esa-sceva/satcom-chunk-collection huggingface dataset:
# Text-Guided Refinement for Referring Image Segmentation
## 2 Related Work
### Referring Expression Grounding  
Referring expression grounding is a task that aims to localize objects in an image based on a natural language description. It includes two branches: localization and segmentation. Some approaches to referring image localization adopt a two-stage framework where object detectors generate candidate regions, which are ranked by their relevance to the referring expression. Methods such as CNN-LSTM architectures [29] select the region with the highest posterior probability, while others [30; 31] optimize the joint probability of the target object and the expression. More recently, one-stage frameworks [32; 33] have emerged, directly predicting target region coordinates in an end-to-end manner, reducing reliance on excessive candidate boxes. For referring image segmentation, early methods [1; 2; 3; 4; 5] rely on simple concatenation of language and visual features, followed by fully convolutional networks for pixel-wise mask prediction. Recent advancements [10; 11; 12; 34; 35] introduce self-attention and cross-attention mechanisms to better integrate linguistic and visual information. Cycle-consistency learning [36] and adversarial training [37] are also explored to boost the segmentation performance. For example, [PERSON] et al. [12] use non-local modules to enhance pixel-word mixed feature, and [PERSON] et al. [38] propose asymmetric cross-guided attention between visual and linguistic modalities. Bi-directional relationship inference networks [10] provide mutual guidance between modalities, and [PERSON] et al. [35] model relationships among entities and attributes. LSCM [11] uses dependency parsing to guide multi-modal context learning. While these methods have advanced the field, most rely on encoder stage fusion, potentially reducing the consistency between language and vision in the semantic space. Different from the previous works, we propose the Text-Guided Refinement Network, which focuses on decoder-stage multi-modal fusion. Unlike methods that perform fusion exclusively during encoding, our approach leverages a cascaded pyramid structure and a text-guided gating mechanism to enable continuous interaction between linguistic and visual features throughout the decoding process and ensure the precise alignment of multi-modal features cross scales.
## 2 Related Work  
### Behavioral State Monitoring  
The most commonly used method for monitoring infant sleep is polysomnography (PSG), which involves attaching multiple electrodes and sensors to the infant's body. While PSG remains the clinical gold standard due to its comprehensive nature, its application in neonatal settings is challenging. The multitude of cables can interfere with caregiving procedures, limit infant mobility, and irritate the baby's sensitive skin--potentially increasing the risk of infection.  
In recent years, numerous attempts have been made to develop non-invasive or minimally invasive alternatives for assessing sleep and activity in infants. Notably, [PERSON] et al. [9] developed a system of actigraphy combined with video for monitoring sleep and wake in infants. Similarly [PERSON] et al. [10] applied video measurements as well for sleep-wake cycle observations. Although these works mark important progress toward non-contact behavioral assessment, they are limited in scope--typically distinguishing only between binary states (sleep vs. wakefulness), without accounting for the nuanced behavioral categories used in developmental care protocols.  
To the best of our knowledge, we know of no algorithm that categorizes infants into behavioral phases according to the globally recognized NIDCAP (Newborn Individualized Developmental Care and Assessment Program) protocol while also accounting for situations such as medical interventions--thus providing a truly holistic monitoring approach.  
In response to these gaps, we have developed a holistic, non-contact monitoring system designed for use in clinical environments. In our earlier work [11], we introduced an algorithm capable of identifying quiet periods, estimating pulse and respiration rates, and recognizing disruptive events such as medical procedures--all from a single video stream. In this study, we build on that foundation by presenting a GRU-stack-based classification model that maps visual features to NIDCAP behavioral categories. We further enhance this approach by incorporating physiological data, specifically pulse rate and pulse rate variability, extracted from photoplethysmography (PPG) signals recorded by standard patient monitors.
