esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In Colombia, most of the energy is produced by using water resources. However, the morphological impact of damming has not been thoroughly studied yet. Therefore, upstream and downstream changes in the channel width and sinuosity along the river due to the Betania, Prado, Salvajina, and Urra I Dams, four of the oldest hydroelectric projects, were estimated. These changes were reported by using aerial photographs and satellite images to compare the river before and after dam construction. The analysis was complemented by including hydrological trends and geological characteristics of the areas to evaluate their relevance on the impacts on channel morphology. It was shown that factors such as valley confinement and the bank's composition are key to determining the magnitude of the impact downstream of the dam. Upstream of the dam, contrastingly, the influence of the reservoir geometry controls the magnitude of the morphological changes, marking the boundaries of affected areas. The impacts of dam construction on river morphology vary notably, but including the geological characteristics of the river reach can be useful to improve predictions of the channel morphology response. The proposed methodology can be used to identify biotic compensation measures for new projects, a task that is not well defined in several countries.  
downstream and upstream changes; morphological evolution; river morphology; credibility; valley confinement +
Footnote †: journal: Applied sciences
## 3 Results
### Bias after Application of Correction Factors  
We then determined the extent to which the original PM\({}_{2.5}\) data measured by ESCORTAIR were improved after applying the correction factor obtained from the model by performing comparative analyses using the outcomes of multivariate regression models (Table 4). Using the corrected data, the final coefficient of determination (R2) between FEM (y) and ESCORTAIR (x) was 0.81. The coefficient for PA was 0.87. We found the difference (median (IQR)) with the calibrated data, compared to FEM, to be 16.3. (8.5-28.0)% for ESCORTAIR and 14.5 (6.1 to 23.5)% for PA for outdoor environments (Figure 3). The bias (mean of the difference) was 13.1% for ESCORTAIR and 7.8% for PA for outdoor. The bias for indoor data was at least similar or lower than the bias level obtained from the outdoor test.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Indoor–Pan-Frying**} & \multicolumn{3}{c}{**Indoor–SHS**} & \multicolumn{3}{c}{**Outdoor–Urban Traffic Hotspot**} \\ \hline  & \multicolumn{3}{c}{Single} & \multicolumn{3}{c}{Multivariate *} & \multicolumn{3}{c}{Single} & \multicolumn{3}{c}{Multivariate *} & \multicolumn{3}{c}{Single} & \multicolumn{3}{c}{Multivariate *} \\ \cline{2-11}  & \(\beta\) & R\({}^{2}\) & \(\beta\) & R\({}^{2}\) & \(\beta\) & R\({}^{2}\) & \(\beta\) & R\({}^{2}\) & \(\beta\) & R\({}^{2}\) & \(\beta\) & R\({}^{2}\) \\ \hline ESCORTAIR & 1.11 & 0.98 & 1.10 & 0.98 & 1.00 & 0.92 & 0.97 & 0.92 & 1.15 & 0.70 & 1.14 & 0.81 \\ PA & 1.92 & 0.94 & 1.90 & 0.94 & 0.87 & 0.89 & 0.81 & 0.90 & 0.70 & 0.83 & 0.71 & 0.87 \\ PDR-1500 & 0.33 & 0.98 & 0.33 & 0.98 & 0.54 & 0.91 & 0.49 & 0.92 & 0.33 & 0.72 & 0.36 & 0.80 \\ SIDEFAK 1 & 0.34 & 0.98 & 0.32 & 0.99 & 0.28 & 0.90 & 0.31 & 0.92 & 0.35 & 0.84 & 0.36 & 0.89 \\ \hline \hline \end{tabular}  
* results obtained after adjusting for temperature and relative humidity.  
\end{table}
Table 4: Slopes obtained from stepwise linear calibration models with adjusted R\({}^{2}\) (Dependent variable: US EPA FEM, Independent variable: IRM).  
Figure 3: Distribution of difference of outdoor measurements against the FEM; BAM for outdoors.
## 4 Experiment
### Ablation Experiments
#### 4.4.3 Comparison of Different Haze Densities  
In order to verify the robustness of the model against different haze densities, the following experiments are designed. In the experiment, the conditions of light, moderate, and heavy haze were simulated by setting different atmospheric light values (A) and scattering coefficients (\(\beta\)). Specifically, A is set to 0.8, 0.9, 0.95, and Random, where \"Random\" represents a random selection in the range of [0.8-1.0]. The \(\beta\) is set to 0.04, 0.08, and Random, where \"Random\" represents a random selection in the range of [0.04-0.08].  
