esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Embedded Space with Low-Level Features for Speech Emotion Recognition
## 2 Materials and Methods  
### Methodology  
The proposed method uses low-level features of the speech signal, which are combined with the features generated by the selected neural network model at the final stage of classification.  
The diagram shown in Figure 1 and the Algorithm 1 shows a general outline of the proposed solution. First, for each recording \(s_{i}\) of emotional speech from the set two representations are determined, base features: \(r_{1}^{i}=\gamma_{1}(s_{i})\) and injected features: \(r_{2}^{i}=\gamma_{2}(s_{i})\) resulting in two sets, \(R_{1}=\{r_{1}^{i}\}_{i=1}^{N}\) and \(R_{2}=\{r_{2}^{i}\}_{i=1}^{N}\). In other words, each \(s_{i}\) recording is represented by a pair \(\{r_{1}^{i},r_{2}^{i}\}\). The functions \(\gamma_{1}\) and \(\gamma_{2}\) return the time-frequency representation of the signal and selected low-level features where \(N\) is the number of recordings in the considered set. In the next step, each pair of features represents subsequent recordings processed as follows, the representation of \(r_{1}\) is processed by neural network and then projected to a given size in the dense layer \(FC_{1}\). As a result, a vector \(x_{1}\) of size \(Z=M-K\) is created, where \(K\) is the number of injected features and \(M\) is the size of the final layer classification \(FC_{2}\). In both cases, \(FC\) represents a single fully connected layer. The \(r_{2}\) representation is normalized in the \(NL\) layer [17], which creates the vector \(x_{2}\). Then, the vectors \(x_{1}\) and \(x_{2}\) are combined in the concatenation process, giving final feature vector \(X=[x_{1}\mid x_{2}]\) with length \(Z\), which is processed by the final classification layer \(FC_{2}\) and then \(softmax\) operation is applied.
# Experimental Study on Damage Constitutive Model of Rock under Thermo-Confining Pressure Coupling
###### Abstract  
Underground engineering frequently encounters the challenges of high temperatures and high confining pressures. The combined effects of temperature and confining pressure can significantly alter the mechanical properties of rock. Evaluating the impact of these factors on rock is a crucial aspect of engineering. This study investigates the mechanical properties of rocks under various temperature and stress conditions, focusing on the deformation and failure characteristics of four typical rock types: granite, red sandstone, gray sandstone, and shale under temperature-confining pressure coupling. The results indicate that high temperatures cause internal structural damage and crack propagation in rocks, leading to a reduction in compressive strength and elastic modulus. Conversely, high confining pressures can inhibit crack propagation and enhance rock deformation capacity. Additionally, significant differences were observed in the mechanical responses of different rocks; red sandstone and shale predominantly exhibited shear failure under the coupled effects of temperature and confining pressure, whereas granite and gray sandstone exhibited bulging failure. Based on the experimental results, an elastic modulus fitting model considering the temperature-confining pressure coupling effect was proposed, and the parameters of the Drucker-Prager criterion were modified. A constitutive model was constructed to accurately reflect the stress-strain state of rocks under the coupled effects of temperature and confining pressure. The constitutive model results show good agreement with the experimental findings.  
confining pressure; damage constitutive model; elastic modulus; rock; temperature +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Methodology
#### 3.3.2 File Format Conversion  
Data in L2D is available in HDF5 or hex cube format. The he5 format has been converted to tiff or hdr format in accordance with the specifications. The open-source software R programming is extremely beneficial in restoring the same. To complete the data format conversion, the following steps must be taken. (1). The pr_convert function is used to import PRISMA L2 data (2B, 2C, and 2D). The pr convert function is used to import PRISMA L2 data (2B, 2C, 2D). It takes a PRISMA L1 hdf5 image as input, as well as the name and format of an output folder, and has a series of switches for selecting which hyperspectral cubes and auxiliary datasets to generate. (2). In VNIR and SWIR, the logical justifications for and against importing VNIR and SWIR hyperspectral cubes are presented. (3). The full logical argument can be used to determine whether a full VNIR+SWIR cube is required.
