esa-sceva/satcom-chunk-collection huggingface dataset:
# The Use of Machine Learning Methods in Road Safety Research in Poland
## 3 Analysis of Variables Describing Road Safety
### The Selection of Variables for the Models
The best classification results were achieved for the \"no injuries\" class, with 21,328 cases correctly classified and a classification error of 9.15%. This indicates very good performance, showcasing the model's ability to identify cases with no injuries accurately. The result is slightly better than that of the initial model. Similarly, the \"minor injuries\" class achieved moderate accuracy. A total of 12,771 cases were correctly classified, with a classification error of 31.93%. The model frequently confused this category with \"no injuries\" and \"severe injuries\". Significant challenges arose in classifying the \"severe injuries\" class. Only 1060 cases were correctly predicted, resulting in a very high classification error of 87.60%. Most cases in this category were misclassified as either \"minor injuries\" or \"no injuries\". The most challenging category to classify was \"deaths\". Only 210 cases were correctly predicted, with a classification error of 90.66%. The model struggled to differentiate this category, often confusing it with \"minor injuries\" or \"severe injuries\". The primary reason for these discrepancies lies in the imbalance among the classes. The model performs best for the dominant class, \"no injuries\", as it contains the largest number of observations. Conversely, the \"severe injuries\" and \"deaths\" categories are more challenging to predict due to their lower representation in the dataset.
## 3 Results and Discussion
### Validation of the Origin Discrimination Table
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multicolumn{4}{c}{**No. of Samples**} & \multirow{2}{*}{**Predictive Rate**} \\ \cline{2-2} \cline{4-5}  & **Total** & **Domestic** & **Foreign** & \\ \hline Total & 60 & 30 & 30 & \(=\frac{59}{60}\times 100=98.3\%\) \\ \hline Domestic & 30 & 29 & 1 & \(=\frac{29}{30}\times 100=96.7\%\) \\ \hline Imported & 30 & 0 & 30 & \(=\frac{30}{30}\times 100=100.0\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Validation results for the discrimination table using judgment values of 11 markers for soybeans. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Statistical Value**} & \multicolumn{2}{c}{**No. of Sample**} \\ \hline TD (True Domestic Product) & 595 & - \\ \hline FD (False Domestic Product) & 35 & - \\ \hline TF (True Foreign Product) & - & 446 \\ \hline FF (False Foreign Product) & - & 20 \\ \hline Total & 630 & 466 \\ \hline Sensitivity & \(=\left(\frac{595}{595+35}\right)\times 100=94.4\%\) & - \\ \hline Selectivity & - & \(=\left(\frac{446}{466+20}\right)\times 100=95.7\%\) \\ \hline Efficiency & \(=\left(\frac{595+446}{599+35+446+20}\right)\times 100=95.0\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification performance parameters of gene-based analysis for discriminating Korean and imported soybeans.
## 4 Results and Discussion
### Results: Preprocessing  
The preprocessing steps required for KEs were simple, and the only requirement was to split the CPG documents into sentences. However, the preprocessing steps required for machine-learning models are more impactful in terms of the final model accuracy and the number of salient terms. We compared the models with applying feature selection techniques and without feature selection. We used the information gain ratio to assign a weight to features and selected top \(k\) features. As mentioned earlier, the value of k highly affects the model accuracy and the salient terms considered by the model. Therefore, we tested the model on different values of \(k\) by apply Algorithm 1. The detail of \(k\) values and their effects on the accuracy of the decision tree model is shown in Figure 4. As shown in Figure 4, initially the accuracy was increasing gradually with an increment of \(k\) value. From \(k=40\) to \(k=79\) the accuracy remained stable with maximum value, while accuracy started to decrease as the value of \(k\) increased from 79. The average number accuracy of the decision tree model in maximum at \(k=40\). Therefore, we selected top 40 features for model training i.e., \(k=40\). The accuracy starts decreasing due to less relevant terms consideration as \(k\) approaches beyond 79.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S.No** & **Patterns** \\ \hline
1 & \(\cdot^{*}\)(give \(\mid\) add \(\mid\) remove) \(\cdot^{*}\) Pharmacologic Substance \(\cdot^{*}\) \\
2 & \(\cdot^{*}\)([I i]n) \(\cdot^{*}\) Population Group \(\cdot^{*}\) \\
3 & \(\cdot^{*}\)(Health Care Activity \(\mid\) Qualitative Concept).* Functional Concept.* \\
4 & \(\cdot^{*}\)Functional Concept \(\cdot^{*}\) Pharmacologic Substance.* \\
