esa-sceva/satcom-chunk-collection huggingface dataset:
# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
###### Abstract
As a result, the noise modeling accuracy is guaranteed, and the computational cost and algorithm delay during the model inference phase are also satisfactory. In addition, we adopt short-time discrete cosine transform (STDCT) [32] instead of the most commonly used short-time Fourier transform (STFT) for TF transformation. Unlike the complex-valued STFT spectrum, the STDCT spectrum is real-valued and implicitly contains both the magnitude and phase information. This escapes the phase estimation problem in the STFT-based methods, thereby simplifying the design of DNN and improving the computational efficiency. The contributions of this study are as follows: (1) A unidirectional information interaction scheme is proposed to optimize the previous bidirectional interactive speech and noise modeling-based dual-branch SE framework so as to effectively reduce the model deployment costs without significant performance degradation. (2) As both the speech and noise branches utilize the CRN as backbone, experiments illustrate that the information interactions between the two branches' encoders, recurrent modules, and decoders are all beneficial and necessary. (3) Ensuring causal inference, experimental results demonstrate the superiority of the proposed unidirectional information interaction-based SE model over existing benchmarks. The performance evaluation results of UniInterNet on both the VoiceBank+DEMAND [33] and DNS-Challenge [34] datasets are presented.
## 4 Results and Discussion
### Simulation Results
#### 4.1.3 Results under Extreme Temperature Conditions
In Figure 14c, the effective sealing lengths of the cross-sections for contact surface I and contact surface II are shown to be 0.7 mm and 0.76 mm, respectively. At this point, the maximum reaction forces along the \(X\)-axis for contact surface I and contact surface II are 35,796 N and 41,539 N, respectively.
# Bearing Fault Diagnosis Method Based on Improved VMD and Parallel Hybrid Neural Network
## 3 Materials and Methods
### Fault Diagnosis Model Design  
In this paper, a parallel hybrid neural network is proposed, which combines the unique advantages of the cyclic neural network in processing time series data with the powerful feature extraction ability of the convolutional neural network and is employed for rapid diagnosis of fault signals. The model uses two parallel networks for more comprehensive feature extraction of fault signals and fuses the extracted features to improve the richness of features. At the same time, the GAP layer is employed for model parameter compression and data dimension transformation. The fault diagnosis process of the parallel hybrid neural network is shown in Figure 4.  
The diagnosis process of the parallel hybrid neural network model is as follows.  
Firstly, the processed fault signal is employed as the input to generate training samples that can be read by BiLSTM and BiGRU layers through data fusion, data interception, and normalization.  
Secondly, in the parallel neural network, the BiLSTM layer and BiGRU layer, respectively, extract the features of each group of training samples with time-series characteristics and then capture and remember the relevant features in the samples. The bidirectional cyclic structure can better handle the long-term dependence in the time series data [31] and transfer the memory information to the lower network layer by layer after completing the feature extraction.  
Figure 3: Flow chart of fault signal processing.  
Thirdly, the CNN layer is employed to deeply mine the output features of the BiLSTM layer and BiGRU layer. By constructing multiple 1D-CNN layers, the small differences between different faults can be extracted from the feature data to improve the diagnosis accuracy of the model.  
Fourthly, the features extracted by the two neural networks are fused by splicing, and the complementarity between features is employed to improve diagnostic accuracy.  
Fifthly, after feature fusion, the use of the gap layer can greatly reduce the parameters of the model and the training and testing time of the model while considering the global information.  
Finally, the output of the GAP layer is input into the SoftMax classifier for a final classification decision, and the diagnosis result is given.
# Detection of Waste Containers Using Computer Vision
## 2 Related Work
### Feature-Based Methods  
Content-based image retrieval (CBIR) [10; 11] involves the retrieval of images based on the representation of visual content to identify images that are relevant to the query. The identification of the types of containers is based on what type of image is returned. The CBIR method used in this application is denoted the vector of locally aggregated descriptors (VLAD) [12; 13].  
