esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Recently, the performance of end-to-end speech recognition has been further improved based on the proposed Conformer framework, which has also been widely used in the field of speech recognition. However, the Conformer model is mostly applied to very widespread languages, such as Chinese and English, and rarely applied to speech recognition of Central and West Asian agglutinative languages. There are more network parameters in the Conformer end-to-end speech recognition model, so the structure of the model is complex, and it consumes more resources. At the same time, we found that there is a long-tail problem in Kazakh, i.e., the distribution of high-frequency words and low-frequency words is not uniform, which makes the recognition accuracy of the model low. For these reasons, we made the following improvements to the Conformer baseline model. First, we constructed a low-rank multi-head self-attention encoder and decoder using low-rank approximation decomposition to reduce the number of parameters of the multi-head self-attention module and model's storage space. Second, to alleviate the long-tail problem in Kazakh, the original softmax function was replaced by a balanced softmax function in the Conformer model; Third, we use connectionist temporal classification (CTC) as an auxiliary task to speed up the model training and build a multi-task lightweight but efficient Conformer speech recognition model with hybrid CTC/Attention. To evaluate the effectiveness of the proposed model, we conduct experiments on the open-source Kazakh language dataset, during which no external language model is used, and the number of parameters is relatively compressed by 7.4% and the storage space is relatively reduced by 13.5 MB, while the training speed and word error rate remain basically unchanged.  
efficient Conformer; Kazakh; low-rank decomposition; multi-head self-attention; balanced softmax +
Footnote †: journal: Medical Informatics  
2023
## 2 Ultrasonic Diffraction Theory Background
### Image Generation  
As mentioned in Section 1, when scanner device with the probes is moved along the direction to be detected, an ultrasonic diffraction image (B-scan) can be synthesized byall A-scans in the scanning path. In the image, every pixel is the sample amplitude in the A-scan and a column of pixels corresponds to an A-scan, as shown in Figure 3. Low amplitudes are shown as black pixels, high amplitudes as white pixels, and zero amplitude as gray pixels [20]. The A-scan at each probe position always contains a surface wave and a backwall wave. The thickness of the specimen and PCS are constant, which makes the arrival time of these two waves in each A-scan the same. Thus, two gray horizontal bands appear at the top and bottom in the image. They, respectively, represent the surface wave and backwall wave.  
The presence of the defect must lie in the region between these two bands, according to ultrasonic time of flight. The defect generally appears as a flat parabola in this region (see Figure 3) due to the large beam range of the ultrasonic diffraction probe. In the scanning process, when the probes are close to but do not begin to overlap with the defect, part of the ultrasound wave with large diffusion angle has actually encountered the defect and the corresponding diffracted wave is received by the receiver, which will cause arc-shaped features at both ends of the defect in the ultrasonic diffraction image. Therefore, the length of the defect cannot be measured simply by the presence of a region with diffraction signals. Obviously, this amplifies the length of the defect to a great extent. Considering this, a standard measurement method is adopted. It manually fits two small parabolas with the two cambered ends of the defect, and the resulting distance (\(\overline{\text{AB}}\) in Figure 3) between the vertex of the two parabolas is the length of the defect.  
However, the measurement results depend on and vary with the experience of the inspector due to the subjective nature of this operation. The different results are obtained even by the same inspector at different times, as in the manually discriminated area in Figure 3, where a defect with an actual length of 30 mm may have a diffracted wave area radiating up to 60 mm. In the region encompassed by diffracted waves, the reliability of manual determination is limited, and the application of machine learning can enhance the consistency of determination outcomes through the comprehensive extraction and recognition of defective signals. In addition, the noise caused by the material grains and the measurement system may make cambered ends of the defect invisible. As shown in Figure 3, they can hardly be found. Therefore, the above method cannot be executed effectively [21].
