esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study shows that the influence of hydropower plants in urban areas on the shaping of water quality from the natural, social, and economic perspectives is ambiguous. The following conclusions can be drawn from the analyses carried out:  
* Differences in the median values of physicochemical parameters within the hydropower plants were small; they amounted to a maximum of 27.80% (NO\({}_{3}\)-N at the Wroclaw II hydropower plant) and usually did not exceed 10%;
* The changes in concentrations for EC and PO\({}_{4}\)-P (Wroclaw II hydropower plant) and pH (Wroclaw I hydropower plant) were found to be statistically significant;
* Water in the area of the Wroclaw I and Wroclaw II hydropower plants was classified as mesotrophic or eutrophic;
* The conditions for the existence of cyprinids and salmonids were not met, respectively, for two out of seven assessed parameters (BOD\({}_{5}\), NO\({}_{2}\)) and for four out of seven parameters (BOD\({}_{5}\), NO\({}_{2}\), temperature, DO in three out of four points);
* Hydropower plants, on average, deteriorated the value of water quality indices by 1.35%; the overall water quality ranged from poor to good, and the average water quality was moderate. This water would require treatment before consumption; however, its quality is acceptable for most water sports, and the water quality is suitable for fish less sensitive to pollution and allows normal industrial production not requiring high-quality water. The values of these indices varied depending on the index and sites;
* The authors plan to continue and expand this research on the analysis of the migration of aquatic organisms through water steps with hydropower buildings in urban areas and investigate the exact characteristics of the capacity achieved by these hydropower plants along with hydrological conditions. The scope of these studies should include not only the impact on the water and land environment, but also the impact on hydrological conditions, landscape, water relations within the hydropower plant, and economic and social issues (e.g., the profitability of this type of project, compliance with the international energy and climate policy and sustainable development goals, and the location of hydropower plants).  
Conceptualization, P.T. and M.W.; methodology, P.T. and M.W.; software, P.T.; validation, P.T.; formal analysis, P.T.; investigation, P.T. and M.W.; resources, P.T. and M.W.; data curation, P.T.; writing--original draft preparation, P.T.; writing--review and editing, A.K. and M.W.; visualization, A.K. and P.T.; supervision, M.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Numerical Results  
As part of the computational validation of the proposed optimization model for siting and sizing STATCOMs in power systems, we implemented the exact MINLP model in the GAMS software with the BONMIN solver using a PC with an AMD Ryzen 7 3700 2.3 GHz processor and 16.0 GB RAM, running on a 64-bit version of Microsoft Windows 10 (Single Language).
## 4 Experiments and Results
### Experimental Data  
Two distinct datasets, each divided into an 80% training set and a 20% test set, were employed in the genre classification experiment involving audio music data. The MAESTRO dataset [23] was used for feature extraction via an autoencoder and for pre-training Deformer. This dataset encompasses a broad spectrum of musical instruments and styles,  
\begin{table}
\begin{tabular}{c c c c} \hline
**Hyperparameters** & **Autoencoder (Training)** & **Pre-Training** & **Fine-Tuning** \\ \hline Kernel Size & \(3\times 3\) & - & - \\ \hline Channels of Encoder & 32, 64, 128 & - & - \\ \hline Channels of Decoder & 128, 64, 32 & - & - \\ \hline Number of Patches & - & 196 & 196 \\ \hline Patch Size & - & \(16\times 16\) & \(16\times 16\) \\ \hline Hidden Size & - & 768 & 768 \\ \hline Intermediate Multiplication & - & 4 & 4 \\ \hline Number of Hidden Layers & - & 12 & 12 \\ \hline Number of Attention Heads & - & 12 & 12 \\ \hline Size of resized Mel Spec. & \multirow{2}{*}{224 \(\times\) 224} & \multirow{2}{*}{224 \(\times\) 224} & \multirow{2}{*}{224 \(\times\) 224} \\ \((W\times H^{\prime})\) & & & \\ \hline Noise Ratio \((\beta)\) & - & 0.75 & - \\ \hline Batch Size & 64 & 16 & 16 \\ \hline Optimizer & AdamW & AdamW & AdamW \\ \hline AdamW Betas & \((0.9,\,0.98)\) & \((0.9,\,0.98)\) & \((0.9,\,0.98)\) \\ \hline Weight Decay & 0.01 & 0.01 & 0.01 \\ \hline Learning Rate \((\eta)\) & \(1\times 10^{-4}\) & - & \(1\times 10^{-6}\) \\ \hline Scheduler & - & WarmupDecayLR & - \\ \hline Minimum Warmup Learning Rate & - & \(1\times 10^{-6}\) & - \\ \hline Maximum Warmup Learning Rate & - & \(1\times 10^{-4}\) & - \\ \hline Warmup Steps & - & 800 & - \\ \hline Total Training Steps \((I)\) & 8000 & 8000 & 20,000 \\ \hline \end{tabular}
\end{table}
Table 2: Hyperparameters used in the autoencoder, pre-training, and fine-tuning experiments.  
