esa-sceva/satcom-chunk-collection huggingface dataset:
# DBCAN: DFormer-Based Cross-Attention Network for RGB Depth Semantic Segmentation
## 2 Related Work  
### Encoder-Decoder-Based Methods  
Recently, researchers (e.g., [PERSON] et al. [14] and [PERSON] et al. [15]) have utilized encoder-decoder architecture for semantic segmentation, aiming to enhance prediction performance by leveraging richer features extracted through the encoder and decoder. These methods have demonstrated impressive results in semantic segmentation tasks. Likewise, in more intricate RGB-D segmentation tasks, this architecture is commonly employed to handle RGB and depth features.  
In encoder structures, a crucial challenge is to extract and integrate features across different modalities. Recent studies [4] have discussed the encoder design implemented in either one-stream or two-stream networks. [PERSON] et al. [16] introduced Spatial Information Guided Convolution (S-Conv) in a one-stream network. Initially, their encoder resembled an RGB semantic segmentation encoder, but S-Conv was employed in each stage to incorporate spatial features from the depth image. The inclusion of S-Conv enhances spatial perception at each stage, thus improving semantic segmentation results. [PERSON] et al. [17] proposed the Multi-modality Non-local Aggregation Module (MNAM) for a two-stream network. MNAM captures spatial and channel dependencies across encoder stages, enabling the extraction of richer features at multiple levels. Similarly, [PERSON] et al. [10]incorporated multiple Global Awareness Attention (GAA) and Local Enhancement Attention (LEA) modules in each stage of the DFormer encoder. They concatenate the features from the last few stages after the encoding stage, enhancing the expression of RGB and spatial features. The design of DFormer allows the encoder to output more effective features that encompass both cross-stage features and inherent semantics.  
Another problem in decoder structure is effective prediction using fused or two-modal features. [PERSON] et al. [18] proposed an up-sampled ResNet decoder for decoding fused features, but it overlooked the role of early encoder features. [PERSON] et al. [9] designed the Multi-Modal Multi-Resolution Fusion (MRF) module to extract encoder features from four stages, resulting in more accurate predictions. [PERSON] et al. [19] introduced L-CFM and G-CFM, applied after the final encoder stage, progressively fusing features from earlier stages. This approach ensures effective feature utilization at each encoder stage and incorporates attention mechanisms in the decoder to enhance prediction accuracy. In this paper, we leverage a subset of encoder stage features in the decoder and introduce FRRM and SSFM to enhance feature expressiveness and inter-modality connections.
## 2 Experimental Details
### Characterization  
In order to investigate the physicochemical properties of the Cu-based absorbent, the surface area and pore properties by the nitrogen adsorption method (Quantachrome Instruments, AUTOSORB-1) were analyzed using Brunauer-Emmett-Teller (BET) and Barrett-Joyner-Halenda (BJH) methods. In addition, X-ray diffraction (XRD, Rigaku, D/MAX-2500, Tokyo, Japan) was used to confirm the crystal structure of the prepared absorbent. Finally, the surface shape and macropores of the absorbent were confirmed by scanning electron microscopy (SEM, Hitachi, S-4100, Tokyo, Japan).
## 3 Results of Computational Experiments  
### Assumptions, Input Data, and Computational Experiments  
The computations verifying the methodology proposed and presented in Section 2 were carried out on a MacBook Pro computer with a 3.1 GHz dual-core Intel Core processor and a Windows 10 operating system embedded in a virtual machine. The model was implemented into the Excel environment, and the computations were performed using Frontline Solvers' evolutionary optimization engines.  
In the experiments, 10 sample instances were analyzed, i.e., sets consisting of 8-20 PLUs, as per customer order, containing only the units to be potentially expanded into SPLUs, see subset \(A2\) on Figure 2. The complete data sets of analysed instances are attached to this article, see Tables A1-A3 in Appendix A. SPLUs were then built from the elementary PLUs based on the mathematical model presented in Section 2, Equations (1)-(11). The results obtained are shown below in Table 4. The analysis included such parameters as the following:  
* Minimum and maximum values: weight, height, and number of PLUs of _top_ and _base_ type;
* The structure of storage carriers, including PLU _top_ and _base_ types;
* Boundary parameters of height and weight of built SPLUs. The results were analyzed taking into account the following:
* The number of SPLUs constructed; the value of the decision variable \(y_{j}\);
* The degree of filling the available height and weight of SPLUs;
* Computing time to solve the problem.
