esa-sceva/satcom-chunk-collection huggingface dataset:
# Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging
## 3 Results
### Minimization Process of Measurement Uncertainty in Medical Imaging  
Figure 12 displays the measurement uncertainty minimization process using standardized phantom data and results from an analysis of measurement uncertainty from medical imaging.  
Environmental variables that are classified as uncertainty elements when measuring medical imaging can have an effect on MRI T1 values. In order to calibrate the measurement values, the phantom T1 value can be calibrated based on the phantom's standardized T1 value affected by environmental variables. Through this process, the measurement value calibration function can be deduced and applied to the patient's measured MRI T1 value. As a result, the calibrated MRI T1 value is attained with the respective value being the same regardless of where the MRI T1 medical imaging was collected as long as the same standardized phantom T1 value is applied.  
Figure 11: Results of Cardiovascular MRI Imaging with De-identifying Methods.  
Figure 10: Original Cardiovascular MRI Imaging.  
For example, in order to detect diseases in specific areas such as cardiovascular symptoms, the patient MR imaging and phantom are captured together. In this case, environmental variables such as sequence design or protocol parameters can be applied as factors of uncertainty and affect the MRI T1 value. We can use a standardized phantom that displays the MRI T1 based value on cardiovascular symptoms. By using the phantom standardized MRI T1 value of cardiovascular symptoms saved on a HL7 FHIR server, we can calibrate the phantom MRI T1 value. By applying the same level of calibration as the phantom MRI T1 value to the patient's MRI T1 measurement value, we can derive the patient's calibrated MRI T1 value.  
Figure 13 shows that when such a process is applied, different medical imaging from different institutions can be decoded on the same quantifiable levels.  
If there is information about the phantom that reflects the characteristics of the other measurement site, it can save the phantom data and use the same method.  
Through the quantified phantom, which minimizes measurement uncertainty elements, MRI results can be translated into quantitative MRI (qMRI) and provide high quality data to artificial intelligence medical imaging diagnosis equipment. Moreover, this process would contribute greatly to clinical practices by allowing the earlier detection of diseases, the replacement and supplementation of biopsies, and precise differentiation of diseases due to numerical differences among others [24].  
Figure 12: Minimization Process of Measurement Uncertainty in Medical Imaging.  
Figure 13: Example of Minimization of Medical Imaging Measurement Uncertainty Elements.
# Diffuse-Field Equalisation of Binaural Ambisonic Rendering
## 4 Evaluation
### Spectral Difference  
Basic spectral difference, calculated from the difference between the magnitude value of each frequency bin of the fast Fourier transforms (FFTs) of two audio files, is not an accurate metric for human auditory perception on its own, as the human auditory system's sensitivity differs greatly depending on relative amplitude, frequency and temporal aspects [38]. Therefore, the objective spectral difference calculation aimed to more accurately represent the perceptual effect of absolute spectral differences, and is here on in referred to as perceptual spectral difference (PSD) [39].  
PSD was calculated between the Ambisonic HRIRs and reference HRIRs as follows. The HRIR data sets were converted to HRTFs, the frequency domain equivalent of HRIRs, using an FFT with a window size of 4096 samples. Each frequency bin value was amplitude weighted according to ISO Standard 226 equal loudness contours [40] to account for human frequency-dependent amplitude sensitivity. The value of each frequency bin was then weighted to reflect human perception of loudness using the sone scale, at a ratio of +10 phonons per doubling of perceived loudness [38; 41; 42], which also accounts for human auditory features such as spectral peaks being more perceptually significant than notches [43]. Equivalent rectangular bandwidth (ERB) filters were utilised to compensate for the linear frequency interval sampling of the FFT. Rendered data sets were normalised to the mean perceptual loudness level of the reference data set, and PSD was calculated as the absolute difference between each frequency bin of the two data sets.  
