esa-sceva/satcom-chunk-collection huggingface dataset:
# RFID Technology as a Low-Cost and Passive Way to Digitize Industrial Analog Indicators
## 4 Results
### RFID Tag and Isolator Simulation Results
#### 4.2.1 RFID Parametric Simulation  
In the next step, after determining and selecting the material and the initial geometry of the antenna to perform impedance matching, we should examine the parameters affecting the input resistance of the antenna and the S11 diagram. The optimal point of each variable was determined separately and in combination by EM simulation software and parametric simulation. Parameters D1- to D3 were static, and their dimensions were defined according to the size of the antenna port and RFID chip. The H parameter was also fixed due to the same thickness of the copper coating on the FR4 layer (about 35 \(\upmu\)m for FR4 1 oz). Due to the limited size of the designed RFID tag described in Section Antenna Pattern and dimension limitation, R parameters were specified in some way to keep the tag dimension less than 20 mm. Therefore, we tried to create the impedance matching by performing parametric analysis in only variables parameter W. The cross-section area and distance of conductors are two critical components in determining the size of capacitors, inductors, and resistors. The sweep in the W parameter simultaneously leads to change in both these components. As a result, the impedance matching can be tuned accurately to the primary operating frequency (900 MHz) by creating a frequency shift. The W parameter was swept in the range of 0.31-0.37 mm, and its effect in the S11 plot was investigated in Figure 7a. As shown in Figure 7b, the simulated input impedance at the UHF RFID central frequency is \(\text{Zin}=10.3+\text{j}234\). The electric field or \"E\" plane shows the polarization or direction of the radio wave. The magnetic field or \"H\" plane lies at a right angle to the \"E\" plane. Co and cross-polarization in the H and E plan are shown in Figure 7c,d.
# Exploring ISAC: Information-Theoretic Insights
## 2 Pre-ISAC: Sensing (Radar) vs. Communication
### Integrated Sensing and Communication (ISAC)  
The ISAC concept originated from observations in communication systems where backscattered signals were typically ignored and not utilized. Subsequent studies revealed that these backscattered signals, though initially overlooked, could provide valuable information that can help the transmitter(s) (Tx(s)) to improve communication performance or simplify coding schemes. In fact, backscattered signals can help any transmitting terminal to better estimate current and future channel conditions at the intended receivers (Rxs) or to identify the receivers' uncertainty about the transmitted data. Accordingly, they can improve transmission performance by adapting future transmissions to the uncertainties to resolve or future channel conditions. Such strategies allow a decrease in error probabilities and simplify coding schemes, and when channels vary only slowly in time, with high dependencies between channel conditions at different time, adaptive schemes can even achieve higher reliable rates, i.e., improve capacity.  
It is not hard to see that in typical ISAC scenarios, all non-extreme operating points of the basic and improved TS schemes are highly suboptimal compared to optimal integrated schemes.
# Mapping Phonation Types by Clustering of Multiple Metrics
## 3 Results  
### The Optimal Number of Clusters  
Now the question arises as to how many clusters \(k\) are optimal for describing the variation in the data. Intuitively, we expect the \(k\) to be limited to a countable range that, to some extent, corresponds to the phonation categories perceived by listeners.  
A frequently used method for determining the optimum number of clusters is the Bayesian Information Criterion (BIC). BIC finds the large sample limit of the Bayes' estimator, which leads to the selection of a model that is the most probable. It depends on whether the data observations are independent and identically distributed. BIC provides no more information than the model itself but selects the model by penalizing the complexity of several competing models. The BIC algorithm was improved by [PERSON] et al., whose variant shows better performance and goodness of fit on a large data set with many data points per cluster.  
Here, the _higher_ the BIC value, the better the tradeoff between the number of clusters and the performance, thus avoiding overfitting.  
