esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Governing Equations
### Reynolds Equation  
The nondimensional Reynolds equation describing fluid film lubrication in journal bearings, considering isothermal operation, constant-density lubricants, and perfect rotor-shell alignment, takes the following form [43]:  
\[\frac{\partial}{\partial\theta}\left[\overline{h}_{n}^{3}\frac{\partial \overline{P}}{\partial\theta}\right]+\left(\frac{D}{L}\right)^{2}\frac{ \partial}{\partial\overline{z}}\left[\overline{h}_{n}^{3}\frac{\partial \overline{P}}{\partial\overline{z}}\right]=6\mu\frac{\partial\overline{h}_{n}}{ \partial\theta}+12\mu\frac{\partial\overline{h}_{n}}{\partial\tau} \tag{1}\]  
The dimensionless quantities appearing in Equation (1) are characterized as:  
\[\theta=\frac{x}{R},\ \ \overline{z}=\frac{z}{L/2},\ \ \overline{P}=\frac{C_{n}^{2} }{bf\theta\omega R^{2}}P,\ \ \overline{h}_{n}=\frac{h_{n}}{C_{m}},\ \ \ \ u=R\omega,\ \ \ \ \ \tau=t\omega \tag{2}\]  
The lubricating film thickness distribution throughout the engineered lobed journal bearing with surface textures is given by the subsequent relation [44].  
\[\overline{h}_{n}=\overline{h}_{n-smooth}+\Delta\overline{h}(\theta,z) \tag{3}\]  
Equation (3) incorporates \(\overline{h}_{n-smooth}\) as the lubricant film dimension separating the rotating shaft and the bearing shell for an untextured circular bearing at all circumferential positions, mathematically described by Equation (4). Furthermore, \(\Delta\overline{h}(\theta,z)\) quantifies the depth of engineered surface textures--including cubic, cylindrical, and semi-ellipsoidal geometries--patterned on the bearing's interior surface [14].  
\[\overline{h}_{n}=\frac{h_{n}}{C_{m}}=\left(\frac{1}{\delta}-\frac{X_{j}}{C_{m} }\cos\theta-\frac{Y_{j}}{C_{m}}\sin\theta+\left(\frac{1}{\delta}-1\right)\cos (\theta-\theta_{0}^{n})\right) \tag{4}\]  
Figure 2: Two-lobe noncircular journal bearing with textured inner surface.  
Within Equation (4), symbol n denotes the lobe count of the two-lobe journal bearing, while variables \(X_{j}\) and \(Y_{j}\) specify the journal center location in static equilibrium conditions relative to the reference coordinate system. Angle \(\theta_{0}^{n}\) corresponds to the lobe's centerline orientation with respect to the X-axis. The noncircularity coefficient \(\delta\), characterizing the bearing geometry, is determined as the ratio between the smallest radial gap (C\({}_{\text{m}}\)) and the maximum conventional radial clearance (C) under concentric shaft-bearing conditions.
## 3 Results
### Thresholds for Pixel Classification  
Figure 6A,B show the identification of pixels as per the optical and thermal approaches. Using Equation 9, the iteration process provided the threshold for the NDVI at 0.425, for the STR at 1.425 and for the Ts\(-\)Ta at 14.85 \({}^{\circ}\)C. In each case, the pixels were divided into four classes. Pixels within Sector Four (refer to Figure 3A,B) were identified as 'irrigated' crop or pasture.
# A BIM-Based Smart System for Fire Evacuation
## 3 Application to a Research Building of Lille University
### Agent-Based Evacuation Simulation (ABS)  
According to [PERSON] et al. (2018) [51], the generated heat, smoke density, and toxicity concentration constitute the main features that affect the occupants' movement. Therefore, the FDS simulations are integrated into Pathfinder to consider humans' movement during the fire. Fifty occupants are distributed randomly in the laboratory, where at least one occupant is placed in each room. The Required Safe Egress Time (RSET) and the exit door used by each occupant were computed. The simulation validation was conducted by checking that the RSET is less than the ASET for all occupants. Several evacuation simulations need to be performed to achieve the optimum goal while using Pathfinder to derive the optimum set of instructions for each tenant considering the real-time location.  
Figure 7 shows, for the kitchen fire scenario, the difference in flow rates between the evacuation under normal conditions and fire circumstances. It indicates that under normal situations, the flow rates on each exit are smooth compared to fire incidents. However, the latter fluctuates all along the evacuation process. Moreover, the total evacuation time in normal conditions is 54.5 s compared to 251.5 s during the fire. This important change in the evacuation time is due to visibility losses and smoke.  
