esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 2 Related Work
### Use of Network Slicing Architecture for Resource Management  
The network slicing architecture supported by NFV and SDN has demonstrated to be an effective solution for implementing resource allocation schemes and algorithms to meet the diverse and simultaneous demands of consumers and vertical markets [27]. For instance, network slicing can be used as a management solution to enhance the network resources sharing required for the dynamic operation of massive IoT infrastructures such as wearable devices [28]. The network slicing paradigm can be used to define entire network slices to cover the elastic demand for network resources through the day and according to different operating and customer requirements such as bandwidth or a desired reliability level for the customers [29]. The reconfiguration, scaling, and migration of virtual resources (e.g., virtual machines) needed for the dynamic operation of mobile networks corresponding to customer-specific workloads are considerations that can also be addressed efficiently (e.g., in terms of bandwidth and latency constraints) with network slicing technology [30]. In addition, the network slicing architectural framework is flexible enough to allow discrimination in network resources allocation among slices, customers, or services according to specific operational requirements; a feature can be exploited in different scenarios and for various purposes. In [31], for example, the authors proposed a two-level prioritization scheme (inter-slice and intra-slice) to implement a heuristic-based admission control mechanism able to dynamically allocate network resources to different slices customers needs and traffic loads. In our proposal, we also use an intra-slice scheme but focused on prioritizing the consumption of certain services if the energy supply is not enough to meet all demand.  
The potential of network slicing for energy management has also been explored. In [32], [PERSON] et al. introduced a dynamic network slicing solution for large-scale energy-harvesting fog computing networks. In the proposed architecture, a regional orchestrator coordinates workload distribution among local fog nodes, providing slices of energy and computational resources to support various types of service requested by end users. The use of network slicing in this use case shows a maximization of the utilization of available resources, dynamic resource allocation according to service demands, and balance of workloads among fog nodes. This information provides insight into the possible improvements in energy efficiency that can be obtained with network slicing in 5G networks.
## 4 Dynamics and Experiments  
### Avionics  
The electronic equipment configuration for the drone and its dual-function landing gear is shown in Figure 9. The main control module uses a Navio+ RP1 4B architecture. The Raspberry Pi 4B, manufactured by the Raspberry Pi Foundation, with its faster processor and larger memory, enables the UAV to handle more complex algorithms and tasks by easily connecting various external devices and sensors. The Navio2, manufactured by Emlid, integrates sensors such as IMU and barometer, providing accurate attitude estimation and position control. It communicates with the landing gear module's STM32F10 SC8T6, manufactured by STMicroelectronics, via UART to switch the dual-function landing gear configuration.  
The drone utilizes the HM30 wireless digital image transmission module, manufactured by Hangzhou Huitong Electronics, for communication with the ground station. This module integrates data transmission, receiver, PWM signal output, and HD image transmission, significantly enhancing the space efficiency of the drone's electronics bay. The power system comprises three 11.1 V 850 mAh 85C lithium batteries connected in parallel, providing power to the electronic speed controller, main control module, and DC gear motor of the quick-release mechanism, and the air side of the wireless digital image transmission system.  
Figure 9: Electronic control system and its associated configurations.
## 4 Discussion  
The comprehensive evaluation of the three RWTD design configurations has yielded significant insights into their performance and functionality, offering valuable implications for medical equipment design and safety. Design 1, named \"Rotate,\" emerged as the superior choice, excelling in criteria such as center of gravity, price, and size. This aligns with established research emphasizing the pivotal role of these factors in ensuring patient safety and affordability in medical equipment design.  
Despite the robust findings, it is crucial to acknowledge the study's limitations. The exclusion of noise analysis due to the constraints of CAD models underscores the necessity for future research to address this aspect, given the potential impact of excessive noise on both patients and healthcare professionals. Similarly, the exclusion of the force required to move criterion underscores the significance of standardization in design elements, emphasizing the need for optimization to enhance maneuverability and minimize physical effort. Additionally, decision matrices can pose challenges in ranking values. Incorporating customer feedback and patient perception in future studies could enhance the ranking algorithm with reduced subjectivity. For example, patient perception of the kinematics could be an interesting scientific contribution to these devices.  
The implications of these findings extend to patient care and medical equipment safety. The emphasis on criteria such as center of gravity and structural strength aligns with the overarching goal of minimizing tipping risks and ensuring the safety of patients and healthcare providers. Considerations like the number of pinch points underscore the importance of prioritizing patient comfort and safety in equipment design.  
