): 0.8) \\ \multirow{2}{*}{HO initiation threshold per SLA} & \(S_{th,SLA2}\): 0.63642 \\  & \(Q_{SLA2}\): 0.75, \(SINR_{SLA2}\): 0.65) \\ \multirow{2}{*}{} & \(S_{th,SLA3}\): 0.45457 \\  & (\(Q_{SLA3}\): 0.6, \(SINR_{SLA3}\): 0.5) \\ \hline Vehicle count & 77,797 \\ \hline Average arrival rate of vehicles & 0.900428241 vehicles/second \\ \hline Average departure rate of vehicles & 0.895439815 vehicles/s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The simulation parameters.
# ML-AKA: An Authentication Protocol for Non-Standalone 5G-Based C-IoT Networks
## 2 Related Work  
Previous researchers have carried out conceptual analyses of IoT security with regard to preventing different security attacks and threats, as well as authentication and privacy preservation. In this work, we focus on the aspects that have received less attention in previous work. To allow a 5G C-IoT system to provide privacy protection and increase the system's attack resistance, more secure and robust protocols have been developed. Furthermore, some of the authentication protocols developed for LTE technology are listed in Table 1. In [4], the authors proposed an authentication for a protocol for secure LTE-based device-to-device (D2D) and IoT communication. In this paper, they explained a Diffie-Hellman Key Exchange (DHKE) for analysis of session key agreement in D2D communication. They also described a Hash-based Message Authentication Code (HMAC) for authentication in D2D. However, these protocols involve more application-based algorithms and are less frequently used for data-sharing applications, as they have the least capability to overcome man-in-the-middle attacks (MITMs). In [5], the authors proposed an authentication system based on a mutual commitment scheme for cellular devices. This proposed protocol cannot be used in many situations as the evaluation process and comparison are performed only during the last phase. This makes the protocol impractical in many situations. The authors of [6] have proposed a key distribution algorithm for D2D communications in an LTE network. Their work uses the LTE framework for D2D communication to reduce communication costs. This algorithm considers the LTE core network as the key functional unit for the generation and distribution of the different session keys for IoT communication. They use an XOR-ed key for authentication purposes. The core network performs a key generation process by XOR operation and uses this XOR-ed key for device identification. In [7], a similar algorithm is proposed to establish a D2D communication key management framework. The authors of [8] have presented a solution for the privacy of user identity. They have proposed a security solution for LTE architecture using pseudonym tags, which helps to hide the actual identity of the device or users. This method provides more secure and efficient identity management compared to other techniques. In [9], the authors create a Simple Password Exponential Key Exchange (SPEKE) protocol for the fourth-generation cellular network. In this protocol, the session keys are shared between the devices through EPC. During this transmission, there is a greater chance of session key hijacking, which could compromise the session IDs. Although all the discussed protocols can be used more efficiently for D2D and IoT communication, no one has proposed a multilevel security protocol that can incorporate multiple steps in order to secure different IoT layers.