esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model Development
### Material Removal Considering the Normal Error of the Polishing Tool  
In practical polishing processes, many factors, such as the polishing pad wear, the concentration change of the polishing slurry, and the motion error of the polishing equipment, affect material removal [15]. The normal error of the polishing tool, that is, the deviation between the rotation axis of the polishing tool and the normal direction of the polishing surface, is an important factor that affects the material removal characteristics. However, the mechanism of its influence on the profile accuracy of the polished surface has rarely been reported. The normal error of the polishing tool directly alters the material removal distribution in the processing area, which in turn affects the surface shape accuracy of the polished surface. The theoretical derivation of material removal presented in this section considered the effect of the normal error of the polishing tool on the material removal characteristics of the polished surface under planetary motion. The formulation of Equation (4) was based on the assumption that the loads applied to the polishing tool are normal to the polished surface. When the polishing axis produces a certain inclination angle with the normal direction of the workpiece surface along any direction, the material removal caused by a single abrasive particle at this time can be expressed as:  
\[\Delta MRV_{m}=2\pi n_{r}{D_{p}}^{1/2}d_{m}(\theta)^{3/2}r_{m}\Delta t\cos\theta, \tag{6}\]  
where \(d_{m}(\theta)\) is the embedded depth of the \(m\)th abrasive particle when the normal error of polishing tool is \(\theta\).
## 2 Signal Transmission System
### Transmitter  
In the developed signal transmission system, the transmitter is mainly used for modulating the baseband signal and coupling it into the human body. As shown in Figure 2, the transmitter can be divided into a Field Programmable Gate Array (FPGA) module, amplifying and filtering module, and electrostatic coupling electrode. Additionally, the baseband signal is stored in the FPGA module, thereby no input port is integrated in the developed transmitter. The structure and function of the three modules can be described as follows: the FPGA module consists of a FPGA (ALTERA EP1C6, EP3C40), a Synchronous Dynamic Random Access Memory (SDRAM), a Programmable Read Only Memory (PROM) and a battery, _etc._ Firstly, it generates a carrier signal with the required frequency, then the carrier signal is modulated by the baseband signal which is stored in the PROM (16 Mbit) with the Differential Binary Phase Shift Keying (DBPSK) method. The amplifying and filtering module consists of a voltage amplifier and a band-pass filter. Due to the fact the Mach-Zehnder electro-optic modulator has extremely high input impedance, therefore, rather than using a complex constant current source to provide enough receiving signal amplitude in the transmitter based on electric sensor [11], here we can use a simple voltage amplifier with adjustable gain for amplifying the modulated signal. Additionally, a 500 kHz-30 MHz band-pass filter is added for filtering out the useless harmonic noise. Finally, the processed signal is coupled into human body by a customized copper electrode, which consists of a circular signal electrode with a radius of 10 mm and a rectangular ground electrode with a size of 10 cm \(\times\) 2 cm. The signal electrode is parallel to the ground electrode, while the distance between them is 10 cm. Meanwhile, the signal electrode is connected with the ground electrode by using rubber material, which has a relative permittivity of 2.6.
# A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model
## 7 Conclusions  
In this paper, we introduce a novel approach for analyzing emergency relief routes that considers the well-being of patients by incorporating the deprivation costs of patients into the objective function. In addition, our model takes into account the total travel distance, penalties for violating time windows, and penalties for exceeding loading capacity. To optimize the model, we propose a new metaheuristic algorithm, HBSO, which integrates SA, LNS, and BSO algorithms. Simulation experiments are conducted to evaluate the effectiveness of the HBSO algorithm, which outperforms existing methods by achieving lower total cost (as reflected by smaller objective function values) and providing more stable solutions with smaller variances. Our study also demonstrates the feasibility of the BSO algorithm for solving route optimization problems. Importantly, our approach aims to alleviate the suffering of patients, which aligns with the humanitarian spirit of emergency relief.  
