esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### RMS Delay Spread  
Delay spread is one of the key parameters for wireless communication. Based on the measurement data, we can obtain the root mean square (RMS) delay spread shown in  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Area** & **Parameter** & **I** & **II** & **III** & **i** & **ii** & **iii** \\ \hline LOS & Delay (ns) & 510 & 3400 & 8320 & 90 & 630 & 140 \\ LOS & Average power (dB) & \(-20.81\) & \(-46.49\) & \(-56.65\) & \(-30.1\) & \(-50.28\) & \(-32.98\) \\ NLOS & Delay (ns) & - & 8530 & \(1.182\times 10^{4}\) & 170 & 970 & - \\ NLOS & Average power (dB) & - & \(-81.8\) & \(-72.54\) & \(-43.53\) & \(-84.72\) & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the average PDP.  
Figure 3: Average PDP from the three selected areas. (**a–c**) are Area I-III in S2I measurement, (**d–f**) are Area i–iii in S2S measurement.  
Equation (3) by the second-order central moment of the power delay profile, after the noise removal using a threshold of 6 dB above the average noise floor [29, 30].  
\[\tau_{rms}=\sqrt{\frac{\int_{-\infty}^{\infty}P_{a}(\tau)\tau^{2}d\tau}{\int_{- \infty}^{\infty}P_{a}(\tau)d\tau}}-\left(\frac{\int_{-\infty}^{\infty}P_{a}( \tau)\tau d\tau}{\int_{-\infty}^{\infty}P_{a}(\tau)d\tau}\right)^{2} \tag{3}\]  
Corresponding to the measurement time and the result of the power delay profiles, Figure 4 plots the time-variant RMS delay spread. We find that the multi-path components can also make impact on the RMS delay spread, which have been shown in areas II and III of S2I measurement and area ii of S2S measurement. The effect from the short distance can also be found in area I of S2I measurement and areas i and iii of S2S measurement. It is noted that both the multi-path components and the shaking of the boat due to the wave can increase the RMS delay spread.  
A statistical analysis of the RMS delay spread is also made through the cumulative distribution function (CDF), as shown in Figure 4. The measured RMS delay spreads  
Figure 4: RMS delay spread and the statistical result of the measurements. (**a**) is for S2I and (**b**) is for S2S.  
are fitted by Lognormal and Generalized Extreme Value distributions, respectively. The result shows that the Lognormal distribution can characterize our measurement data well. Meanwhile, due to the effect of multi-path components and the shaking of the boat, the measurement data also follows the Generalized Extreme Value distribution well. Moreover, Table 3 lists the statistical result obtained from the CDF. It can be found that 50% of the RMS delay spreads for both the measurements are within 17.5 ns, and 75% of those are within 75 ns without an obvious difference. However, 90% of the RMS delay spreads for the S2I measurement is within 265.8 ns, while 146.1 ns for the S2S measurement. This is because the measurement distance of the S2I case is much longer than the S2S case.
# Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction
## 3 Forensic Fingerprint Analysis: Role of Human Errors  
This section introduces the main errors a human may incur in before and during the entire fingerprint identification process, possibly yielding misidentifications (see Figure 4).  
Latent fingerprint identification is composed of four main stages, namely: reveal, capture, feature extraction, and identification. The more common errors typically observed at each one of these stages are as follows [16]:  
**Reveal:**: in this stage, an expert should use specific powders, based on the surface and shape of the object, to reveal the latent fingerprint. The errors one may incur at this stage are:  
* Using a revealing powder that does not correspond to the surface or shape of the object where the latent fingerprint is, which could wrongly reveal the ridges of the latent fingerprints.  
Figure 4: Four stages before and during the fingerprint identification where the human errors could produce misidentification.  
Using more revealing powder than necessary, creating a filling among the fingerprint ridges, and consequently, making it hard to be of use on the next stages.  
in this stage, an expert must be careful at capturing the latent fingerprint under the best possible conditions. Errors that are typically observed at this stage are:  
* While lifting the latent fingerprint, deformations might be created in the ridges creating false bifurcations or false ends on the ridges. As a result, errors are induced that may severely affect the feature extraction stage.
