esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Finer Categorization and Future Research  
In hindsight, it seems evident, from the difference in the differential entropies expression and the difference in the mutual information expressions, that to utilize \(d\) as an effective tool in digital signal analysis, a finer categorization of \(d\) beyond just greater than 0.3, between 0.3 and 0.9, and greater than 0.9, would be much more revealing. For example, even though the fraction of values within an interval does not change, the number of values in the upper portion of the interval could have changed substantially. Future studies should thus incorporate a finer categorization of \(d\) in order to facilitate deeper analyses.  
Even though the new interpretations of \(d\) have been quite revealing for speech coding, even more could be accomplished on this topic with a finer categorization. Additionally, the log likelihood ratio now clearly has a utility in signal analysis, in general, beyond speech, and should find applications to Electromyography (EMG), Electroencephalography (EEG), and Electrocardiogram (ECG) analyses among many other applications, where spectral mismatch alone is not of interest. In particular, the ability to use \(d\) to discover changes in differential entropy in the signals or to recognize the change in the mutual information between processed (for example, filtered or compressed) signals and unprocessed signals should prove useful.  
[PERSON] provided the theoretical developments, [PERSON] performed the speech coding experiments, and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Braking Performance and Response Analysis of Trains on Bridges Under Seismic Excitation
## 2 Materials and Methods
### Simulation of Incentives
#### 2.4.2 Earthquake Simulation  
In the event of an earthquake, ground vibration is first transmitted through the bridge abutments into the entire structural system, and then acts on the bridge and transfers to the track surface, which in turn triggers vibration of the track. This vibration poses an obvious threat to the stable operation of subway trains, which may lead to the deviation of train operation from the normal trajectory or produce unsafe dynamic responses. In order to accurately simulate this situation, a typical seismic wave from the PEER strong earthquake database is selected for ground vibration simulation in this study. The selected earthquake is named Tabas_Iran, and the measurement site is located in Boshrooyeh, with a magnitude of 7.35 and an epicenter distance of 24.07 km. The Tabas_Iran record (RSN 860) was chosen because its low-frequency content matches the dominant modes of a 35 m girder bridge, the recording site has soil properties similar to those of the Wuxi Metro corridor, and its PGA (\(\sim\)0.10 g) equals the design-basis value in the Chinese urban rail seismic guideline. Through this simulation, we are able to analyze in detail how the ground shaking is transmitted through the bridge structure and ultimately affects the tracks and the moving subway trains. The seismic acceleration time-course curves record in detail the changes in the intensity of ground shaking, which is essential for studies evaluating the effects of earthquakes on bridges and track systems. The acceleration time curve of an earthquake is shown in Figure 3. Figure 3a presents the lateral ground acceleration time history of the Tabas_Iran record, with a peak ground acceleration (PGA) of approximately 0.08 m/s\({}^{2}\) occurring around 25 s. Figure 3b shows the corresponding power spectral density (PSD) curve, indicating that the dominant energy is concentrated below 5 Hz, with prominent peaks near 0.8-3 Hz.  
Figure 3: (**a**) Acceleration time history of selected earthquake ground motions; (**b**) Corresponding power spectral density (PSD) curves of the same events.
