esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Estimating UCS Using Linear and Parabolic Equations
The linear fitting equation yielded results with 8 to 12% error, whilst the parabolic fitting yielded results with less than 2.5% error, as shown in Table 5:  
* The \"Estimated-UCS\" using parabolic fitting for sample A provided almost identical results with the \"True-UCS\" (197.26 versus 197.74 MPa, respectively, means a \(-0.2\%\) error);
* For sample B, the \"True-UCS\" is 205.84 MPa, whilst the \"Estimated-UCS\" using the parabolic equation is 210.48 MPa (means a 2.2% error);
* For sample C, the \"True-UCS\" is 212.77 MPa, whilst the \"Estimated-UCS\" using the parabolic equation is 215.47 MPa (means a 1.3% error). As mentioned above, there is a difference of 1 to 2% between the \"True-UCS\" and the parabolic fitting \"Estimated-UCS\" for samples B and C. This difference, although quite small, earns an explanation in the next paragraph. During the test and the calculation of UCS for sample B, the log shows that the test at stage zero confinement was probably stopped a bit too early. The data curve of axial stress (_Sa_) versus axial strain (_Ea_) in Figure 7 shows that the curve is not significantly curving yet. The test with zero confinement could have continued to increase to a slightly higher stress value to gain a higher \"True-UCS\". Due to this early stop, the \"True-UCS\" obtained at this stage of the test might be slightly lower than the actual UCS of the sample. This can be confirmed by the calculation of UCS using the data points from the subsequence steps (steps with confinements of 2, 5, 10, 15, and 20 MPa). The calculation resulted in an \"Estimated-UCS\" equal to 210.48 MPa (using the parabolic equation), slightly higher than the obtained \"True-UCS\" (205.84 MPa). A similar observation and comment can be made for sample C to explain the difference between the \"True-UCS\" (212.77 MPa) and the \"Estimated-UCS\" (215.47 MPa) for this sample. Nonetheless, the differences between the \"True-UCS\" and the \"Estimated-UCS\" in samples B and C are relatively small (2.2% and 1.3%, respectively). From the observations in samples B and C, and also the estimation result for sample A, it can be stated that if the first step (the zero-confinement step) was not stopped that early, then the \"Estimated-UCS\" would be identical to the \"True-UCS\" as demonstrated with sample A. These initial tests that have been performed indicate that with an appropriate fitting equation, the UCS of a rock core can be derived from triaxial test Type II with a high degree of confidence. The fitting equation can be selected as a linear or non-linear equation, depending on the behaviour of the rock sample under the test.
## 3 Data Description and Preparation  
### Data Source  
To investigate the adaptability of models to car-following (CF) behavior in various situations, it is essential to use driving data that provide full spatial-temporal coverage. In the present study, we employed large-scale data obtained from the Natural Driving Research Project, a collaborative endeavor involving GM, Huawei, SAIC, and Tencent, which collected data from 60 drivers for up to 200,000 km.  
The quality and resolution of the NDS data are well guaranteed. The data were collected in a naturalistic driving context to minimize the impact of acquisition devices on driving behavior. Figure 1 displays the data acquisition system. It consists of a Doppler radar, a triaxial accelerometer, a GPS, and four synchronized cameras. These components work together to capture location data and kinematic information for both the subject vehicle (including speed, longitudinal acceleration, and lateral acceleration) and surrounding vehicles. The data record driving information in time series with up to 86 dimensions. The recording frequency for most data is 10 Hz, while for a few dimensions it is 1 Hz. The radar detects road participants around the vehicle and records at a frequency of 10 Hz. It can record the relative position and speed of up to eight objects at the same time. We determine the vehicle being followed by the subject vehicle based on the relative position relationship. Additionally, the system records any manual operations performed by the driver within the vehicle.  
To understand the impact of the driving mode on the modeling accuracy, an extensive comparison of DBDM and DBM is required, which requires enough drivers with enough driving records to be included in the study. In the NDS, natural driving behavior was recorded for a total of 40 Chinese drivers, primarily from the Shanghai metropolitan area, and they were selected at random. The demographic information (gender, driving experience, age) of the 40 drivers is similar to the population of registered drivers in China [32; 33]. The difference between the distribution of NDD on each dimension and theoverall distribution is within 5%. In contrast to the trajectory datasets discussed in Section 2 and used in CF modeling and driving modes, the NDS offers extensive multi-dimensional data with full spatial-temporal coverage. The NDS not only includes a larger number of participating drivers, but it also provides greater traffic flow facility coverage for each driver than other datasets containing the long-term observations of drivers.
