esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Application  
As proof of concept, the fermentation performance of wine-relevant yeasts in SGM was tested in the absence (control) and presence of lipids (50 mg/L linoleic acid) under semi-aerobic conditions (Figure 4). Overall, the highest carbon dioxide production was observed in in _S. cerevisiae_ EC1118 followed by _T. delbrueckii_, _K. marxiamus_ and _M. pulcherrina_. Interestingly, an increase in fermentation performance (carbon dioxide production) was only observed in _K. marxiamus_ and _S. cerevisiae_, whereas minimal differences were observed in other yeasts when linoleic acid was added to SGM (Figure 5). Figures 5 and 6 show the lipid composition of the yeasts investigated in this study at the end of alcoholic fermentation and the corresponding fatty acid and sterol profiles. _M. pulcherrina_ produced the highest relative amount of oleic, linoleic and linolenic acid during fermentations in SGM with no lipids. On the other hand, the highest amount of palmitoleic acid was detected in _K. marxiamus_ at the end of fermentation in SGM without the supplementation of lipids. No linoleic or linolenic acid were detected in _S. cerevisiae_ control fermentations. However, linoleic acid was taken up in all yeasts but to varying degrees. Indeed, the highest uptake of linoleic acid was observed in _M. pulcherrina_, followed by _K. marxiamus_, _T. delbrueckii_ and _S. cerevisiae_. In terms of sterols, the highest ergosterol level was observed in _M. pulcherrina_, whereas high amounts of squalene were observed in _T. delbrueckii_ and _S. cerevisiae_. Overall, all the yeast investigated in this study assimilated linoleic acid. Although selected fatty acids (including linoleic acid) were detected in the supernatant at the end of alcoholic fermentation, these compounds were below the limit of quantification (data not shown).
# Vision-Based Surface Inspection System for Bearing Rollers Using Convolutional Neural Networks
## 3 Surface Inspection Process
### Roller Quality Evaluation  
For defects that occured on the cylindrical surface, no matter which kind of defect it was and what the defect area was, the bearing roller was judged as unqualified. For defects that occurred on the end surfaces, step 3.1, described above, had already excluded defects, such as material lacking at the chamfer and stamp lacking, that caused the outer contour to not be circular in shape. For corrosion, scratch, damage, and grind lacking defects, the bearing roller was judged based on the defect area. The defects with bounding boxes were equivalent to the ROIs (Region of Interest), and the ROIs were analyzed separately using the image processing method. Accordingly, we calculated the defect area on each end surface separately. The process is shown in Figure 7 below.  
Different defects have different impacts on the performance of the roller. Damage has the greatest impact on the performance, followed by scratch, corrosion, and grind lacking. Our surface inspection system had different tolerances for different defects and; therefore, we defined four coefficients for the four defects. When calculating the total defect area, it was necessary to multiply the area of the different defects by the corresponding coefficient. For damage, scratch, corrosion, and grind lacking, the coefficients were defined as 3, 1.5, 1, and 0.8, respectively. The coefficients were defined by multiple experiments based on the inspection effect, and different coefficients could be defined according to different situations.  
After performing median filtering, Otsu thresholding [31], and morphological processing on the ROIs, defects were segmented from the background, and then we calculated the total area of all the defects. If the total defect area was greater than the set threshold, which was about 5% of the end surface area, the bearing roller would be judged as unqualified. The roller would be judged as qualified only when the defect area of each end surface was smaller than the threshold.
