esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Physics Analysis of a Magnetorheological Valve Train with Experimental Validation
## 4 Experimental Study
### Test Procedure  
Before the tests begun, an impedance test was conducted to determine the real-time resistance force from the magnetic plate block and compare it with the magnetic simulation results. Then, the currents required for different values of lift were identified. The trend is illustrated in Figure 12. At higher current, the resistance force was higher, and the lift was smaller. When the valve was fully closed, the required current was 0.61 A.  
The experimental tests were divided into three steps: VVL, VVT, and variable valve lift and timing. First, in the variable lift experiments, the valve lift was controlled by adjusting the magnitude of the applied current. The experiments were conducted for 100%  
Figure 11: Experimental setup for testing the proposed MR valve train.  
Figure 12: Required current for various valve lifts.  
Figure 10: Camshaft with a default cam profile.  
(approximately 6.1 mm), 85% (approximately 5.27 mm), 70% (approximately 4.34 mm), and 0% (fully closed) lifts to confirm the possibility of VVL. Second, variable timing tests were conducted for different valve opening points by changing the timing of the current supply, as depicted in Figure 13. Generally, the cam activates the valve-opening angle (\(117^{\circ}\)). If a late opening angle is required (i.e., \(175^{\circ}\)), the full-magnitude current must be supplied until the required opening angle (i.e., \(175^{\circ}\)) is reached. Then, the current is decreased to zero to ensure that the inlet valve starts to open.  
Owing to design restrictions, the valve closing angle could not be varied. Therefore, the valve-closing angle was fixed as the default cam closing angle. Finally, the variable lift and timing experiments were conducted jointly by controlling the magnitude of the applied current and the energization time point. Tests were conducted for 85% valve lift with various valve opening times and 70% valve lift with various valve timings. Then, these results were compared with the results of the dynamic simulations to verify whether the valve could achieve a high degree of freedom in terms of variable timing and lift.
## 5 Conclusions  
In this study, a monostatic off-ground GPR system was tested to monitor the temporal development of soil freezing and thawing processes in a natural setting over a snow-covered surface. For comparison of the GPR-derived information and to get detailed insight into the soil freeze-thaw state, soil temperature and relative dielectric permittivity were recorded by _in situ_ sensors installed close to the GPR setup. Over the course of nine days, several freeze-thaw cycles of the shallow subsurface could be detected by the _in situ_ sensors in terms of soil temperature and dielectric permittivity changes. Additionally, the freezing front could be delineated by the sensor setup.  
The first results of the off-ground GPR system showed that the measurements were stable at low ambient temperature over the course of the nine days. Additionally, a detectable change in the amplitude of the surface reflection of the time domain GPR signal corresponds to the freezing and thawing cycles observed by the _in situ_ sensors. It was shown that for the GPR setup, the snow cover and changes in the snow cover height and physical properties did not substantially influence the GPR reflection, and therefore, the information of surface reflection is from the soil surface. Finally, shallow soil \(\varepsilon_{r}\) was estimated from the inversion of the surface reflection of the GPR signal, which was in good agreement with \(\varepsilon_{r}\) measured by _in situ_ sensors. Overall, the results represent a promising step toward the application of off-ground GPR measurements for noninvasive monitoring of the shallow soil freeze-thaw cycles at the field scale and can be used to calibrate the footprints of different remote sensing products.
## 4 Results
Therefore, we used laser rangefinders to detect the yaw angle. The experimental result of yaw angle errors is shown in Figure 12. It can be seen that the laser rangefinders improved the measurement precision, with a performance of \(\pm 0.5^{\circ}\). Figure 11: Experimental results of the estimation error in 3D coordinates; (**a**) Errors in translation vector \(T\); (**b**) Errors in rotation matrix \(R\), which can be defined by the three angles: \(\theta\) (roll), \(\phi\) (pitch), \(\psi\) (yaw). Figure 12: Experimental results of yaw angle errors with laser rangefinders. The experimental results of pose estimation for three different electric automobiles with different positions and poses are presented in Figure 13. The four control points of the battery were reprojected to the battery based on the pose information acquired by the pose-estimation system. To further verify the validation of the system, the KUKA robot manipulator was used to grasp the battery based on the relative pose information between the robot and the battery. The results show that the robot can complete the battery replacement with the calculated pose.