## 2 Research Significance  
Current studies offer limited insights into the effects of GPR scan spacing on effectively assessing internal deficiencies of structural concrete members, such as voids, delaminated areas, and locations of reinforcement corrosion and section loss. Recent research has limited information on how the GPR technology can effectively be employed to inspect prestressed concrete box beams and determine the rebars' depth, spacing, and size. This research addresses the knowledge gaps of GPR and suggests an accurate assessment of in-service bridges. This study employs a prestressed concrete box beam of a decommissioned bridge from the West Virginia Division of Highways inventory. The research study aims at enhancing the ability of bridge inspectors to assess the condition of bridge box beams more precisely and effectively in the field. The improved evaluation capability will assist state and federal agencies in allocating their financial and human resources more effectively. This study will ensure efficient and more informed transportation infrastructure investment decisions and potentially save significant costs in the long run.
# Modeling a Milling Dynamometer as a 3 DOF Dynamic System by Stiffness Identification
## 5 Modal Analysis of the Dynamometer  
### Modal Analysis Considering the State Space Model  
In the case of the state-space formulation, the displacements and velocities are considered as unknown variables. Both quantities describe the state of the system at any time.  
From the motion equations, the state-space equations are defined as [21; 22; 23; 24]:  
\[\dot{\mathrm{X}}\ =\ \mathrm{A}\,\mathrm{X}\ +\ \mathrm{B}\,\mathrm{F} \tag{16}\]  
where \(\mathrm{X}\) is the state vector that contains the displacements and velocities for all degrees of freedom, \(\mathrm{A}\) is the a matrix, which contains the characteristics of the system, and \(\mathrm{B}\) is a matrix that refers to the influence of forces described in the input matrix \(\mathrm{F}\). Matrix \(\mathrm{A}\) is given by:  
\[\mathrm{A}\ =\ \left[\begin{array}{cc}0&\mathrm{I}\\ -\mathrm{M}^{-1}\mathrm{K}&-\mathrm{M}^{-1}\mathrm{C}\end{array}\right] \tag{17}\]  
with the notations from Equation (14), a zero matrix \(\mathbf{0}\), and an identity matrix \(\mathrm{I}\).  
The natural frequencies of any mechanical system with a behavior described by Equation (16) are given by the eigenvector of matrix \(\mathrm{A}\) described by (17), where damping is considered to be zero:  
\[\mathrm{A}^{*}\ =\ \left[\begin{array}{cc}0&\mathrm{I}\\ -\mathrm{M}^{-1}\mathrm{K}&0\end{array}\right] \tag{18}\]  
To solve the equation of eigenvalues:  
\[|\,\mathrm{M}\ -\ \mathrm{A}^{*}|\ =\ 0 \tag{19}\]  
Three pairs of six eigenvalues that are complex numbers are obtained. The imaginary part of them represents the natural circular frequency for each degree of freedom.  
The simulation data are as follows: the total mass (upper plate + rings + workpiece) is \(m\ =\ 12\ [\mathrm{kg}]\); stiffness \(k_{1}\) is considered to be the longitudinal stiffness of the ring plus the stiffness of the transducer, where \(k_{1}\ =\ 29.195\times 10^{6}\ [\mathrm{N}/\mathrm{m}]\ +\ 2\times 10^{9}[ \mathrm{N}/\mathrm{m}]\ =\ 2.029195\times 10^{9}\ [\mathrm{N}/\mathrm{m}]\); \(k_{2}\) is considered to be the transverse stiffness of the ring plus the stiffness of the transducer, where \(k_{2}\ =\ 28.894\ \times\ 10^{6}\ [\mathrm{N}/\mathrm{m}]\ +\ 2\times 10^{9}\ [ \mathrm{N}/\mathrm{m}]\ =\ 2.028894\times 10^{9}\ [\mathrm{N}/\mathrm{m}]\); the damping coefficients [21] are \(c_{1}\ =\ 2546.49\ [\mathrm{Ns}/\mathrm{m}]\) and \(c_{2}\ =\ 1469.13\ [\mathrm{Ns}/\mathrm{m}]\); a = 0.087 [m]; b = 0.042 [m]; and the mechanical moment of inertia \(J\ =\ 0.0636\ [\mathrm{kg}\cdot\mathrm{m}^{2}]\).  
