esa-sceva/satcom-chunk-collection huggingface dataset:
# Periodontal Condition and Subgingival Microbiota Characterization in Subjects with Down Syndrome
## 2 Materials and Methods
### Microbiological Processing by Means of Quantitative Polymerase Chain Reaction
#### 2.3.2 Polymerase Chain Reaction  
Multiplex quantitative PCR (qPCR) technology was used for detecting and quantifying the bacterial DNA [32]. The sequence of the primers and probes used for _Aggregatibacter actinomyectemcomitants_ (_A. actinomyectemcomitants_), _Porphyromonas gingivalis_ (_P. gingivalis_) and _Tannerella forysthia_ (_T. forysthia_), targeting against 165 rRNA gene, have been previously reported [33; 34]. PCR amplification was performed in a total reaction mixture volume of 10 \(\upmu\)L, which included 5 \(\upmu\)L of 2\(\times\)TaqMan master mixture (LC 480 Probes Master, Roche Diagnostic GmbH), optimal concentrations of primers and hydrolysis probe (300, 300 and 200 nM for _A. actinomyectemcomitants_; 300, 300 and 300 nM for _P. gingivalis_ and 300, 300 and 200 nM for _T. forysthia_) and 2.5 \(\upmu\)L of DNA from the samples. The no-template control (NTC) consisted of 2.5 \(\upmu\)L of sterile water. Samples were subjected to an initial amplification cycle of 95 \({}^{\circ}\)C for 10 min, followed by 40 cycles at 95 \({}^{\circ}\)C for 15 s and 60 \({}^{\circ}\)C for 1 min in LightCycler(r) 480 II thermocycler (Roche Diagnostic GmbH). Each DNA sample was analyzed in duplicate.  
All assays were performed using calibration curves with a linear quantitative detection range established by the slope range of 3.3-3.6 cycles/log decade, r2 > 0.997 and an efficiency range of 1.9-2.0. Quantification was based on standard curves, which were constructed by plotting cross point cycle (Cp) values generated from qPCR against DNA extracted from serial 10-fold dilutions of purified genomic DNA from each bacterium (log of colony forming units (CFU)/mL).
# The Performance Assessment of a Precast, Panel-Segmented Arch Bridge with Outriggers
## 3 Performance Assessment of the Proposed Arch Bridge System
### Three-Dimensional Structural Analysis Modeling
#### 3.2.1 Material Properties of Structural Analysis Model  
Three-dimensional models were created for the precast panels and backfill soils of the member elements of the proposed arch bridge using the eight-node solid element, and the shell element for the outriggers and V-strips [16]. The equation by [PERSON] was applied to determine the stress-strain relationship [17]. Additionally, the compressive strength (\(\mathrm{f_{cu}}\)) with respect to the strain \(\epsilon_{0}\) and ultimate strain \(\varepsilon_{\mathrm{cu}}\) were set as 0.002 and 0.003, respectively. The standard design compressive strength was 35 MPa and the tensile strength was 4.7 MPa (or 13.5%). The elastic modulus of the precast panel was 28,825.3 MPa; the linear elastic section was assumed to reach 30% of the compressive strength of 35 MPa. Table 2 presents the properties of the precast material. Figure 10 presents the stress-strain relationship of the precast panel with a compressive strength of 35 MPa. The outriggers and V-strips were made from steel with a material yield strength of 280 MPa and a maximum tensile strength of 450 MPa. Moreover, an elastic modulus of 205,000 MPa and unit mass of 7,850 kg/m\({}^{3}\) were applied. Figure 11 presents the stress-strain relationships of the outriggers and V-strips. Under the assumption that the backfill soil is generally used as sandy soil, a unit weight of 1.94 \(\mathrm{\upmu m^{3}}\) was used, as shown in Table 2. This was assumed to be an elastic plastic material according to Mohr-Coulomb theory. An internal friction angle of 30\({}^{\circ}\) was used; 0.33 was used as Poisson's ratio.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Precast Concrete** & **Outriggers and** & \multirow{2}{*}{**Backfill Soil and Fill**} \\  & **Panels** & **V-Strips** & \\ \hline Compressive strength (MPa) & 35 & - & - \\ Yield strength (MPa) & - & 280 & - \\ Tensile Strength (MPa) & 4.74 & 450 & - \\ Elastic modulus (MPa) & 28,825.3 & 205,000 & 200 \\ Unit mass (kg/m\({}^{3}\)) & 2,400 & 7,850 & 1,940 \\ Poisson’s ratio & 0.16 & 0.3 & 0.33 \\ Internal friction angle (\({}^{\circ}\)) & - & - & 30 \\ Coefficient of friction (with & 0.4 & - & 0.5 \\ precast concrete panels) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties of arch members and fill material.  
