esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 3 Navigation Signal Design Considerations
### Ranging Performance
#### 3.3.2 Gabor Bandwidth  
As shown in Equations (8) and (9), the DLL thermal noise jitter depends on \(C/N_{0}\) and the receiver parameters, such as discriminator type, DLL bandwidth, coherent integration time, and correlator spacing. The Gabor bandwidth (so-called root mean square (RMS) bandwidth) is used to evaluate the code tracking performance of RNSS signals independent of the receiver parameters above. The Gabor bandwidth is derived from the Cramer-Rao lower bound (CRLB) of the DLL thermal noise jitter, where the CRLB \(\sigma_{CRLB}^{2}\) and the Gabor bandwidth \(f_{Cabor}\) are expressed as follows [17]:\[\sigma^{2}_{\text{CRLB}}=\frac{B_{L}}{(2\pi)^{2}\frac{C_{\text{s}}}{\text{N}_{0}} \int_{-\frac{B_{L}}{2}}^{\frac{B_{L}}{2}}f^{2}G_{\text{s}}(f)df}=\frac{B_{L}}{(2 \pi)^{2}\frac{C_{\text{s}}}{\text{N}_{0}}f_{\text{G}abor}} \tag{10}\]  
with  
\[f_{Gabor}=\int_{-B_{\text{s}}/2}^{B_{\text{r}}/2}f^{2}G_{\text{s}}(f)df. \tag{11}\]  
From Equation (11), we can expect that the Gabor bandwidth has a higher value when the signal power is more concentrated on the high-frequency components. A higher Gabor bandwidth refers to the signal that has a better code-tracking performance.
## 3 Methodology: Requirements Analysis, Selection of Buildings, and Technologies
### Deriving Technologies and Instrumentation Variants
#### 3.3.1 Requirements
The first consideration is the investment costs, covering the cost of the procurement of equipment. Second is the installation costs, which include direct costs (e.g., the cost of rewiring a building to accommodate wired sensors, or the cost of breaking and joining pipes to install flow meters) and indirect costs, such as the temporary loss of work spaces due to installation work, or the suspension of building heating in order to rework heating piping. Finally, the operational costs are taken into account, which include the cost of maintenance and the associated consumables (e.g., replacement of parts, servicing of components, leasing of ICT infrastructure, etc. ).
## 5 Modulation of Intelligent Techniques for Power System Stability, Control and Protection
On the other hand, the LS-PWM technique guarantees the best switching patterns, but does not evenly distribute power among the switches in the modules. SVPWM, LSPWM, and PS-PWM are contrasted in this research regarding switching patterns and power distribution across modules. A proposed modified carrier-based PWM approach combines the advantages of fair power distribution, equal switch consumption, and the best switching sequences. Line voltage and switching sequence analyses have been performed on each one to close the gap between the three approaches and demonstrate their similarity. Results from simulations are used to support the suggested approach. According to a proposal by [PERSON] et al. [106], random pulse width modulation (RPWM) can lower acoustic noise in loads powered by power electronic converters. The switching period in this method depends on the duty ratio, which is in contrast to standard RPWM systems. In other words, the switching time in each switching cycle depends on the duty ratio and the switching periods of the previous switching cycles. The experimental findings supported the proposed method's capability to lower noise power at a chosen frequency in the voltage spectrum. [PERSON] et al. [107] analyzed a random PWM method for three-phase voltage-controlled inverters with randomized pulse positions. Experiments were conducted to generate and validate closed-form equations for the inverter's line-to-line and line-to-neutral voltages' discrete and continuous power spectra. The theory presented makes it possible to optimize the voltage spectra of randomly modulated inverters numerically. [PERSON] et al. [108] suggested a two-stage, three-phase, wavelet-modulated isolated AC-DC converter for use in electric vehicle (EV) charging systems. A half-bridge resonant CLLC converter was put forward due to its high efficiency, broad gain range, galvanic isolation, and bidirectional power flow. The outcomes demonstrated that the suggested bidirectional converter may be built with wavelet modulation. The performance of the suggested converter results in an output current with less than 10% total harmonic distortion and shallow output voltage ripple, which is in line with expectations.