with contributions from both professionals and amateur musicians. Mel spectrograms derived from raw audio files were used as inputs. The training set was used for model optimization using techniques such as gradient descent, and the test set was designated for performance evaluation using metrics such as MSE. The GTZAN [24] music dataset was exclusively used to fine-tune Deformer. Renowned in genre classification, this dataset consists of one thousand 30 s audio segments across ten distinct genres, including blues, classical, and hip hop. The audio clips were transformed into Mel spectrograms to serve as inputs for the model. The training process involved iterative Deformer updates based on loss minimization, and the test phase assessed the genre-classification capabilities of Deformer in terms of the precision and recall metrics.
## 5 Discussion  
As mentioned above, _OCR_ can not only directly reduce the thermal pore water pressure (Equation (6)), but it can also change the magnitude of the thermal pore water pressure by affecting the value of \(\Lambda\) (Equations (10) and (11)). However, the effect of _OCR_ on \(\Lambda\) is often ignored in existing calculation methods [9; 26], which may be the main reason for overestimating the thermal pore water pressure of highly overconsolidated soil. To quantitatively analyze the effect of \(\Lambda\) on the thermal pore water pressure, two cases are given here, namely, Case 1: the predicted results without considering the variation of \(\Lambda\) (\(\Lambda\) = 6.5), and their comparison with the experimental results; and Case 2: the predicted results considering the variation of \(\Lambda\) (\(\Lambda\) = 5.4, 5.0, and 3.8), and their comparison with the experimental results. The effect of \(\Lambda\) on the thermal pore water pressure is analyzed by comparing the coincidence between experimental results and predicted results. Figure 4a,b show the comparison of experimental results and predicted results for the two cases.  
From Figure 4a, it can be observed that the predicted results without considering the variation of \(\Lambda\) will seriously overestimate the thermal pore water pressure. For clays with _OCR_ equal to 10 and 30, the coefficients of determination (\(R^{2}\)) of predicted results are both 0. When the change in temperature is 35 \({}^{\circ}\)C, the difference between the experimental results and predicted results reaches 61.5% for the clay with _OCR_ equal to 10, and the difference reaches 127.2% for the clay with _OCR_ equal to 30. However, from Figure 4b, it can be found that the coincidence between the predicted results considering the variation of \(\Lambda\) and experimental results was greatly improved. Especially, when _OCR_ is equal to 30, the accuracy of the predicted results was improved by 92.7%, which means that the variation of \(\Lambda\) with _OCR_ cannot be ignored when predicting the thermal pore water pressure inside the highly overconsolidated clay.  
Figure 4: Comparison between experimental results and predicted results of (**a**) Case 1; (**b**) Case 2.
# A Dynamic Multi-Layer Steganography Approach Based on Arabic Letters' Diacritics and Image Layers
## 3 The Proposed Approach
### Experimental and Investigations on the Proposed Method Based on the Steganography Metrics  
#### 4.3.1 Hypotheses  
* First, Table 6 displays the details of the transcripts utilized in our experiments. Four different texts of lengths and properties were employed.
* Secondly, the secrets were randomly selected and comprise a combination of binary digits (zeros and ones).
* Thirdly, the proposed approach was evaluated by a team of five experts using the five-point Delphi method, where decisions were made based on the majority agreement among the team.