5 & \(\cdot^{*}\)Qualitative Concept \(\cdot^{*}\)Functional Concept (should \(\mid\) with \(\mid\) to).* \\
6 & \(\cdot^{*}\)Idea or Concept \(\mid\)/d\(+\)/s\(\cdot^{*}\) \\
7 & \(\cdot^{*}\) should \(\cdot^{*}\) (Functional Concept \(\mid\) Idea or Concept).* \\
8 & \(\cdot^{*}\)Idea or Concept \(\cdot^{*}\) Intellectual Product \(\cdot^{*}\) \\
9 & \(\cdot^{*}\) regardless of \(\mid\) Organism Attribute.* \\
10 & \(\cdot^{*}\) Population Group \(\cdot^{*}\) with \(\cdot^{*}\) (Disease or Syndrome) \(\cdot^{*}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: List of extracted UMLS Patterns.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Guideline** & **Total Sentence** & **Recommendation Sentences** & **Non-Recommendation Sentences** \\ \hline Hypertension & 278 & 78 (28.06\%) & 200 (71.94\%) \\ Rhinosinusitis & 761 & 151 (19.84\%) & 610 (80.16\%) \\ Asthma & 171 & 53 (30.99\%) & 118 (69.01\%) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Details of dataset.
## 3 The Proposed Model
### Data Pre-Processing  
At the initial stage, the presented approach performs the min-max data normalization approach to convert the input data to a compatible format. It can be executed to scale the feature from the zero and one range with the execution in Equation (1):  
\[\
u^{\prime}=\frac{v-\min_{A}}{\max_{A}-\min_{A}} \tag{1}\]  
At this point, \(\min_{A}\) and \(\max_{A}\) signifies the minimal and maximal values of features \(A\). The original and normalized values of the elements, \(A\) have been demonstrated by \(\
u\) and \(\
u^{\prime}\) correspondingly. It is apparent in the above formula that the maximal and minimal feature values were mapped to one and zero correspondingly.
## 4 The Influence of Surface Residual Stress on Corrosion Resistance
### The Influence of Residual Stress on Material Corrosion Rate
Moreover, the shot peening process also causes plastic deformation and increases the dislocation density of the material on the surface of specimens, and the changes mentioned above on the surface quantity could all increase the corrosion rate [33]. Therefore, during the period from the experiment beginning to 1 h of corrosion, the corrosion rate of the specimen surface material increases as the surface compress stress increases. However, after 1-2 h of corrosion, the corrosion rate of all specimens decreases, and with the increase in residual compressive stress on the surface, the corrosion rate shows a trend of gradually decreasing; the corrosion rate of the specimen with a surface residual compress stress of 120.2 MPa is the largest. This phenomenon is due to the electrolysis process, and if there is an uneven and discontinuous anode film, it would lead to pitting on the metal surface, which would lead to more complex surface topography and accelerate the corrosion reaction. Meanwhile, the oxygen precipitated from the anode will also cause a nonuniform, discontinuous anode film, which would also intensify corrosion. However, as corrosion proceeds, the stress concentration point dissolves, the current efficiency of the dissolved metal increases, the defects caused by pitting corrosion are significantly reduced or disappear, and the surface finishing increases, reducing the specimen's corrosion rate material. Meanwhile, the surface finish of the specimen is improved after being corroded during the initial corrosion period, and the promotion of corrosion by the surface topography is weaker than the inhibition of corrosion by residual compressive stress. Therefore, there is a trend that the corrosion rate gradually decreases as the residual compressive stress on the surface increases. When the length of corrosion time is longer than 3 h, the electrolytic corrosion changes into the state of uniform corrosion, and the corrosion rate of each specimen changes less. It should be noted that during the initial stage of the experiment, the surface roughness of the specimens was tested after processing to ensure the consistency of surface roughness. Surface damage, such as surface microcracks, may be caused during the process, which may cause errors and affect the accuracy of the experimental results.
# An Enhanced Message Distribution Mechanism for
## 5 Simulation and Evaluation  
### Latency and Throughput  
In this section, we show more detailed experimental data to verify the effectiveness of the EMD module, in which the experimental environment is similar to Section 3.2. The test software is bench running on a virtual machine, and the tested one is a Ryu controller on another. The data of the latency experiment are the average of three random experiments that complete thirty consecutive packets in-out at least; the throughput experiment lasts more than 5 min. and is repeated three times.  
