esa-sceva/satcom-chunk-collection huggingface dataset:
# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 5 Experimental Results
### Results of Different Implementation Methods  
The experimental settings of the three versions of PPL loss are: version 1 and 3 have the same network structure and settings, both based on TDNN and SP pooling structures; version 2 uses TDNN and LDE pooling and then calculates PPL loss in the pooling layer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Loss Function** & **EER (\%)** & **minDCF (0.01)** \\ \hline Softmax Loss & 3.932 & 0.4103 \\ Center Loss & 3.606 & 0.3647 \\ AM-Softmax Loss & 3.473 & **0.3469** \\ AAM-Softmax Loss & 3.754 & 0.3657 \\ Triplet Loss(end-to-end) & 3.876 & 0.3961 \\ CLLR Loss(end-to-end) & **3.282** & 0.3594 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of the TI-SV task using different loss functions on VoxCeleb.  
Figure 3: The detection error trade-off (DET) curve for VoxCeleb test results of the different loss functions.  
The baseline is the result of not adding the PPL loss. It can be seen from the results in Table 3 that adding PPL loss to the original system can integrate phonetic information into speaker verification systems, thereby improving system performance. Among the three versions of PPL loss implementation methods, version 1 achieves the best minDCF of 0.3234, and version 3 has the best EER of 3.244%. We guess the reason is that both version 1 and version 3 use the features before pooling to calculate PPL loss, and the features contain more speech information. Although version 2 can use phonetic information, it has a limited role because the PPL loss is calculated at the same time as pooling, and the frame-level aggregation and classification process affect each other. As the PPL loss of version 1 has the best performance on minDCF, we chose the PPL loss of version 1 for our subsequent experiments.
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 4 Flexible Antennas for a Sub-6 GHz 5G Band  
The transition to 5G is anticipated to make the best use of the sub-6 GHz band, necessitating a widespread deployment of devices with adequate network coverage. Devices that work on high data rates need an antenna to be wideband to integrate multiple applications. The increasing demand for smart devices needs an antenna to be compact and capable of mounting on any irregular surface. These antennas should also maintain a good performance concerning several parameters such as gain, impedance bandwidth, radiation efficiency, and radiation pattern. The transformation of the antenna from a non-flexible to flexible antenna needs the research of various antenna structures and flexible substrates. The flexible antenna for the recently released sub-6 GHz 5G band thus needs a detailed discussion.  
The following section gives an overview of flexible antennas for the mid-band 5G which includes the single element flexible antenna, the flexible array antenna, and the flexible MIMO antenna. A single element antenna is reviewed with a classification of slot/slit-based, DGS-based, metamaterial-based, DRA-based, and transparent flexible antennas. A flexible array antenna is reviewed on the classification as slot/slit based, metamaterial-based, and transparent flexible antennas. Flexible MIMO antennas are reviewed, by classifying them into slot/slit-based, DGS-based, metamaterial-based, DRA-based, and transparent flexible antennas. The overview of different flexible antennas for the sub-6 GHz 5G band is shown in Figure 6.
## 2 Multi-Beam Massive MIMO Configuration Using CMA in the Uplink  
### Multi-Beam Massive MIMO Configuration Using CMA  
Figure 1 shows the configuration of the uplink using analog multi-beam, and CMA for the digital part [14; 18]. This system only has been proposed configuration and control methods for uplink. In the proposed method, first, \(M\) orthogonal multiple beams are prepared in analog part. It is well known that the Butler matrix [19] and lens antenna realize a multi-beam forming network [20; 21]. A trial circuit example using a Butler matrix circuit is presented in [22].  
Second, the received powers for all users are measured at the outputs of multiple beams. Using several beams, selected according to the powers of all analog multi-beams, the U MU-MIMO transmission without CSI estimation for each user is realized by the proposed system. However, when the AoAs are close among users, signals from multiple users are received in the same beam. Moreover, the signals from users other than the intended user are actually received at the sidelobes and the interference cannot be rejected by the multi-beam forming network alone. In order to realize perfect interference rejection, DBF based blind adaptive arrays are introduced at the outputs of the selected multi-beams.  