# A Designed Calibration Approach for the Measurement -While-Drilling Instrument
## 2 Materials and Methods
### Basic Calibration Algorithm
#### 2.2.3 Inner Product Invariance Method (IPI)  
As we know, the inner product of any two vectors does not change along with their transformation from one coordinate frame to another. This principle can also be employed to identify the error parameters between two sensor triplets of different kinds of vector. That is to say, an unknown coordinate frame can be calibrated by another standard coordinate frame. In this paper, the _acc_ frame would be calibrated first to coincide with the _car_ frame; then the _mag_ frame will be post-calibrated by the calibrated _acc_ frame, to be consistent with the _car_ frame.  
It is assumed that a calibrated vector \(\mathbf{u^{b}_{a}}=(u^{b}_{ax}\), \(u^{b}_{ay}\), \(u^{b}_{az})^{\text{T}}\) of the accelerometer is in the car frame, and another vector \(\mathbf{v^{s}_{m}}=(v^{s}_{mx}\), \(v^{s}_{myr}\), \(v^{s}_{mz})^{\text{T}}\) is the raw output of a tri-axial magnetometer, which is in its own sensor coordinate frame. The inner product of the two vectors can be expressed as the following equation, according to the principle.  
\[\mathbf{u^{b}_{a}}\cdot\mathbf{u^{b}_{m}}=\mathbf{u^{b}_{a}}^{\text{T}}\mathbf{u^{b}_{m}}=\mathbf{u ^{b}_{a}}^{\text{T}}\mathbf{u^{b}_{m}}=\mathbf{u^{b}_{a}}^{\text{T}}\mathbf{u^{b}_{ms}}( \mathbf{v^{s}_{m}}-b_{m})=\mathbf{u^{b}_{a}}^{\text{T}}\mathbf{u^{b}_{ms}}\mathbf{v^{s}_{m}}- \mathbf{u^{b}_{a}}^{\text{T}}\mathbf{u^{b}_{ms}}\mathbf{v^{s}_{m}}=\|\mathbf{G}\|\|\mathbf{M}\|\cos \xi=const \tag{19}\]  
where \(\xi\) is the co-angle of the earth's magnetic dip and \(L^{b}_{ms}\) and \(b_{m}\) are the undetermined coefficient matrix and bias vector for the tri-axial magnetometer. When Equation (19) is expanded with the two vectors, \((\mathbf{u^{b}_{a}}\) and \(\mathbf{v^{s}_{m}})\), and the data of the measuring points in the two datasets (\(\mathbf{D^{b}_{a}}\) and \(\mathbf{D}_{m}\)) are substituted in Equation (19), an overdetermined equation will be established. The solving method is the same as before, and the result needs to be restructured to get the matrix and bias vector. The attention of this method in the process of applying to Attitude and Heading Reference System would not be repeated here, because they were clearly discussed in [31].
## 4 Experimental Setup and Results
### Illegal Graffiti Detector
#### 4.2.2 Results from the Graffiti Detection Model  
Figure 19 displays some successful graffiti detections through the model.  
The Intersection over Union (IoU) was used to evaluate the model's performance. This metric is used to measure the accuracy of an object detector because it is calculated by dividing the overlap area by the union area between the predicted bounding box and the ground-truth bounding box.  
Figure 18: An example of an image where the joining of bounding boxes was necessary.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hyperparameter** & **Value** \\ \hline Model Architecture & Faster R-CNN ResNet50 FPN \\ Learning Rate & 0.005 \\ Momentum & 0.7 \\ Weight Decay & 0.001 \\ Gamma & 0.3 \\ Learning Rate Step Size & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Values for the optimized hyperparameters.  
