esa-sceva/satcom-chunk-collection huggingface dataset:
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 6 Simulation Results
### Compounding  
The greedy wiring approach, cf. Section 4.5, is based on compounding. Each new addition serves to reduce the total squared error by a certain factor, asymptotically by \(4^{-R}\). In the analysis, we have assumed that the target vector is statistically independent of the codebook. In the greedy approach, however, the running target vector depends on the wiring of the previous matrix factor. Thus, this independence assumption is violated. The practically important question is, whether this violation has any significant effect. If so, we could not simply multiply the individual savings of each wiring matrix to get the total saving.  
Consider \(L\) wiring matrices each with two nonzero entries per column, i.e., \(S_{\ell}=1\) for all \(\ell\). Let \(m_{\varepsilon^{2}}\) denote the median of the unconditional distribution of the total squared error \(\varepsilon^{2}\) given in (37). The median of total distortion is shown in Figure 4 vs. \(S\) (approximately the total number of additions per column required for all wiring matrices) and compared to \(m_{\varepsilon^{2}}^{S}\). For unit rate, \(m_{\varepsilon^{2}}^{S}\) is very close to the simulation results. The violation of the independence assumption appears to have a rather minor effect.  
Figure 4: Median total squared error \(D\) for target matrix of unit Frobenius norm vs. the parameter \(S\) (approximately the total number of additions per column required for all wiring matrices). The solid lines refer to \(m_{\varepsilon^{2}}^{S}\), the markers to simulation results. Circles and crosses are for IID Gaussian codebook matrices and mailman codebooks, respectively, all with unit rate. IID Gaussian target matrices. Results are averaged over \(10^{6}\) random matrix entries.  
While for \(N\geq 3\), all curves are close to each other, \(N=2\) is exceptional. This does not imply, however, that small matrix size is beneficial. Contrarily, the number of additions per matrix entry is given in (43), which clearly decays with increasing matrix size.  
The influence of the codebook in Figure 4 is very small. Only for \(N=3\) is a difference between the mailman and the IID Gaussian codebook visible. This is partially a result of codebook evolution, as discussed in Section 4.4.4.
## 4 Discussion
That way, background effects were eliminated allowing vineyards with diverse soil and understory vegetation characteristics to be compared and used in a single 'global' model. Results from the **Global-Mult** model showed that \(\Delta\)NDVI had a significant size effect, being a key factor in predicting \(\Psi_{\text{stem}}\), with only day of year having a stronger size effect and SAVI avg a similar effect (Table 3). In Mevo Beitar, a highly variable vineyard, weekly based correlations ([PERSON]'s \(r\)) of GNDVI, NDVI, EVI and SAVI vs. \(\Psi_{\text{stem}}\) were within the range of 0.28-0.84, with an average value of 0.65 for all indices (Table 2). These results were quite similar to previously reported VI-\(\Psi_{\text{stem}}\) correlations found by others using very-high-resolution images [22; 29]. When time series were used in the regression instead of date-specific data, correlations were even higher (0.80 \(<\)\(r\)\(<\) 0.82). A simple linear regression model (**MB-Reg**) was sufficient to reproduce temporal, as well as spatial variations in \(\Psi_{\text{stem}}\) in the experimental vineyard. In general, correlations from simple specific-date regressions between vegetation indices and \(\Psi_{\text{stem}}\) were also high for the 81 commercial vineyards (\(r\) = 0.72-0.97; Figure 7), implying that Planet-derived time series of vegetation indices like, GNDVI, NDVI, EVI and SAVI may be good indicators of water status at the vineyard-scale level. Finally, the **Global-Mult** model with spectral-based indices derived from Planet satellites was proposed to predict \(\Psi_{\text{stem}}\) in Mediterranean vineyards. After deriving \(\Delta\)NDVI, SAVI avg, NDVI max and NDVI min of a specific vineyard a real-time \(\Psi_{\text{stem}}\) estimation may be provided for that vineyard at a weekly basis. The **Global-Mult** shows promise in improving the efficiency of in-field conventional monitoring efforts.