# Behavior and Damage Characterization of Impulsively Loaded Cross-Laminated Timber Panels
## 3 Results
### Damage Characterization
#### 3.3.3 Influence of Boundary Condition Rotational Rigidity on CLT Damage
The observations on the maximum and residual displacement data are indicative of lower levels of damage in specimens with higher levels of rotational rigidity at the boundary condition in both CLT panel layups tested.
# Constructing Condition Monitoring Model of Harmonic Drive
## 5 Conclusions  
This research proposes a harmonic drive anomaly detection model by combining discrete wavelet transforms, the Log Mel spectrogram, and the EffientNetV2S network architecture. The model uses wavelet transforms to separate the original sample audio into audio components representing each frequency interval, then uses the Log Mel spectrogram to extract features, and finally enters features as inputs into the neural network for training. The detection model exhibited an excellent prediction performance for the DCASE 2022 Dataset and the Harmonic Drive Dataset.  
The proposed detection model uses only the sound of mechanical operation as the anomaly judgment. If data such as vibration information or thermal energy are added to the model, the prediction performance of the model may be further improved.  
The parameter settings of the denoise algorithm were adjusted manually, and the best combination was compared through experiments. The audio pre-processing efficiency may be improved if the optimization algorithm is used to adjust each parameter, thus further enhancing the prediction capability.  
Conceptualization, J.-Y.K., P.-F.W. and C.-Y.H.; methodology, J.-Y.K. and C.-Y.H.; software, J.-Y.K. and C.-Y.H.; validation, J.-Y.K., P.-F.W. and Z.-G.N. writing--original draft preparation, C.-Y.H. and H.-C.L.; writing--review and editing, J.-Y.K., C.-Y.H., P.-F.W., Z.-G.N. and H.-C.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by National Taipei University of Technology-Beijing Institute of Technology Joint Research Program, NTUT-BIT-109-02.  
Not applicable.  
The authors would like to thank LI-MING Machinery Co for assisting in data acquisition and providing machinery for experiments.  
The authors declare no conflict of interest.
# A Cloud Based Disaster Management System
###### Abstract  
The combination of wireless sensor networks (WSNs) and 3D virtual environments opens a new paradigm for their use in natural disaster management applications. It is important to have a realistic virtual environment based on datasets received from WSNs to prepare a backup rescue scenario with an acceptable response time. This paper describes a complete cloud-based system that collects data from wireless sensor nodes deployed in real environments and then builds a 3D environment in near real-time to reflect the incident detected by sensors (fire, gas leaking, etc.). The system's purpose is to be used as a training environment for a rescue team to develop various rescue plans before they are applied in real emergency situations. The proposed cloud architecture combines 3D data streaming and sensor data collection to build an efficient network infrastructure that meets the strict network latency requirements for 3D mobile disaster applications. As compared to other existing systems, the proposed system is truly complete. First, it collects data from sensor nodes and then transfers it using an enhanced Routing Protocol for Low-Power and Lossy Networks (RLP). A 3D modular visualizer with a dynamic game engine was also developed in the cloud for near-real time 3D rendering. This is an advantage for highly-complex rendering algorithms and less powerful devices. An Extensible Markup Language (XML) atomic action concept was used to inject 3D scene modifications into the game engine without stopping or restarting the engine. Finally, a multi-objective multiple traveling salesman problem (AHP-MTSP) algorithm is proposed to generate an efficient rescue plan by assigning robots and multiple unmanned aerial vehicles to disaster target locations, while minimizing a set of predefined objectives that depend on the situation. The results demonstrate that immediate feedback obtained from the reconstructed 3D environment can help to investigate what-if scenarios, allowing for the preparation of effective rescue plans with an appropriate management effort.  
RLP; wireless sensor networks; cloud computing; disaster management; smart 3D virtual environment; traveling salesman problem
## 5 Experiments and Results
### Performance Overhead Evaluation  
While the integrated SPU monitors the program execution in real-time, this inevitably causes some performance overhead to the embedded system. Although we adopted some architectural optimizations in both M-Cache and I-Cache to reduce the performance overhead of CPU-SPU collaborative SoC, it is still necessary to evaluate the final performance overhead induced by the SPU as an indicator of its applied practicability. In our evaluations of the SPU performance overhead, ten various benchmarking programs were selected from Mibench suite [29] to simulate real embedded application scenarios. These ten benchmarks were developed based on the market-proven industry-standard EEMBC-CoreMark; they are comprehensive and advanced performance benchmarks in academia, industry, and military applications.  