## 1 Introduction
The DFNN proved to be an effective tool for accurately predicting porosity, and its success in modeling reservoir properties of the North Adriatic gas reservoir was demonstrated. Moreover, to have an objective comparison of the results, we modeled porosity using the same NN but only with input from two wells. Different methods of implementation of described seismic reservoir characterization results and their integration with the well data in the geological model were tested to find the most appropriate one. The best results in terms of all relevant reservoir properties modeling were achieved by introducing lithology classification and distribution, controlled by the well data and seismic results. After the lithology model was set up satisfactorily, it was utilized to better constrain the porosity distribution based on the well data and DFNN porosity volume as main inputs. The accuracy of porosity evaluation in terms of the accurate range of values but also the spatial distribution through the reservoir volume is crucial for reserves estimation, as it directly and indirectly affects the calculation of the volume of hydrocarbons in place and hydrocarbon resources. The direct impact is visible in reservoir pore volume calculation, but it should be considered that porosity value is also related to and, to some extent, affects fluid saturation and reservoir permeability. The approach presented in this paper has the potential to be applied to other subsurface exploration projects, providing valuable insights into the properties of hydrocarbon reservoirs.
# Corrosion Monitoring Techniques in Subcritical and Supercritical Water Environments
## 3 Invasive Corrosion Monitoring Techniques
### Electrical Resistance Probe  
The electrical resistance probe technique obtains corrosion loss and corrosion rate data by measuring the resistance change of metal specimens. The corrosion products of metals, such as metal oxides, are mostly non-conductive. If current is applied to a metal sheet, as corrosion progresses, the thickness of the metal sheet will decrease, increasing electrical resistance. The corrosion rate can be obtained after extending the information on resistance variation over time to the relationship between material thickness and corrosion time. This type of resistance probe is also known as an electronic mount [86,87].  
As shown in Figure 7, during the monitoring process, one side of the metal specimen is exposed to the environment t, and a current goes through the metal specimen. The electrical resistance of the metal specimen can be calculated as follows:  
\[R=\rho\frac{l}{A} \tag{1}\]  
In the formula, \(\rho\) represents the conductivity of the metal test piece, \(A\) is the cross-sectional area of the metal piece, and \(l\) stands for the length.  
[PERSON] et al. [88,89] used an electrical resistance probe to study the corrosion of pipeline steels, as depicted in Figure 7. As corrosion progresses, by monitoring the change in resistance value, the change in corrosion thickness can be derived based on Formula (1), thereby obtaining information on corrosion loss and corrosion rate. The electrical resistance probe technique is available for measurement in liquid solutions (electrolyte or nonelectrolyte), gas environments, and other media. It has the characteristics of relatively simple operation, easy maintenance, and easy data analysis. Moreover, it can achieve real-time corrosion data collection and transportation. Compared with other monitoring methods, it is highly reliable and has a better economy, making it widely used in industries such as oil and gas production, pipelines, the chemical industry, cultural relic preservation, and power generation.
## 5 Experimental Results
### Generalization Abilities Comparisons of Different Methods  
In order to evaluate the generalization ability of our model, Table 5 recorded the average evaluation scores of Attention-Wave-U-Net [14], SASEGAN [7], and SECS-U-Net (\(L_{alpha}\)) when removing unseen noise. The three models are all trained by the Valentini training set [30], and tested under the 560-Testset, in which noise types are different from in DEMAND [32]. According to the scores of each metric in Table 5, the three models can achieve good denoising performance when dealing with unseen noise. Attention-Wave-U-Net has better SSNR scores, but other indicators are inferior to SECS-U-NET (\(L_{alpha}\)), indicating that our model achieves good overall performance when dealing with unseen noise.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{2}{c}{**Metric**} & \multirow{2}{*}{**PESQ**} & \multirow{2}{*}{**CSIG**} & \multirow{2}{*}{**CBAK**} & \multirow{2}{*}{**COVL**} & \multirow{2}{*}{**STOI**} & \multirow{2}{*}{**SSNR**} \\ \hline