Figure 8 shows that only three occupants used exit 1 during the fire event compared to 11 occupants in normal conditions. This means that most occupants near exit 1 changed their direction to exit 2 because the fire location was close to exit 1. Accordingly, the model guides occupants based on the safest path more than the quickest ones. It should be noted that occupants reached the exit doors in less time than the ASET derived from PyroSim.  
For the electrical fire scenario, the total time for evacuation under FDS conditions is equal to 209.5 s. The flow rates illustrated in Figure 9 indicate a significant change in the occupants' behavior. For example, 14 occupants out of 28 changed exit 2 to exit 1 or exit 3 under fire circumstances. Moreover, the model respects the 83 s ASET for exit 2, confirming that the model privileges the safest evacuation paths.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Limits** & **Exit 1** & **Exit 2** & **Exit 3** \\ \hline Smoke Density & 441 s & 316 s & 254 s \\ \hline Visibility & 585 s & 83 s & 549 s \\ \hline Temperature & NA & NA & NA \\ \hline ASET & 441 s & 83 s & 254 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: ASET computation for door exits in the second scenario (electrical fire scenario).  
Figure 7: Impact of fire on the indoor occupants’ flow rate (kitchen fire scenario).
# Cooperative Guidance Law for the Mother-Cabin of the Anti-UAV Cluster Mother-Son Missile
## 2 Description and Analysis of Cooperative Guidance of Multiple Mother-Cabins  
### The Operational Pattern of Cooperative Interception of UAV Clusters by Mother-Son Missiles  
When a UAV cluster carries out a combat mission, it can generally be divided into the following four phases: first, the carrier aircraft releases the UAVs outside the defense area at a long distance. Then, the UAVs assemble, form up, and fly intensively, and then fly in scattered formations to the mission area. Finally, the UAVs carry out combat missions such as detection, interference, and attack according to the established plan. Among these phases, during the phase of formation and intensive flight over the range of medium and far distances, the communication and coordination functions of the cluster are not yet complete, which is the weakest stage of protection capability. At this stage, the formation is dense, and the cluster as a whole has a weak maneuverability due to the performance of the individual UAVs and the constraints of the formation structure.  
Based on the above characteristics of the cluster, an operational pattern of intercepting UAV clusters over the range of medium and long distances (the phase of formation and intensive flight) by using mother-son missiles is proposed. The mother-son missile in this pattern consists of a turbojet-powered mother-cabin and non-powered son-interceptors carried in the mother-cabin. The targets of interception are medium to large sized UAVs used for cluster combat. Such targets have a long combat range and high altitude, and their payload capacity is sufficient to enable them to perform various combat roles such as reconnaissance, interference, and attack, which pose a high threat level and intercept utility.  
The basic interception strategy is to select high-threat targets within the cluster and divide the selected targets into multiple sub-clusters based on the interception capability of each mother-son missile. Each mother-son missile is then responsible for intercepting one sub-cluster. The basic interception process involves calculating the position of the \"release point\" of each mother-cabin in real time according to the motion state information of each sub-cluster threat's \"center of gravity\". The mother-cabin performs a guided flight according to the \"release point\" and releases the son-interceptor after arriving at the \"release point\". The son-interceptors intercept the designated UAV targets. The basic operational concept is shown in Figure 1. The threat \"center of gravity\" is a spatial particle inside the cluster, which is used to represent the location of the cluster. The \"release point\" is a spatial particle derived from the threat \"center of gravity\" along the horizontal direction. In order to emphasize the research on the cooperative guidance law of the mother-cabin, this paper will not discuss the method of cluster clustering or the calculation methods of the threat \"center of gravity\" and \"release point\".  
Figure 1: Multiple mother-son missiles intercepting UAV clusters.
## 3 Materials and Methods  
### Field Measurements  
Hydrodynamics, bedload transport and morphological changes were monitored using an acoustic Doppler current profiler (SonTek M9-ADCP) on 3 consecutive days in the first trimester of 2018. An external real-time kinematic global positioning system (RTK-GPS) was coupled with the ADCP to improve vessel positional precision (subdecimetric in horizontal). The RTK system used was formed by two Leica 1200 GX GNSS dual frequency GPS receivers (L1 and L2) and a Pacific Crest PDL radio. The GPS mobile station was attached to the ADCP through a metal pole, directly above the ADCP sensor. The GPS base station was fixed at a random point on the right margin (approximately 15 m away from the riverbank), tracked throughout the field campaign.  
