esa-sceva/satcom-chunk-collection huggingface dataset:
# Passive Detection of Low-Altitude Signal Sources Using an Improved Cross-Correlation Algorithm
## 4 Performance Results
### Outdoor Experiments  
In this section, the performance of the outdoor passive detection is evaluated for single and multiple low-altitude signal sources. The false alarm probability and missing alarm probability outdoor are determined using the proposed method and advanced method. In addition, outdoor parameter estimation results are given for the proposed, HOC, and CFAR methods.
# Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
## 4 Results
[40], utilizes FFT images as input to a CNN for feature extraction and classification. The third model (STFT-CNN) integrates STFT images with a CNN, leveraging time-frequency representations for fault detection. These models represent diverse TFD approaches combined with CNN architectures, providing a benchmark to assess the performance and robustness of the proposed method across multiple preprocessing techniques and feature extraction strategies
## 3 Results and Discussions
### XRD Analysis  
The XRD patterns of the catalysts are presented in Figure 3. All the catalysts presented the typical diffraction peaks of anatase TiO\({}_{2}\) (JCPDS 21-1272). The characteristic diffraction peaks of Ce and W species were not observed for all catalysts even the loading amount of Ce and W may exceed the limit of monolayer dispersion. This indicated that the active Ce and W species were in amorphous phase or well dispersed on the TiO\({}_{2}\) surfaces. Similar observations were reported in a previous study [22]. The crystallization degrees of the TiO\({}_{2}\) support were smaller in the Ce\({}_{x}\)W\({}_{0.4-x}\)Ti catalysts compared to the Ce\({}_{0.4}\)Ti and W\({}_{0.4}\)Ti indicating the smaller crystalline sizes of the Ce\({}_{x}\)W\({}_{0.4-x}\)Ti catalysts, considering the lower peak intensities of the major typical diffraction peaks of TiO\({}_{2}\) (1 0 1) at 20 = 25.3\({}^{\circ}\). This phenomenon indicated a smaller crystalline size of the Ce-W-Ti catalysts compared to the Ce\({}_{0.4}\)Ti and W\({}_{0.4}\)Ti catalysts, which was beneficial for the NH\({}_{3}\)-SCR of NO and could be confirmed by the broadening of the characteristic peaks in the Ce-W-Ti catalysts [33; 34].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Samples** & **S\({}_{\text{BET}}\)** & **Pore Volume** & **Average Pore** & **O\({}_{\text{ads}}\)/** & **NH\({}_{3}\) Uptake** \\  & **(m\({}^{2}\cdot\)g\({}^{-1}\))** & **(cm\({}^{3}\cdot\)g\({}^{-1}\))** & **Diameter (nm)** & **(O\({}_{\text{ads}}\) + O\({}_{\text{lab}}\)) (\%)** & **(umol-g\({}^{-1}\))** \\ \hline Ce\({}_{0.4}\)Ti & 26.6 & 0.076 & 9.9 & 17.4 & 7.8 \\ Ce\({}_{0.3}\)W\({}_{0.1}\)Ti & 46.8 & 0.086 & 7.7 & 27.6 & 64.7 \\ Ce\({}_{0.2}\)W\({}_{0.2}\)Ti & 108.1 & 0.121 & 3.9 & 33.8 & 193.9 \\ Ce\({}_{0.0}\)W\({}_{0.3}\)Ti & 68.4 & 0.085 & 4.6 & 21.0 & 62.3 \\ W\({}_{0.4}\)Ti & 16.8 & 0.069 & 16.2 & 16.0 & 20.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Textural properties of the Ce-W-Ti catalysts.  
Figure 3: X-ray diffraction (XRD) patterns of the Ce-W-Ti catalysts.
# An Accurate Platform for Investigating TCP Performance in Wi-Fi Networks
## 3 Emulator-Based Approach for Investigating TCP Performance in a Wi-Fi Network
### Congestion Control Algorithms
#### 3.2.1 Loss-Based Algorithms  
_Binary Increase Congestion Control (BIC)_[34] uses additive increase and binary search to increase the congestion window size.  
_CUBIC_[35] is an improved version of BIC. CUBIC is now the default CCA on Linux kernels.  
