esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
It is the magnetite content that gives the color difference to this rock which can be seen in gray, gray-oily black, and green colors. Serpentine rocks are used as aggregates in normal concrete. Several studies have examined the use of geological materials in concrete structures as radiation-shielding materials. [PERSON] et al. [27] examined the suitability of serpentinite aggregates for radiation-shielding concrete and demonstrated that serpentinite exhibited neutron-absorption performance in the range of 12-33%. [PERSON] et al. [28] conducted an experimental study on the radiation-shielding parameters of magnetite- and serpentine-doped ceramics. [PERSON] et al. [29] reported that the addition of boric acid to serpentine concrete enhanced its thermal and fast neutron-attenuation performance while decreasing its gamma-attenuation capability. [PERSON] et al. [30] compared coarse serpentine aggregates (SCAg) with fine serpentine aggregates (SFAg), concluding that the shielding, mechanical, and physical properties of the concrete were significantly affected by the natural porosity of the materials. Utilizing modern technologies, the shielding performance of iron-magnetite-serpentine cement concrete (IMSCC) for gamma and neutron radiation in nuclear reactor shielding was evaluated [31]. Despite its low hydrogen concentration, high-neutron-absorption cross-sections were observed. In the application of serpentine concrete as a neutron-shielding material, researchers used the Monte Carlo code and determined that the average gamma dose was 4.395 \(\pm\) 0.122 \(\mu\)Sv/h, which is lower than the dose measured for ordinary concrete (10.399 \(\pm\) 0.083 \(\mu\)Sv/h) [3]. Rare earth elements (REEs) are currently incorporated into composite materials to provide effective neutron shielding. Among the heavy rare earth elements, including Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Sc, and Y, gadolinium (Gd), samarium (Sm), and europium (Eu) are known in the literature for their high neutron-absorption cross-sections (for 2200 m/s neutrons) of 49,700 barns, 5922 barns, and 4530 barns, respectively. These elements are commonly used in shielding, control, and structural materials to ensure the safe operation of nuclear reactors and play an active role in processes such as slowing down and scattering fast neutrons, absorbing thermal neutrons, and capturing secondary particles [32; 33; 34]. A review of the literature reveals a lack of experimental studies investigating the radiation-shielding properties of REEs in combination with natural rocks, as well as the testing of such materials for radiation-protection purposes. Therefore, this study is significant in terms of comparing the radiation-shielding properties of serpentinite rocks, particularly in relation to their rare earth element content, within the Guleman ophiolite complex, which presents extensive surface exposure.
## Materials and Methods
### Efficient Transmit Delay Calculation  
To reduce the total number of computations for transmit delay calculation, we propose reusing the delay values calculated earlier. This method can be applied when the steering angle \(\theta_{PW}\) is an integer multiple of the angle between the direction vectors of two adjacent elements. Under this condition, Equation (1) can be developed as follows:  
\[\begin{split}&\tau_{TX}(\theta_{PW},n)=\tau_{TX}(k\cdot d\theta,n)\\ &=R\cdot\cos(k\cdot d\theta-\theta_{ele,n})/c\\ &=R\cdot\cos(k\cdot d\theta-\left((n-1)\cdot d\theta+\theta_{ele,1 }\right))/\text{c}\\ &=R\cdot\cos(0-\left((n-k-1)\cdot d\theta+\theta_{ele,1}\right) )/\text{c}\\ &=\tau_{TX}(0,n-k)\ (k=k_{min},k_{min}+1,\dots,k_{max})\end{split} \tag{2}\]  
where \(d\theta\) is the angle between the direction vectors of two adjacent elements, namely, \(d\theta=\theta_{ele,n+1}-\theta_{ele,n}\), and \(k_{min}\) and \(k_{max}\) are the minimum and the maximum values of an integer \(k\) (\(k_{min}<0<k_{max}\)). As a result of the equality in Equation (2), the transmit delay of the \(n^{\text{th}}\)-element for \(\theta_{PW}=k\cdot d\theta\) is equal to the delay of the (\(n\)-\(k\))th-element for \(\theta_{PW}=0\). By applying the equality to the delay calculation, the delays for \(\theta_{PW}=k\cdot d\theta\) can be obtained from the delays for \(\theta_{PW}=0\) as shown in Figure 6. In addition, the steering angles of plane-waves are selected with symmetry [11] so that \(-k_{min}=k_{max}\) for simplicity.  