While keeping the configuration of the K-means model constant, we iterate the cluster number \(k\) from 1 to 10. Figure 4 shows the BIC tendency across females, males, and children with all six metrics. The BIC approaches its maximum at \(k=6\) clusters, then remains constant for males, while for females and children, the same occurs for \(k=4\). Notice that the absolute values of BIC across the three groups do not imply an actual difference in information because the amount of data points is not normalized across groups. Here, the BIC suggests the optimal value of the cluster number for the model using six chosen metrics to be 4 to 6. A larger \(k\) will be penalized by the BIC algorithm, in order to avoid overfitting.  
Compared to the models with all six metrics, models with only one category of metrics (acoustic or EGG) show a different tendency in Figure 5. The models with only acoustic metrics reach a local maximum at the beginning of the curves of BIC, \(k=2\) for children, \(k=3\) for females, and \(k=4\) for males. The models with only EGG metrics, on the other hand, reach their maximum at \(k=4\) for all three groups. This can be taken to mean that, with the present data set and with the chosen metrics, the EGG signal can resolve a larger number of phonatory conditions than can the acoustic signal. In other words, for this data set, the models with only EGG metrics can yield higher distinguishability of phonation types while retaining a lower risk of overfitting.
## 3 Proposed System Model
### Incentive Mechanism in VN  
In a VN, sensory information is collected from smart vehicles and stored in IPFS. Whenever a smart vehicle requests a service, edge nodes communicate with the RSU. The RSU then checks the blockchain and gives response against the service request. Some smart vehicles are not involved in service sharing process due to their selfishness or lack of trust. Hence, the system's performance is compromised. To overcome this limitation, the incentive mechanism is proposed. Whenever some sensory information is provided by an ordinary node, it is awarded with incentives. This incentive amount is given in the form of cryptocurrency, which is further converted to local currency for getting different benefits from it. In our proposed system, the incentive is given as a reward after valid service sharing. By the involvement of incentive mechanism, problems of selfishness and low user participation are tackled. Algorithm 1 gives the basic flow of service provisioning to ordinary nodes and incentive provisioning to edge nodes.  
```
1 Initialization
2 Inputs: Ordinary node (\(o_{n}\)), Set of ordinary nodes (\(O_{n}\)), Edge node (\(e_{n}\)), Set of edge nodes (\(E_{n}\)), Service required (\(Ser_{req}\)), Set of services (\(S_{n}\)), RSU
3 Outputs: Incentive given (\(Inc_{giv}\)), Incentive denied (\(Inc_{den}\))
4 for\(Ser_{req}\) by \(o_{n}\) from \(e_{n}\), where \(o_{n}\in O_{n}\) and \(e_{n}\in E_{n}\)do
5 if(\(Ser_{req}\in S_{n}\))then
6 if(\(Ser_{req}\exists\) at \(e_{n}\))then
7 Grant service to the ordinary node, \(o_{n}\)
8 else
9 Forward the service request to RSU and goto step 12
10
11 end if
12
13 end if
14 if(\(Ser_{req}\exists\) at RSU)then
15 Grant service to the ordinary node, \(o_{n}\)
16 else
17 Give response to \(o_{n}\): \"Invalid request\"
18 end if
19
20 end if
21 else
22 Give response to \(o_{n}\): \"Invalid request\"
23 end if
24
25 end if
26
27 end if
28
29 ifService is provided to \(o_{n}\)then
30 Incentives given (\(Inc_{given}\)) to \(e_{n}\)
31 else
32 Incentives not given (\(Inc_{den}\)) to \(e_{n}\)
33 end if
34
35 end if
36
37 end if
38
39 end if
40
41 end for
```  
**Algorithm 1**Algorithm of Service and Incentive Provisioning in VN.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 3 Methodology
### Step 3: Analyze Threats to Base Energy Security
#### 3.3.1 Step 3.1: Reference Baseline Threats
Today, the probability of an explosion by a vehicle-born IED or other means of explosives still exists. A heighten intent of disrupting military operations and comprising national security makes military installations more favorable to this type of external event in comparison to civilian grid facilities. Terrorist attacks such as Beirut in 1983, 11 September 2001, and most recently in 2019 a vehicle borne IED outside the U.S. Embassy in eastern Kabul have changed the paradigm of force protection on military installations [48; 49]. Fuel Delivery Disruption with Low Probability or Resupply: Disruption of Fuel delivery may happen to any military installation. For instance, supply chain contracts and limited