esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources
## 2 Background and Related Work
### The Cloud-RAN Architecture  
The RAN comprises the User Equipment (UE), the air interface, antennas, the Remote Radio Units (RRUs), the BBU, and a network link connecting the RRU and the BBU, known as Fronthaul. The RAN is connected to the Core Network (CN) via a transport network called Backhaul. As depicted in Figure 1, within Cloud-RAN, the BBU is implemented as a software-defined wireless networking application (vBBU). By leveraging virtualization technologies, multiple vBBUs can be deployed on a centralized MEC server, sharing both processing and network resources [1].  
The Cloud-RAN architecture presents a new paradigm for RAN design by enabling the sharing of computing and networking resources. Nevertheless, the centralization of vBBUs brings about stringent latency constraints and capacity demands for both the Fronthaul network and the MEC server that houses the vBBUs. In response to these challenges, the 3 GPP has proposed a functional split of the vBBU protocol stack [20].  
Processing a portion of the vBBU functions locally near the antennas helps alleviate bandwidth and latency demands on the Fronthaul [21]. This approach is supported by the IEEE 1914 working group that has defined two logical split point placements [22]: the Distributed Unit (DU), near the cell tower, and the Centralized Unit (CU), based on the Mobile Network Operator's edge server. The introduction of these split points reclassified the mobile transport network segments and their respective latency and capacity requirements [23]. The Fronthaul is the segment between the RRU and the DU, the Midhaul connects the DU and the CU with data rate requirements that vary based on the selected functional split, and the Backhaul connects the Cloud-RAN with the CN. Collectively, these transport segments constitute the mobile Crosshaul (Xhaul).  
As illustrated in Figure 2, the dotted red line signifies the split option as defined by the 3 GPP [20]. In this model, L1 corresponds to physical layer functions (such as low PHY and high PHY), L2 identifies link layer functions (such as MAC and RLC), and L3 is associated with network layer functions (such as PDCP and RRC). Functions to the left of a given option are instantiated at the CU, while functions to the right are allocated to the DU. The more functions designated to the DU, the less stringent the latency and capacity demands.
## 4 IEC's Challenges
We note that a distributed IEC may not have sufficient computing resources to handle all account requests and full reliance on the cloud poses challenges in providing critical latency services. Therefore, it is self-evident to distribute critical large data/latency accounts to distributed IEC servers while moving account-intensive and delay-tolerant tasks to the DCcloud [64]. The coexistence of a distributed IEC and a cloud core is an important issue and more research is needed for their interactions. _Coexistence of human-to-human traffic and IEC traffic:_ Integrating both traditional human-to-human (H2H) traffic (for example, voice, data, and video) and IEC traffic in the 5G network is a challenging task due to the massive paired IoT connections With the various quality of service requirements and the unique characteristics of the IEC movement [11]. For example, the Internet of Things system consists of human-type devices (HTDs) and machine-type devices (MTDs) that may run different types of applications, for example, MTD with sensors and smart homes, and HTD with video games. While MTDs have a mixed set of QoS requirements, such as latency, reliability, and energy efficiency, HTDs typically require a high-speed rate with a limited energy budget [65]. Likewise, the IEC system must be designed in a way that meets the QoS requirements of H2H traffic while preserving the unique characteristics of M2M traffic (for example, real-time response and context awareness).