The use of the BSO algorithm for optimizing vehicle routes has practical implications for material distribution in real-world scenarios. Moreover, the performance of the HBSO algorithm in the emergency relief routing model provides valuable insights for emergency relief actions. Our proposed HBSO algorithm effectively enhances the performance of the BSO algorithm in emergency relief problems by integrating LNS and SA selection steps, enabling the algorithm to obtain superior solutions and avoid local optima. Future research could explore the application of HBSO algorithms to other disaster management problems, such as resource allocation and emergency evacuation planning, or adapt the algorithms to solve optimization problems in other domains, such as transportation or logistics. In addition, future research could investigate the performance of HBSO algorithms on larger scale problems or on different types of input data. However, it should be noted that obtaining accurate patient location information in real-life emergency relief operations may not always be feasible, as opposed to the simulation experiments presented in this study. Therefore, developing an efficient method for rapidly updating relief routes as patient locations change in real-time is a critical research topic that warrants further investigation. In our future research, we plan to focus on developing a method that can effectively and efficiently update relief routes in response to changing patient location information to improve the timeliness and effectiveness of emergency relief operations.  
Investigation and writing--original draft preparation, X.W.; writing--original draft preparation, J.Z.; conceptualization, X.Y. ([PERSON]); investigation, X.Y. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by Social Science Foundation of Chinese Ministry of Education (No. 22 YJC630144), Social Science Research in Colleges and Universities in Jiangsu province (2020 SJA0182).
# A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices
## 2 Methodology
### Data Types
#### 2.2.1 The Input Data  
Two data sets are required as the inputs for this approach: the OSM road data and the crash shapefile. The OSM road data should contain the geometric info (a list of point coordinates) and the \"highway\" attribute. Note that \"highway\" in British English is used to indicate any type of road, such as motorway, primary, route, footway, and pedestrian, within OSM. In this research, we selected only the highways for cars, such as motorway, primary, secondary, tertiary, residential, and service, since we focused on traffic intersections. The crash shapefile should contain the geometric info (x-coordinate, y-coordinate) that is used to locate crashes on intersections. Crash-related factors [41; 42], such as environment and driver, and crash attributes, such as crash type and date, are not necessary but are suggested.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Type** & **File Format** & **Feature Type** & **Description** \\ \hline OSM Road & Input & osm & Line/Point/Region & Widely used open access geographic data \\ Crash & Input & shapefile & Point & The geographic data of crashes \\ Road junction & Intermediate & PostGreSQL & Point & The road junction spatial table \\  & & Table & Point & generated \\  & & & & by osm2 pgrouping software \\ Road segment & Intermediate & PostGreSQL & Line & The road segment spatial table \\  & & Table & & by osm2 pgrouping software \\ Crash table & Intermediate & PostGreSQL & Point & The crash spatial table converted \\  & & Table & & by shp2 pgsql tool \\ Intersection & Intermediate & PostGreSQL & Point & The intersection spatial table with \\  & & Table & & number of crashes \\ KDSWMCI table & Intermediate & PostGreSQL & & extracted from road junction \\  & & & & Stores the crash-intersection spatial \\  & & & Table & / & relationships \\ INDSWMI file & Output & SWM & / & SWM format file with INDSWMI info \\  & & & & from ArcGIS \\  & & & & The result of intersection hotspot \\ intersections & Output & Shapefile & Line & analysis \\ \hline \hline \end{tabular}
\end{table}
Table 1: An overview of data types.
# An ARCore Based User Centric Assistive Navigation System for Visually Impaired People
## 3 Design of ANSVIP  
### Information Flow in System Design  
Most tasks in real life are difficult to accomplish using a single sensor or functional unit. Instead, they require collaboration (cooperation, competition, or coordination) from multiple functional units or sensing agents in intelligent systems to make the most favorable final synthesis. In such a collaborative context, each functional unit has its own duty and cooperates via the agreed channel, thereby maximizing the effectiveness of shared resources to achieve the goal.  
