esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Designed NISC-Based Architectures
### Demapper Architecture
#### 2.2.1 Control Unit  
The control unit is mainly responsible for loading _CWs_, which are stored in the control memory _CMem_ into the components of the _DemaNlSC_ module. In fact, it shares a similar architecture to the control unit used in the equalizer described in the previous chapter. To accomplish this functionality, the unit handles the address of _CMem_ memory and constructs links to distribute the control-signal bits of _CWs_ to appropriate components. In addition, the control unit manages the input data flow from _YMem_, _pMem_ and _Constellation LUT_. These basic tasks reveal the simple hardware structure required to implement the control unit.
# MIDOM--A DICOM-Based Medical Image Communication System
## 4 Evaluation
### Latency Reduction
#### 4.3.3 MR-1  
When it comes to the MR-1 benchmark, results can be seen in Figure 8. Again, both CBPC2 and CBPC cause significant reductions in latency, with CBPC2 paying off better for Wi-Fi, due to a large increase in latency reduction, and for 3G, due to a minor yet not insignificant reduction in the context of this network type. CBPC2 gains the expected result in sense of additional latency reduction for LTE network. For each network profile, latency is reduced for approximately 25% relative to raw image transfer. ZIP compression is steady at around 50% for each network type but is still significantly worse than CPBC2 and CBPC for each network profile, again rendering them plausible for use.
## 3 Results
### AA Analysis  
Quantitative profiling of AAs in 18 crabapple flower cultivars using UPLC-MS/MS revealed variations in total AA content across cultivars, as depicted in Figure 7A,B. The cultivar 'Kelsey' exhibited the highest total AA concentration at 110.00 mg/L, whereas 'Radiant' reported the lowest at 30.90 mg/L. A detailed component analysis identified Glu, asparagine Asn, Asp, Ser, and Ala as the five predominant AAs, which collectively constituted 83.57% of the total AA content (refer to Table S5).  
To investigate the differential AA profiles across various aroma types: fruit-almond, fruit-sweet, and mixed, an OPLS-DA model was developed. This model employed the relative AA contents as predictor variables and aroma type as the response variable, as illustrated in Figure 7C. The classification model achieved a reasonable fit (R\({}^{2}\)Y = 0.631) and predictive capability (Q\({}^{2}\) = 0.520). The reliability of the model was further affirmed through 200 permutation tests, which indicated no evidence of overfitting as the Q\({}^{2}\) intercept in the permutation tests was below zero, thereby validating the model's efficacy in distinguishing between aroma types based on AA profiles, as shown in Figure 7D.  
In the corresponding OPLS-DA score plot for AAs, distinct distribution patterns were observed among the three aroma-type groups within the principal component space. Samples characterized by a fruit-almond aroma were dispersed across multiple clusters ranging from t [1] (\(-0.5\) to \(4\)) and t [2] (\(-2\) to \(11\)), reflecting a high degree of metabolic heterogeneity within this group. Conversely, fruit-sweet samples predominantly clustered towards the lower end of t [1] (\(-2\) to \(0\)) and spanned a moderate to high range on t [2] (1 to \(4\)), indicating greater homogeneity within this group. 'Tamlin' and 'Radiant' exhibited similar t [1] values but showed some separation along the t [2] axis. Although they belong to different aroma types, this distribution pattern may reflect partial metabolic similarities. However, the possibility of sample size effects cannot be excluded.