## 1 Introduction
Therefore, in this context, the performance analysis of asymptotic ergodic capacity is considered based on the above-mentioned three factors in maritime environments based on mobile platforms. We statistically analyze ergodic capacity performance based on our system and channel models. Subsequently, using the moment expressions, we derive the closed-form expression of asymptotic ergodic capacity with both HD and IM/DD.
## 4 Numerical Examples
### Inversion Analysis of a High Earth-Rockfill Dam Using the Step-by-Step Method  
#### 4.2.1 Problem Description  
In this section, inversion analysis using the step-by-step method is carried out and the material parameters of the bottom of a high earth-rockfill dam during construction periods are obtained. Calculated values are compared with those by conventional method.  
The model of a 240-meter high earth-rockfill dam is displayed in Figure 5. The material parameters for design are listed in Table 5. Fifty construction steps are considered in this discrete model and the monitoring arrangement of the core wall can be seen in Figure 6. The monitoring compression values of the bottom of the core wall (marked by red lines in Figure 6) are displayed in Figure 7. The combinations of material parameters for
## 1 Introduction
### Wear Measurement Methods
#### 1.6.7 Macroscopic Visual Wear Evaluation  
The macroscopic visual inspection of disc cutters is the most basic qualitative method for determining wear and is usually performed prior to any techniques that use specialized tools and equipment to measure wear. Basic types of wear can be detected with this method while disc cutters are still installed on the TBM's cutter head [34].  
Based on a visual inspection, the wear of disc cutters can generally be classified into normal wear and abnormal wear with the corresponding subcategories [36].  
Many researchers who have studied the wear of disc cutters have also performed macroscopic wear evaluations [12; 19; 34; 38; 53; 60]. [PERSON] et al. [12] conducted field research to categorize the chipping patterns of disc cutters through macroscopic observations of failure patterns and fracture surface features. Their work made an important contribution to understanding the mechanism of chipping and its morphological features through visual observations. [PERSON] et al. [19] visually depicted the extent of cutter ring wear in different types of TBM disc cutters along two different tunnel sections. Similarly, [PERSON] et al. [34] outlined five basic macroscopic wear types for cutter rings observed in TBM tunneling. [PERSON] et al. [38] emphasized the importance of macroscopic observations in identifying abnormal wear patterns such as the cracking, spalling, mushrooming, and chipping of cutter rings. They discussed the importance of these observations in detecting wear irregularities. [PERSON] et al. [53] analyzed the wear patterns and characteristics of disc cutters at a macroscopic level to understand the effects of various parameters such as rock strength, abrasiveness, machine parameters (thrust force, torque, etc.), and operating conditions oncutter wear and penetration rate in tunnel construction. Furthermore, [PERSON] et al. [60] used a TBM case study in Shenzhen to determine the failure modes of disc cutters through visual observation. Their study showed that about 91.71% of cutter tool failures were due to uniform wear, suggesting that this is the main cause of failure.  
Based on the information provided by these authors, who focus primarily on describing and analyzing different macroscopic types of wear in TBM hard-rock drilling, the drawbacks or limitations of their observations were explicitly not addressed. However, it is important to point out that there may be limitations or drawbacks in any study, such as possible biases in observations, variations in field conditions, or problems in generalizing the results. Further details or a comprehensive analysis would be required to identify the possible drawbacks of these macroscopic observations.
# Analysis of Heavy Metal Sources in Xutuan Mining Area Based on APCS-MLR and PMF Model
## 3 Results and Discussion
### Heavy Metal Risk Assessment  
Changes in external environments can lead to the re-release of heavy metals from sediments into water bodies, causing secondary pollution. Therefore, it is essential to assess the risks associated with heavy metals using appropriate evaluation methods to accurately reflect their pollution level in the environment. In this study, the Igeo and risk assessment coding methods were used to assess the risks associated with the sediment heavy metal elements. The evaluation results are shown in Figure 4.  
The Igeo- and RAC-based heavy metal risk assessment results are shown in Figure 3. The obtained results showed comparatively higher Igeo values of Cd, reaching 3.48 and thus belonging to the strong pollution level, whereas the Igeo values of As reached 1.38, belonging to the moderate pollution level. In contrast, the Cu, Cr, Zn, and Pb Igeo values were lower  
Figure 4: Geological accumulation index and ecological risk assessment index.  