**Method** & & & & & & \\ \hline \multirow{8}{*}{Noisy} & \(-5\) dB & 1.10 & 2.01 & 1.42 & 1.48 & 0.805 & \(-5.91\) \\  & 0 dB & 1.22 & 2.36 & 1.68 & 1.73 & 0.852 & \(-3.64\) \\  & 5 dB & 1.44 & 2.79 & 2.02 & 2.08 & 0.903 & \(-0.87\) \\  & 10 dB & 1.82 & 3.30 & 2.46 & 2.54 & 0.939 & 2.45 \\  & Average & 1.40 & 2.62 & 1.90 & 1.96 & 0.875 & \(-1.99\) \\ \hline \multirow{8}{*}{Attention-Wave-U-Net} & \(-5\) dB & 1.47 & 2.69 & 2.47 & 2.05 & 0.861 & **6.02** \\  & 0 dB & 1.76 & 3.07 & 2.71 & 2.39 & 0.885 & **7.30** \\ \cline{1-1}  & 5 dB & 2.03 & 3.39 & 2.95 & 2.69 & 0.917 & **8.73** \\ \cline{1-1}  & 10 dB & 2.37 & 3.78 & 3.23 & 3.07 & 0.943 & **10.09** \\ \cline{1-1}  & Average & 1.91 & 3.23 & 2.84 & 2.55 & 0.902 & **8.04** \\ \hline \multirow{8}{*}{SASEGAN} & \(-5\) dB & 1.32 & 2.36 & 2.23 & 1.79 & 0.829 & 4.00 \\  & 0 dB & 1.52 & 2.60 & 2.46 & 2.02 & 0.868 & 5.77 \\ \cline{1-1}  & 5 dB & 1.85 & 3.00 & 2.77 & 2.40 & 0.910 & 7.65 \\ \cline{1-1}  & 10 dB & 2.23 & 3.40 & 3.07 & 2.80 & 0.940 & 9.07 \\ \cline{1-1}  & Average & 1.73 & 2.84 & 2.63 & 2.25 & 0.887 & 6.62 \\ \hline \multirow{8}{*}{SECS-U-Net \(L_{alpha}\)} & \(-5\) dB & 1.61 & 2.88 & 2.49 & 2.23 & 0.862 & 4.61 \\ \cline{1-1}  & 0 dB & 1.92 & 3.17 & 2.76 & 2.54 & 0.885 & 6.22 \\ \cline{1-1}  & 5 dB & 2.22 & 3.54 & 3.03 & 2.88 & 0.923 & 8.01 \\ \cline{1-1}  & 10 dB & 2.61 & 3.99 & 3.36 & 3.31 & 0.950 & 9.88 \\ \cline{1-1}  & Average & **2.09** & **3.40** & **2.91** & **2.74** & **0.905** & **7.18** \\ \hline \end{tabular}
\end{table}
Table 5: Objective evaluation results of different models under unseen noise in open dataset (the bold indicates our metric scores and the metric scores that exceed ours in the reference models).  
Figure 5: Comparison of parameters of SECS-U-Net and other reference methods.  
Figure 6 shows the spectrograms of enhanced speech generated from the above three models when the noise is outside the training set and SNR is -5 dB. The results show that all of them can achieve a good denoising. However, the proposed model loses less speech information and retains more speech details.
## 1 Introduction
### Paper Contributions  
This paper focuses on the above-mentioned issue. In particular, an EMS for ensuring that transient frequency deviations do not exceed a defined limit is developed. Accordingly, the main contribution of this paper are:  
* The parameters that, being available by the EMS, may influence the frequency deviations are analyzed.
* The the maximum frequency deviation in front of the maximum power imbalance is formulated. This formulation uses the above-mentioned parameters.
* An EMS including a frequency constraint is formulated.
* The validation of the proposed EMS using dynamic simulation and laboratory platform is presented.  
Particularly, this paper proposes a power dispatch optimization algorithm for PV-battery-diesel-based microgrids including demand and PV forecasting. To deal with uncertainty, the problem is based on stochastic optimization and computed on-line, in a similar way to the rolling horizon technique. The algorithm, which maximizes the PV generation, considers a frequency variation constraint obtainedby analyzing multiple off-line dynamic simulations and performing a statistical study. The result shows that the minimum system frequency depends on the number of connected diesel generators, the battery power generation/consumption and the PV power generation. The algorithm was tested using simulation software (MATLAB-SIMULINK for simulation; and GAMS for solving the MILP optimization problem, using the SCIP solver) and validated in a laboratory platform. Particularly, three different days (based on real second-by-second data) were simulated. Then, one of the simulated days was tested in a laboratory-scaled microgrid platform.