## 3 Results and Discussions  
### Choice of Extraction Kit for Fungal DNA Extraction  
Generally, to test the efficiency of a DNA extraction, DNA concentration and purity were assessed. For DNA extraction, we used an E.Z.N.A. Plant DNA Kit, which yielded a lower DNA concentration but gave higher absorbance readings at 260/280 and 260/230 compared to the ChargeSwitch gDNA kit (Table 1). The higher 260/230 and 260/280 values indicate a greater purity and thus better quality [20; 21]. The lower 260/230 values could be caused by the presence of phenolic compounds, salts, and/or solvents in the samples [22; 23]. As aspen leaves are rich in phenolic compounds and as the presence of phenolics and proteins can hamper the downstream amplicon library preparation [24], the ChargeSwitch DNA Kit was judged to be suboptimal for aspen leaves if the regular washing steps were followed. [PERSON] et al. [22] added an extra washing step. Given our large number of samples, we valued the time that could be saved by omitting additional washing steps, and instead we opted for the E.Z.N.A. Plant DNA kit, which was also superior in terms of the quality of the DNA that it generated.  
One of the most important steps when extracting fungal DNA involves the removal of the thick layer of polysaccharides, proteins, and glycoproteins, melanin, chitin, and other polymers that encapsulate the fungal mycelium and provide an enzymatic and chemical barrier [25; 26]. Both of the DNA extraction kits that we selected bind and remove cellular contaminants. The E.Z.N.A. kit uses a spin-based method to absorb the DNA in a spin column before it is released by elution. ChargeSwitch uses an approach in which the DNA binds to a magnetic solid surface coated with binding antibodies, after which contaminants can be removed by increasing the number of washing steps. We used Nanodrop readings to assess the quality of the extracted DNA in our samples. The advantage of this method is that it allows for the processing of many samples. Another possibility is to employ a fluorometric method such as Qubit which may strengthen the decision of extraction kits, which may provide stronger support for decisions regarding choice of extraction kit and protocol optimization. We only tested the DNA extraction protocol on two samples; more robust testing could be achieved by increasing the numbers of both biological and technical replicates.
### Needs of ISM, WI-FI, and Wireless Lan Applications
#### 1.1.1 Industrial-Scientific-Medical Bands  
In the 2.4 GHz ISM band, ISM applications require 0.5 GHz bandwidth. For unauthorised industrial, scientific, and medical use, ISM bands are internationally designated. Other bands are 902-928 MHz and 2.4-2.4835 GHz. For Bluetooth and Zigbee, the 2.4 GHz spectrum is crucial. For scientific research and industrial automation, the 1 GHz bandwidth meets several ISM applications that need greater spectrum. High-frequency applications can use 5.8 GHz ISM channels with 1.7 GHz bandwidth.
# Tube-Based Robust Model Predictive Control for Autonomous Vehicle with Complex Road Scenarios
## 2 Problem Statement and Preliminaries  
To formulate this control problem in a mathematically tractable way, a unified dynamic vehicle-tire model is first constructed and simplified into a linearized form. This model serves as the basis for the development of a Tube-based Robust Model Predictive Control (Tube-RMPC) strategy. The aim is to ensure that the actual system trajectory remains within a predefined robust tube surrounding the nominal prediction, despite the presence of external disturbances and parameter variations.
# Complex-Valued CNN-Based Defect Reconstruction of Carbon Steel from Eddy Current Signals
## 3 Materials and Methods
### Dataset  
The dataset used in this study contains 46,000 eddy current inspection signal samples collected by the Nondestructive Testing and Evaluation Laboratory of Kyoto University, Japan. The specimens were carbon steel blocks with artificially introduced defects, precisely fabricated by electrical discharge machining (EDM) to simulate crack-type defects of varying depths and orientations encountered in practice. Eddy current signals were acquired using a pancake coil probe under four excitation frequencies of 5 kHz, 20 kHz,  
Figure 1: Carbon steel sample used.  
Figure 2: Schematic diagram of defects in carbon steel.  