# Respiratory Muscle Strength as a Predictor of VO2 max and Aerobic Endurance in Competitive Athletes
## 2 Materials and Methods
### Data Collection Tools
#### Assessment of Respiratory Muscle Strength  
Athletes were briefed on the procedures before the tests. Respiratory function and respiratory muscle strength were assessed utilizing a digital spirometer (Pony FX Cosmed, Italy). The evaluations were conducted with athletes seated comfortably in an upright position. Throughout the tests, participants utilized a mouthpiece and wore a nose clip. They were instructed to seal their lips tightly around the mouthpiece to prevent any airleakage from the spirometer. To familiarize athletes with the device's operation, a few trial tests were conducted before the formal assessments. Each test was repeated thrice with a maximum rest period of three minutes between trials. The highest measurement score obtained was utilized for statistical analysis. During the maximal voluntary ventilation (MVV) test, athletes were directed to breathe deeply, rapidly, and forcefully for 12 s. Following the completion of the test, athletes were asked to briefly hold their breath to prevent respiratory alkalosis, and the MVV value was recorded. MVV: The greatest amount of air that can be inhaled and exhaled within a given period. MIP (Maximal Inspiratory Pressure): The maximum pressure generated during inhalation. MEP (Maximal Expiratory Pressure): The maximum pressure generated during exhalation. To evaluate respiratory muscle strength, MIP and MEP tests were administered. For the MIP test, athletes were instructed to fully exhale before taking a deep, rapid, and forceful inhalation. Conversely, for the MEP test, athletes were prompted to inhale fully before exhaling swiftly and forcefully. Each test was repeated thrice with rest intervals between measurement cycles. The most favorable results obtained from all tests were selected for inclusion in the subsequent analysis [22].  
Additionally, the MIP and MEP tests, which provide information about respiratory muscle strength using the intraoral pressure method, are tests accepted by the European Respiratory Society (ERS) and American Thoracic Society (ATS). These tests are recognized as non-invasive methods for measuring respiratory muscle strength. During exercise, respiratory muscle activation can be assessed by intramuscular EMG or surface electrodes. However, invasive procedures should be performed under medical supervision, while action potential measurements taken using surface electrodes can be challenging to control during high-intensity exercise due to factors like electrode displacement or excessive interference. In previous studies that examined the relationship between respiratory muscle strength and exercise, the MIP/MEP method (which measures intraoral pressure) has been used [23; 24].
## 4 Conclusions  
The ship resistance test plays a very important role in ship design, which mainly involves the selection of the ship's power equipment. Therefore, it is of great significance to determine the size of ship resistance and its influencing factors and then analyze the change in actual ship power. Based on the standard ship resistance experimental test data, the influence of various factors on ship resistance and the effect of multiple factors on the power data of a real ship under the condition of coexistence are studied in the paper. The conclusions are as follows:  
* There is a significant positive correlation between ship resistance and speed, which conforms to the exponential equation change law. The ship resistance also gradually increases with increasing speed. Ship speed, displacement, and appendages can all have impacts on ship resistance, in which speed is the most important factor, and displacement has a greater impact than ship appendages.  
Figure 6: Actual ship power response surface under two-factor condition. (**a**) Coupled effects of displacement and ship speed on power (**b**) Coupled effects of appendage and ship speed on power. (**c**) Coupled effects of displacement and appendage on power.  
According to the correlation analysis, the maximum correlation coefficient between ship speed and resistance or power is 0.99. The correlation between displacement and ship resistance or power is much greater than that of appendages. The correlation analysis further proves the degree of influence of each factor on ship resistance.
* Actual ship power and speed, displacement, and appendage form a quadratic polynomial relationship. The multi-factor interaction analysis results show that speed and displacement have the greatest influence on actual ship power. The main reference factors in the selection of marine power equipment are speed and displacement.  
Conceptualization, X.Z. and X.L.; methodology, X.Z.; software, N.L.; validation, X.L.; formal analysis, X.L.; investigation, X.Z.; resources, X.Z.; data curation, N.L.; writing--original draft preparation, X.Z.; writing--review and editing, B.Z.; visualization, N.L.; supervision, X.L.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Qingdao Marine Science and Technology Center, grant number 2022 QNLM030002-2.  
Not applicable.  