# Spin-Orbital Transformation in a Tight Focus of an Optical Vortex with Circular Polarization
## 5 Transformation of the Longitudinal Energy Flux into the Transverse Energy Flux  
It is interesting to follow how the total longitudinal energy flux (the lengthwise Poynting vector component averaged over the beam cross-section) changes while tight focusing of the beam (1). It was shown in [36] that the distribution of the longitudinal Poynting vector component in the focus \(P_{z}=\text{Re}\left(E_{x}^{*}H_{y}-E_{y}^{*}H_{x}\right)\) is equal to the lengthwise projection of the SAM:  
\[P_{z}=S_{z}=\gamma_{0,m}^{2}-\gamma_{2,m+2}^{2}. \tag{17}\]Therefore, the total longitudinal energy flux averaged over the beam cross-section in the focus will be equal to (11):  
\[\hat{P}_{z}=\int\limits_{0}^{\infty}\int\limits_{0}^{2\pi}P_{z}pd\rho d\vartheta =W_{0}-W_{2}. \tag{18}\]  
In the initial plane of the field (1), the denseness of the longitudinal energy flow and the total longitudinal energy flux are equal to the expressions:  
\[P_{z,0}=\left|A(\psi)\right|^{2},\ \hat{P}_{z,0}=W. \tag{19}\]  
It can be seen from a comparison of (18) and (19) that, upon focusing, the total longitudinal energy flux decreases in the same way as the total longitudinal SAM. Due to the fact that during focusing, a longitudinal component of the electric field strength \(E_{z}\) appears, a transverse energy flux also appears. This occurs simultaneously with the SOC. That is, a transverse energy flow appears as the SAM decreases and the OAM appears. This means that part of the initial longitudinal energy flux is converted into a transverse energy flux, and the longitudinal flux decreases by exactly the same amount as the longitudinal SAM decreases. It should be noted that that the value of the total longitudinal energy flux does not depend on the value of the topological charge, but depends only on the numerical aperture \(\sin\beta\) of the focusing optical system and on the initial radially symmetric amplitude \(A(\psi)\) of the light field. These facts follow from (8) and (18).
## 4 Simulator Implementation for WirelessHART Simulations
### The WirelessHART System  
WirelessHART represents the state-of-the-art in wireless industrial mesh networking [1]. Within the scope of the present paper, WirelessHART itself defines the lower four layers of the network stack. It provides end-to-end routing capabilities. The routing is determined by the network manager in the form of routing graphs. Routing graphs are propagated into field devices, and the packets carry a routing graph identification, so that intermediate devices can forward the packets to the appropriate next-hop node. The routing graphs are set up redundantly, so that every node is reachable over multiple routes, both in the upstream and downstream directions, if possible. This feature provides resilience against temporary single-link failures within the network. The construction of reliable routing graphs for WirelessHART is extensively discussed in [15].  
The data link layer follows the principles of the time synchronized mesh protocol [16]. Communication is organized in a time division multiple access (TDMA) structure with a fixed 10-ms time slot length. The network maintains accurate slot timing and the network-wide absolute slot number (ASN).  
WirelessHART relies on link layer acknowledgments. One complete transaction (_i.e._, data transmission, acknowledgment and the necessary guard times, switching times, _etc._) must fit into one timeslot. Network communication is organized into periodically repeating superframes. WirelessHART is statically scheduled in order to provide guarantees on the end-to-end delay in delay-sensitive applications. The deterministic network schedule is set up by the network manager, based on the communication requirements (superframes) and the statistics collected by the nodes. Only one node is allowed to transmit in a particular time slot on a single carrier frequency, which prevents collisions and the resulting uncertainties. Sub-schedules that define which node is eligible to transmit and which node(s) need(s) to wake up and listen within a particular time slot, are downloaded into the nodes.  
A salient feature, frequency hopping over at most 16 channels in the 2.4-GHz industrial, scientific and medical (ISM) band, each being 5 MHz wide, provides a very effective frequency diversity to randomize channel fading and mitigate external interference present on a channel. Additionally, channels experiencing significant interference might be blacklisted.