# Effects of Drying Process on the Volatile and Non-Volatile Flavor Compounds of _Lentinual edodes_
###### Abstract  
In this study, fresh _Lentinual edodes_ was dehydrated using freeze-drying (FD), hot-air drying (HAD), and natural drying (ND), and the volatile and non-volatile flavor compounds were analyzed. The drying process changed the contents of eight-carbon compounds and resulted in a weaker \"mushroom flavor\" for dried _L. edodes_. HAD mushrooms had higher levels of cyclic sulfur compounds (56.55 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{g}\)) and showed a stronger typical shitake mushroom aroma than those of fresh (7.24 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{g}\)), ND (0.04 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{g}\)), and FD mushrooms (3.90 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{g}\)). The levels of 5\({}^{\prime}\)-nucleotide increased, whereas the levels of organic acids and free amino acids decreased after the drying process. The dried _L. edodes_ treated with FD had the lowest levels of total free amino acids (29.13 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{g}\)). However, it had the highest levels of omami taste amino acids (3.97 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{g}\)), bitter taste amino acids (6.28 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{g}\)) and equivalent omami concentration (EUC) value (29.88 \(\mathrm{\SIUnitSymbolMicro g}\) monosodium glutamate (MSG) per 100 \(\mathrm{\SIUnitSymbolMicro g}\)). The results indicated that FD was an effective drying method to produce omami flavor in dried mushrooms. Meanwhile, HAD can be used to produce a typical shitake mushroom aroma. Our results provide a theoretical basis to manufacture _L. edodes_ products with a desirable flavor for daily cuisine or in a processed form.  
Lentinual edodes; drying methods; volatile compounds; non-volatile compounds; sulfur compounds; free amino acids; 5\({}^{\prime}\)-nucleotide
## 4 Simulation Results
### iQBER Analysis
#### 4.2.2 iQBER Analysis  
MEO  
In this section, we vary the iQBER of the MEO-source to conduct a feasibility analysis by leveraging the requirements for the on-board source emission rate (250 MHz) and transmitted block size (\(10^{12}\) bits) that are derived from Section 4.1.1. For a single MEO satellite pass, when assuming a receiver aperture diameter of 1 m, the only scenario that yields non-zero key rates under the assumption of tip-tilt correction is when the SNSPDs are employed as detection units. Figure 7 presents the expected SKR values over time assuming different iQBER values. It has been observed that MEO satellites can grant QKD links with imperfect sources if SNSPDs are employed in the detection setup for long time windows. On the other hand, the maximum achieved rates were about four orders of magnitude lower compared with the LEO orbit under the same detection assumptions. To enhance the performance in the MEO case, where the source's iQBER is high, improvements in the overall link budget should be considered, such as selecting a bigger receiver aperture, AO correction, or a GHz-scale source.
# Real-Time SLAM and Faster Object Detection on a Wheeled Lifting Robot with Mobile-ROS Interaction
## 4 Experiments  
### Implementation Details  
The proposed YOLOv8-R was implemented by Python 3.10.9 and PyTorch 1.13.1+cu117 and was trained on a PC equipped with an NVIDIA GeForce RTX 4060 laptop GPU (Santa Clara, CA, USA) for 100 epochs. The dataset consists of about 1000 images that were captured using a USB camera in various industrial and logistical settings. These images include two types of objects: cola and box. The dataset was divided into training, validation, and test sets with a ratio of 7:2:1. The training sets were expanded through preprocessing and data augmentation. All other parameters followed the default settings of YOLOv8. For a fair comparison, other state-of-the-art detectors used the settings from their official code and were trained for the same number of epochs on the same dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Model No.** & **Features** \\ \hline \multirow{3}{*}{Control Core Board} & \multirow{3}{*}{RoboMaster Development Board C} & STM32 main control chip, rich \\  & & interfaces, compact structure, \\  & & integrated high-precision IMU \\  & & sensor, strong protection features \\ \hline \multirow{3}{*}{Wheel Motor} & \multirow{3}{*}{RoboMaster M3508} & CAN bus control, dual closed-loop, \\  & & max output power: 220 W, torque: \\  & & 5 N:m \\ \hline \multirow{3}{*}{Lifting Motor} & \multirow{3}{*}{RoboMaster M2006} & High precision, small size, max \\  & & torque: 1000 mN-m, power: 44 W, \\  & & max speed: 500 rpm \\ \hline \multirow{3}{*}{Yaw Motor} & \multirow{3}{*}{RoboMaster GM6020} & Powered by 24V DC, speed control \\  & & via CAN/PWM, built-in angle \\ \cline{1-1}  & & sensor, FOC technology \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the control core board and three motors.  