The research data have been presented in the current paper.  
All authors would like to express sincere thanks to the editor and reviewers for their valuable comments.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Representing FIREFLY Spectra
#### 2.3.1 Deriving Baselines for Indirect SIF Retrievals  
To ascertain the locations and properties of absorption features that could potentially be used for indirect SIF retrieval, simulated spectra were derived for the full spectral range of each spatial pixel of FIREFLY, as described in Section 2.3. Each simulated spectrum (recorded at this stage in relative radiance units) was then searched for upward inflections. Upward inflections were then treated as the lower-radiance \"valley\" bands needed for indirect SIF retrievals (as described in Section 2.1), and each was paired with the \"peak\" band that would provide the deepest proportional depth within a search window of \(\pm\)3 bands. During this process it was ensured that no valley shared a peak with another absorption feature.
# Development of Security Rules and Mechanisms to Protect Data from Assaults
## 3 Proposed Algorithm
### Cipher XORation  
In Cipher XORation, the cipher matrix text and user-defined key obtained from the Caesar matrix algorithm are XORed. The biggest advantage of Cipher XORation is that the attacker will not understand the technique applied to the bits. Whenever an attacker tries to apply a technique to bits, the attacker will obtain a different result that will be irrelevant. Cipher XORation provides more security to the data with the help of the key, and data decryption is be possible until the valid key is used.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Screening Models
#### 3.1.2 Biochllor  
This tool operates within the environment of a Microsoft Excel spreadsheet. It utilizes calculations derived from Domenico's analytical solvent transport model (equation) to simulate various processes including 1D advection, 3D dispersion, linear adsorption, and biotransformation through reductive de-chlorination, the primary biotransformation process observed at most chlorinated solvent sites [88; 383].  
BIOCHLOR can be successfully and reliably applied to sites for which general assumptions apply (e.g., steady groundwater flow, a vertical, planar source, and first-order decay). Examples of its application can be found in the work of Clement et al. [384] and Kuchovsky and Sracek [385]. The recently developed BIOCHLOR-ISO is an add-in to BIOCHLOR. This tool is based on an analytical solution, and is able to reproduce both the natural attenuation processes and the isotope fractionation that occurs in biological radiation [76]. BIOCHLOR-ISO is a dual isotope approach, which means that carbon and chlorine isotopes are included in the calculations [386].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Dimension** & **Flow** & **Transport** & **Biodegradation** & **Kinetics** & **Sorption** & **Cost** \\ \hline BioBalance & 1D flow & Vad, S, S-S & 3D dispersion & +(AN) & F-O & + & Free \\ \hline BIOCHLOR & 1D flow, 2D transport & S, S-S & 1D advection, 3D dispersion & + (AN) & F-O & + & Free \\ \hline BIOSCREEN & 1D flow, 2D transport & S, S-S & 1D advection, 3D dispersion & + (AN, AE) & F-O, I & + & Free \\ \hline CapSim & 1D flow & S, S-S, T & 1D transport & + & F-O & + & Free \\ \hline CDISCO & 1D transport & S, S-S, & 1D transport, & - & F-O & + & Free \\ \hline HSSM & 1D flow, 1D transport in & & & & & & \\ \begin{tabular}{c} FASM \\ \end{tabular} & Vad, S, S-S, T & Vad. zone, and 2D in saturated zone & - & - & - & Free \\ \hline NAS & 1D flow, combined transport & S, S-S, T & \begin{tabular}{c} 2D analytical 3D \\ numerical \\ \end{tabular} & + (AN, AE) & F-O & + & Free \\ \hline REMChlor & \begin{tabular}{c} 1D flow, 2D transport \\ \end{tabular} & S, S-S & \begin{tabular}{c} 1D advection, 3D dispersion \\ dispersion \\ \end{tabular} & + (AN, AE) & F-O & + & Free \\ \hline REMFuel & \begin{tabular}{c} 1D flow, 2D transport \\ transport \\ \end{tabular} & S, S-S & \begin{tabular}{c} 1D advection, 3D dispersion \\ \end{tabular} & + (AN, AE) & \begin{tabular}{c} Zero-O; F-O, \\ M \\ \end{tabular} & + & Free \\ \hline RT1D & \begin{tabular}{c} 1D flow, 1D transport \\ \end{tabular} & S, S-S & 1D transport & + (AN, AE) & \
\begin{tabular}{c} F-O, M or \\ other \\ user-defined \\ \end{tabular} & + & Free \\ \hline SourceDK & 1D & S, S-S &
\begin{tabular}{c} 1D advection and \\ dispersion \\ \end{tabular} & + & F-O & + & Free \\ \hline \hline \end{tabular}
\end{table}
Table 1: Screening tools and modelled processes.