refining capacity within the United States limit the DOD's fuel supply domestically with reliance on extensive supply chains from foreign suppliers [50]. A single natural disaster not directly impacting a base can cause delay in fuel supply to base installations, which is why this type of external event is considered as a more likely occurrence for military installations. Other potential disruptions to fuel delivery such as protests blocking shipments, adversary destruction of fuel tankers, and other scenarios are of particular concern to military installations. Many military installations are heavily reliant on fuel delivery to power generators that are expected to function in order to support critical loads during an island mode situation. The above internal and external threats are the minimal list of threats that we believe base energy managers should use to assess their microgrid and proposed nanogrid architectures. We take this opportunity to vigorously remind the practitioner that location-specific threats either from a military installation itself or from natural or human-made phenomena locally or regionally must also be considered. For example, a military installation such as 29 Palms in the Mojave Desert has a higher probability of a rocket launched from the Mojave Spaceport crashing into microgrid infrastructure than Naval Support Activity Monterey which is far from any rocket launch trajectories. Another example is Camp Santiago in Puerto Rico and Camp Lejeune in North Carolina where both installations have a higher probability of a hurricane destroying microgrid and grid infrastructure due to the frequency of hurricanes in their locations versus North American west coast facilities.
## 2 Beam Characteristics
### Beam Energy  
There are different scenarios considered for future FLASH treatments. The most straightforward is the so-called 'transmission irradiation', where the beam'shoots-through' the patient at a very high energy. The most challenging but also closest to state of the art treatments instead involves sequential position and energy variation--the so-called 'pencil beam scanning'. The first is easier to realize and allows reaching higher dose rates (because of the lower scattering of high energy beams in tissues) but will generally increase the dose to the normal tissues, which in turn might be clinically unacceptable since the ultra-high  
\begin{table}
\begin{tabular}{l c c c} \hline
**Accelerator Type** & **Isochronous Cyclotron** & **Synchrocyclotron** & **Synchrotron** \\ \hline RF Frequency (MHz) & 50–100 & 50–100 & 1–10 \\ Bunch Length (ns) & 1–2 & 1–2 & 25–200 \\ Treatment Pulse Width (s) & - & (1–20) \(\times\) 10\({}^{-6}\) & 0.5–5 \\ Pulse Repetition Rate (Hz) & continuous & \(\sim\)10\({}^{3}\) & 0.5 \\ \hline \end{tabular}
\end{table}
Table 1: Typical beam time structure for different clinical accelerators.  
dose rates are expected to to spare normal tissue by only about 30-50% [2; 25]. Especially for protons, the use of transmission irradiation only is unlikely, as such a solution seems not fully to exploit the sparing properties of protons. Eventually, there are some compromise solutions, such as the use of ridge or pin filters to form a spread-out Bragg peak [23]. Depending on the solution, a different number of energies will be required.  
The range of energies used for proton therapy is typically 70-230 MeV. The performance of most diagnostic devices does not depend on energy variation in such a narrow range. The potential energy-dependent response of detectors can usually be corrected by applying a corresponding factor.  
Regarding the different machines mentioned before, the only difference is that synchro-cyclotron and cyclotron produce beams of fixed energy, unlike the synchrotron. Only the latter does not generally require an energy degrader, as the energy can be varied between cycles. Therefore, the energy spread of the beam is also smaller. Energy change with a degrader typically takes at least 100 ms [26]; thus, sequential energy changes might be too slow for future FLASH treatments. There are concepts of diminishing this time to about 10 ms [27], which is still long for FLASH applications. As previously mentioned, energy degraders also limit beam intensity transported to the treatment area and, thus, the dose rate. A solution, such as the previously mentioned ridge filter, would allow transporting a mono-energetic beam through a beamline. For synchrotrons, rapid cycling with ultrafast energy changes is potentially possible. However, this requires further studies and major developments [23].