Future research directions are identified in optimizing the device's motion range from 0 to 90 degrees tilt for enhanced patient comfort. Additionally, exploring advanced technologies such as automation and smart sensors can further elevate the RWTD's functionality, making it more accessible and safer for both patients and healthcare providers. Prioritizing criteria such as center of gravity, structural strength, and pinch points remains paramount for patient safety, harmonizing with the device's smooth tilt range. The evaluation of patients' experiences will be crucial in optimizing both comfort and safety aspects.  
In conclusion, the focus of future research should be on refining the RWTD design by enhancing tilt motion capabilities, integrating emerging technologies, and addressing design limitations. The ultimate objective is to create a device that optimizes patient care, streamlines clinical processes and prioritizes comfort, safety, and usability for both patients and healthcare professionals.
## 3 Results and Discussion
### Key Aroma Compounds in Non-Smoked Bacon Identified via GC-O-Ms
As shown in Figure 3b, the sensitivity of different substances varies depending on the detection method used. GC-O-MS has more advantages in aldehyde detection, while GC \(\times\) GC-TOFMS has a higher sensitivity to hydrocarbons and alcohols. This may be due to the fact that the materials used in the volatile extraction process have a higher affinity for aldehydes and alcohols, and the chromatographic columns used are polar columns, which have a higher degree of separation for such substances. This leads to the higher sensitivity of these two methods for aldehydes and alcohols. As a result, bacon aroma profiles detected by both methods may tend to be more fatty or even rancid. If there is less extraction or a poorer separation of esters, more sweetness will be lost in the aroma profile. The sensitivity of GC-IMS to various compounds was relatively uniform, which could be attributed to the lack of selectivity in the volatile extraction process. Also, the limited database restricts the species of compounds that can be used. This can lead to the possibility that important compounds may be missed, and the aroma profile of the bacon may not be truly reflected. In addition, most of the alcohols, ketones, and esters detected by GC-IMS were not characterized via the other two methods. The results of this research were consistent with a previous study, which illustrated that only 3.50% of total aroma compounds in grilled lamb shashliks could be detected by all three methods (GC-MS, GC \(\times\) GC-TOFMS, and GC-IMS) [34]. In general, GC-IMS is better at the detection of trace aroma compounds and their comparison between different samples. Meanwhile, GC \(\times\) GC-TOFMS provided a better separation effect and extreme sensitivity. However, the human sense of smell is a more sensitive detector, and the methods combined with it led to more realistic results. GC-O-MS could combine instrumental detection with human senses and had better quantitative capability than the other two methods [15]. A combination of all three methods can be used to maximize the understanding of all the aroma compounds in non-smoked Bacon. At the same time, it can provide a reference extraction and detection method for studying the target compounds in future research.
## 2 Materials and Methods
### Remote Sensing Data and Analysis
#### 2.5.3 Vegetation Index Calculation  
The surface reflectance normalized difference vegetation index (NDVI) is most commonly applied to optical images for crop information [(7)]; the resulting values can then be calculated for leaf area index and canopy cover conversion. NDVI is calculated as:  
\[\mathrm{NDVI}=\frac{\mathrm{P_{NIR}-P_{RED}}}{\mathrm{P_{NIR}+P_{RED}}} \tag{4}\]  
where \(\mathrm{P_{NIR}}\) is the surface reflectance in the near infrared band and \(\mathrm{P_{RED}}\) represents the surface reflectance in the red spectral band.
## 1 Introduction
In this paper, we investigated the propagation properties of signal vortex modes carried by MG beam in weak oceanic turbulence. To accurately describe the effect of ocean turbulence on this beam, in the study, we first studied and established the wave structure function of the beam wave. Then, we derived the expression of transmitting probability of the signal vortex modes for MG beam. By the transmitting probability of the signal vortex modes for MG beam, we discussed the influences of beam structure parameters and oceanic turbulence parameters on the transmitting probability of signal vortex modes in numerical calculation.
# StereoVO: Learning Stereo Visual Odometry Approach Based on Optical Flow and Depth Information
## 6 Conclusions  
We propose a novel data-driven Deep Convolutional Neural Network (StereoVO) that encodes geometrical features in stereo image pairs to estimate camera motion. The main idea is to use CNN to extract features of stereo image pairs in image sequences, then match the features with proposed SSIM cost volume to obtain depth information, fuse depth information with optical flow, and finally achieve the regression of the camera motion among consecutive stereo images. The performance of our approach is better than the monocular versions of ORB-SLAM2, ORB-SLAM3, and DeepVO, PCNN, GeoNet, Flowdometry, and TartanVO. In terms of the memory occupation, our approach is close to the learning-based monocular VO. The proposed method achieves real-time performance and a higher running rate than the stereo versions of ORB-SLAM2 and ORB-SLAM3.  