## 3 Multi-hop Cluster Based Stable Backbone Trees (MCBT)
### Validation of the Proposed Method and the Effect of Weight Factor (\(\omega\))
Since cluster heads have higher degree, MCBT balances energy consumption by distributing the traffic load among nodes around the cluster head.
## 2 Prior Work
### Binocular Stereo Vision Location Theory  
#### 2.1.1 Camera Imaging Model  
The camera imaging model describes the image formation of the geometric mathematical model transformation, and establishes the coordinate transformation relationship between the two-dimensional image plane and three-dimensional space. Camera imaging models are generally divided into linear models and nonlinear models, of which the most commonly used is the pinhole imaging model, a linear model [13]. In order to describe an object in the world coordinate system as an image in a plane two-dimensional image coordinate system, four coordinate systems of the pinhole imaging model need to be established to complete this transformation, which are the pixel coordinate system _(O0-uv)_, image coordinate system _(O1-xy)_, camera coordinate system _(Oc-XcYcZc)_ and world coordinate system _(Ow-XwYwZw)_, as shown in Figure 2.  
Figure 1: The systematic research route of the new binocular spatial location of sugarcane nodes based on the improved YOLOv4.  
For the ideal pinhole imaging model, the relationship between the world coordinate system and the pixel coordinate system can be established by mutual transformation of the four coordinate systems, as shown in Formula (1).  
\[Z_{\text{C}}\left[\begin{array}{c}u\\ v\\ 1\end{array}\right]=\left[\begin{array}{cccc}f_{x}&0&u_{0}&0\\ 0&f_{y}&v_{0}&0\\ 0&0&1&0\end{array}\right]\left[\begin{array}{cc}R&t\\ 0^{T}&1\end{array}\right]\left[\begin{array}{c}X_{\text{W}}\\ Y_{\text{W}}\\ Z_{\text{W}}\\ 1\end{array}\right]=MN\left[\begin{array}{c}X_{\text{W}}\\ Y_{\text{W}}\\ 2_{\text{W}}\\ 1\end{array}\right] \tag{1}\]  
The four parameters \(f_{x},f_{y}\), \(u_{0}\) and \(v_{0}\) are internal parameters of the camera, which are determined by the internal structure of the camera when it was manufactured. Correspondingly, \(R\) and \(t\) are the external parameters of the camera. The internal and external parameters of the camera are all obtained by camera calibration. From Formula (1), if the internal parameter matrix \(M\) and the external parameter matrix \(N\) are known, the position (\(u\), \(v\)) of any point in space with the coordinates (_Xw_, _Yw_, _Zw_, \(1\))\({}^{T}\) can be acquired in the image pixel coordinate system. However, since M is a 3 \(\times\) 4 irreversible matrix, the position of the point (\(u\), \(v\)) in the world coordinate system cannot be derived by its coordinates in the image pixel coordinate system. Hence, a stereo vision system is required.
# SALMA: A Novel Middlebox Infrastructure System Based on Integrated Subnets
## 6 Implementation
### Policy Challenge  
The proposed solution needs to maintain the policy sequence where the flow visits the right middleboxes in the right sequence. In today's network, when a flow needs to be examined by a sequence of middleboxes (e.g., firewall \(\rightarrow\) IDS \(\rightarrow\) load balancer), these middleboxes are installed in the flow path. In this section, we shed light on the policy sequence algorithm implemented in our solution. The algorithm defines how the edge switches will forward the in-scope traffic to the middleboxes' subnet. It also explains how the middleboxes' subnet switch maintains the policy sequence. Furthermore, we show in this section how the proposed algorithm handles both loop and loop-free scenarios. Let us  
Figure 4: The middlebox manager panel.  