In the atmospheric scattering model, the atmospheric light value and scattering coefficient are key factors that determine haze intensity and image blur degree. They affect image sharpness and detail recovery by controlling light scattering and absorption, respectively. The atmospheric light value represents the intensity of light in the atmosphere that is scattered and eventually reaches the observer. When the atmospheric light value is low, more light in the atmosphere is absorbed or scattered, resulting in greater haze and a significant loss of contrast and detail in the image. The scattering coefficient represents the degree to which the atmosphere scatters light per unit distance. When the scattering coefficient is high, there are more haze particles in the atmosphere, and the scattering effect of light is strengthened, making the image more blurred and the visual effect more atomized.  
The results of different haze densities are shown in Table 5. As shown in Figure 13a,c, the light haze in the image has less influence, the atmospheric light value is higher, and the scattering coefficient is lower. Image details can be recovered well by the model, and the degree of color restoration is good. The visual effect is close to that of the original image. The high PSNR and SSIM values indicate the robustness of the model under light haze conditions. With the decrease in atmospheric light value and the increase in scattering coefficient, the contrast of the image decreases, and the details are blurred, as shown in Figure 13b. The overall structure of the image can still be recovered fairly well. The PSNR and SSIM values decreased compared with light haze, but the model could still maintain a good recovery effect. As shown in Figure 13d, under the condition of heavy haze, the image detail is greatly lost. Under this condition, the recovery ability of FA-CycleGAN is obviously limited. The model can restore the outline of the image to a certain extent, but the loss of color detail is significant. As shown in Figure 13e, under the condition of random haze, the performance of the model will also fluctuate because the values of A and \(\beta\) are randomly selected within a certain range. However, in most cases, FA-CycleGAN is able to recover the details of the image fairly well.
## 3 Modified Hybrid Integration Processing
### Communication Compensation of Receiver Signal
Stacking all time samples of the \(n\)th symbol within one pulse into a vector yields\[\widetilde{\mathbf{z}}_{n}=\sum_{l=0}^{L-1}\psi_{l}\beta_{R,l}\beta_{v,l}\widetilde{ \beta}_{v,l}\tilde{\mathbf{u}}(v_{l})\otimes\tilde{\mathbf{u}}(R_{l})+\tilde{ \mathbf{n}}_{n}, \tag{14}\]  
\[\text{where }\widetilde{\mathbf{z}}_{n}=\left[\widetilde{\mathbf{z}}_{n,0}^{T}, \widetilde{\mathbf{z}}_{n,1}^{T},\dots,\widetilde{\mathbf{z}}_{n,P-1}^{T}\right]^{T}, \tilde{\mathbf{n}}_{n}=\left[\tilde{\mathbf{n}}_{n,0}^{T},\tilde{\mathbf{n}}_{ n,1}^{T},\dots,\tilde{\mathbf{n}}_{n,P-1}^{T}\right]^{T},\tilde{\mathbf{u}}(v_{l})= \left[\widetilde{\beta}_{v,l}(0)\widetilde{\beta}_{v,l}(1)\right.\] \[\left.\dots\widetilde{\beta}_{v,l}(P-1)\right]^{T}\text{ and }\otimes\text{ denotes the Kronecker product.}\]  
Let \(\mathbf{Z}=\left[\widetilde{\mathbf{z}}_{0},\widetilde{\mathbf{z}}_{1},\dots, \widetilde{\mathbf{z}}_{N-1}\right]^{T}\), \(\tilde{y}_{i}(n)=\psi_{l}\beta_{R,l}\beta_{v,l}\beta_{v,l}(n),\tilde{\mathbf{y}} _{l}=\left[\tilde{y}_{l}(0),\tilde{y}_{l}(1),\dots,\tilde{y}_{l}(N_{s}-1) \right]^{T}\), \(\mathbf{N}=\left[\tilde{\mathbf{n}}_{0},\tilde{\mathbf{n}}_{1},\dots,\tilde {\mathbf{n}}_{N_{s-1}}\right]^{T}\), \(\mathbf{A}(v,R)=\left[\tilde{\mathbf{u}}(v_{0},R_{0}),\tilde{\mathbf{u}}(v_{ 1},R_{1}),\dots,\tilde{\mathbf{u}}(v_{L-1},R_{L-1})\right]^{T}\) and \(\tilde{\mathbf{u}}(v_{l},R_{l})=\tilde{\mathbf{u}}(v_{l})\otimes\tilde{ \mathbf{u}}(R_{l})\). The received signal can be rewritten as  
\[\mathbf{Z}=\mathbf{A}(v,R)\tilde{\mathbf{y}}_{l}+\mathbf{N}. \tag{15}\]