Figure 3: Proportions of the heavy metal forms in the sediments of the study area.  
than 0, indicating the lack of associated accumulation. The RAC values of the six heavy metal elements in the sediments of the subsidence areas followed the decreasing order of Cd (29%) > Cu (19%) > Pb (16%) > Zn (10%) > As (8%) > Cr (1%). Hence, it can be concluded that Cu, Cd, Pb, and Zn were associated with moderate risks (10% \(\leq\) RAC < 30%), while As and Cr were related to low (RAC < 10%) and zero (RAC < 1%) risks, respectively. This is consistent with the results of [PERSON] et al. [37]. Previous studies have shown that Cd has a high degree of pollution and ecological risk in China and globally [38; 39]. The contents of active states of Cd, Cu, Pb, and Zn in the sediments were relatively high, which is conducive to their release into the water and causes environmental pollution. Cr and As were mainly in the stable residual form. The high proportion of non-residual Cd (75.11%) in sediments, combined with its Igo values (3.48) and RAC risk level (29%), suggests significant bioavailability. The high toxicity of heavy metals in high concentrations to aquatic organisms leads to reduced growth, mortality, and reduced species diversity, with potential impacts on ecosystems and human health [40]. Our observed mean Cd content (1.98 mg/kg) exceeds both China's soil Class II standard (0.6 mg/kg) and National Oceanic and Atmospheric Administration (NOAA) guidelines (1.2 mg/kg) [41], posing acute risks to aquatic food webs. Conversely, As poses lower immediate risks due to its residual dominance (50.94%), but As is a global public health threat and a serious contaminant of drinking water with long-term carcinogenic potential [42]. The two evaluation methods used in this study showed the high Cd-associated risk in the study area, making it urgent to implement effective risk reduction and protection measures.
## 3 Results and Discussion
### HS-SPME Method Optimization
#### 3.1.4 The Effect of Salt Addition
The “ns” stands for “not statistically significant”. The color represents different salt concentrations. In summary, the following parameters were optimized (Table 2). Figure 5: The linear regression between the partition coefficient and Log2 fold change of each of the 46 selected compounds. [PERSON]’s correlation coefficient = \(-0.53\). The solid line represents the linear regression line with the equation of \(\mathrm{Y}=-0.4679\times\mathrm{X}+1.478\). Figure 6: Boxplot of four selected compounds with partition coefficients (\(K_{\mathrm{e}w}\)) from low to high. (**a,b**) show two compounds (2,3-Butanedione and Ethanol) with low \(K_{\mathrm{e}w}\) which demonstrate increasing peak area as the salt concentration increases; (**c,d**) illustrate two compounds (Decane and 1,3-bis (1,1-dimethyl ethyl)-benzene) with high \(K_{\mathrm{e}w}\) which demonstrate no change and a decreasing peak area, respectively, as the salt concentration increase.
## 4 Discussion
### Wind-Induced Soil Erosion
#### 4.1.4 Evaluation of the Dust Control Effectiveness  
On both tested soil types, the results demonstrated that applications at the biopolymers' respective plateau concentrations resulted in high dust control effectiveness >99%, even at a low application rate of 0.2 L/m\({}^{2}\) (Figures 6 and 7, and Table A3). For the given experimental setup and methodology, this implies that a low application rate was already sufficient to properly coat and agglomerate the surface particles to a wind erosion-resisting crust. However, careful interpretation is required when one is comparing these results with previous studies due to the different experimental setups and tested parameters.  
Compared to most previous research, this study tested relatively low biopolymer dosages with concentrations between 0.05 and 1.5 wt% and application rates between 0.2 and 0.6 L/m\({}^{2}\), but also, less demanding wind tunnel conditions (horizontal sample placement and 13.6 m/s). Two studies performed wind tunnel tests with similar parameters [39; 40]. [PERSON] et al. [40] tested Xanthan gum (0.1 wt% and 0.4 L/m\({}^{2}\)) and Chitosan (0.1 wt% and 0.5 L/m\({}^{2}\)) on horizontally placed sand with silt (SM) samples at 7.2 m/s, and they also reported effectiveness >99% (Table A6). Similarly, [PERSON] et al. [39] tested a commercial hydrogel (\(\geq\)4 wt% and 0.3 L/m\({}^{2}\)) on sand with silt (SM) and silt clay loam (CH) samples at velocities of 9 and 11 m/s, and they also reported effectiveness >86%. It is thus concluded that the results of this study are consistent with previous studies testing similar parameters. In contrast to this study, previous studies often tested substantially higher concentrations (from 0.4 up to 10 wt%) and application rates (from 1 to 3.5 L/m\({}^{2}\)), but they also subjected the samples to more challenging testing conditions, such as an angular sample placement and velocities ranging from 14.8 to 41.6 m/s [12; 13; 22; 23; 24]. Most of these studies also showed very high dust control effectiveness >90%.  
