esa-sceva/satcom-chunk-collection huggingface dataset:
# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### What are the Main Problems Deep Learning Is Being Used to Solve?
#### 3.1.5 Anomaly Detection  
Future 5G networks will lead with different types of devices over heterogeneous wireless networks with higher data rates, lower latency and lower power consumption. Autonomous management mechanisms will be needed to reduce the control and monitoring of these complex networks [37].  
Anomaly detection systems are important to identify malicious network flows that may impact users and the network performance. However, developing these systems remains a considerable challenge due to the large data volume generated in 5G systems [38; 39].  
Four articles addressing the anomaly detection problem using deep learning in 5G were identified in this systematic review. In [38; 40], the authors deal with cyber security defense systems in 5G networks, proposing the use of deep learning models that are capable of extracting features from network flows and the quick identification of cyber threats.  
In [10; 41], the authors proposed a deep learning-based solution to detect anomalies in the network traffic, considering two types of behavior as network anomalies: sleeping cells and soared traffic. Sleeping cells can happen due to failures in the antenna hardware or random access channel (RACH) failures due to RACH misconfiguration, while soared traffic can result in network congestion, where traffic increases but with relatively smaller throughput to satisfy the users' demand. Recall that RACH is the channel responsible for giving users radio resources so when RACH is not working properly we effectively have a sleepy cell with no transmission activity taking place.
###### Abstract  
Adopting Industry 4.0 technologies across sectors is critical for enhancing operational efficiency and competitiveness. However, empirical studies on the determinants of such adoption have yielded inconsistent results. This study conducted a systematic review and meta-analysis based on the Technology Acceptance Model and its extensions. A total of 47 empirical studies were extracted from five academic databases and included in the meta-analysis. The findings confirmed that perceived usefulness (PU), perceived ease of use (PEOU), and social influence (SI) significantly and positively influenced behavioral intention (BI) toward adopting Industry 4.0 technologies. Among them, PU exhibits the strongest correlation with BI (r = 0.528), followed by PEOU (r = 0.469) and SI (r = 0.487). Subgroup analyses based on geographical region, organization size, and sector showed consistent significance in effect sizes, although moderating effects across subgroups were not statistically significant. The findings of this study contributed to the literature with an in-depth understanding of the acceptance of Industry 4.0 technologies in various sectors and how moderators influence the acceptance. Practically, the findings provided evidence-based guidance for policymakers, technology developers, and business leaders to tailor adoption strategies and foster digital transformation across sectors.  
Keywords:Industry 4.0 technologies; technology acceptance model; systematic review; meta-analysis +
Footnote †: journal: Applied sciences
## 1 Introduction
### Feature-Based Classification  
IDSs employ traffic packets in network flows, by source/destination IP, source/destination port, timestamp, and protocol [9] to function correctly and identify an anomalous activity effectively [10]. In another research study [11], the authors mentioned the full-flux exchange of successive network packets between an IP address port and a port at a different IP address using a specific application protocol is a unidirectional exchange of packets. For efficient flow management, machine learning operations, and processing traffic classification is therefore necessary.  
Flow technique is a recent development that can overcome various additional restrictions, such as unsaved port numbers, and encrypted packets. In the flow-based method, flow features are used as discriminators to exploit the variety of network packets and various classes [12]. In addition, due to the lack of payload, flow-based methods are preferable rather than payload methods for data protection problems. A flow-based classification of traffic uses high precision, accuracy approaches, and a vast study field.
# Enhanced Bit Repair IP Fast Reroute Mechanism for Rapid Network Recovery
## 6 Discussion of the Obtained Results
### Testing in Real Networks  
Currently, only a few existing IP FRR solutions, such as Equal Cost Load Balancing and LFA, are integrated into real router operating systems (for example, Cisco IOS or Juniper JunOS). Therefore, the implementation of test setups to obtain some real measurements is complicated. Testing the new FRR mechanism using a suitable network simulator such as OMNeT++ or NS2 seems to be the most appropriate solution. We have decided to develop and test new FRR solutions in the OMNeT++ simulator, because of its reliability, good framework base, and large community. Currently, we have successfully implemented some of the existing FRR solutions, such as LFA and R-LFA, into the OMNeT++ simulator. Therefore, the evaluation of the EB-REP mechanism was performed in the OMNeT++ simulator and we used it to compare the EB-REP with other FRR solutions.