PSD between reference HRIRs and Ambisonically generated HRIRs, with and without DFE, was calculated for 16020 directions for each tested order. Figure 6 displays the mean absolute values of PSD between Ambisonic HRIRs and reference HRIRs with and without DFE over the sphere (mean of left and right ear PSD calculations). The figures show DFE implementation improves spectral reproduction for a large amount of the sphere, particularly for 1 st and 5 th-orders, but makes it worse at lateral directions.  
Figure 6: PSD between HRTFs and reference HRTFs with and without DFE (mean of left and right ear PSD calculations): (**a**) \(M=1\), no DFE, (**b**) \(M=3\), no DFE, (**c**) \(M=5\), no DFE, (**d**) \(M=1\), with DFE, (**e**) \(M=3\), with DFE and (**f**) \(M=5\), with DFE. PSD: Perceptual spectral difference; HRTF: Head-related transfer function; DFE: Diffuse-field equalisation.  
The mean absolute values of PSD between reference HRIRs and Ambisonic HRIRs with and without DFE across all angles on the sphere are presented in Table 3. Values were solid-angle weighted to account for the clustering of points at the poles in Gaussian quadrature. The table shows that DFE reduces the overall PSD between Ambisonic HRTFs and reference HRTFs for all three tested configurations.
# Performance of 5G Trials for Industrial Automation
## 6 Summary  
We have presented 5G over-the-air performance measurements conducted in realistic industrial production environments with realistic deployment settings. Our empirical results provide insightful understanding of 5G system performance for some manufacturing use cases. We have applied traffic models to represent realistic industrial traffic as described in 5G-ACIA, 5G-SMART, and 3 GPP. A proper realization of industrial use cases over 5G and its validation is ongoing work within the 5G-SMART project [9; 10; 11].  
We have shown that besides the use of higher subcarrier spacing and more suitable TDD patterns, 5G systems with scheduling enhancements, prioritization, robust control and data channels, faster control feedback mechanisms, etc., can achieve the very low latencies that are required for some of demanding industrial use cases. However, for early 5G networks and devices that are designed for MBB services, we observed an increase in the latency for higher reliability guarantees. The achievable latency is further dependent on the 5G device as well as on the network configuration. We also observed that redundant transmissions, for instance, via FRER, can reduce the E2E latency when high reliability is  
Figure 10: Downlink latency with and without redundant transmissions using FRER.  
required. We evaluated 5G features that have been specified for ultra-reliable and low-latency communication. We observed that with these features, very low latencies could be achieved with high reliability guarantees, which are required in some stringent industrial IoT applications.  
Conceptualization and validation, J.T., J.S., P.d.B., L.G., J.F., B.V. and J.A.: methodology, software, formal analysis, and research, J.A. and C.A.; writing, J.A., C.A., P.d.B., J.S., L.G., B.V., J.F., N.K., D.H. and R.H.S. All authors have read and agreed to the published version of the manuscript.  
This work was performed in the framework of the H2020 project 5G-SMART co-funded by the EU under grant number 857008. This information reflects the consortium's view, but the consortium is not liable for any use that may be made of any of the information contained therein.  
The authors would like to acknowledge the contributions of their colleagues from ABB, Bosch, Ericsson, and Fraunhofer IPT.  
The authors declare no conflict of interest.
## 4 Pool-Experiments Verification and Discussion  
In this section, the effectiveness of the proposed methods (i.e., adaptive sliding mode PID control based on FAT) is validated on the underwater manipulator of the UVIC-I AUV, and in the pool-environment, the experimental results of the proposed methods are compared with the method in [17]. The UVIC-I AUV, as shown in Figure 2a, is a prototype vehicle designed for underwater operations, mainly consisting of AUVs and underwater manipulator.  
