esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of the Critical Speed of Ultra-High-Speed Diamond Drilling Technology in Marble
## 2 Rock Breaking Simulation Model
### Geometric Model, Boundary Conditions and Material Properties  
Using ABAQUS v.2022 software, we established a concise nonlinear dynamic model depicting the interaction between diamonds and rocks, as shown in Figure 3. In this model, the diamond particles have a diameter of 0.5 mm and interact with a matrix measuring 0.6 \(\times\) 0.6 \(\times\) 0.4 mm. The exposed height of the diamond is 0.17 mm, roughly 1/3 of its dimensions. To enhance simulation accuracy, we compared cutting force change curves for octahedral and spherical diamonds in the rock fragmentation process (Figure 4). Despite transient spikes with octahedral diamonds, our focus was on average stress. The overall performance of the two simulations was essentially consistent, leading us to adopt spherical diamonds for modeling and analysis.  
Figure 3: ID bit and rock model.  
Both the impregnated diamond bit and rock samples are meshed using tetrahedral solid elements (C3D4); the mesh of the bit and rock is set to be 4-node linear cells by reduced integration and hourglass control method. Simultaneously, to enhance the modeling and computational accuracy, we refined the meshing around the contact zones between the diamond and the rock components, setting the grid size to 0.01 mm, totaling 555,528 elements in the rock sample model. The numerical model adheres to the International System of Units (m-N-kg-S), establishing reference point RP-1 for applying displacement and rotational speed to the diamonds. The simulation involves two steps:  
* In Step 1, the impregnated block is pressed into the rock along the z-axis by 0.1 mm;
* In Step 2, downward displacement and rotational speed are simultaneously applied to the impregnated block around the z-axis.  
The rock sample is fully constrained, with complete fixation at the bottom and all sides. The entire model maintains a mechanical fixed drilling speed of the drill bit at 0.05 m/s. The drill bit and rock adopt a surface-to-surface contact approach, with a friction coefficient of 0.3 set between the rock sample and the drill bit. Mesh structures are depicted in Figure 5, and basic material parameters are detailed in Table 1. In the simulation test, we kept the mechanical drilling speed constant while altering the rotational speed of the drilling teeth. To mimic the actual drilling conditions of the pregnant diamond bit, we opted for a mechanical drilling speed of 0.05 m/s for the test.  
Figure 4: Verification of the influence of diamond shape on model accuracy. **(a)**Spherical diamond; **(b)** Octahedral diamond.  
Figure 5: Mesh and boundary conditions.
## 2 Materials and Methods
### Rice Straw Hydrolysate  
RS was kindly provided by a rice mill located in Valencia, Spain. The residue was dried at ambient temperature. The RS was ground to a reduced size in the range of 2 to 3 mm. For preservation, the RS was packaged in bundles of approximately 6 kg each and stored in a moisture-free room until use. The material was subjected to physicochemical characterization (see Table 1) according to the National Renewable Energy Laboratory (NREL) procedures [22].  
RS underwent dilute acid-assisted thermal pretreatment according to the procedure described by [PERSON] et al. [23]. A total solids concentration of 7% _w/w_ RS was used for the hydrolysis process. Sulfuric acid (96% purity and density of 1.84 g/mL) supplied by AppliChem (Darmstadt, Germany) was used at a concentration of 1.5% _v/v_ to perform the acid hydrolysis, which was carried out in a 10 L borosilicate bottle with a working volume of 5 L. The homogenized solution was then autoclaved (Raypa, Terrassa, Spain) at 121 \({}^{\circ}\)C for 20 min. At the end of the hydrolysis, the borosilicate bottle was cooled to stop the reaction, and the resulting hydrolysate was filtered through a #20 sieve (850 \(\upmu\)m) to recover the liquid fraction, which was stored in a sterile plastic bottle at 4 \({}^{\circ}\)C until needed. The physicochemical characterization of the hydrolysate is shown in Table 2.  
\begin{table}
\begin{tabular}{l c} \hline
**Parameter (\% _w/w_ on a Dry Basis)** & **RS** \\ \hline Moisture & 7.2 \(\pm\) 0.8 \\ Ash & 12.7 \(\pm\) 0.9 \\ Total solids & 92.3 \(\pm\) 0.8 \\ Acid-insoluble lignin & 20.5 \(\pm\) 0.2 \\ Acid-soluble lignin & 4.5 \(\pm\) 0.1 \\ Total lignin & 25.0 \(\pm\) 0.2 \\ Cellulose & 25.4 \(\pm\) 0.2 \\ Hemicellulose & 7.8 \(\pm\) 0.1 \\ \hline \end{tabular}
\end{table}
Table 1: Physicochemical composition of raw RS.