First, the selected ten benchmarks are preprocessed under offline analysis and static extraction via running Perl scripts to generate the \(INF_{ref}\) blocks of BBs; then, the benchmarks are compiled by the GNU Cross Toolchain or32-elf-gcc and downloaded into FPGA for program execution, respectively. Furthermore, the numbers of total instructions and all the BBs of each benchmark are counted, and the \(INF_{ref}\) table required on-chip storage space is also calculated.  
Considering that the hit rates of I-Cache and M-Cache could influence the SPU performance overhead, we used the or1 ksim [30] simulation software to record the hit rates of the I-Cache and M-Cache, respectively. Hence, the SPU average performance overheads under the different benchmarks can be calculated according to the indicator of CPI on the SoC with and without integrating the SPU.
## 2 Results and Discussion
### Effect of PAW Treatment on the Main VOCs in Rocket Leaves
#### 2.1.1 Glucosinolate Hydrolysis Products (GHPs)
All these compounds have been reported before in rocket leaves [5,15,17,19]. As shown in Figure 1, erucin nitrile (5-methylthiopentanentirile) and erucin (4-methylthioputyl isothiamate) were the major compounds detected in the chromatogram of the control rocket sample. As reported by [20,21], erucin and erucin nitrile are the degradation products of glucorevint (one of the major GSIs found in rocket leaves) (Figure 2). Besides being the major VOC in rocket leaves [15,19], erucin has been reported as one of the most potent odour-active compounds in rocket, being associated with radish and typical rocket aroma [15,17]. However, erucin nitrile was reported as a major compound in samples obtained from dried plant material [5]. This suggests that drying of the plant material, in our case, contributed to the degradation of glucoerucin towards erucin nitrile being the major VOC in the control sample, followed by the erucin (Table 1). Non-significant changes in the total relative abundance of this class in all PAW treated samples were observed. Additionally, the process carried out at 2 and 10 min did not show any significant changes in the individual contents of this class. On the other hand, significant qualitative and quantitative changes were observed for both PAW-5 and -20 samples. Regarding PAW-5, three compounds could not be identified, which are 5-methyl hexanenitrile, heptanontril, and 4-methylthio butanenitrile. A significant decrease in the former two compounds was also observed in PAW-20.
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 3 Results
### Distribution of Vegetation Heights  
The distribution of vegetation heights represented by digital height models (DHM) reveals a maximum vegetation height of 65.06 m in November 2018 and 64.07 m in April 2019 for all the surveyed sites. The average vegetation height ranged from 16.72 m to 21.74 m in November 2018 and from 16.89 m to 22.15 m in April 2019 (Table 3). With an estimated height of 21.74 m in November 2018 and 22.15 m in April 2019, vegetation at Site 6 is the largest among all surveyed sites, as evidenced by the spatial distribution of vegetation heights (Figure 11). The histograms of vegetation height frequencies in November 2018 and April 2019 (Figure 12) generally show two peaks across all sites that reflect the presence of two types of vegetation: low vegetation with a height of less than 25 m and dominant vegetation with a height greater than 25 m. The low vegetation is made up of cocoa tree crops and grassy recrusts and natural regeneration. The dominant vegetation corresponds to all the trees constituting the forest. However, a third peak observed in April 2019 could signal the start of a weed control operation in April 2019.
