esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Analysis of Volatile Compounds by GC-IT-MS  
Volatile compounds were isolated using headspace solid-phase microextraction (HS-SPME), according to the modified method proposed by [PERSON]\(\mathrm{\ddot{c}}\)ic [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [41]. SPME fiber used was divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS), 1 cm length, 50/30 \(\upmu\)m film thickness (Supelco, Bellefonte, PA, USA). Four grams of EVOO sample (or a standard solution) were placed in a 10 mL glass vial containing a micro-stirring bar, and sealed. The headspace in the vial was equilibrated at 40 \({}^{\circ}\)C for 15 min, and the extraction was carried out at 40 \({}^{\circ}\)C for 40 min with stirring at 800 rpm. Thermal desorption of analytes was achieved in the GC injection port in splitless mode at 245 \({}^{\circ}\)C for 3 min. Identification and quantification of volatile compounds was performed using a Varian 3900 GC coupled to a Varian Saturn 2100 T ion trap mass spectrometer (IT-MS) (Varian Inc., Harbor City, CA, USA). A capillary column Rtx-WAX (60 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m film thickness; Restek, Bellefonte, PA, USA) was used. Initial oven temperature was 40 \({}^{\circ}\)C, increased to 210 \({}^{\circ}\)C at 2 \({}^{\circ}\)C/min, increased to 245 \({}^{\circ}\)C at 20 \({}^{\circ}\)C/min, and kept for 20 min. Injector, transfer line and ion trap temperatures were 245, 180, and 120 \({}^{\circ}\)C, respectively. Mass spectra were acquired in EI mode (70 eV) at 1 s/scan, full scan with a range of 30-450 \(m/z\). The carrier gas was helium (1.2 mL/min).  
Identification was performed by comparing retention times and mass spectra with those of pure standards, and with mass spectra from NIST05 library. Identification by comparison with mass spectra was considered satisfactory if spectra reverse match numbers (RM) higher than 800 were obtained. If in a particular sample the mass spectra were not clear (RM \(<\) 800), identification was considered satisfactory if the ratios of a quantifier and three most abundant characteristic ions reasonably matched those in the reference spectra of a given compound. Linear retention indices (relative to C7-C24 n-alkanes) were calculated and compared to those from literature. When standards were available, standard calibration curves based on quantifier ions were used for quantification. Linearity was satisfactory with coefficient of determination higher than 0.99 for all the standards.  
For other compounds semi-quantitative analysis was carried out, and their concentrations (\(\upmu\)g or mg/kg) were expressed as equivalents of the compounds with similar chemical structure for which standards were available, assuming a response factor equal to one.
# CCFNet: Collaborative Cross-Fusion Network for Medical Image Segmentation
## 1 Introduction
This method capitalizes on the self-attention mechanism's robust long-range dependency capabilities to ensure accurate medical image segmentation. Considering the complementarity of the two network features, the CCFM sequentially delivers global context from the Transformer branch to the feature maps through the spatial feature injector (SFI) block. This integration significantly boosts the global perceptual capabilities of the CNN branch. Likewise, the collaborative self-attention fusion (CSF) block progressively reintroduces the local features from the CNN branch back into the Transformer, enriching the local detail and creating a dynamic interplay of fused features. Finally, local-global feature complementarity can be achieved, and the network's feature-encoding capabilities can be enhanced. The experiments utilize Synapse, an open accessible dataset for multi-organ medical image segmentation. When the average Dice similarity coefficient (DSC) scores are compared with those from other hybrid models, our proposed CCFNet shows improved accuracy in organ segmentation. This paper's important contributions are summarized below:  
* We propose CCFNet, a collaborative cross-fusion network, to integrate Transformer-based global representations with convolutional local features in a parallel interactive manner. Compared with other fusion methods, the collaborative cross-fusion module can not only encode the hierarchical local and global representations independently  
Figure 1: Comparison of different CNN and Transformer integration strategies. (**a**) Serial or embedded fusion strategy; (**b**) CNN and Transformer branch fusion strategy; (**c**) parallel fusion strategy of CNN and Transformer at each layer; (**d**) CCFNet fusion strategy. The arrows in the figure indicate the direction of data flow in the feature map. but also aggregate the global and local representations efficiently, maximizing the capabilities of the CNN and Transformer. * In the collaborative cross-fusion module, the CSF block is designed to adaptively fuse the correlation between the local tokens and the global tokens and reorganize the two features to introduce the convolution-specific inductive bias into the Transformer. The spatial feature injector block is designed to reduce the spatial information gap between local and global features, avoiding the asymmetry of extracted features and introducing the global information of the Transformer into the CNN. * On two publicly accessible medical image segmentation datasets, CCFNet outperforms other competitive segmentation models, validating its effectiveness and superiority.