# A Helping Hand to the Elderly: Securing Their Freedom through the HAIE Framework
## 5 Case Studies
### Case Study 1: [PERSON]: IoT-Wearable Device: Smart Watch  
[PERSON], an 85-year-old cardiac patient, is becoming more fragile, and has recently undergone coronary artery bypass heart surgery. He is a widower, and his family lives interstate. [PERSON] prefers to live alone at his home in a small town (Figure 15). [PERSON], [PERSON]'s daughter, is concerned about her father's health, so she gifted him a smartwatch (an  
Figure 14: Mapping the components of the HAIE into the framework content.  
IoT-based wearable) to keep track of [PERSON] without disturbing his independence. The deployment of case study 1 can be viewed in Appendix A Figure A1b.  
[PERSON]'s Outcome:  
[PERSON]'s experience with keeping track of his health has been positive. His daughter, [PERSON], feels relieved and believes that [PERSON] is safe while living independently. She is able to monitor all of [PERSON]'s health activities and consult with the doctor using the reports provided by the watch. The watch offers various healthcare features [34]:  
* Heart rate notifications;
* Irregular rhythm notifications;
* ECG application;
* Low cardio fitness notifications;
* Blood oxygen level;
* Fall detection.  
Highlights for additional research in pattern recognition:  
[PERSON]'s case study emphasizes the importance of technology in promoting well-being and how preserving autonomy can enable the continuation of traditional family roles despite changing medical requirements. The technology, which can be remotely controlled over the internet through an app and the cloud, was not extensively utilized or valued by [PERSON]. Due to [PERSON]'s declining health, he became unable to perform a crucial activity in his life. Therefore, [PERSON] finds this function to be very beneficial as it allows her to access his vitals via the app. Here are some of the key highlights from the case study:  
* In response to a particular task, smart technology may be used in conjunction with home alterations.
* We investigate how new technologies are purchased and maintained by elderly individuals on restricted budgets.
* We consider how smart technology can promote independence and self-care, improving wellness by boosting feelings of value and independence.  
Limitations of Smart wearable  
Even though the watch has many features that help [PERSON] keep track of [PERSON]'s health, it also has some disadvantages:  
* The watch is expensive; thus, many people cannot afford it.  
Figure 15: Case Study 1: [PERSON].  
* The watch has a maximum battery backup of 20 h, which means it must be charged almost daily.
* The watch requires active cellular or Wi-Fi connectivity to transfer data.
* The watch requires high maintenance.
# Coarse-Fine Combined Bridge Crack Detection Based on Deep Learning
## 5 Conclusions  
To solve the incomplete crack detection and segmentation caused by the complex background and small proportion in the actual bridge crack images, this paper proposed a coarse-fine combined bridge crack detection method of \"double detection + single segmentation\" based on deep learning. The images of old civil bridges and viaduct bridges against a complex background and the images of a bridge crack against a simple background were used as datasets. By setting up model effectiveness evaluation experiments, the efficient Faster R-CNN and YOLOV5(x) and U-Net were selected as the basic networks for fine crack detection, the bridge crack refined detection was realised, and the effectiveness and practicality of the selected network model for crack refined detection were verified.  
In the experiment, the YOLOV5(x) model was preferred for the initial and fine detection of bridge cracks, as it could preserve the original shape of the cracks and had a strong anti-interference capability. The detected crack information was accurate, detailed and complete. The preferred U-Net model was applied for fine crack segmentation; the AC-value of its crack segmentation results was as high as 98.37%, and the crack segmentation effect was fine and complete.  
The \"double detection + single segmentation\" coarse-fine combined bridge crack detection method based on deep learning can realise the high efficiency and fine detection of bridge cracks and can provide help for future bridge crack detection. It also has a certain reference value for high-quality image processing and application in AI intelligent detection of construction damage in real time.  
Conceptualization, K.M. and M.H.; methodology, K.M. and X.M.; validation, X.M.; formal analysis, M.H.; investigation, J.L. and J.M.; resources, J.M.; data curation, X.M. and M.H.; writing--original draft preparation, X.M. and M.H; writing--review and editing, K.M.; visualization, J.L.; supervision, Y.X.; project administration, K.M.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript. This research was funded by the Henan Provincial Science and Technology Research Project (Grant No. 222102220035). Not applicable. Not applicable. Not applicable. Data Availability Statement: The data will be made available on request. The authors would like to thank the team for sharing the crack image dataset and the reviewers for their insightful comments. The authors declare no conflicts of interest.