80 kHz, and 320 kHz to achieve penetration detection at different depth ranges. Each signal represents the impedance response at a discrete location on the specimen surface. The dataset was divided approximately in the ratio of 7:2:1 into training, validation, and testing sets, consisting of 32,195, 9200, and 4605 samples, respectively. It includes 16 defect types with variations in depth and size, and the sample distribution of the testing set for each defect category is presented in Table 1. Each signal is represented in complex form, encompassing variations in both the real and imaginary components of the impedance.
# Analytical Selection of Leakage Inductance for Single-Phase Dual Active Bridge Converters
## 3 Reactive Power Command Generation and Calculation of Leakage Inductance
### Generation of Reactive Power Command
The following section provides a detailed formula for calculating the critical value of \(L_{emax}\). The input voltage of a system may fluctuate around the designed value and affect the operation of the converter. The converter can achieve zero reactive power output across all loads if the voltage exceeds the designed value. However, if the voltage falls below the designed value, achieving zero reactive power becomes unattainable at near and full load conditions. In such instances, the reactive power command is determined as follows:  
Using Equations (22) and (26) the reactive power command can be obtained as  
\[Q_{1}(k)=\frac{v^{2}(k)}{\omega_{s}L_{e}}-\sqrt{\frac{2v^{2}(k)\cdot P_{1}(k )}{\omega_{s}L_{e}}-{P_{1}}^{2}(k)} \tag{28}\]  
where \(v(k)\) is defined as  
\[v(k)=\frac{1}{2}\sqrt{1+4{\omega_{s}}^{2}{L_{e}}^{2}\cdot{P_{1}}^{2}(k)},\, \left(0\leq v(k)\leq\frac{4V_{in}}{\sqrt{2}\pi}\right) \tag{29}\]  
Figure 6: Range of reactive power across different load conditions. Equation (28) serves to generate a zero reactive power command when the voltage applied to the converter is equal to or exceeds the designed value. Conversely, if the voltage falls below the designated threshold, the equation yields a power command according to the maximum allowable DC-link voltage.
## 4 MIMO Performance Parameters
### Total Active Reflection Coefficient (TARC)  
The _TARC_ defines the degree of coupling and channel independence among different ports. To quantify the interaction between nearby cells in MIMO systems [36], use:  
\[TARC=-\sqrt{\frac{(S_{11}+S_{12})^{2}+(S_{21}+S_{22})^{2}}{2}} \tag{4}\]  
The _TARC_ value should be lower than 0 dB [36]. Figure 16 represents the reflectance of the _TARC_ response for the circular disk MIMO observed at \(-32.89\) dB at \(4.6\) GHz, \(-17.42\) dB at \(8.64\) GHz, and \(-12.93\) dB at \(11.52\) GHz. The CSRR-based MIMO antenna represents the reflectance of \(-12.2\) dB at \(9.05\) GHz. It is observed that the proposed antenna provides channel independence and a lower coupling value.  
Figure 16: The _TARC_ response of the presented circular disk and CSRR-based MIMO antenna structure.  
Figure 15: The diversity gain of the proposed antenna. The \(DG\) for circular disk MIMO structure is \(9.93\) dB and for the CSRR-based MIMO antenna is \(9.77\) dB.
## 3 Theoretical Part
### Digitalization and 3D Modeling of Cultural Heritage
#### 3.3.2 The Role of 3D Modeling in Documentation and Preservation  
Three-dimensional modeling plays a crucial role in the process of documenting and preserving cultural heritage. This technology allows for the creation of accurate digital replicas of cultural artifacts and monuments, which can be used for various purposes, including restoration, education, and exhibition in museums or on virtual platforms [32].  
The 3D modeling process typically involves technologies such as photogrammetry, laser scanning, and computed tomography, which allow for capturing detailed data about the physical characteristics of objects. These data are then processed to create a three-dimensional digital model that can be rotated, analyzed, and modified on a computer [33].  