_Hamilton-TCP(H-TCP)_[36] was designed for high-speed, high-latency networks. It changes the rate of increase of the congestion window during a period after congestion occurs.  
_Highspeed_[37] adjusts the congestion window size following predefined low and high speeds.  
_Hybla_[38], a modified version of Reno, was developed for heterogeneous environments.  
_Reno_[39] operates following the Additive Increase Multiplicative Decrease (AIMD) algorithm.  
_Scalable_[40] is characterized by the fact that recovery from congestion is independent of the size of the congestion window.
# Effect of Pole Head Profile on Torque Performance of a Disc-Type Multipole Magnetorheological Brake
## 5 Results  
Simulation results of the magnetic flux density of a magnetorheological brake were analyzed. Considering the optimized brake parameter range, the present study on varied pole head geometries observes their effect on braking torque. The simulation process for different types of pole head profiles primarily focuses on the best values for pole head radius (varied from 7.5 mm to 8.5 mm), shank dimensions (varied from 7.0 mm to 8.0 mm) and the number of wire turns around the coil that affect output shear force.  
As shown in Figure 6, the braking torque for all pole head profiles at 270 wire turns and center of pole radii of 28.5 mm were discussed. The circle profile obtained the best output value of 15.655 N-m at an 8.5 mm pole head radius and 8.0 mm shank radius. The trapezoid profile obtained the best output value of 14.02 N-m using an 8.0 mm pole head radius and 8.0 mm shank radius. The square profile obtained the best output value of 13.45 N-m using an 8.0 mm pole head radius and 8.0 mm shank radius. But, for the 8.5 mm pole head radii, the square shape had a drawback in that it overlapped with the other pole head, since the dimension of the magnetorheological brake studied had a fixed parameter of 38 mm rotor radius. The polygon profile obtained the best output value of 15.36 N-m using an 8.0 mm pole head radius and 8.0 mm shank radius. Similar to the square profile, the polygon pole head of 8.5 mm radii also had a drawback in that it overlapped with the other pole head, since the dimension of the magnetorheological brake studied had a fixed parameter of 38 mm rotor radius. The hexagon profile obtained the best output value of 16.27 N-m at an 8.0 mm pole head radius and 8.0 mm shank radius. In the ellipse profile, due to its shape, the possible pole head radius was 6.3 mm, the shank radius was 6.5 mm, the center of the pole radius was 27 mm and the output braking torque value was 16.09 N-m. Therefore, the obtained results for different pole head profiles were influenced based on different parameters such as effective area of MR fluid, maximum magnetic flux flow, radial dimensions and shank radius.
## 3 Results and Discussion
### BLUF Modules in Association with the Effector Domains
#### 3.3.16 RhoGEF Domain  
The RhoGEF protein is a guanine nucleotide exchange factor (GEF) responsible for the activation of Rho family GTPases (Rho, Rac, and Cdc42) [139; 140; 141]; it controls a diverse array of cellular processes, including cellular differentiation [142], cell morphology [143], cell motility and adhesion [144], phagocytosis [145], cytokinesis [146], smooth muscle contraction [147], and the etiology of human disease such as hypertension [148] and cancer [149]. The Rho family proteins are generally found in two different conformational states, i.e., active GTP-bound and inactive GDP-bound [150]. The Rho family GTPases have a conserved domain of -200 amino acid residues known as the RhoGEF domain or Dbl homology (DH) domain, which encodes a GEF specific to different Rho family members [151]. In addition to the RhoGEF domain, Rho family GTPases also have another functionally independent conserved domain (-100 amino acid residue), i.e., the pleckstrin homology (PH) domain, located at the C-terminus of the RhoGEF domain [152]. The C-terminal PH domain is generally involved in intracellular targeting and regulates the function of the RhoGEF domain. The RhoGEF domain has an \(\alpha\)-helix bundle-like structure with three conserved regions, i.e., conserved region 1 (CR1), conserved region 2 (CR2) and conserved region 3 (CR3). Among these three conserved regions, CR1 and CR3 interact partly with \(\alpha\)-6 and the DH/PH junction site, forming the Rho GTPase binding pocket.