## 2 Material and Methods
### Estimation of DPPH Mediated Free Radical Scavenging Activity  
The antioxidant assay was explored by using 1,1 difenyl-2-picryl-hydrazyl (DPPH) radical assay for both _P. emblica_ and _A. indica_ extract. In brief, stock solution (1 mg/mL) of _P. emblica_ or _A. indica_ extract was diluted to different concentrations (50-600 ug/mL) by serial dilution in methanol. Now, 0.3 mM DPPH was prepared in analytical grade methanol, and 1 mL of this solution was mixed with different dilutions (2500 uL) of extracts. The reaction mixtures were mixed well manually. The tubes were kept away from the light for half an hour at room temperature. After completion of incubation, the absorbance of samples was recorded spectrophotometrically at 517 nm. DPPH is completely soluble in MeOH. DPPH in methanol solution was used as a control. Hence, methanol was used as a blank to set the spectrophotometer to zero before measuring absorbance of our control and samples. The percentage of free radical reduced of _P. emblica_ or _A. indica_ extract was calculated in accordance with the following equation.  
\[\text{Percentage of free radical reduced= [(Nc -Ns)/Nc]}\times 100\]  
where Nc means the control absorbance, and Ns denotes the sample solution absorbance.
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 5 Modification Processes of Kaolin: Enhancing Industrial Applicability  
Kaolin is a widely available and cost-effective layered silicate mineral. Prior to any modifications, the raw material is collected, crushed, and sieved to achieve the desired particle size. However, unprocessed kaolin often exhibits limitations such as low purity, large particle size, hardness, and suboptimal performance, rendering it inadequate for modern industrial applications. To overcome these limitations and increase kaolin's value and economic effectiveness across various industries, various methods have been developed to enhance its specific surface area, catalytic activity, and adsorption efficiency. Its high specific surface area, ion exchange capacity, and unique layered structure provide excellent reactive activity and adsorption capabilities.  
These modification techniques include heat treatment by calcination, mechanochemical activation, chemical (acid-base) modification, intercalation/exfoliation treatment, and coating modification, among others (Figure 6).
## 4 Numerical Methods and Validation  
### Governing Equations and Numerical Methods  
To analyze the airflow driven by the axial-flow fan in the outdoor unit numerically, the following three-dimensional incompressible unsteady Reynolds-Averaged Navier-Stokes (RANS) equations are used as the governing equations:  
\[\frac{\partial}{\partial x_{j}}\Big{(}\rho u_{j}\Big{)}=0, \tag{1}\]  
\[\frac{\partial}{\partial t}(\rho u_{i})+\frac{\partial}{\partial x_{j}}(\rho u _{j}u_{i})=-\frac{\partial p}{\partial x_{i}}+\frac{\partial}{\partial x_{j}} \bigg{(}\mu\bigg{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j} }{\partial x_{i}}\bigg{)}-\rho\overline{u_{j}^{\prime}u_{i}^{\prime}}\bigg{)} \tag{2}\]  
Figure 3: Measured overall sound pressure level versus rotating speed.  
Figure 2: Schematic diagram of the actual fan-performance tester.  
Equations (1) and (2) are numerically solved using a finite volume method based on the unstructured grids. The Re-Normalization Group (RNG) k-\(\epsilon\) model is used as the turbulence model [13; 14; 15; 16], and the standard wall function is used to model near-wall flows. For the numerical solutions of turbulent kinetic energy and turbulent dissipation rate, the first-order upwind discretization scheme is selected. Second-order upwind discretization scheme is used to solve the momentum term, the Semi-implicit Method for Pressure-Linked Equations (SIMPLE) scheme is used to solve the pressure-velocity coupling equations. The first order implicit method is selected for the transient discretization. The time step is 0.01/angular velocity. The under-relaxation factors are set to be 0.3, 0.7, 0.8, and 0.8 for pressure, momentum, turbulent kinetic energy, and turbulent viscosity, respectively. The volume flow rate at the discharge zone is used as the convergence criteria. The sliding mesh technique is used to simulate the rotating of the axial fan. These numerical methods are realized using the commercial CFD solver of ANSYS fluent.  