# Text-Guided Refinement for Referring Image Segmentation
## 3 Method
### Language-Embedded Visual Encoder  
To integrate the multi-modal information effectively and enable the preliminary alignment between the referring expression and image content, we designed a language-embedded visual encoder that injects textual context into early visual features. This allows the model to be guided by language before decoding begins. Given a referring expression as \(S=\{s_{1},s_{2},\ldots,s_{T}\}\), each word \(s_{t}\) is first embedded into a 300-dimensional vector using GloVe embeddings [41], following [34]. These are passed through a LSTM, and the final hidden state \(h_{T}\) is used as the global text representation. To condition the visual encoder on this language representation, we enhance the visual feature map \(V_{i}\) extracted from the backbone network by embedding linguistic relevance. For each spatial location \((x,y)\), we compute the similarity between the visual feature \(V_{i}(x,y)\) and the text embedding \(h_{T}\). The attention weight is defined as follows:  
\[A(x,y)=\text{softmax}(\langle V_{i}(x,y),h_{T}\rangle), \tag{8}\]  
where \(\langle\cdot,\cdot\rangle\) denotes the dot product, and softmax ensures spatial normalization. The text feature is then modulated by the attention weights to form spatially aware text embedding:  
\[T^{\prime}(x,y)=A(x,y)\cdot h_{T}. \tag{9}\]  
Subsequently, we concatenate \(T^{\prime}(x,y)\) with \(V_{i}(x,y)\) to form the final language-embedded feature:  
\[V_{i}^{*}(x,y)=\text{Concat}(V_{i}(x,y),T^{\prime}(x,y)). \tag{10}\]  
Finally, a \(1\times 1\) convolution is applied to \(V_{i}^{*}\) to reduce the channel dimension, ensuring compatibility with downstream modules. This process ensures that the resulting visual features are contextually modulated by the referring expression, producing language-aware feature maps that guide the model's attention toward semantically relevant regions. As a result, it benefits subsequent multi-modal fusion, particularly in complex scenes.
## 1 Introduction  
Ferroelectrets (FE) are polymeric foam films with elongated pores in the film plane that can be poled and present a piezoelectric response [1; 2]. They have been widely researched and used. One of the most interesting uses of FE films is for air-coupled ultrasonic transducers, where flat and focused designs have been used in Nondestructive Tests NDT [3; 4; 5]. The very low impedance of this type of materials permits the construction of transducers without matching layers, though the response is quite resonant and, hence, the bandwidth reduced, alternatively they have also been used as active matching layers [6].  
Flat transducers were first tried for water immersion in [3; 7] where they demonstrated a poor sensitivity but a very wide band response. Later, in [8], the use of matching layers was tried to increase both sensitivity and water resistance with limited succeed. In spite of the poor sensitivity, the large bandwidth together with other advantages, like the flexibility and the possibility to miniaturize the transducers, motivated further study of this application. In particular, the use of different FE materials, different protective coatings and focused designs to improve resolution.  
In this work, we present the pulse-echo response in water immersion of transducers based on two different FE materials with the purpose of providing some insight into the path to improve FE material properties for water coupled ultrasonic transducers. Flat and spherically focused prototypes were fabricated and tested and the use of different thin polymeric coatings intended to protect the transducer surface without having any negative impact on either the sensitivity or the frequency bandwidth were tried. Transducer response in pulse-echo operation mode (both time domain and sensitivity in the frequency domain) was measured as well as the acoustic field distribution.