# Tracking and Registration Technology Based on Panoramic Cameras
## 4 Experiments
### Limitations Analysis  
In this chapter, a lightweight camera pose estimation model was designed to ensure the real-time performance of panoramic camera tracking and registration. However, the model still exhibits certain limitations. First, the depth estimation sub-network performs only two downsampling operations on the input image. Although this design contributes to the lightweight nature of the model, it also limits the network's ability to capture comprehensive global contextual information, thereby affecting the accuracy of depth estimation and ultimately reducing the precision of pose estimation. Second, the model processes image data at a resolution of 256 \(\times\) 512. When applied to higher-resolution panoramic images, the accuracy of the predicted depth maps may degrade significantly, which restricts the model's overall performance and application scope. To overcome these limitations, we consider incorporating model distillation techniques. For instance, a larger teacher model with stronger representation capabilities can be used to guide the training of our lightweight panoramic pose estimation network. Such approaches may help retain high-resolution performance while maintaining inference efficiency, and this will be an important direction for our future work.
## 5 Experimental Results
### Simulation Experiment
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Parameter** & **Resolution (m)** & **PSLR (dB)** & **ISLR (dB)** \\ \hline \multirow{3}{*}{Theoretical value} & Range & 0.1329 & \(-13.27\) & \(-10.24\) \\  & Azimuth & 0.1256 & \(-13.27\) & \(-10.24\) \\ \multirow{3}{*}{Proposed method} & Range & 0.1338 & \(-13.26\) & \(-10.21\) \\  & Azimuth & 0.1288 & \(-12.05\) & \(-9.70\) \\ \multirow{3}{*}{KT-CICPF} & Range & 0.1463 & \(-17.03\) & \(-14.46\) \\  & Azimuth & 0.1589 & \(-18.77\) & \(-16.66\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Focusing Performance. Figure 6: Scattering point coordinate distribution. (**a**) Scatters coordinate distribution of T1. (**b**) Scatters coordinate distribution of T2. relatively concentrated and the coupling degree with range migration is small. The peak of the \(F_{0}\)-\(F_{1}\) plane constructed by the KT-CICPF is divergent. The reason for this phenomenon is that the residual range migration by the KT-CICPF method is larger, resulting in a large parameter estimation error. For the target T1, Figure 8b,e illustrates the same phenomenon. In this paper, when constructing the \(F_{0}\)-\(F_{1}\) plane, Equation (29) is used to compensate for the DCCF to reduce the influence of the third-order range migration on the \(F_{0}\)-\(F_{1}\) parameter plane. Figures 7c,f and 8c,f show refocused results of the two methods. It can be seen that the imaging results of KT-CICPF methods are severely defocused in azimuth. The proposed method can better maintain the shape of the target. Figure 8: The focused images of T2. (**a**) Peak signal extracted by DCCF. (**b**) \(F_{0}\)–\(F_{1}\) parametric plane by proposed method. (**c**) Imaging by proposed method (16\(\times\) upsampling). (**d**) Spectrum of peak signal. (**e**) \(F_{0}\)–\(F_{1}\) parametric plane by KT-CICPF. (**f**) Imaging by KT-CICPF (16\(\times\) upsampling). Figure 7: The focused images of T1. (**a**) Peak signal extracted by DCCF. (**b**) \(F_{0}\)–\(F_{1}\) parametric plane by proposed method. (**c**) Imaging by proposed method (16\(\times\) upsampling). (**d**) Spectrum of peak signal. (**e**) \(F_{0}\)–\(F_{1}\) parametric plane by KT-CICPF. (**f**) Imaging by KT-CICPF (16\(\times\) upsampling).