Ficovs-Williams and Hawkings (FW-H) equation [17] in the following form is used for the prediction of radiated acoustic pressure:  
\[4\pi c^{2}H\rho^{\prime}\Big{(}\vec{x},t\Big{)}=\frac{\partial}{\partial t} \int_{S}\frac{p_{0}\overrightarrow{v}\cdot\overrightarrow{n}}{r|1-M_{r}|}dS \Big{(}\overrightarrow{\eta}\Big{)}-\frac{\partial}{\partial x_{i}}\int_{S} \frac{p_{ij}\pi_{j}}{r|1-M_{r}|}dS\Big{(}\overrightarrow{\eta}\Big{)}+\frac{ \partial^{2}}{\partial x_{i}\partial x_{j}}\int_{V}\frac{T_{ij}}{r|1-M_{r}|}d ^{3}\overrightarrow{\eta}. \tag{3}\]  
Note that the volume source, the last term in the right-hand side of Equation (3), which is known to be negligible at low Mach number flow, is neglected.
## 2 Materials and Methods
### HS-SPME Experimental Conditions  
The SPME holder for manual sampling and fiber coatings was purchased from Supelco (Aldrich, Bellefonte, PA, USA) and the SPME fiber DVB/CAR/PDMS (1 cm stable-flexTM fused silica fiber, coated with partially cross-linked 50/30 \(\upmu\)m divinylbenzene/carboxen/polydimethylsiloxane) was conditioned before use, according to the manufacturer's recommendations. To avoid any cross-over contamination due to own fiber coating, blanks corresponding to the analysis of the fiber coating not submitted to any extraction procedure were run between sets of three analyses. All measurements were made with three replicates, each replicate representing the analysis of one different aliquot of each coffee sample.  
* Coffee powders: the analysis of the powdered coffees was performed based on a previous study [(29)]. Briefly, for each HS-SPME assay, 1.2 g of sample was placed into a 12 mL glass vial that was capped with a PTFE septum and a screw cap (Chromaol, Hertfordshire, UK). The vial was placed in a thermostatic bath adjusted to 55.0 \(\pm\) 0.1 \({}^{\circ}\)C, and the SPME fiber DVB/CAR/PDMS was inserted into the headspace for 12 min.
* Espresso coffee brew: the SPME assay used for the espresso coffee brew was defined to simulate the consumer's perception when drinking an espresso coffee. So, based on a previously reported work [(30)], each espresso coffee sample (40 mL \(\pm\) 2) was extracted directly into a thermostatized SPME glass vial (120 mL, 60.0 \(\pm\) 0.1 \({}^{\circ}\)C, for 5 min), which was sealed and kept at 60 \({}^{\circ}\)C, at constant stirring (ca. 400 rpm), and the DVB/CAR/PDMS fiber was immediately inserted into the sample headspace for 3 min. The temperature of 60 \({}^{\circ}\)C was chosen since it is about the same temperature as the coffee would normally be when consumed by the consumer [(17)].  
To establish a basis for comparison between the coffee powder and the respective brew, the conditions for the GC \(\times\) GC - ToFMS analysis were the same for the two types of coffee samples.
# Landsat Surface Product Validation Instrumentation:
## 3 ASD and UAS Technology Assessment
### Accuracy
#### 3.2.5 55% Permaflect Panel Accuracy  
The accuracy error for the Permaflect 55% panel is shown in Figure 14. As the name suggests, the reflectance of this panel was close to 55% at all wavelengths. Accuracy errors tend to be small for the EROS ASD systems, with a positive trend as a function of wavelength; they range from \(-0.005\) in the Coastal Aerosol band to +0.025 in the SWIR2 band. SDSU measurements are all biased positive and range from 0.025 to 0.065. UAS measurement errors are slightly larger than the SDSU errors. Since the UAS calibration was based on the ELM calibration approach using the SDSU measurement of the Permaflect panels, this result is consistent with the calibration traceability of the instrumentation. Overall, these results again indicate an accuracy of 0.02 reflectance units is possible with ASD-based systems.