## 4 Discussion
The occurrence of errors in the NDVI scenario was sporadic in certain regions. After that, the next step was to plot the NDVI time series curves so that monitoring would be more accurate. At the moment, leaf area index, leaf chlorophyll content, and biomass are some of the indicators that are used with remote sensing technology to track the growth of crops. The main reason why maize NDVI was used in this study was to track the growth of maize on a regional level. Additionally, it is anticipated that future studies will integrate several indicators of maize growth to comprehensively monitor its development, thereby enhancing our comprehension of maize growth dynamics. At the same time, a small-scale experimental area can be set up for future research to improve the reliability of remote sensing technology for tracking crop growth. It is possible to check how well remote sensing technology works for monitoring crop growth by comparing the values that were measured of crop growth in the test area with the data from the remote sensing system. The Google Earth Engine (GEE) platform demonstrates a high efficiency in handling substantial volumes of remote sensing data. By leveraging the GEE cloud infrastructure, we successfully extracted maize cultivation patterns from the Huinong District, spanning the years 2017 to 2020. This enabled us to promptly generate accurate maps depicting the spatial distribution of maize planting areas. However, there are some limitations in terms of computational capacity when using complementary resources on the Google Earth Engine (GEE) platform. In instances where the study area is extensive, the computation process may encounter occasional errors such as \"computation timeout,\" \"computation exceeds the limit,\" or browser crashes. However, it is anticipated that these issues will be progressively resolved through subsequent enhancements. Through further enhancements, there will be a progressive improvement. This study utilized a large dataset obtained from the GEE cloud platform to monitor the growth of maize. The practicality of this approach in terms of its application value was examined. This study can be used as a starting point for future work that aims to create a business-oriented operational system for the remote sensing of agricultural data. This system will be built upon the GEE cloud platform, with the aim of enhancing the effectiveness of monitoring and analytic processes.
# Addressing the Non-Stationarity and Complexity of Time Series Data for Long-Term Forecasts
###### Abstract  
Real-life time series datasets exhibit complications that hinder the study of time series forecasting (TSF). These datasets inherently exhibit non-stationarity as their distributions vary over time. Furthermore, the intricate inter- and intra-series relationships among data points pose challenges for modeling. Many existing TSF models overlook one or both of these issues, resulting in inaccurate forecasts. This study proposes a novel TSF model designed to address the challenges posed by real-life data, delivering accurate forecasts in both multivariate and univariate settings. First, we propose methods termed \"weak-stationarizing\" and \"non-stationarity restoring\" to mitigate distributional shift. These methods enable the removal and restoration of non-stationary components from individual data points as needed. Second, we utilize the spectral decomposition of weak-stationary time series to extract informative features for forecasting. To learn features from the spectral decomposition of weak-stationary time series, we exploit a mixer architecture to find inter- and intra-series dependencies from the unraveled representation of the overall time series. To ensure the efficacy of our model, we conduct comparative evaluations against state-of-the-art models using six real-world datasets spanning diverse fields. Across each dataset, our model consistently outperforms or yields comparable results to existing models.  
Keywords:time series forecasting; non-stationary; weak-stationary; multivariate; univariate; spectral decomposition; ConvMixer +
Footnote †: journal: Applied
## 4 Results
### Infected Density Study Results  
In this study, the severity of damage caused by _Zyginidia pullula_ on maize plants was analyzed using various feature extraction techniques and machine learning classifiers, ANN, RF and SVM. The classification performance for different feature extractors and classifiers is presented in Table 6.  
Among single descriptor models, ResNet50 with ANN achieved the highest accuracy 82.65%, outperforming EfficientNetB2 and DenseNet201. Combining ResNet50 with Gabor or HSV features further improved performance, reaching 83.03% and 82.46% accuracy with ANN, respectively. The best-performing combination was ResNet50 + Gabor + HSV, achieving 83.78% accuracy with ANN and 79.96% with SVM. RF consistently performed lower than ANN and SVM across all feature sets, reaching a maximum accuracy of 71.75%.  