Using the tools of MATLAB(r) R2024a software, we obtained the following values for the rotational frequencies: \(\omega_{n1}=\ \omega_{n2}\ =\ 1.8521\ \times\ 10^{4}\ [\mathrm{rad}/\mathrm{s}]\), and \(\omega_{n3}\ =\ 2.4581\ \times\ 10^{4}\ [\mathrm{rad}/\mathrm{s}]\). Corresponding to these circular frequencies are the following natural frequencies: \(f_{n1}\ =\ f_{n2}\ =\ 2947.7\ [\mathrm{Hz}]\), and \(f_{n3}\ =\ 3912.2\ [\mathrm{Hz}]\).
## 4 Methodology
### Case Study 1: Live Indoor Site  
The live indoor site was located in Glasgow city and consisted of a three-story open plan office building, Figure 3 of an undeveloped flat open area. The floors were essentially rectangular with 12 \(\times\) 45 meters with a small taper at one end for an overall area of 510 square meters per floor. The roof and large part of the outer skin of the top two floors were metal. Against the building long side was a two-story atrium linking this building to an adjacent two-story building. There were two LoRAWAN connected environmental monitoring stations on each floor, with an additional monitoring station located in a bathroom area on the ground-floor. A portable monitoring station was located in different building areas or outdoors as required. The seven fixed monitoring stations measured several environmental parameters and reported values approximately every 15 min. The mobile monitoring station measured the same environmental parameters and transmitted these values every seven to eight seconds if turned on. All monitoring stations requested an ACK when they transmitted.  
Correct transmission and reception of packets from the monitoring stations to the network were identified from the monotonic incrementing of the packet up-counter for a specific monitoring station, and lost packets were identified from a missing count. Lost acknowledgements from the NS to the monitoring stations via a gateway were inferred by monitoring stations sending a repeat transmission of a packet approximately twenty seconds after a previous successful transmission. The monitoring stations were designed to resend several times, and if an ACK were not received, the monitoring stations would attempt to rejoin the network. Monitoring stations utilised the Multitech mDot [43] LoRAWAN module.  
Figure 2: Anonymised JSON packet from a NS including gateway RF data and time stamps.  
Monitoring stations communicated with two Multitech 'Conduit' gateways [44] in the building, one on the top-floor and another on the ground-floor. Initially, one gateway used a mobile network backhaul, but the mobile network latency was extremely variable, with latency commonly exceeding 1500 ms, it would have compromised the correct operation of the network.
###### Abstract  
Small mN-class gridded ion thrusters are usually tested in a vacuum chamber without the use of a neutralizer, relying on self-neutralization of the ion beam due to interaction with facility walls. Langmuir probe measurements performed immediately downstream of such a thruster reveal values of the plasma potential of several hundreds of volts. If this locally very high potential indeed exists, it would have significant impact on the erosion rate of RIT grids and thus reduce the lifetime of thrusters compared to the generally accepted plasma potential of a few tens of volts. Further measurements performed with a movable Langmuir and emissive probes indicate that the probe mount violates the ability of the ion beam to self-neutralize. This is concluded due to dependence of the measured potential value on the degree of neutralization introduced in the experiment. Particle-in-cell and direct-simulation Monte Carlo simulations of the ion beam corresponding to experimental conditions (ion energy \(E_{\text{Xe}^{+}}=1.5\,\text{keV}\) and ion beam current \(I_{\text{Xe}^{+}}=17\,\text{mA}\)) are carried out to determine the phenomena responsible for the self-neutralization; mainly, reactions with neutral species such as ionization by electron or ion impact and secondary electron emission (SEE) from the facility walls are compared. Reasonable agreement with measurements is achieved, and SEE is determined to be the primary source of electrons, indicating that facility and measurement disturbance effects majorly influence testing of (non-neutralized) ion beams. Further, limitations of the applicability of probe diagnostics on non-neutralized ion beams are described.  
ion beam; self neutralization; Langmuir probe; PIC; DSMC; secondary electron emission +
Footnote †: journal: Applied Physics Letters
## 1 Introduction
A significant amount of research is in progress to regulate the practical application of simplified operational measures, some of which includes studies addressing operational limitations based on the standard WST or modified WST for specific failure modes and ship types [15; 16; 17]. Studies are also being conducted on the application of guidance [18; 19]. In addition, some research focuses on variation in stability estimation when different weather data sources are used [20; 21]. Meanwhile, other studies used a numerical approach by incorporating operational limitations [22] and route navigation simulations to assess the failure modes and using ship-traffic-based AIS data [23]. However, the limits on the recommended standard WST are not assessed, given that successful validation with the same will achieve ships with unrestricted operation. Therefore, it is important to assess the recommended standard WST's safety limit that holds valid for major seasons and regions. However, such a study is not available, and, therefore, is carried out in this paper using hindcast data. The study allows to identify the existing limitation of the standard WST. The following paper is outlined as follows. Section 2 discusses the methodology used for the study, Section 3 briefly describes the ship used for the case study and Section 4 covers the discussion and the result.