Figure 9: Analysis model of a precast, panel-segmented arch bridge with outriggers: (**a**) 3D structural analysis model; (**b**) precast panel and outrigger details; (**c**) Step 2 loading condition model; (**d**) Step 5 loading condition model.
# On the Utilization of Reversible Colour Transforms for Lossless 2-D Data Compression
## 4 Experimental Setup And Results
### Lossless Compression Of Colour Images  
The BBWCA was tested on sets of colour images to guess the efficiency of the RCTs. Systems comprised of BMF, FLIC, STUFF IT, FP8, GRALIC, and BMF were used as benchmark schemes for the evaluation purposes. Image compression benchmark website [48] features the afore-mentioned techniques serving as the core of the most recent compression schemes. The proposed scheme was gauged against the formerly crafted YUV RCT based BBWCA [15]. The Kodak test images [49] shown in Figure 4 were used for initial testing. All the pictures have a resolution of 768 \(\times\) 512 \(\times\) 3 pixels totaling 1152 KB each. Compression results for these images are listed in the tabular form in Table 2. The YA6 UA6 VA6 RCT based BBWCA compressed the archive of images to 4199 KB in opposition to the raw data set of 11,520 KB. Subsequently, the size of 4231 KB was achieved using GRALIC method. The YA6 UA6 VA6 RCT based BBWCA peaks the benchmark compression schemes as it compresses the well homogenized gray levels resulting from the RCT. Figure 4: Kodak Test Images. Proposed scheme tested over natural images acquired from Uncompressed Colour Image Database (UCID) of the University of Loughborough as shown in Figure 5. In this case, the proposed system employed the existing distinguished top performer YA6 UA6 VA6 RCT for BBWCA. PNG, FP8, BMF, FLIC, GRALIC and BBWCA (YUV) were utilized as benchmark schemes. Compression results are presented in Table 3. BBWCA surpasses the benchmark schemes by showing a significant reduction of the total archive of size 14,450 KB to a compressed size of 5519 KB with a Compression Ratio (CR) of 2.62. It is considered as a significant enhancement in the pre-existed RCT based BBWCA scheme equipped with the ability of reduction of the 7829 KB archive data with CR as 1.85. The proposed lossless compression technique was also extended to other 2-D data. Section 4.2 exhibits the compression capability of the proposed scheme to RASTER maps. Section 4.3 and 4.4 represent the lossless compression for the CFA and EEG images respectively.
## 2 Measurement Method and Experimental Setup Design
### Martian Air Sample Preparation
The first type is the Heimann vacuum sensor HVS Vac 04 (HEIMANN Sensor GmbH, Dresden, Germany), which is a miniature Pirani-type sensor based on a heated resistor structure on a thin micromachined membrane. The second type is a miniature absolute piezoresistive pressure transducer XT-140 (Kulite Semiconductor Products, INC, Leonia, NJ, USA) based on standard miniature silicon diaphragm. The XT-140 is used for measuring the pressure higher than 30 mbar, while the HVS Vac 04 is exploited for low pressure measurements. These sensors were calibrated at different temperatures using high precision laboratory pressure sensors: ACM200 (Atovac, Yongin-si, Gyeonggi-do, Cheoin-gu, Korea) and Inficon DI200 (INFICON AG, Balzers, Liechtenstein). There are also two types of sensors used for temperature measurements. The first one is a high precision semiconductor NTC MC65F103A temperature sensor (Amphenol Corporation, Wallingford, CT, USA). The second one is platinum temperature sensor 701-102 BAB-B00 (Honeywell International Inc., Charlotte, NC, USA). Thereby, one of each pressure sensors are installed inside the ICOS cell, as well as two MC65F103A and one 701-102 BAB-B00 temperature sensors are installed outside the ICOS cell on its surface to precisely measure current values of pressure and temperature. In addition, there are pressure and temperature sensors inside the M-DLS to be aware of the instrument state during operation. Thus, the Martian air preparation system allows us to form purified Martian air sample with the controlled values of its pressure and temperature, which is important for further spectroscopic measurements.
# Missing Data Imputation in the Internet of Things Sensor Networks
## 5 Results
### Sensor Calibration  
After missing values were imputed using different techniques, we investigated the effect of each imputation technique for the purpose of sensor calibration. Sensor calibration commonly require complete observations of the reference and sensor measurement. Therefore, the use of efficient imputation strategies is paramount.  