## 4 Materials and Methods
The LOS displacement raster has been resampled to a pixel dimension of \(20\times 30\) m. Using the GMT5 SAR program, three components of the view geometry were determined (i.e., look vectors: horizontal north-south and east-west, vertical up-down) to calculate the incidence angle for each pixel representing the area of deformation. Based on the geometrical relationships between the Sentinel-1A/B satellites orbit and the measured point on the Earth's surface, the incidence angle was determined using the Formula (1) [65]:  
\[\theta=arctg\bigg{(}\frac{\sqrt{look\_N^{2}+look\_E^{2}}}{look\_U}\bigg{)} \tag{1}\]  
where:  
\(\theta\) is incidence angle;  
\(look\_N\) is horizontal north-south of look vector;  
\(look\_E\) is horizontal east-west of look vector;  
\(look\_U\) is vertical up-down of look vector. The value of the incidence angle for Sentinel-1A/B satellites ranges from 29\({}^{\circ}\) to 46\({}^{\circ}\). In the next step vertical displacements were calculated using the following Formula (2) [66; 67]:  
\[d_{V}=\frac{d_{LOS}}{\cos(\theta)} \tag{2}\]  
where:  
\(d_{V}\) is the vertical displacement;  
\(d_{LOS}\) is the LOS displacement;  
\(\theta\) is the incidence angle. Once vertical displacements for each image pair were calculated, spatial statistics with the use of GIS raster map algebra's cell statistic functions were used to determine shape of the deformation area after each of the induced shocks. The functions calculate a per-cell statistic from multiple rasters, such as maximum, minimum, mean or range values. In our case, the number of input rasters (vertical displacement maps) varied from 2 to 8 depending on the event, and the shape of deformation (vertical displacement at each location) was determined as mean value of all spatially congruent cells in raster maps. These results were used in the next stage alongside descriptive summaries of vertical displacements to reflect the shape of terrain deformations. Descriptive statistics calculated in the study included: minimum, maximum, and mean vertical displacement values. The results have been presented in the form of graphs, and Table 3 shows the Results part. In addition, the Pearson correlation coefficient test statistic was calculated to assess statistically the potential relationship between the maximum observed value of vertical displacements, extent of deformation, and energy of each event.
# PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables
## 4 Results
### Results of Indoor Location for PlaMoS Remote Node  
Results show that the centroid localization algorithm used by PlaMoS was able to locate the remote mobile node to within 7.6 m. To obtain this result, the data packages transmitted were reviewed in real-time while the patient moved among the rooms. Figure 19 shows the variance in meters between real location of the patient and the location saved it by PlaMoS.  
Figure 18: Values obtained by the PlaMoS remote node.  
The accuracy depends on the number of nodes in the WSN infrastructure. The location accuracy increases proportionally with the number of network nodes.
## 4 Implementation
### Proposed Scheme
#### 4.4.3 Maximum Communication Distance  
The proposed scheme can communicate with multiple users with only a single camera using RoI to detect the source of LEDs. However, the number of users is limited because of the difference in the focal length, transmit distance, and distance between two consecutive LEDs. Figure 13 shows this relationship.  