The proposed configuration can be regarded as a fast Fourier transform (FFT)-based CMA adaptive array [23]. The configuration in [23] is the DBF configuration, and multi-beam forming network is used in the digital parts. On the other hand, the main feature of proposed configuration is that multi-beams are realized in the analog part. Therefore, the number of frequency converters and analog-to-digital/digital-to-analog converters can be reduced, unlike in the DBF configuration. Moreover, the transmission timings of the UTs need not be synchronized.
# High Resolution STEM Images of the Human Tooth Enamel Crystals
## 1 Introduction  
### Human Tooth Structure  
The human tooth is made up of dentin, which is a connective tissue that gives shape and stiffness. In the crown, dentin is covered by enamel, the most wear-resistant tissue in the human body. Enamel is responsible for protecting teeth from wear and tear caused by chewing as well as corrosion from acids produced from food debris. The dentin-enamel junction zone is where dentin meets enamel.  
Dentin is made up of a 70% inorganic material, a 20% organic material, and 10% water. Enamel is composed of a 90% inorganic material, a 5% organic material, and 3% water [1]. The inorganic component is calcium phosphate named hydroxyapatite (HAP, Ca\({}_{10}\)(PO\({}_{4}\))\({}_{6}\)(OH)\({}_{2}\)). The organic part is collagen. The EDS analyses of enamel and dentin indicate the existence of substitute elements, such as Mg, Na, and Cl, and residual organic elements, such as C and N, in addition to the HAP elements Ca, P, and O [2,3].  
At the micrometric level, enamel is made up of elongated structures arranged in rows with sinuous trajectories from the dentin-enamel junction to the enamel surface named rods or prisms. This prismatic structure has a keyhole-type shape. At the nanometer level, the enamel prisms are made up of ribbon-shaped crystals which are approximately 70 to 170 nm long, aligned with the length of the rods. When cross-sectioned, the crystallites appear as semi-polygons with a 20-to-50 nm thickness range [1]. In the center of the enamel crystals, small concentrations of Mg\({}^{2+}\) and Na\({}^{+}\) have been reported [3], which indicates modifications in the elements of the HAP unit cell at this region. Understanding thestructure and chemistry of enamel at the nanoscale is essential for elucidating the enamel dissolution process and for developing new dental treatments.
###### Abstract  
This paper presents a stochastic inference problem suited to a classification approach in a time-varying observation model with continuous-valued unknown parameterization. The utilization of an artificial neural network (ANN)-based classifier is considered, and the concept of a training process via the backpropagation algorithm is used. The main objective is the minimization of resources required for the training of the classifier in the parametric observation model. To reach this, it is proposed that the weights of the ANN classifier vary continuously with the change of the observation model parameters. This behavior is then used in an update-based backpropagation algorithm. This proposed idea is demonstrated on several procedures, which re-use previously trained weights as prior information when updating the classifier after a channel phase change. This approach successfully saves resources needed for re-training the ANN. The new approach is verified via a simulation on an example communication system with the two-way relay slowly fading channel.  
wireless physical-layer network coding; machine learning; artificial neural networks; backpropagation; two-way relay channel; decision regions +
Footnote †: journal: International Journal of Information Technology
# Application of Iron-Modified Activated Carbon for Phosphate Removal in Aqueous Systems
## 3 Results and Discussion  
### Soil Sample Acquisition  
The soil used in the study was collected at Sediment Measurement Network Site 1, located near Chudong Intake Tower within the Chudong Water area, Daecheong Lake, as marked in Figure 1a. To analyze the particle size distribution of the soil used in the experiment, the collected soil samples were washed and subjected to a sieve test. The results are presented in (b). Based on the analysis, \(D_{10}\) (the effective diameter) was 0.150 mm and \(D_{60}\) was 0.250 mm, yielding a uniformity coefficient (\(D_{60}/D_{10}\)) of approximately 1.67. This can be considered relatively uniform in particle size distribution. The Chudong water area experiences consistent foot traffic and hosts various commercial and recreational facilities. The waterfront includes walking trails and an array of trees, contributing to the influx of organic materials, such as leaves and branches, as well as other pollutants. Juwon Stream, which flows into the Chudong water area, has a relatively small watershed, and features a topography that widens significantly near the inflow zone. Due to these characteristics, the Chudong water area is less influenced by fluctuations in inflow volume and water quality caused by rainfall compared to other regions of Daecheong Lake [34]. The sediment in the Chudong water area consists of clay, silt, and sand particles, with its soil texture classified as silt, clay. Particles such as clay and fine silt, which have small sizes and high specific surface areas, are known to effectively adsorb nitrogen and phosphorus [35]. These soil characteristics can lead to internal loading.  