# Coastal Zone Classification Based on U-Net and Remote Sensing
## 2 Experimental Methods
### U-Net Network  
The U-Net network model is an end-to-end, U-shaped symmetric network, originally derived from the FCN architecture [67]. U-Net is known for achieving good segmentation performance, even when the training sample size is relatively small [68; 69]. The network consists of two main parts: an encoder and a decoder. The encoder, located on the left side, is responsible for extracting feature information from the input image. The decoder, on the right side, accurately locates and restores detailed information from the extracted feature image [70]. The encoder is composed of four groups of identical coding blocks. Each coding block employs two convolutional layers with a kernel size of 3 \(\times\) 3. The rectified linear unit (ReLU) is used as the activation function, and a 2 \(\times\) 2 max-pooling filter with a stride of 2 is applied for down-sampling. With each down-sampling operation, the spatial dimensions of the feature map are halved, while the depth of the feature map doubles. The decoder mirrors the encoder with four identical decoding blocks. In each decoding block, a 2 \(\times\) 2 kernel is first used for transposed convolution to up-sample the feature map, effectively halving its depth. This is followed by a skip connection that merges the up-sampled feature map with the corresponding feature map from the encoder. Two additional convolutional layers with 3 \(\times\) 3 kernels are then applied, reducing the feature map's spatial dimensions to half of its original size. The final layer of the U-Net employs a 1 \(\times\) 1 convolution kernel to map the 64-dimensional feature vector to a 256 \(\times\) 256 output image. The Softmax function was then used to generate a classification map with the same resolution as the input image. The detailed structure of the U-Net network is illustrated in Figure 3.  
Figure 2: Workflow of a coastal classification framework.
# Bioacoustic Classification of Antillean Manatee
## 3 Results and Discussion
### Results for Experiment #6--Comparing the CNN-Based and Signal Processing-Based Approaches  
The performance of the CNN networks and the FFT-based harmonic search methods are presented in Tables 8 and 9, respectively. The CNN approach presented the higher accuracy and F1 scores than the harmonic search method for each river (Accuracy: 0.905 for Changuinola River and 0.919 for San San River; F1 Scores: 0.891 for Changuinola River and 0.889 for San San River).  
The greatest precision values for both rivers were obtained by the harmonic search method on operation mode # 3 (0.992 for Changuinola River and 0.923 in San San River). However, for those cases the recall scores were very small (0.422 for Changuinola River and 0.557 for San San River). In general, the CNN approach present better scores for the harmonic search approach with a greater accuracy and greater balance between precision and recall (F1 Score).  
CNN combinations of spectrogram types and architectures provided no strikingly different results, with the exception of the linear spectrograms in the Changuinola River which provided significantly smaller recalls (and F1 Scores). This may be explained by the presence of noise background and interferences with more power in this river and the fact that those networks were trained with San San River samples that did not have such noisy conditions. For this river, the effect of noise was less  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**Experiment \#4**} & \multicolumn{3}{c}{**Experiment \#5**} \\ \hline
**Spectrogram Type** & **Network Architecture** & **AUC** & **Precision** & **Recall** & **F1** & **AUC** & **Precision** & **Recall** & **F1** \\ \hline \multirow{2}{*}{Binary} & Linear w/Dropout & 0.954 & 0.98 & 0.93 & 0.902 & 0.864 & 0.921 & 0.585 & 0.715 \\ \cline{2-9}  & Pyramidal w/Dropout & 0.951 & 0.94 & 0.89 & 0.91 & 0.876 & 0.872 & 0.65 & 0.744 \\ \hline \multirow{2}{*}{Linear} & Linear w/Dropout & 0.908 & 0.95 & 0.81 & 0.87 & 0.912 & 0.958 & 0.465 & 0.626 \\ \cline{2-9}  & Pyramidal w/Dropout & 0.927 & 0.92 & 0.85 & 0.88 & 0.849 & 0.944 & 0.505 & 0.657 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results for Experiment #4 and #5 on different metrics of classification assessment.  