# DFD-SLAM: Visual SLAM with Deep Features in Dynamic Environment
## 4 Results  
### Experiment Introduction  
We evaluated DFD-SLAM's performance on TUM-VI and TUM-RGBD datasets against leading visual SLAM algorithms [32; 33]. TUM-VI features complex scenes with frame rotations and loop closures, while TUM-RGBD focuses on indoor dynamics. On the TUM-VI dataset, we compared ORB-SLAM3 [16], VINS-Mono [2], and HFNet-SLAM [7]. ORB-SLAM3 serves as our benchmark framework, and the improvements in DFD-SLAM are based on ORB-SLAM3. VINS-Mono is a classic visual-inertial SLAM system. HFNet-SLAM, a recently developed SLAM system based on HFNet, demonstrates exceptional performance and is a similar type of work to DFD-SLAM when dealing with static environments. In the TUM-RGBD dataset, we compared our system with ORB-SLAM3 (O3) [16], DynaSLAM (Dyna) [27], DS-SLAM (DS) [29], Crowd-SLAM (Crowd) [8], Locrf (Locrf) [34], CDS-SLAM (CDS) [9], and PR-SLAM (PR) [35] systems. Among them, DynaSLAM and DS-SLAM are classic dynamic SLAM systems, and even today, DynaSLAM still demonstrates outstanding practical accuracy. Crowd-SLAM highlights the degradation of tracking accuracy due to excessive feature point removal, making it an important comparison object. Locrf applies an innovative method to ensure accuracy in dynamic environments and has high real-time performance. CDS-SLAM and PR-SLAM are the newest dynamic SLAM systems before the publication of this paper, showing relatively superior accuracy and real-time performance. We use absolute trajectory error (ATE) and relative pose error (RPE) to evaluate trajectories. ATE and RPE are critical metrics in SLAM systems as they provide comprehensive insights into the global consistency and local accuracy of the estimated trajectory, respectively. Calculating the RMSE (Root Mean Square Error) for these metrics allows for quantifiable and comparable evaluation of a SLAM system's precision and robustness in different scenarios. ATE is the Euclidean distance between transformed trajectories, representing the absolute error between two trajectory paths. RPE measures the pose differences at regular time intervals in the transformed trajectory, providing a finer representation of the accuracy of the SLAM system. All calculations are performed using Root Mean Square Error (RMSE). When performing a quantitative analysis of the improvement in absolute trajectory error before and after the enhancement, we use the following formula for calculation. Let the value before the improvement be \(ATE_{pre}\) and the value after the improvement be \(ATE_{cur}\). The calculation method for the improvement boost is shown by Equation (19):  
\[boost=\frac{ATE_{pre}-ATE_{cur}}{ATE_{pre}} \tag{19}\]  
In the tables below, the boost is calculated using this method. When calculating the overall improvement, the boost for different scenarios is averaged across all sequences and reported as a percentage. Tests were conducted on Ubuntu 18.04 (British company Canonical, London, UK) with an Intel-12700H CPU (Intel, Santa Clara, CA, USA), GeForce GTX 1070 Ti GPU (Nvidia, Santa Clara, CA, USA), and 16 GB RAM.
# Structural Particularities, Prediction, and Synthesis Methods in High-Entropy Alloys
## 2 Structural Particularities of HEAs, Prediction and Estimation Concepts
### Definition and Composition  
One of the pioneering works in the field defines high-entropy alloys (HEAs) as \"alloys composed of five or more principal elements in equimolar ratios\" [1]. While the requirement for equimolar concentrations is restrictive, the same work extends this definition by specifying that \"HEAs can contain principal elements with concentrations ranging from 5% to 35% atomic percent\". This broader definition implies that HEAs do not need to be equimolar, significantly increasing the number of possible high-entropy alloys. HEAs can also include minor elements to modify the alloy's basic properties, further expanding the scope of HEAs [8].  
The \"high entropy\" concept definition is based on the entropy value. An alternative definition, derived from [PERSON]'s theory, classifies alloys into low-entropy (SSS, ideal! 0.69 R, where SSS, ideal is the total configurational entropy in an ideal system and R is the gas constant), medium-entropy (0.69 R! SSS, ideal! 1.61 R), and high-entropy alloys (SSS, ideal? 1.61 R) [8]. Although [PERSON]'s equation provides a straightforward method for estimating SSS, which is ideal in alloy compositions, it assumes random atomic occupation of lattice sites.
