esa-sceva/satcom-chunk-collection huggingface dataset:
# Photoconductive Semiconductor Switches: Materials, Physics, and Applications
## 8 Application Case Study: A Medium-Voltage PCSS Breaker  
Medium-voltage direct-current (MVDC) electric power, ranging from 5 kV to 65 kV, provides several advantages over alternating current (AC) systems, including reduced power losses and lighter cables [90]. Additionally, MVDC systems offer superior control and efficiency in converting electrical energy for various applications, including energy storage, renewable energy generation, and electric vehicle (EV) charging stations [91]. However, the adoption of DC systems is constrained by the need for affordable, reliable, and rapid fault detection and protection mechanisms. Unlike AC systems, which naturally experience zero current crossing, aiding fault protection, DC complicates the implementation of effective fault protection strategies [92].  
Traditional mechanical circuit breakers (CBs) are hindered by slow response times, diminished reliability after repeated switching, low efficiency, and substantial size. These drawbacks can be addressed by solid-state circuit breakers (SSCBs), which leverage semiconductor devices for switching, offering significantly faster response times than mechanical CBs and minimizing energy dissipation during faults [93]. Advances in wide-bandgap (WBG) semiconductors, like silicon carbide (SiC) and gallium nitride (GaN), have outperformed silicon (Si)-based systems in efficiency and performance [88]. However, the limitation of commercially available SiC devices to 3.3 kV necessitates connecting multiple semiconductors in series for MVDC circuit breakers to enhance voltage tolerance beyond individual elements. This series operation demands precise synchronization to avoid transistor overvoltage during transitions and to maintain leakage current balance in steady state.
# Three-Dimensional Human Head Reconstruction Using Smartphone-Based Close-Range Video Photogrammetry
## 2 Materials and Methods
### 3D Reconstruction Algorithms
#### 2.1.2 Methodology for Improvement of 3D Head Reconstruction  
Here, we propose a methodology for the improvement of 3D head reconstruction. We seek reconstruction improvement by increasing the reconstruction quality and reducing the number of required computations. We define the model quality by the amount of semantic noise and reconstruction errors--the higher level of noise and errors, the lower the quality of the model. The methodology is a list of possible solutions that systematically originated from the factors that negatively affect the reconstruction process and quality of the head model.  
We have summarized the factors that may negatively affect the reconstruction process and quality of the reconstructed head model (discussed in previous Section 2.1.1):  
1. Changing background--due to the movement of the head in respect of the background or existence of other moving objects in the background;
2. Motion blur and rolling shutter distortion--due to low light conditions and faster movement of the camera, shivering of hand;
3. Defocus distortions--if the camera focuses on background objects;
4. Head out of frame limits--stumbles making selfie videos;
5. Too many frames--due to the inefficient design of camera positioning around the head and, as a consequence, acquired long recording (excess of redundant frames only slows down reconstruction process).  
These key modifications of the general-purpose 3D reconstruction algorithm should improve 3D head reconstruction from selfie videos by weakening factors that negatively affect the reconstruction process and quality of the model:  
1. Elimination of image background--suppresses the negative influence of the changing background to the reconstruction process; reduces the amount of semantic noise; background elimination frees from computations in the background region of the image;
2. Selection of the highest quality frames--as a result, reconstruction errors are reduced because images with motion blur, defocus distortions, and images, where the head is out of frame limits, are removed; reduces the number of frames that are redundant, so the computational load is reduced; removal of redundant frames enables moving the camera slowly while capturing in order to reduce motion blur and rolling shutter distortions.  
Specifics of the implementation solutions of these modifications will be presented and discussed in Section 2.1.4.
# Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics
## Materials and Methods
### Hyperspectral Imaging System  
The hyperspectral imaging system was applied to acquire hyperspectral images of wheat seeds in a dark environment through line-scanning way. Based on these references [14; 15; 16; 17], the appropriate near-infrared spectral range of 874-1734 nm for measurements was determined in this experiment. Through each single scanning, HSI can obtain spectral information of a batch of samples that covers hundreds of successive wavebands. In this study, 256 wavebands of 320 pixels could be acquired. Moreover, the whole system reaches 5 nm spectral resolution. Five major components constitute the whole HSI system: an imaging spectrograph (ImSpector N17E; Spectral Imaging Ltd., Oulu, Finland), a high-performance CCD camera (C8484-05; Hamamatsu, Hamamatsu City, Japan) with a camera lens (OLES22; Specim, Spectral Imaging Ltd., Oulu, Finland), an illumination unit that consists of two 150W tungsten halogen lamps (Fiber-Lite DC950 Illuminator; Dolan Jenner Industries Inc., Boxborough, MA, USA) to ensure sufficient light, a mobile platform controlled by a stepper motor (Isuzu Optics Corp., Taiwan, China) to convey the samples, a matched computer equipped with the image acquisition software (Xenics N17E, Isuzu Optics Corp., Taiwan, China).
# Fault Prediction of Mechanical Equipment Based on Hilbert-Full-Vector Spectrum and TCDAN
## 2 Fault Feature Extraction
### Hilbert-Envelope Spectrum  
The principle of signal enveloping using the Hilbert transform is to first make the original signal generate a 90\({}^{\circ}\) phase shift, to form an analytical signal with the original signal [29; 30]. Let \(S(t)\) be a real signal, and its Hilbert transformation is defined as:  
\[S_{h}(t)=S(t)*\frac{1}{\pi t}=\frac{1}{\pi}\int_{-\infty}^{+\infty}\frac{S(\tau )}{t-\tau}\mathrm{d}\tau \tag{1}\]  
where \(*\) indicates the convolutional calculation. Then, the original signal \(S(t)\) and its Hilbert transformation signal \(S_{h}(t)\) can constitute an analytical signal \(S_{a}(t)\):  
\[S_{a}(t)=S(t)+jS_{h}(t) \tag{2}\]  
Its amplitude \(A(t)\):  
\[A(t)=\sqrt{S^{2}(t)+S_{h}^{2}(t)} \tag{3}\]  
\(A(t)\) is the envelope of the real signal \(S(t)\). Then the Hilbert-envelope spectrum of the original signal can be obtained by FFT operation on the envelope signal.
###### Abstract  
Existing studies on data acquisition in vehicular networks often take the mobile vehicular nodes as data carriers. However, their autonomous movements, limited resources and security risks impact the quality of services. In this article, we propose a data acquisition model using stable matching of bipartite graph in cooperative vehicle-infrastructure systems, namely, DAS. Contents are distributed to roadside units, while vehicular nodes support supplementary storage. The original distribution problem is formulated as a stable matching problem of bipartite graph, where the data and the storage cells compose two sides of vertices. Regarding the factors relevant with the access ratio and delay, the preference rankings for contents and roadside units are calculated, respectively. With a multi-replica preprocessing algorithm to handle the potential one-to-many mapping, the matching problem is addressed in polynomial time. In addition, vehicular nodes carry and forward assistant contents to deliver the failed packets because of bandwidth competition. Furthermore, an incentive strategy is put forward to boost the vehicle cooperation and to achieve a fair bandwidth allocation at roadside units. Experiments show that DAS achieves a high access ratio and a small storage cost with an acceptable delay.  
v 2017, 1327; doi:10.3390/s17061327
## 3 Result and Discussion
### Water Absorption Behaviour  
Figure 15a-d depict the proportion of water absorbed by sisal- and flax-fibre-reinforced hybrid composites following different concentrations of NaOH treatment (0, 1 wt.%, 3 wt.%, and 5 wt.%). The Indians' attitudes toward water have shifted drastically through time. Fibre reinforcement generally causes polymer composites to retain a lot of moisture. The moisture absorption of flax- and sisal-fibre-reinforced composite materials with 3 wt.% and 5 wt.% NaOH concentrations was moderate, whereas the moisture content of NaOH-treated flax- and sisal-fibre-reinforced composite materials with 1 wt.% and 0 wt.% concentrations was high. The mechanical-fractured sample study revealed that fibre composites containing  
Figure 14: Microstructural Images of **(a)** Plain epoxy; **(b)** 0 wt.% of NaOH Treated; **(c)** 1 wt.% NaOH Treated; **(d)** 3 wt.% of NaOH Treated; **(e)** 5 wt.% of NaOH Treated composites; **(f)** 5 wt.% NaOH Treated Tensile failure; **(g)** 5 wt.% NaOH Treated Flexural failure; **(h)** 5 wt.% NaOH Treated Impact failure.  