# An Overview of the Process Mechanisms in the Laser Powder Directed Energy Deposition
## 4 Solidification Mechanisms
### Surface Quality
[176] showed that the surface finish is improved using quasi-continuous laser irradiation instead of fully continuous laser irradiation. This was attributed to the reduction of the thermal gradient and Marangoni flow in the melt pool. One of the main methods used to reduce top surface roughness is surface scanning without powder feeding. This method, in analogy with the L-PBF process, is named remelting. [PERSON], et al. [181] studied the effect of laser remelting on surface roughness. Figure 35 illustrates the surface profile data before and after remelting. In their work, the surface quality was evaluated in terms of maximum profile height (Rt) along two directions parallel (X-direction) and perpendicular (Y-direction) to the deposition path. Before remelting, a lower value of Rt was observed in the analyzed samples. Moreover, a clear correspondence between waviness and hatching distance was identified on the deposited samples. This correspondence was not observed after remelting. Figure 35: Height profile measured on samples (**a**) without and (**b**) with laser remelting (reproduced with permission from [181]; Copyright © 2013 Elsevier). Dimensional accuracy is another important factor used to describe surface quality. In fact, based on dimensional and geometrical deviation, the post-processing operation can be designed [168]. [PERSON], et al. [182] and [PERSON], et al. [168] reported that dimensional deviation depends on process parameters. [PERSON], et al. [183], based on the dimensional results, showed that the accuracy of the LP-DED process falls into the coarse tolerance class. However, the accuracy can be increased by optimizing the process parameters [168,183]. For example, [PERSON], et al. [168] observed that travel speed significantly influences the dimensional deviation, which decreases as the travel speed increases. On the other hand, the dimensional deviation was not significantly influenced by the laser power. Table 5 summarizes the main aspects of the solidification process and its most important parameters.
## 6 Conclusions  
Optimal memory utilization is indispensable across all applications, especially in those demanding high memory usage, where it becomes imperative. Given the evolving trend in memory development, storage systems must adapt to these new architectures. In this research, a pioneering approach is presented that maximizes the use of unoccupied space in adjacent storage units for record storage. This approach significantly lowers PM access and optimizes bandwidth consumption. By efficiently making use of available capacity in nearby nodes, redundant directory doubling procedures are postponed, resulting in a more effective deployment of persistent memory space. Additionally, scalability tests have demonstrated that this scheme performs admirably in multi-threaded environments. This strategy enables us to efficiently harness the persistent memory capacity. To enhance performance, we put forward a minimal locking approach where a bucket is locked only during a write request, enabling concurrent access/read operations on buckets within the segments by other processes. This facilitates multiple access points, leading to increased scalability in multi-threaded environments. Furthermore, our scheme exhibits superior performance compared to an alternative approach for both uniform and skewed data distributions. Our approach exhibits superior performance compared to current leading methods, regardless of whether the data distribution is uniform or skewed. As a result, it presents an alternative solution to enhance CCEH by mitigating the overhead linked with split management and deferring the resource-intensive directory doubling operation in systems employing an extendible hashing scheme on persistent memory.  
In the future, we intend to assess the scheme across a diverse array of applications and various workloads in real-world scenarios to further refine it for use at a production level. Additionally, we envisage incorporating additional tests and seeking peer evaluations to establish a comprehensive framework adept at handling hashing for a wide spectrum of applications, based on the insights gleaned from these evaluations.  
Conceptualization and methodology, D.R. and H.Y.Y.; methodology, D.R. and H.Y.Y.; experimental activity, data processing, and validation D.R.; writing original draft preparation, review and editing, D.R., H.Y.Y. and J.H.; supervision and funding acquisition, D.R. and J.H. All authors have read and agreed to the published version of the manuscript.  
This work is partially funded by the National Research Foundation of Korea (NRF) grant funded by the Korean government through BK21 Stage 4 Graduate School Innovation Education Research Group (No. 3789). (Corresponding Author: [PERSON]).  
Not applicable.  
**Data Availability Statement:** Avialable on request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Research on Fleet Size of Demand Response Shuttle Bus Based on Minimum Cost Method
## 3 Cost-Based Fleet Size Model  
The objective function is constructed with the objective of minimizing the total cost of DRC, and the total system cost includes user cost and vehicle operation cost, and the objective function is as Equation (14):  
\[\text{min}C=C_{u}+C_{o} \tag{14}\]  
Substituting the user cost Equation (12) and operational cost Equation (13) constructed in the previous Section into Equation (14), the cost objective function of the DRC over the operating cycle can be obtained as Equation (15):  
\[\text{min}C=B*(c_{0}+c_{1}K)+P_{w}*Q*\frac{(1+\alpha)T}{2}+P_{v}*\frac{Q*S}{2v} \tag{15}\]  
The constraints are Equations (16) and (17):  
\[k(f)\leq K \tag{16}\]  
\[\sum_{i=1}^{n}T_{i}\leq ST \tag{17}\]  
where Equation (16) indicates that the number of passengers on board at any moment will not exceed the rated capacity of the bus vehicle, and Equation (17) indicates that the sum of the time spent on picking up and dropping off passengers for detours should be less than or equal to the slack time.  
