esa-sceva/satcom-chunk-collection huggingface dataset:
# Solid-State Transformer-Based DC Power Distribution Network for Shipboard Applications
## 3 Overall System Control  
The architecture of the proposed DC shipboard power distribution network is depicted in Figure 3. The proposed system encompasses five main components: the generator side voltage source converter (GS-VSC), the multiphase electric propulsion system, the load side voltage source inverter (LS-VSI), the dual active bridge (DAB) converter, and the PV renewable power generator with the required battery storage media. Each of these components entails a detailed investigation of the components sizing, efficient converter operation, and appropriate control technique. In this section, each system component is briefly explained.  
The GS-VSR, DAB, and LS-VSI constitute the main parts of the employed SST. The SST provides a more compact size and better system controllability. It also adds new functionalities when replacing the bulky conventional electromagnetic transformers. Reducing the size and weight increases fuel-saving and reduces the required space [33]. It also facilitates the successful integration of renewables, such as fuel cells, PV modules, and  
Figure 3: Proposed architecture of DC shipboard power distribution network.  
energy storage systems. Moreover, the high-power electric motors used for propulsion can directly be integrated into the available high voltage DC bus as is clear from the system shown in Figure 3.  
For the proposed system, the power coming from the generators is converted to DC via an AC/DC converter connected to the high voltage DC (HVDC) bus. The propulsion system can, therefore, be directly fed from this HVDC bus without disrupting the downstream loads/renewables connected to the low voltage DC (LVDC) side of the SST. Therefore, the SST resolves the impedance matching problem between the LVDC and HVDC sides on this local grid. It also allows the efficient integration of lower power sources and energy storage devices, which further reduces the encountered volume and complexity problems of the conventional systems.  
High power electric converters are vital interfaces between the functional blocks and the DC distribution system. Rectifiers for generators take the tasks of HVDC bus voltage regulation and power sharing. Inverters for propulsion motors are responsible for driving the motor under different operating conditions. Bidirectional DC/DC converters for energy storage systems (ESSs) are used to provide supply-demand balance and voltage fluctuation mitigation.
## 4 Upscaling to Real World Components  
As described above, a different wavelength is used for the measurement system than the design wavelength of the optics. This results in a spot offset in the focal plane, since the objective lens used here is not designed for a wavelength nearly 100 nm different from the process laser. As shown in Figure 6, the positioning error increases the further the beam is deflected. For the lens considered here, the maximum spot offset is 171.8 \(\upmu\)m. No direct measurement of the laser ablation is possible. Now it is possible to adjust the machining process to correct the offset. This requires an interruption of the machining process  
objective lens system and the measuring wavelength, as it is used for image field calibration with the process laser. However, this would require significant changes to the process. In addition to the actual processing and the necessary extension for synchronization with the measuring system, a change of the correction data and a repositioning of the scanner would have to take place for each individual measurement in order to be able to measure the same location.  
As shown in Figure 7, the spot offset to the border of the objective lens increases. This non-linear or non-constant offset cannot be compensated by additional optical elements, for example, so that a dynamic control is necessary. This dynamic control was made possible by adding another scanner exclusively for the measurement system. As illustrated in Figure 7. At the same time, this opens up new possibilities for process monitoring. It is not only possible to inspect the ablation point, but also a upstream or downstream inspection of the workpiece while the process is running.  
Figure 6: Lateral offset between the process spot and the not corrected measuring spot in dependency of the deflection of the two main-galvanometers.
# Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems
## 3 Machine Learning Algorithms and Techniques
### Random Forest  
Random Forest is a supervised algorithm which combines the output of multiple decision trees to reach a single result [24]. It generates a random subset of features, which ensures low correlation among decision trees. This is a key difference between decision trees and random forest. While decision trees consider all possible features splits, random forest only select a subset of those features. Examples that use this technique can be found in [6; 8; 9; 10; 11; 13; 14].
## 3 Results  
### Cell Size and Bending Test Findings  
The mean \(\pm\) standard deviation value of the cell size of the I-SEMS was 0.251 \(\pm\) 0.02 mm\({}^{2}\), whereas that of the K-SEMS was 3.712 \(\pm\) 0.37 mm\({}^{2}\) (Figure 2a,c). The cell size of the I-SEMS was 14.7 times smaller than that of the K-SEMS. In the bending test, the I-SEMS showed highly flexible bending without kinking and maintained an inner lumen of the stent (Figure 2b). However, the K-SEMS was not able to withstand the bending force and showed severe kinking in the middle portion, which means an inner lumen of the stent could not be maintained (Figure 2d).