The example label shown in Figure 7 is basically the same as the previous one; \(simple\) and \(Hedera\) represent the simple_switch app and Hereda app running alone; \(+red\) means that there is also a redundant app running at the same time, \(+3 red\) means three redundant apps; and, \(+EMD\) means the controller runs with the EMD module in this experiment. Each cluster of bars represents a set of experiments, using cbench to simulate \(1/4/16/64\) switches for experiments.  
The redundant app only registers with the controller and listens to the event of the packet-in message; after receiving the event notification, it does not analyze the data packet, but it simply performs ten plus-one operations in a loop, and then ends. We use this method to simulate the certain apps, which may be more computationally expensive when processing uninteresting messages. It can be seen that, when comparing clusters \(1/2/3\) or clusters \(6/7/8\), the latency will increase significantly with the increase of redundant apps, increased up to \(93.1\%\) at most (from \(99.02\) us to \(191.2\) us by simple_switch when the number of switches is \(16\)). This is what we do not want to see; in many cases, the controller is required to have high performance, even when multiple apps are running in parallel [26].  
Figure 6: The Migration of The Forwarding Tree of Figure 5.  
When we add redundant apps, we make the controller run using a EMD module at the same time, which is the 4/5/9/10 cluster bar in Figure 7. It can be concluded that EMD can eliminate the performance degradation that is caused by multi-app parallelism to a certain extent. Take the comparative experiments of \(sim\), \(sim+3 red\), and \(sim+3 red+EMD\) in latency experiment as an example. When [PERSON] runs the simple_switch app alone, the average response latency is 103.443 us, but it rises to 195.135 us after adding three redundant apps with an increase of 88.6%; the average response latency after using the EMD module was only 114.842 us, an increase of only 11.02%. The latency is reduced by 41.15% when compared with the case of no EMD.  
The throughput, also known as the response number per second, also has a corresponding trend. When it is increased to three redundant apps, the performance drops drastically. Based on the two experiments, whether it is the single packet in-out in the idle state or the maximum responses under the stress test, EMD has greatly improved as compared with the original NBI.
## 2 Derivation of the Azimuth and the Elevation Error under Frequency Mismatch
### Partial Derivative of the Cost Function with Respect to Elevation
#### 2.2.2 Second Condition for the Partial Derivative with Respect to the Azimuth at  
\(\theta=\theta^{(0)}+\Delta\theta,\psi=\psi^{(0)}+\Delta\psi\) to be Zero: \(\cos(\psi^{(0)}+\Delta\psi)=0\)  
From (16), we have:\[\sin(\psi^{(0)}+\Delta\psi)=1 \tag{37}\]  
Substituting (16) and (37) in (25) yields:  
\[\cos(\psi^{(0)})\cos(\Delta\theta)=0. \tag{38}\]  
For (38) to be true, \(\cos(\Delta\theta)\) should be equal to zero, implying that \(\Delta\theta\) should be odd multiples of \(\frac{\pi}{2}\). As previously stated, \(\Delta\theta\) is much smaller than \(\frac{\pi}{2}\) since \(\Delta\theta\) denotes a small azimuth estimation error. Therefore, (38) cannot be true, which implies that, for \(\cos(\psi^{(0)}+\Delta\psi)=0\) in (16), it is impossible to find \(\Delta\theta\) satisfying (25), which is the constraint that the partial derivative of the cost function with respect to elevation should be zero.  
Summary of the Analytic Derivation of the Azimuth Estimation Error and the Elevation Estimation Error  
Necessary conditions for minimizing the cost function are that both the partial derivative of the cost function with respect to \(\theta\) and that with respect to \(\psi\) should simultaneously be zero. Based on these two constraints, expressions of \(\Delta\theta\) and \(\Delta\psi\) are derived under the [PERSON] approximation.  
It turns out that the partial derivative of the cost function of an interferometer algorithm with respect to \(\theta\) at \(\theta=\theta^{(0)}+\Delta\theta,\psi=\psi^{(0)}+\Delta\psi\) is zero if either of two condition is satisfied:  
\[\Delta\theta=0 \tag{39}\] \[\cos(\psi^{(0)}+\Delta\psi)=0. \tag{40}\]
## 5 Simulation Result
### Hysteresis Current Control (HCC)  
After compensation, the simulation results of HCC under 50V DC-link voltage conditions are shown in Figure 5a and Table 2. The harmonic currents are compensated, and the total source current harmonic distortion (THD\({}_{six}\)) is reduced from 33.7% to about 11.3%. Also, the compensated \(i_{sn}\) is 0.49 Arms and the compensated \(i_{sx}\) is around 2.50 Arms. Additionally, the three-phase power factors (FFs) are the same and increase to 0.99, and the Q\({}_{Total}\) reduces to 4.2 var. The total switching loss (total SW loss) of HCC is 25.0 W.  