## 3 Results and Discussion
### Detection of P-Wave Arrival
Thus, the integrated result improves on both seismic-only and GPS-only methods, by providing the full spectrum of seismic motions from the detection of P-wave arrivals to the estimation of permanent offsets. When P-waves are detected at four or more near-field GPS/strong motion pairs, the epicenter, the velocity of earthquake wave and the origin time can be determined by using a least squares method as follows:  
Figure 6: An enlarged view of the first 20 s of the coseismic displacements and velocities in all three components on the collocated AKT006 (seismic) and 0183 (GPS) stations during the Tohoku-oki earthquake on 11 March 2011. The 1 Hz GPS-only and 100 Hz tightly-integrated displacements and velocities are respectively shown by the blue dotted lines and red lines. The bottom sub-figures are STA/LTA ratio results based on tightly-integrated results, which show the first arrival time of seismic wave. The sub-figures show, from left to right, the north, east and up components. \[ \begin{cases}(\dfrac{x_{1}-x_{0}}{d_{1}^{0}}-\dfrac{x_{2}-x_{0}}{d_{2}^{0}})\cdot dx +(\dfrac{y_{1}-y_{0}}{d_{1}^{0}}-\dfrac{y_{2}-y_{0}}{d_{2}^{0}})\cdot dy+(t_{1}- t_{2})\cdot v-(d_{1}^{0}-d_{2}^{0})=0\\ (\dfrac{x_{1}-x_{0}}{d_{1}^{0}}-\dfrac{x_{3}-x_{0}}{d_{3}^{0}})\cdot dx+(\dfrac{ y_{1}-y_{0}}{d_{1}^{0}}-\dfrac{y_{3}-y_{0}}{d_{3}^{0}})\cdot dy+(t_{1}-t_{3}) \cdot v-(d_{1}^{0}-d_{3}^{0})=0\\ \vdots\\ (\dfrac{x_{1}-x_{0}}{d_{1}^{0}}-\dfrac{x_{n}-x_{0}}{d_{n}^{0}})\cdot dx+( \dfrac{y_{1}-y_{0}}{d_{1}^{0}}-\dfrac{y_{n}-y_{0}}{d_{n}^{0}})\cdot dy+(t_{1}- t_{n})\cdot v-(d_{1}^{0}-d_{n}^{0})=0\\ t_{0}=\dfrac{\sum\limits_{i=1}^{n}(t_{i}-\dfrac{d_{i}}{v})}{n}\end{cases}  \tag{10}\]  
where, \(x_{0}\),\(y_{0}\) denote the approximate coordinates of the epicenter; \(x_{i}\),\(y_{i}\)(\(i=1\),\(\cdots n\))denote the coordinates of the \(i\)th station; \(d_{i}^{0}\) denotes the distance from the \(i\)th station to the approximate coordinates of epicenter; \(dx\),\(dy\) denote the increments of epicenter; \(v\) denotes velocity of earthquake wave; \(t_{i}\) denotes the arrival time of earthquake wave at the \(i\)th station; \(d_{i}\) denotes the distance from the \(i\)th station to the epicenter; \(t_{0}\) denotes the origin time. Several iterations are required to avoid the linearization error. In order to test this technique, the five GPS/strong motion pairs where P-wave is detected earliest during the El Mayor-Cucapah earthquake are used. The detected earthquake P-wave arrival time is 0.09 s, 0.15 s, 0.11 s, 0.10 s, and 0.13 s later than the USGS reference values of P-wave arrival time at the five pairs. The epicenter estimate is roughly 2.5 km away from the U.S. Geological Survey (USGS) epicenter estimate. The origin time estimate is 0.12 s later than the USGS reference value of 22:40:57 (GPS time). The accurate detection of P-wave arrival is critical for earthquake early warning, as it allows for prediction of the arrival of the destructive S-wave. The P-wave-based earthquake parameters such as epicenter and origin time can be released before the S-wave arrival.
## 3 Results
### Evaluation Metrics  
To rank the quality of the proposed solutions, various metrics are used. The F1-Score is based on the metrics of Recall, Precision, F1-score, Accuracy, and Matthews Correlation Coefficient (MCC). The corresponding equations define the measurements from Equations (1)-(5) [37; 38; 39].  
Where:  