* Output an out of focus or low-resolution photo of the latent fingerprint.
* Lack of use of a rule next to the latent fingerprint that may enable one to estimate the real scale of the fingerprint.  
during this stage, the examiners will extract all features necessary for the identification stage. The more common flaws one may experience at this stage are:  
* False features can be added from errors originated at the capture stage.
* Actual features could be missed by examiners.
* The position or angle of some features could be shifted due to human perception.  
any flaw committed at previous stages will affect identification performance. However, the main errors that may show up at this stage are:  
* The ranking provides the corresponding impression, but the experts obviate this matching.
* The ranking does not provide the corresponding impression due to human errors issued at the previous stages.
* The expert issues a true positive identification when really it is a false positive.  
All the human errors previously described above negatively affect the identification rate. One of the most important features for fingerprint identification is minutiae. A minutia is a minute detail on the ridges of a fingerprint, often ridge ending or bifurcation. Minutiae are one of the most used features for fingerprint identification [5; 21; 22]. Hence, we will analyze minutiae's impact on fingerprint identification and the matching algorithms upon the possible appearance of human errors.
# Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model
## 3 Results  
This study uses the compaction process of a specific earth-rock dam in Southwestern China as a case study. The power station controls a watershed area of approximately 65,700 km\({}^{2}\), with a total reservoir capacity of 10.154 billion m\({}^{3}\) and an active storage capacity of 6.56 billion m\({}^{3}\), thereby providing multi-year regulation capability. The main structures of the project consist of a rockfill dam with a gravel-core wall, a spillway, a flood discharge tunnel, a draw-off tunnel, an underground powerhouse, and other related facilities. The proposed compaction quality prediction model for the earth-rock dam, based on the IFA-RF model, is applied to predict the actual compaction process. A comparative analysis is conducted between the proposed model and traditional compaction quality prediction models, such as RF, BPNN, and multiple linear regression (MLR), to validate the consistency, superiority, and robustness of the proposed model.
## 3 Results and Discussion  
### Dynamic Regeneration of Exhausted GAC  
Following the scaling rules for scaling down the water filter (see Section 2.6), a dynamic regeneration was performed. Figure 3a displays the regeneration breakthrough curve using 7.4 wt.% HCl after applying RS5 CT experiments. The breakthrough time was determined as the desorption time for reaching the equilibrium concentration at which \(c(H^{+})_{i}/(c(H^{+})_{0}\) equals 0.05. This ratio was chosen, reducing the amount of HCl needed for regeneration and thus lowering the cost of this process in a real power plant installation. The breakthrough curve using 7.4 wt.% HCl shows a sharp and relatively fast transition during the regeneration, indicative of a high desorption rate with a breakthrough time of 184 min, as shown in Figure 3. HCl reacts with the adsorbed compounds on the GAC particles (mainly CaCO\({}_{3}\)), leading to a fast regeneration of the GAC in the column. HCl breakthrough curve presents a slightly narrow mass-transfer zone, and as a result, the desorption capacity increases.  
Figure 2: Calibration curve of hardness of synthetic water solutions (in mg CaCO\({}_{3}\)/L) as a function of the conductivity (in \(\mu\)S/cm).
