esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Predictive Capability of the Models
#### 4.3.3 Analysis of Prediction Results  
Figure 11 shows how the three models perform in predicting the humidity and temperature at the end of the roadway. As can be discerned, the hybrid learning model optimized by the GA has more excellent performance in predicting the humidity and temperature at the end of the roadway than the single LSTM model. Moreover, after dimension reduction by the PCA, the dimensions of the input features are reduced from 21 to 5 on the basis of reflecting the data feature information to the maximum extent, so the optimization time is decreased significantly in the intelligent optimization by the GA. It is worth mentioning  
Figure 10: Comparison between predicted and actual relative humidities \(RH_{out}\) on the testing set among the three models. (**a**) LSTM. (**b**) LSTM-GA. (**c**) PCA-LSTM-GA.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \begin{tabular}{c} **Number of** \\ **Units in LSTM** \\ **Layer** \\ \end{tabular} & \multirow{2}{*}{**Batch Size**} & \
\begin{tabular}{c} **Optimizer** \\ **Learning Rate** \\ \end{tabular} &
\begin{tabular}{c} **Dropout Layer** \\ **Parameter** \\ \end{tabular} \\ \hline LSTM-GA & 42 & 20 & 0.00654 & 0.254 \\ \hline PCA-LSTM-GA & 16 & 21 & 0.009253 & 0.1163 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal hyperparameters of the models for relative humidities prediction.  
that the prediction performance is not lowered but even improved. This indicates that the PCA algorithm can effectively reduce the interference of data redundancy in model establishment and has an active effect on model optimization.  
On the whole, the PCA-GA-LSTM model succeeds in predicting the humidity and temperature at the end of the roadway effectively. The performance of this model can be optimized instantly according to different geological conditions of the roadway, and the hybrid machine learning model has good robustness, so the model is suitable for predicting the humidity and temperature at the end of the roadway.
## 2 Materials and Methods  
The algorithm framework is divided into three main sections: inputs, processes, and outputs. Figure 1 outlines the proposed algorithm framework for acoustic ray tracing. The algorithm requires inputs, such as the factory's 3D model, the location of noise sources, their respective Sound Pressure Levels (SPLs), and the receiver's location. The maximum exposure limits and the Noise Reduction Rating (NRR) variables are established according to the Occupational Safety and Health (Noise Exposure) Regulations 2019 [29] from the Department of Safety and Health, Malaysia. The algorithm is initiated by plotting the floor plan, positioning the source and receiver on the floor plan, and executing the ray tracing engine based on the simulation model.  
The ray tracing method can be integrated into MATLAB, allowing users to customise it for specific scenarios. However, the ray tracing engine in MATLAB is primarily optimised for radio signal processing rather than noise calculation [30]. Since the ray tracing engine in MATLAB is not for acoustic purposes, additional computations must be implemented in the code to determine the resultant SPL. Consequently, the data must be converted to be suitable for acoustic purposes. This process involves calculating the distance and identifying the reflecting surfaces for each generated ray. Subsequently, the integration with the Safety and Health Act can be incorporated into the program, creating a comprehensive simulation that identifies issues and provides solutions. Based on the resultant SPL, the program calculatesthe maximum allowable exposure time and the minimum Noise Reduction Rating (NRR) required for an ear protection device to be adequate over an 8-hour shift.  
The ray tracing uses the Shooting and Bouncing Rays (SBR) method. The SBR method launches rays from a geodesic sphere centred at the noise source. The geodesic sphere enables the model to launch approximately uniformly spaced rays. Then, the method traces every ray from the source and can model different interactions between the rays and surrounding objects, such as reflections and edge diffractions. When a ray hits a flat surface, the ray reflects based on the law of reflection. When a ray hits an edge, it spawns many diffracted rays based on the law of diffraction. Each diffracted ray has the same angle with the diffracting edge as the incident ray. The diffraction point then becomes a new launching point, and the SBR method traces the diffracted rays in the same way as the rays launched from the source.  