In order to build a VLAD, descriptors are required, and there is a multitude of them to choose from. In this study, Oriented FAST and Rotated BRIEF (ORB) [14] was chosen because, unlike other state-of-art descriptors, such as speeded up robust features (SURF) [15] and scale-invariant feature transform (SIFT) [16], it is patent-free. Being patent-free is important as this application is intended to have commercial use. Usually, this kind of descriptor is separated into three distinct phases [4; 16]. In this application, only the first two were necessary.  
The first phase is feature detection or extraction. In this phase, an image is searched for locations that are considered likely to match on other images. The second phase is feature description. In this phase, each region around a detected feature is transformed into a numerical descriptor that follows a model to have invariant characteristics.  
Each phase introduces important choices. In the feature detection phase, choices need to be made on a feature detection method and the parameters that define a good feature; more precisely, that make an ideal feature. This has been extensively studied in [17] and more recently in [18].  
In the feature description phase, the problem lies in image transformations. Such transformations can alter a feature's orientation, illumination, scale, and affine deformation. For these reasons, the chosen descriptor needs to be invariant to the transformations an image can go through [4]. ORB has some shortcomings; however, it still manages to achieve good results, as demonstrated in [19].
# Phytochemical and Functional Properties of Fruit and Vegetable Processing By-Products
## 2 Materials and Methods
### Determination of Bioactive Compounds and Antioxidant Capacity
FLC was obtained from a calibration curve, using catechin as a standard, and the results were expressed as milligram of catechin equivalents per 100 g of dry weight (mg CAE 100 g\({}^{-1}\) dw). Briefly, a volume of 3 mL of 4% (_w/v_) vanillin in methanol and 0.5 mL of a known dilution of the extract were added to a test tube. After 3-5 min, 1.5 mL of concentrated HCl was added to the solution, mixed, and incubated for 15 min at 20 \({}^{\circ}\)C. The absorbance was read at 500 nm using a UV-Vis Spectrophotometer. A blank was prepared by substituting the vanillin solution in the reaction mix with methanol. Determination of CAE was performed by CA standard curve (0.025-0.500 mg mL\({}^{-1}\)). Pigments, i.e., chlorophyll a (Chl A), chlorophyll b (Chl B), and carotenoids (TCC), were determined using acetone as a solvent. Briefly, aliquots of 0.5 g of freeze-dried samples were extracted with 8 mL of iced acetone (\(-\)20 \({}^{\circ}\)C) containing 0.01% butylated hydroxy-toluene (BHT; 0.5 mg mL\({}^{-1}\)) as an antioxidant. To avoid degradation and isomerization of carotenoids, amber glassware was used, and sample preparation was performed under dim light conditions. The extraction was performed at room temperature for 1 h under continuous stirring (300 rpm). Then, the extract was filtered through Whatman No 1 paper (Sigma-Aldrich; Milan, Italy). The residue was extracted once again in the same way. Then, the filtrates were combined together and immediately analyzed. The supernatant was separated and the absorbances were read at 400-700 nm on the Evolution 300 UV-Vis Spectrophotometer. It was recorded that chlorophyll a showed the maximum absorbance at 662 nm, chlorophyll b at 646 nm, and total caroten at 470 nm. The amount of these pigments was calculated as follows. Chl A = 11.75 A\({}_{662}-2.350\) A\({}_{645}\)  
Chl B = 18.61 A\({}_{645}-3.960\) A\({}_{662}\)  
TCC = 1000 A\({}_{470}-2.270\) Chl A \(-\) 81.4 Chl B/227  
The results were expressed as micrograms of chlorophyll a, chlorophyll b, and \(\beta\) caroten equivalent per grams of dry weight (\(\mu\)g g\({}^{-1}\) ChIAE dw, \(\mu\)g g\({}^{-1}\) ChIBE dw, \(\mu\)g g\({}^{-1}\)\(\beta\)CE dw, respectively). The antioxidant activity evaluation of the extracts was performed with 2,2-diphenyl-1-picrylhydrazyl (DPPH). Briefly, 1.5 mL of DPPH solution was added to 1.5 mL of the extract and, after 15 min, absorbance at 515 nm was determined using the UV-Vis Spectrophotometer. The percent inhibition activity of extract was calculated as follows:  
\[[(\text{A0}-\text{A1})/\text{A0}]\times 100\]  
where A0 was the absorbance of the control and A1 was the absorbance of the extract. Trolox (0.5-10 \(\mu\)g mL\({}^{-1}\)) was used as the reference compound and AA was expressed as \(\mu\)g of Trolox equivalent (TE) mg\({}^{-1}\) dw.