Figure 9: Receiver Operator Characteristics (ROC) curves for binary representation for pyramidal with dropout architecture for (**a**) Experiment #4 and (**b**) Experiment #5.  
present in the binarized spectrogram, since the thresholding process eliminates most noise components from the spectrogram. In the case of Changuinola River, it would be of interest to do further testing using a training database from the same river with the same noise conditions.  
Concerning the computational complexity, the harmonic search method implied the computation of three FFT of size \(N=8192\) for this implementation. Each FFT required \(\mathcal{O}(N\log N)\) operations. Other steps in this method as finding maximum values of a vector and comparisons of vectors required \(\mathcal{O}(N)\) operations.
## 1 Introduction
The provided instances are derived from the actual geographic data of urban distribution centers (UDCs), regional hubs for postal services, shopping malls, restaurant businesses, residential facilities and convenience stores in the cities of Seoul and Busan, Korea. The real road distance and road time matrices are built using the T map API. T map, owned by SK Telecom, is the most accurate and the most used map service provider in Korea [5; 6]. Using the realistically created road distance and road time matrices, this paper reveals the frequency and distances by which the air distance differs from reality. The findings not  
Figure 1: A figurative explanation on using map APIs: (**a**) The air distance underrepresents the actual travel distance; (**b**) the outbound and return trip costs are different. only explain the necessities of using map APIs when planning deliveries in urban areas, but also provide insights and guidelines for urban and logistics planners. The rest of the paper is organized as follows. Section 2 provides a literature review and background information. The ACVRP and its existing benchmark instances are summarized, and a recent trend of including the use of map APIs in solving VRP variants is reviewed. In Section 3, first, the need for new realistic benchmark instances is explained, and then methods, reasons and sources for the new data sets are presented. Using the road distance and road time in the new benchmark sets, Section 4 conducts an analysis on three issues: how air distance can be deceiving when measuring costs, how outgoing and return trips differ in cost, and the magnitude in which road distances exceed air distances on average. Section 5 provides some possible limitations on the proposal and analysis of the new benchmarks. Section 6 concludes the paper with a summary and suggestions on future research.
## 5 Simulation and Discussion
### ABS Control System Simulation
#### 5.2.2 Snowy Pavement Braking  
Figure 8 shows the braking effect of the three ABS control strategies, ADRC, SMC, and PID, on a snowy road surface. From Figure 8a, it can be seen that the braking distance under the ADRC strategy is the smallest, but the differences among the three strategies are minor, around 0.2 m. Figure 8b,c indicates that the difference in braking time between ADRC and SMC is negligible, with neither exceeding 11 s, while the braking time for PID is the longest, surpassing 11 s.  
Figure 7: Braking performance on dry cement pavement. (**a**) Braking distance. (**b**) Vehicle speed. (**c**) Wheel speed. (**d**) Slip.  
In terms of slip ratio control, as shown in Figure 8d, there is no overshooting phenomenon in ADRC, and the response time of ADRC to reach the steady-state value is faster than that of SMC and PID. The comparison of the braking effects of the different ABS control strategies involves multiple factors. On snowy roads, the advantages of the ADRC strategy in terms of braking distance and braking time are not significant. However, considering the overall controller's response time, the results show that the ADRC algorithm offers superior control performance.