The AUVs is equipped with eight thrusters, two to actuate surge, two to actuate sway and yaw, and four vertical thrusters to actuate heave, roll, and pitch, and sensors such as depth, velocity, and attitude angle [28]. The underwater manipulator, as shown in Figure 2b,c, with five joints (5 DOF), the detailed structure is shown in [9], and the main parameters are shown in Table 1. Moreover, during the underwater experiments, the experiments are obtained in a pool measuring 8.0 m \(\times\) 5.0 m \(\times\) 5.0 m, and the AUVs is in a dynamic positioning state, maintaining a stable attitude, as shown in Figure 2d. The main parameters of the adaptive sliding mode PID controller are given as follows: \(\omega=0.2\), \(k_{\text{p}}=0.5\), \(k_{\text{i}}=0.1\), \(k_{\text{d}}=1\), \(h=1\), \(\lambda_{1}=10\), \(\lambda_{2}=1\), \(A=[0\ 1;0.5\ 0.1]\), \(Q=[400\ 0;\ 0400]\), \(K_{1}=1.5\).
# A Multi-Strategy ALNS for the VRP with Flexible Time Windows and Delivery Locations
## 4 Multi-Strategy ALNS with a Simulated Annealing Mechanism
### The Search Mechanism
#### 4.3.2 Insertion Operators  
The insertion operators reinsert destroyed parts by means of random, heuristic, or local search. It aims to improve the quality of the solution. The design of the insertion  
Figure 5: Illustration of removal operators. **(a)** Example customer–locations table mapping customers (F–K) to potential delivery locations (18–31). **(b)** Schematic representation of routes where removal occurred (top panel shows two routes, with delivery locations 18, 20, 23, 27, 31, 32), delivery locations to be removed (e.g., delivery location 20), and node removal costing diagram (bottom panel), where selected customers (e.g., delivery option 20, which belongs to customer G) are moved to the “request bank” (list of removed customers).  
operation can affect the quality of the solution and is therefore usually adapted to the characteristics of the problem.  
In MSALNS, \(\Gamma\) customers in \(\Omega\) are fixed to a partial solution using the insert operator. The insert operator performs only viable inserts. The insertion operators perform only feasible insertions.  
Figure 6 is an example illustrating how to calculate the different insertion metrics of customer H. The darker colors are used to indicate better metric values.  
In MSALNS, the classical operator random insertion [49], greedy insertion, and cost regret insertion [44] are adapted. At the same time, in order to better construct high-quality neighborhood solutions, MSALNS constructs the following operators from the perspective of customer satisfaction:  
1. **Satisfaction Best Insertion:** The operator selects the lowest insertion cost customer \(i\) from \(\Omega\) each time then selects the optional location \(a\) that brings the best satisfaction and performs the insertion.
# Enhanced Knowledge Graph Embedding by Jointly Learning Soft Rules and Facts
## 5 Experiment  
When conducting experiments on SoLE, we wanted to explore the following two questions:  
* Whether the devised joint training algorithm and the introduction of forward chaining of SoLE really provided benefits for embeddings compared to the state-of-the-art KG embedding models with soft rules: To test this, we evaluated the performance of SoLE on the link prediction task, which has been widely applied in previous KG embedding works.
* Considering the groundings generated by forward chaining, whether they were more helpful for embeddings than those generated by one time inference and to what degree if this is true: To do this, we compared the effect of forward chaining and one time inference on the link prediction task and analyzed the results.  
Besides, we will discuss the influence of the PCA confidence threshold on our method, the convergence of the training process in SoLE, and the whole runtime of SoLE. Notably, we used the TensorFlow framework (GPU) along with Python 3.6 to conduct our experiments. All experiments were executed on a Linux server with processor Intel(R) Xeon(R) Gold 5118 CPU @ 2.30 GHz, 128 GB RAM, and an NVIDIA GeForce GTX 1080 GPU.
# Cell Disruption and Hydrolysis of _Microchloropysis salina_ Biomass as a Feedback for Fermentation
## 3 Results and Discussion
### Mechanical Cell Disruption
#### 3.2.1 Ultrasonication
Ultrasonication of microalgae has been mainly applied as a pretreatment for biodiesel, bioethanol, and biogas production and has been shown to adequately break algal cells in low-density biomass suspensions, increasing the soluble fraction of organic matter, proteins, carbohydrates, and lipids [35]. On the other hand, for microalgal biomass hydrolysis, where the cell wall itself is the target rather than  
Figure 2: Disruption of _M. salina_ biomass by ultrasonication. (**a**) Ultrasonication of microalgal biomass with various solids contents ranging between 10 and 25%, as the legend indicates, for 40 min; (**b**) ultrasonication of microalgal biomass with a 15% solids content for 90 min. Error bars show the standard deviation of triplicate experiments. an intracellular product to be released, ultrasonication on an industrial scale might be too energy- and time-intensive compared to other mechanical disruption methods, considering the high frequencies and treatment duration required for effective cell disintegration.
