esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### ELISA Method Validation
In the case of immunoassay, the pretreatment of the sample represents a critical step since most of the extractive methods reported in literature applied to cosmetic samples are based on the use of organic solvents. Since immunoassay are based on the use of biological probes, organic solvents are not ideal for developing such bioassays. For this reason, the proposed pre-treatment procedure was based on the extraction of target analytes using THF followed by a 1:5 dilution in PBS in order to avoid an undesired effect of THF on the immunoassay performances. Therefore, in order to evaluate the recovery of the target analytes obtained through this procedure, cream and serum samples spiked with low, medium, and high concentration of different corticosteroids were analyzed. The added concentrations were selected considering the dynamic ranges reported for the different \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Steroid**} & \multicolumn{2}{c}{**PBS/THF**} & \multicolumn{2}{c}{**CREAM**} & \multicolumn{2}{c}{**SERUM**} \\ \cline{2-7}  & \multicolumn{1}{c}{**Upper Limit**} & \multicolumn{1}{c}{**LOD (ng/mL)**} & \multicolumn{1}{c}{**LOD (ng/mL)**} & \multicolumn{1}{c}{**Upper Limit**} & \multicolumn{1}{c}{**LOD (ng/mL)**} & \multicolumn{1}{c}{**Upper Limit**} \\ \cline{2-7}  & \multicolumn{1}{c}{**LOD (ng/mL)**} & \multicolumn{1}{c}{**LOD (ng/mL)**} & \multicolumn{1}{c}{**LOD (ng/mL)**} & \multicolumn{1}{c}{**LOD (ng/mL)**} & \multicolumn{1}{c}{**Upper Limit**} \\ \hline FM & 0.02 & 0.002 & 0.59 & 0.015 & 1.610 & 0.009 \\ DM & 0.04 & 0.005 & 1.8 & 0.067 & 0.03 & 0.12 \\ BC & 0.80 & 0.88 & 1.8 & 0.068 & 12.1 & 6.20 \\ BM 17-V & 4.7 & 3.27 & 13.8 & 0.53 & 67 & 65.46 \\ MP & 0.22 & 3.13 & 51.2 & 36.2 & 5 & 30.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Concentration ranges and LOD for calibration curves generated using standard and spiked solution in different matrices. Figure 2: Calibration curves obtained in PBS/THF (circle symbols), cream A (triangular symbols), and serum (square symbols) for the different corticosteroids. corticosteroids. The results are shown in Table 4. The recovery values were between 70 and 92%, and the performance was better in the case of medium and high concentrations. Paired t-tests were performed to evaluate the difference between the real concentration and the obtained values, and it was found that there were no significative differences for all the analyzed samples (n = 3 and \(p\) = 0.01). The assay also showed a satisfactory precision, providing a mean CV% of below 25%.
# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 3 Proposed Method  
We present the method in three parts. First, we provide definitions and the problem statement. Next, we provide a comprehensive overview of the data processing. Then, we describe the detailed process of constructing the vessel portrait. Finally, we provide a comprehensive description of our proposed model VEPO-S2S including the Multi-level Vessel Trajectory Representation Module and the Feature Fusion and Decoding Module.
## 3 Multi-Scale Damage Detection Results Using pNDE
### Macro-Scale Damage Detection in Composites  
Low-velocity impact load can cause severe structural damage, such as delamination and transverseply crack, in composite laminates. Demonstration of the reliable detection of macro-scale damage in composites using the developed pNDE method is critical for the engineering application. As shown in Figure 6, the macro-scale delamination and transverse ply cracks were successfully detected and represented using C-scan images at 5 different depths in the range 0-1.6 mm. The edge of transverseply cracks was clearly drawn using the dashed line. In addition, the area of delamination at different depths was also highlighted in each subfigure. In Figure 6a-e, the size of the delamination area gradually reduced, indicating that the size of delamination was relatively large near the composite surface. This observation was reasonable since the excitation energy was reduced as it penetrated deep into composites, resulting in reduced delamination area as the depth increased. The detected macro-scale delamination and transverse ply cracks matched with the optical image of the damaged composite sample shown in Figure 6f. A typical photoacoustic signal received by the 20 MHz center frequency transducer is shown in Figure 6g. The corresponding single-sided photoacoustic signal frequency amplitude spectrum is also shown in Figure 6h. Due to the increased scan steps, the spatial resolution of macro-scale pNDE scanning was lower than that of the micro-scale pNDE detection. This is reasonable, since the extremely high spatial resolution was not necessary to identify macro-scale damage in composites. In addition, the optimization of pNDE parameters for macro-scale damage detection was able to significantly increase the scanning speed by choosing the relatively large scanning step length during the detection. Therefore, it is critical to adjust the pNDE parameters following the potential damage size and required spatial resolution, allowing the developed system to be suitable for both micro- and macro-scale damage detection in composites.  