Figure 5: Simulated B-mode images of point targets acquired using (**a**) 9 plane-waves and (**b**) 65 plane-waves with \(\theta_{PW}=\pm 37.7^{\circ}\). The simulation was conducted by a Field II ultrasound simulation program [22] with a C5-2 curved array transducer (a 128-element transducer with a pitch of 425 \(\upmu\)m, a center frequency of 3.1 MHz, and a convex angle of 75.54 degree).
## 3 Results and Their Analysis
An offshore wind farm had a bigger impact on the environment as compared to a land-based wind farm, for exactly half of the analyzed categories that make up the nonergonomic impacts (emission of organic substances causing respiratory system diseases, radioactive, carcinogenic, and ecotoxic elements) (Figure 3). An analysis of four impact categories that make up the nonfunctionality impacts shows that two of them, that is, fossil fuels extraction processes (58,004 Pt--land-based wind farm, 41,713 Pt--offshore wind power plant) and minerals (12,208 Pt--land based farm, 11,996 Pt--offshore wind power plant) have a particularly significant impact on the value of this index. Higher level of nonfunctional impact was found for a land-based wind power plant (processes connected with land use, extraction of minerals and fossil fuels), for all the three categories (Figure 4). Figure 3: Results of grouping and weighing environmental impacts for ergonomic impacts involved in life cycles of land based and offshore wind farms. Authors’ own research. The value of nonecological impact of the land based and offshore wind power plant includes eight categories. The one, characterized by the highest level of impacts involves the fossil fuels extraction processes (58,004 Pt--land based wind fam, 41,713 Pt--offshore wind power plant) and processes of minerals extraction to a smaller degree (12,208 Pt--land based wind power plant, 11,996 Pt--offshore wind power plant), emission of compounds which cause the climate change (9138 Pt--land based wind farm, 9875 Pt--offshore wind power plant) and substances with ecotoxic properties (9860 Pt--land based wind power plant, 10,297 Pt--offshore wind power plant). The offshore wind power plant was characterized by a higher value of the nonecological impact index (as compared to land-based wind power plant), for three categories: emission of radioactive elements, emission of ecotoxic compounds, and emission of substances that contribute to global warming (Figure 5).
# Deep Learning and Embedding Based Latent Factor Model for Collaborative Recommender Systems
## 4 The Proposed Model
### DMF Method  
The authors of [8] introduced DMF for top-N item recommendations. To modify the representation of the user and item, the model first maps users' and items' input vectors into latently structured spaces. In this space, users and items are represented by low-dimensional vectors. DMF uses latent features to compute the rank of items for specified users based on similarity results. DMF uses both explicit and implicit feedback as input features. On the other hand, DELCR uses dense latent factors as input features, and we developed the method to predict the ratings rather than predict the rank. For model optimization, we use distinct loss functions from DMF. In DMF, they filled the missing values in the rating matrix with zeros. We believed that this resulted in biases, which in turn affected the performance of recommendations. In our case, we learned these missed values from existing rating values using embedding techniques. In addition, we used an embedding layer to create a dense vector representation that addresses the problem of sparsity in our scenario. In particular, our approach maps the input data into a low-dimensional dense vector before mapping the features into a dual deep neural network structure. In general, our model is a pure collaborative filtering method because, unlike DMF, it predicts user preference for an item based on previous interactions.
## 3 Methodology
### Condition Factor Selection  
In landslide susceptibility evaluations, the PCC serves as a significant metric for analyzing the relationship among conditional factors. A PCC value nearing an absolute value of 1 signifies a more robust correlation between the two variables being analyzed [9; 51]. In the DL training, high-correlation data not only causes data redundancy and reduces model execution efficiency, but it also has an impact on prediction accuracy. However, correlation analysis does not analyze the dependency between the character variable (LCFs) and the dependent variable (landslide class label) to select the appropriate condition factor that triggers landslides. Given this issue, the study employed the MI to evaluate the association between the predictors and the outcome variables. Finally, the PCC and MI were combined to choose the LCFs.  