Hence, the resulting dust control effectiveness reported by different studies cannot simply be compared with each other, and this also does not enable us to infer the potential performance of a dust suppressant in field conditions. However, dust control effectiveness is a valuable parameter for comparing the performances of a combination of application rate, concentration, and biopolymer type within an experimental study. Moreover, for the tested biopolymers, it enables the definition of application parameters at which the tested biopolymers will likely demonstrate similar performances.
## 2 Related Works
### Meta Learning Object Detection  
Meta-learning object detection builds on top of meta-learning classification and adds the task of object localization, needed to learn more effectively the rich prior knowledge from base classes. Based on meta-learning, few-shot object detection methods design meta-learners to train models with sufficient base-class samples during meta-training, learning universal meta-knowledge across tasks. During meta-testing, learned meta-knowledge is used to predict parameters based on class-related parameters of small-sample images.  
[PERSON] et al. [25] proposed a one-stage meta-learning object detection method based on YOLOv2 [26]. They converted the support set image of each class into an n-dimensional vector using convolution and used this to perform channel-wise reweighting of the feature of the query set, which was then predicted by the predictor head. In one-stage meta-learning object detection, the anchor box coordinates in the regression process were generated directly from the query set image features, but due to the small number of training samples, the regression process did not fully converge, and foreground and background objects were easily confused. [PERSON] et al. [27] proposed Meta R-CNN with a meta-learner called PRN (Predictor-head Remodeling Network) based on Faster R-CNN, which constructed a class attention vector and fused it with ROI features of the query set to perform object detection. [PERSON] et al. [28] improved Meta R-CNN by introducing feature subtraction to enhance the fusion of support set class attention vectors and query features. [PERSON] et al. [29] combined the transformer with meta-learning to propose Meta-DETR, which learns class-agnostic meta-knowledge using an encoder-decoder architecture inspired by Deformable DETR [30]. [PERSON] et al. [31] proposed Meta Faster R-CNN, which utilizes the original Faster R-CNN for base class detection and designs Meta-RPN, a coarse-grained prototype matching network, and Meta-Classifier, a fine-grained prototype matching network for new class detection, generating anchors for new class objects and performing classification and regression.  
Currently, meta-learning-based few-shot object detection performs well on public datasets dominated by natural scenes, but there is still a lot of room for improvement in defect detection. For example, in defect detection, the global similarity between normal and defective parts is high, which can prevent generic meta-learning methods from fully utilizing defect features for detection. In this paper, we propose a meta-learning-based object detection method specifically for defect detection to improve the detection precision of defective components in power transmission lines.
## 3 Extraction of Vital Products
### Lipids  
Oleaginous microorganisms are a vital source of lipids mostly as neutral lipids, phospholipids, glycolipids and free fatty acids (FFA), which can be effectively recovered via UAE. Solvent-free UAE was reported by [PERSON] et al. [41] for the extraction of lipid from fresh _Nannochloropsis oculata_ microalgae. [PERSON] et al. [42] utilized UAE for extraction of lipid from _Chloella protothecoides_ microalgae where the yield was improved by 42%. Microorganisms, such as oleaginous yeast _Trichosporo oleaginousus_ (ATCC20509), oleaginous fungus (SkF-5), were also subjected to UAE for lipid recovery, which resulted in a substantial reduction in the duration of extraction (12 h to just 15 min) as compared to conventional methods [43]. Venturi-type hydrodynamic cavitation (HC) reactor can also be an ideal possibility to be deployed for the extraction of lipid from microalgae. High extraction yield of lipids (25.9-99%) from microalgae (_Nannochloropsis salina_) was obtained via HC extraction as compared to ultrasound-assisted extraction (5.4-26.9%). Under optimum conditions, HC recovered maximum lipid content from wet microalgae, even though the extracted lipid from wet biomass was tough compared to the dry form [44]. HC has also demonstrated high lipid extraction efficiency from dry _Nannochloropsis sp._ microalgae, which is vital for biodiesel production [45].