Around the Chudong Intake Tower, the total phosphorus (TP) concentration in the sediment varies between 1000 mg/kg, annually. This level exceeds the sediment dredging threshold of 800 mg/kg set for the Paldang Dam and the 1000 mg/kg criterion for sediment in the Han River [35]. Meanwhile, the concentration of total nitrogen (TN) in the water remains consistent throughout the year; however, the concentration of TP increases between August and October each year, reaching as high as 0.05 mg/L. As a result, harmful algae thrive from September to October, with densities recorded between 2000-5000 cells/mL.
## 2 Methodology for Measuring the Rate of Corrosion Reinforcement in Cores Drilled from The Structure
### Measurements of Reinforcement Corrosion Using the Method of Linear Polarization Resistance (LPR)  
The three-electrode system was arranged on the drilled cylindrical cores as specified in the procedure described in Section 2.2 to take polarization measurements of corrosion rate of reinforcement. A fragment or fragments of steel rebars were used as the working electrode, the auxiliary electrode was the conductive coating, and the reference electrode was the graphite rod. All three electrodes were connected to the potentiostat Gamry Reference 600 (cf. Figure 2).  
Following the testing methodology presented in Section 2.3, which includes the use of laboratory-simulated effects of temperature and relative humidity of the tested structure, two pairs of boundary parameters \(T\) and \(RH\) were determined. It was determined that temperature \(T\) = 30 \({}^{\circ}\)C and relative humidity \(RH\) = 99% increased the rate of corrosion in reinforcing steel in concrete without the direct contact with water in the tank. On the other hand, average water temperature \(T\) = 7 \({}^{\circ}\)C and full immersion of cores in water collected from the tested tanks were regarded as characteristic parameters for concrete zone with the permanent contact with water. Results from the LPR method in the form of four sets of polarization curves are illustrated in Figure 6. Two sets of polarization curves presented in one row (Figure 6a,b) refer to the tank T1, while the curves shown in the second row (Figure 6c,d) refer to the tank T2. For clear comparison diagrams in the left column (Figure 6a,c) were obtained from the reinforcement of cylindrical cores immersed in water at temperature \(T\) = 7 \({}^{\circ}\)C, while those in the right column (Figure 6b,d) were obtained at the climate chamber with the determined air temperature \(T\) = 30 \({}^{\circ}\)C and relative humidity \(RH\) = 99%.  
Figure 5: Photos from the inside of tested tanks for fresh water: (**a**) a fragment of the internal wall and floor slab of the tank: 1—smudge of water in the tank, 2—cylindrical concrete cores drilled below and above the line 1 that indicates water level in the tank; and (**b**) perimeter and internal walls, columns and beam from the reinforced concrete frame that supports the floor slab of the tank.
# Innovative Approach Integrating Machine Learning Models for Coiled Tubing Fatigue Modeling
## 7 Correlation Analysis
### Influence of Dominant Features on Fatigue Behavior
#### 7.3.3 Wall Thickness: Stress Distribution Impact  
Moderate influence was observed for wall thickness, indicating that while thicker tubing can better distribute stress, other factors (e.g., bending radius) play a more dominant role. Thicker walls reduce stress intensity factors (SIFs), which slow crack propagation. However, when tubing undergoes repeated bending cycles, the overall strain distribution may still be dictated by geometric constraints rather than just thickness. ML models recognize wall thickness as a secondary factor, aligning with structural fatigue theories.