## 3 Results and Discussion
### An Optimal Variable Set Applicable to Moisture Characteristics of Tea Leaves  
The BOSS method was used to optimize the variables of different layers of the wavelet reconstruction matrix. As shown in Figure 6, the optimal set of each layer was roughly distributed around the moisture absorption peak (1200, 1400, 2400, and 2600 nm). Due to the randomness of the variable selection, BOSS was repeated 30 times to reduce the statistical errors. Therefore, the top 10 variables with the highest occurrence frequency were taken as the preferable variables in each layer after 30 cycles in the test. After the combination of the preferable variables of L1-L6, the optimal variable set V was obtained. As the number of decomposition layers increased, some moisture features were optimized and some irrelevant information was eliminated. The optimal variables decomposed by L1-L6 were superimposed to obtain 55 optimal variables in the regions of 800-1000, 1100-1400, 1500-1700, 1900-2000, and 2300-2600 nm. The considerable number of variables are in the range of 800-1100, 1200-1400, and 1700-2000 nm. The wavelength ranges were mainly represented by the fundamental frequency vibration of the free -OH group, as well as the combination and octave vibration absorption.
# Advancements in Gaze Coordinate Prediction Using Deep Learning: A Novel Ensemble Loss Approach
## 3 Materials and Methods
### Models  
In this paper, we performed an experiment to evaluate the accuracy improvement when applying ensemble loss to an existing deep learning model for eye tracking. To achieve this, we leveraged various existing deep learning models designed for eye tracking and applied them for feature extraction and training, and in the final step, we added a fully connected layer to regress x and y coordinates based on the features extracted from these models. A model involving ensemble loss is described in Figure 1. One of the deep learning models used in this paper is AFF-Net [23]. AFF-Net is a deep-learning-based model used in existing eye tracking, and extracts features by inputting 2D image information of the face and eyes. AFF-Net is a model that improves eye tracking accuracy by adaptively fusing two eye features and extracting the eye features derived from facial appearance characteristics and is a model that demonstrates cutting-edge performance. As you can see in Figure 1, we learn the features of the face and eye images and the landmark coordinates of their locations. These learned features are used for training by applying ensemble loss.  
The second model is an eye tracking model using the residual neural network (ResNet) proposed by [PERSON] [21]. ResNet can solve the performance saturation problem caused by deep networks by using residual blocks. Additionally, it has excellent learning stability and can learn complex features, making it suitable for eye tracking. As you can see in Figure 1, face images, eye images, and head pose information are taken as inputs to the ResNet-18 model to learn features, and then ensemble loss is applied in the final stage of training.  
The third model is iTracker [7]. iTracker is a deep learning model that predicts gaze using a deep convolutional neural network (CNN) using input consisting of cropped images of both eyes and the face, along with facial position coordinates, called a face grid, from image data. Although iTracker has a simple structure, it provides fast and accurate gaze prediction because it integrates facial grid information. As seen in Figure 1, we extract features based on CNN and similarly apply ensemble loss for training.
## 4 Conclusions
Desorption kinetics for Sr(II) elution from AO-APEI sorbent using 0.5 M CaCl\({}_{2}\)/0.2 M HCl solution--Modeling of kinetic profiles using the PFORE and PSORE adapted to desorption (SD: 1 g L\({}^{-1}\); q\({}_{0}\): 76 mg Sr g\({}^{-1}\) = 0.867 mmol Sr g\({}^{-1}\); Temperature: 21 \(\pm\) 1 \({}^{\circ}\)C; agitation speed: 140 rpm). Figure S11. XPS (survey) spectra of AO-APEI beads after being in contact with seawater samples (Beihai, China and Da Nan, Vietnam). Figure S12. Selectivity for Sr(II) removal from 2 samples of seawater (SD: 0.2 g L\({}^{-1}\); pH\({}_{0}\): 7.98 (Da Nan seawater sample), 7.63 (Biehei seawater sample). Figure S13. Example of EDX analysis (element cartography) of AO-APEI beads after contact with seawater (analysis of surface and section). **Author Contributions**: Conceptualization, [PERSON] and M.F.H. ; methodology, K.A.M.S. and S.L. ; software, T.F. and T.V. ; validation, Y.W. and M.F.H. ; formal analysis, K.A.M.S. and S.L. ; investigation, T.F. and T.V. ; resources, Y.W. ; data curation, E.G. and M.F.H. ; writing--original draft preparation, E.G. and M.F.H. ; writing--review and editing, E.G., Y.W. and M.F.H. ; visualization, Y.W., and E.G. ; supervision, Y.W. and M.F.H. ; project administration, Y.W. ; funding acquisition, Y.W. and E.G. **Funding:** [PERSON] acknowledges the China Scholarship Council (CSC, Grant N\({}^{\circ}\) 2018 DFH009897) for MSc fellowship. [PERSON] M.F.H. thanks the funded scholarships through the Talented Young Scientists Program, China, and the College of Resources Environment and Materials, Guangxi University, China. **Acknowledgments:** [PERSON] and [PERSON] acknowledge Institut Francais d'Egypte (France) and Science and Technology Development Fund (Egypt) for supporting the bi-lateral collaboration between IMT-Mines Ales and Nuclear Materials Authority. [PERSON] thanks the financial support of NSFC Project No.11675102 and the support of School of Nuclear Science and Engineering at Shanghai Jiao Tong University. Authors also acknowledge the technical assistance of [PERSON]. **Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Discussion and Conclusions