Conceptualization, C.D. and H.L.; methodology, C.D.; software, C.D. and S.J.; validation, K.T. and S.J.; formal analysis, C.D.; investigation, C.D.; resources, H.L. and K.T.; data curation, C.D., K.T. and S.J.; writing--original draft, C.D.; writing--review and editing, C.D., H.L., S.J. and K.T.; Visualization, C.D.; supervision, H.L. and K.T.; funding acquisition, C.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Hunan Research Innovation Project for Postgraduate Students (1053320184209).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **RMSE** & **Mean** & **Std.** \\ \hline MLF-VO & 0.105 & 0.078 & 0.068 \\ StereoVO & 0.089 & 0.070 & 0.055 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average RPE results on KITTI Seq. 07, 09, and 10. Our method outperforms MLF-VO in all metrics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Data** & **GPU (G)** & **Speed (s/Frame)** \\ \hline ORB-SLAM2-S [18] & stereo & - & 0.1 \\ ORB-SLAM3-S [23] & stereo & - & 0.12 \\ MLF-VO [29] & monocular & 1.7 & 0.03 \\ StereoVO & stereo & 1.9 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The average processing time (seconds) for each image on KITTI sequence 07, 09, and 10. ‘-’ means not needed.
## 2 System Model  
In FBMC/OQAM systems, the transmitted signal is given in the following form [15]:  
\[x(t)=\sum\limits_{m=0}^{N-1}\sum\limits_{n}d_{m,n}g_{m,n}(t) \tag{1}\]  
where \(d_{m,n}\) are real valued OQAM symbols, and \(g_{m,n}(t)\) denotes the synthesis basis, which can be obtained by the prototype function \(g(t)\) in the following way:  
\[g_{m,n}(t)=g(t-n\tau_{0})e^{i2\pi mF_{0}t}e^{i\phi_{m,n}} \tag{2}\]  
where \(N\) is an even number of sub-carriers, the sub-carrier spacing of \(F_{0}=1/T_{0}=1/2\tau_{0}\), and \(\phi_{m,n}\) an additional phase term. \(T_{0}\) denotes OFDM symbol duration, and \(\tau_{0}\) denotes the time offset between the real and imaginary parts of the OQAM symbols. \(m\) is the sub-carrier index and \(n\) is the OQAM symbol time index.  
The design of pulse \(g\) enables the associated sub-carrier functions \(g_{m,n}\) to be orthogonal in the real field,  
\[\Re\{\big{\langle}g_{m,n}\big{|}g_{p,q}\big{\rangle}\}=\Re\{\sum\limits_{t}g_ {m,n}(t)g_{p,q}^{\ast}(t)\}=\delta_{m,p}\delta_{n,q} \tag{3}\]  
where \(\delta_{i,j}\) denotes Kronecker delta, \(\delta_{m,p}=0\) if \(m\
eq p\) and \(\delta_{m,p}=1\) if \(m=p\). We can find that, even in the distortion-free channel and with perfect time and frequency synchronization, some purely imaginary inter-carrier interference at the output still be existed, thus, we set interference weights  
\[\big{\langle}g\big{\rangle}_{m,n}^{p,q}=-j\big{\langle}g_{m,n}\big{|}g_{p,q} \big{\rangle} \tag{4}\]  
where \(\big{\langle}g_{m,n}\big{|}g_{p,q}\big{\rangle}\) denotes a purely imaginary term for \((m,n)\
eq(p,q)\).  
Through the channel, with an additive noise, the received signal can be expressed as  
\[r(t)=\sum\limits_{m=0}^{M-1}\sum\limits_{n}d_{m,n}g_{m,n}(t)H_{m,n}(t)+\eta(t) \tag{5}\]  
with  
\[H_{m,n}(t)=\int\limits_{0}^{\tau_{\max}}h(t,\tau)e^{-2j\pi mF_{0}\tau}d\tau \tag{6}\]  
where \(h(t,\tau)\) denotes the channel impulse response, and \(H_{m,n}(t)\) denotes a complex response of the channel at instant \(t\). Figure 1 shows an implementation diagram of the FBMC/OQAM system.