## 7 Mechanisms of PGPB (Plant Growth Promoting Bacteria) towards Salinity Tolerance
### Direct Mechanisms  
#### 7.1.1 Nitrogen Fixation  
Nitrogen is an essential plant nutrient [170]. In particular, legumes are influenced by salt stress frequently related to a lower growth of the host plant, weak symbiotic formation of root-nodule microorganisms [171] with a decrease of the nitrogen-fixing capacity [172]. Symbiotic associations of plants with soil microbes such as nitrogen-fixing bacteria (_Frankia_ and _Rhizobia_) and mycorrhizal fungi may have a positive impact on plant salt stress tolerance [173]. Additionally, stress-tolerant rhizbial strains may help legume plants to preserve the level of N fixation and plant productivity [174]. Salinity tolerant Rhizobium bacteria are more efficient in promoting plant growth under saline conditions [175]. Consequently, it is advisable to select indigenous rhizbial strains that are efficient even under salinity [176]. For example, _A. nilotica_ inoculated with a salinity resistant Rhizobia strain was capable of maintaining an N-fixing rate of almost 70% at 12 dS/m compared to its highest fixing rate (2 dS/m) [177]. The simultaneous treatment of cowpea seedlings with arbuscular mycorrhizal fungi (AMF) and N-fixing bacteria like _Azospirillum brasilense_ enhanced plant N concentration by 230% in comparison to 151% and 94% in seedlings treated individually with N-fixing strains or AMF at 7.2 dS/m salinity [178]. It has been shown that the association of stress-tolerant varieties and stress-tolerant rhizobia can lead to synergistic benefits in legume plant development under saline conditions [179].  
Figure 2: PGPB (Plant growth promoting bacteria) salinity resistance strategies and mechanisms of plant growth improvement under salt stress (SMPGSS: stimulation mechanisms of plant growth under salt stress; MSHS: mechanisms surviving high salinity, PGPB: plant growth-promoting bacteria, EPS: exopolysaccharide).
# A Vector Sensor-Based Acoustic Characterization System for Marine Renewable Energy
## 4 Discussion and Summary
The contamination of acoustic measurements by non-acoustic pressure fluctuations can be induced either from ambient flow or from the relative motion between the sensor and surrounding fluid [9]. An acoustic flow suppression shield acts as a physical barrier between the turbulence-inducing flow and the hydrophone, while remaining acoustically transparent. Flow noise contamination can be particularly confounding when using vector sensors, since these sensors measure acoustic particle motion using accelerometers that physically respond to the propagation of an acoustic wave. In this paper, a flow shield made of ballistic nylon was found to be an effective suppressor of flow noise for a vector sensor in an energetic tidal flow, with flow noise suppression on the order of 10-15 dB. However, it is noted that, while the flow shield does result in flow noise suppression (Figures 6 and 7), the suppression is not total. This is evident in some deviation toward higher values in the \(M_{2}\) metric, even in the presence of a flow shield, indicating residual non-acoustic contamination. In highly energetic current environments, where flow noise can exceed 50 dB [16], work remains to be done in further suppressing flow noise contamination. Finally, plane-wave beamforming techniques were applied to the vector sensor data to obtain the three-dimensional bearing to a source at a known depth, which was then used to infer its location. The application of beamforming to locate a source using an array of hydrophones along with complex acoustic propagation models is a rich field with a long history [33]. The utility of the bearing information, inherent to vector sensor measurements, allows for three-dimensional low-frequency source localization which previously required large two-dimensional hydrophone arrays. Beamforming of vector sensor array data to locate a ship was recently demonstrated [20], and it was applied here to locate a sub-surface source, albeit at a known depth. It was found that, while errors in source location were less than 4 m with a 200-m source-receiver separation (2% error), achieving this error requires careful measurement of location of the vector sensor spacing. Furthermore, it is critical that the inertial motion unit is properly calibrated to allow for the accurate translation of relative azimuth and bearing estimates into true earth coordinates. The NoiseSpotter described in this paper ties together all the above work to provide a new tool for commercial noise assessments, useful for the MRE industry, as well as for any application that requires geolocation of acoustic sources below 3 kHz. Typical sources of sound in this frequency range include wave and tidal energy converters, baleen and mysticete whales, ships, and low-frequency sonar.