Figure 6: A pulsed laser-induced pNDE system to detect macro-scale delamination and transverse ply cracks in composites. (**a–e**) The 2D C-scan images showing the size of delamination and transverse ply cracking. (**f**) Optical images using the detected impact damage of composites. (**g**) A plot of the raw photoacoustic signal. (**h**) Single-sided photoacoustic signal frequency amplitude spectrum plot.
## 3 Mechanical Design
### Tuning Holes Design  
There may exist deviation between the actual frequency and the desired value when the fabrication of the deflecting cavity is completed. Therefore, the deflecting cavity is required to have frequency tuning capability. In this paper, perturbation of the cavity radius is adopted to tune the frequency.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**End Wall Thickness/mm** & **Largest Deformation/mm** & **Frequency Drift/kHz** \\ \hline
10 & 0.0609 & \(-69.19\) \\
15 & 0.0266 & \(-33.57\) \\
21 & 0.0142 & \(-19.55\) \\
23 & 0.0119 & \(-16.75\) \\
25 & 0.0101 & \(-14.48\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation results of the largest deformation and frequency drift with different end wall thickness.  
Figure 8: Schematic of the waveguide brazing with the middle cell. (**a**) Transition plate; (**b**) Main part of cavity; (**c**) After brazing.  
The electric field of the working mode is concentrated inside the cavity, which is surrounded by the magnetic field. There is magnetic field distributing over the cavity wall concentrated on the upper and lower part, so volume perturbations of the magnetic field are performed to adjust the resonant frequency. The tuning scheme is carried out on all the three cells. One pair of tuning holes are located at the upper and lower part for each end cell and four tuning holes are placed at an angle of 45\({}^{\circ}\) from the \(y\)-axis for the middle cell. All the eight holes with reserved rods have the same size. The outer diameter of the tuning hole is 30 mm, and the remaining wall thickness is 4 mm. There are M8 screw threads in the center of each reserved rod with diameter of 16 mm, and the holes can be pushed or pulled to adjust the resonant frequency. The deformation produced by applying different forces to the tuners are calculated in CST. The results show that, when the adjustment is 627 kHz which has met the requirement, the deformation value is 0.54 mm.
# LoRaWAN Path Loss Measurements in an Urban Scenario Including Environmental Effects
###### Abstract  
LoRaWAN is a widespread protocol by which Internet of things end nodes (ENs) can exchange information over long distances via their gateways. To deploy the ENs, it is mandatory to perform a link budget analysis, which allows for determining adequate radio parameters like path loss (PL). Thus, designers use PL models developed based on theoretical approaches or empirical data. Some previous measurement campaigns have been performed to characterize this phenomenon, primarily based on distance and frequency. However, previous works have shown that weather variations also impact PL, so using the conventional approaches and available datasets without capturing important environmental effects can lead to inaccurate predictions. Therefore, this paper delivers a data descriptor that includes a set of LoRaWAN measurements performed in Medellin, Colombia, including PL, distance, frequency, temperature, relative humidity, barometric pressure, particulate matter, and energy, among other things. This dataset can be used by designers who need to fit highly accurate PL models. As an example of the dataset usage, we provide some model fittings including log-distance, and multiple linear regression models with environmental effects. This analysis shows that including such variables improves path loss predictions with an RMSE of 1.84 dB and an R\({}^{2}\) of 0.917.  
LoRaWAN; path loss; environmental variables +
Footnote †: journal: Data 2023, 8, 4.