# Impact of Heat Exchanger Effectiveness and EGR on Energy and Emission Performance of a CI Engine
## 3 Results and Discussion
### Investigation of Engine Environmental Performance Indicators
However, at medium load (0.6 MPa), with EGR active, the specific HC emission increased by around 16% (from 0.048 g/kWh to 0.056 g/kWh), primarily due to the reduced combustion temperature and lower air quantity in the cylinder, both of which negatively affect the combustion process. Increasing HEE intensity up to 100% at engine loads of 0.2 MPa and 0.4 MPa even increased the specific HC emission (by up to 18%), as it reduced the combustion temperature, while the additional air did not improve combustion due to the already high excess air ratio (above 2.4). A slight positive effect of HEE (up to 5%) in reducing HC emissions was observed only in the case of EGR on at a load of 0.6 MPa. Since the specific HC emission is relatively low due to the excess air, HC should not be considered a primary concern when developing the HEE and EGR control strategy. Figure 17: Effect of engine load and heat exchanger effectiveness on specific NO\({}_{\rm x}\) emission. As engine load increases, the CO\({}_{2}\) concentration (%) rises due to the greater amount of fuel in the combustible mixture. However, as engine power increases, the specific CO\({}_{2}\) emission (g/kWh) decreases (Figure 19). The EGR system significantly increases the volumetric concentration of CO\({}_{2}\) in the exhaust gases (by 30-40%), as part of the intake air is replaced by exhaust gases, which have a high CO\({}_{2}\) content. Moreover, EGR slightly increases brake-specific fuel consumption (Figure 15a). On the other hand, in the case of EGR off, the intake air mass is reduced by approximately 20-28% (Figure 7), which significantly lowers the absolute mass emissions of exhaust pollutants, which are directly proportional to the specific CO\({}_{2}\) emissions. When comparing EGR-off and EGR-on conditions across various engine load levels (BMEP = 0.2 MPa to 0.6 MPa), the calculated specific CO\({}_{2}\) emissions under EGR-on conditions increased by approximately 3.0%. Increasing HEE intensity to 100% at a load of BMEP = 0.4 MPa under EGR-on conditions resulted in a relative reduction in specific CO\({}_{2}\) emissions of up to 4%, thereby offsetting the emission increase caused by EGR. Under EGR-off conditions, increasing HEE intensity led to a 1.7-3.7% reduction in specific CO\({}_{2}\) emissions due to improved brake thermal efficiency and reduced brake specific fuel consumption (Figure 15).
# Experimentation in 5G and beyond Networks: State of the Art and the Way Forward
###### Abstract  
After first being standardized by the 3 rd Generation Partnership Project (3 GPP) in Release 15, 5 th Generation (5G) mobile systems have been rapidly deployed worldwide [1]. This initial phase is being complemented by the release of enhanced versions of the standards, which are expected to enable the evolution of current 5G deployments towards Beyond-5G (B5G) and 6 th Generation (6G) systems [2]. Therefore, the need for testing and validating current and planned 5G/B5G technologies and features via data-driven and measurement-based analyses has significantly increased in the last few years, ultimately resulting in key experimental research activities that bring benefit to the entire telecommunications ecosystem, including research institutions, standardization bodies, network operators, technology and equipment providers, enterprises, and verticals [3, 4, 5, 6, 7].  