To better understand the model's classification behavior at the class level, the confusion matrix for the ResNet50 + Gabor + HSV combination using the ANN classifier is presented in Table 7. This table provides detailed insights into the true and misclassified instances across low, medium, and high severity categories.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Features** & **ANN** & **RF** & **SVM** \\ \hline Single descriptor & Dense201 & 0.8184 & 0.6637 & 0.8077 \\  & EfficientNetB2 & 0.8083 & 0.6637 & 0.7777 \\  & Resnet50 & 0.8265 & 0.6706 & 0.8058 \\  & Gabor & 0.6493 & 0.6180 & 0.6487 \\  & HSV & 0.7213 & 0.6737 & 0.7044 \\  & GLCM & 0.6844 & 0.6568 & 0.6493 \\ \hline Hybrid of two descriptors & Dense201 + Gabor & 0.8165 & 0.6612 & 0.8027 \\  & Dense201 + HSV & 0.8159 & 0.6574 & 0.7902 \\  & Dense201 + GLCM & 0.8227 & 0.6631 & 0.8058 \\  & EfficientNetB2 + Gabor & 0.8165 & 0.6505 & 0.7933 \\  & EfficientNetB2 + HSV & 0.8215 & 0.6581 & 0.7852 \\  & EfficientNetB2 + GLCM & 0.8202 & 0.6562 & 0.7783 \\  & Resnet50 + Gabor & 0.8303 & 0.6649 & 0.8046 \\  & Resnet50 + HSV & 0.8246 & 0.6612 & 0.7983 \\  & Resnet50 + GLCM & 0.8209 & 0.6687 & 0.8071 \\  & HSV + GLCM + Gabor & 0.7507 & 0.7175 & 0.7000 \\  & HSV + GLCM & 0.7376 & 0.7138 & 0.7100 \\  & HSV + Gabor & 0.6931 & 0.7125 & 0.6718 \\ \hline Hybrid of three descriptors & Resnet50 + Gabor + HSV & 0.8378 & 0.6581 & 0.7996 \\  & EfficientNetB2 + Gabor + HSV & 0.8190 & 0.6574 & 0.7889 \\  & Dense201 + Gabor + HSV & 0.8240 & 0.6612 & 0.7964 \\ \hline Hybrid of four descriptors & Resnet50 + HSV + GLCM + Gabor & 0.8209 & 0.6637 & 0.8040 \\  & EfficientNetB2 + HSV + GLCM + Gabor & 0.8159 & 0.6587 & 0.7833 \\  & Dense201 + HSV + GLCM + Gabor & 0.8353 & 0.6624 & 0.7952 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy scores for the prediction performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Actual\_Predicted** & **Low** & **Medium** & **High** \\ \hline Low & 966 & 7 & 63 \\ Medium & 7 & 137 & 39 \\ High & 108 & 35 & 235 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Confusion Matrix for ResNet50 + Gabor + HSV.
# Artificial Intelligence-Based Discontinuous Reception for Energy Saving in 5G Networks
## 2 Related Work
### DRX in LTE
#### 2.1.2 Sleep State  
A UE turns off the radio components during sleep state in order to save power. UE cannot receive or send the data packets during DRX sleep state but can only monitor the PDCCH for any incoming data. DRX in a sleep state is controlled by the following parameters:  
* ON time
* Short sleep cycle
* Short sleep timer
* Long sleep cycle
* Long sleep timer  
During ON time, a UE monitors the PDCCH. ON time always starts after completion of each sleep cycle. A short sleep cycle is a small duration of time that saves the UE energy by switching off the transceiver part. A short sleep cycle is repeated up to \(N_{sc}\) number of short sleep cycles. After the expiry of \(N_{sc}\), a UE transits to the long sleep cycle. The long sleep cycle and long sleep timer are similar to short sleep cycle and short sleep timer but have a longer time period than the counterpart, respectively.  
Figure 2 delineates the timing diagram of LTE-DRX. As shown in Figure 2, all the data transmission and reception take place during the active state. After reception of each new packet, inactivity timer restarts. In the case that no new packet reaches and inactivity timer finishes the countdown timer, then UE switches to short sleep cycle. After every short sleep cycle, a UE monitors the PDCCH for any incoming packet during the ON time. If no new packet arrives before the completion of short sleep timer, the UE switches to the long sleep cycle and remains there until intimation of the new packet is received. UE transits from a long sleep to idle state if no new packets arrive and the long sleep timer expires.
## 3 Results
### Phytochemical Fingerprinting via GC-MS  
GC-MS chromatograms demonstrated various peaks of volatile organic phytochemicals in all extracts of UD, CBP and IR. On comparison of the mass spectra (MS) of the phytoconstituents with the Wiley library, the ten major phytocompounds were characterized and identified in UD, eleven compounds were detected in CBP, and three phytoconstituents were present in the ethanolic extract of IR, as demonstrated in Figure 3.  
Figure 3: GC–MS chromatograms of the volatile fractions of herbal extracts of **(A)** leaves of _Urtica dioica_ (UD), **(B)** whole plants of _Capsella bursa-pastoris_ (CBP), and **(C)** rhizomes of _Inula racemosa_ (IR).