# EE-UWSNs: A Joint Energy-Efficient MAC and Routing Protocol for Underwater Sensor Networks
## 3 The Proposed EE-UWSNs Protocol
### Performance Metrics  
The proposed EE-UWSNs protocol is evaluated in many Key Performance Indicators (KPIs), which are:  
* Energy Consumption: It refers to the amount of sensor energy consumed to perform aggregation, transmission, and reception of data [45]. It is measured in units of joules (watts per second) [46].
* Number of Collisions: A collision occurs when more than one sensor node sends packets at the same time, resulting in packet corruption [47]. Therefore, the source node needs to retransmit the lost packet and this leads to energy wastage. There are several collision avoidance protocols that are based on using RTS and CTS packets prior to sending data [48]. Decreasing the number of collisions is an important issue to save the energy of sensors.
* End-to-End Delay: This is a function of several parameters, which includes the transmission delay, the propagation delay, the queuing delay, processing delay and the number of retransmissions [49]. The transmission delay is calculated by dividing the size of a packet by the transmission rate. The propagation delay can be estimated by dividing the distance between a sensor and a sink by the speed of sound (1500 m/s) [50]. Processing delay refers to the time taken by sensor nodes to process packets [51; 52]. The queuing delay is the time that a packet spends waiting in a node's queue until it departs [53].
* Jitter: Jitter of the packet delay is a critical factor in determining the quality of service in UWSNs. The jitter is defined as the variation in the packet delays [54].
# Risk Assessment of Soil Contamination with Heavy Metals from Municipal Sewage Sludge
## 2 Materials and Methods
### Contamination Assessment Methodology
#### 2.3.3 Environmental Risk Determinant (ERD)  
Analysing the mobility of heavy metals, one can conclude that only the FIV fraction is fully stable and does not migrate to the environment. The fractions FI and FII are considered mobile, while FIII can be mobile under certain conditions, i.e., when microorganisms fully process organic matter in the soil and when a storm occurs under the influence of ozone. Metals in water-soluble compounds and metals associated with carbonates are considered the most mobile. Metals bound to iron and manganese oxides are released to the environment much more slowly. Under certain conditions of pH and oxidation-reduction potential, FII bound metals can show significant bioavailability [44]. The environmental risk assessment is based on the first three fractions, taking into account the level of individual predisposition of each fraction to release heavy metals into the soil environment. The _ERD_ determines the content of an element in the heavy metal group, depending on its content in the four fractions. Each fraction is assigned an appropriate weight depending on the 0-1 scale. The authors proposed the use of the _ERD_ indicator because none of the indicators using the issue of mobility take into account the weight of individual fractions. It should be taken into account that FI, FII, and FIII fractions are mobile, but the FI fraction is much more mobile than FII and FIII, which takes into account the formula for the _ERD_ indicator. The weighting ranges adopted are proposed on the basis of an analysis of the scale of the other indicators. Its determinant is described by the formula:  
\[ERD=F_{p1}+F_{p2}+F_{p3} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c} \hline \(E_{r}^{i}\) & _PERI_ & **Potential Environmental Risk** \\ \hline \(<\)40 & \(<\)150 & Low \\
40–80 & 150–300 & Medium \\
80–320 & 300–600 & High \\ \(>\)320 & \(>\)600 & Very high \\ \hline \end{tabular}
\end{table}
Table 7: Interpretation of the potential environmental risk indicator (PERI) value [38; 39; 40; 41].  
where:  
\(F_{p1}=F_{1}\); \(F_{1}\)--metal content in fraction FI on a scale of 0-1; \(F_{p2}=F_{2}\)2; F2--metal content in fraction FII on a scale of 0-1; \(F_{p3}=F_{3}\)3; F3--metal content in fraction FIII on a scale of 0-1.  