Different supervised machine learning techniques were employed to calibrate the sensor data (MLR, DT and RF). To assess the effect of the imputation techniques on the calibration process, we conducted our analysis using the results of the imputed data at 30% missing rate. Calibrating sensor data using the BFMVI data showed stronger results when contrasted with the original data. The results in Table 3 shows the calibration results of the different imputation methods on the \(C_{6}H_{6}\) sensor. Figure 4 also shows the response of some meteorological factors on the imputation techniques. On each calibration method, the result of the BFMVI imputed data showed more accurate results. The RI, missForest and \(k\)NN  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Missing Period** & **KNN** & **RI** & **EM** & **missForest** & **BFMVI** \\ \hline
1 Day & 0.0872 & 0.115932 & 0.526072 & 0.11624 & 0.001016 \\
1 Week & 0.225566 & 0.341941 & 1.185411 & 0.328895 & 0.002169 \\
1 Month & 0.770596 & 0.763162 & 2.785029 & 0.750043 & 0.005263 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of Imputation Techniques on \(C_{6}H_{6}\) Missing Values occurring over Consecutive Periods.  
Figure 3: Comparison of Imputation Techniques on \(C_{6}H_{6}\) Missing Values occurring over Consecutive Periods.  
methods also showed good performance with \(R^{2}\) score \(>0.9\) for all calibration processes. Calibrating the the sensor with the MLR model trained on the BFMVI imputed data showed the best performance with an RMSE score of 0.031. Overall, calibrating the sensor on the filtered CCA data showed promising results. However, there was a noticeable reduction in error from the calibration on the imputed data compared to the original CCA data.  
Figure 4: Scatter plot showing the effect of various imputation techniques on the sensor data color-coded with Temperature (T) and Relative Humidity (RH) meteorological factors: (**a**,**b**) Original data, (**c**,**d**) \(k\)NN Imputation, (**e**,**f**) Regression Imputation, (**g**,**h**) EM Imputation, (**i**,**j**) missForest Imputation and (**k**,**l**) BFMVI.
# Integrated Replay Spoofing-Aware Text-Independent Speaker Verification
## 4 Experiments and Results
### Results Analysis  
Table 3 describes the results of the proposed E2E framework with a monolithic approach. System #1 refers to the proposed architecture that jointly optimizes SID, PAD, and ISV loss (see Figure 1a). System #2-SE is the result of applying squeeze-excitation (SE) [26] based on its recent application to PAD [9]. System #3 describes the result of assigning three max feature map (MFM) blocks [18] for SID as well as for PAD after the first three MFM blocks. Because most of the system's performance measures deteriorated compared to the SV baseline, we concluded that the monolithic E2E approach was not ideal for the ISV task. While the results of the experiments were different from what we expected, they nevertheless served as a springboard for establishing a new hypothesis.  
Table 4 addresses the validation of our hypothesis in Section 3 that the discriminative information for the SV and the PAD task are distinct based on the results of Table 3. To validate our hypothesis, we trained our SV and PAD baselines with and without additional loss for extracting common embeddings. Here, the first and third rows refer to the SV and PAD baselines and the second and fourth rows refer to the usage of the MTL framework. The results demonstrated that, in both baselines, additionally adopting another loss function degraded performance.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**System** & **ZE-EER (SV)** & **PAD-EER** & **ISV-EER** \\ \hline \#1 & **18.52** & **15.73** & 18.44 \\ \#2-SE & 18.99 & 15.90 & **17.90** \\ \#3-split & 19.43 & 37.31 & 26.40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of various architectures using the proposed monolithic E2E framework for the ISV task. Numbers in bold represent the best results.  
Table 5 summarizes the results of performance improvement across various attempts to improve the performance of the proposed method in the back-end modular approach. The comparison of Systems #4 and #5 shows the effectiveness of using multiplication of the SV result and PAD prediction for the ISV task. System #6 refers to the result of setting weights to the SV task in the training phase where we set the \(\alpha\) to 20. System #7 shows the result of reducing the number of nodes per hidden layer.  
Finally, Table 6 compares our proposed modular approach with the SV baseline and existing work [12] using official trials. The results demonstrated that the proposed approach stabilizes unbalanced performance between ZE-EER and PAD-EER. Compared with the SV baseline, which does not consider PAD attacks, we achieved a relative improvement of 21.77%. It is important to note here that we were unable to compare the ISV-EER with that of [PERSON] et al. [12], although it is the only study that reported performance using official trials. Because it proposed a unified threshold for conducting SV and PAD tasks, ISV-EER results using the full trial do not exist.