# Prediction of Arrival Time of Vessels Considering Future Weather Conditions
## 2 Previous Studies
### Overview
#### 2.1.2 (B) Voyage Speed Calculation  
The en route voyage speed is calculated from past voyages with the same departure and destination ports, and it is used to calculate the time required to reach the destination port. First, data for the same departure and destination port are extracted from the operation data of the considered vessel, and the average voyage speed is calculated. The route obtained from (A) is used to calculate the distance from the current position to the destination port, which is then divided by the average voyage speed to yield the time required to reach the destination port. This time is added to the current time to determine the arrival time at the destination port.
## 4 Results
### Off-Grid Operation
#### 4.2.2 Unsymmetrical Load in Steady States  
Measurements of electric energy quality parameters in a steady state were carried out by measuring the phase voltages and currents of an unsymmetrical resistive load. The measurement results for the phase loads 5.5 kW, 5.5 kW, 0 kW are shown in the figures below. The waveforms of phase voltages and values of phase voltages and currents are shown in Figure 18. The power values and voltage harmonics are shown in Figure 19. The voltage asymmetry is shown in Figure 20.  
Figure 16: Phase load power values, root mean square (RMS) values of phase voltages and voltage harmonics. Off-grid mode, symmetrical load. The colors of the lines in the harmonics diagram represent the individual phases. Source: author’s development.  
Figure 17: Phase voltage unbalance. Off-grid mode, symmetrical load. The colors of the arrows in the unbalance diagram represent the individual phases. Source: author’s development.  
Figure 18: Waveforms and values of phase voltages and currents. Off-grid mode, unsymmetrical load. Source: author’s development.  
The most important result of the test was checking the asymmetry of the phase voltages. The voltage values in individual phases did not exceed the permissible values \(\pm\)10% of the rated value. For a rated voltage of 230 V the permissible range is 207-253 V. For a resistive load, there is only one basic harmonic.  
For the Fluke manufacturer's stated measurement accuracies, the measured values are: (1) voltage of phases L1: 250.56 \(\pm\) 0.23 V, L2: 219.27 \(\pm\) 0.23 V, L3: 223.14 \(\pm\) 0.23 V; (2) current of phases L1: 0, L2: 23.2 \(\pm\) 0.6 A, L3: 23.5 \(\pm\) 0.6 A; (3) active power of phases L1: 0, L2: 5.08 \(\pm\) 0.1 kW, L3: 5.25 \(\pm\) 0.1 kW. In this example, the effect of the voltage value on the active power of a resistive load can be seen.
## 2 Materials and Methods
### Training and Testing Setup  
The training and testing of both networks were conducted on a machine equipped with an Intel i7-6700 HQ CPU (8 cores) running at 2.6 GHz base frequency, an NVIDIA GeForce GTX 950M GPU, and 16 GB RAM. The CFO network underwent training for 90 epochs, employing a learning rate of \(10^{-3}\). To enhance the learning process, a learning rate scheduler known as ReduceLROnPlateau was utilized, reducing the learning rate by a factor of 0.5 with a patience of 10. The Adam optimizer was employed with the same learning rate and optimizer parameters were set to \(\beta_{1}=0.5\) and \(\beta_{2}=0.999\).  
Similarly, the U-Net-based autoencoder network was trained for 35 epochs, employing the same learning rate and Adam optimizer parameters. However, the patience value for the learning rate scheduler was adjusted to 5. This configuration facilitated effective network training and optimization of model performance.  
Table 4 provides a concise summary of the training hyperparameters utilized in our study.  
With the hardware setup detailed in Table 5, our trained neural network can efficiently execute a single CFO estimation and correction event for an individual signal in approximately 0.6 ms. Furthermore, the other neural network can perform phase offset estimation, correction, and denoising for the same signal in approximately 2.2 ms.
# Cavity Detection in Steel-Pipe Culverts Using Infrared Thermography +
## 5 Experimental Results  
### Experiment 1: Specimen 1, Cavity Configuration 1  
Raw thermal images (Figure 5(a)) were corrupted with noise and could not provide any clear sign of the presence of a cavity. Pre- and post-processing, as shown in Figure 5(b)-f, noticeably contributed to a better understanding and easier analysis of thermograms. Color mapping was also helpful and carried out at different levels of granularity to achieve a desirable output.  