## 5 Classification Results  
### Convolutional Neural Network (CNN) Architecture  
The convolutional neural network (CNN) technique is used for automatic feature extraction and classification using spectrogram images as input data. Figure 28 shows the complete single training scheme in which three layers of convolutions are used, followed by ReLU and pooling layers subsequently. A fully connected layer is used afterward to connect all the regions, as a result of the previous layers and finally after the softmax function implementation to attain the probabilities the classification results are attained in the classification layer.  
Figure 28: CNN architecture with layers sequence where FS = convolutional filter size, FN = number of convolutional filters.  
Results of the proposed technique outperformed the recent advance techniques added to the literature. [PERSON] et al. [28] proposed a weighted hidden Markov model for CNC Milling tool health prediction, attaining an 81.64% accuracy in the best case. [PERSON] et al. [29] used a deep learning model on the impulse response of tool vibration data to predict tool health, achieving a 98.1% accuracy at most, while the proposed technique has been able to have a consistent accuracy lying in between the range of 99% to 100% for all six cases using acoustic emission and convolutional neural network for CNC milling tool health monitoring.
## 2 Materials and Methods  
### Materials Synthesis  
All of the chemicals used in this work were of analytical reagent grade. Ce(NO\({}_{3}\))\({}_{3}\):6H\({}_{2}\)O (purity \(\geq\)99.0%, Fluka, Bucharest, Romania), Zn(CH\({}_{3}\)COO)\({}_{2}\cdot\)2H\({}_{2}\)O (purity \(\geq\)99%, Sigma-Aldrich, St. Louis, MO, USA) were used as precursors for the preparation of ceria-zinc materials. HO\({}_{2}\)CCO\({}_{2}\)H (purity \(\geq\)99%, Sigma-Aldrich, St. Louis, MO, USA), NaOH (purity \(\geq\)98%, Honeywell Fluka, Seelze, Germany) and absolute ethanol (purity 99.8%, ACROS Organics, Geel, Belgium) were also employed during the synthesis procedure.  
Bare ceria nanorods were initially prepared by the hydrothermal method, as described in detail in our previous work [21]. CeO\({}_{2}\)/ZnO mixed oxides of different Zn:Ce ratios (0.2, 0.4, 0.6) were prepared by a modified hydrothermal method. In particular, 0.38 g HO\({}_{2}\)CCO\({}_{2}\)H, 0.64 g Zn(CH\({}_{3}\)COO)\({}_{2}\cdot\)2H\({}_{2}\)O and a certain quantity of CeO\({}_{2}\) nanorods (2.42, 1.21, 0.81 g for Zn/Ce = 0.2, 0.4 and 0.6, respectively), were dispersed in 40 mL of double deionized water under stirring for 20 min. Then, the suspension obtained was transferred to a plastic bottle and aged at 70 \({}^{\circ}\)C for 1 h. The precipitate was recovered by centrifugation, washed with double deionized water and ethanol, dried at room temperature overnight, and calcined at 500 \({}^{\circ}\)C for 2 h under air flow (heating ramp 5 \({}^{\circ}\)C/min). For comparison purposes, a pure zinc oxide sample was also prepared by the same method. The as-prepared materials are denoted as CeO\({}_{2}\)/ZnO-x, where x refers to Zn:Ce atomic ratio.  
For comparison purposes, a mechanical mixture (CeO\({}_{2}\)+ZnO-0.4) was synthesized in agate by hand, by physically mixing ceria nanorods and ZnO, with the same composition as the CeO\({}_{2}\)/ZnO-0.4 sample.
# Recent Advances in Si-Compatible Nanostructured Photodetectors
## 1 Introduction
The integration of silicon, germanium, and tin heterostructures with a silicon chip and their compatibility with silicon electronic and photonic circuits has great potential for the development of low-cost telecommunication optoelectronic modules for fiber optic communication lines operating in the telecommunication wavelength range [7; 8; 9; 10; 11; 12; 13]. One of the main methods for obtaining low-dimensional structures today is molecular beam epitaxy, the main advantages of which include low temperatures and growth rates, the possibility of abrupt interruption and resumption of growth, and precise control of the composition of the main substance and impurity concentrations. In this case, the analysis of the grown structures and the control of the necessary parameters can be carried out directly in the synthesis process. Molecular beam epitaxy and other modern technological and material advances in semiconductors have brought the possibility of fabricating heterostructures utilizing quantum mechanical features. For example, creating quantum wells and self-assembled quantum dots allowed researchers to manufacture better photodetectors with higher performance. In this review, we considered the latest and most modern photodetectors in the current scientific literature, stating the types and materials while highlighting the features of each work with special attention to Ge-Si photodetectors. These heterostructures with quantum wells and dots have a number of unique properties that made it striking to scientists' attention and device applications. Since silicon is the dominating semiconductor material in the electronic industry over the past decades, and as germanium nanostructures are very compatible with silicon, that made them a better fit and promising candidate to use in future technologies.