The role of 3D modeling in documenting cultural heritage is particularly important in the context of restoration and conservation. Digital models enable conservators to analyze the condition of artifacts accurately, plan restoration interventions, and simulate different restoration scenarios before physical work begins. This reduces the risk of damaging original items and allows conservators to make informed decisions about the best preservation methods [34].  
Additionally, 3D models can be used to create replicas of cultural artifacts through 3D printing, allowing for museum work and education without the need to handle sensitive originals. Furthermore, 3D models enable the creation of interactive virtual tours and exhibitions, where visitors can explore cultural objects in a digital environment, further enriching their experience and understanding of cultural heritage [35].
# Online Learning Based Underwater Robotic Thruster Fault Detection
## 6 Experimental Results and Analysis
### Pool Test Fault Detection Results and Analysis
It can be seen from Figure 13 that after the second model update, the model residual still exceeds the threshold. Therefore, the algorithm detected that a thruster fault occurred in the 1282 nd sampling period. After the thruster fault was detected, the algorithm continuously updated the model online, as the residual of the model continued to exceed the threshold. When the model was updated after the failure, more and more data were generated in the fault state in the modelling data, and the new-built model came closer to the real fault state model. Then, the model residual gradually reduced. By observing the new-built model residuals, corresponding to the 1283-1324 sampling periods in Figure 13, it was found that the model residual trend was consistent with the above analysis. The above-mentioned online modelling process ended at the 1324 th sampling period, and so lasted about 43 sampling periods, which was close to the minimum modelling data set size \(w_{\text{min}}=50\) in the proposed algorithm. At this point, the algorithm had built a fault state model through online learning. In the 1380 th sample, the thruster inhaled the second cloth strips and the fault state of the thruster changed, which meant the occurrence of a new fault. The proposed method also successfully detected the new fault by the adaptive fault detection strategy based on online model update, which partly proves the online learning ability of the online learning-based modeling algorithm in the presence of work status changes. Model residuals of the fixed model-based fault detection method during pool test of the saucer-shape AUV is shown in Figure 15. As mentioned before, parameters in the fixed model-based fault detection are the same as that uses in the proposed online learning model-based method. Figure 15: Model residuals of the fixed model-based fault detection method during pool test of the saucer-shape AUV. As can be seen in Figure 15, after the 700 th sampling period, due to the change of thruster work status, residuals of the fixed model become large and exceeds the threshold multiple times, resulting in a series of misdiagnosis. In contrast, as shown in Figure 13, when residual exceeds the threshold at the 700 th sampling period, the model online update process starts and thus avoid misdiagnosis.
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 3 Methodology
### Proposed Protocol Design
#### 3.2.5 Secure Communication  
Once \(k_{sym}\) is established, all further communication is encrypted by a symmetric protocol. In our protocol, we have used AES-256 in cipher block chaining (CBC) mode. For message encryption, the sender uses \(k_{sym}\) to encrypt the plaintext message:  
\[\text{Ciphertext}=e_{k_{sym}}(\text{Plaintext}).\]  
The message integrity of symmetric encryption in our protocol is ensured through an HMAC also generated using \(k_{sym}\).  
Message \(\text{HMAC}=\text{HMAC}_{k_{sym}}(\text{Ciphertext})\).  
The encrypted ciphertext and the HMAC are sent together to the receiver. Upon receipt, the receiver first verifies the HMAC to confirm the integrity and authenticity of the ciphertext:  
\[\text{Computed HMAC}=\text{HMAC}_{k_{sym}}(\text{Ciphertext}^{\prime}).\]  
If the computed HMAC matches the received HMAC, the ciphertext is deemed intact. The receiver then decrypts the ciphertext using \(k_{sym}\) to retrieve the plaintext message:  
\[\text{Plaintext}=d_{k_{sym}}(\text{Ciphertext}^{\prime}).\]  
This method ensures both confidentiality and integrity of the communication. The symmetric key \(k_{sym}\) provides consistent encryption and HMAC generation, protecting against unauthorized tampering or interception of messages within a smart grid environment.