# Potential of Plant Stem Cells as Helpful Agents for Skin Disorders--A Narrative Review
## 5 Promising Agent for Skin Aging Treatment: Extracellular Vesicles  
Extracellular vesicles (EVs) have received a significant amount of attention, emerging as potential agents for the treatment of skin aging [97; 98; 99; 100] and facilitators of intercellular communication through biomolecules. Despite restricted understanding, plant-derived EVs show promise in skin rejuvenation because they exhibit favorable biological characteristics for treating age-related skin conditions [102]. It is postulated that plant stem cells release exosome-like structures when cultured in vitro and that these vesicles may harbor anti-inflammatory and potentially regenerative functions. Recent findings indicate that exosome-like structures from plant stem cells might stimulate skin fibroblast proliferation, collagen production, and in vitro wound healing. Furthermore, they can reduce pigmentation of the melanocytes and decrease anti-inflammatory function in the macrophages [103]. EVs are small lipid bilayer nanoparticles released into the extracellular space by various cell types. They may be classified into three types based on size and biogenesis: exosomes (50-200 nm), microcviseles (100-1000 nm), and apoptotic bodies (500-5000 nm) [104; 105; 106]. Interestingly, several sources reported that callus-derived EVs seemed promising as far as skin aging therapies were concerned [62]. The mechanism of anti-senescence action of callus-derived EVs involves inhibition of ROS production, reduction in melanin production, and increased expression of essential skin proteins (filaggrin--FLG, aquaporin 3--AQP3, collagen type 1--COL1) in fibroblasts [62]. Further characterization and functional studies of exosomes derived from plant stem cells are necessary.
## 2 Site Conditions  
The port discussed in the study is located in Conakry, Guinea. The port is the main port where 90% of Guinea's foreign trade is carried out and has strategic importance as it is on the city's main sea border. When the capacity of the port is examined, more than 450 ships use the port annually. The average waiting time for ships to anchor is 0.7 days and the annual unloading capacity of the port is more than 3.4 million tons. It was planned to increase the capacity of the port, and the old quay wall was used for this purpose. The old quay wall was built as a stone fortification, and it was determined that there was damage due to abrasion due to the influence of the sea. In addition, since special cranes moving on the cranes will be used to increase the capacity, it is planned to connect the crane system to a reinforced concrete structure due to deformation limitations. For this purpose, first, the structural project of the existing quay wall was determined based on old data and field observations (Figure 1).  
Then, soil investigations were carried out in the field in order to determine the soil conditions, and the borehole locations are presented in Figure 2. Borehole depths vary between 10 and 30 m, and when determining the borehole depths, the depth that will not damage the existing quay wall and the depth affected by the stress coming from the structure are taken into account. Since the soil in the field generally consists of cohesionless soils, soil conditions must be determined by in situ tests. For this reason, soil properties were tried to be determined by the SPT test, which is suitable for such cohesionless soils. A standard penetration test (SPT) was performed at regular intervals of 1.5 m for all the boreholes (Figure 3). Trial pits (TP) were opened to verify the borehole work, and these are shown in Figure 2a. The depth of the trial pits is 0.8 m. At this depth, the soil profile observed is 0.00-0.20, yellowish fine sandy soil; 0.20-0.50 m random fill; and 0.50-0.80 m reddish gravelly laterite. The soil profile obtained from BH-1 and BH-1A boreholes is shown in Figure 4. The study area generally consists of sand gravel-laterite mixtures followed by yellowish-white, light brownish-red medium dense sand-green dark gray clay-brown clay soils. Laboratory experiments were carried out on the samples obtained from the boreholes carried out in the region where the problem was taken into account. According to the laboratory results, the cohesion value varies between c = 60 and 88 kPa and the internal friction angle value varies between \(\phi\) = 2 and 7\({}^{\circ}\). The groundwater level at the study area was observed between 2.50 and 2.90 m according to the borehole locations.  
Figure 1: Structural profile of the existing quay wall.  
Figure 2: Boreholes layout plan (BH: Borehole, TP: Trial Pit). (**a**) Boreholes location (first study). (**b**) Boreholes location (second study).
## 4 Security
### Threat Analysis  
To protect industrial WSNs efficiently against potential attackers, the following main security criteria have to be considered: confidentiality of information, integrity of information, authentication of communication peers and availability of information [35]. The first criterion ensures that the data access is restricted to authorized parties only, while the second ensures their protection against alteration and modifications by either malicious parties or the harsh surrounding environment. The authentication of communication peers allows guaranteeing that the exchanged data are coming from trusted devices. At last, the information availability ensures that data and services are accessible even in case of attacks.  
Figure 12: Interoperability of WirelessHART and ISA100.11.a networks [38].  
To perturb or even break down industrial WSNs, the attackers can therefore target one or several of the aforementioned criteria and conduct the appropriate attack(s).