# Leveraging Hybrid RF-VLP for High-Accuracy Indoor Localization with Sparse Anchors
## 4 Results and Discussions
### The Effect of BLE Transmission Power on Hybrid System Localization Accuracy  
The proposed system utilizes BLE signals for inter-receiver distance estimation and local coordinate construction. To assess the impact of BLE transmission power on ranging and positioning performance, we systematically test the system under varying power levels. Changes in transmission power directly influence the signal strength and communication range between receivers, enabling a controlled evaluation of the trade-offs between power consumption, communication range, and positioning accuracy. For the experiments, 12 receivers are deployed at fixed positions within the test area, as illustrated in Figure 7a, alongside the LED anchors. The BLE transmission power is adjusted across all receivers, and the resulting localization errors are analyzed. The proposed hybrid positioning method is applied, with the experimental results presented in Figure 7. It can be observed that the positioning error decreases as the BLE transmission power increases. However, once the transmission power exceeds \(-3\) dBm, the improvement in positioning accuracy becomes marginal, indicating diminishing returns beyond this point.  
Higher transmission power enhances the signal coverage and signal-to-noise ratio, reducing the communication bit error rate and further improving the positioning accuracy. Considering the trade-off between localization error and system power consumption, \(-3\) dBm is selected as the optimal BLE transmission power for subsequent experiments.
## 3 Multi-Channel Membrane CDI (MC-MCDI)
### Operational Studies and Principle of Mechanism
Furthermore, the benefit of the salinity gradient in MC-MCDI was also thoroughly examined in battery electrodes like Prussian blue analogs (Figure 8A) [35]. Although the battery type electrodes have garnered attention because of their high ion removal capacity, the mass transfer at the interface of the electrode and low saline feed is too limited to maintain the long-term stability. As shown in Figure 8B, using the MC-MCDI, ion removal capacity, and the maximum salt removal rate demonstrated to a three-fold increase as the side channel concentration increased from 10 to 1000 mM, preventing performance degradation over 100 cycles [35]. It is clear that the salinity gradient in MC-MCDI could improve desalination performance such as ion removal capacity, charge efficiency, and mass transfer. It should be noted that the exceptional desalination performance is accomplished with a simple operational condition and without a complicated cell module or any additional electrode modifications. Figure 8: (**A**) Schematic illustration and (**B**) desalination capacity as well as the maximum salt removal rate of multichannel membrane CDI (MC-MCDI) using battery type electrodes (Reprinted with permission from [35]. Copyright (2019) American Chemical Society). Figure 7: Ion removal mechanism of MC-MCDI in semi-continuous operation: Anion exchange membrane (AEM) and cation exchange membrane (CEM), and MC and SC denote the middle and side channels, respectively. (Reprinted with permission from Reference [32]).
###### Abstract  
A planar, microstrip line-fed, quad-port, multiple-input-multiple-output (MIMO) antenna with dual-band rejection features is proposed for ultra-wideband (UWB) applications. The proposed MIMO antenna design consists of four identical octagonal-shaped radiating elements, which are placed orthogonally to each other. The dual-band rejection property (3.5 GHz and 5.5 GHz corresponding to Wi-MAX and WLAN bands) was obtained by introducing a hexagonal-shaped complementary split-ring resonator (HCSRR) in the radiators of the designed antenna. The MIMO antenna was etched on low-cost FR-4 dielectric substrate of size \(58\times 58\times 0.8\) mm\({}^{3}\). Isolation higher than 18 dB and envelope correlation coefficient (ECC) lesser than 0.07 was observed for the MIMO/diversity antenna in the operating range of 3-16 GHz. The presented four-port UWB MIMO antenna configuration was fabricated, and the experimental results validate the simulation outcomes.  
antenna; MIMO; octagonal; planar; UWB +
Footnote †: journal: Applied Sciences