On the one hand, experimental studies make it possible to identify how network deployment, configurations, and features affect achievable performance, while guiding the derivation of solutions for system enhancement and optimization. On the other hand, they also bring complex challenges to address, including (but not limited to) the definition of Key Performance Indicators (KPIs) and of proper measurement and testing methodologies. Furthermore, the popular approach of using Artificial Intelligence (AI) and Machine Learning (ML) to address several network aspects clearly requires system experimentation activities in order to be able to properly design AI/ML solutions on top of trustable data-driven analyses [8, 9].  
With the above context, the Special Issue \"Experimentation in 5G and beyond Networks: State of the Art and the Way Forward\" collected original contributions on experimental aspects related to 5G/B5G networks and systems. The call for papers for this Special Issue included topics such as the design, implementation, and usage of 5G/B5G experimental testbeds and platforms, open-source tools for system monitoring and management, end-to-end measurement and validation of 5G/B5G KPIs, and ML/AI-based solutions for the enhancement of different 5G/B5G system aspects, including (but not limited to) New Radio (NR) [10, 11], Core Network (CN) [12], Software-Defined Networking (SDN) and Network Function Virtualization (NFV) [13], cloud/edge computing and network slicing [14], and vertical-specific solutions in the context of enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communication (URLLC) and massive Machine-Type Communication (mMTC) [15].
## 4 Major Components of Network Slicing System
### Open Source Mano (OSM)  
The open-source management and orchestration (OSM) platform is a project of ETSI which provides telco operators an E2E network service orchestrator for the deployment of network services automatically. OSM has been developed based on NFV standards and facilitates the orchestrator to interact with other components, such as infrastructure managers VIM, NFVI, and network functions (PNFs and VNFs). Moreover, it provides the feature of on-demand creation of networks, e.g., network as a service (NaaS). It also provides an interactive GUI where the client can easily deploy, manage, and monitor the network resources. It can also support and facilitate the clients to deploy E2E network slices according to their needs at run-time, although the complete life cycles of network slices can also be managed by the OSM [18]. In our network slicing system, the IBN component OSM policy configurator can interact with the orchestrator with the help of a REST API by generating the slice configuration in JSON string and sending them to the orchestrator for NFs deployments. After that, the orchestrator deploys the required slice resources with the help of VIM (OpenStack).
# A Vision Servo System for Automated Harvest of Sweet Pepper in Korean Greenhouse Environment
## 1 Introduction
Regions of sweet pepper fruit were detected using the reflection characteristic (that fruits reflected light more than leaves) with LED (Light Emitting Diode) lights, and the recognition success rate for detecting sweet pepper fruits was 79.2%. [PERSON] et al. [18] developed a harvesting robot with a cable-driven gripper to harvest strawberries. A cable-driven gripper used in the system was designed to be robust against local errors introduced by the vision module, and the harvesting success rate was 53.57%. [PERSON] et al. [19] suggested a method of angle estimation between the fruit and stem for a harvest robot. The method used to estimate the angle between the fruit and stem theoretically increased the success rate from 14% to 52% and impacted the harvest performance. In order to automate the harvest work, a suitable harvest algorithm is needed for each country's cultivation environment. Compared to other countries, South Korea lacks research on the development of harvest robots for the automation of agricultural work [6]. This study introduces a vision servo control (image-based closed-loop control) [20] system for a Korean greenhouse environment to improve the accuracy of sweet pepper harvest. In addition, this study developed an automated sweet pepper harvest robot system based on a manipulator and an end-effector which could detect a sweet pepper via stereo cameras, decide whether or not to harvest it, carry out the work of harvest using the acquired information, and then verify its efficiency.
# A DRL-Based Satellite Service Allocation Method in LEO Satellite Networks
## 3 Problem Statement  
Due to the diverse requirements of edge services for various resources and the significant differences in the availability of resources on different satellites, there is an uneven distribution of resources in space, which also exhibit tidal-like variations over time, and an NP-hard problem arises in achieving the optimal match between services and resources. The SSA problem aims to assign each service to the most suitable satellite, thereby enhancing global resource utilization efficiency. This section analyzes the SSA problem, models its characteristics, and transforms it into a MDP, laying the groundwork for the introduction of a reinforcement learning solution. The definitions of relevant symbols are listed in the Table 1.  