# A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network
## 4 Simulation Results and Discussion  
In this section, simulation environment and datasets, experiments nature, performance parametric and results are discussed in detail. The discussion is based on connectivity feature and its impact on our work. Here, prospect and usefulness is inspected in various simulation environments.
## 4 Experiments  
In order to validate and characterise the DAQ with embedded processing, we set up a WMS system and carried out measurements on it. For the WMS system, we used verified gas absorption data from the HITRAN database [22]. Water vapour (i.e., steam) at 1391 nm absorption line was used. The gas phase chamber is assumed to contain a mixture of water vapour and air, with the experiment aimed at accurately determining the concentration (i.e., the mole fraction) of the water vapour, assuming a constant temperature and pressure of 298 K and 1 atm, respectively, in the enclosure. The spectral absorption profile at these parameters is shown in Figure 4. The laser scan rate was set to 125 Hz, and the modulation frequency, \(f_{m}\), was 125 kHz. For WMS, to maximise the second harmonic signal, the modulation amplitude should be approximately 2.2 times the half width at half maximum (HWHM) of the absorption feature [23]. For our experiment, the modulation amplitude chosen was set as 0.006 cm\({}^{-1}\), consistent with previous work [17]. The path length of the modulated laser output was set as 30 cm. Using the absorption data from HITRAN, transmitted signals were generated for six known relative concentrations of water vapour. An absorption-free signal is also generated at same conditions, with no water vapour present.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Resource** & **Utilisation** \\ \hline LUT & 1258 \\ Flip Flops & 998 \\ Block RAM & 2 \\ DSP & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Resource overhead of real-time demodulation firmware.  
Figure 3: RTL schematic of implemented real-time demodulation.  
To account for noise from the photo detector (i.e., thermal noise) and signal amplification circuits, additive white Gaussian noise (AWGN) was added to the signals. The SNR of 56 dB was used based on estimate from previous work [17], in which an in-depth analysis of the noise components in a typical DAQ system for WMS is presented. We refer readers interested in the full analysis of the various noise sources to [17]. Noise from signal digitalisation circuits was excluded from the generated transmitted signal, as this will be incurred in our DAQ. Figure 5 show the signal generated.  
The signals (with and without absorption) were digitised and loaded into an arbitrary waveform generator, the output of which is directly connected to our DAQ system. The DAQ system digitises the signals and extracts the harmonics of interest (i.e., \(S_{1,Z_{\text{/}}n}\), \(S_{Q,Z_{\text{/}}n}\), and \(A_{1/f_{\text{m}}}\)), and as explained in Section 3.2, these were then transmitted in real time to a high-level processor, where post-processing was implemented to extract \(S_{2f/1f}\) and gas concentration for each data frame. Figure 6 shows the hardware set up for the experiments.  
Figure 4: Spectral absorption profile of water around 1391.  
Figure 5: Transmitted signal with 56 dB additive white Gaussian noise.
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 3 Locking Detection, Assessment, and Resolution  
The proposed ideas of locking detection, assessment, and resolution are based on application of three existing numerical techniques. The first one is the equilibrated residual method of error estimation [43; 44], here applied to solution of the local problems of two types. The second idea is sensitivity analysis based on comparison of the solutions to two local problems which differ with the longitudinal orders of approximation. The results of such a comparison can be used for the locking detection or assessment. If the locking is detected, then the mesh can be modified through the increase of the longitudinal order of approximation, by means of the standard \(p\)-enrichment technique [41; 42].  
The mentioned three techniques can be combined with the three-step \(hp\)-adaptive procedure controlled by the approximation error [51; 52] or the automatic, iterative \(hp\)-adaptation driven by the interpolation error [41; 42; 53].
## 4 Experimental Results
### Discussion about the Hyper-Parameters of the Proposed Semi-Supervised Learning
#### 4.5.3 Different Training Epochs in Self-Supervised Stage  
The effect of different training epochs for self-supervised learning was explored. In this experiment, the training epochs for self-supervised learning were set to 50, 100, and 200. The other experimental setups were the same as in the first experiment.  