_PCC_ includes the conditional factors \(E\) and \(F\), and the \(PCC(E,F)\) of \(E\) and \(F\) is defined as follows:  
\[PCC(E,F)=\frac{\sum_{i=1}^{n}\left(E_{i}-\overline{E}\right)\left(F_{i}- \overline{F}\right)}{\sqrt{\sum_{i=1}^{n}\left(E_{i}-\overline{E}\right)^{2}} \sqrt{\sum_{i=1}^{n}\left(F_{i}-\overline{F}\right)^{2}}}, \tag{1}\]  
where \(E_{i}\) and \(F_{i}\) denote the \(i\) th conditional factor layer of \(E\) and \(F\), respectively, while \(\overline{E}\) and \(\overline{F}\) denote the sample averages of \(E\) and \(F\). According to PCC [52], if PCC > 0.7, the variables are strongly correlated, warranting consideration for exclusion from further analysis.  
_MI_ includes the condition factor \(E\) and landslide type variable \(G\), and the \(MI(E,G)\) of \(E\) and \(G\) is defined as follows:  
\[MI(E,G)=\sum_{g\in G}\sum_{e\in E}p(e,g)\log\left(\frac{p(e,g)}{p(e)p(g)} \right), \tag{2}\]  
where \(p(e,g)\) denotes the joint probability density function of the stochastic variables \(E\) and \(G\), \(p(e)\) and \(p(g)\) denotes the edge probability density functions of \(E\) and \(G\), respectively. According to the properties of MI [53], the properties of MI indicate that an increase in the mutual information value signifies a stronger dependence between the features and the label classes.
## 7 Current Challenges and Problems  
Biomedical device technology has been recently used for medical applications in the human body by monitoring or recording many signals such as EMG, ECG, EOG and ERG and monitoring or diagnosing the current conditions of implantable devices such as retinal implants, cochlear implants, pacemakers, brain implants and for wireless capsule endoscopy. Two of the most important issues for biomedical implants are patients' safety and comfort. These can be achieved by reducing the power consumption, switching losses, choosing suitable modulation and carrier frequency according to a MICS table and by increasing the efficient energy transfer capabilities, for example, using near-field wireless energy transfer that results in electromagnetic fields that would not heat the tissues. However, for example, heating of pacemaker leads is the major problem undermining magnetic resonance imaging. Therefore, hundreds of patients with a pacemaker or implantable cardioverter face safety and reliability issues [90,91]. Typically a wireless biomedical devices consists of two parts, the external part that transmits RF signals to the internal part through an inductive coupling downlink using variety of modulated signals such as ASK, FSK, PSK (BPSK, QPSK, DBPSK, DQPSK, OQPSK), and it must transmit with high efficiency and low power consumption. Thus, an aim of research into implantable biomedical devices is to develop such devices with low power consumption, efficient energy transfer, the highest data rate, efficient power amplifiers, small size and low-cost, so many parameters need to be considered, as discussed in the following sections.
applied sciences Article  
Optimal Buccal Site for Mini-Implant Placement on Attached Gingiva of Posterior Maxilla: A CBCT Study  
[PERSON] 1, [PERSON] 2 and [PERSON] 3,*(c)  
This research aims to investigate the optimal buccal site on the attached gingiva of the posterior maxilla for mini-implant placement for anchorage purposes in orthodontics. In 23 female patients, mini-implants were implemented between the roots of the first molar and second premolar, in the maxilla, for anchorage purposes. A CBCT was acquired for diagnostic purposes, and intraoral scanning was performed. Using the digital model that was the result of combining the DICOM and STL files, the cortical bone thickness and density, as well as the trabecular bone density, were measured on three axial bone slices corresponding to the three defined height levels (lower, middle, upper) on the attached gingiva, in the interradicular area. Pearson and eta correlation tests were performed in order to investigate possible correlations between height in the attached gingiva, and the corresponding cortical bone thickness and density, as well as the corresponding trabecular bone density. The correlations regarding the height level in the attached gingiva were medium for the cortical bone thickness, and weak for the cortical bone density, while a strong correlation was found between the cortical bone thickness and density. The upper level of the attached gingiva, between the second premolar and the first molar in the maxilla, is the optimal site for mini-implant placement, as the cortical bone thickness and density are probably greater than in the lower and middle level.  