## 2 Related Work
CPU and memory consumption of k3s has been initially attempted with RPi devices [41]. Memory and storage requirements of a Kubernetes compatible container solution (FLEDGE) has been compared to k3s with edge devices (RPi) [42]. The results indicated that FLEDGE has lower memory requirements (25-30%) on ARM-devices than k3s. DLASE [43] enables execution of ML-based models (object detection) on edge nodes with OpenBalena and Tensorflow Serving, while packaging the deployed models with Docker. ML techniques have been applied for optimisation of energy consumption in the context of facility management. An energy consumption optimisation solution has been presented for an office environment [44]. Edge gateways were used for data collection and training of the model, and cloud was used as a secondary storage, and for improving prediction latency (offloading). Another smart city-related system applied edge-nodes for data collection, and a DNN was trained continuously for optimising energy consumption in power plants [20]. An ML platform has been proposed for optimising energy consumption in power plants with minimal interruption to the prediction service [21]. The review indicates that different RA approaches [16; 17; 24; 25; 26; 27] have been designed for edge computing systems. There exist several techniques [12] for optimising training and inference of models within the cloud-edge continuum. Further, Rancher and k3s provide an interesting solution for managing services across the cloud-edge continuum. Finally, various solutions have been proposed [20; 21; 23; 44] for optimising energy consumption in plants and facilities based on ML-based models. However, an architecture has not been presented (to the authors' best knowledge), which enables continuous training and deployment of models on edge-devices for optimising energy consumption in the context of facility management, when ML-based services are managed in Kubernetes-clusters with Rancher and k3s.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbols** & **Definitions** & **Symbols** & **Definitions** \\ \hline \(\mathrm{G}_{\alpha,\beta}\) & Channel gain & \(\gamma\) & Distance power gradient \\ \(\mathrm{g}_{\mathrm{op}}\) & Gain of the optical filter & \(\mathrm{L}_{\mathrm{p}}\) & Path loss using Bluetooth low energy \\ \(\mathrm{\psi}_{\mathrm{in}}\) & Angle of incidence & \(\mathrm{P}_{\mathrm{tr}}\) & Transmitted power using BLE \\ \(\mathrm{\psi}_{\mathrm{ir}}\) & Angle of irradiance & \(\lambda\) & Selection score \\  & Area of the light-emitting diode & & \\ \(\mathrm{A}_{\mathrm{c}}\) & (LED)-projected image on an image & \(\zeta_{\mathrm{c}}\) & Condition factor \\  & sensor & & \\ \(\mathrm{m}_{\mathrm{i}}\) & Lambertian emission index & \(\zeta_{\mathrm{c,1}}\) and \(\zeta_{\mathrm{c,min}}\) & Thresholds of the condition factor \\ \(\mathrm{d}_{\alpha,\beta}\) & Euclidean distance between the & & \\  & camera and LED & \(\sigma_{\mathrm{ins}}\) & Instantaneous heart rate \\ \(\mathrm{\partial}_{\mathrm{AOV}}\) & Angle of view of camera & \(\sigma_{\mathrm{tar}}\) & Target heart rate \\ \(\mathrm{\psi}_{1/2}\) & Half-intensity radiation angle & \(\tau\) & Monitoring interval \\ \(\mathrm{\rho}\) & Pixel edge length & \(\Gamma_{\mathrm{crit}}\) & A threshold above \(\Gamma_{\mathrm{min}}\) \\ \(\mathrm{A}_{\mathrm{l}}\) & Effective area of LED & \(\mathrm{P}_{\mathrm{im}}\times\mathrm{d}_{\mathrm{im}}\). & Image sensor dimension \\ \(\mathrm{f}_{\mathrm{o}}\) & Focal length & \(\vartheta\) & Distance between two cameras \\  & & Minimum part of \(\mathrm{q}_{\mathrm{im}}\) that must \\ \(\mathrm{\eta}\) & Signal-to-interference-plus-noise ratio & \(\mathrm{m}_{\mathrm{im}}\) & appear inside the image sensor in \\  & & & data decoding \\ \(\mathrm{\
u}\) & Responsivity & \(\mathrm{d}_{\mathrm{d}}\) & Vertical distance from LED to camera \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of symbols.
## 4 Performance Evaluation
### Simulation Setup
#### 4.2.3 \(\sigma\): Lognormal Shadowing Standard Deviation Variable  
In this subsection, the impact of varying \(\sigma\) is studied. Simulations parameters are listed in Table 5. Figure 5 presents boxplots illustrating the results of 100 runs. They show how the data error is extended, and red lines show the median of the error. It can be observed that NRMSE values increase with the \(\sigma\) value, without exceeding 0.12 when \(\sigma=6\) dB. Correspondingly to intuitive expectations, where \(\sigma\) introduces randomness in the received signal power, the median localization error progressively increases from 2% to 8% of the range.  
Figure 4: Inter-node distance estimation NRMSE for variable PLE values.  
Figure 5: Inter-node distance estimation NRMSE for variable \(\sigma\), lognormal shadowing standard deviation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters [Unit]** & **Values (s)** \\ \hline \(\gamma\): PLE & 3 \\ \hline \(\sigma\): Lognormal shadowing standard deviation [dB] & (1;2;3;4;5;6) \\ \hline \(m\): Nakagami parameter & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Variable parameter setup 3.