The classification of the \(ERD\) results is: 0 < \(ERD\)\(\leq\) 0.35--low risk; 0.35 \(ERD\)\(\leq\) 0.6--medium risk; 0.6 < \(ERD\)\(\leq\) 0.8--high risk; 0.8 < \(ERD\)--very high risk.
# Algorithmic Techniques for GPU Scheduling: A Comprehensive Survey
## 2 Models
### Components of a Model
In clusters dominated by ML workloads, scheduling metrics often extend beyond runtime. For example, SLAQ (SLA-aware queuing scheduler) evaluates performance based on the final model accuracy or training loss, rather than just job duration. Other models incorporate job-specific deadlines or accuracy targets directly into the objective function [108]. In summary, a rigorous scheduling model precisely defines (1) what constitutes a feasible schedule--through resource and timing constraints--and (2) what constitutes a good schedule--through one or more explicitly stated objective functions. This formalism underpins the systematic design and analysis of effective scheduling algorithms. Design takeawaysEach scheduling objective entails trade-offs that directly impact real-world performance. For example, minimizing average latency can degrade tail-latency \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Objective** & **Formulation** & **Definition** \\ \hline Minimizing average waiting time & \(\min\frac{1}{n}\sum_{j=1}^{n}W_{j}\) & Shorten the average time jobs wait in the queue before execution. \\ \hline Minimizing average completion time & \(\min\frac{1}{n}\sum_{j=1}^{n}C_{j}\) & Minimize the average turnaround time between job submission and completion. \\ \hline Maximizing throughput & \(\max\frac{\#\text{jobs finished time}}{\text{time}}\) & Increase the number of jobs completed per unit time. \\ \hline Maximizing utilization & \(\max\frac{\text{total busy time}}{\text{total available time}}\) & Maximize the fraction of total time during which GPUs are actively utilized. \\ \hline Maximizing fairness & \(\min\max_{j}\left(\frac{C_{j}}{T_{j}}\right)\) & Equalize resource allocation by preventing any job \(j\) from experiencing excessive slowdown relative to its service time \(T_{j}\). \\ \hline Minimizing energy consumption & \(\min\sum_{j=1}^{n}E_{j}\) & Reduce the total energy consumed by the GPU cluster during job scheduling. \\ \hline \end{tabular} Note: Each expression is minimized (or maximized) over the feasible schedule set \(\mathcal{S}\).  
\end{table} 
Table 3: Scheduling objectives: formulations and definitions. SLOs, particularly under heavy-tailed job-size distributions--a common pattern in DL training workloads. Similarly, maximizing GPU-hour throughput may lead to starvation of short, interactive inference jobs unless fairness constraints are explicitly enforced. In multi-tenant clusters, optimizing solely for utilization can exacerbate interference between jobs, resulting in volatile 95 th-percentile latencies. As a result, effective scheduling demands a careful balance between latency, throughput, and fairness. Many modern schedulers address this by adopting weighted or multi-objective formulations that explicitly navigate these competing goals.