The initial phase of this research involves designing the simulation algorithm. This includes planning, designing, and integrating the MATLAB ray tracing method. Subsequently, various variables are tested and selected to ensure the algorithm functions as intended, necessitating multiple simulations. The third phase involves manual testing, where the algorithm's functionality is compared with a manual calculation approach. Finally, the algorithm is tested and validated by comparing its results with real-world data, and its performance is thoroughly evaluated.
# A Novel Network Protocol Syntax Extracting Method for Grammar-Based Fuzzing
###### Abstract  
Network protocol syntax information plays a crucial role in grammar-based fuzzing. Current network protocol syntax extraction methods are less versatile, inefficient, and the extracted information is not comprehensive. This paper proposes a novel method for extracting syntax information, which innovatively extracts network protocol syntax from Wireshark protocol dissector files. The extracted syntax information includes packet types of the protocol, the constituent fields of each packet type, and detailed attributes of each field. Based on this method, an automated system for network protocol syntax information extraction was developed. The experiment was conducted with this system on a variety of protocols including DCCP, DNP3.0, Modbus TCP, and S7 COMM. The experimental results show that compared with the current methods, our method has a better performance in terms of efficiency and versatility and at the same time ensures the comprehensiveness and accuracy of the extracted syntax information.  
network security; Wireshark; fuzzing; network protocol; protocol syntax extraction Article
# Privacy Auditing in Differential Private Machine Learning:
## 2 Preliminaries
### Centralized and Local Models of Differential Privacy  
The two main common models [82; 83] for ensuring data privacy, each with different applications and mechanisms, are the _central_ model and the _local_ models. In differential privacy, the user datasets are noised either at the data center after receiving client's data or it is noised by each user of the data locally.  
The classic centralized version of DP requires a trusted curator who is responsible for adding noise to the data before distributing or analyzing. In centralized differential privacy, the data and model are collocated, and the noise is added to the original dataset after it has been aggregated in a trusted data center. A major problem with centralized differential privacy setting is that users still need to trust a central authority, namely the administrator of the dataset, to maintain their privacy [84]. There is also the risk of a hostile curator [85].  
In the local differential privacy model, the data are made differentially private before they come under the control of the curator of the dataset [86]. Noise is added directly to the user's dataset [87] before it is transmitted to the data center for further processing [88]. In the trade-off between privacy and accuracy, both centralized and local paradigms of DP can reduce the overall accuracy of the converged model due to the randomization of information shared by users [85].  
Another taxonomic approach is the general distinguish DP into single-party learning and the multi-party learning [89]. _Single-party learning_ means that the training data of each data owner is stored in a central place. In _multi-party learning_, on the other hand, there are several data owners who each keep their own datasets locally and are often unable to exchange raw data for data protection reasons.
## 2 Bio-Inspired Mechanisms Used in Locomotion
### Cilia  
The core structure of a cilium was identified as the axoneme [95]. The axoneme consists of dynein motors which are assembled onto a scaffold of nine cylindrically arranged doublet microtubules. Dynein motors can generate a linear motion, which makes the neighboring doublet microtubules slide over each other and create an oscillatory beating pattern [96]. However, the exact mechanism causing the beating pattern is still unclear [97]. The search for any chemical modification which regulates the dynein motors was unsuccessful over the past decade. Therefore, this beating pattern must be regulated by the intrinsic physical properties of the axoneme [98]. A single cilium can beat rhythmically and its motion can be divided into two stages; effective stroke and recovery stroke as shown in Figure 5a [99]. During the effective stroke, the cilium is straight and pushes a large amount of fluid and in the recovery stroke, it pulls a small amount of fluid as it is closer to the cell surface [100]. When cilia arrange in an array, the hydrodynamic interactions make the adjacent cilia beat out-of-phase [101]. This motion is similar to a wave-like motion and is known as a metachronal wave which is shown in Figure 5b [102].  