## 6 Conclusions  
In this study, we try to improve the accuracy of classifying human emotion by EEG signals. The innovation of our methods involves two aspects. First, we propose a new method for the EEG feature extraction and representation. EEG frequency features (PSD) are extracted from different EEG channels and mapped to a two-dimensional plane to construct the EEG MFI. EEG MFI sequences are built from the raw EEG signal. The EEG MFI sequences fuse together the spatial, frequency domain, and time characteristics of the raw EEG signal. Another aspect is our proposal of a hybrid deep neural network that deals with the EEG MFI sequences and recognizes the emotions. The hybrid deep neural networks combined Convolution Neural Networks and Long-Short-Term-Memory Recurrent Neural Networks. In the hybrid structure, CNN is used to learn temporary image patterns from EEG MFI sequences, and LSTM RNN is used to classify human emotions.  
With our method, empirical research is carried out with the DEAP dataset. We compare our results with those from the baseline methods and find that the emotion classification accuracy of our method reached 75.21%, which is higher than the accuracies from the baseline methods. In the baseline methods, we chose a 'CNN + RNN' Neural Network without LSTM unit to compare with our method. We find that the LSTM unit showed the time sensitivity. Furthermore, we reviewed the state of the art of human emotion recognition by EEG signals. Compared with similar studies, our study improves the classification accuracy.  
Figure 10: MFI sequences with different time windows corresponding to Subject 4 (corresponding to the emotion HVHA).  
Additionally, we analyzed the effects of different time windows on classification accuracy and found that time windows corresponding to two to three seconds achieved good classification accuracy, and the corresponding classification accuracy decreased from the time window division after four seconds. Given these results, we inferred that MFI sequences from a smaller time window represent more details of the variation of the EEG signal. We select Subject 4 to seek corresponding evidence in the MFI sequence and find that, with smaller size time windows, MFI reveals more details about activation in the FP\({}_{1}\) and FP\({}_{2}\) area.  
This work is supported by the National Basic Research Program of China (2014 CB744600), the National Natural Science Foundation of China (61420106005), and the International Science & Technology Cooperation Program of China (2013 DFA32180). [PERSON] and [PERSON] proposed the method of the construction of EEG MFI sequences; [PERSON] and [PERSON] proposed the framework of the hybrid neural networks; [PERSON] contributed to the EEG feature extraction and analysis method; [PERSON] and [PERSON] designed and performed the experiment; [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 6 Proposed Joint Mode Selection and Resource Allocation Scheme (SD-Scheme)  
In the present part, we introduce an SD-scheme underlaying cellular networks. Joint mode selection, channel assignment, and power allocation are considered with the aim of optimizing the sum data rate, SE, and EE. First, the mode selection issue is tackled by employing a greedy algorithm based on maximum SINR to select the optimum mode among DM, RM, and LM for every D2D pair. After that, the matching algorithm is implemented to tackle the problem of sub-channel assignment by exploiting the two-sided preference lists to optimize the utilization of spectrum resources. Finally, the power allocation issue is solved by introducing SARSA-based RL to obtain the optimum power for each D2D pair in the proposed scheme.  
Several important factors inspired the decision to choose the SARSA algorithm for this study. Firstly, the modeling of the network in the complicated and proposed scenario of resource allocation for D2D communication is simply unpracticable. Therefore, the model-free feature of this system is particularly advantageous in this particular scenario. Moreover, the SARSA algorithm is highly applicable to decentralized decision-making and enables agents to self-sufficiently learn the most optimal policies, which aligns effectively with the architecture of the proposed network. The proposed approach utilizes an SARSA algorithm to train and update the power level states by considering the environment feedback information.
# Cloud-Based Single-Frequency Snapshot RTK Positioning
## 2 Methodology
### SRTK Performance  
This section introduces the RTK measurement model and the method used to fix the carrier phase ambiguities in a single epoch. Then the parameters of interest for this work are discussed as well as their impact on SRTK performance.