1. The number of sleepy states that were incorrectly labeled \"normal\" is shown in the false-negative column.
2. The True Positive metric indicates the percentage of drowsy states that were accurately identified.
3. The True Negative () value indicates the proportion of false negatives correctly identified as false positives.
4. The number of times a normal status was mistakenly labeled as a drowsy status is shown in the false positive.  
\[Accuracy=\frac{No.of\ correctly\ detected\ images}{Total\ No.of\ images}\times 100=\frac{(T_{N}+T_{P})}{(T_{P}+F_{P}+T_{N}+F_{N})} \times 100 \tag{1}\]  
\[Recall=TPR=T_{P}/(T_{P}+F_{N})=(1-FNR) \tag{2}\]  
\[precision=T_{P}/(T_{P}+F_{P}) \tag{3}\]  
\[F1=2\times((precision\times recall)/(precision+recall)) \tag{4}\]  
\[MCC=\frac{(T_{P}\times T_{N})-(F_{P}\times F_{N})}{\sqrt{((T_{P}+F_{P})\times (T_{P}+F_{N})\times(T_{N}+F_{P})\times(T_{N}+F_{N}))}}\times 100 \tag{5}\]
## 3 Related Work
### Privacy-Preserving Deep Learning (PPDL)
#### 3.1.3 Works Based on Homomorphic Encryption  
In homomorphic encryption (HE) [47], parties encrypt their data, and the server conducts operations on the ciphertexts and delivers the encrypted prediction to the parties. CryptoNets [11] was the first work to show how to apply homomorphic encryption to neural networks. They approximate the non-linear activation functions and transform a neural network to polynomials, and hence any polynomial function can be computed over encrypted data using homomorphic encryption [11]. CryptoDL is a similar work that approximates low-degree polynomials for activation functions and evaluates the convolutional neural networks over the encrypted data [48]. Furthermore, homomorphic encryption began to be adopted as a solution to protect the privacy of the learning process in combination with other technologies [49; 50]. However, many of these works are limited to the predictive phase since the decryption and encryption processes add communication and computational overhead. To overcome the communication and computational issues, ref. [51] proposed a GPU acceleration solution to accelerate the full homomorphic evaluation of the CNN model on encrypted data. Due to the higher number of operations and the longer runtime, it does not tolerate the training stage on encrypted data.  
Despite all efforts, the fundamental issue with using HE with neural networks is that they add a computational overhead to the training phase. Also, in practice, the poor approximation of the activation function might cause neural networks to perform poorly. It also generates larger encrypted messages, which consume more memory [52].
# Contamination with Pharmaceuticals in Aquatic Environment: Focus on Analytical Methodologies
###### Abstract  
The growing prevalence of pharmaceutical compounds in the environment raises significant concerns due to their potential impacts on ecological and human health. This present manuscript focuses on the methods used to extract and determine these pharmaceuticals in water samples. It provides a comprehensive analysis of the extraction techniques and analytical approaches employed for the identification and quantification of pharmaceuticals in environmental water. Due to their chemical properties and widespread use, pharmaceuticals persist in the environment and contaminate water bodies, soil, and sediments. The presence of pharmaceuticals in the aquatic environment has been linked to several adverse effects on aquatic organisms, including the disruption of physiological processes and reproductive impairment. Furthermore, pharmaceuticals in the environment can affect human health through food and drinking water contamination and contribute to antibiotic resistance. The analysis of pharmaceutical contaminants in water samples presents several challenges due to the complex matrix and low concentrations of target substances. Various sample preparation techniques and protocols, including solid-phase extraction (more than 76% of the studied literature) and QuEChERS (quick, easy, cheap, effective, rugged, and safe), coupled with liquid chromatography-tandem-mass spectrometry, are commonly used for their determination. These methods offer high sensitivity, selectivity, and efficiency in identifying and quantifying pharmaceuticals in environmental samples. It is, therefore, essential that ongoing research is conducted in order to develop more efficient analytical methods and mitigation strategies to address pharmaceutical contamination in the environmental water effectively. It is also crucial that increased awareness and regulatory measures are put in place in order to minimize the environmental and human health risks associated with pharmaceutical pollutants.  