## 2 Materials and Methods
### Simulation Platform of Rice Transplanter Navigation Control
#### 2.1.2 Design of Preview Tracking Algorithm Based on Small Angle Linearization  
In this study, a preview tracking control algorithm is designed based on the preview control theory [18], and its structure is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Description** & **Symbol** & **Description** \\ \hline \((x,y)\) & Position of control point \(P\) & \(\varphi\) & Vehicle heading angle \\ \(v\) & Forward velocity & \(\delta_{f}\) & Steering angle \\ \(v_{y}\) & Lateral velocity & \(l\) & Wheelbase length \\ \(\dot{\beta}\) & Sideslip angle & \(R\) & Turning radius \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameterization of the kinematic model.  
Figure 3: Kinematic model of rice transplanter (sideslip perturbation).  
Taking the preview straight line \(PP_{a}\) as an auxiliary line, based on the small angle linearization method and the pole assignment method [19], the steering angle is obtained as  
\[\delta_{f}=\frac{2\cdot l}{\
u}\left(\psi_{e}+\frac{1}{2\cdot\
u}p_{e}\right), \tag{4}\]  
where \(p_{e}\) is the lateral deviation from control point \(P\) to \(PP_{a}\).  
It can be seen as a rice transplanter always traveling along \(PP_{a}\), when \(p_{e}=0\) (\(P\) is always on the \(PP_{a}\)), then Equation (4) is changed as  
\[\delta_{f}=\frac{2\cdot l}{\
u}\left(\psi_{e}+\frac{1}{2\cdot\
u}\cdot 0\right) =\frac{2\cdot l}{\
u}\psi_{e}, \tag{5}\]  
According to the preview tracking model, as shown in Figure 4,  
\[\psi=\varphi_{t}-arctan\left(\frac{P_{e}}{d}\right), \tag{6}\]  
\[\psi_{e}=\varphi-\psi, \tag{7}\]  
Therefore,  
\[\psi_{e}=\varphi-\psi+arctan\left(\frac{P_{e}}{d}\right)=\varphi_{e}+arctan \left(\frac{P_{e}}{d}\right). \tag{8}\]  
The optimal steering angle can be obtained by Equations (5) and (8) as  
\[\delta_{f}=k\left(\varphi_{e}+arctan\left(\frac{P_{e}}{d}\right)\right). \tag{9}\]
## 2 Materials and Methods  
Coconut husk waste collected from local stores was cleaned with normal tap water and dried in an electrical oven to remove impurities and moisture. The fibers of the husks were manually separated from the coconut fruit, and approximately 10 g of the fibers were placed in an alumina crucible. After that, they were subjected to fire at 500-700 \({}^{\circ}\)C for 2 h under a constant heating rate of 10 \({}^{\circ}\)C. The coconut husk ash (CHA) obtained was cooled down inside an electrical furnace and collected for acid treatment. The CHA was added to 20 mL of 5 N (normality) sulfuric acid in a beaker. After that, the mixture was heated up to 50 \({}^{\circ}\)C, and continuously stirred for 1 h at a speed of 300 r.p.m. using a hotplate magnetic stirrer under a fume hood. After that, the filtration process was carried out using Whatmann No. 41 ashless filter paper, whereby distilled water was used three times to remove excess acid in the residue. The treated CHA was then characterized to determine its properties for potential glass-ceramics applications. A quantitative analysis of the sample's chemical compositions was carried out using an X-ray fluorescence (XRF) XRF-EDX-720/7000 (Shimadzu, Kyoto, Japan) machine. Thermographic analysis (TGA) and difference thermogravimetry (DTG) were implemented using a Mettler Toledo TGA/DSC 1 HT (Mettler Toledo, Kusnacht, Switzerland) in order to measure the rate of mass loss over the temperature of the coconut husk. The TGA was carried out in the presence of oxygen gas, with a constant heating rate of 10 \({}^{\circ}\)C/min. The FTIR analysis was conducted using a Thermo Nicolet Nexus 47 FT-IR (ThermoFisher Scientific, Waltham, MA, USA) in a common range of 280 to 4000 cm\({}^{-1}\). Then, the phase identification of the samples was carried by using an X-ray diffraction (XRD) Phillips X'Pert High Pro PANanalytical Diffractometer (Malvern Pananalytical, Almelo (The Netherlands) and Malvern (UK)), with a data range of 20 from 20\({}^{\circ}\) to 80\({}^{\circ}\), while a field emission scanning electron microscopy (FESEM) Nova NanoSEM 30 (FEI, Hillsboro, OR, USA) was used to analyze the morphological structure of the samples. Meanwhile, the particle size of the samples was determined using a particle size analyzer (PSA) Malvern Instrument Zetasizer Nano S (Malvern Pananalytical, Almelo (The Netherlands) and Malvern (UK)). In this study, UV-3600 Shimadzu (Shimadzu, Kyoto, Japan) was used in the UV-VIS analysis, and the absorption edge observed in the spectrum was used to calculate the optical band gap for the materials. The overall methodology described is shown in the schematic diagram in Figure 1.