_Article_  
**Modeling the Long-Term Deformation of a Geodesic Spherical Frame Structure Made from Wood Plastic Composite Lumber**  
**[PERSON] \({}^{1,2}\)\({}^{\text{\textregistered}}\), [PERSON] \({}^{3,*}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Department of Civil and Environmental Engineering, Advanced Structures and Composites Center, University of Maine, Orono, ME 04469, USA; [EMAIL_ADDRESS] (M.A.A.A.); [EMAIL_ADDRESS] (R.A.L.-A.);  
\({}^{2}\) Department of Civil Engineering, College of Engineering, University of Misan, Amarah, Iraq  
\({}^{3}\) School of Forest Resources, Advanced Structures and Composites Center, University of Maine, Orono, ME 04469, USA  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS]  
Received: 28 May 2020; Accepted: 18 July 2020; Published: 21 July 2020  
**Abstract:** The long-term deformation of a geodesic spherical frame structure with a diameter of 20 m made from wood plastic composite (WPC) lumber (struts) is described using the Norton-Bailey power law model to predict the service life creep behavior (the creep strain (\(\varepsilon_{\sigma\tau}\))) of the WPC. The Norton-Bailey power law model parameters, \(A\) the power law multiplier, \(n\) the stress order, and \(m\) the time order, were obtained from experimental four-point bending flexural creep measurements of WPC lumber subjected to three levels of flexural stress: 7, 14, and 29% of the ultimate flexural strength for 200 days. The parameters obtained from the experiments showed good agreement to the model of the WPC lumber in flexure. The Norton-Bailey power law parameters were then implemented to describe the long-term deformation of the spherical frame structure. The limit of failure was considered when the WPC creep strain reaches the value of 1%. However, the FEA predicted the maximum creep strain to be 20% of the failure strain. This modeling approach is considered useful to describe and predict the long-term deformation of aquacultural structures made from viscoelastic materials during the envisioned service life (10 years) based on experimental creep data for the members that form the structure.  
**Keywords:** Norton-Bailey; power law; creep; wood plastic composite; finite element analysis
## 3 System Model
### Attacker Model  
The prime objective for the attacker is to breach high-value nodes within the Intranet. Originating from an external network, the attacker initiates their assault from a singular entry point. Once they gain control over a specific service node that provides external services, they use it as a stepping stone for lateral movements within the internal network, as illustrated in Figure 2. Leveraging this initial node, the attacker can gather information about adjacent nodes. However, due to their limited understanding of the system, they cannot pinpoint the direct route to the target node. Thus, we posit that the attacker cannot access the entire Intranet's topology at once and is restricted from making jumpsacross nodes. Their movement is constrained to lateral transitions from one node to the subsequent one, continuing this pattern until they either locate the target node or get detected by the defender.  
Furthermore, we base our model on the assumption that the attacker is a logical entity, striving to maximize their attack gains within a set number of moves without getting detected. However, they lack the capability to identify deception assets, making them vulnerable to any decoys set up by the defender. An attack is deemed successful if the attacker locates the target node, while it is deemed a failure if they fall for a honeypot, granting the defender insights into their attack strategy.
## 2 Materials and Methods
### Construction of Overexpression Plasmids and S. indica Peg-Mediated Transformation  
For creating a _SiTPS_-overexpressing mutant, the _SiTPS_ coding sequence was cloned into a Nhel and Pmel digested K167 vector, a _S. indica_-compatible vector developed in the [PERSON] lab ([PERSON] and [PERSON], unpublished; modified from [46]) using the InFusion(r) HD Cloning Kit (Takara Bio, Kusatsu, Japan). The resulting plasmid K167_SiTPSov carried the _SiTPS_ coding sequence under the control of a strong promoter (FGB1 promoter-Fungal Glucan-Binding 1, PIIN_03211) (Supplementary Figure S4). The empty vector named K167_ev was transformed as control.  
_S. indica_ protoplasts were isolated and transformed with K167_SiTPSov and K167_ev through polyethylene glycol (PEG)-mediated transformation according to [47]. Young mycelium from a 7-day-old _S. indica_ culture was harvested, homogenized and left to regenerate for three further days. The regenerated mycelium was treated with 20 mL SMC solution (1.33 M sorbitol, 50 mM CaCl\({}_{2}\) and 20 mM MES) containing 0.02 g/mL lysing enzyme from _Trichoderma harzianum_ (Sigma-Aldrich, St. Louis, MO, USA) at 32 \({}^{\circ}\)C. After 2 h, protoplasts were harvested and transformed with 7-10 ug linearized and purified plasmid in the presence of 40% PEG 3350 and heparin (15 mg/mL). Protoplasts were regenerated on plates with two layers of malt yeast peptome (MYP) agar supplemented with 0.3 M sucrose (0.7% _w/v_ malt extract, 0.1% _w/v_ peptome, 0.05% _w/v_ yeast extract). The bottom medium contained 1.2% agar and hygromycin B (80 ug/mL). The top medium (0.6% agar and no antibiotics) was mixed with the transformation mixture and then quickly poured on to the solidified bottom medium. The plates were incubated at 28 \({}^{\circ}\)C. After 10-14 days, regenerated colonies were transferred to CM plates supplemented with 80 ug/mL hygromycin B and verified for carrying either K167_SiTPS or K167_EV. _S. indica_ mutants that were notimpaired in growth and with the right mating type were selected and used for further experiments (Supplementary Figure S5).