###### Abstract  
Novel techniques such as mmWave transmission and massive MIMO have proven to present many attractive features able to support high data demand for 5G NR technologies. Towards the standardization of 5G networks, channel modeling has become an important step in order to test the reliability of theoretical studies. In this paper, we study the performance of a 5G network at mmWave range for the downlink. We consider a single trisectorized base station equipped with planar arrays, and we model users as a spatial Poisson process in a hexagonal grid. We adopt the latest 3 GPP channel model described in TR 38.901 and we provide a thorough description and step-by-step tutorial of it along with our customizations and MATLAB scripts for channel generation in the presented scenario. Moreover, we evaluate the performance of Multi-User Multi-Layer MIMO techniques, such as Signal-to-Leakage-plus-Noise Ratio (_SLNR_) precoding and MMSE combined with different system configurations by means of achievable per-user rate.  
5G; mmWave; Multi-User MIMO; channel modeling; precoding Article  
Performance Analysis of Multi-User MIMO Schemes under Realistic 3 GPP 3-D Channel Model for 5G mmWave Cellular Networks
## 5 Discussion
### Which NLP and ML Techniques Were Used to Analyze Textual Data Associated with the Legal Text?  
Tables 2-6 summarize the NLP techniques used for analyzing textual data in the legal domain.  
First, several efforts were proposed for Human Rights. All studies reviewed present a binary classifier. Some attempt to classify violation or non-violation of an article from the ECHR. The corpus was vectorized using strategies, such as TF-IDF, n-grams, Bag-of-Words, word embedding, and paragraph embeddings. SVM was the most popular used supervised learning algorithm. Other algorithms were KNN, NB, CART, and various Random Forest variants.  
For analyzing documents concerning constitutional law and ruling prediction of Supreme Courts, most studies address the problem of binary classification using classical ML algorithms, such as decision trees, RF, SVM, and XGBoost. Furthermore, recent DL techniques were used, such as CNN, LSTM, and their variants, such as BiLSTM, BI-GRU, Attention, and RoBERT. The authors used classical techniques for text vectorization, such as  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Nro.** & **Reference** & **Corpus Size** & **Lang.** & **Model** & **Repr.** & **Metric** & **Min(\%)** & **Max(\%)** & **Year** \\ \hline
1 & [33] & 584 & E & SVM & BoW, n-grams, & Accuracy & 62 & 79 & 2016 \\ \hline
2 & [36] & 584 & E & KNN, LR, Bagging, RF, SVM & Topics & Accuracy & 66.5 & 87.5 & 2017 \\ \hline
3 & [38] & 1942 & E & SVM & TF-IDF n-grams & Accuracy & 66 & 77 & 2018 \\ \hline
4 & [37] & 9703 & E & GB, RF, SGB, DT, and QDA & n-grams. word embeddings and doc2 vec & Accuracy & 68.83 & 68.83 & 2019 \\ \hline
5 & [39] & 684 & E & SVM, GBN, KNN, Ensembles & BoW + Spectral clustering & Accuracy & 69.6 & 86.1 & 2019 \\ \hline
6 & [40] & 18,420 & E & AdaBoost, Bagging, NB, DT, Ensemble Extra Tree, Extra Tree, GB, KNN, SVM, NN, and RF & TF-IDF & n-grams & Accuracy & 75.86 & 98.32 & 2020 \\ \hline
7 & [35] & 10 & E & Abstract Dialectical Framework & Arguments & Accuracy & - & 79 & 2020 \\ \hline
8 & [34] & 11,532 & E & SVM & TF-IDF & Accuracy & 77 & 79 & 2020 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of ruling decision prediction in Human Rights, where E = English.  
binary, TF, TF-IDF representation, embeddings, and fuzzy representation. Finally, accuracy and F-measure measures were used to measure the effectiveness of the classification task.  
Regarding Tax Law, Intellectual Property, and Administrative Law, algorithms such as NB, SVM, XGBboost, decision trees, logistic regression, BERT, Transformers, CNN, and LSTM, were used for the prediction task. Furthermore, some authors proposed modifications to the BERT algorithm, such as ooBERT, LEGAL-BERT, and ft-BERT. The TF-IDF vector representation of documents is used. In addition to these techniques, two adapted ones were used to improve results, such as normalized thesaurus and weighted TF-IDF. The F-measure is the most used evaluation measure in this category.