The slender shape of the cilium and time-varying bending moments ensure the generation of non-reciprocal motion required to propel in a low Reynolds number regime [103]. The dynamics of cilia can be analyzed using the rotor and rower models [104]. In addition, the effect of metachronal waves on the flow has been numerically analyzed using superparamagnetic cilia actuated by a rotating magnetic field [105]. Similar to flagella, several microorganisms use cilia to generate drag-based propulsion within a fluid. Paramecium and stentor are such microorganisms that use cilia for propulsion [106]. ParaLiker (shown in Figure 6a) mimics the motion of Paramecium and has two arrays of cilia with an in-plane beating action [107]. The maximum velocity of a microrobot with two cilia arrays as shown in Figure 6b, was measured in both silicon oil and DI water and it was proved that the maximum velocity and the step-out frequency in silicon oil are smaller due to its high viscosity. This robot was demonstrated for particle transportation in microenvironments [108].  
Figure 4: Helical and planar wave propagation initiating mechanisms [88].  
Figure 5: Cilium motion: (**a**) Effective and recovery stroke of a cilium; (**b**) metachronal wave generated by an array of cilia.
## 1 Introduction
After that, the proportionate MEE (PNMEE) algorithm is proposed to utilize the sparse property in nature systems [16]. However, the PMEE algorithm has a high computational burden, which makes it not good for practical engineering applications. To reduce the computational burden, the maximum correntropy criterion (MCC) was developed using a similarity function to act as a novel cost function [27]. Finally, the MCC has a comparable complexity compared with the basic LMS algorithm, while it can provide a robustness-like performance as that of the MEE algorithm. Thereby, the MCC algorithm is a proper selective method for practical engineering applications in non-Gaussian environments. However, the MCC does not use the sparse-structure information in the existing sparse system. Although a Pt technique was integrated into the normalized MCC (NMCC) to form a proportionate NMCC (PNMCC) algorithm to handle the sparse signals, the PNMCC's convergence becomes slow after the early iterations and the PNMCC cannot fully use the sparse information of the natural sparse signals. An improved proportionate normalized MCC (PNMCC) and a correntropy induced metric (CIM) penalized PNMCC (CIM-PNMCC) are considered for fully taking use of the inherent sparsity characteristics in the sparse systems. The developed PNMCC algorithm is realized by incorporating the proportionate-type technique, the normalization method and a generalized Gaussian density function (GGDF) into the MCC algorithm, while the CIM-PNMCC algorithm is implemented by means of the CIM theory which is integrated into the newly presented PNMCC algorithm to form an amazing zero attractor. The proposed PNMCC and the CIM-PMCC algorithms are mathematically given in detail and they are used for estimating sparse systems. These two algorithms perform better for identifying the sparse systems in non-Gaussian noises compared with the PNLMS algorithm. The paper is well constructed as follows. We review the MCC and zero attraction methods in Section 2. In Section 3, the PNMCC and CIM-PNMCC algorithms will be proposed. The performance for sparse system identification of the developed algorithms is discussed in Section 4. Finally, we give a short summary of this paper. Notations:  
\(\left\|\cdot\right\|\)\(l_{2}\)-norm  
\((\cdot)^{T}\)Transpose operation for a matrix or a vector  
\(\mathbf{x}\)with bold frontVector or Matrix
## 2 Materials and Methods
### Feedback Conditions  
In both groups, the tape provided the participants with continuous tactile feedback on their spine flexion by augmenting skin stretching. While leukotape (altapharma sport tape, Rossmann GmbH, Burgwedel, Germany) was applied to the participants' back in one group, kinesio tape (altapharma kinesio tape, Rossmann GmbH, Burgwedel, Germany) was used in the other group. The tape was applied superficial to the participants' left and right lumbar extensor muscles on the lower back, from the 12 th thoracic to the 1 st sacral verteb. During the application of the tape, the participants were asked to remain in a standing position with a neutral spine. While the length of the tape corresponded to the distance between the two vertebrae in the leukocyte group, two centimeters was subtracted from this distance in the kinesio tape group, and the tape was stretched to apply it from T12 to S1, thereby representing an approximate 10-15% baseline stretch. As leukocyte tended to peel off in previous studies Sze  
by six additional markers, and one marker was applied to the box. Data were exported for analysis to Visual 3D software (C-Motion, Inc., Boyds, MD, USA), and they were low-pass-filtered using a bidirectional low-pass filter with a cut-off frequency of 15 Hz before computing the outcomes of interest.  