Figure 5: An example of variations in policy sequences.  
assume the following sequence of middleboxes is installed physically between endpoints to face their exchanged flows:  
\[\text{FW}\rightarrow\text{IDS}\rightarrow\text{Proxy}.\]  
When these middleboxes are moved from one location to another inside the enterprise network, it is necessary to forward the exchanged bidirectional flows between the endpoints to the right middleboxes. In addition, the bidirectional flows must visit the middleboxes while following the right sequence.  
The middlebox manager steers the in-scope traffic to one of the preconfigured tunnels between edge switches and the subnets of the middleboxes, as shown in Figure 6. The selection criteria of middleboxes' subnets is based on the load-balancing algorithm and the policy sequence. As illustrated in Figure 6, the physical arrangement of middleboxes in the subnet cannot satisfy all the policy sequences. For instance, the incoming HTTP traffic must follow the subsequent sequence: IDS \(\rightarrow\) FW \(\rightarrow\) Proxy, and the sequence of middleboxes visited in the case of outgoing Internet is FW \(\rightarrow\) IDS. Thus, we can not physically reorder the middleboxes in the subnet to satisfy all sequences. To solve this issue, we propose to install the middleboxes in the subnet in any order. Since this order is known to the middlebox manager, it will be responsible for installing the appropriate flow entries to maintain the appropriate sequence. Unfortunately, there are only a few fields in the IP and MAC headers that can be utilized for this purpose. Flags such as DSCP and ECN are used by other solutions, such as the WAN optimizer and the QoS configuration in routers. Furthermore, end-to-end flows and middleboxes, such as IDS, proxy, and firewall, are sensitive to L4 fields (e.g., destination port number). Nevertheless, we propose two alternatives to maintain the policy sequence.
## 4 Experimental Validation  
The experimental platform is built up to further verify the validity of the established model. Figure 16 shows the photo of the experimental setup. A mechanical machine tool spindle, which is driven by belt driving system, is applied to explore these issues. A high-precision dual ball artifact mounted in the rotor is used as the testing target, through which eccentricity can be adjusted manually. The NI (National Instruments) data acquisition (DAQ) card (USB 6356) and capacitive displacement sensor with nanometer-level resolution are used to acquire the displacement signals. Table 2 shows the technical parameters of the capacitive sensors used in the experiment. Data acquisition and processing  
Figure 14: Effects of external disturbance force on radial synchronous errors.  
Figure 15: Variation trends of radial errors with different amplitude and order of external disturbance force.  
software is developed by LabVIEW software and can be used for online analysis. The displacement data can be also saved for off-line processing.  
In order to investigate the dynamic rotational error quantitatively, two experiments are designed. The schematic diagram of unbalanced mass and disturbance force verification experiment is illustrated in Figure 17. In the actual operation of the SRB system, there are many factors that affect its dynamic rotational error. It is difficult to eliminate the impact of these factors effectively. To study the influence of a certain factor, the proportion of this factor can be artificially enlarged. Compared with other factors, it will produce more obvious changes.  
When the SRB system is not subjected to any artificial excitation force, rotational error motion can also occur due to the influence of some unknown factors. The spindle rotational error measured in this state is used as the initial value for comparison. The polar plot is shown in Figure 18. The whirling speed is 300 rpm. The corresponding TIR, synchronous and asynchronous errors are 14.41, 0.55 and 2.86 \(\upmu\)m, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Parameters** & **Item** & **Parameter** \\ \hline Sensing area diameter & 2/mm & Linearity error & 0.1\% \\ Measurement range & 250/\(\upmu\)m & Operating temperature & 4–50/\(\mathrm{\SIUnitSymbolCelsius}\) \\ Output voltage & \(\pm\)10/V & Output sensitivity & 0.08/V/\(\upmu\)m \\ Connection mode & Differential & Ambient temperature & 20 \(\pm\)1/\(\mathrm{\SIUnitSymbolCelsius}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical parameters of capacitive sensor.  
Figure 16: Test setup of disturbance force verification experiment.  
Figure 17: Schematic diagram of unbalanced mass and disturbance force verification experiment.