Keywords: pharmaceuticals; environmental samples; analysis; extraction; determination +
Footnote †: journal: Applied Sciences
## 2 Direct Problem
### Kinetics  
The transfer of metal ions from the solution to the adsorption sites within the adsorbent particles is restricted by mass transfer resistance, which determines the time required to reach the equilibrium state. In the temporal progress of this process in the Linear Driving Force (LDF) model approach, it is formally assumed that the transportation of the adsorbate at the adsorption sites inside the particle occurs in a fictitious film, in a comparable process to what happens externally in the diffusion film. Consequently, the solid phase concentration gradient is replaced by a linear difference between the equilibrium amount on the outer surface of the particle and the amount adsorbed by the particle [25; 31]. Thus, the equation used to describe this mechanism is approximated by Equation (3):  
\[\frac{\partial q}{\partial t}=-k_{s}(q-q_{s}) \tag{3}\]  
where \(q\) (mg/g) is the amount adsorbed by the particle, \(q_{S}\) (mg/g) is the equilibrium amount on the outer surface of the particle, and \(k_{S}\) (min\({}^{-1}\)) is the intra-particle mass transfer coefficient. [PERSON] [31] found the following relationship (Equation (4)) between the mass coefficient, \(k_{S}\) and the surface diffusion coefficient, \(D_{S}\) (cm\({}^{2}\)/min):  
\[k_{s}=\frac{15D_{s}}{r_{p}^{2}} \tag{4}\]
# Ontology-Based System for Dynamic Risk Management in Administrative Domains
## 3 System Architecture
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Device** &
\begin{tabular}{c} **Penetration Operating** \\ **System** \\ \end{tabular} & **Description** \\ \hline Mobile Phone & iOS and Android & Smart phones based on iOS or Android operating systems. \\ \hline Computer & Windows, Linux, and MacOS & Any device oriented to develop work or entertainment tasks based on Windows, Linux, or MacOS. \\ \hline Server & Windows Server and Linux & Workstations and computers dedicated to serving services from inside of the administration domain to external entities. Only servers based on Windows Server OS or Linux. \\ \hline Router & Not applicable & Network device responsible for the interconnection of the internal network and the external network of an administration or between different LANs of the administration. \\ \hline Switch & Not applicable & Network device responsible for the interconnection of different elements inside an internal network of an administration. \\ \hline IoT device & Not applicable & Any kind of device with at least two features: internet access and computational capability. In the case of this research, smart watches and smart assistants (Alexa, Google Home, etc.) are included here. \\ \hline Industrial & Not applicable & Devices involved within industrial control systems which are focused on controlling industrial process. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification of the different devices related to each actor within the administrative domain. (antecedent), the consequent is executed, i.e., the impact on the assets of the affected device is calculated. In addition, the system is designed to receive threat events from various sources. Thus, a module has also been designed to characterize the trust in information sources. That means, given a source of information, its threat events will be considered with more or less relevance depending on its trust level. Figure 3 depicts the whole system described above. This generic proposal can be adapted to the specific requirements of any administrative domain. In this case, because the project that supported the research presented in this article has been funded by the Spanish Ministry of Defense, it has been necessary to adapt its functionality to the idiosyncrasies of the administrative organization of that country. The Spanish Administration is divided into 15 autonomous communities and 2 autonomous cities (Ceuta and Melilla). In this sense, if threats are geolocalized, the system is able to calculate risks in the different geographical administrative domains. Thus, the system will calculate the following risks in different Spanish Administrative domains:  
* Global risk of Spain. * Risk of a specific asset in Spain. * Global risk of an autonomous community. * Risk of a specific asset in an autonomous community.
# Fault-Tolerant Tripod Gait Planning and Verification of a Hexopod Robot
## 2 The Modeling of Hexapod Robot  
### General Definition of Robot Gait  
Robot gait refers to the way that a robot moves its legs regularly in the process of motion. When a robot is walking, legs support the weight of the body and make it move in the walking direction. Different gaits can be distinguished by the order and phase of the transfer leg. Gait parameters [21] are defined as follows.  
1. Support phase and transfer phase: the state of legs supported on the ground is called the support phase, and the state of legs lifting in the air is called the transfer phase.
2. Locomotion period \(T\): the duration required for legs to complete one transfer phase and one support phase, including transfer phase period \(T_{t}\) and support phase period \(T_{s}\), \(T=T_{s}+T_{t}\).
3. Duty factor \(\kappa\): the ratio of the support phase period to the locomotion period; the value range is \(0\leq\kappa\leq 1\), \(\kappa=T_{s}/T=1-T_{t}/T\).
4. Leg phase \(\phi\): relative position of a leg in a locomotion period at the time of lifting.
5. Stride \(S\): the distance that the center of gravity (COG) of the robot moves in a locomotion period.
6. Stroke \(\lambda\): the distance that the leg moves from the previous landing point to the next in the transfer phase.  
Moreover, to provide an effective evaluation index for the locomotion of robot, a variety of stability evaluation indexes have been proposed, such as longitudinal stability margin (LSM) [22] and energy stability margin (ESM) [23]. However, the static stability margin (SSM) \(S_{M}\) is versatile when the motion velocity is not high. The stability margin represents the margin from the current equilibrium state to the instability state of the robot, which is represented by the minimum vertical distance between the projection point of the COG on the foot support plane and the boundary of the support polygon [24], as shown in Figure 1.  