## 4 Discussion of Results
### Pipeline Scale Analysis
Figure 13: Linear regression fit of the bare land increase within the pipeline segment buffer of the four zones labelled in the central panel of Figure 11. This plot illustrates the higher degradation rate in Zone B (East of Bille), compared to Zone C, despite the former having less degraded forest area at the onset of the study period, capturing the impact on a previously unaffected area. The results also highlight Zone D as comparably stable throughout the study period. again illustrates the degradation expansion associated with the impact on a previously unaffected area. In contrast, the western portion of the Bille pipeline (Zone D) shows minimal impact on the surrounding environment over the 8-year study period. This is reflected in the PII's low rating for this zone, contrasting sharply with areas of more severe degradation and this is further supported by the absence of reported spills in the region. It is also important to acknowledge that Zone A, which also contains sample pipeline 4 in the top panel of Figure 11, has a distinctly higher rating at a granular scale as opposed to the pipeline extent scale. Though this pipeline has a small total impact area, it has a similar spread rate to Zone B, which has been suggested as a region in need of urgent intervention. This underscores the utility of evaluating pipeline impact at a more granular scale, as a pipeline with a small footprint may still contain sections with significant degradation rates, warranting closer inspection and targeted intervention. It is also noteworthy that segments of pipeline 3 north of Bille, which had no reported spills during this study, showed unexpectedly high PII values. Further examination using Sentinel-2 RGB composites from 2023 revealed no clear evidence of anthropogenic drivers such as deforestation for agriculture or logging, nor did it show a spatial pattern consistent with such activities. This suggests the possibility of under-reporting or perhaps of oil-bankering activities contributing to the pollution-induced deforestation observed in these areas.
## 6 Discussion and Future Directions
### Federated Learning  
FL is a type of machine learning strategy that is distributed in its approach, where models are allowed to be trained over decentralized devices or servers while keeping local data [8; 168; 39]. In FL, participants collaboratively receive a global model obtained from the central server, train the model locally, and submit local update to the central server without releasing the raw data [140]. The aggregates from all the local model updates are then collected by the central server to form a new global model [140]. This process continues repeated until convergence is achieved. This FL approach has enabled the removal of privacy threats for vehicles by performing TFP tasks locally, though FL still heavily relies on the central server to collate and manage the local model update [168]. FL was developed by Google in 2016 to solve the problem of data integration in vehicular networks [140]. One of the shortfalls of FL system is that model updates are obtained from centralized sources--the global server. This makes the system vulnerable to attacks such as single-point failure [39].
# A Virtualization Infrastructure Cost Model for 5G Network Slice Provisioning in a Smart Factory
## 1 Introduction
Thus, the virtualization infrastructure cost modeling needs to be adaptable to different scenarios. The economic benefits of SDN and NFV in providing significant cost savings have been widely studied [9; 10; 11]. Different data center topologies have been compared in [12], and savings through virtualization in the Total Costs of Ownership (TCO) for different data center architectures have been studied in [11]. Furthermore, different VNF placement strategies in different data center architectures are studied for VNF service chains, with four VNFs per chain [13]. However, the cost modeling for virtualization infrastructure has not been studied, especially from a market-based approach that considers the perspectives of slice customers and slice providers in provisioning the required one-to-one, one-to-many, many-to-many, or many-to-one slice type allocation. Moreover, the demand for slices is expected to evolve over time, and a demand forecast for the number of devices to be supported per network slice must be conducted. Additionally, different deployment scenarios for the virtualization infrastructure must be compared. In this paper, a slice demand-based cost model is developed for the virtualization infrastructure for slice provisioning, and the model is applied to a real smart factory as a case study. The smart factory vertical with a multi-tenant, multi-use case ecosystem requires the provisioning of use-case-specific network slices. The allocation of slice types to customers' device types in the model is accomplished for a smart factory, but the cost model is scalable to other verticals as well. The model considers one-to-one, one-to-many, many-to-many, or many-to-one slice type allocation between the slice customer and slice provider, conducts a slice demand forecast over 2021-2030, and compares the costs of three different deployment scenarios. Further sensitivity analysis is performed for the cost model by varying the growth of devices, the number of factory customers, the level of isolation between the slices, and resource overbooking. The rest of the paper provides the relevant background, followed by the developed cost model, case evaluation for a smart factory accompanied with sensitivity analysis of key input parameters, and followed by conclusions.