The M9-ADCP was applied to measure water discharges at cross-section P1, following procedures regarding quality control [23]. Static ADCP moving-bed tests were also performed at 6 verticals locations along the cross-section P1 (Figure 2a), to evaluate the average velocity of sediment particles moving close to the riverbed. These results were used to estimate bedload transport rates.  
In addition to discharge measurements and moving-bed tests, the ADCP was used for two bathymetric surveys (integrated with simultaneous flow velocity collection), using the HydroSurveyor software capabilities [24]. The surveyed reach was approximately 1 km long (380 m upstream and 620 m downstream of the cross-section P1) (Figure 2b). Each bathymetric survey lasted 3 h and the time interval between them was near 24 h. The collection frequency was 1 Hz, with 5 depth points measured per second (from the ADCP vertical beam and its 4 slanted beams). During the survey, navigation was conducted with longitudinal, transversal and oblique track lines, trying to cover the biggest possible area of the river. At regions of higher river width, e.g., near the confluence with the Coxim River (downstream), the transversal distance between longitudinal track lines reached 30 m. However, near the region where the modified ISSDOT method was applied, the transversal distance was below 20 m.  
The distances between consecutive depth points (1 Hz frequency) measured with the ADCP varied according to the boat speed and local depth. Since maximum depths were approximately 5-6 m and boat speed did not exceed 20 km/h (normally less than 15 km/h), the distance between successive measured points was less than one meter (usually some decimeters), with overlapping data due to the 5-beam scheme. The width of each surveyed \"swath\" varied mainly between 1 m and 3 m.  
Figure 2: (a) Cross-section P1 and verticals of static measurements; (b) depth points measured during the ADCP bathymetric survey. Flow occurs from right to left.  
For logistic reasons, it was not possible to conduct all types of surveys (water discharges, moving-bed tests, bathymetry, flow velocities, and bedload samples) on the same days. Table 1 and Figure 3 summarize the temporal resolution and the equipment used.
# AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods
## 4 Experiments
### Security Alert Visualization  
\(t\)-distributed stochastic neighbor embedding (\(t\)-SNE) [71] is a dimensionality reduction technique that is particularly well suited for visualizing high-dimensional datasets. The \(t\)-SNE algorithm measures the similarity between data points in a high-dimensional space and projects them onto a lower-dimensional space, thereby allowing us to effectively reduce the dimensionality of the data while preserving the local structure of the data. We mapped the negative, positive, and synthetic security alert samples from the enterprise datasets in a two-dimensional layout.  
Figure 4 presents a \(t\)-SNE visualization of the enterprise dataset used in the experiments. Disk and cross-markers represent negative and positive alert samples, respectively, and the letters A-F indicate the appliance ID. The alerts were color coded according to the issuing appliance, with the same color indicating positive and negative samples from the same appliance. This figure indicates that the dataset was highly imbalanced, with most alerts being negative. In addition, the alerts were largely clustered according to the issuing appliance, as indicated by the letters A-F, with appliances E and F issuing the most alerts.  
Figure 5 shows the distribution for the alert samples issued by Appliances A and B. The blue disks represent negative alert samples, and the red crosses represent positive samples. The figure indicates that negative samples comprise the majority class. Positive samples formed tight clusters and were highly separable from negative ones. Synthetic samples are shown as green crosses for SMOTE (Figure 5a), ADASYN (Figure 5b), SVMSMOTE (Figure 5c), and MAS (Figure 5d). As shown in Table 2, appliance A had more positive alert samples than Appliance B, resulting in more synthetic alert samples being generated for Appliance B by each oversampling method. Each method employs a different data generation scheme, resulting in a unique distribution of synthetic samples.  
The alert distribution obtained using MAS (Figure 5d) alert samples resulted in high-quality synthetic alert samples clustered around the positive ones characterized by reduced class overlap. More data points were synthesized using MAS for Appliance B compared with the other methods, indicating that the method resulted in a more balanced data  
Figure 4: Visualizing unique security alert samples using \(t\)-SNE.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Traffic** & **(\#) Records** & **(\#) Sample** & **Imbalance Rate (\%)** \\ \hline Attack & 164,675 & 4000 & \\ Normal & 93,005 & 37,000 & **9.756** \\ Total & 257,680 & 41,000 & \\ \hline \hline \end{tabular} The data imbalance rate is shown in bold font.  