5% NaOH had strong fibre-to-matrix bonding capabilities. As a consequence, excellent water-resistant characteristics were developed. Due to good bonding, the polymer matrix is tightly wrapped around the fibre, preventing water molecules from contacting the exterior [45]. Liquid fragments may effortlessly enter the fibre surface in the 0-wt.% and 1 wt.% fibre-reinforced composites due to the weak interaction between the matrix and the fibre. As a consequence, the water absorption of the material increased. The 0-wt.%, 1 wt.%, 3 & 5 wt.% flax- and sisal-fibre-reinforced composites reached saturation point after 60 h of incubation. As can be observed, all nanomaterials display a normal polymeric water uptake response that follows [PERSON]'s baseline. In general, including nanofillers reduces the water absorption of altered hybrids compared to ordinary epoxy. The nanofiller's exceptional resistance characteristics cause this phenomenon. Including nanofillers with a high surface area can provide a difficult route for hydrogen atoms to infiltrate into the nanocomposite. The maximal water absorption of aluminium oxide reinforced polymer nanocomposites diminishes steadily as the filler concentration increases [41]. Compared to plain epoxy, the maximum moisture uptake of aluminium-loaded epoxy nanomaterials is reduced by 12.1, 19.4, and 22.4%, respectively, with the inclusion of 2, 4, and 6 wt.% aluminium oxide. Similarly, adding 2, 4, and 6 wt.% aluminium oxide reduces water retention by 9.14, 10.21, and 16.54%, respectively. Surprisingly, the resistance characteristics of nanomaterials loaded with aluminium oxide outperform those of other unfilled biocomposites.  
Figure 15: Moisture Absorption Behaviours of (**a**) Untreated; (**b**) 1 wt.% of NaOH; (**c**) 3 wt.% of NaOH; (**d**) 5 wt.% NaOH treated sisal/flax/Nano Al\({}_{2}\)O\({}_{3}\)/Epoxy based hybrid composites.
# Spam Classification Based on Signed Network Analysis
## 5 Performance Evaluation
### Results  
Table 5 shows the comparison result. The node-ranking-based detection approaches, SybilRank and NFS, show poor spam detection performance for all indicators; they achieve detection accuracies of 28% and 57%, respectively, and F1 scores of less than 51%, while the other approaches achieve accuracies of more than 90%. The proposed schemes outperform both CatchSync and TSP in terms of accuracy and precision. They achieve an accuracy of more than 92% and a precision of almost 95%. For recall, however, the proposed schemes have lower scores than CatchSync and TSP. Although they show the highest recall performance, their false positive rates are close to 10%, implying that they aggressively classify spammers. On the other hand, the proposed schemes show more stable detection performance; they can achieve almost the same level of detection performance while maintaining a low false positive rate. TSP resembles our approach in that the triad patterns are utilized, but it considers only undirected networks, which leads to performance degradation.  
For the proposed method, we obtain the best performance result when we use only the social status features; when the balance feature is used, the classification performance is slightly degraded. This is because the balance feature ignores the information about the edge direction of the Twitter dataset, which results in a weak fit to the data. Nevertheless, we can see that it is still more effective for spam classification than conventional node-ranking-related features.
# Fast Reroute Mechanism for Satellite Networks Based on Segment Routing and Dual Timers Switching
## 2 Related Work
### Node Behavior Design
#### 3.2.3 Endpoint Behavior  
When the egress node (stack bottom) receives a packet, it checks if the packet is part of the SR backup path and if the label in the stack is the bottom label. If both conditions are met, the node is recognized as the stack bottom. The stack bottom node then removes the SR node label from the stack, converts the packet into a underlay routing packet, performs a underlay routing table lookup, and forwards the packet based on the matching outgoing port.
# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 4 Performance Evaluation
### Performance Measures
#### 4.2.4 Energy Consumption  
The proposed method was formulated to minimize energy consumption by incorporating the most effective cells into the handover process. The energy consumption presents the approximated energy usage value for the period of time a UE attempted to handover to a cell. The algorithm contributed to energy consumption due to the restricted scanning of cells within UE movements, the ST role in preventing unnecessary HOs, and the reinforcement of positive rewards. The element therein for the consumption is the approximated transmission power multiplied by the duration of the handover attempt (in milliseconds, for instance). Optimizing energy consumption is critical in this mechanism, since the handover should be rapid to prevent wasteful power utilization, hence prolonging the battery life (limited power source) of UE devices. The less time used for the handover, the less energy is consumed. This demonstrates the power efficiency of the algorithm.  
To calculate the total energy expenditure during the handover, the following formulas are applied:  
\[E_{total}=E_{base}+E_{retransmissions} \tag{8}\]  
where  
* \(E_{base}=P_{t}\times T\).
* \(E_{retransmissions}=N_{r}\times E_{base}\).  
Thus, the total energy expenditure is  
\[E_{total}=E_{base}+N_{r}\times(P_{t}\times T) \tag{9}\]  
where  
* \(P_{t}\) is the Transmit Power (W).
* \(T_{S}\) is the Time on Air (s).
* \(N_{r}\) represents the number of retransmissions.
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 3 Comparative Analysis of IEEE 802.11 ah with Previous IEEE 802.11 Amendments
### Comparison of IEEE 802.11 Amendments Based on MAC Features
#### 3.1.2. Distributed Channel Access (DCF)  
It is the basic random access MAC protocol of IEEE 802.11 standard that includes CSMA with Collision Avoidance (CSMA/CA), a sort of listen before talk mechanism. Furthermore, it encompasses binary exponential back-off rules to manage the retransmission of collided frames. It works as follows. Before initiating a transmission, a station senses the channel to determine whether it is busy. If the medium is sensed idle during a period of time called the Distributed Inter-frame Space (_DIFS_), the station is allowed to transmit. If the medium is sensed busy, the transmission is delayed until the channel is idle again. In this case, a slotted binary exponential back-off interval is uniformly chosen in [0, _CW_-1], where _CW_ is the contention window. After each data frame is successfully received, the receiver transmits an acknowledgment frame after a Short Inter-frame Space (_SIFS_) period.
# A New Association Scheme for Handling Node Mobility in Cluster-Tree Wireless Sensor Networks
## 3 System and Network Model  
Recently, WSN are also being used to support monitoring applications in health, agriculture, industrial and smart cities domains [1]. Regarding the health domain, also called Internet of Medical Things (IoMT), sensor monitoring applications are proposed for a wide range of applications, but they bring many challenges concerning reliable and timely delivery of critical signals [29]. For example, heart rate data have stringent time constraints, and one of the major challenges in this context is to guarantee the quality of service for the data transfer of from several sensors that have different priorities, where a delay could have serious consequences for patients [30].  
This paper addresses, as an application example, a research issue that has been found in a real health monitoring system involving a Body Area Network (BAN). The proposed architecture is intended to monitor a set of mobile nodes, which are communicating through a set of infrastructure nodes that form a multi-hop network. This multi-hop architecture, adapted from [31], is composed of three layers: Intra-BAN, Inter-BAN and Beyond-BAN. The Inter-BAN layer is based on the node backbone concept, serving as a communication infrastructure for one or more MNs, localized in the Intra-BAN layers, to transfer their data towards the monitoring system in the Beyond-BAN layer. This last layer is formed by network gateways and cloud services. It is important to highlight that more recent works, e.g., [29], also define similar architectures for individual and group health monitoring systems, where one of the many issues is to guarantee the message transmission from MNs to the gateway.  
Basically, the main target of this paper is to improve the quality of the communication line between MNs and the Inter-BAN layer, guaranteeing reliable monitoring communication from the patient till the beyond-BAN layer. Within this context, this paper contributes to the association mechanism of WSNs structured in cluster-tree topology. The main contributions can be divided into two main parts: network infrastructure and handover mechanism.