###### Abstract  
Objective: To evaluate the affective responses to running with imposed velocity or self-selected time trials in runners, as well as the effects on volume load (VL) and training impulse (TRIMP). Anxiety was also determined. We established the level of association between the dependent variables. Methods: Three visits were carried out. The 1 st visit consisted of a maximum running effort test (VO\({}_{2}\)Max). In the 2 nd and 3 rd visits, participants were divided between the time limit (T\({}_{\text{Lim}}\)) or time trial 1000 m running at self-selected intensity (T\({}_{1000}\)). Participants responded to the felt arousal, feeling, and anxiety SUDS scale before, during, and after T\({}_{\text{Lim}}\) and T\({}_{1000}\). Results: T\({}_{\text{Lim}}\) vs. T\({}_{1000}\) (\(p<0.001\)) and V\({}_{\text{Peak}}\)\(\times\) V\({}_{1000}\) (\(p=0.013\)) showed differences, but did not influence VLT\({}_{\text{Lim}}\) vs. VL\({}_{1000}\) (\(3181.34\)\(\pm\)\(872.22\) vs. \(3570.60\)\(\pm\)\(323.3\); \(p=0.062\)). TRIMP showed no differences (\(p=0.068\)). Arousal did not differ between the pre-exercise (\(p=0.772\)) and post-exercise (\(p=0.083\)) conditions but was different during (\(p=0.035\)). There were differences between groups in the pre-exercise (\(p=0.012\)), during (\(p<0.001\)), and post-exercise (\(p<0.001\)) conditions for feeling and anxiety scores. The correlation between TRIMP and affective scores showed an association with T\({}_{\text{Lim}}\) (\(\tau=0.46;p=0.043\)). Conclusion: The self-selected exercise generated positive affective responses, but the same did not occur for the imposed T\({}_{\text{Lim}}\). VL and TRIMP presented equality. There was association between TRIMP and the T\({}_{\text{Lim}}\) feeling scale. T\({}_{\text{Lim}}\) significantly increased anxiety scores.  
affective responses; affect; anxiety; high intensity exercise +
Footnote †: journal: Applied sciences
# Effect of Controlled Expiratory Pressures on Cerebrovascular Changes During Valsalva Maneuver
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were performed using Jamovi ver. 2.2.5 (The Jamovi project (2021)). One-factorial repeated-measures (RM) ANOVA was used to analyze significant differences between before and after VM performance for every outcome. NIRS data measured inreal-time were divided into four sections (PRE = before VM, VM = during VM, POST1 = the first after VM, and POST2 = the second after VM), and hemodynamic changes in the brain caused by VM were analyzed. To analyze the stable NIRS data, the PRE VM data used the average value of data acquired for 15 s, the VM data was the average of data for 15 s, which was the strain stage, and the POST1 section was 15 s after VM termination (the first 15 s of POST period). For POST2, the average value of the second 15 s after VM was used for analysis. Therefore, an RMANOVA with four within-factors was performed to analyze the NIRS data. The Doppler ultrasound data were divided into three sections (PRE, POST1, and POST2). The average data for 15 s in the pre-stage was used for PRE, and two 15 s averaged data measured for 30 s after VM were used for POST1 and POST2, respectively. Accordingly, RMANOVA with three within-factor analyses was performed. Where the assumption of sphericity could not be assumed, adjustments to the degrees of freedom were made (\(\epsilon>0.75\) = Huynh-Feldt; \(\epsilon<0.75\) = Greenhouse-Geisser). Post hoc pairwise comparisons were conducted using [PERSON]'s tests. Finally, a paired t-test was performed on PWV and stiffness index to compare changes before and after the VM. Both the PWV and stiffness index satisfied the assumption of normality by performing the Shapiro-Wilk test.
## 3 The Proposed Algorithm Based on Novel Detection Probability
### An Novel Detection Probability Model
Assuming that the active detection method is adopted, the wave front is the cylindrical wave when distance is far away, \(\text{TL}=10\log(r)\). According to Equation (21), \(\text{SNR}=\text{SL}-2\text{TL}-\text{NL}+\text{TS}+\text{DI}\), combining Equation (28). The relationship between the detection probability, the false detection probability and the distance is shown in Equation (29). \[P_{d}=1-\text{N}\bigg{(}\text{N}^{-1}\Big{(}1-P_{f}\Big{)}-\sqrt{2(\text{SL}-2 \text{TL}-\text{NL}+\text{TS}+\text{DI})}\bigg{)} \tag{29}\]  
Given the uncertainty of the false detection probability, we cannot get the analytic relationship between the detection probability and distance, here choosing the Naiman Pearson criterion as the criterion. That is, the detection probability is maximized under the condition of false detection probability. When the false detection probability is fixed, we can get the analytic expression between the detection probability and target distance. Assuming the source level \(\text{SL}=120\) dB, the fifth grade sea condition \(\text{NL}=55\) dB,\(\text{TS}=10\) dB, and ignoring the direction index \(\text{DI}~{}=~{}0\), the relationship between the detection probability and the distance is shown in Figure 4.  