The diagram depicted in Figure 22 synthesizes the workflow followed in this research work. It reached the most suitable models for the problem previously described within those tested with the help of transfer learning and data augmentation.  
The workflow allowed the development and training of two models: (1) an image classification model that classifies an image according to three classes: street art, illegal graffiti, and no graffiti classes; and (2) a graffiti identification model that provides the location of illegal graffiti identified by the classification model. For the image classification problem, ResNet50 showed the best results, presenting an overall accuracy of 81% and an F1-score of 86%, 81%, and 66% for the street art, illegal graffiti, and no graffiti classes, respectively. Meanwhile, for the illegal graffiti identification model, the best performance was obtained using a Faster R-CNN architecture ResNet50 FPN, having 0.703 of the IoU in the test set.
# In-Vehicle Data for Predicting Road Conditions and Driving Style Using Machine Learning
## 7 Results
### Driving Style Classification Results  
Table 8 shows the accuracy, the precision, the recall and the F-score of driving style classification and Table 9 shows the confusion matrix of the predictions.  
Table 10 shows the feature importance for driving style classification. Fuel consumption was more important for decision trees and random forests, while manifold air pressure was more important for SVM.
## 4 Experiments and Analysis
### Experimental Setup and Implementation Details  
#### 4.2.1 Experiment Details  
All experiments were implemented under the Pytorch [51] framework and accelerated on the NVIDIA RTX A100 GPU (40 GB). During training, the training epochs were set to 400, and the batch size was 30. We used the ADAM optimizer as the optimization algorithm. The learning rate was set to \(4\times 10^{-4}\) first, and the default values of \(\beta_{1}\) and \(\beta_{2}\) were 0.5 and 0.999, respectively. We used CyclicLR to adjust the learning rate, with an initial momentum of 0.9 and 0.999. Data augmentation included horizontal flipping, random cropping, and randomly rotating the image to \(90,180,\) and \(270^{\circ}\).  
Throughout the training process, we standardized the resolution of both the input and output to \(256\times 256\). From the UIEB dataset, we randomly selected 800 pairs of original images and their corresponding clear images to compose the training set for our model.  
During the training validation phase, we employed the remaining 90 images from the UIEB dataset (referred to as T90) to evaluate our method's performance on degraded images. For the formal testing stage, we continued to use the T90 dataset; however, in contrast to the training phase, we refrained from making any alterations to the format and size of the images, aiming to restore the model's capability to enhance real underwater images. Furthermore, to assess the generalization capacity, we conducted evaluations using the C60 and U45 datasets with various methods. To assess the generalization capability of our model, some comparative experiments were conducted on the EUVP dataset. We used the ImageNet sub-dataset under the EUVP dataset to contain 3700 pairs of images for training and randomly sampled 1000 pairs of images from the Dark sub-dataset for testing.
# Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
## 2 Methodology
### Node Extraction and Edge Construction
#### 2.3.2 Node Extraction and Edge Construction for Hallways
This process continues until the distances between all intersection points are greater than the set threshold, ensuring that the geometric relationships within the hallway are accurately reflected. Based on our prior knowledge, not all hallways require sub-node extraction. To avoid unnecessary sub-node extraction and improve the efficiency of the algorithm, this study classifies hallway network extraction into two types based on the length of the hallways. For hallways with a length less than or equal to 4 m, we extracted only the centroid as the  
Figure 8: An illustration of multiple sub-node extraction for longer hallways. Figure 7: Principal component analysis (PCA) of the hallway point cloud: red arrow for the main direction, yellow for the secondary. representative node. However, for hallways with a length longer than 4 m, we applied sub-node extraction to enhance the accuracy of the navigation network. In this way, a short hallway is represented by a single node, which is connected to its associated door nodes to form the corresponding edges. In contrast, a longer hallway is represented by multiple sub-nodes within the navigation network, with each sub-node connected to either adjacent doors or neighboring sub-nodes, thereby forming the edges of the network.