For the SSA problem, we primarily focus on two constraints. The first one is that UE should be able to establish an effective communication link with LEO satellites:  
\[\frac{\overrightarrow{Loc_{\pi_{t}}^{Sat}}\cdot\overrightarrow{Loc_{k}^{UL}}}{ \left|Loc_{\pi_{t}}^{Sat}\right|\left|Loc_{k}^{UE}\right|}\geqslant\alpha_{\pi_{t },k}\,\forall t\ and\ \pi_{t}\
eq 0. \tag{18}\]  
The second constraint is the resource constraint mentioned in formula [n], as follows:  
\[\sum_{\forall k\in\mathcal{K}}\delta_{\pi_{t},k}(t)\mathcal{D}_{\pi_{t},k}^{ UE}\prec\mathcal{V}_{\pi_{t}}^{Sat},\forall t\ and\ \pi_{t}\
eq 0. \tag{19}\]  
Due to the presence of constrained multidimensional resources in the ISTN and varying resource utilization patterns across different services, we assume that to achieve desired service effects, each service has specific resource requirements. Satellite resources exceeding these requirements do not contribute to higher service quality for UE. Given the limited resources of satellites, there is competition among different services for the utilization of the same resources. The deployment of each service can be independently determined based on the current satellite's remaining resources. With a sufficiently fine-grained time granularity, in each timeslot \(t\), only one service is allocated to an appropriate satellite, and the satellite resource status is updated. This process continues in the next timeslot, with suitable resources being allocated for the service. Inspired by [36; 37; 38], we propose the DRL-based approach in this paper. In this scenario, akin to the edge user allocation (EUA) problem mentioned in [39], we can model the SSA problem as an MDP as shown in Figure 4. This includes the satellite network state \(S\), satellite service allocation actions \(A\), and the effectiveness of service allocation \(R\). Therefore, the length of the Markov chain equals the number of users requesting services in each timeslot. \(S\) represents the satellite network state in each timeslot.
## 2 Technology and Methods  
The hybrid power supply modulator is mainly divided into three parts, a switching converter, a linear amplifier, and a hysteresis comparator, as shown in Figure 2. The linear amplifier realizes the linear amplification of the envelope signal, and mainly consists of operational amplifiers and push-pull circuits. The linear amplifier mainly provides voltage and a small current to the power modulator, as shown in Figure 3. The output stage adopts a class-AB structure. The class-AB structure has three advantages: first, the power transistor in the structure is in a micro-conduction state, and the power consumption is small in this  
Figure 1: Basic block diagram of envelope tracking.  
state; second, the output of the first stage amplifier can track the input signal better; and, third, the bias mode of the class-AB type allows the transistor to have a larger current output capability [38].  
The switching converter is directly controlled by the hysteresis comparator. When the envelope signal is in the low-frequency band, the switching converter is turned on and off to provide current to the power supply modulator. The linear amplifier mainly deals with high-frequency signals. In Figure 2, module A is the linear amplifier, module B is the hysteresis comparator, and module C is the switching converter. \(i_{out}=i_{sw}+i_{linear}\), where \(i_{linear}\) is the output current of the linear amplifier, \(isw\) is the current of switching converter, and \(i_{out}\) is the current of the whole circuit, as shown in Figure 3.  
When the input signal (vip) frequency is low, the slew rate of the switching converter \(\text{SR}_{\text{isw}}\) is much higher than the slew rate of \(\text{SR}_{\text{iload}}\)[39], and the switching converter can track the input signal. The linear amplifier cancels the ripple from the switching converter. When the input signal (vip) is high frequency, the slew rate of the switching converter \(\text{SR}_{\text{isw}}\) is lower than the slew rate of \(\text{SR}_{\text{iload}}\); when this happens, the switching converter cannot  
Figure 3: The key waveform of current through a hybrid power modulator.  
Figure 2: Typical architecture of a hybrid power supply modulator.  
track the input signal, and the linear amplifiers provide a high slew rate of SRlinear to track the high-frequency signal assisting the switching converter.