## 5 Discussion
### Individual Acoustic Cues Weighting Strategies  
The perceived sound intensity is regarded as a primary cue for determining the distance to a sound source. It, along with the reverberation-related cue, are the two primary cues involved in auditory distance perception of frontal sources. Earlier research has produced contradictory findings regarding the relative role of intensity or a reverberation-related cue for auditory distance perception [34; 35; 36]. [PERSON] and [PERSON] [5] and [PERSON] [4; 12] concluded that listeners weight differently the acoustic cues to produce auditory distance judgments, and that the weighting of cues might be influenced by the listening situation. The different acoustic weighting cues employed by participants are analyzed under the scope of the individual compression coefficients (see Figure 4). These coefficients quantify the compression effect for each model and each participant.  
Individual participant's compression coefficients for the envelope-based model and the rendering method based on measurements are homogeneous. Each participant presents a similar compression effect for these two rendering methods. In contrast, the difference between the compression coefficients associated with the rendering method based on measurements and the intensity-based model suggests that different strategies were used to judge the distance of sound sources. Participants for which the value of the compression coefficients estimated on the intensity model is comparable to the value obtained with the rendering method based on measurements mainly base their auditory distance judgments on intensity. Some participants obtain a compression coefficient \(a\) that is nearly equal to 0 for the intensity-based model only, then do not use intensity as a distance cue, and rely primarily on a reverberation-related distance cue.  
This result corroborates the findings of [PERSON] [12]. It shows that the acoustic cues weighting strategies is mainly an individual characteristic. In the following section, the influence of environmental context on the weighting of acoustic cues is examined, to determine if it can be linked to some characteristics of the listening situation or if it is only an idiosyncratic characteristic.
## 4 Discussion
### Efficiency Value Analysis between Variables  
The analysis of efficiency values between variables aims to see the efficiency values reviewed for each DMU, as shown in Figures 4-6. Figure 4 describes the analysis of the performance efficiency value of DMU X (pre-incubation organization) in the last 5 years,  
Figure 3: Results of measuring efficiency of TTO performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & & **DMU** & **Score** & **Description** \\ \hline
1 & & & 2016 & 1 & v \\ \cline{3-5}
2 & & 2017 & 1 & v \\ \cline{3-5}
3 & X & & 2018 & 0.857 & \\ \cline{3-5}
4 & & & 2019 & 1 & v \\ \cline{3-5}
5 & & & 2020 & 1 & v \\ \hline
6 & & & 2016 & 1 & v \\ \cline{3-5}
7 & & & 2017 & 1 & v \\ \cline{3-5}
8 & Y & & 2018 & 1 & \\ \cline{3-5}
9 & & & 2019 & 0.667 & \\ \cline{3-5}
10 & & & 2020 & 1 & v \\ \hline
11 & & & 2016 & 1 & v \\ \cline{3-5}
12 & & & 2017 & 1 & v \\ \cline{3-5}
13 & Z & & 2018 & 0.4 & \\ \cline{3-5}
14 & & & 2019 & 1 & v \\ \cline{3-5}
15 & & & 2020 & 0.2 & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Efficient DMU table.  
namely, 2016 to 2020. Based on the figure, the pre-incubation organization at LPIK ITB achieved performance efficiency levels in 2019 and 2020. Based on the data obtained, 2019 and 2020 is the peak period for LPIK ITB to produce a finding/innovation during the period under study.  
Figure 4: Analysis of DMU X efficiency value.  
Figure 5: Analysis of DMU Y efficiency value.  
Figure 5 describes the analysis of the efficiency value of the performance of DMU Y (Incubation Organization) in the last 5 years, from 2016 to 2020. It can be seen that, in the last five years, the performance of the incubation organization at LPIK ITB has not maximized the existing inputs, so the output produced has not yet been realized efficiently. However, in 2017, it was seen that the output produced was efficient by producing 64 innovations that were ready to be commercialized. Then, 2020 was also approaching efficiency by producing 46 innovations that were ready to be commercialized in the post-incubation stage.  
Figure 6 below describes the analysis of the performance efficiency value of DMU Z (Post-incubation Organization) in the last 5 years, 2016 to 2020. It can be seen that, in 2019, the output produced was efficient. Based on the data obtained, in that year, 32 startups were produced. This startup has various business fields that refer to the four main priorities of research innovation at LPIK ITB. From this analysis, it was found that there are three main priorities in the post-incubation process to accelerate tenant graduates who produce a startup, namely, product/service sales, technology accessibility, capital and market, and further business.