When the support polygon is a triangle, the projection point of the COG on the support plane is \(M\), and the stability margin can be calculated by the following equation:  
\[S_{M}=min\left(\frac{\overrightarrow{P_{1}M}\times P_{1}^{\overrightarrow{P_{ 2}}}}{|P_{1}P_{2}|},\frac{\overrightarrow{P_{2}M}\times P_{2}^{\overrightarrow {P_{3}}}}{|P_{2}P_{3}|},\frac{\overrightarrow{P_{3}M}\times P_{3}^{ \overrightarrow{P_{1}}}}{|P_{3}P_{1}|}\right) \tag{1}\]
## 2 Design and Simulation  
The schematic diagram of the multi-sized particles sensor based on the surface acoustic wave resonator with a cascade sampler we designed is shown in Figure 1. The SAW chip is the fundamental functional element of the sensor and can be classified as a resonator and delay-line structure. Although the SAW delay-line can provide a larger area for coating sensitive films, the insertion loss of the chip is usually high. The resonator has the characteristics of high Q-value and low insertion loss, which is beneficial to improve the detection limit in mass sensing [18; 19]. The particulates in the air were selected by an impactor and deposited on the sensitive area of the SAW placed on the impaction plate, changing the propagation velocity of the surface acoustic wave with the mass loading effect, resulting in changes in chip amplitude and phase frequency response. The phase signal was discriminated by the phase discriminating circuit and converted into a voltage signal (mV) for output. The changed value of the output voltage represented the mass of the detected particulate matter.  
In this study, we designed a three-stage cascade impactor combined by the LCP stage, PM\({}_{2.5-10}\) stage, PM\({}_{1-2.5}\) stage, inlet stage, and outlet stage. Figure 2 shows the schematic diagram of our cascade impactor. We label the first stage \"LCP\" because it will collect and measure particles larger than 10 \(\upmu\)m, which are large coarse particles; the second stage will collect and measure particles between 2.5 and 10 \(\upmu\)m, which we label \"PM\({}_{2.5-10}\)\"; the third stage was labeled \"PM\({}_{1-2.5}\)\", and will collect and measure finer particles between 1 and 2.5 \(\upmu\)m.  
Figure 1: Schematic diagram of the multi-sized particles sensor based on the surface acoustic wave resonator and cascade impactor (single stage).
# Research on the Prediction of Insertion Resistance of Wheel Loader Based on PSO-LSTM
## 2 Experimental Data Acquisition and Analysis
### Insertion Depth  
Insertion depth is a key factor in determining the magnitude of insertion resistance. In this paper, the real insertion distance of the wheel loader is obtained by a fifth wheel gauge hinged to the rear of a wheel loader. Due to the limited accuracy of the fifth wheel gauge, the interval between two adjacent data measured by it is 9 mm, so the curve of the whole insertion distance shows a stepped-up trend. The insertion depth of gravel and sand of 1000 mm is shown in Figure 2. To exclude the influence of speed on insertion resistance and to ensure that the insertion speed of the wheel loader is maintained as uniformly as possible. In this paper, the engine speed is set to 1500 rpm, and the initial speed is about 3.42 km/h. Meanwhile, to ensure that the bucket always remains horizontal during insertion. In this paper, the displacements of the lift cylinder and the tilt cylinder during insertion are monitored, where the initial displacement of the lift cylinder is about 1411 mm and the initial displacement of the tilt cylinder is about 1402 mm, and when the displacement changes within \(-1\) mm to 1 mm, it can be considered that the bucket remains horizontal. The engine speed and cylinder displacement for the insertion of gravel and sand with a depth of 1000 mm are shown in Figure 3, respectively. As can be seen from Figure 3, the engine speed is maintained at about 1500 rpm, but there will be a step up and down. The displacement of the lift cylinder and the tilt cylinder in the gravel remains stable, while the lift cylinder and the tilt cylinder in the sand show a smaller decrease, in which the change magnitude of the displacement of the lift cylinder is about 0.6 mm, while the change magnitude of the tilt cylinder is about 0.1 mm, which meets the requirement of keeping the bucket horizontal.  
Figure 1: Wheel loader inserts into the pile. (**a**) The insertion of gravel; (**b**) The insertion of sand.  
Figure 3: Engine speed and cylinder displacement. (**a**) Gravel; (**b**) Sand.  
Figure 2: Insertion depth measured by the fifth wheel gauge.