# Rebirth of Distributed AI--A Review of eHealth Research
## 8 Selected IoMT-Based Health Applications
### Tele-Surgery or Autonomous Surgery
Achieving less than 1 ms round-trip latency over long distances under 5G is nearly impossible, therefore a better performance is expected from 6G with its extremely reliable and low latency communication (ERLLC). * Ultra-high bandwidth: a minimum of 1 Tbps is required to stream the ultra-high-definition images and videos [30]. * High-precision stream bundle synchronization: the network needs to manage a massive number of synchronized streams originating from different sensors, an object at different angles, or a processed volumetric fusion [50]. * Ultra-high reliability: reliability is related to the capability of a network to carry out a preferred operation with very low error rates. The current state-of-the-art healthcare system is unable to provide telesurgery due mainly to communication issues. 6G can provide these capabilities via its core services of Enhanced Mobile Broadband (eMBB), Extremely Reliable and Low Latency Communication (ERLLC), and Mobile Broad Bandwidth and Low Latency (MBBLL) [30]. Federated learning can enrich telesurgery with pre-trained models to enhance the efficiency of the operations in the operation room, and also to reduce the human errors with predictive and prescriptive models. Think of a model that is trained using the medical history of the patients with similar health condition across different health institutions. Or another model that is pre-trained using previous surgeons' decisions made in a similar surgery across different health institutions. Such models can assist the surgeons prior and during the telesurgury. The former, can give the surgeons a 360 view of the patient's health status which can help detecting overlooked critical points that the surgeon might not have looked at. While the latter, can assist the surgeons in making the highly beneficial decisions when they are in doubts.
# Energy Consumption Analysis of ISO/IEC 29192-2 Standard Lightweight Ciphers
## 3 ISO/IEC 29192-2 Standard Lightweight Ciphers  
ISO (International Organization for Standardization) standards are guidelines that set internationally accepted criteria for quality, safety, efficiency and reliability. These standards are developed to ensure that products, services, and systems are implemented consistently and securely around the world. In particular, in the area of security and technology, ISO standards provide requirements to maintain high levels of security while supporting the interoperability of different devices and software. Standards such as ISO/IEC 29192-2 focus on enabling secure data communication in resource-constrained environments such as IoT devices and embedded systems by defining lightweight cryptography algorithms. Within the scope of ISO/IEC 29192-2:2012, the encryption algorithms PRESENT [6] and CLEFIA [7] were accepted as standards in 2012. The LEA [29] encryption algorithm was included in the ISO/IEC 29192-2:2019 document in 2019, and three encryption algorithms were recognized as standards by ISO, and their implementation was recommended. The following requirements for lightweight cryptography are also recommended by ISO/IEC 29192-2:2019. To achieve different levels of security, various lengths of blocks and keys are recommended. For a high level of security, a block length of 128 bits and a key length of 256 bits are required. For a normal level of security, both the block length and the key length should be 128 bits. Meanwhile, for a sufficient level of security, a block length of 64 bits combined with a key length of 128 bits is considered adequate. The CLEFIA, PRESENT, and LEA algorithms announced in the ISO/IEC 29192-2:2019 standard were examined and analyzed in this work. The algorithms' operating performances and energy consumption for the determined scenario have been measured, interpreted, and conveyed in the findings section.
## 3 Results
### The Effect of Materials on the Peak Linear Acceleration and the Rotational Acceleration  
The original geometry of the child helmet was employed to study the effect of materials. Comparisons of the peak linear acceleration obtained from the DOT drop test are shown in Figure 11 for all five combinations of helmet materials.  
Figure 11: Comparisons of the peak linear acceleration obtained from the DOT drop test for five combinations of helmet material.  
Figure 10: Maximum principal strain on the brain obtained from the inclined drop tests at (**a**) crown, (**b**) front, (**c**) rear, and (**d**) side locations.  
For the same density of EPS, it was found that the simulations with the Al-foam outer shell (AIF_EPS90 and AIF_EPS50) showed an 18-20% lower peak linear acceleration than those with ABS outer shell (ABS_EPS90 and ABS_EPS50) at the crown and the front impact locations, while the simulations with ABS outer shell showed a 10% lower peak linear acceleration than those with the Al-foam outer shell at the rear impact location. If the ABS was used, the peak linear acceleration at the side impact location was 30% lower. When comparing the model with the different density of the inner liner EPS, the one with 50 kg/m\({}^{3}\) (ABS_EPS50 and AIF_EPS50) expressed a little lower peak linear acceleration compared to EPS 90 kg/m\({}^{3}\) (ABS_EPS90 and AIF_EPS90) for both outer shell materials.  