mini-implants; attached gingiva; cortical bone; cortical bone density; CBCT
## 4 Results and Discussions
### Discussion and Benchmark against Recent Studies
Mean surface flow velocity along the PMG glacier (16 sections, 7.5 km starting from the front and upstream, images from SENTINEL2, Res. 10 m). We also report (bars) percentage deviation from the mean value in each of the sections, for each acquisition date, colored seasonally. Same background color indicates same season (season color code as in Figure 8), and a different plot indicates different images/dates (date of second image). authors, such glacier behaves differently from other glaciers in the area, such as the PMG, and comparison may only be qualitative. Caluving rate assessment, which gives possibly the most interesting results, clearly suffers from (poor) estimation of its terms (namely, flow rate, front position and ablation), all subject to considerable noise. Analysis of noise in calving rates however would require assessment of flow velocity as estimated via satellite, against those assessed in the same periods from ground observations, which was not possible here, if not only qualitatively. Moreover, actual front positioning from local assessment would be probably necessary. Ablation would also need to be measured on the ground in some points and maybe modelled accurately glacier-wise. All such pieces of information are sparsely available. Here, for the purpose of the paper, we could state that our estimates reasonably well match those from other authors in other periods and thereby seemingly indicate substantial stability of the glacier. Overall, very little weather and glaciological local information is available for the PMG, and full understanding of all mechanisms leading to glacier's dynamics and stability is difficult without further field investigation. Notwithstanding, our findings are of interest given that we investigated for the first time PMG with SENTINEL2 data that we know of, and we extended the present knowledge by 4 years as given in the literature. Recently, ICESat-2 satellite, a NASA follow-up mission to ICE-Sat (2003-2010, Res. 60-70 m) was launched, with the goal to continue measuring and monitoring the impacts of the changing environment. The ICESat-2 satellite carries an improved laser altimeter called ATLAS (Advanced Topographic Laser Altimeter System), designed to measure ice-sheet topography, sea ice freeboard as well as cloud and atmospheric properties and global vegetation, with a resolution of about 20 m, i.e., slightly coarser than, but comparable to, Landsat7-8 and Sentinel 2 here. In the future, some attempts at using also data from this satellite may be pursued, including for the PMG area, to gain further insight.
# Dual Hybrid Attention Mechanism-Based U-Net for Building Segmentation in Remote Sensing Images
###### Abstract  
High-resolution remote sensing images (HRRSI) have important theoretical and practical value in urban planning. However, current segmentation methods often struggle with issues like blurred edges and loss of detailed information due to the intricate backgrounds and rich semantics in high-resolution remote sensing images. To tackle these challenges, this paper proposes an end-to-end attention-based Convolutional Neural Network (CNN) called Double Hybrid Attention U-Net (DHAU-Net). We designed a new Double Hybrid Attention structure consisting of dual-parallel hybrid attention modules to replace the skip connections in U-Net, which can eliminate redundant information interference and enhances the collection and utilization of important shallow features. Comprehensive experiments on the Massachusetts remote sensing building dataset and the Inria aerial image labeling dataset demonstrate that our proposed method achieves effective pixel-level building segmentation in urban remote sensing images by eliminating redundant information interference and making full use of shallow features, and improves the segmentation performance without significant time costs (approximately 15%). The evaluation metrics reveal significant results, with an accuracy rate of 0.9808, precision reaching 0.9300, an F1 score of 0.9112, a mean intersection over union (mIoU) of 0.9088, and a recall rate of 0.8932.  
deep learning; remote sensing image; attention mechanism; U-net; semantic segmentation. Article
## 2 Materials and Methods
### Hardware-Specific Optimization and Implementation  
Xilinx Vitis-AI is a platform for deploying machine learning models on Xilinx hardware, such as FPGAs. It enhances efficiency by optimizing models through techniques like quantization, which reduces their size and complexity while preserving accuracy. Supporting popular frameworks like TensorFlow and PyTorch, it simplifies the integration of pretrained models [17]. Conceptual pipeline of the Xilinx Vitis-AI is depicted in Figure 3. In this work, after building and training models on PC, to deploy them on the FPGA efficiently, the model was imported to the Vitis-AI platform and a streamlined workflow was implemented to optimize hardware resource utilization while preserving model accuracy through the following steps:  
Figure 2: VGG16 and VGG19 model architecture using transfer learning in this work.  