## 4 Testing the Problem Formulation  
To evaluate the mathematical formulation previously presented in Section 3.1, the performance of HES of Figure 1 is analyzed using a GA and a typical system with a wind generator of 75 kW (\(P_{W}^{max}\) = 75 kW), and the load profile of Figure 3 has been used. Cut-in, rated, and cut-out wind speeds equal to 3, 12, and 25 m/s, respectively, have been assumed.  
Regarding the fuel-based generator, a diesel unit of 100 kW (\(P_{D}^{max}\) = 100 kW) with minimum operating power of 50% (\(P_{D}^{min}\) = 50 kW) has been assumed.  
As the effects of wind generation and BESS management on the reduction of fuel consumption have been deeply studied in the technical literature [41], special attention to the influence of these devices on GHG emissions has been paid in this work. Thus, the GHG-emission measurements published in [42] have been adopted.  
During the GA implementation, the initial population is randomly initialized, so that an operator has to be incorporated in order to fix the elements of the matrix \(G_{(k)}\) to +1 at those hours at which NL is negative (Figure 2). Additionally, such operator has to be included after the application of mutation operator.  
Regarding the GA parameters, a population with 75 individuals, 100 generations, a crossover rate of 90%, and a mutation rate of 5% were considered.  
Wind speed profile has been modeled by using the general purposes profile of (40) [7,10], which depends on the average wind speed (\(S_{W}^{a}\)), diurnal pattern strength (\(S_{W}^{b}\)), and the hour of peak wind speed (\(S_{W}^{c}\)).  
\[S_{W(t)}=S_{W}^{a}\Big{(}1+S_{W}^{b}\cos\Big{[}\Big{(}\frac{2\pi}{T}\Big{)}(t- S_{W}^{c})\Big{)}\Big{]}\;\forall\;t=1,\ldots,T. \tag{40}\]  
Different values of average wind speed and diurnal pattern strength have been considered. Specifically, \(S_{W}^{b}\) = 0, 0.1, 0.2, 0.3, 0.4 and \(S_{W}^{c}\) = 15 h were evaluated through the analysis of three case studies. These are typical values for places located in the United States. In this way, different values of \(S_{W}^{b}\) allow us to evaluate the wind speed profile with high or low oscillation, and consequently their impact on the operation of BESS.  
A typical VRFB of 5 kW/20 kWh (\(P_{B}^{max}\) = 5 kW/\(E_{B}^{max}\) = 20 kWh) has been considered. Minimum and maximum SOC were assumed as 15 and 90% (\(SOC_{B}^{min}\) = 0.15 and \(SOC_{B}^{max}\) = 0.9), respectively, and minimum and maximum voltage were assumed as 42 and 56.5 V (\(U_{B}^{min}\) = 42 V and \(U_{B}^{max}\) = 56.5 V), respectively. The entire bank is composed of 10 of these batteries connected in parallel.  
The simulation and optimization analysis were implemented in MATLAB(r), using a personal computer with i7-3630 QM CPU at 2.4 GHz, 8 GB of memory and a 64-bit operating system.  
The previously mentioned cases are carefully discussed in the next subsections.  
Figure 3: Load profile.
## 4 Design of Deep Reinforcement Learning Controller for Stabilized Platform
### Network Structure Design  
In line with the preceding discussion and following the principles of the DDPG algorithm, the designed DDPG controller adopts a dual network framework encompassing the Actor and the Critic, each consisting of both online and target networks. These network structures exhibit complete structural congruence aside from their parameterization. Figure 4 provides a graphical representation of the Actor and Critic network architectures. The Actor network's input layer interfaces with the state vector, denoted as \(s_{t}\). It includes two fully connected neural layers, consisting of 64 and 32 nodes, with Rectified Linear Unit (ReLU) activation functions. The output layer produces the action \(a_{t}\), representing the control variable chosen by the agent within the current-state context. Conversely, the Critic network's input layer accommodates both the state vector, \(s_{t}\), and the action, \(a_{t}\). The output layer generates the value associated with the agent's action selection under the present-state conditions. The overall structural composition of the Critic network mirrors that of the Actor network, maintaining consistency throughout.  
In accordance with the state vector, reward function, and network structure design of the DDPG controller as elucidated earlier, the iterative process of tracking the tool face angle unfolds. During each iteration, the network parameters undergo calculated adjustments until the system achieves convergence with the desired ideal state.