# Bioaugmentation Treatment of a PAH-Polluted Soil in a Slurry Bioreactor
## Materials and Methods
### Analytical Methods  
#### 2.3.1 PAH Analyses  
Triplicate slurry samples of 15 mL were obtained from each experiment at 0, 1, 3, 7, and 15 days. They were extracted with hexane:dichloromethane (1:1, _v/v_) in a Soxtherm system (Gerhardt). The extract was concentrated by rotary evaporation. The PAH concentrations were determined by means of a modified Environmental Protection Agency (EPA) method 8272. In brief, the extracts were injected into a 7890A gas chromatograph (GC) System coupled to a 5975C Inert XL Mass Selective Detector (MSD) with a Triple-Axis Detector (Agilent Technologies). A capillary column DB-5 ms (5% phenyl 95% dimethylpolysiloxane) 30 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m film (Agilent Technologies) was used, with helium as the carrier gas at 1 mL/min. The initial oven temperature was 70 \({}^{\circ}\)C (held for 2 min) and was ramped up at 20 \({}^{\circ}\)C/min to 220 \({}^{\circ}\)C, raised to 270 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min (held for 1 min), then raised to 290 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min (held for 1 min), and finally ramped up at 10 \({}^{\circ}\)C/min to 300 \({}^{\circ}\)C (held for 7 min). The GC injector was operated in splitless mode for 2 min, and its temperature was maintained at 260 \({}^{\circ}\)C. The mass spectrometer was operated in selected ion monitoring mode (SIM), and the quantification of the _m/z_ relations was as follows: 128 (Naphthalene); 152, 153, and 154 (Aeenaphthylene and Acenaphthene); 165 and 166 (Fluorene); 178 (Phenanthrene and Anthracene); 202 (Fluoranthene and Pyrene); 228 (Benz[a]anthracene and Chrysene); 252 (Benzo[b]fluoranthene, Benzo[k]fluoranthene, and Benzo[a]pyrene); and 276 and 278 (Dibenz[a,h]anthracene, Benzo[ghi]perylene, and Indene[1,2,3-cd]pyrene). The MS worked in electron ionization mode (EI) at 70 eV, and it was calibrated daily by auto-tuning with perfluoritbutylamine (PFTBA). PAH calibration mixtures (AccuStandard) were used.
## 4 Experiments
### Performance with Competing Noises  
This subsection considers the localization performance with only competing noises, since this would be the general case in the acoustic environment. The experiment also indicated the generalization ability of the trained time-frequency selection neural network in unseen scenarios. The MAE results are reported in Table 3 with a subset of the tested environments investigated using a typical reverberation time of 360 ms. The SNRs were set to be 0 dB, 5 dB, and 10 dB. A directional Noise (N2) was also included in evaluation. The noise signal was drawn from the Noisex92 database, and it was then convolved with the measured impulse responses. The localization accuracies differed in different noisy conditions, and as expected, the directional noise affected the performance badly for the SRP-PHAT and cWMM algorithms. The time-frequency selection neural network was able to deal with this case since the information of the target speaker was well kept in the adaptation utterance. Overall, the performance of the algorithms was better than that when an interfering speaker existed. The white noise scenario turned out to be the easiest test case, where the MAEs were around 2\({}^{\circ}\)-4\({}^{\circ}\). Again, the time-frequency selection proved its effectiveness.
## 2 Related Works
### Electricity Theft Detection
#### 2.2.1 Supervised Detectors  
Supervised electricity theft detectors are binary classifiers trained on datasets containing both benign and malicious electricity usage samples. In [11], [PERSON] et al. utilized the Irish Smart Energy Trial (ISET) dataset to train a binary SVM to identify potential electricity theft cases. Since the ISET dataset comprises only benign samples, the authors employed six attack functions to synthesize malicious samples from the benign ones. The results indicated the effectiveness of the SVM in identifying the six types of electricity theft cyberattacks. In [12], [PERSON] et al. proposed combining DTs with SVM for improved electricity theft detection. Instead of depending on a single DT, [PERSON] et al. [13] proposed utilizing ensembles of DTs, including LightGBM, CATBoost, and XGBoost. The results demonstrated that employing ensemble learning boosts the detection performance. Furthermore, [PERSON] et al. [14] further enhanced electricity theft detection by training an XGBoost on both electricity consumption samples from the Endesa dataset and auxiliary data, including the customers' locations and the technical specifications of their SMs.  
Apart from previous works, the authors of [31; 32; 33] explored supervised electricity theft detectors using deep learning (DL) algorithms. DL improves electricity theft detection due to its ability to capture temporal correlations in electricity usage data and uncover hidden patterns. In [31], [PERSON] et al. compared between shallow ML and DL models for electricity theft detection. The experimental results utilizing a synthetic dataset indicated that DL models, including CNNs, long short-term memory (LSTM), and Autoencoders outperform shallow ML models, including DT and random forest (RF). In [32], [PERSON] et al. proposed a hybrid DL model to identify potential theft cases. Their model is a combination of a fully connected feed-forward neural network (FCNN) and a CNN. Experimental results using the State Grid Corporation of China (SGCC) dataset showed the advantage of the hybrid detector over standalone FCNN and CNN detectors. In [33], [PERSON] et al. investigated the electricity theft problem in net metering systems. They developed a multi-stage detector using a combination of a CNN and RNN, trained on multiple data sources in addition to power readings. At each stage, the detector is provided with additional auxiliary data to enhance its performance. However, all of the above works utilized supervised learning, where detectors are designed to detect specific attacks, and their performance is unpredictable when encountering new, unseen attacks [34].
# PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
## 3 Experiments and Results
### Experimental Setup
#### 3.2.2 Verification Indicator  
To systematically quantify and evaluate the performance of our road extraction model in classification tasks, we employed a confusion matrix as the principal evaluative metric.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Value** \\ \hline Epoch & 100 \\ \hline Batch Size & 4 \\  & (Massachusetts Roads Dataset) \\ \hline Initial Learning Rate & \(1\times 10^{-4}\) \\ \hline e & \(1\times 10^{-8}\) \\ \hline Weight Decay & 0 \\ \hline Beta 1 & 0.9 \\ \hline Beta 2 & 0.999 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameter settings in experimental training.  
This matrix delineates four key parameters: True Positives (_TP_), representing the accurate classification of pixels as road; True Negatives (_TN_), indicating correctly identified non-road pixels; False Positives (_FP_), specifying erroneously classified road pixels; and False Negatives (_FN_), marking road pixels incorrectly categorized as non-road. Guided by these fundamental metrics, we computed three principal evaluative indices:  
Precision (_P_): this index gauges the model's accuracy in labeling pixels as roads (i.e., true positives). The formula employed is as follows:  
\[P=\frac{TP}{TP+FP} \tag{4}\]  
Recall (_R_): this is employed to assess the model's capacity to correctly identify pixels that are, in fact, roads (i.e., true positives). The computational formula is as follows:  
\[R=\frac{TP}{TP+FN} \tag{5}\]  
Intersection over union (_IoU_): this metric assesses the overlap between the model's predicted region and the actual ground truth, offering a robust measure of spatial accuracy. The computation is as follows:  
\[IOU=\frac{TP}{TP+FP+FN} \tag{6}\]  
Collectively, these indices provide a comprehensive and nuanced evaluation of model performance, focusing not only on identification accuracy but also on the model's ability to handle different classifications (i.e., true and false positives and negatives). Through this integrated evaluation framework, we aim to illuminate the model's strengths and weaknesses in road extraction tasks, thereby furnishing a holistic and in-depth understanding of its capabilities.
# Removal of Phosphate from Aqueous Solution by
## 3 Results and Discussion
### Adsorption Kinetics and Isotherm Adsorption
#### 3.3.2 Adsorption Isotherm  
Figure 8 depicts the adsorption isotherm based on the equilibrium concentration and equilibrium adsorption capacity of the phosphate solution. The equilibrium adsorption capacity of zeolite, biochar, and PZC 7:3 for phosphate was rapidly increased with the increase of the equilibrium concentration at the lower initial phosphate concentration. When starting with a high concentration of phosphate, the equilibrium adsorption capacity grew gradually along with the equilibrium concentration. This phenomenon is consistent with the findings of previous studies [18; 54]. This was attributed to the extensive adsorption sites on the surface of zeolite, biochar, and PZC 7:3 at a lower starting solution concentration, which were able to make complete contact with phosphate. Therefore, phosphate was easily adsorbed at a lower concentration by zeolite, biochar, and PZC 7:3. However, in the high concentration, the adsorption sites of zeolite, biochar, and PZC 7:3 gradually reached saturation, and then the adsorption capacity slowly increased to equilibrium.  