# Experimental Performance Analysis of Wi-SUN Channel Modelling Applied to Smart Grid Applications
## 2 AMI Architecture and Last-Mile Communication
### Mr-Odm  
MR-OFDM provides high data rates and long-range communication, which has been overcoming the ISI caused by the multi-path fading channel and the interference thanks to the concept that MR-OFDM spreads the information into multiple sub-carriers within a data frame. Furthermore, the multi-path fading channel has experienced an independently individual frequency response in each subcarrier; therefore, at the receiver, the received signals are retrieved in each subcarrier by using the orthogonality property of the OFDM signal and the estimated channel impulse response achieved by sending the pilot signals. FEC techniques and frequency repetition can also be used for performance enhancement. The achievable data rate varies between 50 and 800 kbps, which is higher than MR-FSK and MR-OQPSK; however, MR-OFDM requires more complex circuitry and high-power consumption.
_applied sciences_  
_Article_  
**Hybrid Analytic Hierarchy Process-Artificial Neural Network Model for Predicting the Major Risks and Quality of Taiwanese Construction Projects**  
[PERSON] 1, [PERSON] 2,*(r) and [PERSON] 3(r)  
1 Department of Construction Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 824, Taiwan; [EMAIL_ADDRESS]  
2 Department of Civil Engineering, The Republic of China Military Academy, No. 1, Weiwu Rd., Fengshan, Kaohsiung 830, Taiwan  
3 Institute of Engineering Science and Technology, National Kaohsiung University of Science and Technology, Kaohsiung 824, Taiwan; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS] Tel.: +886-7-7456290  
**Abstract** Construction projects are associated with risks, which influence projects' performance and quality. To ensure the on-time completion of construction projects, project managers often use risk assessment and management methods to reduce risks in the project life cycle. Identifying risk factors and the relationship between major risk factors and the quality of construction projects facilitates construction management. In this study, 948 project records of construction inspection from 1993 to 2020 were collected from the Public Construction Management Information System (PCMIS) of the Taiwan central government to conduct an expert survey to identify five risk dimensions and 19 major risk factors associated with Taiwanese construction projects. The hybrid analytic hierarchy process (AHP) and an artificial neural network (ANN) were employed to develop a model for predicting major risk factors and construction quality. The AHP was used to calculate the weight of major risk factors to verify their influence on construction. The ANN was adopted to extract the features of major risk factors to predict the quality of a construction project. The accuracy of the prediction model was 85%. The project managers can reference the prediction results obtained with the proposed method to perform effective risk management and devise decision-making strategies for construction management.  
**Keywords:** analytic hierarchy process; artificial neural network; major risk factors; project quality
## 3 SAM-Based Approach to Improve Panoramic Image Segmentation in Indoor Construction Environments
### Object Extract  
Applying SAM to panoramic images of indoor construction sites allows for the extraction of valid object masks based on input prompts. However, since the model operates on a single image at a time, it must be configured to run iteratively to extract all objects within the image. To achieve this, we configured SAM to be executed repeatedly on a single image and set a termination condition based on the background proportion. Specifically, after each execution of SAM, the detected object masks were stored and combined to generate a mask representing the detected regions (foreground). Subsequently, the accumulated detectedregions were integrated into a single mask, which was then used to calculate the proportion of undetected regions (background). Finally, SAM was continuously executed until the background proportion was reduced to less than 2% of the original image, ensuring the continuous detection of objects. This sequence of processes is illustrated in Figure 2.  
However, a simple iterative execution of SAM resulted in duplicated detections and overlapping objects, necessitating additional processing. To address these issues, this study developed three techniques, namely (1) the adjustment of input parameters, (2) updating of input images and prompt labels, and (3) handling of duplicate and overlapping objects. These techniques enable SAM to iteratively detect new objects without redundant detections.