For the lifting task, the lumbar angle was computed as a planar angle (YZ) at each frame between the TV12-L3 and L3-S1 segments, using the pelvis as a reference. The angle was normalized by the lumbar angle recorded during the static pose, in which the participants adopted a neutral standing position. The start and the end of each lift were defined by the box marker's vertical position. Peak angles were extracted as the maximum lumbar angles between the beginning and the end of the lift.  
For the repositioning task, we were interested in the torso angle. Consistent with previous research, the segment between S1 (proximal) and TV12 (distal) was considered rigid and homogenous. The torso angle was calculated as the angle of the S1-T12 segment on the sagittal plane (YZ). The laboratory's coordinate system was chosen as a reference for the calculation of the torso angle.  
As a measure of gross lumbar proprioception, repositioning errors were computed as the difference in torso angular displacements between the target position (i.e., 30 or 60 degrees) and the orientation of the torso adopted by the participant in their perception of this target position using the following formula, adapted from [14, 21, 23, 24, 25, 26]:  
\[\mid\text{RE}\mid\text{=RPi}-\text{TP}\]  
where RP is the reached position, i is the number of trials (n = 5), TP is the target position, and RE is the repositioning error. RE refers to the number of errors and indicates the accuracy, regardless of the direction.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps Derived from a Combination of Field Biomass, LiDAR Data and Optical and/or Radar Data
#### 3.4.13 Global Forest AGB Maps from Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data  
[PERSON] et al. [99] provided a global forest biomass map at a spatial resolution of 1 km for 2004 by integrating field data collected from published literature, optical imagery, GLAS data, climate surfaces, and topographic data using the Random Forest model. Comparing the entire biomass map with field data, an R\({}^{2}\) of 0.56 and RMSE of 87.53 Mg/ha was achieved.
## 1 Introduction
Nevertheless, its field of application, already pointed out by other researchers [8], would be found in aerostatic or very light UAS combined with solar panels, nowadays of very low cost, light and flexible to obtain the necessary energy as primary source for the high voltage converter. Some papers have been recently published in which the fundamentals of EHD thrusters have been applied to electric propulsion flight. Although the value of these studies is undeniable, methodologically they are not as rigorous as previous works and in some cases are inconclusive. Authors of present a study, somewhat similar to those carried out in [12; 13; 14; 15], although it does not go as deeply into the physical analyses. By first time, the architecture of the DC/DC converters used to excite the electrodes is shown. To the best of the authors' knowledge, apart from the well-known test carried out by the Massachusetts Institute of Technology with an extremely light 5 m glider, and the use of ionic wind for drag reduction in fixed-wing aircrafts presented in [16], no other attempts to connect electrohydrodynamic propulsion with atmospheric flight exist. In [16] an asymmetrical capacitor is built on a wing section with one of the electrodes placed on the leading edge. With a handmade wind tunnel and a rather elementary instrumentation, the drag coefficient is measured for different angles of attack. Despite the rudimentary nature of the system, at low relative wind speeds (around 7 m/s) a reduction of the drag coefficient and increases in lift varying between 2 and 4%, depending on the angle of attack, are observed. From the above review it can be concluded that the use of EHD thrusters as an aircraft propulsion system is a subject that has aroused and arouses interest, and its future viability, either as a pure propulsion method or combined with electric propulsion in atmospheric flight aircraft, manned or remotely piloted, presents a wide field of study. For this reason, this paper presents a study, which to the best of the authors' knowledge has not been carried out, in which the performance of electrohydrodynamic propulsion is quantified using simple thruster topologies, to compare it with conventional electric propulsion, taking into consideration, for the latter, the weight, power consumption the drawbacks that arise when trying to scale up the phenomenon. For the sake of clarity, the following section will start by presenting the structure of the most elementary EHD thruster together with the equations governing its behavior, and then continue with the methodology, instruments and tests performed.