\end{table}
Table 3: Statistics of the UNSW-NB15 benchmark dataset.  
distribution across all appliances. This suggests the potential for the improved performance of a security alert data analysis system trained on MAS alert samples.
## 4 Conclusions  
In this paper, a high-performance 0.34 THz sub-harmonic mixer combining two-stage reduced matching technology with an improved active circuit model is designed, fabricated, and measured. For this mixer, the measured SSB conversion loss is almost better than 12 dB and the best value is 8.95 dB. The experimental measurements of this mixer are consistent with the simulated results, which powerfully demonstrate the effectiveness and correctness of this model. We hope that this work will be able to facilitate the development of terahertz sub-harmonic mixers in the future.  
Conceptualization, methodology, software, W.F.; validation, W.F., P.Y. and X.S.; investigation, W.F.; resources, S.L.; writing--original draft preparation, W.F.; writing--review and editing, Y.Z.; supervision, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China 2018 YFB1801503, the National Natural Science Foundation of China (61931006, U20A20212, 61921002), and the Fundamental Research Funds for the Central Universities no. ZYGX2020 ZB011.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Freq (GHz)** & **Conversion Loss (dB)** & **LO Power (mW)** & **RF Return Loss (dB)** & **Reduced Matching Technology** & **Method** \\ \hline
[13] & 200–240 & 7–12 (SSB) & 3 & N.A. & Two-stage & GDM \\
[17] & 320–360 & 7.2–24.1 (SSB) & 7–11 & N.A. & Single-stage & N.A. \\
[18] & 320–360 & 7–10 (DSB) & 6 & N.A. & Single-stage & GDM \\  & 6–9 (min) & 9–10 (SSB) & 2.5–3 & \textgreater{}10 & Single-stage & SDM \\
[19] & 290–310 & 6–8 (DSB) & 9–12 (SSB) & 5.5 & \textgreater{}12 & Two-stage & An improved active circuit model \\ This work & 320–360 & 8.95 (min) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of some published sub-harmonic mixers.  
Figure 10: The experimental results of a 0.34 THz sub-harmonic mixer combining two-stage reduced matching technology with an improved active circuit model. **(a)** IF frequency (\(f_{\text{IF}}=f_{\text{RF}}-2f_{\text{LO}}\)): 1 GHz and 2 GHz; **(b)** IF frequency (\(f_{\text{IF}}=2f_{\text{LO}}-f_{\text{RF}}\)): 1 GHz and 2 GHz; **(c)** LO frequency: 163 GHz and 165 GHz; **(d)** LO frequency: 170 GHz and 172 GHz; **(e)** The simulated and measured RF return loss when the LO frequency is fixed at 165 GHz; **(f)** The comparison of SSB conversion loss.
# The Impact of Gravity on Different Height Systems: A Case Study on Mt. Medvednica
## 3 Leveling Survey
### Geometrtic Leveling  
Geometric leveling is a method for determining height differences between points, such as \(A\) and \(B\), with the leveling instrument and leveling staffs. By using horizontal sight, the leveling instrument, placed between two staffs, reads the sections on the two leveling staffs at points \(A\) and \(B\), thereby determining the height difference between these points (Figure 9) [22].  
The height difference between points \(A\) and \(B\) is obtained as follows [23]:  
\[\Delta h=\ l_{B}-l_{A}. \tag{31}\]  
If the height of point \(A\) is known, this difference is added to the known height to determine the height of point \(B\).  
Depending on the accuracy, geometric leveling can be divided into the following [24]:  
* High accuracy leveling (cro. _Nivelman visoke tonosti_--NVT);
* Precise leveling (cro. _Precizni nivelman_--PN);
* Technical leveling with increased accuracy (cro. _Telmicki nivelman povecane tonosti_--TNPT);
* Technical leveling (cro. _Telmicki nivelman_--TN);
* City leveling (cro. _Gradski nivelman_--GN).  