Figure 4: Key components of the AMSeWL-R system’s electronic control unit. (**a**) The core control board integrates essential processing capabilities. (**b**) The wheel motor is used for optimal locomotion efficiency. (**c**) The lift motor provides precise elevation control. (**d**) The robot arm yaw axis motor ensures accurate rotational movement. Detailed specifications and functionalities for these components are provided in Table 1.
## 4 Discussion
### Challenges and Limitations
#### 4.4.2 Model Complexity and Computational Challenges  
Many of the forecasting models employed in agricultural price prediction are computationally intensive, especially when multiple techniques are combined or when deep learning architectures are used [72]. Thus, even though LSTM, CNN, and many other hybrid techniques tend to provide better efficiency than traditional techniques most of the time, they can be quite computationally heavy. This malleability is typical for longer training times, scaling resource consumption, and issues of scaling, particularly in real-time applications or large volumes of data. Furthermore, using more models or techniques simultaneously, for example, the complicated system that integrates ARIMA and deep learning methods, may do a better job when it is used to make predictions, but they come with high model complexity that might be hard to address and control. For instance, models developed to integrate different algorithms such as LSTM-CNN registered great  
Figure 10: Evolution of agriculture commodity price prediction techniques.  
performance; nevertheless, they demand massive computer processing and could hardly be implemented at an extremely large scale.
## 2 Finite Element Analysis Modeling and Validation
### Establishment of RPC Column Model Under Coupled Fire and Explosion  
For the temperature field simulation, eight-node linear heat transfer hexahedral unit DC3D8 was selected for the concrete unit, and the grid size of the concrete was taken as 0.02 m; two-node linear three-dimensional truss unit T3D2 was used for the reinforcement bars, the reinforcement cages were gridded at 0.02 m, and the temperature field results were imported into the explosion simulation through the restart function. For the explosion simulation, the concrete unit was selected as the eight-node hexahedral linear reduced integration unit C3D8R. The boundary conditions of the RPC column model were fully fixed at both ends, and the connection between the reinforcement and the concrete adopted the separated model, where the reinforcement and the concrete form a common junction without considering the bonding slip between the two. During finite element simulation, the damage model was selected as the concrete plastic damage model.  
When analyzing the dynamic response of the structure under high temperature-explosion coupling, the method of applying the temperature load first and then the explosion load was chosen, combining the implicit static analysis and displaying the dynamic analysis of ABAQUS. First, an implicit transient heat transfer analysis of ABAQUS was used to perform the temperature field simulation of the concrete column to obtain the internal temperature field distribution and the stress state of the concrete column; thereafter, the temperature field results obtained were imported into the explosion model in the predefined field command through the restart function to realize the conversion of the thermal analysis to the dynamic analysis. Among the variables, the temperature-time curve in the temperature field simulation was selected from the fire-resistant experimental curve for building components recommended by the International Standards Organization (ISO), and only the uniform temperature field was considered; after the temperature field was imported during the explosion simulation stage, the explosion load was applied to the structure by using the CONWEP loading method.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 4 Results and Discussion
### Throughput Performance
#### 4.2.2 Node Throughput for BATMAN Routing Protocol  
Figure 10 shows the throughput performance of the laptop nodes for the BATMAN routing protocol. Throughout the simulation, the throughput stabilizes after an initial period of fluctuation, with all the nodes converging to similar throughput values around 500-1000 kbps. The close alignment of the throughput across the heterogeneous laptop nodes suggests effective load balancing and efficient routing across the network.  
Figure 8: AODV’s throughput for mobile nodes.  
Figure 9: AODV’s throughput for fixed nodes.  
Figure 11 shows the throughput performance of the fixed nodes for the BATMAN routing protocol. The red and blue lines depict distinct fixed nodes, showcasing variations in throughput across the simulation time. Node 0 consistently maintains higher throughput compared to Node 1, with peaks exceeding 60,000 kbps. This contrast demonstrates the differential load distribution and routing efficiency, highlighting BATMAN's ability to adapt to network conditions, although some performance disparities between nodes are evident.  
Figure 12 illustrates the throughput performance of the mobile nodes for the BATMAN routing protocol. The data reveal variations in throughput across five different mobile hosts, with some hosts achieving peaks of nearly 60,000 kbps during the simulation period. These differences underscore the impact of mobility and node heterogeneity on the overall network performance, where frequent changes in the node positions influence the BATMAN protocol's adaptive capabilities in managing route discovery and data transmission.  