### Theoretical Contributions  
This subsection outlines the novel theoretical insights generated by the present study, shedding light on the broader scholarly discourse within the field. Firstly, by synthesizing the existing literature, this research advances the understanding of performing LCA for LEO constellation satellites with the usage of the average mass budget. It introduces a novel perspective by combining the average mass budget method and parametric method, which results in a more accurate estimation of mass budget for an LEO constellation, which is essential for performing a life cycle assessment. Moreover, through rigorous analysis, this study extends theoretical boundaries by developing a new theory with the help of FCC orbital data as input and combining the existing average mass budget table for LEO satellites and the parametric method, which depends on using mathematical equations. Additionally, by addressing gaps and inconsistencies in previous research, this study contributes to refining and clarifying theoretical propositions, offering a more nuanced understanding of performing life cycle assessment for satellites without readily available mass budget data. Overall, the theoretical contributions of this research serve to enrich the theoretical landscape of \"sustainability for space\" and provide valuable insights for future research endeavors.
# Object-Tracking Algorithm Combining Motion Direction and Time Series
## 4 Experimental Results and Analysis
### Implementation Details  
The base tracker used in this work is STARK-ST50 [25]. Note that instead of tracking the result scoring mechanism, we incorporate the TRSM module to determine the reliability of tracking results. The tracker is trained using Equation (6), while the TRSM module is trained using Equation (7).  
To construct the historical templates in the TRSM module, we select the initial target template and the dynamic template updated over time in the STARK algorithm. The TRSM module employs a pre-trained ResNet-50 [36] as its feature extraction network, which has also been utilized in STARK-ST50. The scaling factor of this network is 16 (s = 16). The encoder and decoder sections in the encoding-decoding architecture have two layers each, and we set the number of heads to 8 and dimensions to \(C=256\). Additionally, the fully connected network's hidden layer has 256 neurons.  
During the tracking process, we set the reliability threshold \(\tau\) to 0.6 based on experience. The TRSM module determines the reliability of the tracking results, and the template is updated only if it exceeds this threshold. As referenced in [31], we use the hyperparameter \(\epsilon=4\) in the loss function to control the attention of aspect ratio loss in the total loss. Following the research on the linear combination of L1 loss and GIoU loss in DETR, we set the weights \(\lambda_{L_{1}}\) and \(\lambda_{\text{SIoU}}\) to 5.0 and 2.0, respectively. The aforementioned parameters are applicable to all datasets.  
The algorithm is trained in two steps: the tracker is trained first, and then the TRSM module is trained separately with fixed tracker model parameters. The experiments were conducted on an Ubuntu Server system equipped with four 24 G RTX3090s. We trained our algorithm on various datasets, including LaSOT, GOT-10K, COCO [37], and TrackingNet.
# Software Fault Localization Based on SALSA Algorithm
## 4 Experimental Settings
### Evaluation Metrics  
* Top-N  
Top-N is an important metric for evaluating the performance of fault localization techniques, with the core being to check whether the actual fault location is accurately included in the top N code locations recommended by the fault localization technique. Specifically, within the Top-N metric framework, these techniques first recommend the Top-N code locations that are most likely to contain faults as candidate areas. Then, the effectiveness of the fault localization technique is assessed by verifying whether faults exist within these candidate areas. In short, it involves calculating how many of the Top-N recommended locations are indeed the actual locations of the faults. Clearly, the larger the value of Top-N, the better the performance of the technique, as it reflects that more accurate fault locations have been successfully identified and included in the Top-N recommended positions.