## Materials and Methods
### Satellite Data
#### 2.6.2 Building Time Series of Planet's Vegetation Indices in Google Earth Engine  
We used Google Earth Engine (GEE; [[https://eartheengine.google.com/](https://eartheengine.google.com/)]([https://eartheengine.google.com/](https://eartheengine.google.com/))), which is a freely available cloud-based geospatial processing platform that combines a large catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities [50]. All downloaded PS-clipped TIFFs, as well as the shapefile vector layers of the 82 vineyards (i.e., polygons), were uploaded as image- and feature collections to GEE. Then, GEE JavaScript API was used to: (1) calculate the four vegetation indices (VIs) for all images; (2) merge the number of images to a single image per date using a maximum value criteria (i.e., creating an image mosaic by selecting the maximum value for each pixel from all available images for that day) [43]. In this way, noisy data caused by cloud contamination and/or sensor view uncertainties were eliminated from the image; (3) generate time series of the four VIs; (4) calculate the time series of the averaged VI over the entire area of the vineyard (i.e., over all pixels within the vineyard area), for the 82 vineyards, and for the 60 plots within the vineyard area of Mevo Beitar (Figure 1b).
## 4 System Model
### Dataset
Scheduling_class and priority are the two important attributes in this table. Tasks and jobs are classified according to their sensitivity to delays in execution by a scheduling class. One number represents the scheduling class, with 3 being a more time-sensitive job and 0 indicating a non-production job. Priorities are assigned to everything, and those integers are translated into a scale from 0 (the lowest) to 360 or more (the highest). Higher-priority items often get more preference than lower-priority items. * **Instance Event Table** The data in this table pertain to occurrences that have occurred in instances (tasks and alloc instances). Time, scheduling class, priority, and so on, are only a few of the initial sets of fields that are also included in the CollectionEvents table. Instance-specific attributes may consist of machine id, resource request, and others. Resource requests show how much memory and CPU an instance can use (called its limit). Tasks that are used too much may be slowed down or stopped (for resources such as memory). Because the scheduler may over-commit system resources, there may not be enough to meet the needs of all tasks during their run-time, even if each task is not used as much as it could be. This could kill a low-priority job(s). * **Instance Usage Table** The amount of resource utilization that is reported for an alloc instance is the amount of resource utilization for all of the jobs that are executed inside it during each sample period. This table contains 18 fields including cpu_usage_distribution, tail_cpu_usage_distribution, and cycles_per_instruction. The cpu usage distribution and tail cpu usage distribution vectors each give in-depth information on the distribution of the amount of NCUs used by the CPU throughout the course of the five-minute measurement frame. The statistics known as Cycles Per Instruction (CPI) and Memory Accesses Per Instruction (MAI) are gathered from the performance counters of the CPU; however, not all machines get this information. Memory accesses are determined by the measurements of the cache level most recently used.
## 3 Results and Discussion
### Identification of Volatile Biomarkers for Four Different Apricot Cultivars  
The variable importance for the projection (VIP) was found to distinguish the most characteristic variables based on the result of the PLS-DA classification above, and the VIP plot is given in Figure 4. Generally speaking, a VIP value greater than 1.0 means significance to the overall volatile profile, whereas a VIP value less than 0.5 shows its unimportance [42]. In this study, the VIP values larger than 1.0 were picked out to produce the volatile clustering heatmap of the four apricot cultivars (Figure 5 and Figure S3). According to the row clustering results, the compounds (VIP > 1.0) were divided into four categories. It was obvious that the relative contents of salicaldehyde and eucalypt in XB apricots were the most abundant in the first group. Category II included 14 volatiles. Most of these compounds in the DBY apricots were richer than in the other three cultivars. In particular, the third category included decanal, ocimenol, non-2-enal, acetophenone, myrcenol and 7-hexadecenal, and their relative content was higher in LPH apricots compared to the other apricot cultivars. In the 4 th category, most compounds were relatively abundant in LPH and YOU cultivars except for 1-methyl-4-(1-methylethyl)-2-cyclohexen-1-ol and 2,4-dimethyl-cyclo-hexanol.  
The seven differentiated volatile compounds whose VIP > 1.0 are shown in Table 4. Particularly, damascenone only existed in DBY apricots. 2,4-Dimethyl-cyclohexanol was only present in YOU apricot samples. Three compounds, i.e., acetophenone, myrcenol and 7-hexadecenal, were only found in LPH apricots. Moreover, eucalypt and salicaldehyde were characteristic volatiles for XB apricots.  
Figure 4: The variable importance for the projection in PLS-DA model. DBY, Dabaiyou apricot; LPH, Lupuhongdaike apricot; YOU, You apricot; XB, Xiaobai apricot.