The simulation results of HCC under 40V DC-link voltage conditions are shown in Figure 6a and Table 3. The THD\({}_{six}\) reduces to about 15.9%. Also, the compensated \(i_{sn}\) is 0.79 Arms and the compensated \(i_{sx}\) is around 2.49 Arms. Additionally, the three-phase PFs are the same and increase to 0.99, and the Q\({}_{Total}\) reduces to 10.0 var. The total SW loss of HCC is 12.3 W.
# Effect of Local Cyclic Loading on Direct Shear Strength Characteristics of Shear-Zone Soil
## 4 Results
### Studying the Effect of Normal Stress  
The cyclic direct shear tests exhibited in this section were carried out with the normal stresses of 100 kPa, 200 kPa, and 400 kPa, and shear stresses of sinusoidal waveforms. A total of 100 cycles were imposed to the shear-zone soil for each stress level. For the load-controlled local cyclic direct shear tests, the applied shear stress amplitude, \(\tau_{\text{a}}\), was 5% of the shear strength reached under monotonic loading (\(\tau_{\text{fm}}\)). The applied normal stress, the vertical displacement, the shear displacement, and the horizontal shear stress were recorded automatically during the direct shearing process.  
Figure 8 shows the shear stress plotted against shear displacement for shear-zone soil at a normal stress of 100 kPa, 200 kPa, and 400 kPa. It indicates that an increase in the normal stress enhanced the initial slope of the curves and the stiffness of the shear-zone soil. Compared with the monotonic direct shear test, local cyclic loading did not change the stress-displacement tendency but decreased shear strength, which could be attributed mainly to the different  
Figure 6: Monotonic direct shear tests of shear-zone soil under different normal stresses: (**a**) shear stress versus shear displacement; (**b**) vertical displacement versus shear displacement.  
Figure 7: Shear stress as a function of the applied normal stress under monotonic loading.  
cyclic loading [29]. After 100 cycles the specimens were subjected to more accumulated plastic deformation and fatigue damage, leading to lower shear strength.  
Figure 8: Shear stress versus shear displacement curves under different normal stress (\(N=100\)). (**a**) \(T=10\) s; (**b**) \(T=60\) s; (**c**) \(T=90\) s.  
Figure 9 presents the dimensionless normalized shear strength ratio (\(f_{\rm cm}\)) as a function of the normal stress for the shear-zone soil under monotonic and cyclic loading. Apparently, the shear strength ratio of the shear-zone soil increases with increasing normal stress because of shearing contraction due to the rearrangement of soil particles. In addition, longer cyclic periods (\(T=90\) s) implies that the specimen is weaker and less resistant to the shear stress, due to the fatigue deteriorating effect of the cyclic stress. For example, the shear strength parameters of the shear-zone soil are \(c=45.715\) kPa, \(c=43.185\) kPa, \(c=39.55\) kPa, \(\varphi=20.35^{\circ}\), \(\varphi=20.00^{\circ}\), and \(\varphi=20.06^{\circ}\) under 10 s, 60 s, and 90 s, respectively. The shear strength ratio is in the range of 0.79-0.94 for shear-zone soil under different normal stress and cyclic loading periods.
# Authenticity at Risk: Key Factors in the Generation and Detection of Audio Deepfakes +
## 1 Introduction
Thus, the choice of this dataset is based on the fact that the aforementioned recordings were made in realistic environments, without any constraints, and under authentic acoustic and emotional conditions. This is relevant for investigating the factors that may influence both the generation and detection of deepfakes, thus allowing us to identify in which real contexts it is easier to be fooled by deepfakes. The main contribution of this work is a structured analysis of the impact of acoustic environments on both the generation and detection of deepfakes, an aspect that has not been systematically explored so far. In particular, we have evaluated (1) the influence of the speakers, (2) the type of acoustic environment, (3) the signal-to-noise ratio of speech over the background environment and (4) the models chosen for the detection. A highlight of our analysis is that we employ a real-life dataset, which allows the conditions of the generated audio to resemble real-world situations. However, the fact that this dataset is totally unconstrained and only weakly labeled compounds the analysis of the aforementioned factors since first, both the type of environment and the signal-to-noise (SNR) ratio had to be empirically estimated and second, a high imbalance of the classes of environments and SNRs appears. This paper is organized as follows. We review the existing literature on the generation and detection of deepfakes in Section 2. Section 3, describes our chosen dataset-_WELIVE-_, explaining in detail how these data were collected. Section 4 explains the methodology employed including how the different factors used to interpret the results were obtained followed by the experimental settings specifications in Section 5 including pre-processing data, as well as the systems employed for the generation and detection of deepfakes in the recorded audios. In the Section 6, the findings of the experiments are presented. Finally, in the Sections 7 and 8, we discuss the implications of the results obtained and propose directions for future research to improve the detection of deepfakes when they are generated from real acoustic environments, in order to protect victims of gender-based violence from impersonation attacks.