The drop tests for the rear and side locations employed the hemisphere anvil. The impact area was localized, hence resulting in localized deformation. ABS usually resists penetration better than Al-foam. The peak linear acceleration was smaller for the helmet with ABS outer shell than the helmet with the Al-foam outer shell. When comparing the inner liner material with ABS outer shell, it was found that the Al-foam inner liner showed a much lower peak linear acceleration at the side location only.  
Figure 12 shows comparisons of the rotational acceleration obtained from two different outer shell materials, ABS and Al-foam. The simulations with ABS outer shell showed a 7% lower rotational acceleration than those with the Al-foam outer shell only at the inclined-crown location. The simulations with the Al-foam outer shell a expressed significantly lower rotational acceleration at the other impact locations. The simulations with ABS outer shell expressed the rotational acceleration more than 8000 rad/s\({}^{2}\) threshold at all impact locations. The 50 kg/m\({}^{3}\) density EPS liner showed to a slightly lower rotational acceleration than the 90 kg/m\({}^{3}\) EPS. It can be seen that the inner liner with 50 kg/m\({}^{3}\) showed a lower peak linear acceleration and rotational acceleration, while the Al-foam outer shell showed a lower peak linear acceleration and a rotational acceleration in most of impact locations. The Al-foam outer shell with 50 kg/m\({}^{3}\) EPS liner was selected for later parametric studies.
# Efficient Parallel Implementation of CTR Mode of ARX-Based Block Ciphers on ARMv8 Microcontrollers
## 2 Background
### NEON Engine and ASIMD Instructions
#### 2.2.2. HIGHT Block Cipher  
In CHES'06 [2], [PERSON] et al. proposed the HIGHT block cipher to provide confidentiality in environments where only limited computation and power are allowed such as RFID and sensors. HIGHT was designed in a feistel structure and is a lightweight block cipher that encrypts 64-bit block through a 128-bit secret key. The HIGHT' round function is composed of lightweight arithmetic such as modular addition by \(2^{8}\), rotate (\(F_{0}\) and \(F_{1}\) functions), and \(XOR\) operations in 8-bit units, so it is very suitable for low-end processors with limited resources. Furthermore, HIGHT was designated as an ISO/IEC 18033-3 [15] in 2010. In the key schedule process of HIGHT, the delta value is updated through Linear-Feedback Shift Register (LFSR), and subkeys are generated by computing the modular addition by \(2^{8}\) on one byte of the master key and the delta value. In LFSR, the connected polynomial uses \(x^{7}+x^{3}+1\), so the delta has a period of 127. By repeating the above process, a total of 128-byte subkeys are generated by the delta value. In addition, 128-byte subkeys and an 8-byte part of the master key are required for the encryption process. In the encryption process of HIGHT, it is composed of an initial and final conversion performed at the beginning and end of encryption, and round functions. The initial and final transformations operate byte-wise \(XOR\) operation and modular addition by \(2^{8}\). The round function performs \(F_{0}\), \(F_{1}\) function, \(XOR\), and modular addition by \(2^{8}\) operations. The \(F_{0}\) and \(F_{1}\) functions, which have the largest computational overheads in the round function, perform rotate and \(XOR\) operations. After each round operation is completed, the result values are rotated one byte to the left. Figure 3 shows the round function of HIGHT. A total of 32 round functions are performed to encrypt 64-bit plaintext. The rotate operation is not performed to the result values in the last round. Finally, the final conversion is performed on the result value. The encryption of HIGHT is completed.
# Integration and Field Evaluation of an IoV System for Enhancing Road Safety
## 3 Human-Machine Interface Architecture
### Mobile Applications  
The mobile applications (Android and iOS) are implemented as multi-threaded applications. They are used as the terminal devices of non-equipped vehicles and each contains three main components. The first component, called Location Tracking Service (LMS), is a service that runs in the background and implements communication with the device's GPS hardware. The element operates continuously and receives every second the longitude, latitude, altitude, direction and speed. After that, the data is provided in the third element.  
The second component, called MQTT Service, is a service that runs in the background and implements the communication with the MQTT broker in a separate thread. The service subscribes to the topic \"ODOS2020/HMI_active/#[StationID]\" to receive the messages provided by the DSS. Messages are encoded and decoded using the C programming language as a structure. Thus, the mobile application implements a specific wrapper/module that can be used by all components to post and receive messages. The messages received by the mobile application are of type HMI_active (Figure 5). The service then decodes the message using the module and converts it to a string sent to the third component.  
The third component implements the entire functionality responsible for presenting the User Interface (UI) elements and audio messages to the driver/rider. This component runs on the main thread. The component also contains two broadcast receivers to receive messages from the other two components (Location Tracking Service and MQTT Service). The first broadcast receiver is responsible for receiving the data provided by the LMS and the second for receiving data from the MQTT Service.