In the demand response bus operation process, the slack time is an essential factor for smooth vehicle operation, which is the extra time pre-set by the vehicle to allow the DRC to respond to the new travel demand and drive away from the base route, i.e., the difference between the vehicle travel time and the time required for the vehicle to complete the base route. It is expressed as Equation (18)  
\[ST=T_{c}-\frac{2L}{v}=\frac{2W}{3v}+\frac{LW^{2}\tau}{6 vf}-\frac{2L}{v}*\frac {f}{LW\tau+f} \tag{18}\]  
In addition to the above constraints, there exists a relationship between vehicle occupancy rate, frequency of departure and vehicle type in the DRC operating model, which can be expressed as Equation (19):  
\[O_{B}=\frac{LW\tau}{fK}*\frac{L}{S} \tag{19}\]  
where \(O_{B}\) represents the average occupancy rate of the vehicle, taking the value in the range of [0, 1]. When taking \(O_{B}\) as 1, it means that the vehicle is fully loaded.  
According to the above equation, the frequency function for vehicle type \(K\) can be derived as shown in Equation (20):  
\[K(f)=\frac{L^{2}W\tau}{Sf}=\frac{L*Q}{f*(2L*\frac{Q}{Q+f}+\frac{2W}{3}+\frac{ W}{6}*\frac{Q}{f})} \tag{20}\]  
Limited by the scheduling algorithm and constrained operational decisions, it is very difficult to find the resolution of the optimal departure frequency, so this paper adopts a numerical simulation method, using [PERSON]'s iterative method, to solve the model, so as to obtain the optimal departure frequency \(f^{*}\) based on \(f^{*}\) and the actual effective utilization rate of vehicles \(\beta\).  
The expression for the optimal fleet size per unit hour can be obtained as Equation (21):  
\[B=\frac{f^{*}}{\beta} \tag{21}\]  
where \(\beta\) is generally taken as its reciprocal, taking the value of 1.05.  
According to the above two models, we can obtain the corresponding algorithm diagram. It is shown in Figure 2.  
Figure 2: Algorithm diagram of cost model of DRC and cost-based fleet size model.
# Data Downloaded via Parachute from a NASA Super-Pressure Balloon
## 3 Descent and Recovery
### Predicted Descent Trajectories  
We predict the DRS descent trajectories and landing sites with PyBalloon software [6] that integrates a path followed by a test particle through winds described by Global Forecast System (GFS) weather models. When we did this live, we predicted descent vectors \(\sim\)61.3 km on heading 107\({}^{\circ}\) and a flight time of \(\sim\)32 min. However, the fast ground speed and high-latency communications meant that our uncertainty in the landing site was dominated by the unpredictable timing of release (2 min uncertainty extended our error ellipses by 8 km). We, therefore, aimed for a large region of open, uninhabited land west of main road 12. These specific circumstances are unusual and unlikely to be repeated.  
Knowing with hindsight the precise times of release, we predicted landing sites for DRS1 at \(-47.8594^{\circ}\), \(-69.6307^{\circ}\) (altitude 878 m), and DRS2 at \(-47.8557^{\circ}\), \(-69.6559^{\circ}\) (altitude 920 m), with 68% confidence limits of \(\sigma_{\parallel}=2.1\) km in the main direction of motion and \(\sigma_{\perp}=1.9\) km perpendicular to that. With even more hindsight, we were able to interpolate between GFS model wind speeds issued before _and after_ the DRS release. These lengthened the model descent vectors by \(\sim\)1.1 km, to predicted landing sites for DRS1 at \(-47.8655^{\circ}\), \(-69.6153\) (altitude 863 m) and DRS2 at \(-47.8600^{\circ}\), \(-69.6444^{\circ}\) (altitude 850 m), with 68% confidence limits \(\sigma_{\parallel}=2.3\) km and \(\sigma_{\perp}=1.9\) km (see Figure 6). Even in the absence of any additional information, the open terrain would have made it possible (albeit time-consuming) to search for bright orange parachutes in an area this size.