# Eight-Port Modified E-Slot MIMO Antenna Array with Enhanced Isolation for 5G Mobile Phone
## 3 Results and Discussions
### Parametric Analysis, S-Parameters, Gain, and Efficiency  
Variations of some design parameters were investigated in this study. Figure 4(a)-e shows the plots of the scattering parameters as a dependent variable on \(L\), \(t\), \(d\), \(g\), and \(I\), respectively. Sweeps were performed to validate the impact of the aforementioned factors on system performance. From Figure 4(a), it is clear that with the initial parameter value of 15 mm, the first resonance is at 3.53 GHz, so also all other values remain at that frequency. The only effect here is that the magnitude of the resonance increases with an increase in the \(L\) value.  
Figure 4(b) is a plot of \(t\) variation, which indicates how the frequency resonance varies with an increase in the \(t\) parameter. The resonance point moved toward the higher frequency band with an increase in the \(t\) value, so also the magnitude increased with the increase in the parameter. The variation of the \(d\) parameter in Figure 4(c) shows little effect when the value is varied from 2 to 4 mm; at an interval of 0.5. Similarly, the \(g\) parameter was also varied, and the effect can be seen in Figure 4(d). Figure 4(e) shows the experimental results of the \(I\)-shape variation on the antenna frame. As can be seen from the simulated scattering parameters in Figure 4(e), the S11 shifts to a lower resonance frequency with an increase in the length of the \(I\) parameter.  
Figure 5(a) depicts both the simulated values as well as the measured reflection coefficient plot for Ant 1 through Ant 4. This work achieved a magnitude better than 18 dB for the four antennas, covering the (3.4-3.65) GHz. Figure 5(b) shows the port isolation plots between the neighboring antennas, for which we obtained isolation better than 21 dB in all possible combinations. From the depicted measured antenna efficiency in Figure 6(a), it is clear that our system achieved an efficiency value between 34-76% within the frequency of interest with antenna 8 having the maximum efficiency of 70%. We consider the minimum and maximum efficiencies of the antennas as quoted in Table 2. Figure 6(b) indicates the gain of the antenna. A gain value higher than 3.1 dB is obtained with a maximum value of around 4.8 dB. Note that the maximum value of the gain is quoted as provided in Table 2.  
Figure 4: Simulated S-parameter (**a**) with no decoupling element (**b**) with the decoupling element.  
Figure 5: Scattering parameter plots (**a**) parametric study of the parameter \(L\) (**b**) parametric study of \(t\) parameter (**c**) parametric study of \(d\) parameter (**d**) parametric study of \(g\) parameter (**e**) parametric study of \(l\) parameter (**f**) envelope correlation coefficient.
## 3 Method  
### The \(\Delta t\)-\(\log(T)\) Logarithmic Model  
Current research primarily focuses on utilizing displacement monitoring data from the accelerated deformation phase for short-term landslide prediction. The reciprocal velocity method is applied after the initiation of the accelerated deformation phase, assuming that the deformation velocity tends to infinity at the instant of slope failure. By performing a linear fit of the reciprocal of velocity versus time, a trend line can be obtained, and the intersection of this trend line with the time axis represents the predicted time of failure (\(T_{f}\)). Therefore, starting from the Time of Start Point (\(T_{SP}\)), the linear relationship between the reciprocal of velocity and time can be expressed using a formula. [PERSON] [26] provided a method for calculating the reciprocal of velocity, and [PERSON], and [PERSON], [28] successfully applied this method to an open-pit mine in Nevada. The calculation formula is as follows (1).  
\[\frac{1}{V}=-A(T-T_{SP})+B \tag{1}\]  
where \(V\) represents the deformation velocity, \(T\) represents the time corresponding to different velocities during the landslide process, \(T_{SP}\) represents the starting time of the acceleration phase of the slope, and \(A\) and \(B\) are constants of the fitted curve.  