It can be seen from Figure 4, when at a certain distance, the false detection probability is greater, the detection probability greater; when the detection probability is certain, the false detection probability is greater with the distance further. No matter how the false detection probability is chosen, the detection probability decreases with an increase in distance, and when the false detection probability is smaller, the detection probability decays faster.
## 2 Methods
### Borehole Image Preprocessing  
When the structural plane intersects the borehole at an oblique angle, the intersection of the plane with the borehole forms a standard sinusoidal curve, as shown in Figure 2a. When the structural plane is perpendicular to the borehole, the intersection between the structural plane and the borehole forms a horizontal straight line, as shown in Figure 2b.  
Figure 1: Borehole image data acquisition area. A1: Zhulinshan Coal Mine; A2: Huahong Coal Mine.  
Finally, when the structural plane is parallel to the borehole, the intersection between the structural plane and the borehole forms two vertical lines, as shown in Figure 2c.  
The mechanical properties of engineering rock mass depend mainly on the characteristics of the structural plane, such as occurrence, continuity, and aperture. The occurrence and continuity of the structural plane can be expressed by the dip angle and the trace length, respectively. The structural plane aperture refers to the vertical distance between the two sides of the structural plane. We must not only identify the structural plane but also describe its location and degree of development. The structural planes are categorized according to the dip angle and trace length into five categories: (A) the long, longitudinal structural plane; (B) the short, longitudinal structural plane; (C) the long, transverse structural plane; (D) the short, transverse structural plane; and (E) the fracture zone, and different colors are used to distinguish the different types of structural planes for later model learning. Figure 3 shows the corresponding characteristics and descriptions.  
Figure 3: Five types of structural planes.  
Figure 2: Plane of borehole wall expansion for different types of intersections: (a) oblique, (b) horizontal, and (c) vertical.  
High-quality training samples and outstanding features can improve the accuracy of the deep learning model. However, borehole images contain a large number of data (many borehole images can reach lengths exceeding ten meters), which can make fracture information difficult to identify due to the borehole environment. Thus, segmentation sampling and feature enhancement are used to preprocess borehole images in this study, which can accelerate the speed of deep learning and reduce the operation time.  
To better recognize the borehole peep image, each pixel in the borehole image is processed before image training. Taking the target pixel as the center, the image is expanded to a specific length and width to obtain the extended borehole image so that each pixel can be regarded as a training sample for the deep learning network.  
To improve the definition of cracks in the original image, the difference between cracks and other features in the borehole image is expanded, and the features of non-research objects are suppressed. Consequently, the original borehole peep image is processed through image denoising and contrast enhancement. The processed image is then input into the deep learning network, and the preprocessing greatly increases the discrimination to satisfy the training requirements.
# A Novel Power-Saving Transmission Scheme for Multiple-Component-Carrier Cellular Systems
## 1 Introduction
Thus, the proposed scheme has considerable potential in terms of reducing the energy costs and CO\({}_{2}\) emissions associated with cellular networks. The next sections of this paper are organized as follows: Section 2 gives an overview of existing related literature in the field. Section 3 introduces a system model and power consumption model for multi-CC cellular networks and provides a problem formulation for the model to show the objective function to be optimized and define constraints. Next, in Section 4, a proposed novel transmission scheme for use in the model is detailed, and a time complexity analysis of this scheme is performed. Results in terms of power-saving and fairness performances are subsequently shown and discussed in Section 5. Finally, Section 6 relates the conclusions of this study.
# Detecting Suspicious Texts Using Machine Learning Techniques
## 5 Experiments
### Human Baseline vs. ML Techniques  
The performance of the classifiers compared with the human for further investigation. To eliminate the chance of human biases in data labelling and evaluation phases, we have assigned two new experts who manually label the testing texts into one of the predefined categories. Among 1400 test text samples, 621 texts are from non-suspicious (\(C_{ns}\)) class and 779 texts are from suspicious (\(C_{s}\)) class. The accuracy of each class can be computed by the ratio between the number of correctly predicted texts and the total number of texts of that class by using the confusion matrix. Suppose that a system can correctly predict 730 texts among 779 suspicious texts; then, its accuracy in suspicious class will be 93.7% (730/779). As the tf-idf outperformed the BoW in the previous evaluation, we thus compared the performance of the classifiers only for the tf-idf feature extraction technique with experts. Table 8 exhibits the summary of comparison.  