# Outage Probability Analysis in Relaying Cooperative Systems with NOMA Considering Power Splitting
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} NOMA & Non-orthogonal multiple access \\ SWIPT & Simultaneous wireless information and power transfer \\ WSN & wireless sensor network \\ EH & Energy harvesting \\ IT & Information transmission \\ SIC & Successive interference cancellation \\ PS & Power splitting \\ AF & Amplify-and-forward \\ DF & Decode-and-forward \\ OP & Outage probability \\ ABEP & Average bit error probability \\ \end{tabular}
## 4 Materials and Methods
### Experimental Setup at CNA  
The test campaign was performed at the Van der Graaf 3 megavolts (MV) Tandem accelerator at CNA, providing quasi-mono energetic protons (alphas) at various energies from 0.6 (1) to 5 (5.6) MeV with an energy spread \(\Delta E_{beam}<3\%\). The full detector module was placed on the specific sample holder into the irradiation vacuum chamber (achieving a pressure in the order of \(10^{-6}\) mbar). The Timepix3 Radiation Monitor was masked when the spot beam was focused to set each experimental configuration (i.e., beam energy and flux). Afterwards, the beam was swept using the magnetic scanning system installed in the irradiation beam line, located at 15\({}^{\circ}\) from the main line. During the irradiation, a very low particle flux was requested from the facility to minimize the risk of pile-up in the detector module. In this instance, a particle flux lower than \(10^{4}\) particles/(cm\({}^{2}\)-s) was achieved for these irradiation tests.  
The geometry of the Timepix3 Radiation Monitor detector is shown in Figure 4, along with a sketch of the experimental signature of an incoming charged particle at an angle \(\theta\) releasing charge carriers before stopping. The module presents a first aluminium metalisation layer on top, with a known thickness of \(T_{Al}=500\,\mathrm{nm}\), followed by a silicon dead layer of an unknown thickness \(T_{DL}\). Beyond this, due to the partial depletion of the sensor, the active region does not consist of the entire \(D=300\,\mathrm{\SIUnitSymbolMicro m}\) sensor thickness, but only a partial thickness \(W=250\,\mathrm{\SIUnitSymbolMicro m}\) (as anticipated in Section 3), while the thickness \(W\) depends on the bias voltage and can be calculated, as is performed for the detector under exam, the dead layer is usually unknown due to intellectual property rights of the manufacturing process. A measurement of the dead layer was obtained by performing an angular scan of the deposited energy with a \(597\,\mathrm{keV}\) proton beam, as further described in the next paragraphs.  
Figure 4 also presents the data of proton and alpha range and stopping power in silicon, as extracted from the National Institute of Standards and Technology (NIST) database [45]. Notably, the range of alphas is shorter than the one of protons for the same kinetic energy due to the higher mass. All alpha beams used during the test campaign are hence expected to fully stop within the active layer of the sensor. For protons, assuming an orthogonal incidence on the pixel (\(\theta=0^{\circ}\)), the beam range approaches the thickness \(W\) of the active layer when the beam energy is around \(5\,\mathrm{MeV}\); beyond this, protons are expected to deposit a significant portion of energy in a non-depleted layer of silicon, or even to pass through the entire pixel thickness without stopping. To avoid this, proton beam energies above \(5\,\mathrm{MeV}\) were not used during the calibration campaign, as alphas within the available beam energies have significantly shorter ranges.
# Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention
## 3 Research Methods  
### Tcn  
The Temporal Convolutional Network (TCN) is a specialized variant of the Convolutional Neural Network designed for time series data analysis, introduced by [PERSON] in 2017 [22]. Building upon traditional CNN frameworks, TCNs incorporate unique features such as causal convolutions, dilated convolutions, and residual connections to enhance their performance on sequential data. Unlike conventional CNNs, TCNs excel in capturing long-term dependencies within the data using dilated convolutions, without necessitating a substantial increase in parameters or computational complexity. A notable advantage of TCNs is their inherent parallelism, allowing the simultaneous processing of sequence elements through convolutional operations. This attribute renders TCNs more efficient than recursive models like LSTMs during the training phase. Furthermore, TCNs streamline the model architecture by employing standard convolutional layers in succession, thereby eliminating the need for the intricate gating mechanisms and state update protocols typical of LSTM models. This simplification not only reduces model complexity but also facilitates easier comprehension and implementation.  