As mentioned before, we hypothesize that the existence of a cavity behind the specimen leads to variations in thermal radiation from the specimen surface. Although the presence of a cavity is evident in the processed thermograms (orange/yellow patch at the bottom of Figure 5(c)-f), further investigation was made through ATC which is a quantitative approach. Figure 7 shows the temperature-time graph for two randomly selected points, one from the sound areas (without any cavity, referred to as \"sa\") and the other from the defective areas (with cavity, referred to as \"da\"). From this graph, it is apparent that during the transition phase (cooling down), the temperature of defective regions (red curve) cools down slower than nondefective areas (black curve). The blue curve which is the thermal contrast between the two temperatures clearly shows this.  
Thermograms were also analyzed using PCT (Figure 8), and the most informative output was EOF 3. After studying the effect of filtering and other parameters of the experiment setup on the final result, we noticed that the thermal images recorded during the cooling down period, rather than the entire sequence, should be used as input to the PCT algorithm to obtain more informative results. A deeper look at the temperature graph  
Figure 6: Impact of pre- and post-processing on image quality; experiment 1. (**a**) An example of the raw image. (**b**) The processed image. (**c–f**) Color mapping (64, 10, 5, and 3 levels).  
Figure 7: ATC; experiment 1. (**a**) Selected points. (**b**) Temperature-time graph and thermal contrast.  
(Figure 7) revels the underlying cause. At heap up, drastic changes occur in temperature of both sound and defective areas. As PCT is a method of obtaining major variables, it also extracts the variations of temperature in thermal excitation phase, and this makes the process of cavity identification more difficult because of blending ineffectual information to EOFs. Despite the useful role of the temporal filtering at ATC analysis, it did not perform very well for PCT. For each setup, we repeated the experiment with four different durations of the thermal stimulation. The best result was achieved when the duration was around five seconds.
# Server Operation and Virtualization to Save Energy and Cost in Future Sustainable Computing
## 2 Background
### Virtualization  
The capacity of an emulation test bed scales when experimental nodes are mapped onto limited physical resources [18]. For example, the DETER (Defense Technology Experimental Research Laboratory) containers system (DETERLab, The University of Southern California, Los Angeles, CA, USA) [18; 19] can support experiments that are two orders of magnitude larger than the test bed. Most emulation test beds support various state-of-the-art virtualization techniques. With root access on test bed machines, users are able to create different types of virtual machines on the provisioned test bed machines [19]. For instance, if multiple experimental nodes with different operating systems are hosted on a single test bed machine, the user can apply full virtualization solutions, such as KVM [20], VMware [21], or Virtual-Box [22], to create the virtual machines with different guest operating systems. If the user needs to minimize the virtualization overhead, the virtual machines can be created using lightweight OS-level virtualization techniques such as LXC [23]. VMware, Inc. has the largest market share in the virtualization technology. They already have technologies that support Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS), all of which are verified as virtualized services.  
Conversion to a virtualized system will bring about next several benefits, so we have performed the virtualization work for the server and network laying emphasis on them in this paper. The benefits are: reduction in the costs involving the Data Center Management and new investments; human resource efficiency related to the repetitive tasks; and achievement of environment-friendly IT [23; 24] by being able to fulfill low-power operation by securing the rack space and working environment with constant temperature and humidity. The virtualization technology can be largely divided into the application virtualization, server virtualization, network virtualization, desktop virtualization and storage virtualization.  
One of the most compelling aspects of distributed cloud environments based on wide area networks is reducing costs while offering redundancy, reliability, and geo-replication. Furthermore, other motivations of distributed cloud include reducing wide-area traffic and latency, efficient computation at the edge, and virtualization [25; 26; 27; 28; 29; 30; 31], while research challenges for networking are summarized as follows: distributed cloud communications in WAN, network virtualization, network security and privacy, high performance, edge network architecture with middleware, and scalability [32; 33; 34; 35; 36; 37].