The results are reported in Table 7. The results show that the accuracy increases with the increase of training epochs when fine-tuning with a very small number of labeled samples (\(\leq\)2). However, 100 epochs self-supervised training show the best accuracy when the number of labeled samples is larger than 2. Usually, longer self-supervised training allows the encoder to learn more distinguishable visual representations. However, this phenomenon suggests that self-supervised learning may be sensitive to the setting of some hyper-parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Number of Labeled Data per Class for Fine-Tuning**} \\ \cline{2-10}
**Batch Size in Self-Supervised Stage** & **1** & **2** & **4** & **8** & **16** & **32** & **64** & **128** \\ \hline
16 & 64.08 & 74.76 & 83.90 & 92.25 & 95.26 & 96.82 & 98.12 & 98.63 \\
32 & **69.81** & **76.04** & **87.74** & **93.95** & **97.15** & **97.56** & **98.03** & **98.84** \\
64 & 31.40 & 48.95 & 59.18 & 75.28 & 84.63 & 92.28 & 94.69 & 97.14 \\
128 & 25.26 & 44.21 & 45.28 & 70.67 & 72.79 & 83.88 & 90.46 & 95.60 \\
256 & 53.67 & 68.99 & 83.33 & 92.81 & 95.40 & 96.83 & 97.94 & 98.56 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The test accuracy (%) of the MsvNetLite with different batch sizes in the self-supervised stage. The bold number is the best result in each column. The bold number is the best result in each column.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Number of Unlabeled Data per Class**}} & \multicolumn{6}{c}{**Number of Labeled Data per Class for Fine-Tuning**} \\ \cline{2-10}
**in Self-Supervised Stage** & **1** & **2** & **4** & **8** & **16** & **32** & **64** & **128** \\ \hline
3600 & 51.78 & 64.22 & 77.60 & 86.17 & 92.12 & 94.92 & 97.04 & 98.28 \\
6000 & **69.81** & **76.04** & **87.74** & **93.95** & **97.15** & **97.56** & **98.03** & **98.84** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The test accuracy (%) of the MsvNetLite with different numbers of unlabeled data per class in the self-supervised stage. The bold number is the best result in each column.
## 5 Discussion  
Although the proposed network demonstrates advanced performance on publicly available datasets, its results merit deeper discussion in comparison with existing approaches. A primary constraint of this study is the relatively small dataset size, which may limit the model's ability to generalize effectively. As shown in Table 4, the diagnostic performance under operating conditions C and D exhibits some degradation. However, compared to well-established methods such as GoogleNet and ResNet, our approach achieves consistently higher diagnostic accuracy across most operating conditions. This significant improvement is primarily attributed to the integration of two-dimensional transformation and multi-path information fusion. These innovations enable the model to capture fault-specific patterns more effectively, outperforming traditional one-dimensional approaches, which often struggle with complex or overlapping features. Moreover, while existing studies have shown limitations in handling fault diagnosis under varying operational conditions, our method demonstrates superior adaptability within the tested datasets. For example, models like ResNet tend to lose sensitivity when fault patterns become subtle or exhibit high variability. In contrast, our approach maintains its robustness by leveraging enriched feature representations through the fusion of spatial and frequency-domain information.
# Sentiment Analysis of Students' Feedback on E-Learning Using a Hybrid Fuzzy Model
## 3 Methodology
### Data Preprocessing  
The collected tweets, similar to any other data obtained from online platforms, were in an unstructured format. Moreover, due to the tweets being short and having a limited number of characters, users usually use an informal writing style, including special characters, emojis, numbers, and links. Consequently, Twitter's raw data contain a lot of noise and need to undergo a preprocessing phase before any analysis task [40]. Furthermore, the tweets were written in the Arabic language, which is vibrant and morphologically rich. Given that the collected tweets were Saudi tweets, this means they contain a huge variety of written dialects.  
Based on related works and practical considerations, the preprocessing phase of the texts written in the Arabic language should include several essential processing steps: data cleaning, normalization, and stemming [12; 41]. They are effective steps that have a positive effect on the overall model's performance. We applied these steps to our dataset, and they are explained below.