To ensure the predictions are based solely on current and historical data, TCNs leverage extended convolution kernel sizes or additional network layers, ensuring the outputs at any given time are influenced only by present and preceding inputs. The inclusion of a residual module, which sums the inputs or outputs of one layer with the subsequent convolutional layer outputs, enhances gradient flow throughout the network. This feature addresses the challenges of gradient vanishing or explosion often encountered in deep network training and promotes the learning of more complex feature representations by enabling direct information transfer.  
The test data were predicted using LSTM and BP, with the results shown in Table 1. Therefore, based on the above considerations, this study adopts the TCN model for time series prediction.  
As depicted in the aforementioned Figure 2, the structural framework of the Temporal Convolutional Network (TCN) is characterized by a kernel size of 3 and a dilation factor sequence of [1; 2; 4; 8].
## 2 Food Safety Management Systems around the World
### Managerial Systematic Approach to Food Safety
#### 2.1.5 Food Fraud and Food Defense  
Food fraud and food defense are major challenges globally, affecting both developed and developing countries. Food fraud involves intentional adulteration or misrepresentation of food products for financial gain, while food defense focuses on safeguarding food products against deliberate contamination.  
Developing countries face greater vulnerabilities due to inadequate regulatory frameworks, weak enforcement, and poor infrastructure. Examples include the sale of misbranded and adulterated food products in India and the 2008 melamine scandal in China, where contaminated milk powder caused public outrage and exposed flaws in their food safety system [52; 53].  
Developed countries also experience food fraud incidents, such as the European horsemeat scandal and the U.S. honey laundering case, emphasizing the need for robust preventive measures. Food defense is a concern in developed countries, as the food supply-chain infrastructure may be targeted by terrorists, posing a risk to national security [34].  
Both developed and developing countries must address these challenges by implementing effective regulations, strengthening enforcement mechanisms, improving infrastructure, and enhancing surveillance and monitoring systems. These measures are crucial to ensure the authenticity and safety of food products [54; 55].
# A Comparative Study on the Laser Welding of Ti6 Al4V Alloy Sheets in Flat and Horizontal Positions
## 4 Discussion
### Effects of Welding Position on Static Tensile Properties  
For laser welds made in Ti6 Al4V, weld toe undercuts may result in high stress concentrations, which in turn lead to crack initiation and propagation during tensile testing [16]. This seems true in this work in the case of the horizontal welds made with higher heat input (2.2 kW, 8 mm/s), which had the largest undercut. However, this was not the case for the flat welds made with the same heat input, which still also fractured through the weld metal, in spite of their smaller undercuts (ref. Table 4). Evidently, the fracture position is determined not only by the degree of undercut but also other factors. It has been indicated that higher heat input will make the microstructure in the (martensitic) weld coarser, which then gives a lower toughness and a greater tendency toward cracking [16]. This could explain why all of the welds produced with a higher heat input failed through the weld metal, while those made with lower heat input failed through the base metal.  
For welding conditions with higher heat input (2.2 kW, 8 mm/s), a greater amount of porosity is formed (ref. Figure 6), and this may also deteriorate weld strength. This strength deterioration is not noted in flat welds, because their porosity levels are low overall. By contrast, a great number of pores are entrapped in horizontal welds, which could then explain the notable decrease in weld strength. Figure 11 shows the comparative fractographs of a horizontal and a flat weld, both made with higher heat inputs. As Figure 11 shows, large pores are seen in the fracture face of the horizontal weld, while porosity is not detected in that of the flat weld.  
Irrespective of welding position, all welds that fractured through the base metal had greater ductility than those that fractured through the weld metal. This is because the martensite of the weld metal has worse ductility than the phases (\(\alpha+\beta\)) of the base metal Ti6 Al4V, as previously reported [17; 18; 19; 20; 21; 22; 23]. In addition, as porosity is to the detriment of the ductility of welds when its amount reaches a certain limit [24], the great amount of porosity present in the horizontal welds could make the ductility even worse.  
Figure 10: Schematic diagrams showing the movement of pores for (**a**) the flat position and (**b**) the horizontal position welding.