* EXAM (Expected Additional Mistakes)  
EXAM is another commonly used metric for evaluating the performance of fault localization techniques. It is primarily used to measure the percentage of elements that need to be inspected before the actual fault location is found. Specifically, EXAM calculates how many additional elements need to be inspected to find the actual fault location if the fault is not discovered in the Top-N recommended code locations. The formula is as follows:  
\[\text{EXAM}=\frac{\sum_{i=N+1}^{|E|}{(i-N)\cdot P(i)}}{|E|-N} \tag{7}\]  
\(N\) is the number of the top \(N\) recommended code locations, and \(E\) is the set of all possible elements, i.e., the root set. \(P(i)\) represents the probability that the i-th element is the fault location; in this paper, \(P(i)=i/\mid E\mid\).
# Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging
## 2 Materials and Methods
The image processing quality is considered high if \(PSNR\geq Q\), where \(Q\) describes the difference between the two images almost imperceptible for human eyes. \(Q=40\) dB for images with 8 BPC [44,45]. We propose to generalize \(Q\) to the case of images with 12 and 16 BPC using formula  
\[Q=5B. \tag{4}\]  
Thus, \(Q\) is equal 40 dB, 60 dB and 80 dB for images with 8, 12 and 16 BPC respectively.
# Scenario-Based Configuration Refinement for High-Load Cellular Networks: An Operator View
## 2 Analysis of High Traffic Load Cells
### High Traffic Load Cells Identification and Current Situation  
Taking the perspective of telecommunication operators powered by field data, the performance of access and single user data rate are two significant metrics used to evaluate service quality of cells as well as user experience. The performance of access is often related to the capacity of telecom equipment designed by vendors. More specifically, the loads of the universal main processing and transmission unit (UMPT) and LTE baseband processing units (LBBP) are considered when conducting configuration refinement. For the single user data rate, we always investigate two factors, the physical resource block (PRB) utilization ratio and the average number of online users (i.e., ANOU). Thus, after comprehensively considering the comments from vendors, we identify the maximum number of online users (MNOU), the PRB utilization ratio (or the average number of online users (ANOU)), and loads of UMPT and LBBP as three significant indicators of high traffic load cells. As shown in Table 1, these three indicators present different thresholds embedded by telecom equipment manufactured by different vendors.  
Using these three indicators, we take cellular networks deployed in Jiangsu province as examples to study the features of high traffic load cellular networks. We monitor the field data of cellular networks multiple times in several weeks, and all field data reported in this paper are average means. As statistics show in Table 2, we observe that network performance metrics of cells with high traffic loads, that is, access success rate and single user downlink speed, are significantly lower than those of all cells. Moreover, around 81% of cells with high traffic loads are located in university campuses, which is shown in Figure 2. Based on these observations, we analyze the relationships between three indicators and the network performance metrics (i.e., access success rate and single user downlink speed) in Sections 2.3 and 2.4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Huawei** & **ZTE** & **Ericsson** & ** DTT** & **Bell** \\ \hline MNOU & & \textgreater{}300 & & \textgreater{}180 \\ PRB utilization & & ANOU \textgreater{} 50, downlink PRB utilization ratio \textgreater{} 70\%, downlink traffic \textgreater{} 2 GB, & & \\ ratio (or ANOU) & & & or uplink PRB utilization ratio \textgreater{} 500 MB & & \\ LBBP CPU & & & & \\  & utilization ratio \textgreater{} & UMPT CPU & & LBBP or UMPT & utilization ratio \textgreater{} \\ and LBBP & 80\% or UMPT CPU & utilization ratio \textgreater{} & & \\ utilization ratio \textgreater{} & 80\% & & utilization \textgreater{} 60\% & & \\  & 65\% & & & ratio \textgreater{} 85\% & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main indicators of high traffic load cells for different vendors.  
Figure 2: Percentage of high traffic load cells.