# Static Analysis for ECMAScript String Manipulation Programs
## 1 Introduction  
Dynamic languages, for instance JavaScript or Python, have seen an important growth in a very wide range of fields and applications. Common features in these languages are dynamic typing (typing occurs during program execution, at run-time) and implicit type conversion [1], which lighten the development phase and allow programs not to block execution in the presence of unexpected or unpredictable situations. Moreover, one important aspect of dynamic languages is the way strings may be used. In JavaScript, for example, strings can be either used to access property objects or transformed into executable code by using the global function eval. In this way, dynamic languages provide multiple string features that simplify the writing of programs, allowing, at the same time, statically unpredictable executions which might make them harder to understand [1]. For this reason, string obfuscation (e.g., string splitting) is becoming one of the most common obfuscation techniques in JavaScript malwares [2], making it hard to statically analyze code. Consider, for example, the JavaScript program fragment in Figure 1 where strings are manipulated, de-obfuscated, combined together into the variable d and finally transformed into executable code, the statement ws = new ActiveXObject(WScript.Shell). This command, in Internet Explorer, opens a shell which may execute malicious commands. The command is not hard-coded in the fragment but it is built at run-time and the initial values of i,j and k are unknown, as is the number of iterations of the loops.  
All these observations suggest that, in order to statically understand statements which are dynamically generated and executed, it may be extremely useful to statically analyze the string value of d. Unfortunately existing static analyzers for dynamic languages [3; 4; 5; 6], might fail to precisely analyze strings in dynamic contexts. For instance, in the example above, TAJS [3], JSAI [4] and SAFE [5], lose precision on the eval input value and any information gathered so far about it. Namely, the issue of analyzing dynamic languages, even if tackled by sophisticated tools as the cited ones, still lacks formal approaches for handling the dynamic features of string manipulation, such as dynamic typing, implicit type conversion and dynamic code generation. Instead, in [7], a new approach for dynamic language analysis is proposed based on finite state automata for abstracting strings, coming with both a precise string abstraction able to infer string properties in general and a sound abstract interpreter for dynamically-generated code.
## 4 UAV Path Planning with Cooperative Detection Using BLSSPIO
### Simulation  
The BSLSPIO is utilized to generate the optimal \(\overline{V}_{y}^{m,n}\) for each UAV while minimizing the cost. The dimension of the BSLSPIO aligns with the waypoints represented by \(N\). Then, the global velocity \(\mathbf{V}^{m,n}\) and position \(\mathbf{P}^{m,n}\) can be obtained. The parameters are detailed in Table 5, and the path-planning results are illustrated in Figure 6. The procedure is summarized by the following steps. Step 1: Initialize the UAVs states, environmental settings, communication range, the UAVs' number, start points and destination points, etc. Step 2: Assign values to constraints, such as the start time, the arrival time, and the time stamp. Then, calculate the constant lateral \(\overline{V}_{x}^{m,n}\) and the variation range of the longitudinal \(\overline{V}_{y}^{m,n}\) under the local frame. Step 3: Initialize the parameters of the BSLSPIO. The individual solutions are composed of the velocity and position. The elements that need to be optimized are \(\overline{V}_{y}^{m,n}\). Step 4: Select a candidate solution and calculate the cost \(F_{cost}^{m}\) per iteration. Step 5: Update the candidate solution using the BSLSPIO. When the candidate solutions are identified, output the optimal components; otherwise, go to Step 4. Each UAV executes the BSLSPIO process in parallel, which helps reduce the computation cost. The velocities generated by the optimal solutions represent the desired velocities in each local frame. Once the velocities for the UAV's navigation points have been determined, the positions of each waypoint can be calculated. A path-smoothing technique is applied to eliminate the sharp corners and polish the paths. As illustrated in Figure 6, the outlooks of detection units (represented by the forbidden areas) are simplified to hemispheres. All UAVs navigate around the detection units and obstacles, generating paths from the start points (marked in circles) and the end points (marked in stars). Different colors denote different UAVs. Although there are some overlaps and crossing points among different UAVs' paths, this does not imply that there are collisions among UAVs, as their arrival times differ. Figure 7 displays the relative distance between UAVs. The results demonstrate that the distances satisfy the constraints of safe distance and communication range, which is consistent with the proposed models. In other words, the paths are safe and realizable during the cooperation. If the trajectories are not in the same direction, the distances are not in the same direction.