The relationship between the focal length and work distance [27] is:  
\[\frac{d[\text{m}]}{\text{D\_LEDs}[\text{m}]}=\frac{f[\text{mm}]}{\text{d\_LEDs }[\text{mm}]} \tag{7}\]  
where the focal length of the camera and the work distance is represented by \(f\) and \(d\), respectively. D_LEDs is the distance between two LEDs in a realistic experiment, and d_LEDs is the distance between two LEDs in the image. Equation (7) can be used to calculate the minimum distance between two LEDs. By applying the Nyquist theorem, RoIs can detect the minimum distance between two LEDs in two pixels. The value of the minimum distance between two LEDs is calculated as follows:  
\[\text{D\_LEDs}[\text{m}]=2\;\times\;\frac{d[\text{m}]}{f[\text{mm}]}\;\times \;\frac{h\_sensor\_image[\text{m}]}{N\_pixel\_image\_row} \tag{8}\]  
where \(h\_sensor\_image\) and \(N\_pixel\_image\_\) are the height of the sensor image in millimeters and the number of pixel rows in the image, respectively. The theoretical distance is simplified by \(d\) [m] since the work distance also depends on the light power. The interference occurs when the power of the transmitter is smaller than that of the noise lights. Due to this reason, we can define the maximum number of users based on the monitoring environs.
## 3 Analysis and Validation of the Physical Model
### Validation with Varying Speeds and Prewhirl Angles  
The set of performance curves of PO003 was calculated following the work of the authors of [26]. The plot shown in Figure 5a displays the measured data (Data) and the characteristics calculated by the physical model (Physically), assuming a mechanical power of \(P_{m}=240\) W. In addition to the solid curves calculated by the physical model, dashed curves representing the calculated curves using the affinity laws (Affinity) are also shown. According to the authors of [12], the mechanical power has a large influence on the calculation of the set of performance curves. For the calculation, it was assumed that \(P_{m}\propto n^{1.3}\)[10]. The physical model effectively captures the set of performance curves when mechanical power is correctly defined. Figure 5b illustrates the measured data points (Data) and the characteristic curve calculations derived from the physical model (Physically) for PO001 (left-hand side) and PO002 (right-hand side). The motor frequency (\(f_{M}\)) has been adjusted to three different values \(f_{M}\in[40\,\mathrm{Hz}\), \(45\,\mathrm{Hz}\), \(50\,\mathrm{Hz}]\), thus changing the pump speed. The physical model can also accurately replicate the set of performance curves for these two pumps. In contrast to PO003, the speeds of PO001 and PO002 have been slightly reduced. The mechanical power of these two pumps would be determined via measurements.  
Figure 5: **(a)** Data points (Data) of PO003 with values from [26] and calculated characteristics from the physical model (Physically) and the affinity laws (Affinity); **(b)** data points (Data) of the set of performance curves of PO001 (left-hand side) and PO002 (right-hand side), as well as the characteristic curves calculated by the physical model (Physically); **(c)** data points (Data) of PO001 (left-hand side) and PO002 (right-hand side) and their characteristics—calculated by the physical model (Physically)—for varying prewhirl angles (\(\Delta\epsilon_{1}\)).  
Figure 5c shows the measurement data points (Data) and the characteristic curves (Physically) for PO001 (left-hand side) and PO002 (right-hand side), calculated by the physical model at different prewhirl angles (\(\Delta a_{1}\)). The characteristic curves show a high degree of correspondence with the measured values. In the case of PO002, the physical model exhibits a deviation within the range of \(Q=0-30\) m\({}^{3}\)/h. According to this, the recirculation power (\(P_{Rez}\)) is also influenced by the prewhirl bodies. It is notable that the recirculation performance increases for both positive and negative prewhirl angles. The recirculation power also changes for PO001. In comparison to PO002, there are only insignificant changes.
## 2 Description of the Project and Ground Conditions
### Field Test Conditions
#### 2.2.2. _Arrangement of Measuring Points_  
The purpose of the layout without the isolation trench is to measure ground vibration at different distances from the impact point under standard compaction energy and to serve as a control group with the layout of the vibration isolation trench. Table 3 shows the horizontal distance between the tamping and measuring point. Figure 5 shows the layout of the measurement, and the positions of the sensors were determined according to the rule of being denser closer to the impact point. At the same time, to measure the response of the dike, three-axis acceleration sensors were installed at the toe, middle, and crest of the dike. The impact point was hit three times, and the time-domain curve data of the vibration acceleration caused by strong compaction at each measurement point was detected and collected.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Soil Layer** & **Thickness** & **The Allowable Bearing Capacity f\({}_{\text{sk}}\)** & **Compressive Modulus** \\  & **(m)** & **(kPa)** & **(MPa)** \\ \hline \#1 plain fill & 2.23 & Not suitable for direct use as a natural foundation & - \\ \#2 clay & 1.94 & 100–120 & 2.83 \\ \#3 silty clay & 9.15 & 50–70 & 2.86 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Recommended values for the allowable bearing capacity and compression modulus of foundation soil.  