Specifically, an assistive navigation system is composed of two parts: the cognitive system and the guidance system. The cognitive system aims to understand the world, including the micro-scale surroundings and the macro-scale scene; the guidance system aims to properly deliver the micro-scale guidance command, the macro-scale plan, as well as semantic scene understanding, to the user. The collaboration of the two allows the machine to understand the scene, and then the user acquires understanding from the machine, as shown in Figure 2.  
The proposed ANSVIP uses an ARCore-supported smartphone as the major carrier and uses ARCore-based SLAM (Simultaneous Localization and Mapping) to track motion so as to create a scene understanding along with mapping. The human-scale understanding of motion and space is processed to produce a short and safe path towards the goal. The corresponding micro-motion guidance is delivered to the user using haptic interaction, while the macro-path clues are delivered using audio interaction.  
Based on the information flow in an assistive navigation system, we design the ANSVIP structure as follows:  
Firstly, the system should be fully aware of the information related to the user's location during navigation. Unlike GPS-based solutions that are commonly used outdoors, our system has to use computer vision-based SLAM since indoor GPS signals are unreliable. SLAM is based on the Google ARCore, which integrates the vision and inert sensor hardware to support area learning.  
Secondly, the system should be capable of conveying the abstracted systemic cognition to the user. Unlike the conventional exclusive audio interaction, we propose a haptic-based cooperative mechanism. This allows us to replace the popular turn-by-turn guidance with a more continuous motion guidance.  
The working logic among the ANSVIP components is shown in Figure 3. Details of the major components are discussed in the following subsections:  
Figure 2: The information flow in the assistive system: The assistive system core aims to understand the world and translate the essential understanding to the user.
# Acoustic Improvement of Stator-Rotor Interaction with Nonuniform Trailing Edge Blowing
## 5 Experimental Results
### Flow Field Results  
The results presented are for measurement location #1 which is, as indicated in Figure 3, located downstream and in the wake of the stator blade. The speed of the rotor was measured to be 4472.7 rpm (\(f\) = 74.55 Hz). The added mass flows of NTEB ranged from 0 m\({}^{3}\)/h to 6 m\({}^{3}\)/h. The maximal added mass flow is less than 5 \(\%\) of the total inlet flow. The data sampling frequency is 20 kHz. A phase-locked averaging technology was adopted for the data processing, which was facilitated through a Hall sensor for phase locking. Since there will be vortices in the wake region, and because the dynamic four-hole probe cannot be used to measure the vortex speed, the dynamic pressure from one hole of the probe is analyzed, and the results are shown in Figure 7.  
Figure 7 reveals that the axial pressure adopts negative values in the wake region, which indicates a loss of axial velocity. However, with increasing added mass flow, those negative values have a positive growth. When the added mass flow has its maximum of 6.0 m\({}^{3}\)/h, the regions displaying negative pressure values are much smaller. In this case, the stator wake is obviously improved. Meanwhile, the period (T = 11) induced by the stator wake and rotor rotation in Figure 7 reflects the number and contours of rotor blades.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**BPF Order** & **1 st BPF** & **2 nd BPF** & **3 rd BPF** & **4 th BPF** & **5 th BPF** \\
**Frequency** & **(894.54 Hz)** & **(1789.08 Hz)** & **(2683.62 Hz)** & **(3578.16 Hz)** & **(4472.7 Hz)** \\ \hline Without NTEB (N) & 4.59 & 0.95 & 1.54 & 0.95 & 0.37 \\ With NTEB (N) & 1.66 & 0.69 & 0.88 & 0.44 & 0.23 \\ Reduction Ratio & 63.83\% & 27.37\% & 42.86\% & 53.68\% & 37.83\% \\ \hline \end{tabular}
\end{table}
Table 1: The first five orders of blade-passing frequencies (BPF).  