The above experimental findings were used in the Langmuir and Freundlich models to fit the adsorption of phosphate by different materials. The experimental data fitting parameters are shown in Figure 8 and Table 4, where the maximum adsorption capacities of zeolite, biochar, and PZC 7:3 were 0.69, 3.60, and 2.41 mg/g, respectively. Based on \(R^{2}\) (Table 4), the Freundlich model better described the biochar and PZC 7:3 adsorption processes of phosphate, while the Langmuir model better outlined the zeolite adsorption process of phosphate. In the previous publication, it was re  
Figure 8: Fitting of Langmuir and Freundlich models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{3}{c}{**Pseudo-First-Order Model**} & \multicolumn{3}{c}{**Pseudo-Second-Order Model**} \\ \cline{2-7}  & \(k_{1}\) & \(q_{e}\) & \(R^{2}\) & \(k_{2}\) & \(q_{e}\) & \(R^{2}\) \\ \hline zeolite & 0.0096 & 0.0901 & 0.9701 & 0.1123 & 0.1016 & 0.9792 \\ biochar & 0.0936 & 1.0452 & 0.8711 & 0.1423 & 1.0965 & 0.9545 \\ PZC 7:3 & 0.0793 & 0.9855 & 0.9102 & 0.1279 & 1.0326 & 0.9734 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Kinetic parameters of phosphate adsorption.  
equation could be utilized to explain the monolayer adsorption of adsorbates on adsorbents and the Freundlich equation could be used to describe the empirical model of chemisorption of adsorbates on inhomogeneous surfaces [70; 71]. It was shown that physical adsorption occurred during phosphate adsorption by zeolite, while chemisorption occurred during phosphate adsorption by biochar and PZC 7:3.
# Research on the Aerodynamic Characteristics of Tractor-Trailers with a Parametric Cab Design
###### Abstract  
As tractor-trailer ownership has increased year-by-year, corresponding energy consumption and environmental issues have gradually become a heated issue. Approximately 45 percent of the total aerodynamic drag of tractor-trailers is attributed to the flow at the front surface of the cab, and the gap between the cab and trailer. Therefore, this study has taken a new approach to designing the shape of the cab inspired by the external forebody of the cheetah. A parametric design protruding cab was devised in this study; the length of the protruding part and the angle between this protruding part and the A pillar were two design variables. Computational fluid dynamics simulation was conducted to investigate the drag reduction effects of these new cab styling designs, and the proposed cab reduced the drag by a maximum of 8.49%. The flow characteristics around the whole body of the tractor-trailer baseline model and the parametric cab design vehicle model were analyzed using the velocity streamline graph to illuminate the change of the flow field and the drag-reduction mechanism of the proposed design. A vortex formed above the protruding part of the cab and it acted as a 'vortex cushion' to accelerate the speed of the air flow; accordingly, the positive and negative pressure distribution on the front surface of the cab and trailer also changed. The new parametric design has provided the possibility to adjust the forebody of the cab to an aerodynamically optimized shape, and these findings have offered useful information for the development of a new design method of the tractor-trailer to reduce aerodynamic drag and improve fuel economy.  
tractor-trailer; parametric design; drag reduction; vortex cushion effect +
Footnote †: journal: Applied Sciences
## 4 Materials and Methods
### Analysis of Degradome Sequencing Data  
Clean degradome sequencing tags were obtained by removing low-quality and adaptor sequences from raw tags. The clean tags were aligned to _V. ashei_ unigenes using the Bowtie program with no mismatches allowed. The rRNA, tRNA, snRNA, snRNA, polyN, and other repeated sequences were removed based on searches of the NCBI GenBank and Rfam 11.0 databases. The miRNA target genes were identified by mapping the degradome tags to the _V. ashei_ unigenes and conducting an analysis using the CleaveLand pipeline with standard criteria for target cleavage, including a \(p\) value cut-off of 0.05. The target transcripts were classified into five categories based on the following criteria: Category 0--more than one tag at the cleavage site, with the abundance at the cleavage site equal to the maximum on the transcript, and only one maximum on the transcript; Category 1--more than one tag at the cleavage site, with the abundance at the cleavage site equal to the maximum on the transcript, and more than one maximum on the transcript; Category 2--more than one tag at the cleavage site, with the abundance at the cleavage site less than the maximum, but higher than the median for the transcript; Category 3--more than one tag at the cleavage site, with the abundance at the cleavage site equal to or less than the median for the transcript; Category 4--only one tag at the cleavage site.