Figure 4: The monitoring system and sensors.  
The layout with the isolation trench was designed to investigate the effects of the vibration isolation trench on the ground vibration induced by strong compaction and the isolation effect on the dike. Figure 6 shows the profile layout of the measurement. Trenches, as shown in Figure 7, with a length of 16 m, a width of 1 m, and a depth of 1 m, were set up at a distance of 10 m from the compaction point along the #2 measuring line. The sensor layout followed the same principle of arranging them from dense to sparse, and the distance between the trench and the adjacent two sensors was 2 m. In theory, the closer to the isolation trench, the more obvious the isolation effect, but the soil near the isolation trench is prone to collapse, which may cause damage to the foundation. Therefore, installing sensors too close to the isolation trench can lead to invalid measurement results.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{8}{c}{**The Horizontal Distance between Tamping and Measuring Point (m)**} \\ \cline{2-10}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline \#1 & 8 & 12 & 16 & 20 & 25 & 30 & 35 & 40 & 52.5 & 65 \\ \#2 & 8 & 12 & 16 & 20 & 25 & 30 & 35 & 40 & 52.5 & 65 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The horizontal distance between tamping and measuring points on the survey lines.  
Figure 5: The layout of the measurements without the isolation trench.  
Figure 6: The layout of the measurements with the isolation trench.
# Mycelium-Based Thermal Insulation for Domestic Cooling Footprint Reduction: A Review
## 3 Mycelium as a Base for Thermal Insulation
### Mycelium: Insights into its Applications
Furthermore, the prevalent utilization of conventional insulation  
Figure 4: Schematic representation of mycorrhiza: Mutualistic association between tree roots and fungal mycelium [10]. materials often manifests as ill-suited for the exigencies of prevailing climatic extremities, thereby amplifying inherent operational inefficiencies. This review paper aims to thoroughly examine the utilization of mycelium-based thermal insulation to significantly reduce the environmental impact associated with domestic cooling. The paper embarks on a comprehensive journey by investigating the multifaceted dimensions of mycelium, encompassing its concealed attributes within the realm of fungi, its intricate growth process, diverse applications across various fields, and its ecological significance in nutrient exchange, decomposition, and bioremediation. Mycelium-based composites' mechanical, physical, and chemical characteristics are systematically scrutinized, contributing to a comprehensive understanding of these materials' structural and functional properties. In tandem, an inclusive overview of conventional insulation materials is presented, facilitating a holistic evaluation of mycelium-based solutions within the larger context of insulation technology. The paper particularly emphasizes the potential of mycelium to offer sustainable and innovative avenues for indoor temperature regulation, efficient particulate matter absorption, and inherent biodegradability.