# Test Suite Prioritization Based on Optimization Approach Using Reinforcement Learning
## 3 Materials and Methods
functionequalidaction(current-state)  action=random.choice(valid-actions[current-state])  IF action = (\(-1\)):  getvalidation(current-state);  ENDIF  RETURN action endfunction for all episode i in 1000 episodes: do  start-state=0  current-state=start-state  while current-state!=goalstate: do  action=getvalidation(current-state)  next-state=transition-matrix[current-state][action]  future-rewards=[] for all action-next in valid-actions[next-state]: do  Add to future-rewards q-matrix[next-state][action-next])  q-state=reward[current-state][action]+gamma*max(future-rewards)  q-matrix[current-state][action]=q-state  current-state=next-state endfor endwhile  endfor  OUTPUT q-matrix
```  
**Algorithm 1** The proposed learning algorithm for test suite selection. The sequence extraction algorithm creates a state sequence from the initial to the final state from the final updated Q-table based on the greater \(Q\) value. This sequence is called the highest future reward sequence. Algorithm 2 presents the proposed highest future reward sequence extraction algorithm. ```
0: Updated Q-Table and testing environment matrix Output: Sequence of states with highest future reward \(a\leftarrow\)Length of Updated Q-Table \(A\leftarrow\)Array \(c\leftarrow\)Actions \(n=0\) \(m=0\) while\(n\
eq\)ado \(A[m]=state\) for all\(Ci+1\in\)cdo  start from state n, select action highest reward  Perform action on the testing environment matrix and move to the next state  update n  variable to the next state  n = Next State endfor  m = m + 1 endwhile  Output: print A[m]
```  
**Algorithm 2** The Sequence extraction algorithm. The prioritization algorithm takes tester logs and the highest future reward sequence from the Q-Learning algorithm as the input. The proposed prioritization algorithm is presented in Algorithm 3. ```
0: Highest future reward sequence and test suite T
0: prioritized order test-suit
0:\(L\leftarrow\) Length of Test suite T
0:\(F\leftarrow\) Highest future reward sequence
0:\(S=\) Hash table with two values \((\)length,similarity\()\)
0:\(i=0\)
0:\(k=0\) for all Ti \(\in\) L do
0: S[similarity]=compute similarity index of Ti  S[Length]=Length of Ti  sort S by similarity DESC for all k \(\in\) S do  if similarity S[k]=-similarity of S[k + 1]  swap item of S with maximum length first endfor endfor  Output: Print hash table S
```  
**Algorithm 3** The proposed prioritization algorithm. The highest future reward sequence is mapped into the tester log suite, and the cosine similarity of the future reward sequence with each tester log is calculated using Equation (3):  
\[cos(\Theta)=\frac{A\cdot B}{||A||\cdot||B||}=\frac{\sum_{i=1}^{n}A_{i}B_{i}}{ \sqrt{\sum_{i=1}^{n}A_{i}^{2}}\sqrt{\sum_{i=1}^{n}B_{i}^{2}}} \tag{3}\]  
Tester logs with the highest cosine similarity value are prioritized high and vice versa. If test cases have the same cosine similarity index, then the test case's length is considered as secondary parameter and Test case with greater length value is prioritized high.
# Towards Digital-Twin Assisted Software-Defined Quantum Satellite Networks
###### Abstract  
The Quantum Internet (QI) necessitates a complete revision of the classical protocol stack and the technologies used, whereas its operating principles depend on the physical laws governing quantum mechanics. Recent experiments demonstrate that Optical Fibers (OFs) allow connections only in urban areas. Therefore, a novel Quantum Satellite Backbone (QSB) composed of a considerable number of Quantum Satellite Repeaters (QSRs) deployed in Low Earth Orbit (LEO) would allow for the overcoming of typical OFs' attenuation problems. Nevertheless, the dynamic nature of the scenario represents a challenge for novel satellite networks, making their design and management complicated. Therefore, we have designed an ad hoc QSB considering the interaction between Digital Twin (DT) and Software-Defined Networking (SDN). In addition to defining the system architecture, we present a DT monitoring protocol that allows efficient status recovery for the creation of multiple End-to-End (E2E) entanglement states. Moreover, we have evaluated the system performance by assessing the path monitoring and configuration time, the time required to establish the E2E entanglement, and the fidelity between a couple of Ground Stations (GSs) interconnected through the QSB, also conducting a deep analysis of the created temporal paths.  