# _photonics_
## 4 Simulation Results
### QKD Link Performance of an LEO Satellite Constellation over Greek QCI
#### 4.3.1 Single Satellite Pass over a Single OGS  
As the first step, a single pass of a satellite over the OGS of Helmos was examined. In Figure 5, the normalized SKR and the satellite-OGS distance are depicted as a function of time for one LEO satellite pass over the Helmos OGS.  
The duration of this single pass was found to be 340 s or 5.7 min. The maximum SKR value that was obtained was \(3.33\times 10^{-4}\) bps/pulse, which corresponded to the maximum elevation angle of \(67.2^{\circ}\). The total number of key bits that were distilled from this single pass, assuming a 100 MHz Alice's repetition rate [25; 26], was about 1.99 Mbits. It should be mentioned that the elevation angle of the satellite may differ from day to day, since the  
Figure 4: Path of one out of 10 satellites during a single day of the year. The route lines slowly drift to the left day by day. Note that the satellite can be seen coming with the direction, either from South to North or from North to South. In the faint red circle, a single pass of the satellite over Greece is depicted.  
Figure 5: Normalized SKR (bps/pulse) of one out of 10 satellites (left) and distance (km) (right) over time for a single pass over the observatory in Helmos with a receiver’s telescope aperture of 2.3 m. The link is considered available, if the elevation angle exceeds \(20^{\circ}\).  
satellite does not always follow the exact same path over the OGS. Therefore, the satellite can be visible for either shorter or longer time lapses.
## 3 Earth Surface Modelling Advances and Links with EO Datasets
### Land-Surface Reservoirs
#### 3.1.3 Permanent Snow and Ice  
In glaciers and over ice-sheets such as Antarctica and Greenland, the representation of snow for weather and seasonal forecasting has slightly different challenges. The spatial variability is somewhat reduced and open-snow is the dominant cover. There is also less emphasis on the mass balance as this is often not directly relevant. It is, however, essential to correctly represent the albedo to resolve the snow energy balance with a particular focus on melting. EO snow albedo, along with snow cover, plays a crucial role in model evaluation and development [121] with some important examples of data assimilation [122].  
The surface skin temperature provides information particularly over Antarctica where polar orbiting satellites have multiple passages per day. The presence of snow largely affects the diurnal cycle of LST, for which current state-of-the-art models show some limitations. Recent results have  
Figure 9: Offline simulation results with the older (CTR ECMWF) snow scheme (black line—before 2009 revision), and NEW (red line—after 2009 revision) for the 2004–2005 winter season at Fraser forest (**a**–**c**) and open (**d**–**f**) sites. Snow mass (**a**,**d**), snow depth (**b**,**e**) and snow density (**c**,**f**) are shown, with time on the \(x\)-axis (from 1 October 2004 to 1 June 2005). Observations are represented by open blue circles.  
also shown the potential for monitoring internal temperature of the ice sheet in Antarctica [123; 124] using SMOS data. EOs can provide reliable information of LST over snow and ice in clear-sky conditions [125]. However, its application for model evaluation has been limited so far because of the poor coverage of geostationary satellites in high latitudes and because of the low temporal resolution of polar satellites far from the poles. LST data have been used to evaluate the diurnal cycle of skin temperature in the ECMWF ERA-Interim reanalysis over Antarctica, showing a homogeneous and persistent (throughout the year) warm bias over the Antarctic plateau [126]. Numerical experiments by [127] indicated that this could be associated to the high thermal inertia of deep snowpacks in the bulk (single-layer) snow scheme used in the reanalysis model (see Figure 10), indicating the potential of more sophisticated multi-layer snowpack schemes in future operational weather forecasting models.