## 2 System Model  
### Uplink Multiuser MIMO OFDMA Systems  
We consider the uplink multiuser MIMO orthogonal frequency-division multiple access (OFDMA) systems [32; 33] with \(KN_{t}\) user antennas, and \(N_{R}\) antennas at the BS, where \(K\) is the user number, each user is equipped with \(N_{t}\) transmit antennas, and \(N_{R}\gg KN_{t}\). Then, we use \(N_{T}\) to represent the total number of user antennas. The transmitted signal and the received signal can be expressed as \(\mathbf{s}=\left[s_{1},s_{2},\cdots,s_{N_{T}}\right]^{T}\) and \(\mathbf{y}=\left[y_{1},y_{2},\cdots,y_{N_{R}}\right]^{T}\), respectively. In addition, we use bold and capitals to denote vector and matrix, respectively. Hence, the system model can be represented by the following Equation (1):  
\[\mathbf{y}=H\mathbf{s}+\mathbf{n}, \tag{1}\]  
where \(H\) is the \(N_{R}\times N_{T}\) complex channel matrix and \(\mathbf{n}\) is the \(N_{R}\times 1\) noise vector. Moreover, it can be expressed as  
\[\begin{bmatrix}y_{1}\\ \vdots\\ y_{N_{R}}\end{bmatrix}=\begin{bmatrix}h_{11}&\cdots&h_{1N_{T}}\\ \vdots&\ddots&\vdots\\ h_{N_{R}1}&\cdots&h_{N_{R}N_{T}}\end{bmatrix}\begin{bmatrix}s_{1}\\ \vdots\\ s_{N_{T}}\end{bmatrix}+\begin{bmatrix}n_{1}\\ \vdots\\ n_{N_{R}}\end{bmatrix}. \tag{2}\]
# Soft X-ray Microscopy Techniques for Medical and Biological Imaging at TwinMic--[PERSON]
## 3 Results
### STXM Coupled with LEXRF  
#### 3.1.1 Nanotoxicology  
NPs, such as cobalt ferrites, are increasingly being used in the biomedical field, as drug-delivery agents, biosensors, or imaging contrast agents [82; 83]. The study of the complex NP-cell interaction is crucial to better understand their uptake mechanism and cytotoxicity effects.  
To this aim, the capabilities offered by the TwinMic microscope are particularly suited to study the behavior of CoFe\({}_{2}\)O\({}_{4}\) NPs at the single-cell level [43; 77]. STXM sub-micron resolution, combined with LEXRF trace element sensitivity, can provide the simultaneous correlation between cellular changes in shape and morphology and the elemental distributions of both cells and NPs.  
Figure 5 shows an example of LEXRF mapping performed on mouse fibroblast cells exposed to NPs. Cells are well visible by the distribution of C, O, Na, and Mg, with a higher level of O and Na in the areas corresponding to the cell nuclei. The co-localization of Co and Fe reveals the distribution of NPs, which appear to be preferentially located in the perinuclear/cytoplasmic region of the cells. Moreover, NPs often appear to coalesce between them, forming aggregates or vesicles. More details can be found in [43; 77; 84].