## 2 Materials and Methods
### Experimental Measurements  
The experimental measurements were performed on the IPM components using the commercial measurement equipment DEWEsoft(r) (Trbovlje, Slovenia)) in our laboratory settings (Laboratory of electrical machines, Faculty of electrical engineering, University of Ljubljana). The components of the IPM motor on which the experimental modal analysis was performed are shown in Figure 4 (i.e., stator stack only (Figure 4a), stator with the slot winding and end-windings (Figure 4b), rotor without magnets (Figure 4c) and rotor with magnets (Figure 4d)). All of the components of the IPM motor have been prototyped in this study according to the results obtained with the numerical and analytical models described. All components were freely suspended on a cord with neglected mass in order to ensure the free vibration condition. The area marked with white circles in Figure 4a-d indicates the location of the sensor positioned on the IPM component surfaces during the experimental assessment. The reference sensitivity of the impact hammer (Dytran Instruments, Inc., Chatsworth, LA, USA) used was 48.50 mV/Lb.F (Figure 4e). The reference values of the accelerometer sensor sensitivities in X, Y and Z directions were 10.05 mV/g, 9.91 mV/g and 10.08 mV/g, respectively, (Figure 4f). The modal response of the excited mechanical structures was measured via the structural frequency response function (FRF), which typically carries information about the natural frequency and modal damping. The quality of the FRF signal was controlled by appropriate selection of the impact hammer tip, which was selected to ensure as short as possible duration time that the hammer is in contact with the mechanical structure (i.e., IPM motor component). The locations of modal excitation with the hammer were carefully selected in order to comply with the repeatability, reciprocity and linearity of the output signal. The FRF response was measured and analyzed with the specialized Dewesoft X3 (Trbovlje, Slovenia) data acquisition software in order to acquire the natural frequencies of each of the IPM components. The measured FRF signals were further processed and visualized using Matlab R2020a (MathWorks, Natick, MA, USA) software. The obtained results of the natural frequencies were then compared to the results of the numerical and analytical modal analysis performed in this study.
# Mountain Muon Tomography Using a Liquid Scintillator Detector
## 2 Materials and Methods
### Muon Tomography Using a Liquid Scintillator Detector
#### 2.2.2 Event Rate Calculation  
In simulations, the setup of the cosmic-ray event rate in the detector is crucial for the acquisition of the data taking time required to achieve a specific imaging resolution. For the calculation of the event rate in a detector placed somewhere inside or outside the mountain, the first parameter that we need to know is \(R_{0}\), which is defined as the event rate of the detector when there is no geological object above the plane in which it is located. \(R_{0}\) depends on the detector's parameters, including the geometry, size, target mass, muon detection efficiency, and so on. The second parameter is the survival probability \(P\), which is defined as the ratio of cosmic-ray muon fluxes before and after attenuation in the mountain. We can obtain \(P\) via simulations based on a satellite image of the mountain terrain, the reference rock density, and the detector's position. The product of \(R_{0}\) and \(P\) gives the calculated event rate. The event number, namely the product of the event rate and the data taking time, affects the imaging resolution. In addition, \(R_{0}\) is essential for the acquisition of the reference radar image, which is defined in Section 2.4.  
There are two methods to calculate the \(R_{0}\) of the one-ton detector:  
1. As mentioned in Section 2.2.1, the measured event rate is \[R_{\rm CJPL}=(48.4\pm 2.6_{\rm stat.})\times 10^{-7}\ {\rm Hz}\] (4) at the CJPL inside Jinping Mountain. Based on a satellite image of the Jinping Mountain terrain, obtained from the NASA SRTM3 dataset [28], and the reference density, we obtain the survival probability \(P_{\rm CJPL}=2.1\times 10^{-8}\). Furthermore, we have: \[R_{0}=R_{\rm CJPL}/P_{\rm CJPL}=(230\pm 12_{\rm stat.})\ {\rm Hz}.\] (5)
2. The muon flux at sea level is known to be about 1 cm\({}^{-2}\)min\({}^{-1}\). We assume that the sensitive area of the detector is \(\frac{1}{4}\pi\left(1.3\ {\rm m}\right)^{2}\) and the detection efficiency is 100%. Then, \(R_{0}\) is about 220 Hz.  
The conclusions of the above two methods are consistent. Therefore, we use \(R_{0}=230\ {\rm Hz}\) in the following simulations. In actual mountain imaging, we can place the detector on the open ground and measure a more accurate \(R_{0}\).  
Figure 3: Views of the one-ton spherical liquid scintillator detector: (**a**) external structure and (**b**) internal structure.