Figure 6: Blade-passing frequencies of axial thrust.
# Privacy and Utility of Private Synthetic Data for Medical Data Analyses
## 3 Data Privatization Techniques
### Traditional Techniques  
A common approach for protecting the privacy of individuals contained in a dataset is \(k\)-anonymity, which was first introduced by [PERSON] [3]. The main principle of \(k\)-anonymity is to first remove all identifiers and then transform the dataset so that there are groups of \(k\) entries with the same quasi-identifier. Records with matching values on quasi-identifying attributes form an equivalence class, preventing a sensitive attribute from being linked to a single person [16]. This can be achieved by the generalization and suppression of the (quasi-)identifying attributes or by the addition of fake data. When using generalization, the value of a quasi-identifier is replaced with a more general super category. For example, a certain age will be replaced with a suitable age range. Suppression can be used for attributes where there is no good way to generalize the values. Then, the value can be suppressed, which in general means the removal of the value [3].  
\(k\)-anonymity has non-negligible weaknesses. For example, an attacker with background knowledge can assign a specific probability to each sensitive attribute within an equivalence class, which can significantly compromise an individual's privacy. To address this weakness, the definition of \(l\)-diversity was evolved [15]. This is based on \(k\)-anonymity and uses the same suppression and generalization techniques but enforces a stronger privacy guarantee. \(l\)-diversity necessitates that there are at least \(l\) distinct sensitive attributes per equivalence class. While this protects from some possible attacks in \(k\)-anonymity, it still has many pitfalls. If an attacker learns that their target has either prostate cancer or colon cancer, then highly sensitive information has already been leaked. With knowledge of the target's gender, this information might even become more accurate. There are also more sophisticated definitions such as \(t\)-closeness or \(\delta\)-presence that try to remedy these shortcomings but have their own weaknesses. Since their development and use was mostly discontinued after DP was released, they are not discussed in this paper [17; 18].
# Functional Connectome Fingerprinting Through Tucker Tensor Decomposition
## 3 Results
### Dimensionality Reduction Methods in Fingerprinting  
In Table 2, a comparison between different dimensionality reductions methods versus a baseline (consisting of vectorized FCs) for parcellation granularity of 414 is shown. When computing PCA, the number of principal components used to obtain the identifiability matrix was chosen to be the maximizer of subject identifiability as introduced in the work of [PERSON] and [PERSON] (2018) [9]. The reported matching rates for CP decomposition are the highest among rank-\(R\) decompositions, with \(R\in\{100,200,300,400,414\}\). Equivalently, the reported matching rates for Tucker-based methods are the highest among all considered combinations of brain parcellation rank \(R_{1}\) and participant rank \(R_{2}\). Among the presented dimensionality reduction methods, Tucker decomposition (ST-HOSVD and HOOI) demonstrates, by a considerable margin, the highest fingerprint.  
Considering the high dimensionality of neuroimaging data, it is crucial to consider the scalability of Tucker-based methods for the purpose of fingerprinting. In Table 3, the runtime (seconds) of the most computationally demanding decomposition of a tensor of dimensions \(PG\times PG\times 426\), where \(PG\) denotes the parcellation granularity, is presented for each dimensionality reduction method. For CP and Tucker-based methods, the highest runtime corresponds to a decomposition of maximum possible rank. For example, for a tensor of dimensions \(414\times 414\times 426\), the CP rank is set to \(R=414\), and the ranks for ST-HOSVD and HOOI are set to \(R_{1}=R_{2}=414,R_{3}=426\). Equivalently, a matrix of dimensions \((\frac{414}{2})\times 852\) (columns represent the upper triangular entries of vectorized FCs) all 852 principal component are computed for PCA. As the dimensionality of the data increases with the parcellation granularity, PCA tends to become less computationally expensive than ST-HOSVD. However, the increase in computational cost is negligible compared to the effectiveness of ST-HOSVD in uncovering fingerprints. For reference,  
Figure 2: Fingerprinting gradient as measured by matching rate for all fMRI conditions. For each parcellation granularity, the brain parcellation rank and participant rank were set to their maximum value.  
the reported runtimes were measured on a Lenovo Legion 5i laptop equipped with an Intel Core i7 processor, 32 GB of RAM, and an NVIDIA GeForce RTX 3070 Ti GPU.