# Efficient Next-Hop Selection in Multi-Hop Routing for IoT Enabled Wireless Sensor Networks
## 4 Proposed Solution  
In this section, we present the Efficient Multi-hop Routing Protocol (EMRP) for data exchange in IoT-assisted WSNs. It analyzes the residual energy of nodes to decide about the routing paths. It improves the data exchange between source and destination. The proposed system model utilizes an efficient topology formation mechanism to enhance the lifetime of the network. Moreover, an efficient data collection algorithm considers a hybrid network where every node has distinct features as per the deployment in a topology. For development, the key objectives of our proposed protocol are as follows: (i) More reliable time for data gathering; (ii) Reliable data transmission time; (iii) Efficient energy consumption; (iv) Increased network lifetime; (v) Scalability for handling communication cost; (vi) Flexibility for adding/removing nodes. Our work minimizes energy consumption during data collection and transmission by selecting the suitable next hop. We present a next-hop selection-based efficient data collection algorithm to maximize the network lifetime. A list of notations for EMRP is provided in Table 1.  
Next, we present the proposed work and its methodology. To answer the above-mentioned issues, our proposed solution efficiently handles the data collection, data transmission, and energy issues. The presented algorithm enhances the network lifetime as compared with the data gathering mechanism.  
Figure 2: Energy level of nodes at (**a**) t = 1 and (**b**) t = 3.
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPPThird Generation Partnership Project  
5 GFifth generation  
AoAAngle of arrival  
AoDAngle of departure  
DANL Displayed average noise level  
EIRP Effective isotropically radiated power  
EMElectromagnetic  
FSPL Free space path loss  
HPBW Half power beam width  
IEEEInstitute of Electrical and Electronics Engineers  
IFIntermediate frequency  
ISMIndustrial, scientific, and medical  
ITUInternational Telecommunication Union  
KEDKKnife-edge diffraction  
LOSLine-of-sight  
MACMedium Access Control  
MCSModulation and coding scheme  
MDPIMultidisciplinary Digital Publishing Institute  
MIMOMultiple-Input Multiple-Output  
MoMMethod of moments  
MPCMultipath components  
NLOSNon-line-of-sight  
PERPacket error rate  
PHYphysical layer  
PLPath loss  
POPhysical optics  
RFRadio frequency  
RSSI Received signal strength information  
RX Receiving  
RMSRoot mean squared  
SNRSignal to noise ratio  
TCPTransmission Control Protocol  
TGTerragraph  
TXTransmitting  
UTDUniform theory of diffraction
## 3 Results and Discussion
### Sensory Evaluation
#### 3.1.2 Canonical Variate Analysis  
TDS curves only provided dominance rate, which can be translated to % of panel agreement and does not measure intensity. It was also shown on Figures 1 and 2 that oxidized was the most dominant attribute throughout consumption, which unfortunately did not discriminate samples. Hence, a CVA was applied in the TDS duration data in order to provide a clearer interpretation of the TDS results. In this study, both chilled (Figure 3a)and frozen-thawed (Figure 3b) samples were discriminated by CVA with a high variance for sensory data of 94.00% and 90.45%, respectively.  
For the chilled meat samples (Figure 3a), Hotelling-Lawley MANOVA (F\({}_{(135,192)}\) = 29.655; \(p<0.01\)) showed significant differences between the samples in terms of the temporal flavour attributes measured by TDS. F1 explained 57.34% of the variance, separating the meat samples in terms of PEF processing, where negative scores of the CVA corresponded to control non-PEF samples, and positive scores corresponded to PEF treated samples. F2 (36.67%) further distinguished between the 0- and 7-days storage meat samples. Positive scores of F2 corresponded to 7 days storage samples, while negative scores corresponded to 0-days storage samples. 7 days samples were correlated with oxidized flavour. In contrast, control samples (non-PEF) at 0 days were positively correlated with meat and juicy. PEF treated samples stored for 0 days were correlated with browned and livery attributes.  