Keywords:quantum digital twin; quantum internet; quantum key distribution; quantum satellite backbone; quantum software-defined networking +
Footnote †: journal: Sensors
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 4 Routing Protocols for UAWSNs
### Network Structure-Based Routing  
In network structure-based routing, the way that nodes are connected and how they exchange information depend on the network architecture. Flat routing, a type of network structure-based routing, combines all the sensor nodes together to perform sensing, and all nodes have the same role to play in the network. On the other hand, hierarchical routing has sensor nodes that play different roles in the network. For example, some nodes may send data to the sink node, whereas others may perform the task of sensing only. In tree-based routing, a routing tree is formed among the sensor nodes and the UAV-based sink node is the root of the tree. Location-based routing, which is another type of network structure-based routing, operates on the basis of two assumptions: (a) nodes are position-aware and have knowledge about the position of their neighbors, and (b) the message contains the location information of the destination node.  
Figure 1: Taxonomy of routing protocols for UAWSNs.
## Permeate
### Other Integrated MFCs  
Many research studies have used MFCs in a stacked configuration to achieve upscaling and treatment performance. There were attempts to improve the COD removal by testing an MFC (without the membrane) employed with a biocathode submerged in an SBR. This MFC was able to reach 2.34 W/m\({}^{3}\), with a total of 18.7% elimination of COD from the integrated setup [23]. USB-MFC-BAF was used to attain the concurrent management of molasses wastewater and the production of bioelectricity [26]. In this system, the potential of COD removal was 53.2%, and the supreme energy output reached was 1410.2 mW/m\({}^{2}\). The absolute denaturation of the azo dye AO-7 was obtained through the integration of an MFC and an anaerobic bioreactor [32]. The dual-staged technique was used to achieve a decrease in ecological toxicity and reach 90% COD removal along with the simultaneous generation of bioelectricity. An MFC-COR setup packed with a granular catalyst was used to obtain degradation of the azo dye, Congo red [30]. An amount of 90% of the dye was observed to have degraded in 3 days in a chargeless mixture, and the maximum power density reached was 808.3 mW/m\({}^{3}\).  
In a plug flow environment, [PERSON] et al. [99] combined a number of MFCs in a stacked configuration. With a total volume of 250 L, the flow was horizontal. While treating residential wastewater, the maximum energy obtained in each module was 0.435-0.010 A, with carbon oxygen demand and total nitrogen (TN) elimination rates of 79 \(\pm\) 67% and 71 \(\pm\) 68%, respectively. Moreover, MFC has been applied to a number of other procedures, such as electro-Fenton reactions, to improve treatment efficiency. The strategy for integrating MFCs to improve electro-Fenton reactions is to add hydroxyl ions to the Fenton reactions to speed up the processes. Many studies have shown the generation of H\({}_{2}\)O\({}_{2}\) at the cathode of MFCs. The generation of H\({}_{2}\)O\({}_{2}\) at the cathode can be accomplished using two or four electron transmission processes, whereas in electro-Fenton reactions, ferrous ions are oxidized to ferric ions using oxidants (Figure 9).  
In one of the studies, a laboratory-scale three-chambered MFC-dependent latrine was built for organic material treatment in the anode compartment, nitrification in the central compartment, and denitrification with the help of a biocathode in the third compartment [100]. During the operation, the highest COD elimination of 90%, a nitrate reduction  
Figure 9: MFC integrated with bio-electro-Fenton.  
of 76.8%, and an energy output of 3.4 mW/m\({}^{2}\) were attained. Composting was eventually performed with solid waste [101]. A three-column MFC stacking structure that can be easily plugged in a septic tank to yield an energy output of 142 mW/m\({}^{2}\) was devised. This system is predicted to have a daily power consumption of 24 W/h, which is enough to run a 6 W LED bulb for 4 h. Similarly, 15 MFCs were stacked in a 2.44 m\({}^{3}\) septic tank and linked to a power management circuit, finally discharging the highest current of 1.98 mA and power of 4.51 mW [102].