# Joint Embedding of Semantic and Statistical Features for Effective Code Search
## 4 Experiment and Analysis
### Results Analysis
#### 4.4.2 Rq 2: Analysis of the Impacts on JessCS Effectiveness from Influencing Factors  
This part mainly analyzes the influencing factors of code search effectiveness from the selection of code features and embedding vector dimension. Based on the code search model in this study, only the selection of code features in the code embedding network is changed, and all other conditions are kept unchanged for experiments. We use \(\langle\mathit{tokens}\rangle\), \(\langle\mathit{tokens}+\mathit{method\ name}\rangle\), and \(\langle\mathit{tokens}+\mathit{method\ name}+\mathit{API\ invocation}\rangle\) as code features to train the model, and train 300 rounds on the same dataset, and record the index values for comparison.  
As shown in Figure 10, the horizontal axis feature represents different code feature selections, \(t\) represents the token set, \(m\) represents the method name, and \(a\) represents the API invocation. In terms of code feature selection, selecting \(t+m+a\) as the code feature improves the TOP-1 Precision and MRR indicators by 16.6% and 0.144, compared with \(t\) and \(t+m\), which effectively improves the accuracy of code search.  
According to the code search model in this work, only the dimensions of the code embedding and natural language description embedding vector in the code embedding network are changed, and all other conditions are kept unchanged for the experiments. Three embedding vector dimensions (i.e., 100, 200, and 400) were set and each of them was trained for 300 rounds on the same dataset and model structure. The index values were recorded for comparison.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **SucRate@1** & **SucRate@5** & **SucRate@10** & **MRR** \\ \hline UNIF-based & 0.43 & 0.70 & 0.80 & 0.56 \\ JessCS & 0.63 & 0.83 & 0.90 & 0.71 \\ \hline Improvements & & & & \\ of JessCS & 47\% & 19\% & 13\% & 27\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The accuracy of UNIF-based approach and JessCS results.  
Figure 10: The impacts of code features on the effectiveness of JessCS.  
As shown in Figure 11, the horizontal axis \(d\) represents the dimension of the embedded vector of the code and its corresponding natural language description. For the code search model, changing the dimension of the embedding vector has a limited impact of JessCS effectiveness.  
**Finding 2.**_The joint embedding of method name and API invocation can significantly improve the effectiveness of JessCS. The use of joint embedding network can obtain more effective results than the single use of code tokens. The dimension of embedding vectors cannot impact JessCS significantly. The different dimensions (i.e., 100, 200, and 400) obtain similar results._
## 3 Results and Discussion
### Sensory Evaluation of Wine  
In total, 57 distinct aroma attributes were identified and used in the Quantitative Descriptive Analysis (QDA) of Tera wine sensory profiles. The scores for the aroma groups and their key descriptors are illustrated in Figure 1 for young Tera wines and in Figure 2 for aged Tera wines. A comprehensive overview of all aroma descriptors, along with statistical analysis, is provided in Table 2.  
Figure 1: Perception of aroma group intensities obtained by the QDA of Teraan young wines produced by different vinicitation treatments: QDA—quantitative descriptive analysis; TM7—a control treatment with 7 days of maceration; TM10—prolonged 10-day maceration; TM21—extended 21-day post-fermentation maceration; TPHT—48-h pre-fermentation maceration heating at 45 \({}^{\circ}\)C followed by 8 days of maceration. Figure 2: Perception of aroma group intensities obtained by the QDA of Tera aged wines produced by different vinicitation treatments: QDA—quantitative descriptive analysis; TM7—a control treatment with 7 days of maceration; TM10—prolonged 10-day maceration; TM21—extended 21-day post-fermentation maceration; TPHT—48-h pre-fermentation maceration heating at 45 \({}^{\circ}\)C followed by 8 days of maceration. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Aroma Descriptors**} & \multirow{2}{*}{ \begin{tabular}{c} **Young/Aged** \\ **Wine** ** **TMT** \\ \end{tabular}  } & \multicolumn{4}{c}{**Treatments**} \\ \cline{3-6}  & & **TM7** & **TM10** & **TM21** & **TPHT** \\ \hline \multirow{3}{*}{Fruity} & Y & 7.25 \(\pm\) 0.22 \({}^{\text{c*}}\) & 7.88 \(\pm\) 0.13 \({}^{\text{b*}}\) & 8.42 \(\pm\) 0.