* **Quantization**: Weights and activations were quantized by converting 32-bit floating point to 8-bit fixed point precision through a post-training quantization process using Xilinx Vitis-AI tools, reducing computational and memory requirements considerably while maintaining high classification accuracy.
* **Compilation**: Quantized models were compiled with the Vitis-AI library to generate FPGA-compatible binaries optimized for the Zynq UltraScale + MPSoC. This step included tailoring for the DPU to enhance hardware-specific performance.
* **Deployment on FPGA**: The compiled models were deployed onto the FPGA, where the DPU configuration was fine-tuned for parallel computation. FPGA resources, including Look-Up Tables (LUTs), Digital Signal Processors (DSPs), and Block RAM (BRAM), were leveraged to minimize inference latency and maximize throughput.
* **Model Execution Using the PYNQ Framework**: Using the Python-based PYNQ framework in Jupyter Notebook, the FPGA was navigated to perform inference. Test images from the CIFAR-10 dataset were imported, preprocessed, and transmitted to the FPGA's programmable logic. The classified outputs were retrieved, completing the inference process efficiently.  
This workflow highlights the effectiveness of hardware-specific optimizations for deploying DL models on FPGAs, enabling real-time inference in resource-constrained environments with enhanced computational efficiency.
## 2 Study Area and Datasets  
Daegu Metropolitan City in South Korea was selected as the study area in this research for the following reasons. First, the urban areas of Daegu have been significantly expanded of late [13]. Second, there are long-term measured coarse particle (PM\({}_{10}\)) data acquired by the 11 air quality monitoring stations (AQMSs) in Daegu, which can be used for the study [14]. Daegu has operated these AQMSs since 1973 for sustainably monitoring the air quality condition of the city. Figure 1 shows the locations of the 11 AQMSs in Daegu, South Korea.  
The multi-temporal Landsat satellite images acquired on 13 May 2007 (\"first Landsat image\") and on April 29, 2017 (\"second Landsat image\") were used in this study for the following reasons. First, the urban areas in Daegu significantly expanded during such periods due to the city's urban development policy. Second, both Landsat images were less affected by the prevailing atmospheric conditions then. Figure 2 shows one section each of the first and second Landsat images.  
The first Landsat image was acquired by the Landsat-5 thematic mapper (TM) sensor, and it consists of seven bands (blue: 450-520 nm; green: 520-600 nm; red: 630-690 nm; near-infrared: 770-900 nm; short-wave infrared 1: 1550-1750 nm; short-wave infrared 2: 2080-2350 nm; and thermal: 10,400-12,500 nm) [15]. The second Landsat image was acquired by the Landsat-8 operational land imager (OLI) and the thermal infrared sensor (TIRS), and it consists of nine bands (coastal aerosol: 435-451 nm; blue: 452-512 nm; green: 533-590 nm; red: 636-673 nm; near-infrared: 851-879 nm; short-wave infrared 1: 1566-1651 nm; short-wave infrared 2: 2107-2294 nm; thermal infrared 1: 10,600-11,190 nm; and thermal infrared 2: 11,500-12,510 nm) [15]. Both Landsat images were georeferenced to the coordinate system universal transverse mercator (UTM), zone 52 N, based on the 1984 datum world geodetic system (WGS).  
To measure the annual PM\({}_{10}\) concentrations during the same period for the monitoring of the urban expansion in Daegu using the Landsat 1 and 2 images, the measured PM\({}_{10}\) data between 2007 and 2017 acquired through the Daegu atmospheric information system ([[https://air.daegu.go.kr/](https://air.daegu.go.kr/)]([https://air.daegu.go.kr/](https://air.daegu.go.kr/))) were also utilized.