Figure 11: BATMAN’s throughput for fixed nodes.  
Figure 10: BATMAN’s throughput for laptop nodes (low mobility).
# The Impact of Nonlinear Mobility Models on Straight Line Conflict Detection Algorithm for UAVs
## 3 Nonlinear Mobility Model
### Gauss-Markov Mobility Model
#### 3.1.2 Boundary Handling  
There were several proposals on how the model should behave around the boundaries. [PERSON] and [PERSON] in [15] proposed a direction change close to the boundaries without specifying how to make that change. In [22], the authors proposed a 180\({}^{\circ}\) turn around the boundaries. However, this strategy conflicts with one of the main reasons for proposing this model, which is to avoid sharp turns. Additionally, it will eliminate the temporal dependency feature as the direction is not based on the previous direction.  
The works in [23,24] proposed forcing the mobile node away from the edge by changing the average of the direction when the node enters into a specific margin zone. In that way, the node moves away from the boundary progressively toward the center of the area. Figure 2 shows the proposed angle change.  
Figure 2: Change of mean angle near the boundaries.
###### Abstract
The received signal can be transmitted in the frequency domain or time domain through inverse fiber transmission for dispersion compensation, but this kind of dispersion compensation method requires a high-performance analog-to-digital converter for an AO-OFDM system, and a digital sampling rate that exceeds the symbol rate by several times, making it unrealistic to be widely used in future cost-effective optical networks. When CP is inserted into the AO-OFDM system, we believe that the optical filter, as a core component for generating orthogonal sub-carriers, can also play a larger role in enhancing the anti-dispersion capability of the AO-OFDM system. And changing the Sinc-shaped filter into a Gauss-shaped filter close to the main lobe of the Sinc function can also improve the anti-dispersion capability of the AO-OFDM system. For the WSS based on LCoS, it is much easier to load the phase diagram of the control LCoS to generate a Gauss-shaped filter with a variable center wavelength and adjustable bandwidth than a Sinc-shaped filter with the same flexibility. In Section 2, we will specifically analyze the differences in the implementation methods of the two filters and the orthogonality of the sub-carriers generated by the two filters. The simulation results show that the AO-OFDM system generated by the Gauss-shaped filter had high anti-dispersion ability.
## 4 Results and Discussion  
Table 3 summarizes the overall measurement and simulation results for the three scenarios, i.e., one LOS scenario and two NLOS scenarios. \(\text{PL}_{\text{min,meas}}\) is the minimum measured PL at 60 GHz over all AoAs, measured with the spectrum analyzer-based sounder. \(\text{PL}_{\text{omni,meas}}\) is the omnidirectional PL, by interpolating and summing the received powers over all AoAs. Based on the angular PL measurements, the angular spread (AS) is obtained. \(\text{PL}_{\text{min,TG}}\) is the minimum measured PL using the TG platform, i.e., the PL of the TX-RX beam configuration for which the PL is minimal. \(\text{PL}_{\text{min,sim}}\) is the total PL of the best path from the ray-launching simulations.  
In the next sections, we discuss the measurement and simulation results in detail, starting with the angular PL measurements, continuing with PER measurements and throughput estimation, and concluding with the simulation results from the coverage prediction tool.
# Analyzing Markov Boundary Discovery Algorithms in Ideal Conditions Using the d-Separation Criterion
## 5 Experiment and Results  
An experiment was implemented to illustrate the use of the d-separation criterion when evaluating MBD algorithms. A selection of three Bayesian networks were used throughout the experiment. These are the CHILD, ALARM and MILDEW networks, part of the bnlearn repository [20]. The CHILD network consists of 20 nodes, the ALARMnetwork consists of 37 and MILDEW consists of 35. The repository classifies the size of these three networks as \"medium networks\" [20].  