# Effective Adsorption of Phenoxyacetic Herbicides by Tomato Stem-Derived Activated Carbons
###### Abstract  
Six activated carbons from tomato (_Solanum Lycopersicum_ L.) stems (TS-AC) were synthesized by carbonization and chemical activation using potassium hydroxide (KOH) and sodium hydroxide (NaOH) at temperatures of 550, 650, and 750 C. These TS-ACs were then evaluated as adsorbents to remove 2_4_-dichlorophenoxyacyacetic acid (2,4-D) and 2-methyl-4-chlorophenoxyacyacetic acid (MCPA) from aqueous solutions. The adsorption kinetics of both herbicides followed the pseudo-second-order model, closely correlating with the mesopore volume of the TS-AC. The Langmuir isotherm accurately described the adsorption process for both 2,4-D and MCPA. The porous structure of TS-AC, characterized by micropore volume and specific surface area, significantly influenced the maximum adsorption capacities. The adsorption of both herbicides was pH dependent, but ionic strength had no significant effect. Regeneration testing, conducted over three cycles, showed less than a 15% reduction in herbicide adsorption capacity. This study demonstrates that agricultural waste, specifically tomato stems, can be effectively valorized by using simple activation techniques in TS-AC that are efficient adsorbents to remove organic pollutants, such as herbicides, from aqueous media.  
tomato stems; activated carbon; agricultural waste; chemical activation; surface chemistry; phenoxyacetic herbicides adsorption Article
###### Abstract  
The paper considers the neural network application to detect microstructure defects in dissimilar welded joints using the acoustic emission (AE) method. The peculiarity of the proposed approach is that defect detection is carried out taking into account a priori information about the properties of the AE source and the acoustic waveguide parameters of the testing structure. Industrial process pipelines with dissimilar welded joints were studied as the testing object, and diffusion interlayers formed in fusion zones of welded joints were considered microstructure defects. The simulation of AE signals was carried out using a hybrid method: the signal waveform was determined based on a finite element model, while the amplitudes of AE hits were determined based on a physical experiment on mechanical testing of dissimilar welded joints. Measurement data from industrial process pipelines were used as noise realizations. As a result, a data sample was formed that considered the parameters of the AE source and the parameters of the acoustic waveguide with realistic noise parameters and a signal-to-noise ratio. The proposed method allows for a more accurate determination of the waveform, spectrum, and amplitude parameters of the AE signal. Greater certainty in the useful signal parameters allows for achieving a more accurate and reliable classification result. When using a backpropagation neural network, a percentage of correct classification of more than 90% was obtained for a data set in which the signal-to-noise ratio was less than (\(-5\) dB) in 90% of cases.  
dissimilar welded joints; acoustic emission; diffusion interlayers; neural networks in acoustic emission; classification of acoustic emission signals; acoustic waveguide modeling +
Footnote †: journal: applied sciences  
Article  
Detection of Diffusion Interlayers in Dissimilar Welded Joints in Processing Pipelines by Acoustic Emission Method [PERSON]  
1 Department of Information Technologies and Computer Science, National Research University \"Moscow Power Engineering Institute\", 14, Krasnokazarmenmaya Street, 111250 Moscow, Russia; [EMAIL_ADDRESS] (VB.j.); bardakovv1 [EMAIL_ADDRESS] (V.B.); [EMAIL_ADDRESS] (D.A.); [EMAIL_ADDRESS] (S.U.); [EMAIL_ADDRESS] (M.K.); [EMAIL_ADDRESS] (E.L.)  
LLC \"Interunis-IT\", 20B, Entuziastov Highway, 111024 Moscow, Russia; [EMAIL_ADDRESS]