In terms of frozen-thawed meat samples (Figure 3b), F1 explained 63.06% of the variance. Meat samples were separated along F1 in terms of PEF processing treatment, where negative scores of the CVA corresponded to non-PEF control samples (except FT-C7), and positive scores corresponded to PEF treated samples. F2 (27.39%) further discriminated almost all lamb samples between the 0- and 7-days storage. Positive scores of F2 were associated with 7 days storage samples (except FT-C0 and FR-C0), while negative scores were associated with 0 days samples (except FR-P7, FSk-P7, FT-P7, FSd-P0, and FK-P7). Samples treated with PEF were correlated with oxidized flavour, especially FRib-P7. In contrast, control samples (non-PEF) were positively correlated with meaty and juicy, especially at 0-days storage. PEF treated 0-days samples were correlated with browned and livery attributes.  
Figure 3: Canonical Variate Analysis Biplot of dominance durations of sensations of PEF and non-PEF treated cooked chilled (**a**) and frozen-thawed (**b**) lamb meat of different cuts. Hotelling–Lawley trace MANOVA test showed significant product differences ((**a**): F\({}_{(135,192)}\) = 29.655; \(p<0.001\); (**b**): F\({}_{(135,192)}\) = 12.043; \(p<0.001\)) based on sensory attributes. Sd = Shoulder; Rib = Rib; L = Loin; K = Knuckle; R = Rump; S = Shank; T = Topside; C: control; P: PEF; 0 = 0 days storage; 7 = 7 days storage/post-processing storage.
## 3 Results
### Latency Sensibility Analysis
#### 3.2.2 Symptoms  
Non-accurate images do not let the user's brain to fully elaborate a 3D view of the model; the partial depth perception is then perceived by the user as a blurred view caused by physical factors, causing symptoms like, but not limited to:  
* Nausea;
* Wanes;
* Cold sweating.  
On the other hand, it is worth noticing that the holographic table motion sickness presents less severe symptoms than counterparts such as Virtual Reality headsets and biomechanical simulators [27] due to their lightweight glasses and the still perception of reality. By testing the application on several students attending the laboratory, the most common symptom resulted in dizziness, causing the user to remove the lightweight glasses and rapidly regain consciousness over the surrounding environment.
## 5 Results and Discussion
### RQ3: What Are the Parameters That Are Optimized to Enhance the Performance of LoRaWAN Networks?
#### 5.4.1 Interference and Congestion Management  
In the studies [30; 41], the authors have focused on the challenge of managing interference and network congestion, particularly in dense deployments. The author [51] stated the impact of interference and collision when multiple LoRa devices are involved, especially in dense network deployments. LoRaWAN operates on unlicensed frequencies, and that leads to a high risk of collision and interference from both LoRa and non-LoRa sources. The studies emphasized the need for more effective solutions to mitigate interference, which could include developing advanced ML algorithms that dynamically adjust network parameters to optimize traffic flow and reduce collision rates. For instance, the author [30] suggests exploring multi-agent systems for coordinated resource allocation, which could significantly improve the network's resilience to interference.
# Innovation Potentials and Pathways Merging AI, CPS, and IoT
## 4 Discussion of Innovation Fields
### Field E--'Integrated SCM Sytems'  
The technologies and systems described with the fields 'C' and 'D' will also increasingly start to merge. Such _'integrated SCM systems'_ will be characterized by the real-time analysis, simulation and decision from both sides, manufacturing and transportation/warehousing perspectives. This will enable a new level of supply chain optimization where for example marginal cost information for required warehousing or transportation capacities will be taken into account together with the marginal cost rates and technology situation in manufacturing (cp. [63; 64]). Therefore, new decisions might emerge where for example scheduling decisions in manufacturing are based on high warehousing costs of specific raw materials (prioritizing the production schedule for those products with higher cost-levels in warehousing their specific raw materials). Also, in another scenario, production schedules might dynamically change due to rising transportation prices for specific raw materials. Therefore, a new total cost and quality (as well as possibly sustainability, [65]) perspective throughout the whole supply chain will be possible by applying such integrated SCM systems with the help of AI analysis and decision.