# Robust Near-Optimal Aerocapture Guidance Method Based on Saturation Function
## 4 Simulations and Result Analysis
### Mars Aerocapture Mission
#### 4.2.1 Preliminary Monte Carlo Simulation  
Based on the dispersions mentioned above, 100 Monte Carlo simulations are carried out in the Mars scenario, and the simulation results are shown in Figures 10 and 11.  
Figure 10 shows the relative velocity versus altitude. It can be seen that the velocity decreases mainly in a narrow 20 km altitude zone between 30 and 50 km, and finally, the exit velocity is between 3900 and 4000 m/s. Figure 11 shows the bank angle time histories of the Monte Carlo simulation. From this figure, the bank angle's variation basically follows the bang-bang structure, which ensures the algorithm's near-optimal performance. The bank angle is reversed at about 230 s, and then in some cases, it finally saturates to 180 deg,  
Figure 8: Dispersion of the velocity increments with the different lift-to-drag ratios for Earth aerocapture.  
Figure 9: Nominal results for Mars aerocapture: (**a**) relative velocity versus altitude; (**b**) bank angle versus time.  
while in others, it saturates to 0 due to various parametric disturbances and inaccuracies in propagating the roll dynamics.  
Similarly to the simulation in the Earth scenario, the simulation statistical results in Mode 1 and Mode 2 for the Mars aerocapture can be obtained, as shown in Table 5. Through comparison, it can be seen that the total \(\Delta V\)'s mean value and standard deviation obtained by our method are smaller, and the mean value of \(\Delta V\) in Mode 1 is reduced by 8.0% compared to that of Mode 2, indicating that our method performs better in optimality and robustness. In addition, the final deviation from the target orbit inclination is small, ensuring that the guidance requirements can be well met.  
Figure 11: Bank angle versus time for Mars aerocapture.  
Figure 10: Relative velocity versus altitude for Mars aerocapture.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Mode 1: Total** & \multirow{2}{*}{**Mode 2: Total**} & \multicolumn{2}{c}{**Mode 1:**} & **Mode 2:** \\  & \(\Delta V\)**, m/s** & & **Inclination** & **Inclination** & **Inclination** \\  & & **Deviation, deg** & **Deviation, deg** & **Deviation, deg** \\ \hline Mean & 56.8421 & 61.7943 & 0.0151 & \(-\)0.0763 \\ Maximum & 70.3198 & 145.5577 & 0.4898 & 0.6589 \\ Minimum & 52.9646 & 51.5100 & \(-\)0.4415 & \(-\)0.4480 \\ Standard deviation & 3.7831 & 16.3686 & 0.2111 & 0.2048 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical results from Monte Carlo simulations for Mars aerocapture.
## 2 Optimization
### Classical Iterative Optimization Algorithms
#### 2.1.1 Stochastic Gradient Descent (SGD)  
In iteration \(t\), an approximation of gradient \(\
abla_{\theta}J(\theta_{t-1};\mathcal{D}_{train})\) is calculated using a mini-batch \(\mathcal{D}\subseteq\mathcal{D}_{\text{train}}\) of training data and then used to modify parameters from previous time step \(t-1\) according to the update rule [21]  
\[\mathbf{\theta}_{t}=\mathbf{\theta}_{t-1}-\eta\
abla_{\theta}J(\mathbf{\theta}_{t-1}; \mathcal{D}). \tag{2}\]  
(In the literature, the term _stochastic gradient descent_ is often used for a variant of gradient descent in which one training example is used for approximation of the gradient. When the approximation of gradient is calculated on a mini-batch of training examples, then the term _mini-batch gradient descent_ is used. Here, term SGD refers to mini-batch gradient descent as it is the case in the most deep learning frameworks.) In the rest of the article, \(\
abla J(\mathbf{\theta}_{t-1})\) denotes approximation \(\
abla_{\theta}J(\theta_{t-1};\mathcal{D})\).  
The choice of learning rate \(\eta\) plays a crucial role in the convergence of SGD. Choosing too small learning rate results in slow learning and choosing too high learning rate can lead to divergence. When SGD gets very close to a local optimum, the parameter values sometimes oscillate back and forth around the optima. It also takes a lot of time for SGD to navigate flat regions, which are common around local optima where the gradient is close to zero. These problems led to developing new optimization algorithms that incorporate the momentum term.