The characteristics of all of these can be found in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Leveling Network** & **Line Length [d]** & **Accuracy [U0]** \\ \hline NVT & \(\leq\)300 km & \(\pm\)1.0 mm\(\surd\)km \\ PN & \(\leq\)80 km & \(\pm\)2.0 mm\(\surd\)km \\ TNPT & \(\leq\)50 km & \(\pm\)5.0 mm\(\surd\)km \\ TN & \(\leq\)30 km & \(\pm\)8.0 mm\(\surd\)km \\ GN & - & \(\pm\)2.0 mm\(\surd\)km \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of geometric leveling based on accuracy [24].  
Figure 9: Principle of geodetic leveling (according to [PERSON] [23]).
# Improved Sliding Mode Control for Permanent Magnet Synchronous Motor Speed Regulation System
## 2 SMC Design for the PMSM  
### Dynamical Modeling of PMSMs  
Suppose the magnetic circuit is unsaturated, neglecting the iron losses, and the stator resistance and self- and mutual-inductances for each phase are the same for a surface-mounted PMSM. The mathematical model of the surface-mounted PMSM can be written in state equation form in the \(d\)-\(q\) reference frame as  
\[u_{d}=Ri_{d}+L_{s}\frac{di_{d}}{dt}-p_{n}\omega_{m}L_{s}i_{q} \tag{1}\]\[u_{q}=Ri_{q}+L_{s}\frac{di_{q}}{dt}+p_{n}\omega_{m}L_{s}i_{d}+p_{n}\omega_{m}\psi_{f} \tag{2}\]  
\[J\frac{d\omega_{m}}{dt}=\frac{3}{2}p_{n}\psi_{f}i_{q}-T_{L} \tag{3}\]  
where \(i_{d}\), \(i_{q}\), \(R\), \(L_{s}\), \(u_{d}\), \(u_{q}\), \(p_{n}\), \(\omega_{m}\), \(\Psi_{f}\), \(J\) and \(T_{L}\) are \(d\)-axis and \(q\)-axis currents, stator resistance, stator inductance, \(d\)-_axis_ and \(q\)-_axis_ voltages, the number of pole-pairs, the mechanical angular speed of the rotor, the rotor flux linkage generated by the permanent magnet, the equivalent moment of inertia and the load torque, respectively.  
A typical PMSM vector control system is shown in Figure 1. The differential equations of surface-mounted PMSMs are represented in the rotor reference coordinates based on the above assumptions. For the PMSM, whose rotor flux is generated by a permanent magnet, the excitation component \(i_{d}\) is set to zero, and the torque is produced by current \(i_{q}\). The current Equation of the PMSM described by (1) is rewritten as  
\[\frac{di_{q}}{dt}=\frac{1}{L_{s}}(-Ri_{q}-p_{n}\omega_{m}\psi_{f}+u_{q}) \tag{4}\]  
\[\frac{d\omega_{m}}{dt}=\frac{1}{J}(-T_{L}+\frac{3p_{n}\psi_{f}}{2}i_{q}) \tag{5}\]
# The Concept of Segmented Wind Turbine Blades: A Review
## 7 Mechanical Joints in Segmented Blades
### Experience from Blade Root and Hub Extenders  
To use existing blades on turbines at sites of a lower wind class than the blades were designed for, blade root extenders are placed in-between the hub and the blade roots, increasing the rotor diameter [122]. Blade extenders are generally made out of metal, but can also be manufactured from composite material [123]. They can incorporate a pre-coning [124] or sweep [125]. In a similar approach, the hub is extended, placing the pitching mechanism further out-board forming a hub extender or partial pitch system [50]. This concept was already used in the NASA Mod-2 turbine [126]. [PERSON] [127] investigated a segmented blade of which the inboard portions were essentially blade extenders connected by a truss structure to reduce loads. Furthermore, to provide sufficient solidity at the blade root, an aerodynamic shape with a large chord length is required. This can be made feasible by using a root extender with an aerodynamic shape as suggested by [PERSON] [128]. An overview of these methods is shown in Figure 9.  
Figure 8: Prototype segmented blades using inserts. (**a**) Blade joint developed during the UpWind project [55]. (**b**) Blade joint with alternating long and short bolts [49]. (Reprint with permission [49]; 2014, John Wiley and Sons.)  
Figure 7: Prototype segmented blades using a T-bolt joint. (**a**) DEBRA-25 blade [100]. (Reprint with permission [100]; 2006, Elsevier.) (**b**) Split blade tested under the JOULEIII project [45]. (Reprint with permission [45]; 2000, Knowledge Centre WMC.) (**c**) Blade tested under the MEGAWIND project [52].