# Sound Localization Framework for Construction Site Monitoring
## 3 Literature Review
### Monitoring Approaches Suitable for Construction Sites
#### 3.1.2 Passive Localization Approaches  
Currently, passive localization approaches that use vision data are widely used on construction sites. Vision-based monitoring algorithms have been developed to classify factors such as workers, equipment, and damaged parts. The locations of objects and actions are determined through the vector analysis of the reference points of the objects [18]. [PERSON] et al. developed systems that use stereo cameras installed at construction sites to measure the changes in worker locations to perform localization. [PERSON] and [PERSON] demonstrated systems that use cameras in indoor environments to accurately monitor worker locations and work status [19].  
Despite substantial research and development efforts, there are several limitations regarding the use of vision data-based localization systems on construction sites, such as the positioning and monitoring range of cameras. When a large area needs to be monitored at obstruction-filled sites, several cameras need to be installed. Additionally, the quality of the vision data depends on the camera performance and resolution. Even when high-resolution cameras are used, their performance can be easily degraded by unexpected environmental factors at the construction site, such as dust and foreign substances. In addition, it can be used as a supplementary measure to solve problems regarding continuously monitoring the work status due to the privacy problem.
## 3 Results
### Characteristics of the Measured Surface  
The flatness of the surface has an influence on the measured spectrum, as explained in Section 2.3.1. For the relatively flat tissue slices, SNV normalization can correct these differences [15]. For the spherically shaped lumpectomy surface, SNV normalization proved to be a sufficient method as well in areas of sufficient illumination (red spectra in Figure 5f). Where large differences between Figure 5a,b are observed, these are minimized after applying SNV normalization (Figure 5c,d). However, in areas of shadow (blue and green spectra in Figure 5f), the low illumination resulted in a diffuse reflectance spectrum with a low intensity that could not be corrected for by SNV normalization. We determined the minimum intensity a diffuse reflectance spectrum should have in order to be able to use SNV for normalization. Tissue spectra in Figure 5c were subtracted from tissue spectra from Figure 5d and their maximum absolute difference was plotted against the SCM in Figure 6a. We allow a maximum absolute difference of 0.3, which corresponds to an SCM of 0.995 and 0.992 for NIR and VIS, respectively. Therefore, as shown in Figure 6b, the maximum value of the diffuse reflectance should be at least above 15% (with Spectralon as a reference) for the spectra to be reliable after SNV normalization. Spectra that did not fulfill this criterion (for example the blue and green spectra in Figure 5f) were excluded from further analysis.  
Figure 4: (**a**) Overview of the histopathological information corresponding to the 110 measured locations. The bold numbers correspond to locations for which an extra operation was required due to the malignant tissue at the resection margin. The corresponding H & E sections of these points are shown in (**b**). The red and magenta lines in (**b**) are delineations of the pathologist that indicate the border of IDC (invasive ductal carcinoma)/ILC (invasive lobular carcinoma) and DCIS, respectively. For three locations, the marked location was retrieved on two H & E sections of neighboring tissue slices. The arrows represent a length of 1 mm underneath the resection surface. In (**a**): * Locations that contained malignant tissue over more than 4 mm on the resection surface. The percentages values represent the amount of the specific tissue type in the measured locations based on H & E results.