# Characteristics of Low-Frequency Acoustic Wave Propagation in Ice-Covered Shallow Water Environment
###### Abstract  
Mastering the sound propagation law of low-frequency signals in the Arctic is a major frontier basic research demand to improve the level of detection, communication, and navigation technology. It is of practical significance for long-distance sound propagation and underwater target detection in the Arctic Ocean. Therefore, how to establish an effective model to study the characteristics of the acoustic field in the Arctic area has always been a hot topic in polar acoustic research. Aimed at solving this problem, a mathematical polar acoustic field model with an elastic seafloor is developed based on a range-dependent elastic parabolic equation theory. Moreover, this method is applied to study the characteristics of polar sound propagation for the first attempt. The validity and effectiveness of the method and model are verified by the elastic normal mode method. Simultaneously, the propagation characteristics of low-frequency signals are studied in a polar sound field from three aspects, which are seafloor parameters, sea depth, and ice thickness. The results show that the elastic parabolic equation method can be well utilized to the Arctic low-frequency acoustic field. The analysis of the influence factors of the polar sound field reveals the laws of sound transmission loss of low-frequency signals, which is of great significance to provide information prediction for underwater submarine target detection and target recognition.  
low frequency; polar shallow water environment; elastic parabolic equation +
Footnote †: journal: Applied sciences
## 5 Results and Discussion
### Energy Yield  
In Table 5, the energy yield of the wind farm sitting is presented. The main characteristic of the sitting is the increased losses due to topography (>32%) as several wind turbines are located at inferior positions. The wake effects are calculated to be 3.11%.  
Figure 14: Mean power density at KTD site at 95 m above ground level.  
Figure 13: Mean wind speed at KTD site at 95 m above ground level.  
Figures 15 and 16 show the results of basic and net energy yield with respect to individual unit production and with respect to the wind direction sector, respectively.  
The base energy yield is calculated to equal to 34.45 GWh per year, where the topographic affect change is \(-32.17\%\) and the wake losses are \(-3.11\%\). Therefore, the initial KTD's wind park final energy yield is equal to 22.6541 GWh per year.  
The calculations of the hydropower system show that the efficiency of the system is 78.1%. Therefore, the efficiency of the whole hybrid project (wind park and hydropower-pump system) is approximately 40%. The amount of the electricity that can be produced by the KTD's hybrid system is approximately 26,663,933 kWh/year. The emissions reduction from the operation of this system is estimated to be 15,100,000 tons of CO\({}_{2}\)-equivalent each year, which is acceptable for our study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Wind Turbine** & **Base Yield** & **Topographic Effects** & **Wake Losses** & **Total Yield** \\
**Identifier** & **GWh** & **\% Change** & **\% Loss** & **GWh** \\ \hline
1 & 6.89 & \(-33.41\) & \(-2.70\) & 4.47 \\
2 & 6.89 & \(-26.71\) & \(-5.50\) & 4.77 \\
3 & 6.89 & \(-16.33\) & \(-4.00\) & 5.54 \\
4 & 6.89 & \(-53.50\) & \(-1.44\) & 3.19 \\
5 & 6.89 & \(-30.88\) & \(-1.02\) & 4.72 \\ Total & 34.45 & \(-32.17\) & \(-3.11\) & 22.69 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Wind farm energy yield for the wind farm layout.  
Figure 16: Basic and net energy yield with wind direction for the initial layout.  
Figure 15: Basic and net energy yield for each turbine for the initial layout.