# A Cloud-Based Distributed Architecture to Accelerate Video Encoders
## 4 Experimental Setup and Results  
This section describes the experimental setup (testbed, measured metrics and experiments) used to validate the proposed architecture. Then a scalability and resource usage study is performed varying the number of worker encoder nodes. Finally, the quality of the encoded videos is obtained for different chunk sizes and bit rates.  
All the experiments have been performed applying the H.265/HEVC and VP9 codecs to a Sintel raw video with a resolution of 1080p and YUV 4:2:0 format [25]. This video has a duration of 236.30 s with 6144 frames at 25 fps and a file size of 19.1 GB. The libraries used for each codec have been x265 [23] for HEVC/H.265 and libvpx-vp9 [24] for VP9. The parameters used in the encoding process for each codec is shown in Table 1.  
The encoding has been carried out increasing the number of workers from 1 to 15 to analyze the scalability of the proposed architecture. On the server side, load metrics (CPU and memory utilization) and executed jobs metrics (e.g., number of executed jobs, job execution time, etc.) have been collected for each worker VM during the experiments. Video quality results are evaluated for different video chunk sizes, bit rates, and codecs, and the total codification time has been obtained for each combination.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Codec** & **Tool** & **Input** & **Resolution** & **Encoder** & **Preset** & **Threads** & **fps** & **Bit Rate (Mbps)** \\ \hline
**H.265** & ffmpeg & RAW video & 1080p & libx265 & slow & & 4 & 25 & \{0.75, 1.5, 3, 4.5\} \\
**VP9** & ffmpeg & RAW video & 1080p & libvpx-vp9 & -cpu-used 1 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in the encoding process.
# Zero-Day Threat Mitigation via Deep Learning in Cloud Environments
## 5 Results
### Performance Analysis  
The experimental results demonstrate that the MVT model achieves moderate performance overall. While it exhibits high recall in detecting malware (Class 1), it faces difficulties in accurately identifying benign files (Class 0), largely due to class imbalance within the dataset. This behavior is consistent with previous findings suggesting that transformer-based models tend to perform well on the majority classes while underperforming on minority class representations.  
A key insight is the model's tendency to favor malware detection. Although this bias was partially mitigated using SMOTE [25], it persists and highlights the need for more effective balancing techniques. Compared with traditional models such as Decision Trees and Convolutional Neural Networks, MVT achieved more balanced performance in scenarios involving unseen malware [14].  
The model's robustness was supported by statistical evaluations across multiple experimental configurations, including box plot comparisons. These experiments confirm its generalization capacity. However, the relatively lower performance in detecting benign samples reinforces the importance of advanced data augmentation or reweighting strategies for the minority class.  
In summary, the MVT model shows significant potential in detecting zero-day threats, especially malware, but further optimization is required for benign classification, computational efficiency, and explainability to support its practical deployment.
# GNSS Spoofing Detection Based on Wavelets and Machine Learning
## 3 Methodology
### Implementation Details and OAKBAT Datasets
The datasets of digitized radio signals OAKBAT were developed to investigate intermediate spoofing attacks in the GNSS area and are complementary to the TEXBAT datasets. Unlike the TEXBAT datasets, which are based only on GPS system signals, OAKBAT additionally contains Galileo signals, which was an additional reason for using this dataset in this research. OAKBAT consists of 8 datasets containing only GPS L1 C/A signals and 8 datasets containing only Galileo E1 signals. It was created using the commercial GNSS simulator Orolia Vulnerability Test System, which consists of two Orolia GSG-6 series multi-frequency and multi-constellation GNSS simulators. Simulators use the actual broadcast ephemeris data files from NASA Crustal Dynamic Data Information System for GPS and CNES Navigation and Time Monitoring Facility for Galileo. The system used for recording and playback of the recorded files was a software-defined radio Ettus X310. The parameters used to create the OAKBAT dataset were as follows: environment \"open sky\", date and time of start of all scenarios: 19 March 2020, 09:59:42 UTC, powers for GPS and Galileo satellites: 123.5 dBm and \(-\)125 dBm, sampling frequency 5 MHz, and the data format was int16. The duration of each.bin file is 8 min. For this research, OAKABAT datasets [36] for GPS and Galileo in static scenarios were used. Those datasets were GPS clean, os2, os3, and os4, which are equivalent to the Galileoclean, os10, os11, and os12 datasets. The difference between these spoofing datasets is in the power level advantage of fake signals in relation to the authentic ones, as shown in Table 1.