The experiment contains two groups of algorithm runs, called \(BN\) and \(DS\), based on the source of CI information used by the algorithms when discovering Markov boundaries:  
* The \(BN\) group contains algorithm runs where the CI information was the d-separation criterion exclusively, computed on the three Bayesian networks in their original, abstract form; the runs in the \(BN\) group do not require any sampling or dataset synthesis;
* The \(DS\) group contains algorithm runs that apply statistical CI tests on synthetic datasets randomly generated from the three Bayesian networks; the \(DS\) group also uses the d-separation criterion on the original Bayesian networks, but only to record the accuracy of each test, where 'accurate' means in agreement with the d-separation criterion, while 'inaccurate' means in disagreement.
## 2 Materials and Methods
### Laboratory Micro-Raman Measurements  
Micro-Raman measurements were conducted by using a portable 'BTR 111 Mini-RamTM' (B&W Tek, Inc., Newark, NJ, USA) spectrometer with an excitation wavelength of 785 nm (diode laser), 280 mW maximum laser power at the excitation port, and a charge-coupled device (CCD) detector (thermoelectric cooled, TE). It is possible to continuously adjust the laser output power in order to obtain the best signal-to-noise ratio in the minimum integration time. The 62-3153 cm\({}^{-1}\) spectral range was investigated, with a resolution of 10 cm\({}^{-1}\), and an acquisition time of 10 s \(\times\) 32 scans. The optimal performance of the instrument was guaranteed by a calibration procedure before each measurement using the peak at 520.6 cm\({}^{-1}\) of a silicon chip. The system was equipped with a BAC151B Raman microscope. An 80\(\times\)\(\backslash\)40\(\times\) objective was used, with a working distance of 1.25 mm\(\backslash\)3.98 mm and laser beam spot size 26 \(\upmu\)m\(\backslash\)50 \(\upmu\)m. The maximum power at the samples was \(\sim\)15 mW. The identification of the peaks was obtained by comparing the experimental spectra with those reported in various databases and in the literature [3; 4; 5].
# Multi-Agent Reinforcement Learning: A Review of Challenges and Applications
## 3 The Limits of Multi-Agent Reinforcement Learning
### Partial Observability
#### 3.3.2 Communications between Agents
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **Type of Agent** & **Learning Structure** & **Features** \\ \hline Hysteretic & Value based & Independent learners & Uses a different learning rate for increasing and decreasing Q-values. \\ Q-Learning [26] & Value based & Independent learners & No need for communication between agents \\  & & & Accumulates rewards for a state-action-pair and then update it using the maximum reward \\  & & & Receives the Q-values from agents in the proximity with the objective of minimizing the difference and reaching consensus \\ Actor-Critic with & Actor-critic & Networked agents & Both the policy of the actor and the Q-estimates of the critic are parameterized. The agents share the parameters of their critic to reach consensus \\  & & & Stores a temperature variable in the experience replay to decide the amount of leniency to apply to the updates \\ Multi-Agent Deep Q-Network [33] & Value based & Independent agents & Use of importance sampling and low-dimensional fingerprints to disambiguate samples in the experience replay \\  & & & Integrates the recursive neural network to estimate the non observed \\ Dec-HDQRN [35] & Value based & Independent agents & state and hysteretic Q-learning to address non stationarity. Possibility to use transfer learning to adapt to multi task applications \\ PS-TRPO [36] & Policy optimization & Centralized training & Shavers parameters between agents during training. The policy parameters are bounded to change a Hurst region. Can space progressively to more agents using curriculum learning \\ COMA [39] & Actor-critic & Centralized training & A centralized critic is used only during the training phase. Can differentiate rewards between agents using a correlatetal baseline \\ VDN [40] and QMIX [41] & Value based & Networked agents & The Q-table of the joint action can be factorized as a sum or a combination of independent Q-Tables. The agents communicate for a number of rounds before selecting their action. The communication protocol is learned concurrently with the optimal policy \\ DDRQN [43] & Value based & Networked agents & Uses a deep recursive network architecture in a partial observable setting with the use of parameter sharing to speed-up the learning process \\ Q-RTS [24] & Value based & Centralized training & Agents create a global knowledge Q-matrix combining their most valuable experiences and make updates on a linear combination of the matrix and their local Q-table. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of MARL algorithms.