The experts outperformed the ML classifiers in both classes. Experts can more accurately classify non suspicious texts than suspicious texts. We found approximately 0.5% accuracy deviation between experts. All of the classifiers done well on the suspicious class and performed very poorly on the non-suspicious class. A significant difference has been observed between human baseline and ML classifiers. All of the classifiers were able to identify suspicious texts more precisely than the non suspicious texts. After manual analysis, we traced the reason behind this disparate behaviour. The maximal portion of the non-suspicious texts was accumulated from the newspaper, and, on average, each text has 72.12 words in it. ML-based classifiers did not consider the semantic meaning of a text which is important for the classification of long texts. Thus, the system could not detect non-suspicious  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Approach** & \(C_{ns}\) **Accuracy (\%)** & \(C_{s}\) **Accuracy (\%)** \\ \hline Expert 1 & 98.71 & 98.58 \\ Expert 2 & 99.19 & 98.33 \\ \hline LR + F1 & 72.94 & 92.81 \\ LR + F2 & 65.37 & 94.35 \\ LR + F3 & 52.81 & 97.81 \\ LR + F4 & 72.14 & 93.19 \\ LR + F5 & 72.30 & 93.19 \\ \hline DT + F1 & 70.85 & 83.56 \\ DT + F2 & 68.43 & 88.44 \\ DT + F3 & 42.44 & 97.56 \\ DT + F4 & 57.69 & 82.02 \\ DT + F5 & 68.76 & 83.05 \\ \hline RF + F1 & 66.66 & 97.30 \\ RF + F2 & 67.63 & 96.68 \\ RF + F3 & 52.49 & 98.20 \\ RF + F4 & 66.02 & 96.91 \\ RF + F5 & 67.63 & 96.53 \\ \hline MNB + F1 & 73.42 & 92.04 \\ MNB + F2 & 63.28 & 93.96 \\ MNB + F3 & 41.38 & 98.58 \\ MNB + F4 & 71.65 & 93.58 \\ MNB + F5 & 71.01 & 93.45 \\ \hline SGD + F1 & 72.30 & 81.51 \\ SGD + F2 & 66.82 & 94.09 \\ SGD + F3 & 56.19 & 97.04 \\ SGD + F4 & 72.46 & 92.42 \\ SGD + F5 & 70.04 & 93.53 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy comparison between experts and classifiers with the tf-idf FE technique.  
texts accurately. For this reason, the false-negative value becomes very high, which causes a drop in the recall, and thus affects the system classification accuracy.
## 2 Materials and Methods
### Proposed Model
#### 2.5.1 Semantic Embeddings  
Semantic embeddings act as a vector representation of words, considering their isolated meaning and the context in which they are found. This work used a pre-trained model fed large amounts of text to capture semantic subtleties [27]. Once these embeddings are generated, they are integrated into the model through a dense layer that connects them directly to the attention mechanism.  
To generate the semantic vectors in the model, a process began with selecting a pre-trained language model, such as Word2 Vec or BERT. This model is fed extensive text data sets to learn vector representations of words that capture their meaning and context. Each word in the data set is transformed into a numerical vector in a multidimensional space, where words with similar meanings are positioned closely. These vectors train the chatbot model, providing a solid foundation for advanced semantic processing and generating accurate and contextual responses.  
Figure 2: The general architecture of the proposed model.  
Furthermore, the inclusion of GPT in the model influences the generation of semantic vectors. GPT, being a pre-trained generative model, not only contributes to the semantic understanding of words but improves the contextual generation of text. Combining GPT with models like Word2 Vec or BERT allows a richer understanding of context and semantics in chatbot interactions. Thus, the resulting semantic vectors are more robust, capturing the meaning of individual words and the relationship and coherence in text sequences.  
Table 1 represents the generation of semantic embeddings for various tokens. Each token has an associated six-dimensional embedding vector in this example. These vectors capture the semantic essence of each word, allowing the model to identify relationships and similarities between words based on context and inherent meaning. It is essential to understand that, in real applications, these vectors can have many more dimensions, allowing for an even richer representation of word meaning.