When \(\frac{1}{\
u}=0\), the time \(T\) at this point is the moment of slope failure.  
Formula (1) can be further transformed into the velocity-solving Formula (2).  
\[V=\frac{1}{-A(T-T_{SP})+B} \tag{2}\]  
Figure 2: Registration of radar point clouds with 3D maps.  
By integrating Equation (2), we obtain the solution Formula (3) for displacement \(S\), where \(C\) represents the integration constant.  
\[S=\frac{-\log[-A(T-T_{SP})+B]}{A}+C \tag{3}\]  
Based on the three-stage theory of slope creep and sliding, the displacement-time curve exhibits a stage of uniform deformation prior to the accelerated deformation stage. During this stage, the deformation rate of the slope remains basically constant, and the average velocity of the landslide can be assumed as \(\overline{\
u}\). By applying the physical relationship of displacement, which is obtained by multiplying velocity by the relative time of motion, Equation \(\Delta\mathrm{t}=\frac{\overline{\
u}}{\overline{\
u}}\) transforms into a logarithmic function relationship of time (\(\Delta\mathrm{t}-\log(T)\)), as shown in Equation (4); we can derive a logarithmic function relationship between \(\Delta\mathrm{t}\) (relative time) and \(T\) (absolute time). This transformation serves to unify the dimensional units of the horizontal and vertical axes of the analysis curve. The purpose of unifying the dimensional units of the coordinates on the analysis curve is to address the issue of convergence in the curve fitting of monitoring data, ensuring that the constants \(A\) and \(B\), and the \(C\) value can be accurately calculated through curve fitting, as shown in Figure 3.  
\[\Delta\mathrm{t}=\frac{-\log[-A(T-T_{SP})+B]}{A\overline{\
u}}+\frac{C}{ \overline{\
u}} \tag{4}\]
## 4 Results
### Remote Sensing Techniques
Especially while researchers using the SNAP software, all these hard targets should be masked at the first stage of the processing. For example, in SAR image processing for wind field estimation, the studied areas should be checked out for oil spill and removed from the image because of decreasing the water roughness of the sea surface [85]. Another important weaknesses, the wind direction (SAR images can be analyzed in SNAP software to achieve wind direction with 180 degree ambiguity) obtained from the SAR satellites cannot be verified because the available data will not really show the wind field in the coastal and open seas. This is the main reason for selecting local data (in situ data) in many studies. Another limitation of wind speed estimation based on satellite data are that they measure wind speed data at 10 m above sea surface water. By using this type of data to obtain information about the wind sources at a height of 100 hub meters, it is necessary to implement theoretical models using surface roughness coefficient [76]. Threats: Interdisciplinary knowledge is needed to work with RS methods as this involves obtaining and analyzing satellite data. Then, researchers need to be familiar with various software applications to properly analyze satellite data. It is known that satellites are highly capable of observing the Earth, since they have applications in many different fields. Hence, studying the specific application mainly depends on the software knowledge of researchers.
## 3 A Proposed Robust Scheme Based on Hop-Count Analysis (MHA)
### Route Maintenance in MHA Routing Protocol  
#### 3.5.1 Dealing with Broken Routes and RERR Packets  
As time passes, routes may break. When a node receives a data packet but can not reach the next hop in the routing table, it generates a RERR packet including \(\mathrm{IP_{S}}\) and \(\mathrm{IP_{D}}\) to notify the source/destination node.  
The source/destination node receives the RERR packet, deletes the broken route in memory and uses the RACK packet to notify the destination/source node to use other remaining routes. The destination/source node also deletes the route. Until the next route discovery procedure, the remaining routes are used for transmission.