## 3 Control Strategy
### Off-Center Arm Steering Angle Tracking Error Control Strategy  
In previous research, bridge circuits [40] were used for off-center arm angle tracking control. However, the bridge circuit-based control system exhibits large steady-state errors. To solve this problem, the fuzzy PI control method is employed. It is also possible to use PID for steering angle tracking control, but its parameter adjustment is more complex than PI control [41; 42]. From the previous test, PI control was sufficient for steering angle tracking control. Therefore, a fuzzy PI controller was employed to improve processing efficiency. Fast steering response can be achieved and angle error can be reduced via the auxiliary PI algorithm and self-tuning of the proportional and integral coefficients. The fuzzy method still uses a two-dimensional fuzzy controller and the inputs are the errors of the steering angles and the error rate (\(E_{t}\) and \(\Delta E_{t}\)). The outputs are the \(K_{P}\) and \(K_{I}\) corrections for PI control. In this controller, the seven fuzzy linguistic terms are introduced to describe the input and output variable values: input variable 1: \(E_{t}\)\(\in\)[NB, NM, NS, Z, PS, PM, PB]; input variable 2: \(\Delta E_{t}\)\(\in\)[NB, NM, NS, Z, PS, PM, PB]; output 1: \(K_{P}\)\(\in\)[NB, NM, NS, Z, PS, PM, PB]; and output 2: \(K_{I}\)\(\in\)[NB, NM, NS, Z, PS, PM, PB]. The membership function curves are shown in Figure 7. The membership functions were also fine-tuned experimentally. Figure 8 shows a surface plot of the input and the output fuzzy logic variables of \(\Delta K_{P}\) and \(\Delta K_{I}\).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\Delta K_{P}\)} & \multirow{2}{*}{\(\Delta K_{P}\)/\(\Delta K_{I}\)/\(\Delta K_{I}\)} & \multicolumn{8}{c}{\(\Delta E_{c}\)} \\ \cline{3-10}  & & **NB** & **NM** & **NS** & **Z** & **PS** & **PM** & **PB** \\ \hline \multirow{5}{*}{\(E_{c}\)} & NB & PB/NB/PS & PB/NB/NS & PM/NM/NB & PS/NM/NB & PS/NS/NB & Z/Z/NM & Z/Z/PS \\  & NM & PB/NB/PS & PM/NB/NS & PM/NM/NB & PS/NS/NM & PS/NS/NM & Z/Z/NS & NS/Z/Z \\  & NS & PM/NM/Z & PM/NM/NS & PM/NS/NM & PS/NS/NM & Z/Z/NS & NS/PS/NS & NS/PS/Z \\  & Z & PM/NM/Z & PM/NM/NS & PS/NS/NS & Z/Z/NS & NS/PS/NS & NM/PS/NS & NM/PM/Z \\  & PS & PS/NS/Z & PS/NS/Z & Z/Z/Z & NS/PS/Z & NS/PS/Z & NM/PM/Z & NM/PM/Z \\  & PM & PS/Z/PB & Z/Z/NS & NS/PS/PS & NM/PM/PS & NM/PM/PS & NM/PM/PS & NB/PB/PB \\  & PB & Z/Z/PB & Z/Z/PM & NM/PS/PM & NM/PM/PM & NM/PB/PS & NB/PB/PS & NB/PB/PB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fuzzy rules for \(\Delta K_{P}\), \(\Delta K_{I}\), and \(\Delta K_{D}\).  
Figure 6: Membership function curve for (**a**) coupling error and (**b**) its rate. NB, negative big; NM, negative medium; NS, negative small; ZE, zero; PS, positive small; PM, positive medium; PB, positive big.  
Figure 7: Membership function curve for (**a**) steering angle error and (**b**) its rate.
# AU-Guided Unsupervised Domain-Adaptive Facial Expression Recognition
## 3 Methodology  
### Overview of AdaFER  
The goal of our method is to mitigate the domain gap, including inconsistent annotations and different imaging conditions. Considering the relationship between subjective facial expressions and objective action units, we propose a simple yet efficient AU-guided unsupervised domain-adaptive facial expression recognition (AdaFER) method. Figure 2 illustrates the pipeline of our AdaFER, which mainly consists of two crucial modules: (i) an AU-guided annotation (AGA) module and (ii) an AU-guided triplet training (AGT) module. Given images from a source domain and a target domain, we first utilize a pretrained AU detection model to extract the AUs coding for images from both domains. Then, the AGA module assigns a soft/hard pseudo-label for each image in the target domain by comparing the AUs between the source and target domains. For example, one of the AGA strategies is to assign a target image with a hard label that is the same as a source image if both images have equal AUs. Meanwhile, we mine triplets among the source and target domains according to the AUs. For example, given an anchor image in the target domain, a positive image in the source domain is the one with equal AUs, and a negative image is the one with different AUs. We jointly train the FER model on the source domain with ground truths and on the target domain with pseudo-labels and triplets.