# DeCGAN: Speech Enhancement Algorithm for Air Traffic Control
## 2 Materials and Methods  
In an air traffic control environment, the presence of complex background noise presents significant challenges in effectively capturing and utilizing multi-dimensional information in the time-frequency domain. This includes temporal, spectral, channel, and phase features that are essential for speech enhancement. Therefore, in the present work, the DeCGAN model is proposed specifically to manage complex noise issues in ATC communications.  
The DeCGAN model comprises two primary components: a generator, based on the DeConformer module, and a discriminator, informed by evaluation metric scores. The generator extracts multi-level time-frequency features to produce enhanced speech, while the discriminator assesses the resulting enhanced speech using objective metrics.  
This adversarial process encourages the generator to create higher-quality speech. The subsequent sections of this article offer a comprehensive introduction to the model's architecture and design principles.
###### Abstract  
In an aeroacoustic simulation of human voice production, the effect of the sub-grid scale (SGS) model on the acoustic spectrum was investigated. In the first step, incompressible airflow in a 3D model of larynx with vocal folds undergoing prescribed two-degree-of-freedom oscillation was simulated by laminar and Large-Eddy Simulations (LES), using the One-Equation and Wall-Adaptive Local-Eddy (WALE) SGS models. Second, the aeroacoustic sources and the sound propagation in a domain composed of the larynx and vocal tract were computed by the Perturbed Convective Wave Equation (PCWE) for vowels [u:] and [i:]. The results show that the SGS model has a significant impact not only on the flow field, but also on the spectrum of the sound sampled 1 cm downstream of the lips. With the WALE model, which is known to handle the near-wall and high-shear regions more precisely, the simulations predict significantly higher peak volumetric flow rates of air than those of the One-Equation model, only slightly lower than the laminar simulation. The usage of the WALE SGS model also results in higher sound pressure levels of the higher harmonic frequencies.  
human phonation; vocal folds; turbulent flow; aeroacoustics; LES; WALE SGS; PCWE 1
# Meander Structure Analysis Techniques Using Artificial Neural Networks
## 4 Discussion
It can also be seen that the TDNN with \(N_{\rm d}=8\) already predicted the fast-changing characteristics of \(S_{11}\), representing the reflection of the MS's output (Figure 7c). No larger delay buffer size was attempted, as the current MAE margin was sufficient for MS analysis. In addition, the present results clearly demonstrate the advantages of the TDNN. The third CNN model was used to investigate the possible mapping between an MS's images and its electrodynamical characteristics. CNN models are rarely used in the analysis of microwave devices. In this study, we focused on establishing a basic understanding of the application of CNNs in order to predict the electrodynamical characteristics of various meander structures. Using simple CNN models was a deliberate strategy aimed at unraveling the fundamental aspects of the problem, particularly considering the relatively modest size of the dataset. The results obtained using the CNN model architecture showed low MAEs for \(S_{21}\) and \(S_{11}\) compared with the other neural networks investigated. Furthermore, relatively high standard deviation values were observed, which shows a need for additional post-processing of the signals. In future research, it would be appropriate to explore more advanced CNN architectures, taking into account the specific challenges associated with our task and the modest size of our dataset. Calculations using the MoM method for a particular MS took approximately 120 min on average. The predictions using an FFNN or TDNN took approximately less than 0.1 s. The prediction with a CNN took approximately 0.4 s on average. On the other hand,the training times of all three characteristics using the FFNN, TDNN, and CNN took approximately 3 min, 3.5 min, and 4.5 min, respectively.